The Meteorological Modulators: Quantifying Weather's Influence on Activity-Travel Behavior

The Meteorological Modulators: Quantifying Weather's Influence on Activity-Travel Behavior | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Meteorological Modulators: Quantifying Weather's Influence on Activity-Travel Behavior Nazmul Arefin Khan, Joshua A. Auld This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8703831/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study presents an analysis of the effects of weather conditions on travel demand. It develops a combined database of travel demand and monthly, daily and hourly weather conditions. The trip information of the database was extracted from the 2018-19 CMAP Household Travel Survey. To obtain the weather information, the study selected U.S. Local Climatological Data (LCD) data from National Centers for Environmental Information (NCEI) of NOAA. This study examines the effects of a comprehensive set of weather metrics on activity-travel patterns, focusing on activity participation and mode choice behavior. Descriptive analyses of daily and hourly weather conditions and travel demand provide insights into how activity-travel behavior varies with changing weather. The empirical analysis exclusively models the impacts of multiple hourly weather variables and their interactions with demographic and built environment characteristics using a mixed logit modeling framework. Elasticity analysis is employed to assess the magnitude of weather-related effects. Results indicate that temperature, relative humidity, windspeed and visibility strongly influence activity-travel behavior, particularly discretionary activity participation and active mode choices, whereas participation in mandatory activity and choice of single-occupancy vehicles are found to be less sensitive. Overall, the findings demonstrate weather conditions as critical factors influencing travel behavior and emphasize the importance of explicitly incorporating weather effects into travel demand models. These insights reinforce the need for dynamic travel demand models that incorporate real-time or hourly weather data, and underscore the importance of tailoring transportation policies and infrastructure to support resilient transportation systems. weather travel demand household travel survey local climatological data mixed logit activity participation mode choice Figures Figure 1 Figure 2 Figure 3 Figure 4 1. BACKGROUND Understanding the complex dynamics of activity-travel behavior is critical for effective urban and regional planning, infrastructure development and policy evaluation. Traditional travel demand models often focus on socio-demographic and built environment factors, while the influence of environmental conditions, particularly weather, on daily travel decisions is often disregarded. Weather conditions are a critical, yet underappreciated, factor significantly influencing travel behavior and the overall transportation system. Extreme temperatures, varying precipitation levels, humidity, wind speed, etc. alter the way people prefer to travel, and sometimes not to travel at all ( 1 ). Adverse weather often leads to a decrease in using active transportation modes (e.g., walking, cycling) and an increase in the use of motorized transport (( 2 ), ( 3 )). Similarly, inclement weather may also significantly reduce the overall number of trips undertaken, especially discretionary or leisure-related travel, while essential trips are sometimes rescheduled or delayed to more favorable conditions. As transportation systems aim to become more resilient and responsive, a nuanced understanding of weather-related travel behavior has become increasingly important for effective transportation planning, particularly in the era characterized by the adoption of abundant transportation technology ( 4 ). A major limitation in current transportation modeling is the insufficient incorporation of detailed weather variables, which can significantly reduce the accuracy of travel demand forecasts and hamper effective planning and resource allocation ( 5 ), ( 6 ). Further research is needed to explore how travelers adjust their behavior in response to changing weather conditions, offering valuable insights for building more adaptive and resilient transportation systems. A growing body of literature has investigated the impacts of weather conditions on travel behavior and demand and established measurable influence of weather on activity-travel behavior. Weather's substantial impact on trip generation, destination selection, and modal split has been well-documented in recent research ( 7 ), which suggested that weather variables be integrated into traffic demand models (( 8 ), ( 9 )). Studies predominantly concentrate on individuals’ mode choice decisions, given their responsiveness to prevailing weather patterns. For instance, researchers found that car trips are less sensitive to specific weather details than bicycle trips, which are highly influenced by seasonal weather patterns due to cyclists' direct exposure ( 1 ). While seasons often represent typical weather for models (( 10 ), ( 8 ), ( 2 )), unusual daily weather can have a stronger impact than seasonal norms, potentially masking true variations ( 7 ). Weather forecasts also play a role in travel decisions ( 11 ), though their influence is generally less than immediate weather changes (( 12 ), ( 10 )). More intense weather conditions like heavy rain, snow, extreme temperatures, or strong winds have a greater impact on trip frequency and mode choice decisions ( 12 ). Studies show that increasing precipitation reduces trips, especially leisure ones, and impacts walking and biking more significantly compared to car driving ( 9 ). Conversely, public transport use often increases with precipitation and wind as people shift away from walking and cycling ( 13 ). Heinen et al. ( 14 ) showed that increased rainfall and wind negatively affect cycling – especially for recreation. However, prior studies also found that inclement weather conditions often induce travelers to cancel trips, postpone departures, or alter routes or destinations, instead of primarily switching travel modes (( 15 ), ( 16 ), ( 7 ), ( 17 )). The depth of insights of the existing studies mostly hinges on the aggregate temporal resolution of the weather data, particularly based on monthly or daily average (( 18 ), ( 19 ), ( 3 ), ( 20 )) – primarily due to data unavailability and computational constraints. Such approach has provided critical insights by demonstrating general trends. For instance, higher precipitation and extreme temperatures throughout the day reduce active mode and increase private vehicle usage (( 19 ), ( 21 )), and have significant impacts on daily errands participation decisions ( 20 ). However, weather does not have a uniform effect on travel behavior throughout the day. Although the aggregate analysis provides a broad understanding of the relationships between weather and travel demand, it fails to distinguish between different types of weather events occurring within the same day; thus, it oversimplifies behaviorally distinct contexts. This level of aggregation introduces temporal aggregation bias, where short-term variations that directly influence the way people travel are averaged out, thereby masking the behaviorally appropriate influence of rapidly changing weather. Such lack of sensitivity makes models built on daily or monthly averages less accurate for predicting activity and travel behavior due to dynamic weather changes. In contrast, hourly-level analysis offers a more precise understanding of short-term variations that can capture crucial temporal nuances in activity-travel behavior. This finer temporal resolution enhances behavioral realism and supports more appropriate, responsive, and behaviorally relevant modeling, particularly when higher temporal resolution travel information are available. This approach also enables a better understanding of behavioral adaptations in response to short-term weather condition changes, which is warranted for developing models to appropriately reflect dynamic travel behavior. Despite an increasing interest in high-resolution modeling, the majority of weather-travel demand studies still rely on daily or aggregate temporal resolutions (( 1 ), ( 22 )), which may lead to inaccurate estimation of travel demand. This study addresses aforementioned literature gaps by focusing on the hourly analysis of weather's impact – a crucial step for capturing short-term behavioral variations and deriving more accurate, responsive, and behaviorally relevant insights on activity-travel behavior. To this end, it first identifies the appropriate data sources and joined them with disaggregate travel information to develop an integrated weather-travel demand database with both aggregate and disaggregate weather information. Following this, a wide range of weather metrics is determined for both daily and hourly analysis based on an extensive literature review, and compared to understand the differences between high and low temporal resolution weather effects on activity-travel behavior. Finally, to capture the unobserved heterogeneity occurred due to repeated observations, an activity participation model and a mode choice model are estimated following mixed logit modeling approach to appropriately explore individuals’ decision-making exclusively under different weather conditions. For both models, the mixed logit formulation accommodates unobserved preference heterogeneity by allowing parameters to vary randomly and enhance estimation accuracy by accounting for individual-specific factors relevant in the repeated observations. This way it allows to reveal nuanced interactions between hourly weather and travel behavior across transportation modes, thereby filling a significant void in existing research. 2. DATA 2.1. Data sources This study primarily relies on three different types of data sources – 1) activity-travel data, 2) weather data, and 3) land use and built environment data. Following are the details of the data sources. 2.1.1. Activity-travel data source The activity-travel information used in this study are extracted from the 2018-19 Household Travel Survey (HTS), conducted by the Chicago Metropolitan Agency for Planning (CMAP) as part of the My Daily Travel Survey project in Chicago, USA. Data was collected through multiple methods: telephone interviews, web-surveys, in-person intercept surveys, app usage, online forms, and direct mail. The survey was also heavily promoted via electronic media, email blasts, local school districts, government units, community organizations, and bloggers. Additionally, digital and social media platforms like Facebook, Instagram, Google Display Network, Google Search and Ads, and paid digital influencers were utilized for household recruitment. The reason behind recruiting respondents by using different outreach options was to get a balanced sample among different demographic groups (e.g. age, income, race, mobility-constraint, etc.). For example, the target audience for the social media campaign was Chicago region residents, Hispanic/Latino population, regional commuters, African American population, 65 + age population and lower income households. The telephone and web survey modes were targeted to recruit low-income, socioeconomically disadvantaged, and older members of the population. The direct mail approach used the United States Postal Service (USPS) Every Door Direct Mail (EDDM) tool to distribute materials to residential addresses within selected geographic areas and demographic groups, helping achieve a balanced sample. Through these efforts, over 17,000 households were recruited, with 12,660 completing the survey. A survey was considered complete when all members five-years-old and above reported travel details for the assigned travel day and subsequently all edit checks and post processing errors were able to be cleared. Among the completed surveys, the digital advertising campaign accounted for 1,127 completed household surveys. Intercept surveys conducted in person at public transportation stations, community college campuses and transit centers yielded 111 completed households. The direct mail effort resulted in 354 completed surveys. Local community partners, such as community colleges, faith-based organizations and local chambers of commerce, contributed 1,582 completed surveys, while school districts facilitated the collection of 960 completed surveys. The smartphone application generated the largest share, with 4,397 completed surveys. The remaining responses were obtained through electronic media outreach, telephone surveys, and web-based surveys. The detailed description of the survey design, data collection approach, sample distribution, and data extraction and retrieval processes can be found in ( 23 ). During the data collection, data processing and cleaning were conducted continuously. Critical variables (e.g. the addition of a car that was not originally reported) were updated during survey administration, while non-flow affecting variables (e.g. recoding race based on “Other, specify” responses) were finalized after data collection. Automated edits, range checks, consistency checks, and validity checks were embedded in the survey, supplemented by staff-led frequency reviews and issue resolution. Logic, range, and consistency checks ensured accurate responses, valid data types and values, and consistency across survey files. After reviewing and confirming the responses, 12,068 households were retained. In addition, 323 pilot households were added with the final sample bringing the total to 12,391. The final sample included 12,391 households, totaling 30,683 respondents, and 98,091 trips. The detailed description of the data cleaning, processing and quality checks can be found in ( 23 ). The survey gathered detailed information on various household and member attributes, including income, household size, home ownership status, residence type, vehicle ownership, residential address, member ages, employment status, education, race, occupation, mobility tool ownership, and relationships between members. Methodologically, it is a cross-sectional survey that collected 1- or 2-day trip information, covering activity purposes, trip origins and destinations, departure and arrival times, durations, travel modes, and associated vehicular information. To evaluate the representativeness of the CMAP HTS, weighted estimates from the survey were compared against corresponding benchmarks derived from the American Community Survey (ACS). The ACS serves as a relevant reference due to its large sample size, standardized methodology, and comprehensive coverage of household and person characteristics. Comparisons were conducted using weighted percentage distributions, and differences were assessed using absolute percentage point deviations from ACS benchmarks. The weights were applied to the CMAP HTS data to adjust over- and under-representation. This process involved assigning higher weights to responses from underrepresented categories and lower weights to those from overrepresented categories. By doing so, the final dataset more accurately represented the population distribution and travel behaviors across the entire region. Overall, CMAP HTS exhibits close correspondence with ACS estimates across key household and person attributes, such as household size, vehicle ownership, income, age range, race, etc. The Margin of Error (MOE) at the 90% confidence interval was also calculated using the variance estimates drawn from the replicated weights for a given estimate. MOE is critical to assess whenever estimates are compared. A lower MOE indicates more stable samples with greater confidence in the estimate. American Community Survey (ACS) estimates and MOE were obtained from the U.S. Census Bureau and are based on 2013-17 5-year estimates. The detailed discussion on the comparison can be found in ( 23 ). Table 1 illustrates the comparison for household size and vehicle ownership in a representative county in the CMAP region (Cook County). Table 1 Comparison between CMAP HTS and ACS based on household size and number of vehicles in Cook County ( 23 ). Household Size No. of Vehicles CMAP Estimate CMAP MOE (90%) ACS Estimate ACS MOE (90%) 1 person 0 11.07% 1.09% 10.83% 0.44% 1 19.91% 1.41% 19.42% 0.44% 2 1.26% 0.45% 2.01% 0.44% 3 0.19% 0.16% 0.22% 0.44% ≥ 4 0.06% 0.06% 0.08% 0.44% 2 persons 0 4.13% 0.59% 3.90% 0.44% 1 10.65% 1.12% 11.44% 0.44% 2 12.61% 1.41% 12.57% 0.44% 3 1.40% 0.37% 1.47% 0.44% ≥ 4 0.55% 0.26% 0.28% 0.44% 3 persons 0 1.29% 0.35% 1.46% 0.44% 1 4.54% 0.81% 4.57% 0.44% 2 6.34% 1.03% 5.83% 0.44% 3 2.36% 0.74% 2.68% 0.44% ≥ 4 0.52% 0.32% 0.50% 0.44% ≥ 4 persons 0 1.40% 0.45% 1.54% 0.44% 1 5.07% 0.94% 5.19% 0.44% 2 9.67% 1.34% 9.69% 0.44% 3 4.54% 1.03% 4.01% 0.44% ≥ 4 2.45% 0.71% 2.31% 0.44% 2.1.2. Weather data source To extract the weather information, this study first identifies three prominent and reliable meteorological data sources – 1) NASA Prediction of Worldwide Energy Resources (POWER) database, 2) National Solar Radiation Database (NSRDB) by National Renewable Energy Laboratory (NREL), and 3) U.S. Local Climatological Data (LCD) data from National Centers for Environmental Information (NCEI) of National Oceanic and Atmospheric Administration (NOAA). These sources were selected due to their broad usage in climate, energy, and transportation-related research and their ability to provide temporally resolved weather information. The NASA POWER database is primarily focused on climate research, renewable energy, and agricultural needs and based on model simulations and data assimilation. It relies on a combination of model simulations and data assimilation techniques to generate gridded meteorological variables. The database provides a variety of meteorological and solar data, including near-surface air temperature, relative humidity, rainfall, solar radiation, and wind speed and direction. Although the POWER database offers consistent global coverage and long temporal spans, its reliance on modeled and interpolated data often limits its accuracy at fine spatial scales. It struggles to capture localized weather conditions, complex terrain effects and extreme events, which are critical for analyzing daily travel behavior. The NREL NSRDB database provides a comprehensive suite of weather data related to solar irradiance, atmospheric conditions, and surface properties. It is widely used in solar energy and building energy modeling applications and offers high temporal resolution and broad spatial coverage. However, the NSRDB is not ideal for meteorological or climatological analyses due to its data-filling methods for serial completeness that may introduce systematic biases. Therefore, its applicability for studies requiring accurate representation of observed weather variability and extremes is limited. The NOAA-NCEI LCD database provides high-quality and observation-based weather data at fine temporal and spatial resolutions. The LCD dataset includes hourly, daily, and monthly observations collected directly from surface weather stations and supplemented by satellite observations where applicable. It provides detailed information on a wide range of meteorological variables, including temperature, dew point, humidity, snow depth, snowfall, precipitation, atmospheric pressure, wind speed and direction, sky conditions, and weather type. The specific station-based nature of the LCD database allows for more accurate representation of localized weather conditions, which makes it suitable for regional and local-scale analyses. Given the high temporal resolution, reliance on direct observations and extensive set of weather information, the NOAA-NCEI LCD database is well suited for linking weather conditions to daily activity-travel behavior. Therefore, this study adopts the NOAA-NCEI LCD dataset as the primary weather data source and extracts monthly, daily, and hourly weather information for subsequent analysis. 2.1.3. Land use and built environment data source In addition to the HTS and weather data, this study incorporates land use and built environment measures from the U.S. Environmental Protection Agency’s (EPA) Smart Location Database (SLD). The EPA SLD was developed to support consistent assessment and comparison of location efficiency across geographic areas and provides a comprehensive set of demographics, employment, and built environment indicators for all Census block groups (CBGs) in the United States. 2.2. Data preparation and variables considered The LCD database consists of weather records from multiple weather stations. This study selected Chicago O'Hare International Airport as the reference weather station since it's the official data source for Chicago weather analysis. Since the CMAP HTS data was collected between the years 2018 and 2019, this study extracted weather information from the NOAA-NCEI LCD database from 01/01/2018 to 12/31/2019, which includes monthly, daily and hourly weather information along with exact date, time and station location. Following this, the LCD and HTS databases were joined based on the exact month (monthly average weather), month and date (daily average weather), month, date and time (hourly weather). This study considers a wide range of weather metrics for daily and hourly weather-travel demand analysis. A comprehensive literature review was conducted recently by Petrovic et al. ( 2 ) that explored the most common weather factors used in the existing literature of travel demand. Based on the findings of Petrovic et al. ( 2 ), this study determined the following list of weather metrics to analyze the relationships between travel demand and daily/hourly weather: 1) temperature: dry bulb, wet bulb and dew point temperature, 2) atmospheric pressure: altimeter setting, sea level pressure, station pressure, and pressure change, 3) humidity: relative humidity, 4) wind: wind speed, wind gust, wind direction, 5) precipitation: precipitation, snow depth, snow fall, visibility, 6) weather type, 7) pressure tendency, and 8) sky condition, among others. The joined database includes all demographics, activity, travel and the identified weather information for the days and hours when the survey was administered between 2018 and 2019. The dependent variable of the activity participation model is formulated based on four activity purposes: 1) mandatory activity (e.g. work, school), 2) maintenance activity (e.g. all shopping, non-work errands, service, personal business, civic, religious), 3) discretionary activity (e.g. eat out, leisure, recreation, entertainment, social), and 4) in-home activities. The mode choice model is formulated based on 7 modes collected during the survey: 1) SOV (single-occupancy vehicle), 2) HOV (high-occupancy vehicle), 3) walk, 4) bike, 5) bus, 6) rail, and 7) taxi. To understand the effects of land use and built environment measures, U.S. EPA Smart Location Database (SLD) were merged with the weather and activity-travel database based on the activity locations. The EPA-SLD provides a comprehensive set of variables at the Census Block Group (CBG) level, which are organized into five categories: (1) density, (2) diversity, (3) design, (4) transit accessibility, and (5) destination accessibility. Based on the spatial locations of reported activities, relevant neighborhood attributes, including population density, residential density, activity density, transit service frequency density and distance to the nearest transit stop, were extracted from the EPA-SLD. Density measures were computed as the ratio of units to total land area in the neighborhood. For example, population density was computed by dividing the number of people by total land area in acre, residential density was calculated by taking the ratio of number of housing units per acre, and activity density was calculated as the number of housing units and jobs per acre within a neighborhood ( 24 ). Land-use diversity was measured by quantifying the relative blend of the number of jobs in different employment sectors and residential housing types ( 24 ). The final dataset for estimating the activity participation and mode choice models were constructed by integrating the weather and activity-travel joint database with the corresponding neighborhood-level land use and built environment attributes. Table 2 and Table 3 present the descriptive statistics of the variables retained in the final model estimations. Table 2 Activity participation model variables descriptive statistics Variables Description Mean/ proportion Standard Deviation Temperature Freeze ( 77F) Dummy, if hourly dry bulb temperature is above 77F = 1, 0 otherwise 8.88% - Freeze × household size Freezing temperature interacted with household size 0.385 0.49 Freeze × Age 55 + years Freezing temperature interacted with age above 55 years 8.54% - Freeze ×Vehicle ownership > 1 Freezing temperature interacted with households with multiple number of vehicles 28.52% - Hot × Age 25–40 years Hot temperature interacted with age 25–40 years 3.05% - Hot × Age 55 + years Hot temperature interacted with age above 55 years 2.42% - Hot × Employment: Construction and Retail Hot temperature interacted with the individual's workplace type construction and retail 0.41% - Hot × Households with kids Hot temperature interacted with households with kids 2.88% - Freeze × Population density Freezing temperature interacted with population density in the neighborhood (per acre) 4.512 13.31 Freeze × Residential density Freezing temperature interacted with residential density in the neighborhood (per acre) 2.204 8.53 Hot × Population density Hot temperature interacted with population density in the neighborhood (per acre) 1.898 10.90 Cold × Residential