Does the 15-minute city promote sustainable travel? Quantifying the 15-minute city and assessing its impact on individual motorized travel, active travel, public transit ridership and CO2 emissions

Does the 15-minute city promote sustainable travel? Quantifying the 15-minute city and assessing its impact on individual motorized travel, active travel, public transit ridership and CO2 emissions | 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 Does the 15-minute city promote sustainable travel? Quantifying the 15-minute city and assessing its impact on individual motorized travel, active travel, public transit ridership and CO2 emissions Rui Colaço, João de Abreu e Silva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4359947/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jan, 2025 Read the published version in Networks and Spatial Economics → Version 1 posted 11 You are reading this latest preprint version Abstract Promoting density and implementing mixed land use have long been acknowledged as potentially effective land use based solutions to transportation problems. However, the policy has leaned toward mobility-based solutions, favouring rapid travel instead of high proximity. This tendency seems now to be reversing with the increasing popularity of the 15-minute city. This paper assesses the effectiveness of the 15-minute city in promoting sustainable travel in the Lisbon Metropolitan Area. Our research shows that the 15-minute city increases non-motorized travel among its residents by facilitating engagement with amenities such as supermarkets or green urban areas. Nevertheless, central and dense areas that are not necessarily 15-minute cities also contribute towards more sustainable travel, being more effective at reducing car travel due to increased public transit use. The 15-minute city impact on CO 2 emissions per household is higher than that of central and dense areas since non-motorized travel is presented as a direct alternative to car and transit, while central and dense areas also rely on transit as an alternative to car. Hence, policies combining proximity and density may eventually maximize the benefits of implementing land use based solutions by increasing non-motorized travel and the use of transit and reducing car travel and CO 2 emissions. 15-minute city greenhouse gas emissions active travel sustainable development Figures Figure 1 Figure 2 Figure 3 1. Introduction The "15-minute city" concept was initially introduced in public discussion through an opinion piece by Carlos Moreno in the French newspaper La Tribune (Moreno, 2016 ). The backdrop of the article was that, against an ever-increasing urban population and the breaking of the barriers that had previously separated "daytime activities" from "nightlife activities" (since one could now, for example, shop online and have the item delivered to a nearby pick-up point any moment of the day), a new type of chrono-urbanism, that did not rely on a static sequence of activities, needed to be implemented (Moreno, 2016 ). Moreover, that backdrop included the then-recent signing of the Paris Agreement to limit the rise in global temperature. Hence, this "new chronourbanism" should be implemented by restructuring the urban landscape so that proximity, diversity, density, and ubiquity would allow citizens to fulfil their daily needs sustainably by bringing supply closer to demand, allowing citizens to walk or bike to their destinations in 15 minutes or less. However, the possibility of these "hyperproximities" being linked by "new generation public mobility services (such as) on-demand buses (with or without driver), and multimodal and shared services" was also briefly discussed (Moreno, 2016 ). Since then, the 15-minute city concept has gained traction among decision-makers. The model has started shaping policy among the C40 – a network of mayors of some leading world cities (C40, 2021 ). The Driving Urban Transitions (DUT) Partnership, co-funded by 28 European Union (EU) members, has included a "15-minute City Transition Pathway" to promote a sustainable urban mobility transition (DUT, 2023 ). The signs that this "proximity revival" may be successful are promising. Therefore, we propose to explore the degree to which the 15-minute city may effectively promote non-motorized travel and transit and reduce individual motorized travel and CO 2 emissions. Moreover, we will assess if the number of different amenities at walking distance increases non-motorized engagement with 15-minute urban functions. Furthermore, we will control for the impact of more common land use variables: density, rail-based transit supply and centrality on travel demand that might enhance the "15-minute effect". The research goal is to assess the effectiveness of the 15-minute city (the "net" effect) in promoting sustainable travel in the Lisbon Metropolitan Area (LMA). The paper is organized as follows. Section 2 provides context to the 15-minute city (section 2.1 .) and presents recent research on it, focused on measuring or quantifying the 15-minute city, which will help us identify it in the LMA (subsection 2.2 .). Section 3 explains how we built our dataset and the methods used in the subsequent analysis. The results are presented and discussed in section 4 . Finally, Section 5 presents the key takeaways from the paper, namely answering the research question and pointing ways for further research. 2. Literature Review 2.1. Context on the 15-minute city Advocating for sustainable travel is not new. By 1988, the global number of cars had exceeded 400 million, and motor vehicle emissions had already been recognized as having severe health and environmental effects (Lowe, 1990 ; Walsh, 1990 ). However, the suburban ideal of family togetherness (Miller, 1995 ) demanded car use, and decades of affordable car use had increased suburban sprawl, creating a negative feedback loop that demanded more car travel (Kenworthy and Laube, 1996 ). However, it was already well established in research that dense and compact urban environments that tended to be more mixed favoured shorter trips, non-motorized modes, and the use of transit (Cervero, 1996 ; Frank and Pivo, 1994 ; Kenworthy and Laube, 1996 ). Concepts such as Transit Oriented Development (Calthorpe, 1993 ), where clusters of housing, retail, offices, daycare, recreation, and parks would ideally be located within a 400 m walking radius of transit, also emerged around that time, intended to redesign suburbia to favour non-motorized modes and the use of transit. Hence, promoting density and implementing mixed land use have long been acknowledged as potentially effective land use based solutions to transportation problems (Schwanen and Mokhtarian, 2005 ). However, the policy has leaned toward mobility-based solutions, favouring rapid travel instead of high proximity (Shen et al., 2012). This tendency in policymaking seems now to be reversing with the increasing popularity of the 15-minute city (Moreno et al., 2021 ), suggesting that promoting density and mixed land use might be successfully revived, leading to modal shifts that may favour sustainable travel and urban development, even if not eliminating it. Considering that the 15-minute city intends to restructure the urban landscape so that proximity, diversity and density will allow everyone to have "six essential urban social functions" (working, commerce, healthcare, education, entertainment, living) at walking or cycling distance (Moreno, 2016 ; Moreno et al., 2021 ), one may feel that planning tendencies are only cyclical and inconsequential, if not accompanied by policymaking. However, the "15-minute city" has gained traction after mayor Anne Hidalgo's re-election campaign in Paris in 2020, to which it was central. Moreover, the COVID-19 pandemic further increased the overall interest in the concept since the need to remain home (or nearby) made it not only the "largest work-from-home experiment" (Banjo et al., 2020 ) but also the most intense "stress test" the 15-minute city could have asked for. Uptakes in shopping in local stores (Li et al., 2020 ) and engaging in physical activity in nearby parks (Yang and Xiang, 2021 ) appeared to demonstrate that proximity to amenities was a desirable feature that could remain so even after the pandemic, mainly if local consumption patterns persisted. Moreover, COVID-19 also reduced air pollution drastically (Saladié et al., 2020 ; Venter et al., 2020 ). In an editorial for The Lancet Planetary Health, Allam, Nieuwenhuijsen, Chabaud and Moreno (2022) discuss (backed by the COVID-19 experience) how the 15-minute city could help reduce greenhouse gas (GHG) emissions through proximity, further enhanced by density, diversity, and digitalization (with the latter "replacing" ubiquity, in this context). Still, reducing GHG emissions would be a major accomplishment for the 15-minute city. However, that line of research, to the best of our knowledge, has been pursued only by a handful of researchers (De Leániz and Lobo, 2023 ; Molinaro et al., 2023 ) apart from the team of Allam, Chabaud and Moreno (Allam, Bibri, et al., 2022; Allam, Nieuwenhuijsen, et al., 2022). This apparent lack of interest in connecting the 15-minute city concept with reducing GHG emissions may have one or two (prominent) reasons. The first reason that may justify this apparent lack of interest in the potential of the 15-minute city to reduce GHG emissions is that the 15-minute concept was introduced in connection to "local living". Hence, it became more attached to enhancing human interaction, access to opportunities, and accessibility analyses in general (Abbiasov et al., 2022 ; Birkenfeld et al., 2023 ; Logan et al., 2022 ; Willberg et al., 2023 ). The second reason is that researchers (and practitioners) may consider it a utopia since it is unlikely that the 15-minute city can eliminate commuting to work or study and significantly impact GHG emissions (De Leániz and Lobo, 2023 ). 2.2. Measuring the 15-minute city Research on the 15-minute city has extensively focused on (sustainable) accessibility gains, that is, walking or cycling to the "six essential urban social functions" defined by Moreno et al. ( 2021 ): working, commerce, healthcare, education and entertainment (the sixth is living, with home being assumed as the starting point for most travel). The concept has also been expanded to include public transit in the "30-minute city" (Birkenfeld et al., 2023 ; Levinson, 2020 ). Moreover, the threshold to be considered has also been debated, and the 15-minute city has sometimes been termed the "x-minute city" to include other thresholds (Logan et al., 2022 ; Lu and Diab, 2023 ). These are necessary because, as Willberg et al. ( 2023 ) discuss, the distance reached in 15 minutes can be quite different depending on factors such as an individual's age, the road conditions or the season of the year. Other methodological issues must be considered when assessing the 15-minute city besides the threshold distance. The first relates to the number and type of amenities that should be considered when accounting for the "six essential urban social functions" of Moreno et al. ( 2021 ). The second relates to how one should measure the distance to those, hence being able to map the 15-minute city. Although Clarence Perry's neighbourhood units (Perry, 1929 ) are mentioned frequently in 15-minute city literature (Caselli et al., 2022 ; Hosford et al., 2022 ; Papadopoulos et al., 2023 ), cities have not usually been built according to Perry's concept, and even if they were influenced by it, neighbourhood units have changed, and cities have "outgrown" them. Moreover, contrary to Perry's units, the 15-minute city is "outwards instead of inwards". Each individual is the centre of their own "neighbourhood", and amenities might be found in adjacent blocks or neighbourhoods, not necessarily their own. Hence, researchers have instead considered communities (Weng et al., 2019 ), blocks (Guzman et al., 2024 ) or statistical grids (Willberg et al., 2023 ), which, depending on the place of analysis, usually correspond to the smallest statistical subsection to which population can be reported. Measurements of the 15-minute distance consider the centroid of the statistical unit (Abbiasov et al., 2022 ; Hosford et al., 2022 ; Logan et al., 2022 ) or, more rarely, the individual when data with that level of disaggregation is available (Birkenfeld et al., 2023 ). Finally, the number of categories of amenities included in the analysis can be quite large (e.g., more than 35 in da Silva et al., 2020 ) but usually grouped in no more than 8–10 groups (Abbiasov et al., 2022 ; Ferrer-Ortiz et al., 2022 ; Guzman et al., 2024 ) accounting for commerce, healthcare, education and entertainment. One thing appears to be consensual: most authors do not consider work in their assessments, although some consider access to transport (Ferrer-Ortiz et al., 2022 ; Guzman et al., 2024 ), eventually as a proxy. Our approach to the 15-minute city is described in the following section. 3. Research Framework 3.1. Case study and data The analysis is implemented in the Lisbon Metropolitan Area (LMA), comprising 18 municipalities with a total area of approximately 3,000 km2 and almost 3 million inhabitants, making it the largest metropolitan area in Portugal (INE, 2022 ). The data presented in this paper was compiled from different sources to account for land use patterns and activity opportunities and from a mobility survey (INE, 2018 ) to account for trips. The process of building the database is described below. 