Residential-Relocation, Displacement, and CO2 Emissions Near Light-Rail: A 10-Year Microdata Study

Residential-Relocation, Displacement, and CO2 Emissions Near Light-Rail: A 10-Year Microdata Study | 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 Residential-Relocation, Displacement, and CO 2 Emissions Near Light-Rail: A 10-Year Microdata Study Faria Afrin Zinia, Andy Hong, Reid Ewing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8897368/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Light rail transit (LRT) is often promoted as a sustainable mobility strategy to reduce traffic emissions. Yet its impact on vehicular CO₂ emissions through residential relocation around its stations remains poorly understood. This gap stems from the lack of household-level data to track residential moves and the limited understanding of how built environment changes associated with those moves shape vehicle use. To address this, we used 10 years of Data Axle microdata to track individual household relocation patterns near LRT stations in Salt Lake County, Utah. Our findings indicate that station areas concentrate residential moves among low-income renter households, with net out-migration and a small degree of displacement, indicating subtle socioeconomic and racial filtering in which lower-income, non-white households are replaced by slightly higher-income renters near LRT stations. In our analysis of travel-related environmental outcomes, we found a net increase in estimated vehicle miles traveled (VMT) and CO₂ emissions, as potential VMT reductions associated with station-area built environments are offset by moves to more car-dependent areas and the de-densification of station areas. Our findings suggest that targeted housing policies to retain low-income renters and the densification of station areas through a broader mix of socioeconomic groups may help maximize the environmental benefits of public transit. Light Rail Transit Station areas Built-environment Vehicle Miles Traveled CO2 emission Residential relocation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Transportation is a leading source of CO 2 emissions in the United States, due to high automobile dependency (EPA, 2024 ; Transportation Statistics, 2024 ). To address this, urban planners have increasingly adopted strategies such as new urbanism and smart growth to create more sustainable and livable communities. These approaches focus on reshaping the built environment to reduce VMT by bringing destinations closer together and promoting low-emission transportation options such as public transit, walking, and biking(Choi & Zhang, 2017 ; Kim et al., 2015 ). Resultantly, over the past 25 years, significant investments in LRT and mixed-use, transit-oriented developments have been made to reduce car use and lower emissions (Boarnet et al., 2020 ; Finio, 2024 ). While these investments often improve transportation accessibility, they also affect real estate markets by increasing housing premiums which in turn attracts further investment in urban infrastructure, stimulating additional development (Ibraeva et al., 2020 ; Kaniewska et al., 2024 ). This higher housing cost in transit-served areas may increase displacement risk of low-income and vulnerable populations, and may inadvertently displace them to car-dependent neighborhoods, thus undermining CO 2 reduction goals (Atkinson, 2004 ; Chatman et al., 2019 ; Leung & Yiu, 2022 ). While extensive research has examined how transit investments influence residential relocation and displacement, their downstream impact on travel behavior and CO 2 emissions resulting from changes in the built environment remains understudied (Boarnet, Bostic, Rodnyansky, et al., 2017; Cervero et al., 2002 ; Delmelle & Nilsson, 2019 ; Doucet, 2021 ; Finio, 2024 ; Song & Chapple, 2024 ; Tehrani et al., 2019 ). To our knowledge, no study has yet used longitudinal population data around light rail station areas to directly assess how residential relocation and potential displacement affect household travel behavior and vehicular CO₂ emissions. To address this gap, our research analyzes residential relocation and displacement surrounding light rail station areas using household-level data from 2012 to 2022. We then estimate the likely impact of these moves on VMT and emissions by considering how changes in the built environment associated with these moves influence household travel behavior. 2. Literature review 2.1 LRT, residential relocation, and displacement Despite the remarkable expansion of LRT and its potential influence on residential relocation, research on transit-induced residential relocation and travel behavior remains limited compared to studies on property values and neighborhood change, largely due to data constraints (Delmelle, 2021 ). While LRT can improve access, attract redevelopment, and influence residential relocation, it may also lead to the displacement of low-income households from transit-served areas during this process. According to Grier and Grier (1978), “residential displacement” is defined as a situation where a household is forced to leave its home due to uncontrollable conditions that make continued residence impossible, hazardous, or unaffordable. This occurs even when the household meets all occupancy requirements. While the term “forced” displacement is not always clearly involuntary—many moves stem from subtle pressures. For example, steep rent hikes may leave low-income households with no real choice, blurring the line between voluntary and involuntary relocation (Newman & Owen, 1982 ). While many studies examined gentrification and displacement linked to transit investments, studies on residential mobility near transit stations have been limited compared to studies examining housing price or neighborhood outcomes. Among the limited research,Cervero et al. ( 2002 ) found that station-area residents in the Bay Area were typically younger, lower-income, more diverse, and had lower car ownership.Delmelle & Nilsson ( 2019 ) found no national-scale evidence of increased displacement among lower-income residents near transit, though they did exhibit higher mobility. Their follow-up