density Cold temperature interacted with residential density in the neighborhood (per acre) 2.187 8.77 Relative humidity High (> 75%) Dummy, if hourly relative humidity is over 75% =1, 0 otherwise 33.49% - Discomfort (60% − 75%) Dummy, if hourly relative humidity is between 60% and 75% =1, 0 otherwise 34.44% - High × High income households High relative humidity interacted with above $ 150,000 annual earning households 7.55% - High × No flexible work hours High relative humidity interacted with no flexible work hours in the workplace 12.17% - Discomfort × Households with kids Discomfort level relative humidity interacted with households with kids 14.15% - Discomfort × Activity density Discomfort level relative humidity interacted with activity density (per acre) 20.832 29.51 High × Transit service frequency High relative humidity interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre) 0.173 0.96 Precipitation Light ( 0.35") × Employment: IT and Finance Heavy precipitation (> 0.35") interacted with individual's workplace type IT and finance 4.02% - Heavy × Transit service frequency Heavy precipitation interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre) 0.022 0.12 Windspeed Light air ( 20 mph) Dummy, if hourly windspeed is above 20 mph = 1, 0 otherwise 3.85% - Strong air × Distance to the nearest transit stop Strong air interacted with the distance to the nearest transit stop (miles) 0.521 0.42 Visibility Poor (< 0.60 miles) Dummy, if the visibility is below 0.60 miles = 1, 0 otherwise 2.16% - Poor × Activity density Poor visibility interacted with activity density (per acre) 0.710 6.87 Table 3 Mode choice model variables descriptive statistics Variables Description Mean/ proportion Standard Deviation Travel time Travel time in minutes 22.501 30.80 Temperature Freeze (< 32F) Dummy, if hourly dry bulb temperature is below 32F = 1, 0 otherwise 38.50% - Cold (32F − 50F) Dummy, if hourly dry bulb temperature is in between 32F and 50F = 1, 0 otherwise 27.71% - Cool (51F − 68F) Dummy, if hourly dry bulb temperature is between 51F and 68F = 1, 0 otherwise 13.64% - Warm (69F − 77F) Dummy, if hourly dry bulb temperature is between 69F and 77F = 1, 0 otherwise 11.28% - Freeze × Age 16–24 years Freezing temperature interacted with age 16–24 years 3.68% - Freeze × Age 55 + years Freezing temperature interacted with age 55 + years 8.54% - Freeze × High income households Freezing temperature interacted with above $ 150,000 annual earning households 9.20% - Freeze × Vehicle ownership = 1 Freezing temperature interacted with 1-vehicle households 28.52% - Freeze × Peak hour departure time Freezing temperature interacted with AM and PM PEAK HOUR departure time 20.01% - Hot (> 77F) × Age 16–24 years Hot (> 77F) temperature interacted with age 16–24 years 0.60% - Hot × Age 25–40 years Hot temperature interacted with age 25–40 years 3.05% - Hot × Vehicle ownership > 1 Hot temperature interacted with households with multiple vehicles 5.30% - Hot × Off-Peak hour departure time Hot temperature interacted with AM and PM OFF PEAK HOUR departure time 5.55% - Hot × Population density Hot temperature interacted with population density (per acre) 1.898 10.90 Cool × Activity density Cool temperature interacted with activity density (per acre) 7.005 75.16 Relative humidity Comfort ( 75%) × Low-income households High humidity (> 75%) interacted with households with annual income below $ 50,000 6.33% - High × Employed male High humidity interacted with male employed individuals 10.87% - High × Transit service frequency High humidity interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre) 0.173 0.96 Discomfort × Activity density Discomfort humidity interacted with activity density (per acre) 20.832 29.51 Precipitation Light ( 0.35") × Transit service frequency Heavy precipitation (> 0.35") interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre) 0.022 0.12 Windspeed Light air ( 20 mph) Dummy, if hourly windspeed is above 20 mph = 1, 0 otherwise 3.85% - Strong air × Residential density Strong air interacted with the residential density (per acre) 0.335 3.62 Hourly windspeed × Female Hourly windspeed interacted with female indicator 12.48% - Visibility Clear (> 6.2 miles) Dummy, if the visibility is above 6.2 miles = 1, 0 otherwise 88.52% - Poor (< 0.60 miles) Dummy, if the visibility is below 0.60 miles = 1, 0 otherwise 0.22% - Clear × Distance to nearest transit stop Clear visibility interacted with the distance to the nearest transit stop (miles) 0.135 0.18 Poor × Land use diversity Poor visibility interacted with the land use diversity 0.080 0.22 3. DESCRIPTIVE ANALYSIS OF WEATHER AND TRAVEL DEMAND This study conducts a wide range of analysis to explore the relationships between daily and hourly weather conditions and travel demand. Among them, it presents a few prominent analyses in this section, specifically focusing on the mode choice and activity participation. 3.1 Activity participation Figure 1 a- 1 c presents the changes in activity participation with the change in daily average weather conditions. Average daily weather changes are found to have subtle impacts on activity participation patterns. With the increase in daily average temperature, windspeed and windgust, routine (mandatory) activity participation is found to increase slightly, while non-routine activity participation, as a whole, reduces. Conversely, higher daily average snowdepth, snowfall and precipitation show slight decrease in routine activity participation, and increase in non-routine activity participation. The following charts (1a-1c) exhibit the results only for daily average temperature, snowdepth and windspeed. Figure 2 a- 2 c presents how hourly weather changes impacts on activity participation. Results indicate that hourly higher temperature and visibility decrease routine activity participation, while increasing non-routine activity participation. This demonstrates more nuanced and immediate reactions to weather changes on activity participation patterns. Routine activity participation is found to increase with the increase in hourly relative humidity and precipitation, while non-routine activity participation decreases. The following charts (2a-2c) only show the results of hourly temperature, visibility and relative humidity. 3.2 Mode share Figure 3 a- 3 c presents the changes in mode shares due to daily average weather changes. Average daily weather changes have noticeable impacts on travel choices. Results suggest that auto mode (SOV and HOV) usage is the highest in freezing and cold temperatures and decreases with warmer temperatures. Walking, biking and public transportation mode usage are found higher in warmer conditions. Auto mode shares are found to increase with rainfall, windspeed and windgust (not shown in the figure), while active mode and public transportation mode shares decline. The following charts (3a-3c) only exhibit the analysis of daily average temperature, windspeed and rainfall. In terms of hourly analysis (Fig. 4 a- 4 c), results suggest that with the increase in temperature, windgust, relative humidity, windspeed and rising pressure, SOV usage decreases, while public transportation usage is found to increase slightly. This indicates that hourly weather analysis improves the aggregation bias and temporal nuance in mode choices. The following charts only exhibit hourly temperature, relative humidity and windgust analysis. 4. MODELING APPROACH This study utilizes a mixed logit (MXL) model to estimate the relationship between hourly weather conditions and travel demand, specifically – activity participation and mode choice decisions. In both instances, dependent variables are discrete/categorical, hence, generally a multinomial logit (MNL) model would be more appropriate (( 25 )). The MNL model, however, is based on a restrictive IIA assumption, hence, fails to account for correlations among unobserved factors over time for an individual that may occur due to repeated observation from the same individual. In this study, in the activity participation dataset, multiple episodes of the same activity purpose by an individual on a typical day exist (i.e. repeated observation of an activity by the same individual). Similarly, in the mode choice dataset, records exist for multiple trips made by an individual on a typical day (i.e. repeated observation of the mode choice). Due to such repeated observations, correlations among individual-specific unobserved factors may occur, which may arise the unobserved preference heterogeneity. MXL model can accommodate such heterogeneity across individuals within its modeling framework. In the case of activity participation, the MXL model is formulated based on four activity purposes: 1) mandatory activity, 2) maintenance activity, 3) discretionary activity, and 4) stay-at-home activities. Stay-at-home activity alternative is considered as a reference alternative for all the parameters in the modeling framework. The mode choice model is formulated based on 7 modes collected during the survey: 1) SOV (single-occupancy vehicle), 2) HOV (high-occupancy vehicle), 3) walk, 4) bike, 5) bus, 6) rail, and 7) taxi. Reference alternatives for mode choice model parameters vary based on the hypothesis. For both models, variable choicesets are created based on the availability of alternatives. For instance, mandatory activity was unavailable to the individuals who do not work or go to school, not owning a bike or car resulted in eliminating the alternatives from the choiceset of the corresponding individual, bus mode was unavailable outside of the operating hours and specific distance radius. The mixed logit model employed in this study can be described as follows. Let, an individual n chooses among J possible alternatives. The utility that individual n derives from alternative j at observation (choice occasion) t can be expressed as: $$\:{U}_{njt}=\:{\alpha\:}_{j}+\:{\beta\:}_{n}{X}_{njt}+\:{\epsilon\:}_{njt}$$ 1 Where, α is the alternate-specific constant (ASC), one of which is assumed zero (reference) for identification purposes. X is the column vector of the observed attributes, β is the coefficients of the parameters to be estimated, and 𝜀 is the random error term. The probability of individuals’ observed sequence of choices can be written as: $$\:{P}_{n}\left(t\right|\beta\:)=\:{\prod\:}_{t}\frac{{e}^{{\alpha\:}_{j}+\:{\beta\:}_{n}{X}_{nj(n,t)t}}}{\sum\:_{n=1}^{N}{e}^{{\alpha\:}_{j}+\:{\beta\:}_{n}{X}_{nj(n,t)t}}}$$ 2 Here, j(n,t) is the alternative that an individual n chooses at observation t . As discussed at the beginning of this section, the flexible MXL model formulated in this study accommodates individual-specific unobserved factors that may influence repeated choices from the same individual over time (at observations T ). Random parameters are introduced within the MXL modeling framework to capture such random taste variations (i.e. unobserved heterogeneity) across sample population through the estimation of mean and standard deviations. This study estimates mean (µ) and standard deviation (σ) of random parameters by following a normally distributed density function f(.) . Thus, the choice probability becomes the following equation: $$\:{R}_{n}=\:\int\:{P}_{n}\left(t|\beta\:\right)\:f\left({\beta\:}_{n}\right|\mu\:,\sigma\:)d{\beta\:}_{n}$$ 3 The unconditional log-likelihood function to estimate the parameters can be expressed as: $$\:{L}_{n}=\:\sum\:_{n=1}^{N}ln{R}_{n}$$ 4 Equation 4 is a multivariate integral function that does not have a closed form in general, hence, the choice probability cannot be computed analytically. As a result, parameters are estimated using the simulated maximum likelihood estimation (SMLE) technique. Halton sequences are utilized in this study, since it requires a lower number of draws compared to random draws. Both activity participation and mode choice models are estimated using the PandasBiogeme (( 26 )) software. 5. ESTIMATION RESULTS In this section, the parameter estimation results, model fits and elasticity analysis results are presented. Tables 4 and 5 present the parameter estimation results of the activity participation model and mode choice model, respectively. The model fits are reported at the bottom of Tables 3 and 4 . It includes null and final model log-likelihood and Akaike Information Criteria (AIC) values. To evaluate the model performance, likelihood ratio (LR) test is conducted for both models. In addition, for both model, family-wise error rate (FWER) is controlled using the Holm-Bonferroni procedure at the 5% significance level. This limits the probability of getting false positives arising from multiple hypothesis testing due to extensive set of hypothesis tests conducted simultaneously, specifically for the interaction effects that are motivated theoretically but may be correlated statistically ( 27 ). The Holm-Bonferroni correction method is applied in this study to make sure that the effects are robust after correcting for multiple comparisons, thus strengthening confidence in the relationships between weather conditions, demographics, built environment, and activity-travel demand (( 28 ), ( 29 )). This is a stepwise procedure used to control the family-wise error rate in multiple hypothesis testing while preserving greater statistical power. In this process, all p-values are first sorted from smallest to largest. Starting with the smallest p-value, each is sequentially compared against a progressively adjusted significance threshold. The smallest p-value is compared to an adjusted significance level of \(\:\varvec{\alpha\:}/\varvec{m}\) , where α is the significance level which is assumed as 0.01 in this study, and m is the total number of hypothesis tests. If this hypothesis is rejected, the next smallest p-value is compared to \(\:\varvec{\alpha\:}/(\varvec{m}\:-\:\varvec{k}\:+\:1)\) for the k -th ordered p-value. The process continues until a hypothesis fails to meet its adjusted threshold, at which point that hypothesis and all subsequent hypotheses are interpreted as exploratory. Below is a discussion of the parameter estimation results, model fits, and elasticity analysis results. 5.1. Activity participation Table 4 presents the estimation results of the activity participation model. It offers an understanding of how hourly weather conditions, in conjunction with demographic and household attributes and neighborhood characteristics, influence the likelihood of engaging in mandatory, maintenance, and discretionary activities compared to staying home. In terms of model fits, the log-likelihood value of the final model is found higher than the constant only model. The likelihood ratio (LR) test value is calculated as 70101.98, which is greater than the critical chi-square value (105.19) at the 1% significance level; thus rejecting the constants only model. Furthermore, all the parameters retained in the final model are found to be statistically significant at least at 10% significance level. The statistical significances of the parameters are reassessed using the Holm-Bonferroni procedure to account for multiple hypothesis testing and control the family-wise error rate across weather-related main effects and interaction terms. The starting adjusted significance threshold ( \(\:\varvec{\alpha\:}/\varvec{m}\) ) for the Holm-Bonferroni correction is computed to be 0.000141, which is compared to the smallest p-value in the model. Applying this procedure results in few parameters to be not Holm-Bonferroni significant. However, they are still retained in the final model because of their meaningful coefficient signs and nominal significance. The negative and highly significant constants across all activity types indicates a baseline preference for home-based activities, with discretionary pursuits exhibiting the strongest pull towards staying in. Participation in mandatory out-of-home activities exhibits moderate sensitivity to weather conditions, reflecting the relatively fixed nature of these activities. For mandatory activities, the results reveal that freezing, cold and hot temperatures and discomfortable humidity reduce the probability of staying home, increasing the likelihood of engaging in mandatory activities. Similarly, individuals working in construction and retail industries tend to participate in mandatory activities, even under extreme temperature. Such outcomes may indicate a strong obligation effect – people will often proceed with routine activities regardless of extreme weather or discomfort. This is consistent with previous studies such as Bhat and Srinivasan ( 30 ), which report less weather sensitivity in work-related activity patterns. However, certain segments are more vulnerable to weather, such as older adults (age over 55 years), who are less likely to leave home during freezing and hot temperatures. Similar outcomes are also found for the households with kids, who demonstrate lower tendency to participate in mandatory activities under hot temperatures. Notably, households with multiple vehicles are more likely to engage in mandatory activities during freezing weather, emphasizing that access to private transportation mitigates some weather barriers. High humidity and light air positively influence mandatory activity participation, possibly reflecting tolerability or a lack of barriers when air is stagnant. The interaction between high income and high humidity shows a positive effect, suggesting that higher-income individuals are more likely to pursue mandatory activities even under humid conditions. This is likely due to access to climate-controlled travel or workplaces. The variable “no work flexibility × high humidity” is positively associated with mandatory activity participation, implying that individuals without flexible work option are more likely to travel in humid conditions regardless of comfort – possibly pointing to constraint-driven behavior. This is in line with previous empirical studies showing that weather conditions have asymmetric effects depending on socio-economic and job-related constraints ( 7 ). As expected, poor visibility and strong air tend to decrease individuals’ propensity to participate in mandatory activities. Expectedly, individuals are found to increase their probability to participate in mandatory activities more during low precipitations. Conversely, people working at IT and finance industries exhibit lower propensity to engage in mandatory activities, consistent with evidence that these sectors offer greater opportunities for flexible and remote work arrangements ( 31 ). The interaction effects of weather and neighborhood characteristics on mandatory activity participation suggest that higher population, residential and/or activity densities generally mitigate the deterrent effects of moderately adverse weather, such as cold temperature, low precipitation, discomfort humidity and/or poor visibility. This is likely due to shorter travel distances and greater accessibility to workplaces and services in such neighborhoods. However, under extreme weather conditions (e.g. freezing and hot temperatures), individuals living in high population density areas are less likely to participate in mandatory out-of-home activities, which is consistent with increased generalized travel disutility under adverse conditions in urban settings ( 32 ). Better quality of transportation services in the neighborhoods is found to offset weather-related disutility. Results suggest that higher transit service frequency increases individuals’ probability to participate in mandatory activities during high humidity and heavy precipitation. Under strong wind conditions, closer distance to transit stops is found to be associated with higher participation in mandatory activities. These outcomes perhaps indicate that robust transit supply supports schedule-constrained activities even under unfavorable weather conditions. For maintenance activities, the model shows that freezing temperature, cold temperature, and hot temperature – all increase the likelihood of participation, which may initially appear counterintuitive. However, such activities could include indoor errands or temperature-insensitive obligations such as grocery shopping, making them less elastic to weather. Like mandatory activities, maintenance activities are positively associated with temperature extremes, especially among older adults (age over 55 years). Other extreme weather events, discomfort humidity, and strong air also exhibit positive coefficient values. This might demonstrate the nature of the maintenance activities, e.g. critical daily groceries, attending medical appointments, performing errands, etc. – which might be crucial for daily life and household functioning and cannot be postponed for a better weather. As expected, the positive effect of light air suggests less deterrence compared to windier conditions. However, high income × high humidity shows a negative effect, suggesting that wealthier individuals may delay or substitute maintenance trips during extreme weather conditions. This nuanced difference in the high-income households may reflect a higher level of schedule flexibility or ability to outsource such tasks. Similarly, freezing temperatures decrease the likelihood of maintenance activity participation in larger households, reflecting increased coordination and exposure burdens that make such semi-flexible activities less attractive. Moreover, the maintenance activity participation is more likely to reduce under hot temperature conditions, indicating that semi-flexible errands tend to get postponed or consolidated in households with kids. As expected, the interaction between no work flexibility and high humidity has a negative effect on maintenance activity participation, demonstrating the limited ability of schedule-constrained individuals to avoid uncomfortable weather conditions that lead to reduced participation in semi-flexible maintenance activities. In contrast, positive interaction effects are found between heavy precipitation and employment in IT and finance. Workers in such sectors might have greater access to flexible or remote work arrangements and are better able to adapt their activity schedules, thereby mitigating the disutility associated with adverse weather. Moderate humidity condition, such as discomfort humidity, is found to increase maintenance activity participation in the households with kids, suggesting that few essential maintenance activities in such households are likely to be undertaken under moderately relative humidity. Furthermore, extreme temperatures (i.e. freezing and hot temperature) tend to reduce maintenance activity participation in high population density areas – possibly reflecting higher exposure, congestion, and travel discomfort in urban settings combined with the flexible nature of maintenance activities that allows postponement under adverse conditions ( 16 ). In contrast, denser areas with higher population, residential and/or activity density are found to mitigate the deterrent effects of moderate weather conditions, such as cold temperature, discomfort humidity, low precipitation or poor visibility. These outcomes might suggest the resilience of urban neighborhoods to minor weather disruptions. However, heavy precipitation tends to discourage maintenance activity participation even in transit-rich areas with higher transit service frequency, and greater distance to transit stops further reduce maintenance activity participation during strong winds. Such results underscore the importance of transit accessibility and the sensitivity of maintenance travel to compounded weather-related disutility. Discretionary activities, being the most flexible, show the greatest sensitivity to weather. The results indicate that young adults (aged 25 to 40 years) and older adults (age over 55 years) are more likely to increase discretionary trips during hot temperatures and freezing temperatures. Interestingly, freezing, cold and hot temperatures reduce discretionary activity participation, suggesting aversion to extreme temperatures for leisure purposes, which aligns with prior findings ( 7 ) that social or leisure travel is often the first to be postponed under adverse weather. The interaction between freezing temperatures and larger household size also shows a negative effect, consistent with the higher coordination burden and exposure risk faced by larger households during adverse weather conditions. The higher negative effects of hot temperature and discomfort humidity on discretionary activity participation in the households with kids indicate that such weather conditions substantially discourage to undertake discretionary activities. This is perhaps consistent with the highly flexible nature of these activities and heightened sensitivity to thermal discomfort when children are present. Light air tends to increase the discretionary activity participation, which is expected. High humidity is found to reduce participation. However, high income households tend to increase their discretionary activity participation in high humidity. For high-income households, high humidity possibly acts as a strong incentive to move their discretionary activities indoors to venues they can afford. As expected, light air tends to increase individuals’ discretionary activity participation, whereas poor visibility and strong air are less likely to increase the discretionary activity participation – reinforcing the idea that comfort and perceived safety are key concerns for discretionary travel. Finally, similar to maintenance activity participation, the interaction variable ‘no work flexibility × high humidity’ shows a negative effect on discretionary activity. As expected, compared to staying at home, individuals tend to participate in discretionary activities more during low precipitations. For discretionary activities, extreme temperatures (freezing and hot temperatures) combined with high population or residential density tend to reduce participation, reflecting heightened exposure and discomfort in dense urban settings and the ease with which such highly flexible optional activities can be postponed. In contrast, mild weather conditions (low precipitation and poor visibility) in high population or activity density areas are more likely to increase discretionary activity participation – perhaps, due to shorter distances, lower perceived risk and greater activity location availability in such areas. Transit service frequency offsets high humidity condition by improving accessibility but amplifies deterrence during high precipitation – probably due to crowding or service unreliability during heavy precipitation. The final model specification estimates standard deviations of two random parameters – strong air and high humidity, along with their mean values to explore taste variations across the sample population. All two random parameters are statistically significant and standard deviations of the random parameters are found higher than the mean values – which confirms the presence of unobserved preference heterogeneity across the sample population. Table 4 Activity participation model parameter estimation results Variables Types of Activities (Reference: In-home activity) Mandatory Activity Maintenance Activity Discretionary Activity Coeff sig . Coeff sig . Coeff sig . Constants -0.8749*** -0.4108*** -0.5486*** Temperature Freeze ( 77F) 0.5632*** - -0.1485*** Freeze × household size - -0.5189*** -1.0449*** Freeze × Age 55 + years -0.4854*** - 0.0508*† Freeze × Vehicle ownership > 1 0.5109*** - - Hot × Age 25–40 years 0.2107*** - 0.2614*** Hot × Age 55 + years -0.3025**† 0.4189*** 0.3100*** Hot × Employment: Construction and Retail 0.1176*** - - Hot × Households with kids -0.0949*** -0.5710*** -1.2107*** Freeze × Population density -0.2365*** -0.0814***† -0.8547*** Freeze × Residential density - - -0.2044*** Hot × Population density -0.0240**† -0.0548*** -0.5621*** Cold × Residential density 2.5558*** 3.1900*** - Relative humidity High (> 75%) 0.7049*† - -0.0521*** Discomfort (60% − 75%) 0.0512*** 0.5144**† - High × High income households 0.0549*** -0.1000*** 0.1478***† High × No flexible work hours 0.6218***† -0.0264*** -0.0789** Discomfort × Households with kids - 0.1597*** -0.6421*** Discomfort × Activity density 0.4100*** 1.4178*** - High × Transit service frequency 0.5956*** - 0.4250*** Precipitation Light ( 0.35") × Employment: IT and Finance -0.1635*** 0.2954*† - Heavy × Transit service frequency 0.3205*** -0.9958*** -0.7419*** Windspeed Light air ( 20 mph) -0.2500*† 0.0654*** -0.0946*** Strong air × Distance to the nearest transit stop -0.1208*** -0.6321*** - Visibility Poor ( 20 mph) - 0.4158*** - High humidity (> 75%) 1.3649*** - - Model fits Observations 97118 Log-likelihood (null) -164186.10 Log-likelihood (final) -129135.10 AIC 258426.2 Note: *** 1% significance level, **5% significance level, *10% significance level. Coefficients marked with † are not statistically significant after Holm-Bonferroni correction for multiple testing. 