3.1.1. Travel data Travel data comes from a mobility survey implemented in the LMA in the last trimester of 2017 to a sample of almost 28,000 households, reporting over 120,000 trips. Each trip was characterized by start and endpoint (statistical subsection only, for privacy issues), mode and vehicle (by fuel type), distance and duration. Each household was characterized in socioeconomic terms. The survey allowed for proxy answers and included only one day of travel, which has limitations. For example, a shopping trip may have been "moved" to another day of the week (on purpose – underreporting – or in fact). We refer the reader to Colaço and de Abreu e Silva ( 2024b ) for some of the limitations of the survey for purposes other than assessing commuting patterns – which was its original purpose. However, we believe it is still sufficient to inform the discussion on the potential of the 15-minute city in promoting sustainable travel. 3.1.2. Measuring the 15-minute city The first step was deciding what amenities to include in our analysis. Survey data was used to inform us of our sample's most frequent travel purposes (activities) and, hence, which amenities may be more relevant to have at a 15-minute distance. Work was not accounted for since mixing work and place of residence may be less attainable (e.g., finding specialized labour at walking distance) or even undesirable (e.g., locating a factory at walking distance from a residential location). We associated at least one amenity to each activity, with the results shown in Table 1 . Table 1 "15-minute amenities" Activity %Trips* Amenities Category Escorting a friend or family member (e.g., taking a child to school) 20.51 School (1) Social facility (2) Education and elderly care Going to school 14.92 School (1) Shopping (groceries, supermarket and other) 14.92 Supermarket (2)(3) Commerce (provisioning) Going to a restaurant, café, or disco 7.31 Cafe (2)(3) Restaurant (2)(3) Commerce / Entertainment Practicing outdoor or indoor activities (sports or other) 5.08 Sports Centre (3) Park (3) Green urban areas (4) Entertainment Taking care of personal business (going to the bank, laundry services, hairdresser, other) 3.35 Bank (3) Post office (3) Laundry (2) Hairdresser (2) Services Going for a doctor's appointment, or a medical examination or similar 3.30 Clinic (2) Pharmacy (2) Healthcare Engaging in group activities (community or other associations, volunteering, church, …) 2.05 Cultural and recreational associations (2) Entertainment * Percentage of trips excluding commuting trips. (1) IGeFe ( 2024 ) (2) Dataluso ( 2020 ) (3) OpenStreetMap (OSM) (4) CORINE Land Cover (EEA, 2021 ) The remaining 28.56% of trips correspond to activities to which respondents did not answer about trip purpose (7.95%), to trips whose purpose cannot be associated with a specific amenity ("visiting friends and family", 7.49%) or that are very generic ("other leisure activities", "going for a walk", and similar, adding up to 13.12%). To map the LMA's 15-minute cities, we considered the amenities accessible at the statistical subsection level (roughly equivalent to a census block), related to the 8 activities mentioned in Table 1 (the categories in the same Table exist only to relate the activities to other research (Ferrer-Ortiz et al., 2022 ; Moreno et al., 2021 ). To count how many activities can be found at a 15-minute distance for someone residing in a statistical subsection, we created 600m buffer areas for each amenity. We intersected these with the subsections, one activity at a time. Let us imagine one subsection is smaller than a 600m buffer area and one restaurant is located in its centre. Since the entire subsection (100%) has access to the restaurant, we count one amenity (related to the activity "Going to a restaurant, café, or disco") and proceed to the following. If it is another café, the count is still 1 (we consider the subsection already has access to the activity). If it is an amenity related to a different activity, the count moves up to 2, and so on, until we reach our "full" 15-minute city with access to 8 activities. Moreover, the amenities do not have to be central to the subsection or even inside it as long as 100% of the subsection is contained within the buffer areas. As for the threshold distance of 600m, the average distance for a trip with a duration under 15 minutes, walking or cycling, and falling under one of the eight activities considered, equals 711m in our dataset – a little under the 800m average found by Guzman et al. ( 2024 ) in Bogotá. Since we needed a Euclidean distance to create the buffers, we chose 600m to account for the potential sinuosity of the street network. Moreover, the distance associated with 15 minutes can vary substantially: for example, an older person will likely walk slower than a younger one; the slope and other road conditions will also influence the distance one is willing to walk; and even the weather and time of year may impact it (Willberg et al., 2023 ). For these reasons, we find that a distance of 600m as the crow flies can be a reasonable estimate of the average distance most of our sample is willing to walk. The final result, showing how the number of accessible amenities at 15 minutes is distributed in the LMA, is presented in Fig. 1 (with 8 amenities representing the "full" 15-minute city). 3.1.3. Additional data and summary of the variables After data cleaning, we retained 11,060 households from which we retrieved socioeconomic and travel information. Households that we could not be sure about home location (we considered home to be the destination of the trips whose purpose was reported as "returning home"; if no one in the household had at least one trip with that purpose, we discarded the household), or that had not reported the trip mode or purpose of a trip, or household income, were discarded. As for land use, and apart from the number of amenities available in the subsection (ranging from 0 to 8 and according to Fig. 1 ), we also calculated the percentage of the area of the subsection within a 600m radius of a metro station. The process was the same as the one used to calculate if an amenity is within reach (however, we kept the percentage of the statistical subsection area included within the buffer). Distance from the centroid of the subsection to the central business district and population density of the urban area of the civil parish were also included in the dataset as proxies of centrality and density. At one point, we considered including "Transport" as a Category in the 15-minute city, as did other researchers (Ferrer-Ortiz et al., 2022 ; Guzman et al., 2024 ). Including transport "expands" the 15-minute city to other potential thresholds, such as the 30-minute city (Birkenfeld et al., 2023 ; Levinson, 2020 ). In this aspect, we chose to stay true to Moreno et al. 's vision (2021) because we concluded that including transport-related variables in our models instead of in our 15-minute cities served our discussion better. Separating public transport from active mobility (proximity) will allow us to discuss complimentary policies, namely the 15-minute city "proximity" vs public transit accessibility, and how combining them may contribute to increasing sustainable travel. Finally, since the mobility survey provides information about the distance and duration for each trip, as well as mode choice and vehicle characteristics (fuel type), it is possible to estimate the CO 2 emissions associated with each trip based on the basic formula of the European Environmental Agency's methodology, as given by Eq. 1 : . $$E=EF x TA$$ 1 where EF is the emission factor (g/km), and TA is the transport activity (distance in kilometres travelled in each trip). The emission factor (EF) allows the conversion of the consumption values of travelled distances into emission values. The EF varies according to the mode of transport and respective fuels/energy sources used. A comprehensive explanation of the estimation of the CO 2 emissions can be found in de Abreu e Silva et al. ( 2023 ), which is also the source of the CO 2 emissions estimations (per household) used in this paper (CO 2 e), which is presented in Table 2 , along with the summary of the variables. Table 2 Summary of the variables Variable Description Mean Variance HH_Size Household size 2.38 1.48 P_14less Percentage of children in the household (Number of individuals ≤ 14 / HHSize) 0.10 0.03 P_65more Percentage of older individuals in the household (Number of individuals ≥ 65 / HHSize) 0.24 0.16 P_Univ Percentage of individuals with a university degree in the household (Number of individuals with a university degree / Number of adults) 0.39 0.18 N_Emp Number of employed individuals in the household 1.16 0.80 N_Cars Number of cars in the household 1.32 0.69 Income Household monthly gross income (Euros) / 1000 1.92 1.38 DCBD Distance from the subsection centroid to the Central Business District* (km) 14.31 77.89 PMetro Percentage of the subsection at 600m from a Metro Station 0.14 0.12 PopDens Population Density of the civil parish (residents / km2 of urban area) / 1000 ** 3.79 15.08 PNonMot Percentage of trips using non-motorized modes 0.21 0.11 PPrivMot Percentage of trips using private motorized vehicle 0.56 0.17 PTransit Percentage of trips using transit 0.20 0.10 P15minTrip Percentage of trips directly related to the 15-minute concept: non-motorized trips whose purpose is engaging in an activity which could potentially be found in a 15-minute city (as defined in Table 1 ) and whose travel duration is under or equal to 15 minutes - "15-minute trips" 0.05 0.02 CO 2 e CO 2 emissions (g) (log) 3.13 1.56 Percentage Subsections by number of amenities at a 15-minute distance 8 amenities 20.92 7 amenities 21.28 6 amenities 13.58 5 amenities 8.76 4 amenities 7.57 3 amenities 6.18 2 amenities 5.60 1 amenity 6.03 0 amenities 10.07 Note: *For calculation effects, the CBD is defined as a point - Lisbon's City Hall. **The urban area was calculated by considering the areas classified as urban fabric in the CORINE Land Cover (EEA, 2021 ). As a final note, we used data from 2017 (the survey) and hence, queried OSM for data from before 31-12-2019 (so it could match the one from Dataluso – a private firm collecting Public Register's Office data, which has been used in other analyses (Colaço and de Abreu e Silva, 2022 , 2024a ) and proved trustworthy) and avoid the COVID-19 pandemic impact on, for example, store location. The CORINE Land Cover data is from 2018. The data related to transportation infrastructures comes from the operators or previous analyses of the LMA and has not changed in recent years. Only the population data used to calculate the Population Density comes from the 2021 Census since the alternative would have been to use the 2011. 3.2. Conceptual Model The conceptual model structure examines the relations between household socioeconomic characteristics, car ownership, and land use patterns around the residential location, including the number of 15-minute city amenities, mode choice, CO 2 (GHG) emissions, and engagement in "15-minute trips" (trips directly related to the 15-minute concept: non-motorized trips whose purpose is engaging in an activity which could potentially be found in a 15-minute city and whose travel duration is under or equal to 15minutes). The conceptual framework accounts for the possibility of self-selection due to residential and travel preferences. Self-selection plays a significant role in travel and residential choices, and individuals with an affinity toward a particular travel mode will use it more frequently and likely choose their residential location accordingly (Handy et al., 2005 ). Hence, we hypothesize that socioeconomic characteristics will influence residential patterns and car ownership (number of vehicles) and that the latter will impact their residential location. Households owning more vehicles will be less likely to locate themselves in central and dense areas (de Abreu e Silva et al., 2006 , 2012 ). Since we expect to find a significant association between central and dense areas and the number of amenities available in the area of residence, car ownership is also likely to impact residential location in the 15-minute city. Considering the above, household socioeconomic characteristics are the only exogenous variables in the model and are expected to impact travel outcomes associated with the 15-minute city. Car ownership and residential land use patterns are also likely to influence these outputs. As travel outcomes, we consider mode choice, CO 2 (GHG) emissions, and engagement in "15-minute trips" separately. This means that 5 similar models are estimated, one for each travel outcome. These travel outcomes are the only difference in the specification of the different models. Including all of these travel outcomes in the analysis allows us to pursue the research goal of assessing the effect of the "15-minute city" in promoting sustainable travel by comparing the direct and total effects of the 15-minute city in mode share, CO 2 (GHG) emissions, and engagement in "15-minute trips". The conceptual model is presented in Fig. 2 . 