study (Nilsson & Delmelle, 2020 ) showed unequal sorting: higher-income households tended to move to wealthier areas, while lower-income households did not experience similar upward mobility. A key barrier to studying transit-area residential relocation and displacement is limited household-level data (Bardaka, 2024 ). Decennial censuses miss interim moves, so early studies inferred displacement from demographic trends (Atkinson, 2000 ; McKinnish et al., 2010 ). A notable exception to not compromising the interim residential move is Boarnet et al. ( 2017 ), who used Los Angeles tax data, found that the number of low-income households (< 30% AMI) decreased post-rail, while higher-income households (30–50% AMI) were more likely to remain. Given the data limitation,Baker et al. ( 2021 ) emphasized the potential of longitudinal consumer data for studying residential relocation near transit, an approach we adopt in this study. 2.2 Residential relocation, VMT, and CO 2 emissions Household relocation is closely linked to changes in travel behavior. After moving, individuals often adjust their transportation choices, including mode choice, car ownership, and travel frequency (Xue & Yao, 2022 ). Often, this change in travel behavior is driven by shifts in the built environment. Research has long explored how urban form and built environment can influence travel behavior and reduce VMT, which is a key measure of land use and transportation system performance (Lee & Lee, 2020 ). VMT is often used to assess the impact of light rail-induced household relocation on travel behavior. In this regard, a central framework to study VMT and built environment is the "five Ds"—density, diversity, design, distance to transit, and destination accessibility—which summarize key built environment factors influencing travel behavior (Cervero & Kockelman, 1997 ; Ewing & Cervero, 2010 ; Handy, 2005 ). Empirical studies, including meta-analyses by Ewing and Cervero ( 2010 ), found that these factors—especially density, diversity, and design—are significantly associated with lower household VMT, even if individual elasticities are modest. More recent studies incorporating all five Ds show that compact, mixed-use, and transit-accessible neighborhoods effectively reduce car use, both in the U.S.(Lee & Lee, 2020 ; L. Zhang et al., 2012 ) and internationally (Thao & Ohnmacht, 2020 ). In the context of LRT-induced residential relocation, the built environment and potential travel behavior can be affected in two ways: for households moving into transit-served areas and for those moving out, including displaced households. Several studies have shown that moving into transit-accessible neighborhoods can reduce VMT. For example, Boarnet et al. ( 2020 ) found greater VMT reductions in dense, job-rich areas with light rail access. Adhikari et al., ( 2020 ) noted that people preferring mixed-use environments tend to drive less. Bailey et al. ( 2008 ) found that moving one mile closer to rail transit reduces household VMT by 10.9 miles daily—5.76 miles from increased transit use and 5.19 miles from land use changes like higher density and mixed-use development. Spears et al. ( 2017 ) observed a 10-mile daily VMT drop among households near LA’s Expo Line, driven by tripling transit use. Cao ( 2019 ) also found that neighborhoods served by the Hiawatha LRT in the Twin Cities saw about 20% less driving than comparable areas without LRT. While much research focuses on households relocating into transit-served areas, fewer studies examine travel behavior after moving out. Kamruzzaman et al. ( 2013 ) examined modal shifts through “residential dissonance,” a mismatch between preferred and actual neighborhood types—but did not distinguish displacement, and found that pro-transit individuals often switched to car use over time due to inadequate infrastructure, highlighting the built environment’s influence. Chatman et al. ( 2019 ) found little impact of displacement by higher-income households on VMT or vehicular CO₂ emissions using tract-level data, indicating a gap in the detailed understanding of travel and environmental impact of LRT-induced residential relocation at the micro scale. Given the limited insights on how residential relocation influences driving behavior at the micro scale (household level) in both directions—moving into and out of transit-served areas—we estimate changes in VMT associated with such residential relocation surrounding LRT station areas. Leveraging the widely adopted "five Ds" framework, we model VMT as a function of built environment characteristics and socioeconomic factors. Finally, recognizing the strong link between VMT and transportation-related CO 2 emissions, we also estimate the resulting changes in vehicular CO₂ emissions linked to these residential relocations. 3. Methods 3.1. Study area We focus our analysis on Salt Lake County, Utah—a region that has received substantial investment in LRT infrastructure since the late 1990s. Operated by the Utah Transit Authority (UTA), the TRAX system currently includes the Blue, Red, and Green lines, spanning 42.5 miles and serving 50 stations across key residential, commercial, and transit corridors (Utah Transit Authority (UTA), 2023 ). As of 2024, TRAX recorded approximately 13.5 million annual boardings—a 26.5% increase from the previous year—reflecting a strong post-pandemic recovery, with ridership reaching over 91% of pre-pandemic levels (Utah Transit Authority, 2025 ). Since the opening of the Blue Line in 1999, followed by the Red Line in 2001 and the Green Line in 2011, TRAX has become a core component of a broader multimodal network that includes bus, bus rapid transit (BRT), streetcar, and commuter rail services (Deseret News., 2019). More than just transit stops, TRAX stations are designed to support walking, biking, and transit-oriented development (TOD). Their role is further reinforced by the Housing and Transit Reinvestment Zone Act (HTRZ), which promotes affordable housing development within a 1/3-mile radius of transit stations. To study the effects of transit proximity on residential sorting and travel behavior, researchers often use 1/2-mile or 1/4-mile buffers around light rail stations (Boarnet, Wang, et al., 2017; Cervero et al., 2002 ; Hess & Almeida, 2007 ; Tehrani et al., 2019 ; Werner et al., 2016 ). In our case, the HTRZ policy sets 1/3 mile as the maximum eligible distance for reinvestment, so we conducted a sensitivity analysis using both 1/3-mile and 1/4-mile buffers (Please see Appendix A from supplementary materials). Observing similar residential relocation patterns in both buffer sizes, we defined station areas as Traffic Analysis Zones (TAZs) within a 1/4-mile buffer to enable a more focused analysis consistent with established literature (Fig. 1 ). 