5.2. Mode choice The estimated mode choice model in Table 5 presents a nuanced understanding of how hourly weather conditions influence transportation preferences, with particular attention to interactions between temperature, visibility, windspeed, humidity, demographics and neighborhood characteristics. The final model provides a better fit than the constant only model, as indicated by its higher log-likelihood value. The likelihood ratio (LR) statistic is 133092.6 that exceeds the 1% critical chi-square value of 124.13, leading to rejection of the constant only model. All parameters retained in the final model are statistically significant at least at the 10% level. Furthermore, to control the family-wise error rate arises due to multiple hypothesis tests, statistical significances of all the parameters are re-evaluated using the Holm-Bonferroni procedure. The starting adjusted significance threshold ( \(\:\alpha\:/m\) ) for the Holm-Bonferroni correction method, which is compared to the smallest p-value, is calculated as 0.000087. Although p-values of few parameters do not meet the Holm-Bonferroni’s incrementally adjusted significance thresholds, they are retained in the final model due to their theoretically consistent coefficient signs and nominal statistical significance, thus maintaining behavioral interpretability. The mode choice model uses taxi as the reference category for alternative-specific constants (ASCs) and includes interaction terms to capture weather sensitivity across different demographics, trip and built environment contexts. As expected, ASCs for other modes (e.g., bike, bus, HOV, rail, SOV, walk) are significantly positive, suggesting higher baseline utility for these modes relative to taxi, with walk and SOV showing especially strong preferences. In addition to that, this study utilized a generic travel time parameter, which expectedly came out to be negative and statistically significant. Temperature has a strong influence on mode choice, particularly when examined at more granular levels. For example, the model demonstrates that freezing cold temperatures increase the likelihood of choosing motorized modes (e.g. SOV, HOV, taxi) and bus while strongly discouraging biking and rail mode choice, which is consistent with prior findings in travel behavior literature (e.g., ( 20 ), ( 33 )). This highlights weather-induced shifts from active to motorized travel. Walking is assumed as a reference under freezing conditions, suggesting it remains a necessary baseline for short trips even in extreme cold weather. Moderate temperatures, such as cool and warm temperatures, are also found to decrease the probability of choosing activity modes (walking and biking) compared to SOV, while increasing the tendency to choose public transportation modes (transit or rail). While interacting the temperatures with demographic variables, interesting outcomes are observed. Younger individuals (16–24 years) demonstrate reduced reliance on SOV and HOV under extreme weather conditions, such as freezing and hot temperatures, perhaps indicating lower vehicle access and greater tolerance for discomfort. In contrast, older adults (55+) tend to choose SOV and reduce taxi use during freezing conditions. This is consistent with heightened safety concerns and a preference for private and controlled environments ( 34 ). Such results may suggest that sensitivity to weather varies by age group, which aligns with behavioral studies that report stronger weather sensitivity among more vulnerable populations ( 18 ). High-income households are more likely to prefer HOV and taxi under freezing conditions compared to HOV, while reducing bus use, reflecting their greater access to private and semi-private mobility options. Single-vehicle households increase their propensity to choose HOV during freezing weather, which indicates household-level coordination and ridesharing as adaptive strategies when driving conditions deteriorate. As expected, multiple vehicle households tend to choose SOV during hot temperature weather. The model also incorporates temporal dimensions, such as peak hour and off-peak hour interactions with temperature. For example, during peak hours, freezing conditions increase the probability of choosing bus and SOV modes – likely reflecting urgency or the need for faster and enclosed travel. Interestingly, warm temperatures have a mixed effect. It reduces the likelihood of biking and walking and even deters HOV travel. This is consistent with heat stress literature, which shows that extreme heat can suppress physical activity and encourage air-conditioned travel ( 35 ). Furthermore, hot temperatures in dense areas reduce the likelihood of walking relative to SOV, while increasing the probability of choosing HOV, bus and rail. Dense built environments generally contribute to higher heat exposure ( 36 ), which may increase thermal discomfort for active modes during hot weather conditions and provide viable shared and transit alternatives. In contrast, with the increase in activity density, moderate weather (e.g. cool temperature) tends to increase the probability of choosing walking, biking, bus and taxi mode choices compared to HOV. Cooler temperatures may lower thermal discomfort, thus making active modes more attractive while areas with higher activity locations support short trips and multimodal accessibility. This aligns with the notion that moderate weather encourages non-motorized travel in compact and mixed-use environments ( 37 ). In terms of relative humidity, results suggest that comfortable humidity encourages walking and reduces rail mode choice, while discomfort humidity demonstrates the opposite, supporting earlier findings ( 7 ) that perceived discomfort strongly deters non-motorized travel. High humidity combined with low income (low income households × high humidity) deters rail and SOV use, which might reflect compounded barriers for disadvantaged populations when exposed to extreme weather conditions. Compared to the walking mode, employed males are found to increase their likelihood of SOV and taxi mode choices under high humidity while reducing biking – might be indicating the consistency with time constraints and comfort-seeking behavior among workers ( 15 ). Interestingly, higher transit service frequency in the neighborhoods offsets humidity-related disutility, increasing bus and rail mode choices compared to SOV choice – which might underscore the protective role of high-quality transit infrastructure during extreme weather conditions. Finally, the positive interaction between discomfort-level humidity and activity density for non-auto mode choices suggests that higher activity density in the neighborhoods (i.e. compact urban form) might buffer moderate discomfort, likely through reduced exposure time and improved route choice flexibility. Other weather conditions, such as precipitation, windspeed and visibility also demonstrate expected behavior. Individuals are more likely to choose SOV, walk, bus and taxi, compared to bike during low precipitation. However, heavy precipitation tends to lower walking and biking mode choices while increasing bus and rail mode choices in areas with frequent transit services – reflecting classic weather-induced mode substitution ( 38 ). Wind speed plays a critical role. Light air is found to decrease probability of choosing motorized and public travel modes compared to active modes such as walking and biking. When interacted with land-use diversity, such weather condition is observed to continue to support active travel, while also increasing bus mode – reflecting improved access and comfort in mixed-use areas. Conversely, individuals are less likely to choose biking during strong winds while more likely to choose SOV, HOV, bus and rail modes, which might be consistent with safety and stability concerns. Higher residential density in the neighborhoods amplifies these effects, particularly increasing bus and taxi mode choices. This might highlight how urban structure shapes weather resilience. The negative interaction effects between female and hourly windspeed on active and public modes such as bike, rail and walk indicate that women are more deterred by windy conditions. This finding possibly reinforces gender-based disparities in perceived comfort and safety under adverse conditions – aligning with previous studies that have found women’s mode choices to be more influenced by comfort, safety, and environmental factors ( 39 ). Visibility conditions further reveal interesting patterns. Clear visibility increases the likelihood of bike mode choice but decreases bus and HOV preference compared to the SOV. When interacted clear visibility with distance to the nearest transit stop, it is found that people living near transit stops tend to choose active modes and public transportation while reducing the propensity to choose SOV compared to taxi under the clear weather condition – possibly reflecting better accessibility and improve comfort for outdoor travel in the neighborhoods. In contrast, poor visibility exhibits negative effects for biking and private vehicle choices while increasing bus mode choice in diverse land-use environments. This may suggest that travelers probably perceive professional transit operations as safer and more reliable under poor visibility conditions. The final model specification estimates standard deviations of two random parameters (freezing temperature and poor visibility) along with their mean values to explore taste variations across the sample population. Both random parameters are statistically significant and standard deviations of the random parameters are found higher than the mean values – which confirms the presence of taste variations (unobserved preference heterogeneity) across the sample population. Table 5 Mode choice model parameter estimation results Variables Types of available modes SOV HOV Walk Bike Bus Rail Taxi Coeff sig . Coeff sig . Coeff sig . Coeff sig . Coeff sig . Coeff sig . Coeff sig . Constants 3.0686*** 2.9954*** 2.1635*** 1.0050*** 1.4935*** 2.0965*** Ref. Travel time -0.0028*** -0.0028*** -0.0028*** -0.0028*** -0.0028*** -0.0028*** -0.0028*** Temperature Freeze (< 32F) 1.0635*** 0.9548***† Ref. -1.0074*** 0.6078*** -0.3958*** 0.5611*** Cold (32F − 50F) 0.5963*** - Ref. -0.3548*** 0.7411*** - 0.3651***† Cool (51F − 68F) Ref. -0.4977*** -0.1069*** -0.2046*** 0.2608*** - 0.2400*** Warm (69F − 77F) Ref. -0.2058*** -0.1000**† -0.0840*† - 0.1150*** - Freeze × Age 16–24 years -0.8990*** -0.9888*** - Ref. - - - Freeze × Age 55 + years 0.6322*** - - - Ref. - -0.3984**† Freeze × High income households Ref. 0.2429***† - - -1.3740*** - 0.2636*** Freeze × Vehicle ownership = 1 - 0.6214*** -0.1088*† -0.5120*** - - Ref. Freeze × Peak hour departure time 0.0530**† - Ref. - 0.2959*** 0.8669*** - Hot (> 77F) × Age 16–24 years -0.8522*** -0.7001*† - Ref. 0.3120*** - - Hot × Age 25–40 years Ref. 0.3541*** -0.4158*** - -0.0584*† - - Hot × Vehicle ownership > 1 1.2410*** Ref. - -0.9521***† - -0.0846*** - Hot × Off-Peak hour departure time 0.1863***† -0.1816*** Ref. - - - - Hot × Population density Ref. 0.9410*** -0.0825*† - 1.0353*** 0.2155**† - Cool × Activity density - Ref. 0.2477*** 0.4444*** 0.6189*** - 0.2421*** Relative humidity Comfort ( 75%) × Low-income households -0.6908*** - 0.0950*** - Ref. -0.1147*† - High × Employed male 0.5100*** - Ref. -0.0485*** - - 0.1622*** High × Transit service frequency Ref. - - - 0.6257*** 0.0818*** - Discomfort × Activity density - -0.5641*** 0.8453*** 0.1011*** 0.8741*** 0.4280*** Ref. Precipitation Light ( 0.35") × Transit service frequency Ref. - -0.3158*** -0.6489**† 0.2107*** 0.0954*** - Windspeed Light air ( 20 mph) 0.1626*** 0.4869*** - -0.7416*** Ref. 0.2500*** - Strong air × Residential density - 0.0621*** -0.3892*† -0.1986*** 0.3555*** Ref. 0.5000*** Hourly windspeed × Female Ref. - -0.0748*** -0.8044*** - -0.2849*** - Visibility Clear (> 6.2 miles) Ref. -0.0411*** - 0.3035*** -0.1510*** - - Poor (< 0.60 miles) -0.0846*** Ref. 0.6637*** - - 0.4218***† - Clear × Distance to the nearest transit stop 0.2228*** - -0.4109*** -0.8541*** -1.0547*** - Ref. Poor × Land use diversity -0.4632***† -0.1587*** Ref. -0.9218*** 0.6308**† - - Standard deviations of random parameters Freeze temperature - - - - 1.2108*** - - Poor visibility 0.2589*** - - - - - - Model fits Observations 94481 Log-likelihood (null) -183851.5 Log-likelihood (final) -117305.2 AIC 234772.4 Note: *** 1% significance level, **5% significance level, *10% significance level. Ref. = reference mode. Coefficients marked with † are not statistically significant after Holm-Bonferroni correction for multiple testing. 5. ELASTICITY ANALYSIS This study computes elasticity measures for all explanatory variables to enable a more interpretable assessment of the weather determinants of activity participation and mode choice decisions. The parameter estimation results discussed earlier describe the statistical associations between explanatory variables and travel demand choices. To better convey the magnitude of these effects, the estimation results are complemented with the elasticity analysis, which provides a more interpretable measure of behavioral responsiveness ( 40 ). Table 6 reports the average elasticities for activity participation, while Table 7 presents the corresponding elasticities for mode choice decisions. For binary explanatory variables, elasticities represent the percentage change in the probability of selecting a given alternative when the variable shifts from 0 to 1. For continuous variables, elasticities indicate the percentage change in choice probability associated with a 1% change in the variable. These elasticity results provide insights into the behavioral relevance of the interaction between weather and demographics, built environment and transportation service characteristics, and inform policy interpretation. Overall, the findings indicate the importance of the incorporation of weather related information within the travel demand modeling framework. The elasticity estimates reveal substantial differences in how weather and contextual factors influence activity participation across activity types (Table 6 ). The average elasticity analysis across all alternatives indicates that daily activity participation decisions are mostly affected by the relative humidity and windspeed, followed by visibility and temperature related factors. Precipitation is found to affect the activity participation decisions the least. Across all activity types, extreme weather conditions, particularly temperature and relative humidity, exert the largest impacts, while mild weather conditions and built-environment interactions generally exhibit smaller and moderating effects. Elasticities for discretionary activities are found to have greater impacts, with freezing temperatures and high relative humidity exhibiting the strongest reductions in discretionary activity participation compared to mandatory and maintenance activities. This may confirm the high behavioral elasticity of discretionary activities. Strong wind conditions also have considerable impacts on mandatory and discretionary activity participations that might indicate significant sensitivity to safety and comfort concerns. Interactions between adverse weather and high population or residential density further amplify these effects. Sociodemographic interactions, such as households with children, older adults (55+) and household size, demonstrate moderate elasticities, which primarily affect discretionary and maintenance activity participation. These variables intensify negative weather impacts but do not independently dominate activity participation decisions. Temperature interactions with age and employment type also have medium impacts, indicating heterogeneous but bounded behavioral responses. Furthermore, variables associated with comparatively mild weather conditions (e.g. cold temperatures, light precipitation, light wind) and transport supply or accessibility (for example, transit service frequency, distance to the nearest transit stations, activity density) exhibit relatively lower elasticities across activity types. While these factors demonstrate expected behaviors, their influence is secondary compared to extreme weather and household constraints. Overall, the elasticity analysis of the activity participation model indicates that extreme weather conditions dominate activity participation responses, particularly for discretionary activities, while household characteristics and urban context primarily function as impact amplifiers or buffers. Mandatory activity participation remains the least affected across all variables – perhaps reflecting their low behavioral elasticity and limited scope for suppression. In the case of mode choice model (Table 7 ), the elasticity analysis indicates that the average elasticity analysis across alternatives suggests that temperature, relative humidity, windspeed and visibility have greater impact on the mode choice decisions, while precipitation and mild weather conditions exhibit comparatively lower impacts. The largest impacts are associated with extreme temperature conditions and their interactions. Freezing and cold temperatures exert greater impacts on the preferences of motorized modes (particularly SOV) and transit, while strongly suppressing active modes such as biking. Interaction between temperatures and departure times suggest that peak-hour interactions under freezing weather conditions has the most influential effects – substantially increasing reliance on SOV and transit modes. Interaction between hot temperatures and population density also demonstrates considerable impacts on mode choices, especially on the bus mode choices. Relative humidity effects, specifically discomfort and high humidity interacting with transit service frequency, also demonstrate high elasticity. This indicates strong effects on the possible modal shifts away from SOV and toward transit and walking under uncomfortable conditions. Visibility-related interactions, such as clear conditions combined with distance to the nearest transit stations and poor visibility interacting with land use diversity, are also found to have higher impacts on mode choices. Although windspeed-related factors have considerable standalone effects on the mode choices, moderate impacts are observed in the case of interacting them with the built environment variables. Strong air and gender-specific wind sensitivity (female × hourly windspeed) are found to notably reduce biking and rail choices while increasing reliance on motorized modes. Activity density effects under cool conditions also fall within this range, indicating meaningful but context-dependent adjustments in modal preferences. These variables influence mode choice decisions without dominating behavior across all alternatives. In general, lower impacts are observed for precipitation effects, such as light rainfall, and for mild temperature categories (cool and warm conditions without any interactions). Although these variables often exhibit expected behaviors (e.g. slight higher preference towards public transportation modes over active modes), their impacts are found to be modest relative to the extreme weather condition effects. Similarly, several demographic interactions (e.g. age-specific hot weather effects) exhibit lower impacts – perhaps suggesting localized rather than system-wide behavioral influence. Overall, the elasticity analysis for the mode choice model suggests that extreme weather conditions dominate mode choice decisions, particularly the choices of SOV, transit and bike modes, while precipitation and mild weather conditions play a moderate role. Active travel modes are observed to be most sensitive overall, transit modes show strong sensitivity to service and visibility interactions, and SOV remains comparatively resilient except under few severe weather conditions. Table 6 Activity participation model elasticity analysis results Variables Types of Activities (Reference: In-home activity) Mandatory Activity Maintenance Activity Discretionary Activity Temperature Freeze ( 77F) 0.8557 - -3.6427 Freeze × household size - -1.5219 -2.5410 Freeze × Age 55 + years -3.3924 - 4.5891 Freeze × Vehicle ownership > 1 0.0455 - - Hot × Age 25–40 years 0.1970 - 1.1000 Hot × Age 55 + years -2.3541 0.5476 0.7317 Hot × Employment: Construction and Retail 0.5149 - - Hot × Households with kids -2.5547 -1.2013 -4.1056 Freeze × Population density -3.8000 -2.1088 -2.0014 Freeze × Residential density - - -8.7149 Hot × Population density -0.8521 -1.6228 -3.6553 Cold × Residential density 4.8518 3.4500 - Relative humidity High (> 75%) 5.9889 - -25.6329 Discomfort (60% − 75%) 0.1727 3.2771 - High × High income households 1.7362 -2.3257 4.6605 High × No flexible work hours 3.1867 -2.4798 -10.9208 Discomfort × Households with kids - 1.5788 -0.6421*** Discomfort × Activity density 1.0844 1.8546 - High × Transit service frequency 2.5598 - 2.4804 Precipitation Light (< 0.20") 0.9089 0.3433 0.0715 Light × Population density 0.8950 1.0454 5.4141 Heavy (0.35") × Employment: IT and Finance -0.3146 0.5897 - Heavy × Transit service frequency 1.5121 -1.1248 -3.9847 Windspeed Light air ( 20 mph) -8.4511 0.4582 -2.5750 Strong air × Distance to the nearest transit stop -5.1046 -4.6528 - Visibility Poor (< 0.60 miles) -1.6849 - 9.8236 Poor × Activity density 1.0008 0.8419 4.7777 Table 7 Mode choice model elasticity analysis results Variables Types of available modes SOV HOV Walk Bike Bus Rail Taxi Temperature Freeze (< 32F) 29.4254 3.2186 - -5.5613 2.2859 -4.0942 5.3505 Cold (32F − 50F) 21.0495 - - -2.8447 8.6662 - 4.6615 Cool (51F − 68F) - -0.4086 -2.0360 -0.9854 8.4507 - 10.7861 Warm (69F − 77F) - -1.0214 -2.1380 -0.5671 - 6.2838 - Freeze × Age 16–24 years -2.5230 -1.2215 - - - - - Freeze × Age 55 + years 7.2288 - - - - -2.2368 Freeze × High income households - 1.9381 - - -12.3097 - 1.2489 Freeze × Vehicle ownership = 1 - 4.3135 -6.5449 -5.4082 - - - Freeze × Peak hour departure time 15.1146 - - 19.8954 24.9280 - Hot (> 77F) × Age 16–24 years -0.3400 -0.1199 - - 0.5603 - - Hot × Age 25–40 years - 4.0751 -6.6786 - -11.4472 - - Hot × Multiple vehicle household 9.9377 - - -7.8421 - -0.8650 - Hot × Off-Peak hour departure time 4.1048 -3.4734 - - - - - Hot × Population density - 5.7480 -2.4721 - 19.8662 1.5550 - Cool × Activity density - - 4.5019 9.2416 13.2252 - 3.8990 Relative humidity Comfort ( 75%) × Low-income households -24.3367 - 0.2358 - - -8.9154 - High × Employed male 8.2047 - - -1.4521 3.1646 High × Transit service frequency/acre - - - - 16.8731 4.9731 - Discomfort × Activity density - -1.6532 5.0173 0.8688 4.6986 1.6778 - Precipitation Light ( 0.35") × Transit service frequency - - -7.5025 -10.6543 6.7566 1.5088 - Windspeed Light air ( 20 mph) 2.8235 0.0324 - -6.1737 - 9.8368 - Strong air × Residential density - 4.8911 -10.6305 -4.1862 -6.7584 - 5.8727 Hourly windspeed × Female - - -5.3208 -20.6743 - -11.3254 - Visibility Clear (> 6.2 miles) - -1.2679 - 15.9013 -4.4967 - - Poor (< 0.60 miles) -0.1525 - 0.0108 - 0.1226 - Clear × Distance to nearest transit stop 16.5478 - -0.2418 -19.4872 -25.8567 - - Poor × Land use diversity -12.4127 -0.0159 - -15.4712 -10.6754 - - 6. CONCLUSION This study presents an analysis of the effects of weather conditions on travel demand. It develops a combined database of travel demand and monthly, daily and hourly weather conditions. The trip information of the database was extracted from the 2018-19 CMAP Household Travel Survey. To obtain the weather information, the study identified multiple prominent and reliable meteorological data sources, such as NASA Prediction of Worldwide Energy Resources (POWER) database, NREL National Solar Radiation Database (NSRDB), and U.S. Local Climatological Data (LCD) data from National Centers for Environmental Information (NCEI) of NOAA. After careful investigation, this study selected the NOAA-NCEI LCD database to extract the monthly, daily and hourly weather information. The study identified and analyzed a wide range of weather metrics and their effects on activity-travel pattern, specifically on activity participation and mode choice behavior. The descriptive analysis of the daily and hourly weather and travel demand provides an appropriate understanding of activity-travel behavior changes with the change in weather conditions. The study estimates preliminary activity participation and mode choice models that exclusively examine the impacts of multiple hourly weather metrics on individuals’ activity participation and mode choice behaviors. The activity participation model provides a detailed depiction of how different weather conditions shape participation in various out-of-home activities. Compared to the reference category of home activity, extreme weather, especially freezing temperatures, humidity, and wind, generally discourages non-mandatory trips, while mandatory trips remain more resilient. These findings reinforce the need for adaptive infrastructure and policy, to support vulnerable populations and ensure access to opportunities across different weather scenarios. Results of the mode choice model provide strong empirical evidence that hourly variations in weather conditions significantly shape mode choice behavior. It confirms and extends previous research by demonstrating the dynamic, nonlinear, and interactional nature of weather impacts on mode choice behavior. Furthermore, the magnitudes of the impacts of the determinants are tested in this study by analyzing the elasticity of the variables. Results suggest that generally temperature, relative humidity, windspeed and visibility exert greater impacts on activity-travel behavior, with discretionary activity participation and active mode choices exhibiting the highest elasticities, while mandatory activities and single-occupancy vehicle choices remain comparatively inelastic. Overall, the findings demonstrate that weather conditions dominate travel behavior responses, with demographic and built-environment factors primarily moderating these effects. These insights reinforce the need for dynamic travel demand models that incorporate real-time or hourly weather data and underscore the importance of tailoring transportation policies and infrastructure (e.g., shelter, cooling systems, or bike lanes) to support resilient and responsive transportation systems. One of the limitations of this study is to use the weather data based on only Chicago O’Hare International Airport weather station, therefore, not accounting for the spatial variability of weather conditions across the region. The analysis was focused on exploring temporal variations, effectively relying on regionally averaged weather conditions. An important direction for future research of this study is to incorporate spatially disaggregated weather data by linking trips to the nearest weather stations within the Chicago region, such as O’Hare, Midway, DuPage, Waukegan, Aurora, and Joliet weather stations, to better capture location-specific weather effects. Furthermore, the travel survey data used in this study predate the COVID-19 pandemic and the widespread adoption of remote work. As a result, although the models incorporated indicators for flexible work schedules, they were unable to fully capture the effects of work-from-home arrangements and other online activities (e.g., online food ordering, e-commerce, and on-demand delivery) on the relationship between weather conditions and travel demand. Since CMAP is currently collecting a new wave of the household travel survey that includes detailed information on alternative work arrangements and online activities, an immediate avenue for future research is to update this analysis using the post-pandemic emerging activity-travel data to examine more behaviorally relevant interactions between weather conditions and travel demand. Moreover, this study was unable to examine interaction effects between weather conditions and individuals’ attitudes on travel demand. While such factors could enrich the weather-travel demand analysis by capturing individual perceptions, preferences and risk tolerance, they could not be incorporated due to the absence of lifestyle and attitudinal information in the travel survey data. Future research could address this limitation by integrating attitudinal information from enhanced travel surveys, stated-preference experiments, or complementary data sources to better represent heterogeneous behavioral responses to weather conditions. Methodologically, this study only considers the unobserved preference heterogeneity (random taste variations) that accommodates the variations occurred by repeated observations in the specific preferences for different attributes. The scale heterogeneity (i.e. variance heterogeneity) that captures the variations in the overall consistency or randomness in individuals' choices – is disregarded. Accommodating both scale and preference heterogeneity can provide a more accurate and nuanced understanding of preferences and decision-making behavior under various weather conditions. Therefore, one of the immediate future works of this study is to refine the models by extending the mixed logit model formulation to include both unobserved preference heterogeneity (through random parameters) and scale heterogeneity (through random scale parameters) within its modeling framework. This will assist in appropriately analyzing the choices under dynamic weather conditions and making informed predictions. Another future work includes exploring the effects of weather changes on more activity-travel dimensions such as activity timing, activity location, and activity duration, among others. Nevertheless, this study provides critical insights on how hourly weather conditions affect individuals’ daily activity participation and mode choice decisions. The ultimate goal of developing these weather-informed travel demand models is to enhance the current simulation framework of the multiagent activity-based travel demand model, POLARIS, and make it a responsive and resilient transportation system simulator. Outcomes of this study will be beneficial to conceptualize and develop a weather-informed simulation workflow that will significantly improve the accuracy and responsiveness of travel and activity demand forecasts, providing invaluable insights for transportation planning and operations under varying environmental conditions. Declarations Submitted for TRR revision 105 th Annual Meeting of Transportation Research Board, January 11-15, 2026, Washington D.C AUTHOR CONTRIBUTION STATEMENT The authors confirm contribution to the paper as follows: study conception and design: Nazmul Arefin Khan; data collection: Nazmul Arefin Khan, Joshua Auld; analysis and interpretation of results: Nazmul Arefin Khan; draft manuscript preparation: Nazmul Arefin Khan. All authors reviewed the results and approved the final version of the manuscript. ACKNOWLEDGEMENTS This report and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Integrated Transportation and Energy Cross-Sectoral System of Systems at Scale (ITE-S4), an initiative of the Energy Efficient Mobility Systems (EEMS) Program. Melissa Rossi, a DOE Office of Energy Efficiency and Renewable Energy (EERE) manager, played an important role in establishing the project concept, advancing implementation, and providing guidance. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. References 1. Petrović, D., I. Ivanović, V. Đorić, and J. Jović. Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models. 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Transportation Research Part A: Policy and Practice , Vol. 94, 2016, pp. 360–373. https://doi.org/10.1016/j.tra.2016.09.021. 40. Parady, G., and K. W. Axhausen. Size Matters: The Use and Misuse of Statistical Significance in Discrete Choice Models in the Transportation Academic Literature. Transportation , Vol. 51, No. 6, 2024, pp. 2393–2425. https://doi.org/10.1007/s11116-023-10423-y. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8703831","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580753912,"identity":"5d027425-90c0-427e-8157-27f3c688eb9b","order_by":0,"name":"Nazmul Arefin Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYNCCAhDB+OAAQwUDAx9xWgxABLPBAYYzDAxsJGlhYGwjQos5+/Gnm3kMGOTN25sZDxfOs8tjY2C/+JgHjxbLnoS020AthnPOHGY4PHNbcjEbA0+xMT4tBgcSjoG0MM6QyD9wmHcbc2IbA0+a5Ax8Ws4/bANpsZ8h/5jhMO+ceiK03EhmA2lJnCHBDNTScBiohf2YxAe8Wp6x3ZxjIJE8gyeZ4TDPseOJbcw8zAZ4tZxPf3bjTYWN7Qz2w8yfeWqqE/vZ2x8+SMCjBQokkNjMPAaENaAB9gckaxkFo2AUjIJhDQAFlkfLxvqB4AAAAABJRU5ErkJggg==","orcid":"","institution":"Argonne National Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Nazmul","middleName":"Arefin","lastName":"Khan","suffix":""},{"id":580753913,"identity":"17717206-b79f-421a-b0cd-c5cee2bbb748","order_by":1,"name":"Joshua A. 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BACKGROUND","content":"\u003cp\u003eUnderstanding the complex dynamics of activity-travel behavior is critical for effective urban and regional planning, infrastructure development and policy evaluation. Traditional travel demand models often focus on socio-demographic and built environment factors, while the influence of environmental conditions, particularly weather, on daily travel decisions is often disregarded. Weather conditions are a critical, yet underappreciated, factor significantly influencing travel behavior and the overall transportation system. Extreme temperatures, varying precipitation levels, humidity, wind speed, etc. alter the way people prefer to travel, and sometimes not to travel at all (\u003cem\u003e1\u003c/em\u003e). Adverse weather often leads to a decrease in using active transportation modes (e.g., walking, cycling) and an increase in the use of motorized transport ((\u003cem\u003e2\u003c/em\u003e), (\u003cem\u003e3\u003c/em\u003e)). Similarly, inclement weather may also significantly reduce the overall number of trips undertaken, especially discretionary or leisure-related travel, while essential trips are sometimes rescheduled or delayed to more favorable conditions. As transportation systems aim to become more resilient and responsive, a nuanced understanding of weather-related travel behavior has become increasingly important for effective transportation planning, particularly in the era characterized by the adoption of abundant transportation technology (\u003cem\u003e4\u003c/em\u003e). A major limitation in current transportation modeling is the insufficient incorporation of detailed weather variables, which can significantly reduce the accuracy of travel demand forecasts and hamper effective planning and resource allocation (\u003cem\u003e5\u003c/em\u003e), (\u003cem\u003e6\u003c/em\u003e). Further research is needed to explore how travelers adjust their behavior in response to changing weather conditions, offering valuable insights for building more adaptive and resilient transportation systems.\u003c/p\u003e \u003cp\u003eA growing body of literature has investigated the impacts of weather conditions on travel behavior and demand and established measurable influence of weather on activity-travel behavior. Weather's substantial impact on trip generation, destination selection, and modal split has been well-documented in recent research (\u003cem\u003e7\u003c/em\u003e), which suggested that weather variables be integrated into traffic demand models ((\u003cem\u003e8\u003c/em\u003e), (\u003cem\u003e9\u003c/em\u003e)). Studies predominantly concentrate on individuals\u0026rsquo; mode choice decisions, given their responsiveness to prevailing weather patterns. For instance, researchers found that car trips are less sensitive to specific weather details than bicycle trips, which are highly influenced by seasonal weather patterns due to cyclists' direct exposure (\u003cem\u003e1\u003c/em\u003e). While seasons often represent typical weather for models ((\u003cem\u003e10\u003c/em\u003e), (\u003cem\u003e8\u003c/em\u003e), (\u003cem\u003e2\u003c/em\u003e)), unusual daily weather can have a stronger impact than seasonal norms, potentially masking true variations (\u003cem\u003e7\u003c/em\u003e). Weather forecasts also play a role in travel decisions (\u003cem\u003e11\u003c/em\u003e), though their influence is generally less than immediate weather changes ((\u003cem\u003e12\u003c/em\u003e), (\u003cem\u003e10\u003c/em\u003e)). More intense weather conditions like heavy rain, snow, extreme temperatures, or strong winds have a greater impact on trip frequency and mode choice decisions (\u003cem\u003e12\u003c/em\u003e). Studies show that increasing precipitation reduces trips, especially leisure ones, and impacts walking and biking more significantly compared to car driving (\u003cem\u003e9\u003c/em\u003e). Conversely, public transport use often increases with precipitation and wind as people shift away from walking and cycling (\u003cem\u003e13\u003c/em\u003e). Heinen et al. (\u003cem\u003e14\u003c/em\u003e) showed that increased rainfall and wind negatively affect cycling \u0026ndash; especially for recreation. However, prior studies also found that inclement weather conditions often induce travelers to cancel trips, postpone departures, or alter routes or destinations, instead of primarily switching travel modes ((\u003cem\u003e15\u003c/em\u003e), (\u003cem\u003e16\u003c/em\u003e), (\u003cem\u003e7\u003c/em\u003e), (\u003cem\u003e17\u003c/em\u003e)).\u003c/p\u003e \u003cp\u003eThe depth of insights of the existing studies mostly hinges on the aggregate temporal resolution of the weather data, particularly based on monthly or daily average ((\u003cem\u003e18\u003c/em\u003e), (\u003cem\u003e19\u003c/em\u003e), (\u003cem\u003e3\u003c/em\u003e), (\u003cem\u003e20\u003c/em\u003e)) \u0026ndash; primarily due to data unavailability and computational constraints. Such approach has provided critical insights by demonstrating general trends. For instance, higher precipitation and extreme temperatures throughout the day reduce active mode and increase private vehicle usage ((\u003cem\u003e19\u003c/em\u003e), (\u003cem\u003e21\u003c/em\u003e)), and have significant impacts on daily errands participation decisions (\u003cem\u003e20\u003c/em\u003e). However, weather does not have a uniform effect on travel behavior throughout the day. Although the aggregate analysis provides a broad understanding of the relationships between weather and travel demand, it fails to distinguish between different types of weather events occurring within the same day; thus, it oversimplifies behaviorally distinct contexts. This level of aggregation introduces temporal aggregation bias, where short-term variations that directly influence the way people travel are averaged out, thereby masking the behaviorally appropriate influence of rapidly changing weather. Such lack of sensitivity makes models built on daily or monthly averages less accurate for predicting activity and travel behavior due to dynamic weather changes. In contrast, hourly-level analysis offers a more precise understanding of short-term variations that can capture crucial temporal nuances in activity-travel behavior. This finer temporal resolution enhances behavioral realism and supports more appropriate, responsive, and behaviorally relevant modeling, particularly when higher temporal resolution travel information are available. This approach also enables a better understanding of behavioral adaptations in response to short-term weather condition changes, which is warranted for developing models to appropriately reflect dynamic travel behavior. Despite an increasing interest in high-resolution modeling, the majority of weather-travel demand studies still rely on daily or aggregate temporal resolutions ((\u003cem\u003e1\u003c/em\u003e), (\u003cem\u003e22\u003c/em\u003e)), which may lead to inaccurate estimation of travel demand.\u003c/p\u003e \u003cp\u003eThis study addresses aforementioned literature gaps by focusing on the hourly analysis of weather's impact \u0026ndash; a crucial step for capturing short-term behavioral variations and deriving more accurate, responsive, and behaviorally relevant insights on activity-travel behavior. To this end, it first identifies the appropriate data sources and joined them with disaggregate travel information to develop an integrated weather-travel demand database with both aggregate and disaggregate weather information. Following this, a wide range of weather metrics is determined for both daily and hourly analysis based on an extensive literature review, and compared to understand the differences between high and low temporal resolution weather effects on activity-travel behavior. Finally, to capture the unobserved heterogeneity occurred due to repeated observations, an activity participation model and a mode choice model are estimated following mixed logit modeling approach to appropriately explore individuals\u0026rsquo; decision-making exclusively under different weather conditions. For both models, the mixed logit formulation accommodates unobserved preference heterogeneity by allowing parameters to vary randomly and enhance estimation accuracy by accounting for individual-specific factors relevant in the repeated observations. This way it allows to reveal nuanced interactions between hourly weather and travel behavior across transportation modes, thereby filling a significant void in existing research.\u003c/p\u003e"},{"header":"2. DATA","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data sources\u003c/h2\u003e \u003cp\u003eThis study primarily relies on three different types of data sources \u0026ndash; 1) activity-travel data, 2) weather data, and 3) land use and built environment data. Following are the details of the data sources.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Activity-travel data source\u003c/h2\u003e \u003cp\u003eThe activity-travel information used in this study are extracted from the 2018-19 Household Travel Survey (HTS), conducted by the Chicago Metropolitan Agency for Planning (CMAP) as part of the My Daily Travel Survey project in Chicago, USA. Data was collected through multiple methods: telephone interviews, web-surveys, in-person intercept surveys, app usage, online forms, and direct mail. The survey was also heavily promoted via electronic media, email blasts, local school districts, government units, community organizations, and bloggers. Additionally, digital and social media platforms like Facebook, Instagram, Google Display Network, Google Search and Ads, and paid digital influencers were utilized for household recruitment. The reason behind recruiting respondents by using different outreach options was to get a balanced sample among different demographic groups (e.g. age, income, race, mobility-constraint, etc.). For example, the target audience for the social media campaign was Chicago region residents, Hispanic/Latino population, regional commuters, African American population, 65\u0026thinsp;+\u0026thinsp;age population and lower income households. The telephone and web survey modes were targeted to recruit low-income, socioeconomically disadvantaged, and older members of the population. The direct mail approach used the United States Postal Service (USPS) Every Door Direct Mail (EDDM) tool to distribute materials to residential addresses within selected geographic areas and demographic groups, helping achieve a balanced sample. Through these efforts, over 17,000 households were recruited, with 12,660 completing the survey. A survey was considered complete when all members five-years-old and above reported travel details for the assigned travel day and subsequently all edit checks and post processing errors were able to be cleared. Among the completed surveys, the digital advertising campaign accounted for 1,127 completed household surveys. Intercept surveys conducted in person at public transportation stations, community college campuses and transit centers yielded 111 completed households. The direct mail effort resulted in 354 completed surveys. Local community partners, such as community colleges, faith-based organizations and local chambers of commerce, contributed 1,582 completed surveys, while school districts facilitated the collection of 960 completed surveys. The smartphone application generated the largest share, with 4,397 completed surveys. The remaining responses were obtained through electronic media outreach, telephone surveys, and web-based surveys. The detailed description of the survey design, data collection approach, sample distribution, and data extraction and retrieval processes can be found in (\u003cem\u003e23\u003c/em\u003e). During the data collection, data processing and cleaning were conducted continuously. Critical variables (e.g. the addition of a car that was not originally reported) were updated during survey administration, while non-flow affecting variables (e.g. recoding race based on \u0026ldquo;Other, specify\u0026rdquo; responses) were finalized after data collection. Automated edits, range checks, consistency checks, and validity checks were embedded in the survey, supplemented by staff-led frequency reviews and issue resolution. Logic, range, and consistency checks ensured accurate responses, valid data types and values, and consistency across survey files. After reviewing and confirming the responses, 12,068 households were retained. In addition, 323 pilot households were added with the final sample bringing the total to 12,391. The final sample included 12,391 households, totaling 30,683 respondents, and 98,091 trips. The detailed description of the data cleaning, processing and quality checks can be found in (\u003cem\u003e23\u003c/em\u003e). The survey gathered detailed information on various household and member attributes, including income, household size, home ownership status, residence type, vehicle ownership, residential address, member ages, employment status, education, race, occupation, mobility tool ownership, and relationships between members. Methodologically, it is a cross-sectional survey that collected 1- or 2-day trip information, covering activity purposes, trip origins and destinations, departure and arrival times, durations, travel modes, and associated vehicular information.\u003c/p\u003e \u003cp\u003eTo evaluate the representativeness of the CMAP HTS, weighted estimates from the survey were compared against corresponding benchmarks derived from the American Community Survey (ACS). The ACS serves as a relevant reference due to its large sample size, standardized methodology, and comprehensive coverage of household and person characteristics. Comparisons were conducted using weighted percentage distributions, and differences were assessed using absolute percentage point deviations from ACS benchmarks. The weights were applied to the CMAP HTS data to adjust over- and under-representation. This process involved assigning higher weights to responses from underrepresented categories and lower weights to those from overrepresented categories. By doing so, the final dataset more accurately represented the population distribution and travel behaviors across the entire region. Overall, CMAP HTS exhibits close correspondence with ACS estimates across key household and person attributes, such as household size, vehicle ownership, income, age range, race, etc. The Margin of Error (MOE) at the 90% confidence interval was also calculated using the variance estimates drawn from the replicated weights for a given estimate. MOE is critical to assess whenever estimates are compared. A lower MOE indicates more stable samples with greater confidence in the estimate. American Community Survey (ACS) estimates and MOE were obtained from the U.S. Census Bureau and are based on 2013-17 5-year estimates. The detailed discussion on the comparison can be found in (\u003cem\u003e23\u003c/em\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the comparison for household size and vehicle ownership in a representative county in the CMAP region (Cook County).