3.3. Methodology SEM is a popular modelling technique combining two statistical methods: factor analysis and simultaneous equation model (Schumacker and Lomax, 2010 ). A full-fledged SEM model includes both a measurement submodel and a structural submodel. The measurement submodel associates indicators with latent constructs (similar to factor analysis), and the structural submodel incorporates the relationships between different latent constructs and between these and the observed variables. The SEM models used in this research include a structural submodel (Eq. 2) and a measurement submodel (Eq. 3). η = Bη + Γx + ξ (2) y = Λ y η + ε (3) where: η is a vector (m*1) of the m latent endogenous variables, B is a matrix (m*m) of coefficients of endogenous variables, Γ is a matrix (m*n) of coefficients of exogenous variables, x is a vector (n*1) of the n observed exogenous variables, ξ is a vector (m*1) of errors from structural relation, y is a vector (p*1) of the p observed endogenous variables, Λy is a matrix (p*m) of regression coefficients of y on η; and ε is a vector (p*1) of measurement and errors on y. As some of the endogenous variables included in the models are ordinal, the Weighted Least Squares (WLS) estimation method is used (Muthén and Muthén, 2017 ). The goodness of fit is evaluated using the Comparable Fit Index (CFI) and the absolute Root Mean Square of Approximation (RMSEA). Indirect effects are the product of the direct effects of the different mediating variables in each structural path. Total effects are the sum of both direct and indirect effects. Examining direct and total effects allows for identifying mediation and moderation effects and self-defeating variables due to contrary direct and indirect effects. For a detailed explanation, see (Bollen, 1989 ). 4. Results and Discussion Since we want to assess the effect of the 15-minute city on mode choice, CO 2 (GHG) emissions, and engagement in "15-minute trips", 5 models are implemented following the structure presented in Fig. 2 . Each model is specific to one dimension we want to explore: Model 1, to the percentage of trips using non-motorized modes (PNonMot); Model 2, to the percentage of trips using private motorized vehicles (PPrivMot); Model 3, to the percentage of trips using transit (PTransit); Model 4, to the percentage of "15-minute trips" (P15minTrip); and Model 5, to GHG (CO 2 ) emissions (CO 2 e). We will refer to these models by their number and associated variable for clarity whenever necessary (e.g., Model 1 – PNonMot). The model fit indicators are presented in Table 3 . The results indicate an overall good fit for all models. Table 3 Models fit indicators Model CFI TLI RMSEA Model 1 – PNonMot 0.974 0.957 0.032 Model 2 – PPrivMot 0.960 0.935 0.041 Model 3 – PTransit 0.966 0.943 0.038 Model 4 – P15minTrip 0.972 0.954 0.033 Model 5 – CO 2 e 0.971 0.953 0.035 The measurement submodel is built based on a previous exploratory factor analysis (EFA) using the 3 variables related to land use and transport: DCBD, PMetro and PopDens. One factor was extracted using principal components extraction and varimax rotation, explaining 65.86% of the total variance and presenting a Kaiser–Meyer–Olkin test (KMO) score of 0.671. The factor loadings (positive with population density and proximity to the metro and negative with distance to the CBD) suggest that a latent construct can be built, fitting well with the conceptual model since it can stand for "Central and Dense" areas. The coefficients of the variables are all significant and vary only slightly on the measurements submodels. The results of the EFA and the measurement submodels are presented in Table 4 . The structural submodels are presented in Fig. 3 and are globally aligned with the conceptual model. The difference resides in the lack of statistically significant relationships between land use characteristics ("central and dense") in the vicinity of the residence in Models 1 and 4. The direction of the relationships also follows what was posited in the conceptual model. The effects are negative in some models and positive in others, which is reasonable considering the nature of the different outputs of each model. The standardized direct effects are presented in Table 5 . As was expected (de Abreu e Silva et al., 2006 , 2012 ; Handy et al., 2005 ), having more cars negatively affects living in central and dense areas. Owning one or more cars, which is a materialization of a preference, means that they will be used frequently, which is easier in suburban locations where road travel is rapid although opportunities are scarce (Shen et al., 2012), hence the negative relationship between the number of cars and living in the 15-minute city in all models. Moreover, being central and dense has a high and significant effect on an area having more 15-minute city amenities, which is plausible since most urban services and amenities cannot be provided below a certain threshold of people to make them viable (Christaller, 1933 ; Curtis et al., 2009 ). Considering the "net" effect of the 15-minute city in promoting sustainable travel, Model 1 shows that the 15-minute city increases the percentage of non-motorized travel. At the same time, central and dense locations decrease it, although this latter effect is non-significant. This suggests that centrality, transit supply and density "alone" may not be sufficient to promote a decrease in non-motorized travel, which is supported by Model 4 results: one has to live in a 15-minute city to make a 15-minute non-motorized trip. Although apparently redundant, these results suggest that a) the 15-minute threshold may be appropriate as the maximum distance people are willing to walk or cycle, on average, and b) the chosen amenities are properly capturing those trips (that is, the 15-minute city we mapped in Fig. 1 fits the actual 15-minute cities our sample lives in). Nevertheless, locating in a central and dense area decreases the share of motorized travel, with an effect that more than doubles that of living in a 15-minute city, as seen in Model 2. This is explained in Model 3 since central and dense locations increase transit use while 15-minute cities do not. This does not speak against the 15-minute city – it means that, eventually, 15-minute trips are so short that they are either made by walking or driving. Eventually, the cost of waiting for transit may render it the less appealing alternative in these circumstances. Finally, the net effect of the 15-minute city in CO 2 emissions is negative. 15-minute cities can reduce CO 2 emissions more effectively than centrality, transit supply and density. Living in central and dense locations has a direct positive effect on CO 2 emissions, although statistically non-significant. What the 5 models read together suggest is that in the LMA, living in a central and dense area (which is not, cumulatively, a 15-minute city) will reduce car travel and increase the use of transit, which will still have a positive impact on CO 2 emissions, when compared with non-motorized trips, which is what the 15-minute city promotes. Thus, these results imply that the 15-minute city contribution to reducing CO 2 emission is more robust than living in a central, dense area with a good transit supply. However, the direct effects are insufficient to disentangle the impacts of land use on travel outcomes, as land use also influences the likelihood of living in a 15-minute city, which mediates the effects of land use on travel outcomes. Other relevant insights can be drawn from the standardized direct effects in Table 5 , particularly the ones from the exogenous variables. Income has a positive effect on individual motorized travel (negative on the other modes) and CO 2 emissions. The opposite is found concerning older residents. Income is positively related to the number of cars (all models), while the number of cars is negatively related to central and dense locations. A depiction of the LMA emerges from this analysis: higher-income residents living in the suburbs will drive the most, accounting eventually for the highest share of GHG emissions; older residents living in central and dense areas with abundant amenities will have the most sustainable travel behaviour. Table 6 presents the standardized total effects. Table 5 Standardized direct effects Model 1- PNonMot 2 - PPrivMot 3 - PTransit 4 - P15minTrip 5 - CO 2 e Endogenous variable Regressor Coef p-value Coef p-value Coef p-value Coef p-value Coef p-value PNonMot 15minCity 0.100 0.000 - - - - - - - - Central and Dense -0.026 0.104 - - - - - - - - N_Cars -0.235 0.000 - - - - - - - - Income -0.052 0.000 - - - - - - - - P_65more 0.173 0.000 - - - - - - - - PPrivMot 15minCity - - -0.042 0.006 - - - - - - Central and Dense - - -0.102 0.000 - - - - - - N_Cars - - 0.389 0.000 - - - - - - Income - - 0.050 0.000 - - - - - - P_65more - - -0.036 0.000 - - - - - - PTransit 15minCity - - - - -0.041 0.010 - - - - Central and Dense - - - - 0.161 0.000 - - - - N_Cars - - - - -0.318 0.000 - - - - Income - - - - 0.010 0.443 - - - - P_65more - - - - -0.137 0.000 - - - - P15minTrip 15minCity - - - - - - 0.096 0.000 - - Central and Dense - - - - - - -0.019 0.268 - - N_Cars - - - - - - -0.142 0.000 - - Income - - - - - - -0.046 0.000 - - P_65more - - - - - - 0.127 0.000 - - CO 2 e 15minCity - - - - - - - - -0.060 0.000 Central and Dense - - - - - - - - 0.003 0.817 N_Cars - - - - - - - - 0.315 0.000 Income - - - - - - - - 0.095 0.000 P_65more - - - - - - - - -0.240 0.000 15minCity Central and Dense 0.657 0.000 0.656 0.000 0.653 0.000 0.657 0.000 0.654 0.000 N_Cars -0.059 0.000 -0.057 0.000 -0.062 0.000 -0.058 0.000 -0.062 0.000 Central and Dense N_Cars -0.309 0.000 -0.299 0.000 -0.311 0.000 -0.311 0.000 -0.313 0.000 Income 0.129 0.000 0.126 0.000 0.130 0.000 0.131 0.000 0.128 0.000 HH_Size 0.052 0.001 0.038 0.011 0.062 0.000 0.053 0.000 0.067 0.000 P_14less -0.082 0.000 -0.075 0.000 -0.090 0.000 -0.085 0.000 -0.076 0.000 P_Univ 0.205 0.000 0.208 0.000 0.203 0.000 0.202 0.000 0.203 0.000 N_Cars Income 0.292 0.000 0.286 0.000 0.290 0.000 0.292 0.000 0.294 0.000 HH_Size 0.312 0.000 0.322 0.000 0.314 0.000 0.310 0.000 0.311 0.000 P_14less -0.121 0.000 -0.136 0.000 -0.136 0.000 -0.123 0.000 -0.122 0.000 P_Univ 0.083 0.000 0.085 0.000 0.085 0.000 0.081 0.000 0.088 0.000 N_Emp 0.237 0.000 0.244 0.000 0.245 0.000 0.241 0.000 0.240 0.000 Table 6 Standardized total effects Model 1 - PNonMot 15minCity Central and Dense N_Cars Income P_65more HH_Size P14_less P_Univ N_Emp PNonMot coef. 0.100 0.040 -0.253 -0.121 0.173 -0.077 0.027 -0.013 -0.060 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 15minCity coef. 0.657 -0.262 0.008 -0.048 -0.022 0.113 -0.062 p-value 0.000 0.000 0.341 0.006 0.015 0.000 0.000 Central and Dense coef. -0.309 0.039 -0.045 -0.044 0.179 -0.073 p-value 0.000 0.002 0.002 0.001 0.000 0.000 N_Cars coef. 0.292 0.312 -0.121 0.083 0.237 p-value 0.000 0.000 0.000 0.000 0.000 Model 2 - PPrivMot 15minCity Central and Dense N_Cars Income P_65more HH_Size P14_less P_Univ N_Emp PPrivMot coef. -0.042 -0.129 0.430 0.157 -0.036 0.134 -0.049 0.009 0.105 p-value 0.006 0.000 0.000 0.000 0.000 0.000 0.000 0.059 0.000 15minCity coef. 0.656 -0.254 0.010 -0.057 -0.015 0.115 -0.062 p-value 0.000 0.000 0.240 0.000 0.104 0.000 0.000 Central and Dense coef. -0.299 0.040 -0.058 -0.034 0.183 -0.073 p-value 0.000 0.001 0.000 0.011 0.000 0.000 N_Cars coef. 0.286 0.322 -0.136 0.085 0.244 p-value 0.000 0.000 0.000 0.000 0.000 Model 3 - PTransit 15minCity Central and Dense N_Cars Income P_65more HH_Size P14_less P_Univ N_Emp PTransit coef. -0.041 0.134 -0.357 -0.076 -0.137 -0.104 0.037 -0.003 -0.087 p-value 0.010 0.000 0.000 0.000 0.000 0.000 0.000 0.471 0.000 15minCity coef. 0.653 -0.265 0.008 -0.043 -0.023 0.110 -0.065 p-value 0.000 0.000 0.354 0.000 0.012 0.000 0.000 Central and Dense coef. -0.311 0.040 -0.036 -0.048 0.177 -0.076 p-value 0.000 0.002 0.011 0.000 0.000 0.000 N_Cars coef. 0.290 0.314 -0.136 0.085 0.245 p-value 0.000 0.000 0.000 0.000 0.000 Model 4 - P15minTrip 15minCity Central and Dense N_Cars Income P_65more HH_Size P14_less P_Univ N_Emp P15minTrip coef. 0.096 0.044 -0.162 -0.088 0.127 -0.048 0.016 -0.004 -0.039 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.159 0.000 15minCity coef. 0.657 -0.262 0.009 -0.046 -0.023 0.112 -0.063 p-value 0.000 0.000 0.273 0.000 0.010 0.000 0.000 Central and Dense coef. -0.311 0.040 -0.043 -0.047 0.177 -0.075 p-value 0.000 0.002 0.003 0.001 0.000 0.000 N_Cars coef. 0.292 0.310 -0.123 0.081 0.241 p-value 0.000 0.000 0.000 0.000 0.000 Model 5 - CO 2 e 15minCity Central and Dense N_Cars Income P_65more HH_Size P14_less P_Univ N_Emp CO 2 e coef. -0.060 -0.036 0.330 0.188 -0.240 0.100 -0.038 0.022 0.079 p-value 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 15minCity coef. 0.654 -0.267 0.006 -0.039 -0.017 0.110 -0.064 p-value 0.000 0.000 0.517 0.000 0.062 0.000 0.000 Central and Dense coef. -0.313 0.036 -0.030 -0.038 0.176 -0.075 p-value 0.000 0.004 0.034 0.006 0.000 0.000 N_Cars coef. 0.294 0.311 -0.122 0.088 0.240 p-value 0.000 0.000 0.000 0.000 0.000 The standardized total effects shown in Table 6 help disentangle the impacts of land use on travel outcomes in the LMA. The 15-minute city increases the share of non-motorized travel by more than double compared to central and dense areas (with a magnitude of 0.100 15minCity vs. 0.040 Central and Dense, on Model 1). However, it can only decrease individual private motorized travel by less than one-third compared to those areas (-0.042 15minCity vs. -0.129 Central and Dense on Model 2). The 15-minute city offers proximity and increases PNonMot while reducing PPrivMot and CO 2 emissions. However, central and dense areas also increase transit ridership (PTransit). The models suggest that even when accounting for self-selection, living in a central and dense area will contribute towards more sustainable travel because it is more effective at reducing car trips and increasing public transit trips than the 15-minute city. On the other hand, the 15-minute city is more effective at increasing non-motorized travel. The result is that both the 15-minute city and central and dense areas can help reduce CO 2 emissions: the former with a coefficient of 0.060, the latter with a magnitude of 0.036 (Model 5). Model 5 shows that the central and dense areas effect (-0.036) plus the 15minCity effect (-0.060) could contravene almost 30% of the impact of the number of cars in a household (0.188) in its CO 2 emissions, meaning that (controlling for all other variables) the CO 2 emissions of a household located in a central and dense area which is also a 15 minute-city could be reduced by more than one quarter when compared with a similar household in the suburbs, showing that even an extremely car-dependent household could eventually reduce their CO 2 emissions given the presence of amenities and more transit options. 