3.1. Data and variables To analyze residential relocation, we utilized a relatively novel, proprietary household-level dataset called “Data Axle”. The dataset is available on a yearly basis and is compiled from public records (e.g., property and voter registration), commercial sources (e.g., utility connections, purchase behavior), and over 100 licensed third-party providers, validated using the U.S. Postal Service’s standards and proprietary matching algorithms (Data Axle, n.d.). This data provides household locations (X and Y coordinates, which were geocoded to track households’ moves). The dataset also provides household-level socioeconomic and housing characteristics, including annual household income, housing tenure (likelihood of homeownership), ethnicity, household wealth, and types of housing. Data Axle provides an estimated housing tenure score ranging from 1 (most likely renter) to 9 (most likely homeowner). Following prior research byChapple et al. ( 2022 ), we classify households with tenure scores of 6 or below as renters and those with scores of 7–9 as homeowners, as this approach yields distributions comparable to U.S. Census data. In “Data Axle”, Ethnicity is provided using 168 country-of-origin values, and we recoded these values into standard Census race categories—White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander—based on the country of origin of the household head. We found the recoded values from Data Axle 2022 align with the 2022 ACS 5-year estimates. To obtain the travel, built environment, and socioeconomic characteristics that we used to estimate the travel impacts of household relocation, we compiled data from multiple sources (see Table 1 for details). Household-level VMT data were drawn from the 2023 Utah Household Travel Survey (UHTS 2023). Since such surveys are conducted only once per decade, year-specific VMT and related indicators are unavailable, necessitating reliance on UHTS 2023. However, to ensure the accuracy of our estimates, we validated the results of our VMT models against actual VMT data from the household travel surveys from two distinct time periods (2012 and 2023). The validation analysis ( Please see Appendix B from supplementary materials) confirmed that our estimated VMT data were consistent with the actual VMT measurements from both time periods. The socioeconomic variables were drawn from Data Axle and the US Census, including population, household income, homeownership, vehicle ownership, employment, education, and race. The built environment data consists of the common D variables, such as density (population, employment, and activity density), diversity (land use mix from parcel-level data), design (intersection and road density, four-way intersections, and street connectivity), distance to transit (proximity to rail stations and bus stops, and hourly transit frequency), and destination accessibility (jobs accessible within 30 minutes by car and transit, and distances to downtown and the nearest city center). These variables were compiled from a number of sources, including Longitudinal Employer–Household Dynamics Origin–Destination Employment Statistics (LODES), the Utah Geospatial Resource Center (UGRC), Salt Lake County Parcel Data, the Wasatch Front Regional Council (WFRC), and the General Transit Feed Specification (GTFS), as indicated in Table 1 . We also included mode-preference variables derived from the bike_more/transit_more variables from the 2023 Utah Household Travel Survey, reflecting attitudes favoring biking and transit use. These variables were included to account for potential issues of self-selection, which is known to influence travel behavior (Cao et al., 2009 ) . Table 1 Variable definitions and descriptive statistics (unit: TAZ) Variable Description Data Source Mean SD VMT variables VMT Weighed household vehicle miles traveled UHTS 2023 49.3 39.5 lnVMT Log of weighed household vehicle miles traveled UHTS 2023 3.59 0.88 Socioeconomic and household characteristic variables Household size Average Household size Data Axle 2022 1.63 0.34 % renter households Percent of renter households Data Axle 2022 21.00 27.27 % owner households Percent of owner households Data Axle 2022 61.55 27.97 Median household income Median household income Data Axle 2022 53776.34 35459.23 % black Percentage of black population Data Axle 2022 1.45 2.33 % college degree Percentage of population having some college education (25 years and over) ACS 5 years 2022 21.77 7.81 % employed Percentage of population employed (population 16 years and over) ACS 5 years 2022 69.17 9.20 % unemployed Percentage of population unemployed (population 16 years and over) ACS 5 years 2022 2.87 2.68 % household with no vehicle Percentage of households with no vehicle ACS 5 years 2022 6.63 8.25 % household with 1 vehicle Percentage of households with 1 vehicle ACS 5 years 2022 32.93 16.98 % household with 2 or more vehicle Percentage of households with 2 or more vehicles ACS 5 years 2022 60.44 21.49 Built environment and travel behavior variables Population Total population Data Axle 2022 972.91 653.31 Population density Population density per square mile Data Axle 2022 4,623.27 1,777.81 Employment density Number of employees per square mile LODES 2022 3,783.53 9,076.55 Activity density Population + employment per square mile Data Axle 2022 and LODES 2022 8,406.79 9,190.02 Land use mix (entropy) −[single-family share * ln(single-family share) + multifamily share * ln(multifamily share) + commercial share*ln(commercial share) + industrial share*ln(industrial