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eComparison between CMAP HTS and ACS based on household size and number of vehicles in Cook County\u003c/b\u003e (\u003cem\u003e23\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold Size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of Vehicles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMAP Estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCMAP MOE (90%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eACS Estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eACS MOE (90%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e1 person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2 persons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e3 persons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.83%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4 persons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.34%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Weather data source\u003c/h2\u003e \u003cp\u003eTo extract the weather information, this study first identifies three prominent and reliable meteorological data sources \u0026ndash; 1) NASA Prediction of Worldwide Energy Resources (POWER) database, 2) National Solar Radiation Database (NSRDB) by National Renewable Energy Laboratory (NREL), and 3) U.S. Local Climatological Data (LCD) data from National Centers for Environmental Information (NCEI) of National Oceanic and Atmospheric Administration (NOAA). These sources were selected due to their broad usage in climate, energy, and transportation-related research and their ability to provide temporally resolved weather information. The NASA POWER database is primarily focused on climate research, renewable energy, and agricultural needs and based on model simulations and data assimilation. It relies on a combination of model simulations and data assimilation techniques to generate gridded meteorological variables. The database provides a variety of meteorological and solar data, including near-surface air temperature, relative humidity, rainfall, solar radiation, and wind speed and direction. Although the POWER database offers consistent global coverage and long temporal spans, its reliance on modeled and interpolated data often limits its accuracy at fine spatial scales. It struggles to capture localized weather conditions, complex terrain effects and extreme events, which are critical for analyzing daily travel behavior. The NREL NSRDB database provides a comprehensive suite of weather data related to solar irradiance, atmospheric conditions, and surface properties. It is widely used in solar energy and building energy modeling applications and offers high temporal resolution and broad spatial coverage. However, the NSRDB is not ideal for meteorological or climatological analyses due to its data-filling methods for serial completeness that may introduce systematic biases. Therefore, its applicability for studies requiring accurate representation of observed weather variability and extremes is limited. The NOAA-NCEI LCD database provides high-quality and observation-based weather data at fine temporal and spatial resolutions. The LCD dataset includes hourly, daily, and monthly observations collected directly from surface weather stations and supplemented by satellite observations where applicable. It provides detailed information on a wide range of meteorological variables, including temperature, dew point, humidity, snow depth, snowfall, precipitation, atmospheric pressure, wind speed and direction, sky conditions, and weather type. The specific station-based nature of the LCD database allows for more accurate representation of localized weather conditions, which makes it suitable for regional and local-scale analyses. Given the high temporal resolution, reliance on direct observations and extensive set of weather information, the NOAA-NCEI LCD database is well suited for linking weather conditions to daily activity-travel behavior. Therefore, this study adopts the NOAA-NCEI LCD dataset as the primary weather data source and extracts monthly, daily, and hourly weather information for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Land use and built environment data source\u003c/h2\u003e \u003cp\u003eIn addition to the HTS and weather data, this study incorporates land use and built environment measures from the U.S. Environmental Protection Agency\u0026rsquo;s (EPA) Smart Location Database (SLD). The EPA SLD was developed to support consistent assessment and comparison of location efficiency across geographic areas and provides a comprehensive set of demographics, employment, and built environment indicators for all Census block groups (CBGs) in the United States.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data preparation and variables considered\u003c/h2\u003e \u003cp\u003eThe LCD database consists of weather records from multiple weather stations. This study selected Chicago O'Hare International Airport as the reference weather station since it's the official data source for Chicago weather analysis. Since the CMAP HTS data was collected between the years 2018 and 2019, this study extracted weather information from the NOAA-NCEI LCD database from 01/01/2018 to 12/31/2019, which includes monthly, daily and hourly weather information along with exact date, time and station location. Following this, the LCD and HTS databases were joined based on the exact month (monthly average weather), month and date (daily average weather), month, date and time (hourly weather). This study considers a wide range of weather metrics for daily and hourly weather-travel demand analysis. A comprehensive literature review was conducted recently by Petrovic et al. (\u003cem\u003e2\u003c/em\u003e) that explored the most common weather factors used in the existing literature of travel demand. Based on the findings of Petrovic et al. (\u003cem\u003e2\u003c/em\u003e), this study determined the following list of weather metrics to analyze the relationships between travel demand and daily/hourly weather: 1) temperature: dry bulb, wet bulb and dew point temperature, 2) atmospheric pressure: altimeter setting, sea level pressure, station pressure, and pressure change, 3) humidity: relative humidity, 4) wind: wind speed, wind gust, wind direction, 5) precipitation: precipitation, snow depth, snow fall, visibility, 6) weather type, 7) pressure tendency, and 8) sky condition, among others. The joined database includes all demographics, activity, travel and the identified weather information for the days and hours when the survey was administered between 2018 and 2019. The dependent variable of the activity participation model is formulated based on four activity purposes: 1) mandatory activity (e.g. work, school), 2) maintenance activity (e.g. all shopping, non-work errands, service, personal business, civic, religious), 3) discretionary activity (e.g. eat out, leisure, recreation, entertainment, social), and 4) in-home activities. The mode choice model is formulated based on 7 modes collected during the survey: 1) SOV (single-occupancy vehicle), 2) HOV (high-occupancy vehicle), 3) walk, 4) bike, 5) bus, 6) rail, and 7) taxi.\u003c/p\u003e \u003cp\u003eTo understand the effects of land use and built environment measures, U.S. EPA Smart Location Database (SLD) were merged with the weather and activity-travel database based on the activity locations. The EPA-SLD provides a comprehensive set of variables at the Census Block Group (CBG) level, which are organized into five categories: (1) density, (2) diversity, (3) design, (4) transit accessibility, and (5) destination accessibility. Based on the spatial locations of reported activities, relevant neighborhood attributes, including population density, residential density, activity density, transit service frequency density and distance to the nearest transit stop, were extracted from the EPA-SLD. Density measures were computed as the ratio of units to total land area in the neighborhood. For example, population density was computed by dividing the number of people by total land area in acre, residential density was calculated by taking the ratio of number of housing units per acre, and activity density was calculated as the number of housing units and jobs per acre within a neighborhood (\u003cem\u003e24\u003c/em\u003e). Land-use diversity was measured by quantifying the relative blend of the number of jobs in different employment sectors and residential housing types (\u003cem\u003e24\u003c/em\u003e). The final dataset for estimating the activity participation and mode choice models were constructed by integrating the weather and activity-travel joint database with the corresponding neighborhood-level land use and built environment attributes. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the descriptive statistics of the variables retained in the final model estimations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActivity participation model variables descriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean/\u003c/p\u003e \u003cp\u003eproportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eTemperature\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze (\u0026lt;\u0026thinsp;32F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly dry bulb temperature is below 32F\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold (32F \u0026minus;\u0026thinsp;50F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly dry bulb temperature is in between 32F and 50F\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot (\u0026gt;\u0026thinsp;77F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly dry bulb temperature is above 77F\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; household size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with household size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with age above 55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times;Vehicle ownership\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with households with multiple number of vehicles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with age above 55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Employment: Construction and Retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with the individual's workplace type construction and retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with population density in the neighborhood (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with residential density in the neighborhood (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with population density in the neighborhood (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold temperature interacted with residential density in the neighborhood (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eRelative humidity\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly relative humidity is over 75% =1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort (60% \u0026minus;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly relative humidity is between 60% and 75% =1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; High income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh relative humidity interacted with above \u003cspan\u003e$\u003c/span\u003e150,000 annual earning households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; No flexible work hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh relative humidity interacted with no flexible work hours in the workplace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscomfort level relative humidity interacted with households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscomfort level relative humidity interacted with activity density (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh relative humidity interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003ePrecipitation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight (\u0026lt;\u0026thinsp;0.20\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly precipitation is below 0.20\" =1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLight precipitation interacted with population density (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy (\u0026gt;\u0026thinsp;0.35\") \u0026times; Employment: IT and Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy precipitation (\u0026gt;\u0026thinsp;0.35\") interacted with individual's workplace type IT and finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy precipitation interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eWindspeed\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air (\u0026lt;\u0026thinsp;4 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly windspeed is below 4.0 mph\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air (\u0026gt;\u0026thinsp;20 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly windspeed is above 20 mph\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air \u0026times; Distance to the nearest transit stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong air interacted with the distance to the nearest transit stop (miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eVisibility\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;0.60 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if the visibility is below 0.60 miles\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor visibility interacted with activity density (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMode choice model variables descriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean/\u003c/p\u003e \u003cp\u003eproportion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTravel time in minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eTemperature\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze (\u0026lt;\u0026thinsp;32F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly dry bulb temperature is below 32F\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold (32F \u0026minus;\u0026thinsp;50F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly dry bulb temperature is in between 32F and 50F\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCool (51F \u0026minus;\u0026thinsp;68F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly dry bulb temperature is between 51F and 68F\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarm (69F \u0026minus;\u0026thinsp;77F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly dry bulb temperature is between 69F and 77F\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; High income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with above \u003cspan\u003e$\u003c/span\u003e150,000 annual earning households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Vehicle ownership\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with 1-vehicle households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Peak hour departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreezing temperature interacted with AM and PM PEAK HOUR departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot (\u0026gt;\u0026thinsp;77F) \u0026times; Age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot (\u0026gt;\u0026thinsp;77F) temperature interacted with age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Vehicle ownership\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with households with multiple vehicles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Off-Peak hour departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with AM and PM OFF PEAK HOUR departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot temperature interacted with population density (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCool \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCool temperature interacted with activity density (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eRelative humidity\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComfort (\u0026lt;\u0026thinsp;60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly relative humidity is below 60% =1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort (60% \u0026minus;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly relative humidity is in between 60% and 75% =1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;75%) \u0026times; Low-income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh humidity (\u0026gt;\u0026thinsp;75%) interacted with households with annual income below \u003cspan\u003e$\u003c/span\u003e50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Employed male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh humidity interacted with male employed individuals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh humidity interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscomfort humidity interacted with activity density (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003ePrecipitation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight (\u0026lt;\u0026thinsp;0.20\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly precipitation is below 0.20\" =1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy (\u0026gt;\u0026thinsp;0.35\") \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy precipitation (\u0026gt;\u0026thinsp;0.35\") interacted with the density of aggregate transit service frequency per hour during PM peak period (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eWindspeed\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air (\u0026lt;\u0026thinsp;4 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly windspeed is below 4.0 mph\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air \u0026times; Land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLight air interacted with the land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air (\u0026gt;\u0026thinsp;20 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if hourly windspeed is above 20 mph\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrong air interacted with the residential density (per acre)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHourly windspeed \u0026times; Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHourly windspeed interacted with female indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eVisibility\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear (\u0026gt;\u0026thinsp;6.2 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if the visibility is above 6.2 miles\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;0.60 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy, if the visibility is below 0.60 miles\u0026thinsp;=\u0026thinsp;1, 0 otherwise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear \u0026times; Distance to nearest transit stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClear visibility interacted with the distance to the nearest transit stop (miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor \u0026times; Land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor visibility interacted with the land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. DESCRIPTIVE ANALYSIS OF WEATHER AND TRAVEL DEMAND","content":"\u003cp\u003eThis study conducts a wide range of analysis to explore the relationships between daily and hourly weather conditions and travel demand. Among them, it presents a few prominent analyses in this section, specifically focusing on the mode choice and activity participation.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Activity participation\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec presents the changes in activity participation with the change in daily average weather conditions. Average daily weather changes are found to have subtle impacts on activity participation patterns. With the increase in daily average temperature, windspeed and windgust, routine (mandatory) activity participation is found to increase slightly, while non-routine activity participation, as a whole, reduces. Conversely, higher daily average snowdepth, snowfall and precipitation show slight decrease in routine activity participation, and increase in non-routine activity participation. The following charts (1a-1c) exhibit the results only for daily average temperature, snowdepth and windspeed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec presents how hourly weather changes impacts on activity participation. Results indicate that hourly higher temperature and visibility decrease routine activity participation, while increasing non-routine activity participation. This demonstrates more nuanced and immediate reactions to weather changes on activity participation patterns. Routine activity participation is found to increase with the increase in hourly relative humidity and precipitation, while non-routine activity participation decreases. The following charts (2a-2c) only show the results of hourly temperature, visibility and relative humidity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mode share\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec presents the changes in mode shares due to daily average weather changes. Average daily weather changes have noticeable impacts on travel choices. Results suggest that auto mode (SOV and HOV) usage is the highest in freezing and cold temperatures and decreases with warmer temperatures. Walking, biking and public transportation mode usage are found higher in warmer conditions. Auto mode shares are found to increase with rainfall, windspeed and windgust (not shown in the figure), while active mode and public transportation mode shares decline. The following charts (3a-3c) only exhibit the analysis of daily average temperature, windspeed and rainfall.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of hourly analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), results suggest that with the increase in temperature, windgust, relative humidity, windspeed and rising pressure, SOV usage decreases, while public transportation usage is found to increase slightly. This indicates that hourly weather analysis improves the aggregation bias and temporal nuance in mode choices. The following charts only exhibit hourly temperature, relative humidity and windgust analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. MODELING APPROACH","content":"\u003cp\u003eThis study utilizes a mixed logit (MXL) model to estimate the relationship between hourly weather conditions and travel demand, specifically \u0026ndash; activity participation and mode choice decisions. In both instances, dependent variables are discrete/categorical, hence, generally a multinomial logit (MNL) model would be more appropriate ((\u003cem\u003e25\u003c/em\u003e)). The MNL model, however, is based on a restrictive IIA assumption, hence, fails to account for correlations among unobserved factors over time for an individual that may occur due to repeated observation from the same individual. In this study, in the activity participation dataset, multiple episodes of the same activity purpose by an individual on a typical day exist (i.e. repeated observation of an activity by the same individual). Similarly, in the mode choice dataset, records exist for multiple trips made by an individual on a typical day (i.e. repeated observation of the mode choice). Due to such repeated observations, correlations among individual-specific unobserved factors may occur, which may arise the unobserved preference heterogeneity. MXL model can accommodate such heterogeneity across individuals within its modeling framework.\u003c/p\u003e \u003cp\u003eIn the case of activity participation, the MXL model is formulated based on four activity purposes: 1) mandatory activity, 2) maintenance activity, 3) discretionary activity, and 4) stay-at-home activities. Stay-at-home activity alternative is considered as a reference alternative for all the parameters in the modeling framework. The mode choice model is formulated based on 7 modes collected during the survey: 1) SOV (single-occupancy vehicle), 2) HOV (high-occupancy vehicle), 3) walk, 4) bike, 5) bus, 6) rail, and 7) taxi. Reference alternatives for mode choice model parameters vary based on the hypothesis. For both models, variable choicesets are created based on the availability of alternatives. For instance, mandatory activity was unavailable to the individuals who do not work or go to school, not owning a bike or car resulted in eliminating the alternatives from the choiceset of the corresponding individual, bus mode was unavailable outside of the operating hours and specific distance radius.\u003c/p\u003e \u003cp\u003eThe mixed logit model employed in this study can be described as follows. Let, an individual \u003cem\u003en\u003c/em\u003e chooses among \u003cem\u003eJ\u003c/em\u003e possible alternatives. The utility that individual \u003cem\u003en\u003c/em\u003e derives from alternative \u003cem\u003ej\u003c/em\u003e at observation (choice occasion) \u003cem\u003et\u003c/em\u003e can be expressed as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{U}_{njt}=\\:{\\alpha\\:}_{j}+\\:{\\beta\\:}_{n}{X}_{njt}+\\:{\\epsilon\\:}_{njt}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, α is the alternate-specific constant (ASC), one of which is assumed zero (reference) for identification purposes. \u003cb\u003eX\u003c/b\u003e is the column vector of the observed attributes, \u003cem\u003eβ\u003c/em\u003e is the coefficients of the parameters to be estimated, and \u0026#120576; is the random error term. The probability of individuals\u0026rsquo; observed sequence of choices can be written as:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{P}_{n}\\left(t\\right|\\beta\\:)=\\:{\\prod\\:}_{t}\\frac{{e}^{{\\alpha\\:}_{j}+\\:{\\beta\\:}_{n}{X}_{nj(n,t)t}}}{\\sum\\:_{n=1}^{N}{e}^{{\\alpha\\:}_{j}+\\:{\\beta\\:}_{n}{X}_{nj(n,t)t}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, \u003cem\u003ej(n,t)\u003c/em\u003e is the alternative that an individual \u003cem\u003en\u003c/em\u003e chooses at observation \u003cem\u003et\u003c/em\u003e. As discussed at the beginning of this section, the flexible MXL model formulated in this study accommodates individual-specific unobserved factors that may influence repeated choices from the same individual over time (at observations \u003cem\u003eT\u003c/em\u003e). Random parameters are introduced within the MXL modeling framework to capture such random taste variations (i.e. unobserved heterogeneity) across sample population through the estimation of mean and standard deviations. This study estimates mean (\u0026micro;) and standard deviation (σ) of random parameters by following a normally distributed density function \u003cem\u003ef(.)