5. Conclusions and further research By 2030, 60% of people globally are expected to live in urban areas (United Nations, 2018 ). Making those cities and urban settlements inclusive, safe, resilient and sustainable is one of the goals of the 2030 Agenda for Sustainable Development (United Nations, 2015 ). While this goal includes providing access to sustainable transport systems for all, it also considers that cities' environmental impact must be reduced, which includes paying special attention to air quality (United Nations, 2015 ). Moreover, in the face of climate change, it has also been acknowledged that accelerating the reduction of global greenhouse gas emissions is urgent (United Nations, 2015 ). This paper measured and presented the 15-minute city in the Lisbon Metropolitan Area (section 3.1.2 .) based on the 8 most reported activities from a mobility survey (excluding work). Survey data was then used to evaluate the effectiveness of the 15-minute city in promoting sustainable travel, namely in encouraging non-motorized travel and the use of transit instead of car and in reducing CO 2 emissions per household. Land use characteristics of the place of residence were considered to estimate the "net" effect of the 15-minute city – that is, the effect that proximity to amenities has on the travel-related variables, independent of centrality, rail-based transit supply and density (the land-use variables used in this study), which also allowed us to estimate their combined effect. Our models' results show that living in a 15-minute city increases non-motorized travel by promoting engagement with 15-minute amenities, thus contributing towards sustainable travel. However, central and dense areas, as were defined in the research (through centrality, transit supply and density related variables), also contribute towards more sustainable travel, being more effective at reducing car trips and increasing public transit trips than the 15-minute city. The 15-minute city impact on CO 2 emissions per household is higher than that of central and dense areas because non-motorized travel is presented as a direct alternative to car and transit, while central and dense areas also rely on transit as an alternative to car. Our models' results suggest that a combination of proximity and density can significantly increase non-motorized travel and reduce CO 2 emissions. The 15-minute city offers, eventually, a utopia of proximity, diversity, and density. Even assuming that it cannot or will not be possible to implement 15-minute cities in an entire metropolitan area, there is room for density and proximity to complement one another. The successful 15-minute city must be part of a larger policy package that promotes sustainable travel, also looking at the regional structure of the metropolitan area to maximize the benefits of land use and transport interaction policies. For example, as analyzed here, the 15-minute city concept is quite effective at increasing short, non-motorized trips. However, its role in promoting the use of transit and reducing car use is not so effective, implying that increasing proximity in non-central places might dampen the positive effects of the 15-minute city. On the other hand, centralization and densification policies can contribute, at the same time, to the emergence of areas aligned with the 15-minute city via the generation of agglomeration effects. Further research will focus on collecting more recent data to assess, for example, how phenomena such as rising telework adoption in the post-COVID-19 era and increasing housing prices in city centres may drive residential relocation and promote sprawl and more car-centred development. On the other hand, subcentres of activity may emerge in the periphery, and promoting mixed land use may encourage sustainable travel. Furthermore, some cities have started implementing "15-minute policies", and longitudinal data may help support our conclusions by considering pre-and-post adoption scenarios. Finally, the mobility survey we used is not a time-use survey. We could eventually get a more comprehensive model if we knew, for example, how well the individuals' schedules match the opening hours of the amenities they use or if they were explicitly surveyed about the amenities they need the most around their homes. 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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-4359947","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":325865651,"identity":"22531879-2005-44c1-8218-393e0aa9804e","order_by":0,"name":"Rui Colaço","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie3QQWoDIRQGYAfBbAyztcwwvYJBSDbJ5CojQtY5QBeGAZfNtscoFEqWDoKr3iEzN2gJBAKl1ATqptZCV6H4w5Mn+PF4ApCScq3pXeWXjp4PKHUmfyGNqxvlSfZFYJxQ66/ufYzMJBz65q6u2L7tDut1XQHCN91pN78FxVaHSKkRo40VbGqRKB6oYI5IM35ZTWRpglMIwIg0SPNni6cQU83lmWTKZJKICPnQ/EnlR0+6kzLLKOFK80eEkSd6rAz/kUC3C78XjNgVK7DbBeHhsotQpaFBMmqH/vVYV3lrhgN+d81ImDf3Y4ttsemDY779PfINCYJo/kBSUlJS/mU+AdKyWI6e4jswAAAAAElFTkSuQmCC","orcid":"","institution":"CERIS, Instituto Superior Técnico, Universidade de Lisboa","correspondingAuthor":true,"prefix":"","firstName":"Rui","middleName":"","lastName":"Colaço","suffix":""},{"id":325865652,"identity":"b5e3806c-f411-4c68-8885-e8ec22598fa7","order_by":1,"name":"João de Abreu e Silva","email":"","orcid":"","institution":"CERIS, Instituto Superior Técnico, Universidade de Lisboa","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"de Abreu e","lastName":"Silva","suffix":""}],"badges":[],"createdAt":"2024-05-02 15:17:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4359947/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4359947/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11067-025-09670-6","type":"published","date":"2025-01-22T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61106437,"identity":"e2a36105-383d-4136-836d-0bf34244afb8","added_by":"auto","created_at":"2024-07-25 16:15:41","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":354920,"visible":true,"origin":"","legend":"\u003cp\u003eThe 15-minute city in the Lisbon Metropolitan Area\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4359947/v1/b2c3887df01e348a80123426.jpeg"},{"id":61105919,"identity":"0e3fec23-38eb-4a72-9a59-802b362e275f","added_by":"auto","created_at":"2024-07-25 16:07:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27621,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Model\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4359947/v1/bc7bea30d14b6ed20b1f5ede.png"},{"id":61105920,"identity":"90a40b58-2219-43dc-8f59-77c4f95fe6ee","added_by":"auto","created_at":"2024-07-25 16:07:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104300,"visible":true,"origin":"","legend":"\u003cp\u003eStructural submodels – Empirical relationships between endogenous variables (standardized direct effects)\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4359947/v1/4432b956e4f2f19d7bc53fa0.png"},{"id":74858319,"identity":"4cb5bbab-05cb-43f4-b344-a860bc45bdfa","added_by":"auto","created_at":"2025-01-27 16:07:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1916938,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4359947/v1/9cdbcdf0-77c0-4a13-ab68-97a94866e0c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does the 15-minute city promote sustainable travel? Quantifying the 15-minute city and assessing its impact on individual motorized travel, active travel, public transit ridership and CO2 emissions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe \"15-minute city\" concept was initially introduced in public discussion through an opinion piece by Carlos Moreno in the French newspaper La Tribune (Moreno, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The backdrop of the article was that, against an ever-increasing urban population and the breaking of the barriers that had previously separated \"daytime activities\" from \"nightlife activities\" (since one could now, for example, shop online and have the item delivered to a nearby pick-up point any moment of the day), a new type of chrono-urbanism, that did not rely on a static sequence of activities, needed to be implemented (Moreno, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, that backdrop included the then-recent signing of the Paris Agreement to limit the rise in global temperature. Hence, this \"new chronourbanism\" should be implemented by restructuring the urban landscape so that proximity, diversity, density, and ubiquity would allow citizens to fulfil their daily needs sustainably by bringing supply closer to demand, allowing citizens to walk or bike to their destinations in 15 minutes or less. However, the possibility of these \"hyperproximities\" being linked by \"new generation public mobility services (such as) on-demand buses (with or without driver), and multimodal and shared services\" was also briefly discussed (Moreno, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince then, the 15-minute city concept has gained traction among decision-makers. The model has started shaping policy among the C40 \u0026ndash; a network of mayors of some leading world cities (C40, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Driving Urban Transitions (DUT) Partnership, co-funded by 28 European Union (EU) members, has included a \"15-minute City Transition Pathway\" to promote a sustainable urban mobility transition (DUT, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The signs that this \"proximity revival\" may be successful are promising.\u003c/p\u003e \u003cp\u003eTherefore, we propose to explore the degree to which the 15-minute city may effectively promote non-motorized travel and transit and reduce individual motorized travel and CO\u003csub\u003e2\u003c/sub\u003e emissions. Moreover, we will assess if the number of different amenities at walking distance increases non-motorized engagement with 15-minute urban functions. Furthermore, we will control for the impact of more common land use variables: density, rail-based transit supply and centrality on travel demand that might enhance the \"15-minute effect\". The research goal is to assess the effectiveness of the 15-minute city (the \"net\" effect) in promoting sustainable travel in the Lisbon Metropolitan Area (LMA).\u003c/p\u003e \u003cp\u003eThe paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides context to the 15-minute city (section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e2.1\u003c/span\u003e.) and presents recent research on it, focused on measuring or quantifying the 15-minute city, which will help us identify it in the LMA (subsection \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e.). Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e explains how we built our dataset and the methods used in the subsequent analysis. The results are presented and discussed in section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Finally, Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the key takeaways from the paper, namely answering the research question and pointing ways for further research.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Context on the 15-minute city\u003c/h2\u003e \u003cp\u003eAdvocating for sustainable travel is not new. By 1988, the global number of cars had exceeded 400\u0026nbsp;million, and motor vehicle emissions had already been recognized as having severe health and environmental effects (Lowe, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Walsh, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). However, the suburban ideal of family togetherness (Miller, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) demanded car use, and decades of affordable car use had increased suburban sprawl, creating a negative feedback loop that demanded more car travel (Kenworthy and Laube, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, it was already well established in research that dense and compact urban environments that tended to be more mixed favoured shorter trips, non-motorized modes, and the use of transit (Cervero, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Frank and Pivo, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Kenworthy and Laube, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Concepts such as Transit Oriented Development (Calthorpe, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), where clusters of housing, retail, offices, daycare, recreation, and parks would ideally be located within a 400 m walking radius of transit, also emerged around that time, intended to redesign suburbia to favour non-motorized modes and the use of transit.