share)+public share*ln(public share)] / ln(5), where ln is the natural logarithm Salt Lake County Parcel Data 2018 0.50 0.19 Intersection density Number of intersections per square mile UGRC 2023 410.08 152.58 % 4-way intersection Percentage of 4-way intersections UGRC 2023 28.59 20.67 Street connectivity Beta index, β = ⅇ ∕ v, ⅇ: number of links, v: number of nodes (intersections + dead ends) GTFS 2023, WFRC 2023 0.74 0.18 Road density Road density in terms of link miles per square mile GTFS 2023 25.77 7.95 Distance to nearest rail stop Straight line distance from TAZ centroid to nearest rail stop in miles UGRC 2023 1.51 1.29 Distance to nearest bus stop Straight line distance from TAZ centroid to the nearest bus stop in miles UGRC 2023 0.20 0.11 Transit frequency Public transit frequency per hour GTFS 2023 12.05 12.54 Access to jobs by transit Number of regional jobs within 30 minutes by transit (applies a distance decay function to weight job accessibility based on proximity to the TAZ) WFRC 2023 43,580.00 27,710.74 Access to jobs by auto Number of regional jobs within 30 minutes by auto (applies a distance decay function to weight job accessibility based on proximity to the TAZ) WFRC 2023 437,435.26 76,157.41 Distance to downtown Straight line distance from TAZ centroid to downtown in miles Computed as straight-line distance 6.61 3.94 Distance to the nearest city center Straight line distance from TAZ centroid to the nearest city center in miles Computed as straight-line distance 1.55 0.68 Modal preference to bicycle Individual preference to using more bikes (derived from bike_more variables indicating factors to encourage households to bike more) UHTS 2023 12 10 Modal preference to transit Individual preference to using more transit (derived from transit_more variables indicating factors to increase transit use) UHTS 2023 13 11 Note: All the variables are calculated at the TAZ level; ACS and LODES data was interpolated at the TAZ level; UHTS: Utah Household Travel Survey; LODES: Longitudinal Employer Household-Dynamics; WFRC: Wasatch Front Regional Council; UGRC: Utah Geospatial Resource Center; GTFS: Google Transit Feed Specification. 3.2. Analytical approach 3.2.1. Residential relocation and displacement assessment Although residential relocation occurs among both homeowners and renters, renters are considerably more mobile and face a higher risk of relocation; therefore, our analysis focuses exclusively on renter households, examining those who moved out of and into station areas. Studies show that renter status, low income, and minority (non-White) racial identity are associated with a higher risk of residential displacement (Bates et al., 2017 ; OECD, 2022 ). Also, most often, relocated low-income renters moved to more suburban, car-dependent, low-density areas, which is another recognized form of displacement, as per the literature on the suburbanization of poverty (Blumenberg & King, 1978 ; Blumenberg & Wander, 2023 ). Building on this literature, we conceptualize displaced households as the subset of moved-out households composed of low-income, racial minority renters who relocate to non-transit-served areas. We treated residential displacement as a distinct, one-way process in which low-income, racial minority renter households move out of station areas. A household is classified as displaced if it meets four criteria: (1) it relocates from a station area TAZ to a non-station area TAZ, (2) it falls into one of three income categories—low, very low, or extremely low income, (3) it identifies as non-White (racial-minorities), and (4) it is a renter both before and after the move. Data on these criteria—income, race, and housing tenure—were derived from the Data Axle. Household income was categorized based on county-level Area Median Income (AMI) as: moderate income (80–120% of AMI), low income (50–80%), very low income (30–50%), and extremely low income (below 30%) was inflation-adjusted using Bureau of Economic Analysis indices, followingU.S. Department of Transportation, ( 2023 ) to maintain consistency over the study period. Using this definition, we analyzed displacement by longitudinally tracking renter households who moved out of station areas from 2012 to 2022. We also examined which household moves into station areas to capture potential exclusionary displacement dynamics. Although we used decade-long (2012–2022) data from Data Axle, our analysis of residential relocation and displacement was conducted on a consecutive two-year basis (e.g., 2012–2013, 2013–2014). This approach was adopted to capture short-term household dynamics, as households frequently form and dissolve within the Data Axle records (Acolin et al., 2022 ; Chapple & Song, 2024 ). 3.2.2. Estimating VMT changes over time To estimate the impact of household relocation and displacement on travel, we modeled household-level VMT as a function of socioeconomic characteristics, built environment features, and mode-choice preferences. Early studies primarily used statistical models like ordinary least squares (OLS), hierarchical linear modeling, Multilevel Bayesian Regression, and structural equation modeling (SEM) to model VMT (Choi & Zhang, 2017 ; Currans et al., 2020 ; Gao et al., 2022 ; Lee & Lee, 2020 ; Schneider et al., 2015 ; Thao & Ohnmacht, 2020 b; Zhang et al., 2012 ; Zhang & Zhang, 2020 ). These models are easy to understand and have a straightforward structure, but they don't account for the intricate, non-linear relationships between VMT and built-environment characteristics. In recent studies, Machine Learning (ML) models, especially tree-based models, are being used by researchers to address this concern and improve model performance. These tree-based ML models are flexible with the ability to handle mixed data types and missing values, and the capacity to capture nonlinear relationships (Hu et al., 2021 ; Tian et al., 2024 ). Given the limited application of ML methods in VMT research, this study employs both traditional statistical models and ML-based approaches to compare their performance and contribute to the emerging literature on ML applications in VMT analysis. Statistical models we tested