\u003c/em\u003e. Thus, the choice probability becomes the following equation:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{R}_{n}=\\:\\int\\:{P}_{n}\\left(t|\\beta\\:\\right)\\:f\\left({\\beta\\:}_{n}\\right|\\mu\\:,\\sigma\\:)d{\\beta\\:}_{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe unconditional log-likelihood function to estimate the parameters can be expressed as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:{L}_{n}=\\:\\sum\\:_{n=1}^{N}ln{R}_{n}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eEquation \u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is a multivariate integral function that does not have a closed form in general, hence, the choice probability cannot be computed analytically. As a result, parameters are estimated using the simulated maximum likelihood estimation (SMLE) technique. Halton sequences are utilized in this study, since it requires a lower number of draws compared to random draws. Both activity participation and mode choice models are estimated using the PandasBiogeme ((\u003cem\u003e26\u003c/em\u003e)) software.\u003c/p\u003e"},{"header":"5. ESTIMATION RESULTS","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn this section, the parameter estimation results, model fits and elasticity analysis results are presented. Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present the parameter estimation results of the activity participation model and mode choice model, respectively. The model fits are reported at the bottom of Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It includes null and final model log-likelihood and Akaike Information Criteria (AIC) values. To evaluate the model performance, likelihood ratio (LR) test is conducted for both models. In addition, for both model, family-wise error rate (FWER) is controlled using the Holm-Bonferroni procedure at the 5% significance level. This limits the probability of getting false positives arising from multiple hypothesis testing due to extensive set of hypothesis tests conducted simultaneously, specifically for the interaction effects that are motivated theoretically but may be correlated statistically (\u003cem\u003e27\u003c/em\u003e). The Holm-Bonferroni correction method is applied in this study to make sure that the effects are robust after correcting for multiple comparisons, thus strengthening confidence in the relationships between weather conditions, demographics, built environment, and activity-travel demand ((\u003cem\u003e28\u003c/em\u003e), (\u003cem\u003e29\u003c/em\u003e)). This is a stepwise procedure used to control the family-wise error rate in multiple hypothesis testing while preserving greater statistical power. In this process, all p-values are first sorted from smallest to largest. Starting with the smallest p-value, each is sequentially compared against a progressively adjusted significance threshold. The smallest p-value is compared to an adjusted significance level of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}/\\varvec{m}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003eα\u003c/em\u003e is the significance level which is assumed as 0.01 in this study, and \u003cem\u003em\u003c/em\u003e is the total number of hypothesis tests. If this hypothesis is rejected, the next smallest p-value is compared to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}/(\\varvec{m}\\:-\\:\\varvec{k}\\:+\\:1)\\)\u003c/span\u003e\u003c/span\u003e for the \u003cem\u003ek\u003c/em\u003e-th ordered p-value. The process continues until a hypothesis fails to meet its adjusted threshold, at which point that hypothesis and all subsequent hypotheses are interpreted as exploratory. Below is a discussion of the parameter estimation results, model fits, and elasticity analysis results.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Activity participation\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the estimation results of the activity participation model. It offers an understanding of how hourly weather conditions, in conjunction with demographic and household attributes and neighborhood characteristics, influence the likelihood of engaging in mandatory, maintenance, and discretionary activities compared to staying home. In terms of model fits, the log-likelihood value of the final model is found higher than the constant only model. The likelihood ratio (LR) test value is calculated as 70101.98, which is greater than the critical chi-square value (105.19) at the 1% significance level; thus rejecting the constants only model. Furthermore, all the parameters retained in the final model are found to be statistically significant at least at 10% significance level. The statistical significances of the parameters are reassessed using the Holm-Bonferroni procedure to account for multiple hypothesis testing and control the family-wise error rate across weather-related main effects and interaction terms. The starting adjusted significance threshold (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\alpha\\:}/\\varvec{m}\\)\u003c/span\u003e\u003c/span\u003e) for the Holm-Bonferroni correction is computed to be 0.000141, which is compared to the smallest p-value in the model. Applying this procedure results in few parameters to be not Holm-Bonferroni significant. However, they are still retained in the final model because of their meaningful coefficient signs and nominal significance.\u003c/p\u003e \u003cp\u003eThe negative and highly significant constants across all activity types indicates a baseline preference for home-based activities, with discretionary pursuits exhibiting the strongest pull towards staying in. Participation in mandatory out-of-home activities exhibits moderate sensitivity to weather conditions, reflecting the relatively fixed nature of these activities. For mandatory activities, the results reveal that freezing, cold and hot temperatures and discomfortable humidity reduce the probability of staying home, increasing the likelihood of engaging in mandatory activities. Similarly, individuals working in construction and retail industries tend to participate in mandatory activities, even under extreme temperature. Such outcomes may indicate a strong obligation effect \u0026ndash; people will often proceed with routine activities regardless of extreme weather or discomfort. This is consistent with previous studies such as Bhat and Srinivasan (\u003cem\u003e30\u003c/em\u003e), which report less weather sensitivity in work-related activity patterns. However, certain segments are more vulnerable to weather, such as older adults (age over 55 years), who are less likely to leave home during freezing and hot temperatures. Similar outcomes are also found for the households with kids, who demonstrate lower tendency to participate in mandatory activities under hot temperatures. Notably, households with multiple vehicles are more likely to engage in mandatory activities during freezing weather, emphasizing that access to private transportation mitigates some weather barriers. High humidity and light air positively influence mandatory activity participation, possibly reflecting tolerability or a lack of barriers when air is stagnant. The interaction between high income and high humidity shows a positive effect, suggesting that higher-income individuals are more likely to pursue mandatory activities even under humid conditions. This is likely due to access to climate-controlled travel or workplaces. The variable \u0026ldquo;no work flexibility \u0026times; high humidity\u0026rdquo; is positively associated with mandatory activity participation, implying that individuals without flexible work option are more likely to travel in humid conditions regardless of comfort \u0026ndash; possibly pointing to constraint-driven behavior. This is in line with previous empirical studies showing that weather conditions have asymmetric effects depending on socio-economic and job-related constraints (\u003cem\u003e7\u003c/em\u003e). As expected, poor visibility and strong air tend to decrease individuals\u0026rsquo; propensity to participate in mandatory activities. Expectedly, individuals are found to increase their probability to participate in mandatory activities more during low precipitations. Conversely, people working at IT and finance industries exhibit lower propensity to engage in mandatory activities, consistent with evidence that these sectors offer greater opportunities for flexible and remote work arrangements (\u003cem\u003e31\u003c/em\u003e). The interaction effects of weather and neighborhood characteristics on mandatory activity participation suggest that higher population, residential and/or activity densities generally mitigate the deterrent effects of moderately adverse weather, such as cold temperature, low precipitation, discomfort humidity and/or poor visibility. This is likely due to shorter travel distances and greater accessibility to workplaces and services in such neighborhoods. However, under extreme weather conditions (e.g. freezing and hot temperatures), individuals living in high population density areas are less likely to participate in mandatory out-of-home activities, which is consistent with increased generalized travel disutility under adverse conditions in urban settings (\u003cem\u003e32\u003c/em\u003e). Better quality of transportation services in the neighborhoods is found to offset weather-related disutility. Results suggest that higher transit service frequency increases individuals\u0026rsquo; probability to participate in mandatory activities during high humidity and heavy precipitation. Under strong wind conditions, closer distance to transit stops is found to be associated with higher participation in mandatory activities. These outcomes perhaps indicate that robust transit supply supports schedule-constrained activities even under unfavorable weather conditions.\u003c/p\u003e \u003cp\u003eFor maintenance activities, the model shows that freezing temperature, cold temperature, and hot temperature \u0026ndash; all increase the likelihood of participation, which may initially appear counterintuitive. However, such activities could include indoor errands or temperature-insensitive obligations such as grocery shopping, making them less elastic to weather. Like mandatory activities, maintenance activities are positively associated with temperature extremes, especially among older adults (age over 55 years). Other extreme weather events, discomfort humidity, and strong air also exhibit positive coefficient values. This might demonstrate the nature of the maintenance activities, e.g. critical daily groceries, attending medical appointments, performing errands, etc. \u0026ndash; which might be crucial for daily life and household functioning and cannot be postponed for a better weather. As expected, the positive effect of light air suggests less deterrence compared to windier conditions. However, high income \u0026times; high humidity shows a negative effect, suggesting that wealthier individuals may delay or substitute maintenance trips during extreme weather conditions. This nuanced difference in the high-income households may reflect a higher level of schedule flexibility or ability to outsource such tasks. Similarly, freezing temperatures decrease the likelihood of maintenance activity participation in larger households, reflecting increased coordination and exposure burdens that make such semi-flexible activities less attractive. Moreover, the maintenance activity participation is more likely to reduce under hot temperature conditions, indicating that semi-flexible errands tend to get postponed or consolidated in households with kids. As expected, the interaction between no work flexibility and high humidity has a negative effect on maintenance activity participation, demonstrating the limited ability of schedule-constrained individuals to avoid uncomfortable weather conditions that lead to reduced participation in semi-flexible maintenance activities. In contrast, positive interaction effects are found between heavy precipitation and employment in IT and finance. Workers in such sectors might have greater access to flexible or remote work arrangements and are better able to adapt their activity schedules, thereby mitigating the disutility associated with adverse weather. Moderate humidity condition, such as discomfort humidity, is found to increase maintenance activity participation in the households with kids, suggesting that few essential maintenance activities in such households are likely to be undertaken under moderately relative humidity. Furthermore, extreme temperatures (i.e. freezing and hot temperature) tend to reduce maintenance activity participation in high population density areas \u0026ndash; possibly reflecting higher exposure, congestion, and travel discomfort in urban settings combined with the flexible nature of maintenance activities that allows postponement under adverse conditions (\u003cem\u003e16\u003c/em\u003e). In contrast, denser areas with higher population, residential and/or activity density are found to mitigate the deterrent effects of moderate weather conditions, such as cold temperature, discomfort humidity, low precipitation or poor visibility. These outcomes might suggest the resilience of urban neighborhoods to minor weather disruptions. However, heavy precipitation tends to discourage maintenance activity participation even in transit-rich areas with higher transit service frequency, and greater distance to transit stops further reduce maintenance activity participation during strong winds. Such results underscore the importance of transit accessibility and the sensitivity of maintenance travel to compounded weather-related disutility.\u003c/p\u003e \u003cp\u003eDiscretionary activities, being the most flexible, show the greatest sensitivity to weather. The results indicate that young adults (aged 25 to 40 years) and older adults (age over 55 years) are more likely to increase discretionary trips during hot temperatures and freezing temperatures. Interestingly, freezing, cold and hot temperatures reduce discretionary activity participation, suggesting aversion to extreme temperatures for leisure purposes, which aligns with prior findings (\u003cem\u003e7\u003c/em\u003e) that social or leisure travel is often the first to be postponed under adverse weather. The interaction between freezing temperatures and larger household size also shows a negative effect, consistent with the higher coordination burden and exposure risk faced by larger households during adverse weather conditions. The higher negative effects of hot temperature and discomfort humidity on discretionary activity participation in the households with kids indicate that such weather conditions substantially discourage to undertake discretionary activities. This is perhaps consistent with the highly flexible nature of these activities and heightened sensitivity to thermal discomfort when children are present. Light air tends to increase the discretionary activity participation, which is expected. High humidity is found to reduce participation. However, high income households tend to increase their discretionary activity participation in high humidity. For high-income households, high humidity possibly acts as a strong incentive to move their discretionary activities indoors to venues they can afford. As expected, light air tends to increase individuals\u0026rsquo; discretionary activity participation, whereas poor visibility and strong air are less likely to increase the discretionary activity participation \u0026ndash; reinforcing the idea that comfort and perceived safety are key concerns for discretionary travel. Finally, similar to maintenance activity participation, the interaction variable \u0026lsquo;no work flexibility \u0026times; high humidity\u0026rsquo; shows a negative effect on discretionary activity. As expected, compared to staying at home, individuals tend to participate in discretionary activities more during low precipitations. For discretionary activities, extreme temperatures (freezing and hot temperatures) combined with high population or residential density tend to reduce participation, reflecting heightened exposure and discomfort in dense urban settings and the ease with which such highly flexible optional activities can be postponed. In contrast, mild weather conditions (low precipitation and poor visibility) in high population or activity density areas are more likely to increase discretionary activity participation \u0026ndash; perhaps, due to shorter distances, lower perceived risk and greater activity location availability in such areas. Transit service frequency offsets high humidity condition by improving accessibility but amplifies deterrence during high precipitation \u0026ndash; probably due to crowding or service unreliability during heavy precipitation.\u003c/p\u003e \u003cp\u003eThe final model specification estimates standard deviations of two random parameters \u0026ndash; strong air and high humidity, along with their mean values to explore taste variations across the sample population. All two random parameters are statistically significant and standard deviations of the random parameters are found higher than the mean values \u0026ndash; which confirms the presence of unobserved preference heterogeneity across the sample population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActivity participation model parameter estimation results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTypes of Activities (Reference: In-home activity)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandatory Activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintenance Activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiscretionary Activity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8749***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4108***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.5486***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eTemperature\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze (\u0026lt;\u0026thinsp;32F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1044**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1896***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1549***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold (32F \u0026minus;\u0026thinsp;50F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0985***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0814***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0630***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot (\u0026gt;\u0026thinsp;77F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5632***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1485***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; household size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5189***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.0449***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4854***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0508*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Vehicle ownership\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2614***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3025**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4189***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3100***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Employment: Construction and Retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0949***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5710***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.2107***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2365***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0814***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.8547***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2044***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0240**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0548***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.5621***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5558***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1900***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eRelative humidity\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7049*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0521***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort (60% \u0026minus;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0512***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5144**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; High income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0549***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1478***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; No flexible work hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6218***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0789**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1597***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6421***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4100***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4178***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5956***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4250***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003ePrecipitation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight (\u0026lt;\u0026thinsp;0.20\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1085***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1540***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0585***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9520***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7500**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy (\u0026gt;\u0026thinsp;0.35\") \u0026times; Employment: IT and Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1635***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2954*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3205***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9958***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.7419***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eWindspeed\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air (\u0026lt;\u0026thinsp;4 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6910***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5841***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9105***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air (\u0026gt;\u0026thinsp;20 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2500*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0654***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0946***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air \u0026times; Distance to the nearest transit stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1208***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.6321***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eVisibility\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;0.60 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1186***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0841***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8945***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6902***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4953***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eStandard deviations of random parameters\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air (\u0026gt;\u0026thinsp;20 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4158***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh humidity (\u0026gt;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3649***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eModel fits\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood (null)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-164186.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood (final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-129135.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258426.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: *** 1% significance level, **5% significance level, *10% significance level. Coefficients marked with \u0026dagger; are not statistically significant after Holm-Bonferroni correction for multiple testing.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Mode choice\u003c/h2\u003e \u003cp\u003eThe estimated mode choice model in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a nuanced understanding of how hourly weather conditions influence transportation preferences, with particular attention to interactions between temperature, visibility, windspeed, humidity, demographics and neighborhood characteristics. The final model provides a better fit than the constant only model, as indicated by its higher log-likelihood value. The likelihood ratio (LR) statistic is 133092.6 that exceeds the 1% critical chi-square value of 124.13, leading to rejection of the constant only model. All parameters retained in the final model are statistically significant at least at the 10% level. Furthermore, to control the family-wise error rate arises due to multiple hypothesis tests, statistical significances of all the parameters are re-evaluated using the Holm-Bonferroni procedure. The starting adjusted significance threshold (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:/m\\)\u003c/span\u003e\u003c/span\u003e) for the Holm-Bonferroni correction method, which is compared to the smallest p-value, is calculated as 0.000087. Although p-values of few parameters do not meet the Holm-Bonferroni\u0026rsquo;s incrementally adjusted significance thresholds, they are retained in the final model due to their theoretically consistent coefficient signs and nominal statistical significance, thus maintaining behavioral interpretability.