\u003c/p\u003e \u003cp\u003eHence, promoting density and implementing mixed land use have long been acknowledged as potentially effective land use based solutions to transportation problems (Schwanen and Mokhtarian, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, the policy has leaned toward mobility-based solutions, favouring rapid travel instead of high proximity (Shen et al., 2012). This tendency in policymaking seems now to be reversing with the increasing popularity of the 15-minute city (Moreno et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), suggesting that promoting density and mixed land use might be successfully revived, leading to modal shifts that may favour sustainable travel and urban development, even if not eliminating it.\u003c/p\u003e \u003cp\u003eConsidering that the 15-minute city intends to restructure the urban landscape so that proximity, diversity and density will allow everyone to have \"six essential urban social functions\" (working, commerce, healthcare, education, entertainment, living) at walking or cycling distance (Moreno, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Moreno et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), one may feel that planning tendencies are only cyclical and inconsequential, if not accompanied by policymaking. However, the \"15-minute city\" has gained traction after mayor Anne Hidalgo's re-election campaign in Paris in 2020, to which it was central. Moreover, the COVID-19 pandemic further increased the overall interest in the concept since the need to remain home (or nearby) made it not only the \"largest work-from-home experiment\" (Banjo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but also the most intense \"stress test\" the 15-minute city could have asked for.\u003c/p\u003e \u003cp\u003eUptakes in shopping in local stores (Li et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and engaging in physical activity in nearby parks (Yang and Xiang, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) appeared to demonstrate that proximity to amenities was a desirable feature that could remain so even after the pandemic, mainly if local consumption patterns persisted. Moreover, COVID-19 also reduced air pollution drastically (Saladi\u0026eacute; et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Venter et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In an editorial for The Lancet Planetary Health, Allam, Nieuwenhuijsen, Chabaud and Moreno (2022) discuss (backed by the COVID-19 experience) how the 15-minute city could help reduce greenhouse gas (GHG) emissions through proximity, further enhanced by density, diversity, and digitalization (with the latter \"replacing\" ubiquity, in this context).\u003c/p\u003e \u003cp\u003eStill, reducing GHG emissions would be a major accomplishment for the 15-minute city. However, that line of research, to the best of our knowledge, has been pursued only by a handful of researchers (De Le\u0026aacute;niz and Lobo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Molinaro et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) apart from the team of Allam, Chabaud and Moreno (Allam, Bibri, et al., 2022; Allam, Nieuwenhuijsen, et al., 2022). This apparent lack of interest in connecting the 15-minute city concept with reducing GHG emissions may have one or two (prominent) reasons.\u003c/p\u003e \u003cp\u003eThe first reason that may justify this apparent lack of interest in the potential of the 15-minute city to reduce GHG emissions is that the 15-minute concept was introduced in connection to \"local living\". Hence, it became more attached to enhancing human interaction, access to opportunities, and accessibility analyses in general (Abbiasov et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Birkenfeld et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Logan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Willberg et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The second reason is that researchers (and practitioners) may consider it a utopia since it is unlikely that the 15-minute city can eliminate commuting to work or study and significantly impact GHG emissions (De Le\u0026aacute;niz and Lobo, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measuring the 15-minute city\u003c/h2\u003e \u003cp\u003eResearch on the 15-minute city has extensively focused on (sustainable) accessibility gains, that is, walking or cycling to the \"six essential urban social functions\" defined by Moreno et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e): working, commerce, healthcare, education and entertainment (the sixth is living, with home being assumed as the starting point for most travel). The concept has also been expanded to include public transit in the \"30-minute city\" (Birkenfeld et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Levinson, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the threshold to be considered has also been debated, and the 15-minute city has sometimes been termed the \"x-minute city\" to include other thresholds (Logan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lu and Diab, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These are necessary because, as Willberg et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) discuss, the distance reached in 15 minutes can be quite different depending on factors such as an individual's age, the road conditions or the season of the year.\u003c/p\u003e \u003cp\u003eOther methodological issues must be considered when assessing the 15-minute city besides the threshold distance. The first relates to the number and type of amenities that should be considered when accounting for the \"six essential urban social functions\" of Moreno et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The second relates to how one should measure the distance to those, hence being able to map the 15-minute city.\u003c/p\u003e \u003cp\u003eAlthough Clarence Perry's neighbourhood units (Perry, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1929\u003c/span\u003e) are mentioned frequently in 15-minute city literature (Caselli et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hosford et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Papadopoulos et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), cities have not usually been built according to Perry's concept, and even if they were influenced by it, neighbourhood units have changed, and cities have \"outgrown\" them. Moreover, contrary to Perry's units, the 15-minute city is \"outwards instead of inwards\". Each individual is the centre of their own \"neighbourhood\", and amenities might be found in adjacent blocks or neighbourhoods, not necessarily their own.\u003c/p\u003e \u003cp\u003eHence, researchers have instead considered communities (Weng et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), blocks (Guzman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or statistical grids (Willberg et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which, depending on the place of analysis, usually correspond to the smallest statistical subsection to which population can be reported. Measurements of the 15-minute distance consider the centroid of the statistical unit (Abbiasov et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hosford et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Logan et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or, more rarely, the individual when data with that level of disaggregation is available (Birkenfeld et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the number of categories of amenities included in the analysis can be quite large (e.g., more than 35 in da Silva et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but usually grouped in no more than 8\u0026ndash;10 groups (Abbiasov et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ferrer-Ortiz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guzman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) accounting for commerce, healthcare, education and entertainment. One thing appears to be consensual: most authors do not consider work in their assessments, although some consider access to transport (Ferrer-Ortiz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guzman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), eventually as a proxy. Our approach to the 15-minute city is described in the following section.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Framework","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Case study and data\u003c/h2\u003e \u003cp\u003eThe analysis is implemented in the Lisbon Metropolitan Area (LMA), comprising 18 municipalities with a total area of approximately 3,000 km2 and almost 3\u0026nbsp;million inhabitants, making it the largest metropolitan area in Portugal (INE, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The data presented in this paper was compiled from different sources to account for land use patterns and activity opportunities and from a mobility survey (INE, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) to account for trips. The process of building the database is described below.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Travel data\u003c/h2\u003e \u003cp\u003eTravel data comes from a mobility survey implemented in the LMA in the last trimester of 2017 to a sample of almost 28,000 households, reporting over 120,000 trips. Each trip was characterized by start and endpoint (statistical subsection only, for privacy issues), mode and vehicle (by fuel type), distance and duration. Each household was characterized in socioeconomic terms. The survey allowed for proxy answers and included only one day of travel, which has limitations. For example, a shopping trip may have been \"moved\" to another day of the week (on purpose \u0026ndash; underreporting \u0026ndash; or in fact). We refer the reader to Cola\u0026ccedil;o and de Abreu e Silva (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) for some of the limitations of the survey for purposes other than assessing commuting patterns \u0026ndash; which was its original purpose. However, we believe it is still sufficient to inform the discussion on the potential of the 15-minute city in promoting sustainable travel.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Measuring the 15-minute city\u003c/h2\u003e \u003cp\u003eThe first step was deciding what amenities to include in our analysis. Survey data was used to inform us of our sample's most frequent travel purposes (activities) and, hence, which amenities may be more relevant to have at a 15-minute distance. Work was not accounted for since mixing work and place of residence may be less attainable (e.g., finding specialized labour at walking distance) or even undesirable (e.g., locating a factory at walking distance from a residential location). We associated at least one amenity to each activity, with the results shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\"15-minute amenities\"\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=\"char\" char=\".\" 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\"\u003e \u003cp\u003eActivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%Trips*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmenities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEscorting a friend or family member (e.g., taking a child to school)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchool (1)\u003c/p\u003e \u003cp\u003eSocial facility (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003cp\u003eand elderly care\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoing to school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSchool (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShopping (groceries, supermarket and other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupermarket (2)(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommerce (provisioning)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoing to a restaurant, caf\u0026eacute;, or disco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCafe (2)(3)\u003c/p\u003e \u003cp\u003eRestaurant (2)(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCommerce / Entertainment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePracticing outdoor or indoor activities (sports or other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSports Centre (3)\u003c/p\u003e \u003cp\u003ePark (3)\u003c/p\u003e \u003cp\u003eGreen urban areas (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntertainment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaking care of personal business (going to the bank, laundry services, hairdresser, other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBank (3)\u003c/p\u003e \u003cp\u003ePost office (3)\u003c/p\u003e \u003cp\u003eLaundry (2)\u003c/p\u003e \u003cp\u003eHairdresser (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eServices\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoing for a doctor's appointment, or a medical examination or similar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClinic (2)\u003c/p\u003e \u003cp\u003ePharmacy (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHealthcare\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEngaging in group activities (community or other associations, volunteering, church, \u0026hellip;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCultural and recreational associations (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntertainment\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* Percentage of trips excluding commuting trips.