include the ordinary least squares (OLS), and tree-based ML models include Random Forest, AdaBoost, and XGBoost regressor. In all cases, we modeled VMT as a function of socioeconomic and built environment variables mentioned in Table 1 . We selected the most appropriate variables using feature engineering techniques, including correlation tests and variance inflation factor (VIF) analysis. It should be noted that due to the skewed distribution of VMT data, we followed the same method from prior research(Ewing et al., 2015 ) and applied a logarithmic transformation to address this issue. Initially, we included all variables in the model and then applied feature selection using importance scores (> 0.02), correlation analysis, and VIF (> 10). As ML models do not produce coefficients, we interpreted results using relative variable influence, scaled to 100 for better interpretability. From all models, final VMT model was selected based on common evaluation metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R². RMSE and MAE capture average prediction errors, while R² reflects model fit. Model robustness was ensured through 5-fold cross-validation, in which the dataset was divided into five subsets for iterative training and testing. We then used the best-performing VMT model to assign mean household VMT to household-level residential relocation at the TAZ level. This allowed us to assess the potential impact of residential relocation and displacement on VMT, which was subsequently linked to CO₂ estimation. 3.2.3. Estimating changes in CO 2 emissions VMT is typically used as a proxy for estimating vehicle GHG emissions (Bailey Patricia Mokhtarian & Little, 2008; Boarnet, 2011 ; Glaeser & Kahn, 2010 ; Tuffour et al., 2025 ). Recent studies used advanced models like the Motor Vehicle Emission Simulator (MOVES) developed by the U.S. Environmental Protection Agency (EPA) to estimate GHG emissions from VMT (Choi & Zhang, 2017 ; Lee & Lee, 2020 ). In this study, we followed the same method of using passenger car CO₂ emission rates based on the EPA’s MOVES4 model developed by the Wasatch Front Regional Council (WFRC). MOVES4 incorporates "Vehicle Activity" data from WFRC’s four-step regional travel demand model. MOVES4 also uses recent county-level vehicle registration data by fuel type (gasoline, diesel, CNG, electric, and E85) for passenger vehicle types 21, 31, and 32. Using the WFRC-provided total grams per mile CO2 emission rate, the household-level CO 2 was estimated as: $$\:{CO}_{2}=gpm\:{CO}_{2}\text{*}Household\:VMT$$ Since CO₂ emission rates were only available for 2012, 2017, and 2022 from WFRC, the impact of residential relocation and displacement on vehicular CO₂ emissions was estimated specifically for those years using pre- and post-move VMT. To illustrate the broader trend over the entire study period, the mean VMT and CO₂ emissions across all years were also calculated. 4. Results 4.1. Residential relocation and displacement patterns For the relocation analysis, we tracked 435,024 renter households over the study period 2012–2022, averaging around 43,500 renters per year. Among them, the economic and racial composition of moved households among all households across the period is presented in Table 2 . The vast majority of these moved households—ranging from 81% to 96% among those moving out, and 72% to 93% among those moving in—belong to low, very low, and extremely low-income categories (0–80% of the county’s median income). In contrast, only a small proportion of movers fall into the moderate- or above-moderate-income groups (above 80% of the county’s median income). Figure 2 illustrates the year-by-year distribution of moved households within these dominant income categories, providing a clear visualization of these trends over time. Table 2 Year-wise Adjusted Household Income and Racial Composition of Households Moving into and out of LRT Station Areas Analysis Year Moving out of Station Areas Moving into Station Areas Number of Households (% of County) Households' Income-Mean (SD) % Non-white Households Number of Households (% of County) Households' Income-Mean (SD) % Non-white Households 2012–2013 311 (0.92%) $ 14815 ( $ 15544) 22 228 (0.67%) $ 24024 ( $ 19041) 21 2013–2014 444 (1.04%) $ 16304 ( $ 15625) 23 266 (0.62%) $ 21523 ( $ 17152) 27 2014–2015 432 (1.21%) $ 21643 ( $ 14856) 18 259 (0.73%) $ 30849 ( $ 23026) 24 2015–2016 483 (1.47%) $ 19252 ( $ 14832) 24 255 (0.77%) $ 26435 ( $ 20937) 27 2016–2017 410 (1.08%) $ 27992 ( $ 18684) 30 201 (0.53%) $ 39152 ( $ 30048) 23 2017–2018 692 (1.69%) $ 28011 ( $ 20134) 22 458 (1.12%) $ 38100 ( $ 30058) 24 2018–2019 666 (1.41%) $ 32538 ( $ 29399) 27 509 (1.08%) $ 38969 ( $ 26042) 24 2019–2020 568 (0.99%) $ 34938 ( $ 22729) 23 587 (1.02%) $ 38485 ( $ 24087) 24 2020–2021 808 (1.43%) $ 28850 ( $ 20502) 25 625 (1.10%) $ 43857 ( $ 37647) 25 2021–2022 944 (1.89%) $ 32425 ( $ 22064) 29 395 (0.79%) $ 37797 ( $ 32761) 28 Over the decade, both the number and proportion of households moving out of station areas increased, rising from 311 households (0.92% of total movers) in 2012–2013 to 944 households (1.89%) in 2021–2022. These households typically had lower incomes, with average income increasing from $ 14,815 in 2012–2013 to $ 32,425 in 2021–2022, yet remaining below the average income of those moving into station areas. A large share of these movers consistently fell within the low, very low, and extremely low-income categories (0–80% of the county’s median income), with extremely low-income households (below 30% of the county’s median income) comprising the largest proportion, up to 82% in some years. Households moving into station areas are also predominantly low-income brand, though they have relatively higher incomes than those moving out. Their average income increased from $ 24,024 in 2012–2013 to a peak of $ 43,857 in 2020–2021; however, these levels still fall within the low-income category. Despite this modest increase, low-income households continued to comprise a substantial share of in-movers, though their numbers remained lower than those of households moving out. Disaggregating the low-income category into low-, very low-, and extremely