\u003c/p\u003e \u003cp\u003eThe mode choice model uses taxi as the reference category for alternative-specific constants (ASCs) and includes interaction terms to capture weather sensitivity across different demographics, trip and built environment contexts. As expected, ASCs for other modes (e.g., bike, bus, HOV, rail, SOV, walk) are significantly positive, suggesting higher baseline utility for these modes relative to taxi, with walk and SOV showing especially strong preferences. In addition to that, this study utilized a generic travel time parameter, which expectedly came out to be negative and statistically significant. Temperature has a strong influence on mode choice, particularly when examined at more granular levels. For example, the model demonstrates that freezing cold temperatures increase the likelihood of choosing motorized modes (e.g. SOV, HOV, taxi) and bus while strongly discouraging biking and rail mode choice, which is consistent with prior findings in travel behavior literature (e.g., (\u003cem\u003e20\u003c/em\u003e), (\u003cem\u003e33\u003c/em\u003e)). This highlights weather-induced shifts from active to motorized travel. Walking is assumed as a reference under freezing conditions, suggesting it remains a necessary baseline for short trips even in extreme cold weather. Moderate temperatures, such as cool and warm temperatures, are also found to decrease the probability of choosing activity modes (walking and biking) compared to SOV, while increasing the tendency to choose public transportation modes (transit or rail). While interacting the temperatures with demographic variables, interesting outcomes are observed. Younger individuals (16\u0026ndash;24 years) demonstrate reduced reliance on SOV and HOV under extreme weather conditions, such as freezing and hot temperatures, perhaps indicating lower vehicle access and greater tolerance for discomfort. In contrast, older adults (55+) tend to choose SOV and reduce taxi use during freezing conditions. This is consistent with heightened safety concerns and a preference for private and controlled environments (\u003cem\u003e34\u003c/em\u003e). Such results may suggest that sensitivity to weather varies by age group, which aligns with behavioral studies that report stronger weather sensitivity among more vulnerable populations (\u003cem\u003e18\u003c/em\u003e). High-income households are more likely to prefer HOV and taxi under freezing conditions compared to HOV, while reducing bus use, reflecting their greater access to private and semi-private mobility options. Single-vehicle households increase their propensity to choose HOV during freezing weather, which indicates household-level coordination and ridesharing as adaptive strategies when driving conditions deteriorate. As expected, multiple vehicle households tend to choose SOV during hot temperature weather. The model also incorporates temporal dimensions, such as peak hour and off-peak hour interactions with temperature. For example, during peak hours, freezing conditions increase the probability of choosing bus and SOV modes \u0026ndash; likely reflecting urgency or the need for faster and enclosed travel. Interestingly, warm temperatures have a mixed effect. It reduces the likelihood of biking and walking and even deters HOV travel. This is consistent with heat stress literature, which shows that extreme heat can suppress physical activity and encourage air-conditioned travel (\u003cem\u003e35\u003c/em\u003e). Furthermore, hot temperatures in dense areas reduce the likelihood of walking relative to SOV, while increasing the probability of choosing HOV, bus and rail. Dense built environments generally contribute to higher heat exposure (\u003cem\u003e36\u003c/em\u003e), which may increase thermal discomfort for active modes during hot weather conditions and provide viable shared and transit alternatives. In contrast, with the increase in activity density, moderate weather (e.g. cool temperature) tends to increase the probability of choosing walking, biking, bus and taxi mode choices compared to HOV. Cooler temperatures may lower thermal discomfort, thus making active modes more attractive while areas with higher activity locations support short trips and multimodal accessibility. This aligns with the notion that moderate weather encourages non-motorized travel in compact and mixed-use environments (\u003cem\u003e37\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eIn terms of relative humidity, results suggest that comfortable humidity encourages walking and reduces rail mode choice, while discomfort humidity demonstrates the opposite, supporting earlier findings (\u003cem\u003e7\u003c/em\u003e) that perceived discomfort strongly deters non-motorized travel. High humidity combined with low income (low income households \u0026times; high humidity) deters rail and SOV use, which might reflect compounded barriers for disadvantaged populations when exposed to extreme weather conditions. Compared to the walking mode, employed males are found to increase their likelihood of SOV and taxi mode choices under high humidity while reducing biking \u0026ndash; might be indicating the consistency with time constraints and comfort-seeking behavior among workers (\u003cem\u003e15\u003c/em\u003e). Interestingly, higher transit service frequency in the neighborhoods offsets humidity-related disutility, increasing bus and rail mode choices compared to SOV choice \u0026ndash; which might underscore the protective role of high-quality transit infrastructure during extreme weather conditions. Finally, the positive interaction between discomfort-level humidity and activity density for non-auto mode choices suggests that higher activity density in the neighborhoods (i.e. compact urban form) might buffer moderate discomfort, likely through reduced exposure time and improved route choice flexibility.\u003c/p\u003e \u003cp\u003eOther weather conditions, such as precipitation, windspeed and visibility also demonstrate expected behavior. Individuals are more likely to choose SOV, walk, bus and taxi, compared to bike during low precipitation. However, heavy precipitation tends to lower walking and biking mode choices while increasing bus and rail mode choices in areas with frequent transit services \u0026ndash; reflecting classic weather-induced mode substitution (\u003cem\u003e38\u003c/em\u003e). Wind speed plays a critical role. Light air is found to decrease probability of choosing motorized and public travel modes compared to active modes such as walking and biking. When interacted with land-use diversity, such weather condition is observed to continue to support active travel, while also increasing bus mode \u0026ndash; reflecting improved access and comfort in mixed-use areas. Conversely, individuals are less likely to choose biking during strong winds while more likely to choose SOV, HOV, bus and rail modes, which might be consistent with safety and stability concerns. Higher residential density in the neighborhoods amplifies these effects, particularly increasing bus and taxi mode choices. This might highlight how urban structure shapes weather resilience. The negative interaction effects between female and hourly windspeed on active and public modes such as bike, rail and walk indicate that women are more deterred by windy conditions. This finding possibly reinforces gender-based disparities in perceived comfort and safety under adverse conditions \u0026ndash; aligning with previous studies that have found women\u0026rsquo;s mode choices to be more influenced by comfort, safety, and environmental factors (\u003cem\u003e39\u003c/em\u003e). Visibility conditions further reveal interesting patterns. Clear visibility increases the likelihood of bike mode choice but decreases bus and HOV preference compared to the SOV. When interacted clear visibility with distance to the nearest transit stop, it is found that people living near transit stops tend to choose active modes and public transportation while reducing the propensity to choose SOV compared to taxi under the clear weather condition \u0026ndash; possibly reflecting better accessibility and improve comfort for outdoor travel in the neighborhoods. In contrast, poor visibility exhibits negative effects for biking and private vehicle choices while increasing bus mode choice in diverse land-use environments. This may suggest that travelers probably perceive professional transit operations as safer and more reliable under poor visibility conditions.\u003c/p\u003e \u003cp\u003eThe final model specification estimates standard deviations of two random parameters (freezing temperature and poor visibility) along with their mean values to explore taste variations across the sample population. Both random parameters are statistically significant and standard deviations of the random parameters are found higher than the mean values \u0026ndash; which confirms the presence of taste variations (unobserved preference heterogeneity) across the sample population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMode choice model parameter estimation results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eTypes of available modes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBike\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTaxi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCoeff\u003csup\u003esig\u003c/sup\u003e.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0686***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9954***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1635***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4935***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.0965***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTravel time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.0028***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eTemperature\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze (\u0026lt;\u0026thinsp;32F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0635***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9548***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.0074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6078***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.3958***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5611***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold (32F \u0026minus;\u0026thinsp;50F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5963***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3548***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7411***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3651***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCool (51F \u0026minus;\u0026thinsp;68F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4977***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1069***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2046***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2608***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2400***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarm (69F \u0026minus;\u0026thinsp;77F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2058***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1000**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0840*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1150***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8990***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9888***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6322***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.3984**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; High income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2429***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.3740***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2636***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Vehicle ownership\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6214***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1088*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Peak hour departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0530**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2959***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8669***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot (\u0026gt;\u0026thinsp;77F) \u0026times; Age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8522***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7001*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3541***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4158***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0584*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Vehicle ownership\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.9521***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0846***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Off-Peak hour departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1863***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1816***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0825*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.0353***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2155**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCool \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2477***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4444***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6189***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2421***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eRelative humidity\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComfort (\u0026lt;\u0026thinsp;60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0127***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3854***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.2543***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.5413***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort (60% -75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0463*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0719***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1982***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;75%) \u0026times; Low-income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.6908***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0950***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.1147*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Employed male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5100***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0485***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1622***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6257***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0818***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5641***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8453***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8741***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4280***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003ePrecipitation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight (\u0026lt;\u0026thinsp;0.20\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5410***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2488***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1036***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2547***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy (\u0026gt;\u0026thinsp;0.35\") \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3158***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.6489**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0954***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eWindspeed\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air (\u0026lt;\u0026thinsp;4 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.2418***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2555***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1055***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1486***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air \u0026times; Land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3054***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0845***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0406***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2591***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air (\u0026gt;\u0026thinsp;20 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1626***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4869***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.7416***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2500***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0621***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3892*\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1986***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3555***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.5000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHourly windspeed \u0026times; Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0748***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.8044***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.2849***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eVisibility\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear (\u0026gt;\u0026thinsp;6.2 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0411***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1510***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;0.60 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0846***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6637***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4218***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear \u0026times; Distance to the nearest transit stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2228***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4109***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.8541***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.0547***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor \u0026times; Land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.4632***\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1587***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.9218***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6308**\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eStandard deviations of random parameters\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2108***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor visibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2589***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eModel fits\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood (null)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-183851.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-likelihood (final)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-117305.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234772.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003eNote: *** 1% significance level, **5% significance level, *10% significance level. Ref. = reference mode. Coefficients marked with \u0026dagger; are not statistically significant after Holm-Bonferroni correction for multiple testing.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. ELASTICITY ANALYSIS","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThis study computes elasticity measures for all explanatory variables to enable a more interpretable assessment of the weather determinants of activity participation and mode choice decisions. The parameter estimation results discussed earlier describe the statistical associations between explanatory variables and travel demand choices. To better convey the magnitude of these effects, the estimation results are complemented with the elasticity analysis, which provides a more interpretable measure of behavioral responsiveness (\u003cem\u003e40\u003c/em\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the average elasticities for activity participation, while Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the corresponding elasticities for mode choice decisions. For binary explanatory variables, elasticities represent the percentage change in the probability of selecting a given alternative when the variable shifts from 0 to 1. For continuous variables, elasticities indicate the percentage change in choice probability associated with a 1% change in the variable. These elasticity results provide insights into the behavioral relevance of the interaction between weather and demographics, built environment and transportation service characteristics, and inform policy interpretation. Overall, the findings indicate the importance of the incorporation of weather related information within the travel demand modeling framework.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe elasticity estimates reveal substantial differences in how weather and contextual factors influence activity participation across activity types (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The average elasticity analysis across all alternatives indicates that daily activity participation decisions are mostly affected by the relative humidity and windspeed, followed by visibility and temperature related factors. Precipitation is found to affect the activity participation decisions the least. Across all activity types, extreme weather conditions, particularly temperature and relative humidity, exert the largest impacts, while mild weather conditions and built-environment interactions generally exhibit smaller and moderating effects. Elasticities for discretionary activities are found to have greater impacts, with freezing temperatures and high relative humidity exhibiting the strongest reductions in discretionary activity participation compared to mandatory and maintenance activities. This may confirm the high behavioral elasticity of discretionary activities. Strong wind conditions also have considerable impacts on mandatory and discretionary activity participations that might indicate significant sensitivity to safety and comfort concerns. Interactions between adverse weather and high population or residential density further amplify these effects. Sociodemographic interactions, such as households with children, older adults (55+) and household size, demonstrate moderate elasticities, which primarily affect discretionary and maintenance activity participation. These variables intensify negative weather impacts but do not independently dominate activity participation decisions. Temperature interactions with age and employment type also have medium impacts, indicating heterogeneous but bounded behavioral responses. Furthermore, variables associated with comparatively mild weather conditions (e.g. cold temperatures, light precipitation, light wind) and transport supply or accessibility (for example, transit service frequency, distance to the nearest transit stations, activity density) exhibit relatively lower elasticities across activity types. While these factors demonstrate expected behaviors, their influence is secondary compared to extreme weather and household constraints. Overall, the elasticity analysis of the activity participation model indicates that extreme weather conditions dominate activity participation responses, particularly for discretionary activities, while household characteristics and urban context primarily function as impact amplifiers or buffers. Mandatory activity participation remains the least affected across all variables \u0026ndash; perhaps reflecting their low behavioral elasticity and limited scope for suppression.