\u003c/p\u003e \u003cp\u003e(1) IGeFe (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(2) Dataluso (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e(3) OpenStreetMap (OSM)\u003c/p\u003e \u003cp\u003e(4) CORINE Land Cover (EEA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe remaining 28.56% of trips correspond to activities to which respondents did not answer about trip purpose (7.95%), to trips whose purpose cannot be associated with a specific amenity (\"visiting friends and family\", 7.49%) or that are very generic (\"other leisure activities\", \"going for a walk\", and similar, adding up to 13.12%). To map the LMA's 15-minute cities, we considered the amenities accessible at the statistical subsection level (roughly equivalent to a census block), related to the 8 activities mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (the categories in the same Table exist only to relate the activities to other research (Ferrer-Ortiz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Moreno et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo count how many activities can be found at a 15-minute distance for someone residing in a statistical subsection, we created 600m buffer areas for each amenity. We intersected these with the subsections, one activity at a time. Let us imagine one subsection is smaller than a 600m buffer area and one restaurant is located in its centre. Since the entire subsection (100%) has access to the restaurant, we count one amenity (related to the activity \"Going to a restaurant, caf\u0026eacute;, or disco\") and proceed to the following. If it is another caf\u0026eacute;, the count is still 1 (we consider the subsection already has access to the activity). If it is an amenity related to a different activity, the count moves up to 2, and so on, until we reach our \"full\" 15-minute city with access to 8 activities. Moreover, the amenities do not have to be central to the subsection or even inside it as long as 100% of the subsection is contained within the buffer areas.\u003c/p\u003e \u003cp\u003eAs for the threshold distance of 600m, the average distance for a trip with a duration under 15 minutes, walking or cycling, and falling under one of the eight activities considered, equals 711m in our dataset \u0026ndash; a little under the 800m average found by Guzman et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in Bogot\u0026aacute;. Since we needed a Euclidean distance to create the buffers, we chose 600m to account for the potential sinuosity of the street network. Moreover, the distance associated with 15 minutes can vary substantially: for example, an older person will likely walk slower than a younger one; the slope and other road conditions will also influence the distance one is willing to walk; and even the weather and time of year may impact it (Willberg et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For these reasons, we find that a distance of 600m as the crow flies can be a reasonable estimate of the average distance most of our sample is willing to walk.\u003c/p\u003e \u003cp\u003eThe final result, showing how the number of accessible amenities at 15 minutes is distributed in the LMA, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (with 8 amenities representing the \"full\" 15-minute city).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Additional data and summary of the variables\u003c/h2\u003e \u003cp\u003eAfter data cleaning, we retained 11,060 households from which we retrieved socioeconomic and travel information. Households that we could not be sure about home location (we considered home to be the destination of the trips whose purpose was reported as \"returning home\"; if no one in the household had at least one trip with that purpose, we discarded the household), or that had not reported the trip mode or purpose of a trip, or household income, were discarded.\u003c/p\u003e \u003cp\u003eAs for land use, and apart from the number of amenities available in the subsection (ranging from 0 to 8 and according to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we also calculated the percentage of the area of the subsection within a 600m radius of a metro station. The process was the same as the one used to calculate if an amenity is within reach (however, we kept the percentage of the statistical subsection area included within the buffer). Distance from the centroid of the subsection to the central business district and population density of the urban area of the civil parish were also included in the dataset as proxies of centrality and density.\u003c/p\u003e \u003cp\u003eAt one point, we considered including \"Transport\" as a Category in the 15-minute city, as did other researchers (Ferrer-Ortiz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Guzman et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Including transport \"expands\" the 15-minute city to other potential thresholds, such as the 30-minute city (Birkenfeld et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Levinson, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this aspect, we chose to stay true to Moreno \u003cem\u003eet al.\u003c/em\u003e's vision (2021) because we concluded that including transport-related variables in our models instead of in our 15-minute cities served our discussion better. Separating public transport from active mobility (proximity) will allow us to discuss complimentary policies, namely the 15-minute city \"proximity\" vs public transit accessibility, and how combining them may contribute to increasing sustainable travel.\u003c/p\u003e \u003cp\u003eFinally, since the mobility survey provides information about the distance and duration for each trip, as well as mode choice and vehicle characteristics (fuel type), it is possible to estimate the CO\u003csub\u003e2\u003c/sub\u003e emissions associated with each trip based on the basic formula of the European Environmental Agency's methodology, as given by Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: .\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$E=EF x TA$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere EF is the emission factor (g/km), and TA is the transport activity (distance in kilometres travelled in each trip). The emission factor (EF) allows the conversion of the consumption values of travelled distances into emission values. The EF varies according to the mode of transport and respective fuels/energy sources used. A comprehensive explanation of the estimation of the CO\u003csub\u003e2\u003c/sub\u003e emissions can be found in de Abreu e Silva et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is also the source of the CO\u003csub\u003e2\u003c/sub\u003e emissions estimations (per household) used in this paper (CO\u003csub\u003e2\u003c/sub\u003ee), which is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, along with the summary of the variables.\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\u003eSummary of the variables\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\"\u003e \u003cp\u003eVariable\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 \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_14less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of children in the household (Number of individuals\u0026thinsp;\u0026le;\u0026thinsp;14 / HHSize)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP_65more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of older individuals in the household (Number of individuals \u0026ge; 65 / HHSize)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\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\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of individuals with a university degree in the household (Number of individuals with a university degree / Number of adults)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\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\u003eN_Emp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of employed individuals in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cars in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold monthly gross income (Euros) / 1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from the subsection centroid to the Central Business District* (km)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMetro\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of the subsection at 600m from a Metro Station\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\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\u003ePopDens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation Density of the civil parish (residents / km2 of urban area) / 1000 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNonMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of trips using non-motorized modes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPrivMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of trips using private motorized vehicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTransit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of trips using transit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP15minTrip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of trips directly related to the 15-minute concept: non-motorized trips whose purpose is engaging in an activity which could potentially be found in a 15-minute city (as defined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and whose travel duration is under or equal to 15 minutes - \"15-minute trips\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e emissions (g) (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePercentage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSubsections by number of amenities at a 15-minute distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e20.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e21.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 amenity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: *For calculation effects, the CBD is defined as a point - Lisbon's City Hall. **The urban area was calculated by considering the areas classified as urban fabric in the CORINE Land Cover (EEA, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs a final note, we used data from 2017 (the survey) and hence, queried OSM for data from before 31-12-2019 (so it could match the one from Dataluso \u0026ndash; a private firm collecting Public Register's Office data, which has been used in other analyses (Cola\u0026ccedil;o and de Abreu e Silva, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e) and proved trustworthy) and avoid the COVID-19 pandemic impact on, for example, store location. The CORINE Land Cover data is from 2018. The data related to transportation infrastructures comes from the operators or previous analyses of the LMA and has not changed in recent years. Only the population data used to calculate the Population Density comes from the 2021 Census since the alternative would have been to use the 2011.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Conceptual Model\u003c/h2\u003e \u003cp\u003eThe conceptual model structure examines the relations between household socioeconomic characteristics, car ownership, and land use patterns around the residential location, including the number of 15-minute city amenities, mode choice, CO\u003csub\u003e2\u003c/sub\u003e (GHG) emissions, and engagement in \"15-minute trips\" (trips directly related to the 15-minute concept: non-motorized trips whose purpose is engaging in an activity which could potentially be found in a 15-minute city and whose travel duration is under or equal to 15minutes).\u003c/p\u003e \u003cp\u003eThe conceptual framework accounts for the possibility of self-selection due to residential and travel preferences. Self-selection plays a significant role in travel and residential choices, and individuals with an affinity toward a particular travel mode will use it more frequently and likely choose their residential location accordingly (Handy et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Hence, we hypothesize that socioeconomic characteristics will influence residential patterns and car ownership (number of vehicles) and that the latter will impact their residential location.\u003c/p\u003e \u003cp\u003eHouseholds owning more vehicles will be less likely to locate themselves in central and dense areas (de Abreu e Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Since we expect to find a significant association between central and dense areas and the number of amenities available in the area of residence, car ownership is also likely to impact residential location in the 15-minute city. Considering the above, household socioeconomic characteristics are the only exogenous variables in the model and are expected to impact travel outcomes associated with the 15-minute city. Car ownership and residential land use patterns are also likely to influence these outputs.\u003c/p\u003e \u003cp\u003eAs travel outcomes, we consider mode choice, CO\u003csub\u003e2\u003c/sub\u003e (GHG) emissions, and engagement in \"15-minute trips\" separately. This means that 5 similar models are estimated, one for each travel outcome. These travel outcomes are the only difference in the specification of the different models. Including all of these travel outcomes in the analysis allows us to pursue the research goal of assessing the effect of the \"15-minute city\" in promoting sustainable travel by comparing the direct and total effects of the 15-minute city in mode share, CO\u003csub\u003e2\u003c/sub\u003e (GHG) emissions, and engagement in \"15-minute trips\". The conceptual model is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Methodology\u003c/h2\u003e \u003cp\u003eSEM is a popular modelling technique combining two statistical methods: factor analysis and simultaneous equation model (Schumacker and Lomax, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A full-fledged SEM model includes both a measurement submodel and a structural submodel. The measurement submodel associates indicators with latent constructs (similar to factor analysis), and the structural submodel incorporates the relationships between different latent constructs and between these and the observed variables. The SEM models used in this research include a structural submodel (Eq.