low-income groups provides further insight into the income differences between households moving in and moving out. In each year, the percentage of very low and extremely low-income households was consistently higher among those moving out than among those moving in. Conversely, the proportion of low-income households (just below the moderate-income threshold) was consistently lower among those moving out but relatively higher among those moving in. This pattern might suggest a gradual demographic shift within station areas, where extremely low- and very low-income households are increasingly being replaced by households in slightly higher, though still low-income, brackets. Racial composition data further support this trend (Table 2 ). While the percentage of non-white households moving into station areas remained relatively stable (21%–28%), the proportion of non-white households among those moving out increased from 18% to 30% over the study period. As the absolute number of households moving out was higher than those moving in, the actual number of non-white households leaving station areas exceeded the number moving in, highlighting potential racial dynamics within these residential mobility patterns. We found limited evidence of displacement in station areas over the study period. Displaced households represent less than 0.25% of all renter households in any given year, though they account for approximately 8–13% of households moving out of station areas (Fig. 3 ). Displaced households were predominantly extremely low-income, making up over 85% of the displaced households in the early years (2012–2014), with some diversification later, but no clear shift indicating that the most economically vulnerable residents might be disproportionately impacted by displacement from station areas. 4.3. Effect of residential relocation on household VMT Among all models we tested, the XGBoost regressor performed best, achieving an R² of 0.20, MAE of 0.67, and RMSE of 0.86—closely matching the standard deviation of VMT (0.88) and indicating a strong relative fit (Please see Appendix C1 for model performance comparison). The relative variable importance for this model indicates that socioeconomic and built-environment characteristics contribute almost equally to shaping household travel behavior. Within the socioeconomic category, household size was the most influential predictor (suggesting that larger households have distinct travel patterns that strongly affect per capita VMT), followed by variables such as households with two or more vehicles, renter households, median household income, zero vehicle households, and owner households. On the built environment side, access to jobs by transit stands out as the top factor, followed by the share of 4-way intersections, distance to downtown, access to jobs by auto, and distance to the nearest rail stop (Please see Appendix C2 for details of the relative variable importance). Using the XGBoost regressor, we estimated mean household VMT at the TAZ level. We then plotted the estimated mean VMT for households’ origin and destination TAZs to illustrate how changes in the built-environment and socioeconomic context associated with relocation may influence travel behavior. This analysis was conducted annually from 2012 to 2022 for the three previously defined groups: (1) all renter households moving out of station areas, (2) displaced renter households moving out, and (3) all renter households moving into station areas (Fig. 4 ). Vertical lines represent standard deviations, indicating within-group variation in estimated VMT. Figure 4 (a) illustrates that households moving out of station areas are associated with higher estimated VMT at their destination TAZs compared to their origin TAZs. For example, in 2012–2013, the mean estimated VMT of a household in origin TAZs was slightly below 28 miles, whereas the corresponding estimate for destination TAZs was approximately 33 miles per household. This gap persists over time, with destination TAZ estimates reaching nearly 39 miles by 2021–2022, compared to about 30 miles for origin TAZs. Aggregated across the study period, the difference between destination and origin TAZ estimates averages approximately 5 miles (SD = 2 miles). This increase in potential VMT likely reflects differences in built-environment and accessibility characteristics between origin and destination TAZs. Between 2012 and 2022, households relocating from station areas moved consistently farther from TRAX stations—by over 1 mile on average—as well as moderately farther from bus and other transit stops, and slightly farther from city centers (Please see Appendix D from supplementary materials). Since most of these renter households were not displaced, the spatial patterns might suggest a shift toward increased automobile reliance and reduced transit accessibility. As shown in Fig. 4 (b), displaced households exhibit smaller differences in estimated VMT between origin and destination TAZs, along with greater variability, compared to all households relocating from station areas. In most years, estimated VMT per household in destination TAZs exceeded that of origin TAZs, though the magnitude of the difference was modest. The largest gap appears in 2021–2022, when the mean estimated VMT per household was approximately 29 miles in origin TAZs and nearly 37 miles in destination TAZs. Aggregated across the study period, the average difference between destination and origin TAZ estimates is approximately 1 mile (SD = 3 miles), indicating substantial heterogeneity in relocation contexts among displaced households. Given that these households were disproportionately composed of extremely low-, very low-, and low-income renters from racial minority backgrounds, it is plausible that relocation choices were shaped by both housing affordability constraints and the need to maintain some degree of transit accessibility. Supporting this interpretation, distance measures using GTFS stops indicate that while proximity to TRAX stations consistently declined, changes in access to bus and other transit stops and city centers were more variable (Appendix D). On average, distance