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn the case of mode choice model (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), the elasticity analysis indicates that the average elasticity analysis across alternatives suggests that temperature, relative humidity, windspeed and visibility have greater impact on the mode choice decisions, while precipitation and mild weather conditions exhibit comparatively lower impacts. The largest impacts are associated with extreme temperature conditions and their interactions. Freezing and cold temperatures exert greater impacts on the preferences of motorized modes (particularly SOV) and transit, while strongly suppressing active modes such as biking. Interaction between temperatures and departure times suggest that peak-hour interactions under freezing weather conditions has the most influential effects \u0026ndash; substantially increasing reliance on SOV and transit modes. Interaction between hot temperatures and population density also demonstrates considerable impacts on mode choices, especially on the bus mode choices. Relative humidity effects, specifically discomfort and high humidity interacting with transit service frequency, also demonstrate high elasticity. This indicates strong effects on the possible modal shifts away from SOV and toward transit and walking under uncomfortable conditions. Visibility-related interactions, such as clear conditions combined with distance to the nearest transit stations and poor visibility interacting with land use diversity, are also found to have higher impacts on mode choices. Although windspeed-related factors have considerable standalone effects on the mode choices, moderate impacts are observed in the case of interacting them with the built environment variables. Strong air and gender-specific wind sensitivity (female \u0026times; hourly windspeed) are found to notably reduce biking and rail choices while increasing reliance on motorized modes. Activity density effects under cool conditions also fall within this range, indicating meaningful but context-dependent adjustments in modal preferences. These variables influence mode choice decisions without dominating behavior across all alternatives. In general, lower impacts are observed for precipitation effects, such as light rainfall, and for mild temperature categories (cool and warm conditions without any interactions). Although these variables often exhibit expected behaviors (e.g. slight higher preference towards public transportation modes over active modes), their impacts are found to be modest relative to the extreme weather condition effects. Similarly, several demographic interactions (e.g. age-specific hot weather effects) exhibit lower impacts \u0026ndash; perhaps suggesting localized rather than system-wide behavioral influence. Overall, the elasticity analysis for the mode choice model suggests that extreme weather conditions dominate mode choice decisions, particularly the choices of SOV, transit and bike modes, while precipitation and mild weather conditions play a moderate role. Active travel modes are observed to be most sensitive overall, transit modes show strong sensitivity to service and visibility interactions, and SOV remains comparatively resilient except under few severe weather conditions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eActivity participation model elasticity analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTypes of Activities (Reference: In-home activity)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMandatory Activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaintenance Activity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiscretionary Activity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eTemperature\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze (\u0026lt;\u0026thinsp;32F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-29.9269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold (32F \u0026minus;\u0026thinsp;50F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.8010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.9511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot (\u0026gt;\u0026thinsp;77F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.6427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; household size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.5219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.5410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.3924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Vehicle ownership\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.3541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Employment: Construction and Retail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.5547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.1056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.1088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.0014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.7149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.8521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.6228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.6553\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.8518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eRelative humidity\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-25.6329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort (60% \u0026minus;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; High income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.3257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; No flexible work hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.4798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.9208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Households with kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6421***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003ePrecipitation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight (\u0026lt;\u0026thinsp;0.20\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.4141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy (0.35\") \u0026times; Employment: IT and Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.1248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.9847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eWindspeed\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air (\u0026lt;\u0026thinsp;4 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air (\u0026gt;\u0026thinsp;20 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.4511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.5750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air \u0026times; Distance to the nearest transit stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.1046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.6528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eVisibility\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;0.60 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.6849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.8236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMode choice model elasticity analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eTypes of available modes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWalk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBike\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRail\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTaxi\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eTemperature\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze (\u0026lt;\u0026thinsp;32F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.4254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.5613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.0942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.3505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCold (32F \u0026minus;\u0026thinsp;50F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.0495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.8447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.6662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.6615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCool (51F \u0026minus;\u0026thinsp;68F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.4086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.0360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.9854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.4507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.7861\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarm (69F \u0026minus;\u0026thinsp;77F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.0214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.1380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.5671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.2838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.5230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.2215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Age 55\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.2288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.2368\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; High income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-12.3097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Vehicle ownership\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.5449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.4082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze \u0026times; Peak hour departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.8954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.9280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot (\u0026gt;\u0026thinsp;77F) \u0026times; Age 16\u0026ndash;24 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.3400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.1199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Age 25\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.6786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11.4472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Multiple vehicle household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.8421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.8650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Off-Peak hour departure time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.4734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHot \u0026times; Population density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.4721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.8662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCool \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.2252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.8990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eRelative humidity\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComfort (\u0026lt;\u0026thinsp;60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.5381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-5.5166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort (60% \u0026minus;\u0026thinsp;75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-25.8335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.0616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.6917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.3814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (\u0026gt;\u0026thinsp;75%) \u0026times; Low-income households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-24.3367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8.9154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Employed male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.4521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.1646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh \u0026times; Transit service frequency/acre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.8731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.9731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiscomfort \u0026times; Activity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.6532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.6778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003ePrecipitation\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight (\u0026lt;\u0026thinsp;0.20\")\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.3972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3679\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy (\u0026gt;\u0026thinsp;0.35\") \u0026times; Transit service frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.5025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.6543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.7566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eWindspeed\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air (\u0026lt;\u0026thinsp;4 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.0180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.3690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13.1766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight air \u0026times; Land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.9393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air (\u0026gt;\u0026thinsp;20 mph)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.1737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.8368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrong air \u0026times; Residential density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-10.6305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.1862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.7584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.8727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHourly windspeed \u0026times; Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.3208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-20.6743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11.3254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalicUnderline\" class=\"BoldItalicUnderline\" name=\"Emphasis\"\u003eVisibility\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear (\u0026gt;\u0026thinsp;6.2 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.2679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.9013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.4967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (\u0026lt;\u0026thinsp;0.60 miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear \u0026times; Distance to nearest transit stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.5478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.2418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-19.4872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-25.8567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor \u0026times; Land use diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.4127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.4712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.6754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"6. CONCLUSION","content":"\u003cp\u003eThis study presents an analysis of the effects of weather conditions on travel demand. It develops a combined database of travel demand and monthly, daily and hourly weather conditions. The trip information of the database was extracted from the 2018-19 CMAP Household Travel Survey. To obtain the weather information, the study identified multiple prominent and reliable meteorological data sources, such as NASA Prediction of Worldwide Energy Resources (POWER) database, NREL National Solar Radiation Database (NSRDB), and U.S. Local Climatological Data (LCD) data from National Centers for Environmental Information (NCEI) of NOAA. After careful investigation, this study selected the NOAA-NCEI LCD database to extract the monthly, daily and hourly weather information. The study identified and analyzed a wide range of weather metrics and their effects on activity-travel pattern, specifically on activity participation and mode choice behavior. The descriptive analysis of the daily and hourly weather and travel demand provides an appropriate understanding of activity-travel behavior changes with the change in weather conditions. The study estimates preliminary activity participation and mode choice models that exclusively examine the impacts of multiple hourly weather metrics on individuals\u0026rsquo; activity participation and mode choice behaviors. The activity participation model provides a detailed depiction of how different weather conditions shape participation in various out-of-home activities. Compared to the reference category of home activity, extreme weather, especially freezing temperatures, humidity, and wind, generally discourages non-mandatory trips, while mandatory trips remain more resilient. These findings reinforce the need for adaptive infrastructure and policy, to support vulnerable populations and ensure access to opportunities across different weather scenarios. Results of the mode choice model provide strong empirical evidence that hourly variations in weather conditions significantly shape mode choice behavior. It confirms and extends previous research by demonstrating the dynamic, nonlinear, and interactional nature of weather impacts on mode choice behavior. Furthermore, the magnitudes of the impacts of the determinants are tested in this study by analyzing the elasticity of the variables. Results suggest that generally temperature, relative humidity, windspeed and visibility exert greater impacts on activity-travel behavior, with discretionary activity participation and active mode choices exhibiting the highest elasticities, while mandatory activities and single-occupancy vehicle choices remain comparatively inelastic. Overall, the findings demonstrate that weather conditions dominate travel behavior responses, with demographic and built-environment factors primarily moderating these effects. These insights reinforce the need for dynamic travel demand models that incorporate real-time or hourly weather data and underscore the importance of tailoring transportation policies and infrastructure (e.g., shelter, cooling systems, or bike lanes) to support resilient and responsive transportation systems.\u003c/p\u003e \u003cp\u003eOne of the limitations of this study is to use the weather data based on only Chicago O\u0026rsquo;Hare International Airport weather station, therefore, not accounting for the spatial variability of weather conditions across the region. The analysis was focused on exploring temporal variations, effectively relying on regionally averaged weather conditions. An important direction for future research of this study is to incorporate spatially disaggregated weather data by linking trips to the nearest weather stations within the Chicago region, such as O\u0026rsquo;Hare, Midway, DuPage, Waukegan, Aurora, and Joliet weather stations, to better capture location-specific weather effects. Furthermore, the travel survey data used in this study predate the COVID-19 pandemic and the widespread adoption of remote work. As a result, although the models incorporated indicators for flexible work schedules, they were unable to fully capture the effects of work-from-home arrangements and other online activities (e.g., online food ordering, e-commerce, and on-demand delivery) on the relationship between weather conditions and travel demand. Since CMAP is currently collecting a new wave of the household travel survey that includes detailed information on alternative work arrangements and online activities, an immediate avenue for future research is to update this analysis using the post-pandemic emerging activity-travel data to examine more behaviorally relevant interactions between weather conditions and travel demand. Moreover, this study was unable to examine interaction effects between weather conditions and individuals\u0026rsquo; attitudes on travel demand. While such factors could enrich the weather-travel demand analysis by capturing individual perceptions, preferences and risk tolerance, they could not be incorporated due to the absence of lifestyle and attitudinal information in the travel survey data. Future research could address this limitation by integrating attitudinal information from enhanced travel surveys, stated-preference experiments, or complementary data sources to better represent heterogeneous behavioral responses to weather conditions. Methodologically, this study only considers the unobserved preference heterogeneity (random taste variations) that accommodates the variations occurred by repeated observations in the specific preferences for different attributes. The scale heterogeneity (i.e. variance heterogeneity) that captures the variations in the overall consistency or randomness in individuals' choices \u0026ndash; is disregarded. Accommodating both scale and preference heterogeneity can provide a more accurate and nuanced understanding of preferences and decision-making behavior under various weather conditions. Therefore, one of the immediate future works of this study is to refine the models by extending the mixed logit model formulation to include both unobserved preference heterogeneity (through random parameters) and scale heterogeneity (through random scale parameters) within its modeling framework. This will assist in appropriately analyzing the choices under dynamic weather conditions and making informed predictions. Another future work includes exploring the effects of weather changes on more activity-travel dimensions such as activity timing, activity location, and activity duration, among others. Nevertheless, this study provides critical insights on how hourly weather conditions affect individuals\u0026rsquo; daily activity participation and mode choice decisions. The ultimate goal of developing these weather-informed travel demand models is to enhance the current simulation framework of the multiagent activity-based travel demand model, POLARIS, and make it a responsive and resilient transportation system simulator. Outcomes of this study will be beneficial to conceptualize and develop a weather-informed simulation workflow that will significantly improve the accuracy and responsiveness of travel and activity demand forecasts, providing invaluable insights for transportation planning and operations under varying environmental conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSubmitted for TRR revision\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e105\u003csup\u003eth\u003c/sup\u003e Annual Meeting of Transportation Research Board, January 11-15, 2026, Washington D.C\u003c/p\u003e\u003cp\u003e \u003ch2\u003eAUTHOR CONTRIBUTION STATEMENT\u003c/h2\u003e \u003cp\u003eThe authors confirm contribution to the paper as follows: study conception and design: Nazmul Arefin Khan; data collection: Nazmul Arefin Khan, Joshua Auld; analysis and interpretation of results: Nazmul Arefin Khan; draft manuscript preparation: Nazmul Arefin Khan. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eThis report and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Integrated Transportation and Energy Cross-Sectoral System of Systems at Scale (ITE-S4), an initiative of the Energy Efficient Mobility Systems (EEMS) Program. Melissa Rossi, a DOE Office of Energy Efficiency and Renewable Energy (EERE) manager, played an important role in establishing the project concept, advancing implementation, and providing guidance. The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (Argonne). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e1. Petrović, D., I. Ivanović, V. Đorić, and J. Jović. Impact of Weather Conditions on Travel Demand \u0026ndash; The Most Common Research Methods and Applied Models. \u003cem\u003ePromet - Traffic\u0026amp;Transportation\u003c/em\u003e, Vol. 32, No. 5, 2020, pp. 711\u0026ndash;725. https://doi.org/10.7307/ptt.v32i5.3499.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e2. B\u0026ouml;cker, L. \u003cem\u003eClimate, Weather and Daily Mobility: Transport Mode Choices and Travel Experiences in the Randstad Holland\u003c/em\u003e. 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Size Matters: The Use and Misuse of Statistical Significance in Discrete Choice Models in the Transportation Academic Literature. \u003cem\u003eTransportation\u003c/em\u003e, Vol. 51, No. 6, 2024, pp. 2393\u0026ndash;2425. https://doi.org/10.1007/s11116-023-10423-y.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Argonne National Laboratory","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"weather, travel demand, household travel survey, local climatological data, mixed logit, activity participation, mode choice","lastPublishedDoi":"10.21203/rs.3.rs-8703831/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8703831/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents an analysis of the effects of weather conditions on travel demand. It develops a combined database of travel demand and monthly, daily and hourly weather conditions. The trip information of the database was extracted from the 2018-19 CMAP Household Travel Survey. To obtain the weather information, the study selected U.S. Local Climatological Data (LCD) data from National Centers for Environmental Information (NCEI) of NOAA. This study examines the effects of a comprehensive set of weather metrics on activity-travel patterns, focusing on activity participation and mode choice behavior. Descriptive analyses of daily and hourly weather conditions and travel demand provide insights into how activity-travel behavior varies with changing weather. The empirical analysis exclusively models the impacts of multiple hourly weather variables and their interactions with demographic and built environment characteristics using a mixed logit modeling framework. Elasticity analysis is employed to assess the magnitude of weather-related effects. Results indicate that temperature, relative humidity, windspeed and visibility strongly influence activity-travel behavior, particularly discretionary activity participation and active mode choices, whereas participation in mandatory activity and choice of single-occupancy vehicles are found to be less sensitive. Overall, the findings demonstrate weather conditions as critical factors influencing travel behavior and emphasize the importance of explicitly incorporating weather effects into travel demand models. These insights reinforce the need for dynamic travel demand models that incorporate real-time or hourly weather data, and underscore the importance of tailoring transportation policies and infrastructure to support resilient transportation systems.\u003c/p\u003e","manuscriptTitle":"The Meteorological Modulators: Quantifying Weather's Influence on Activity-Travel Behavior","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 09:33:41","doi":"10.21203/rs.3.rs-8703831/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9aea5c60-1b97-4060-b6b5-b511a148084e","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T09:33:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 09:33:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8703831","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8703831","identity":"rs-8703831","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}