\u0026nbsp;2) and a measurement submodel (Eq.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eη\u0026thinsp;=\u0026thinsp;Bη\u0026thinsp;+\u0026thinsp;Γx\u0026thinsp;+\u0026thinsp;ξ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;Λ\u003csub\u003ey\u003c/sub\u003eη\u0026thinsp;+\u0026thinsp;ε\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3)\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\u003ewhere:\u003c/p\u003e \u003cp\u003eη is a vector (m*1) of the m latent endogenous variables,\u003c/p\u003e \u003cp\u003eB is a matrix (m*m) of coefficients of endogenous variables,\u003c/p\u003e \u003cp\u003eΓ is a matrix (m*n) of coefficients of exogenous variables,\u003c/p\u003e \u003cp\u003ex is a vector (n*1) of the n observed exogenous variables,\u003c/p\u003e \u003cp\u003eξ is a vector (m*1) of errors from structural relation,\u003c/p\u003e \u003cp\u003ey is a vector (p*1) of the p observed endogenous variables,\u003c/p\u003e \u003cp\u003eΛy is a matrix (p*m) of regression coefficients of y on η; and\u003c/p\u003e \u003cp\u003eε is a vector (p*1) of measurement and errors on y.\u003c/p\u003e \u003cp\u003eAs some of the endogenous variables included in the models are ordinal, the Weighted Least Squares (WLS) estimation method is used (Muth\u0026eacute;n and Muth\u0026eacute;n, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The goodness of fit is evaluated using the Comparable Fit Index (CFI) and the absolute Root Mean Square of Approximation (RMSEA). Indirect effects are the product of the direct effects of the different mediating variables in each structural path. Total effects are the sum of both direct and indirect effects. Examining direct and total effects allows for identifying mediation and moderation effects and self-defeating variables due to contrary direct and indirect effects. For a detailed explanation, see (Bollen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eSince we want to assess the effect of the 15-minute city on mode choice, CO\u003csub\u003e2\u003c/sub\u003e (GHG) emissions, and engagement in \"15-minute trips\", 5 models are implemented following the structure presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Each model is specific to one dimension we want to explore: Model 1, to the percentage of trips using non-motorized modes (PNonMot); Model 2, to the percentage of trips using private motorized vehicles (PPrivMot); Model 3, to the percentage of trips using transit (PTransit); Model 4, to the percentage of \"15-minute trips\" (P15minTrip); and Model 5, to GHG (CO\u003csub\u003e2\u003c/sub\u003e) emissions (CO\u003csub\u003e2\u003c/sub\u003ee). We will refer to these models by their number and associated variable for clarity whenever necessary (e.g., Model 1 \u0026ndash; PNonMot).\u003c/p\u003e \u003cp\u003eThe model fit indicators are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results indicate an overall good fit for all models.\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\u003eModels fit indicators\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\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 \u0026ndash; PNonMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u0026ndash; PPrivMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u0026ndash; PTransit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4 \u0026ndash; P15minTrip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 5 \u0026ndash; CO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\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\u003eThe measurement submodel is built based on a previous exploratory factor analysis (EFA) using the 3 variables related to land use and transport: DCBD, PMetro and PopDens. One factor was extracted using principal components extraction and varimax rotation, explaining 65.86% of the total variance and presenting a Kaiser\u0026ndash;Meyer\u0026ndash;Olkin test (KMO) score of 0.671. The factor loadings (positive with population density and proximity to the metro and negative with distance to the CBD) suggest that a latent construct can be built, fitting well with the conceptual model since it can stand for \"Central and Dense\" areas. The coefficients of the variables are all significant and vary only slightly on the measurements submodels. The results of the EFA and the measurement submodels are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e \u003cp\u003eThe structural submodels are presented in Fig.\u0026nbsp;3 and are globally aligned with the conceptual model. The difference resides in the lack of statistically significant relationships between land use characteristics (\"central and dense\") in the vicinity of the residence in Models 1 and 4. The direction of the relationships also follows what was posited in the conceptual model. The effects are negative in some models and positive in others, which is reasonable considering the nature of the different outputs of each model. The standardized direct effects are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs was expected (de Abreu e Silva et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Handy et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), having more cars negatively affects living in central and dense areas. Owning one or more cars, which is a materialization of a preference, means that they will be used frequently, which is easier in suburban locations where road travel is rapid although opportunities are scarce (Shen et al., 2012), hence the negative relationship between the number of cars and living in the 15-minute city in all models. Moreover, being central and dense has a high and significant effect on an area having more 15-minute city amenities, which is plausible since most urban services and amenities cannot be provided below a certain threshold of people to make them viable (Christaller, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1933\u003c/span\u003e; Curtis et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the \"net\" effect of the 15-minute city in promoting sustainable travel, Model 1 shows that the 15-minute city increases the percentage of non-motorized travel. At the same time, central and dense locations decrease it, although this latter effect is non-significant. This suggests that centrality, transit supply and density \"alone\" may not be sufficient to promote a decrease in non-motorized travel, which is supported by Model 4 results: one has to live in a 15-minute city to make a 15-minute non-motorized trip. Although apparently redundant, these results suggest that a) the 15-minute threshold may be appropriate as the maximum distance people are willing to walk or cycle, on average, and b) the chosen amenities are properly capturing those trips (that is, the 15-minute city we mapped in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e fits the actual 15-minute cities our sample lives in).\u003c/p\u003e \u003cp\u003eNevertheless, locating in a central and dense area decreases the share of motorized travel, with an effect that more than doubles that of living in a 15-minute city, as seen in Model 2. This is explained in Model 3 since central and dense locations increase transit use while 15-minute cities do not. This does not speak against the 15-minute city \u0026ndash; it means that, eventually, 15-minute trips are so short that they are either made by walking or driving. Eventually, the cost of waiting for transit may render it the less appealing alternative in these circumstances.\u003c/p\u003e \u003cp\u003eFinally, the net effect of the 15-minute city in CO\u003csub\u003e2\u003c/sub\u003e emissions is negative. 15-minute cities can reduce CO\u003csub\u003e2\u003c/sub\u003e emissions more effectively than centrality, transit supply and density. Living in central and dense locations has a direct positive effect on CO\u003csub\u003e2\u003c/sub\u003e emissions, although statistically non-significant. What the 5 models read together suggest is that in the LMA, living in a central and dense area (which is not, cumulatively, a 15-minute city) will reduce car travel and increase the use of transit, which will still have a positive impact on CO\u003csub\u003e2\u003c/sub\u003e emissions, when compared with non-motorized trips, which is what the 15-minute city promotes. Thus, these results imply that the 15-minute city contribution to reducing CO\u003csub\u003e2\u003c/sub\u003e emission is more robust than living in a central, dense area with a good transit supply. However, the direct effects are insufficient to disentangle the impacts of land use on travel outcomes, as land use also influences the likelihood of living in a 15-minute city, which mediates the effects of land use on travel outcomes.\u003c/p\u003e \u003cp\u003eOther relevant insights can be drawn from the standardized direct effects in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, particularly the ones from the exogenous variables. Income has a positive effect on individual motorized travel (negative on the other modes) and CO\u003csub\u003e2\u003c/sub\u003e emissions. The opposite is found concerning older residents. Income is positively related to the number of cars (all models), while the number of cars is negatively related to central and dense locations. A depiction of the LMA emerges from this analysis: higher-income residents living in the suburbs will drive the most, accounting eventually for the highest share of GHG emissions; older residents living in central and dense areas with abundant amenities will have the most sustainable travel behaviour. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the standardized total effects.\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\u003eStandardized direct effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1- PNonMot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2 - PPrivMot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3 - PTransit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e4 - P15minTrip\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e5 - CO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndogenous variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegressor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCoef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNonMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_65more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPrivMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15minCity\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-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral and Dense\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-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Cars\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\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome\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\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_65more\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-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTransit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15minCity\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-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral and Dense\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\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Cars\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-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome\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\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_65more\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-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP15minTrip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15minCity\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral and Dense\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Cars\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_65more\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15minCity\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral and Dense\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Cars\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome\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 \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_14less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_14less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN_Emp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.000\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=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandardized total effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 - PNonMot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP_65more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP14_less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN_Emp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNonMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2 - PPrivMot\u003c/b\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP_65more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP14_less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN_Emp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPrivMot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e-0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3 - PTransit\u003c/b\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP_65more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP14_less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN_Emp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTransit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e-0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 4 - P15minTrip\u003c/b\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP_65more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP14_less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN_Emp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP15minTrip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e-0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 5 - CO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003cb\u003ee\u003c/b\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP_65more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHH_Size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP14_less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP_Univ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN_Emp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003ee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15minCity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral and Dense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e-0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN_Cars\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoef.\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 \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\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 \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.000\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\u003eThe standardized total effects shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e help disentangle the impacts of land use on travel outcomes in the LMA. The 15-minute city increases the share of non-motorized travel by more than double compared to central and dense areas (with a magnitude of 0.100 15minCity vs. 0.040 Central and Dense, on Model 1). However, it can only decrease individual private motorized travel by less than one-third compared to those areas (-0.042 15minCity vs. -0.129 Central and Dense on Model 2). The 15-minute city offers proximity and increases PNonMot while reducing PPrivMot and CO\u003csub\u003e2\u003c/sub\u003e emissions. However, central and dense areas also increase transit ridership (PTransit).\u003c/p\u003e \u003cp\u003eThe models suggest that even when accounting for self-selection, living in a central and dense area will contribute towards more sustainable travel because it is more effective at reducing car trips and increasing public transit trips than the 15-minute city. On the other hand, the 15-minute city is more effective at increasing non-motorized travel. The result is that both the 15-minute city and central and dense areas can help reduce CO\u003csub\u003e2\u003c/sub\u003e emissions: the former with a coefficient of 0.060, the latter with a magnitude of 0.036 (Model 5).\u003c/p\u003e \u003cp\u003eModel 5 shows that the central and dense areas effect (-0.036) plus the 15minCity effect (-0.060) could contravene almost 30% of the impact of the number of cars in a household (0.188) in its CO\u003csub\u003e2\u003c/sub\u003e emissions, meaning that (controlling for all other variables) the CO\u003csub\u003e2\u003c/sub\u003e emissions of a household located in a central and dense area which is also a 15 minute-city could be reduced by more than one quarter when compared with a similar household in the suburbs, showing that even an extremely car-dependent household could eventually reduce their CO\u003csub\u003e2\u003c/sub\u003e emissions given the presence of amenities and more transit options.\u003c/p\u003e"},{"header":"5. Conclusions and further research","content":"\u003cp\u003eBy 2030, 60% of people globally are expected to live in urban areas (United Nations, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Making those cities and urban settlements inclusive, safe, resilient and sustainable is one of the goals of the 2030 Agenda for Sustainable Development (United Nations, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While this goal includes providing access to sustainable transport systems for all, it also considers that cities' environmental impact must be reduced, which includes paying special attention to air quality (United Nations, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, in the face of climate change, it has also been acknowledged that accelerating the reduction of global greenhouse gas emissions is urgent (United Nations, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper measured and presented the 15-minute city in the Lisbon Metropolitan Area (section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3.1.2\u003c/span\u003e.) based on the 8 most reported activities from a mobility survey (excluding work). Survey data was then used to evaluate the effectiveness of the 15-minute city in promoting sustainable travel, namely in encouraging non-motorized travel and the use of transit instead of car and in reducing CO\u003csub\u003e2\u003c/sub\u003e emissions per household. Land use characteristics of the place of residence were considered to estimate the \"net\" effect of the 15-minute city \u0026ndash; that is, the effect that proximity to amenities has on the travel-related variables, independent of centrality, rail-based transit supply and density (the land-use variables used in this study), which also allowed us to estimate their combined effect.\u003c/p\u003e \u003cp\u003eOur models' results show that living in a 15-minute city increases non-motorized travel by promoting engagement with 15-minute amenities, thus contributing towards sustainable travel. However, central and dense areas, as were defined in the research (through centrality, transit supply and density related variables), also contribute towards more sustainable travel, being more effective at reducing car trips and increasing public transit trips than the 15-minute city. The 15-minute city impact on CO\u003csub\u003e2\u003c/sub\u003e emissions per household is higher than that of central and dense areas because non-motorized travel is presented as a direct alternative to car and transit, while central and dense areas also rely on transit as an alternative to car. Our models' results suggest that a combination of proximity and density can significantly increase non-motorized travel and reduce CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eThe 15-minute city offers, eventually, a \u003cem\u003eutopia\u003c/em\u003e of proximity, diversity, and density. Even assuming that it cannot or will not be possible to implement 15-minute cities in an entire metropolitan area, there is room for density and proximity to complement one another. The successful 15-minute city must be part of a larger policy package that promotes sustainable travel, also looking at the regional structure of the metropolitan area to maximize the benefits of land use and transport interaction policies. For example, as analyzed here, the 15-minute city concept is quite effective at increasing short, non-motorized trips. However, its role in promoting the use of transit and reducing car use is not so effective, implying that increasing proximity in non-central places might dampen the positive effects of the 15-minute city. On the other hand, centralization and densification policies can contribute, at the same time, to the emergence of areas aligned with the 15-minute city via the generation of agglomeration effects.\u003c/p\u003e \u003cp\u003eFurther research will focus on collecting more recent data to assess, for example, how phenomena such as rising telework adoption in the post-COVID-19 era and increasing housing prices in city centres may drive residential relocation and promote sprawl and more car-centred development. On the other hand, subcentres of activity may emerge in the periphery, and promoting mixed land use may encourage sustainable travel. Furthermore, some cities have started implementing \"15-minute policies\", and longitudinal data may help support our conclusions by considering pre-and-post adoption scenarios. Finally, the mobility survey we used is not a time-use survey. We could eventually get a more comprehensive model if we knew, for example, how well the individuals' schedules match the opening hours of the amenities they use or if they were explicitly surveyed about the amenities they need the most around their homes.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eFunding Declaration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe authors gratefully acknowledge the Foundation for Science and Technology (FCT) support through funding UIDB/04625/2020 from the research unit CERIS and for funding the project PTDC/ECI-TRA/4841/2021 (REMOBIL Research Project).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbiasov T, Heine C, Glaeser EL et al (2022) \u003cem\u003eThe 15-Minute City Quantified Using Mobility Data\u003c/em\u003e. \u003cem\u003eNBER Working Paper\u003c/em\u003e. 30752. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2139/ssrn.4306706\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.4306706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllam Z, Bibri SE, Chabaud D et al (2022) The '15-Minute City' concept can shape a net-zero urban future. \u003cem\u003eHumanities and Social Sciences Communications\u003c/em\u003e 9(1). 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However, the policy has leaned toward mobility-based solutions, favouring rapid travel instead of high proximity. This tendency seems now to be reversing with the increasing popularity of the 15-minute city. This paper assesses the effectiveness of the 15-minute city in promoting sustainable travel in the Lisbon Metropolitan Area. Our research shows that the 15-minute city increases non-motorized travel among its residents by facilitating engagement with amenities such as supermarkets or green urban areas. Nevertheless, central and dense areas that are not necessarily 15-minute cities also contribute towards more sustainable travel, being more effective at reducing car travel due to increased public transit use. The 15-minute city impact on CO\u003csub\u003e2\u003c/sub\u003e emissions per household is higher than that of central and dense areas since non-motorized travel is presented as a direct alternative to car and transit, while central and dense areas also rely on transit as an alternative to car. Hence, policies combining proximity and density may eventually maximize the benefits of implementing land use based solutions by increasing non-motorized travel and the use of transit and reducing car travel and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e","manuscriptTitle":"Does the 15-minute city promote sustainable travel? Quantifying the 15-minute city and assessing its impact on individual motorized travel, active travel, public transit ridership and CO2 emissions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 16:07:36","doi":"10.21203/rs.3.rs-4359947/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-06T22:25:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-30T18:43:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127576756180651307622201472685761365306","date":"2024-10-04T15:54:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-15T22:36:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"69435873706244342810973149690553720040","date":"2024-07-03T21:26:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-03T05:58:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68793434494947253933031684160911056451","date":"2024-06-03T20:16:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-24T17:57:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-08T08:18:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-07T23:44:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Networks and Spatial Economics","date":"2024-05-02T15:15:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"networks-and-spatial-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nets","sideBox":"Learn more about [Networks and Spatial Economics](http://link.springer.com/journal/11067)","snPcode":"11067","submissionUrl":"https://submission.nature.com/new-submission/11067/3","title":"Networks and Spatial Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b10c0927-e662-4dda-a6d6-052b82150679","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T15:59:53+00:00","versionOfRecord":{"articleIdentity":"rs-4359947","link":"https://doi.org/10.1007/s11067-025-09670-6","journal":{"identity":"networks-and-spatial-economics","isVorOnly":false,"title":"Networks and Spatial Economics"},"publishedOn":"2025-01-22 15:57:09","publishedOnDateReadable":"January 22nd, 2025"},"versionCreatedAt":"2024-07-25 16:07:36","video":"","vorDoi":"10.1007/s11067-025-09670-6","vorDoiUrl":"https://doi.org/10.1007/s11067-025-09670-6","workflowStages":[]},"version":"v1","identity":"rs-4359947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4359947","identity":"rs-4359947","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}