to bus and other transit stops increased only slightly, by approximately 0.07 miles, with certain years registering small decreases. Similarly, distance to city centers fluctuated between − 0.13 and + 0.24 miles across the study period, yielding a negligible average increase of 0.03 miles. Collectively, these patterns suggest that although displaced households generally lost access to high-capacity rail, many resettled in areas that preserved, or in some cases modestly improved, access to bus transit and urban centers. Figure 4 (c) indicates that households moving into station areas likely experience minimal differences in estimated VMT between origin and destination TAZs. In most years, estimated VMT in destination TAZs is comparable to—or slightly lower than—that of origin TAZs. Across the study period, the average difference between destination and origin TAZ estimates is only + 0.12 miles (SD = 1.6 miles), indicating limited variation in estimated travel intensity associated with relocation contexts. This limited change is likely attributable to modest improvements in the built environment. Access to TRAX stations improved, with average distance reductions ranging from 0.71 to 0.97 miles, while gains in proximity to bus and other transit stops were minimal, typically less than 0.1 miles. Distance to city centers also decreased only slightly, by between 0.02 and 0.24 miles (Appendix D). Although these renter households relocated to more transit-served areas, the incremental nature of these improvements—combined with their pre-existing residential environments—may not have been sufficient to substantially alter VMT. Overall, households moving out of station areas are associated with higher estimated VMT at their destination TAZs, while in-movers show little difference. Given that out-movers exceed in-movers, this imbalance likely contributes to a net increase in aggregate estimated VMT, underscoring the broader implications of residential relocation for transportation-related environmental outcomes. 4.4. Effect of residential relocation on household vehicular CO 2 Figure 5 illustrates that household CO 2 emissions closely follow estimated VMT patterns as they are directly proportional to VMT estimates and vehicle emission rates. Households moving out of station areas (Fig. 5 a) are associated with higher estimated CO₂ emissions in their destination TAZs compared to their origin TAZs. For example, estimated emissions increase from approximately 11,000 grams per housegold in origin TAZs to about 13,500 grams per household in destination TAZs in 2012–2013, and from roughly 10,500 to 13,400 grams in 2021–2022. Across the study period, the average difference between destination and origin TAZ estimates is 2,431 grams (SD = 768), consistent with relocation to more car-dependent environments. Displaced households (Fig. 5 b) exhibit smaller but still positive differences between destination and origin TAZ emission estimates, averaging 1,362 grams (SD = 1,373), with greater variability reflecting heterogeneous relocation contexts. In contrast, households moving into station areas (Fig. 5 c) show minimal differences between origin and destination TAZ estimates, and in some years slightly lower emissions in destination TAZs—for example, declining from approximately 10,500 to just under 10,000 grams in 2021–2022. This group displays the smallest average difference (311 grams, SD = 521) and the lowest variability. Overall, these patterns suggest that when a renter household in a station area is replaced by one relocating from outside the station area, the resulting spatial redistribution is likely associated with higher vehicle-related CO₂ emissions. 5. Discussion Our findings indicate that residential relocation around station areas is overwhelmingly concentrated among low-, very low-, and extremely low-income renter households, who comprise the large majority of both out-movers and in-movers. However, the higher out-migration rate relative to in-migration suggests a subtle de-densification of station areas. This pattern aligns with findings byBoarnet et al. ( 2017 ), who reported a higher out-mobility rate and a widening residential mobility gap between higher- and lower-income households. Although we find limited evidence of displacement—affecting less than 0.25% of renter households annually—the disproportionately higher out-mobility of very low- and extremely low-income households suggests a subtle pattern of income- and race-based filtering within low-income groups. Households at the bottom of the income distribution, many of whom are non-White, appear more likely to move out and may be replaced by renters with slightly higher incomes, though still within the low-income bracket. These patterns are consistent with Song and Chapple ( 2024 ), who find that low-income households are more likely to leave transit-served, gentrifying neighborhoods and less likely to move into them than higher-income households. The higher out-mobility observed in our study also echoes concerns raised by Newman and Wyly ( 2006 ), suggesting that lower in-migration rates among extremely low- and very low-income households may signal diminishing housing opportunities and potential exclusionary displacement pressures in station areas. While the absolute number of displaced households in our study is relatively small, even modest displacement rates may be policy-relevant when coupled with exclusionary dynamics that constrain low-income households’ access to revitalizing neighborhoods. Addressing these pressures may require coordinated housing strategies. Federal programs, such as public housing, Housing Choice Vouchers (Section 8), and the Low-Income Housing Tax Credit, combined with local tools including rent regulation, inclusionary zoning, and city-funded affordable housing initiatives, can help moderate affordability pressures, as demonstrated in New York City (Whitehead & Goering, 2021 ). Moreover, sustaining the density necessary to fully realize the social and environmental benefits of public transit investments will require proactive policies that expand housing opportunities for a diverse range of income groups within station areas. Our VMT estimation based on origin and destination TAZs associated with the residential relocation indicate that these residential relocation patterns have a knock-on effect on VMT and CO 2 emissions. We found that daily household VMT increased when households relocated away from station areas. As shown by previous research, their residential relocation might significantly influence mode choices, with changes in the built environment, commute distance, and socio-economic factors shaping travel decisions; therefore, those who move farther from their workplace often tend to resort to private vehicle ownership and usage when public transportation becomes less accessible or convenient (Zarabi et al., 2019 ; Zhao & Zhang, 2018 ). In such cases, households that already own a car or have the financial capacity to purchase one may exhibit more stable post-relocation VMT patterns and those who cannot afford to buy a car might still rely on public transit. For households displaced from station areas, the estimated increase in mean household VMT at the destination TAZ was smaller but more variable than for all households that moved out of station areas. The higher variability suggests that, after being displaced from station areas, these households may have found housing with less access to essential services and public transit. Despite being displaced, some households may still seek housing near other transit options (e.g. bus), which could explain the higher variability in their destination TAZs' mean VMT. A study by Kamruzzaman et al., ( 2013 ) found that when households relocate due to displacement or affordability issues, their travel habits do not readily adapt to their new surroundings. This could mean that after being displaced, households might have experienced longer walks to existing transit stations, longer commutes in general, or switched to different types of public transit, all of which contribute to the increased variability in destination VMT. In contrast, we found households moving into station areas are expected to have little or no savings in their mean VMT. Our findings—showing minimal VMT reduction among households relocating to station areas with a high share of low-income renters—can be partly explained by the similarity between their previous and current built environments, as well as their socioeconomic status. These households may have already lived in transit-accessible neighborhoods, limiting the potential for further VMT reduction. This finding also aligns withBoarnet et al. ( 2020 ) who found that lower-income households tend to experience smaller VMT reductions than higher-income households after moving into transit-served areas. Together, these results suggest that while proximity to transit can support lower VMT, the magnitude of these benefits varies across income groups. As noted by Boarnet et al. ( 2020 ), while equity concerns should get strong support for the inclusion of low-income housing near rail transit, strategies centered solely on either low- or high-income groups may be less effective than mixed-income approaches. Our results likewise underscore the importance of inclusive, mixed-income housing policies to advance both equity and environmental gains in station areas. Regarding the CO₂ impacts of residential relocation and displacement, our findings indicate a net increase in household vehicle emissions, consistent with the observed VMT patterns. This aligns withBoarnet et al. ( 2017 ) who found that living near station areas can reduce vehicle CO₂ emissions. Our study extends this research by providing a more detailed picture of emissions associated with households moving in, moving out, and being displaced. Limitations There are several limitations in our study. First, we focused only on displacement using proxy indicators such as income, race, and tenure, and were geographically confined to Salt Lake County. The use of proxy measures and the lack of intra-regional analysis may obscure some voluntary moves. Additionally, due to data limitations, specifically the lack of information on housing rents, new housing production, and other market dynamics, we were unable to directly capture exclusionary displacement, where low-income renters are unable to move into station areas. However, by analyzing the composition of households moving into station areas, we offered indirect insight into this phenomenon. Second, we estimated mean household VMT at the TAZ level using socioeconomic and built environment factors, rather than direct household-level travel data. The VMT model was based on 2023 UHTS data and applied across the entire study period, even though our analysis spans multiple two-year intervals. Ideally, VMT would be estimated for each year, but household travel surveys—on which such modeling depends—are typically conducted only once every decade. Due to this limitation, we relied on 2023 data but validated our estimates against the actual 2012 VMT to ensure consistency in trends. While this approach captures general patterns, it may not reflect individual travel behavior and introduces the risk of the ecological fallacy by assigning TAZ-level estimates to the household level. However, key variables such as density and median income are generally more reliably assessed at the area level than at the household level. Third, CO₂ emissions were derived from WFRC’s MOVES model estimates for 2012, 2019, and 2022. We averaged these values to assess the environmental impact of driving behavior over the full period. While not ideal, this approach offers a reasonable approximation of longer-term trends. 6. Conclusion In this study, we examine how light rail investments are associated with renter household relocation patterns in station areas and the resulting implications for vehicle-related CO₂ emissions. We document gradual de-densification of station areas and limited formal displacement, alongside a subtle pattern of income- and race-based sorting within low-income groups. These dynamics suggest a narrowing of housing access for extremely low- and very low-income renters in transit-served neighborhoods. We further show that this compositional shift has measurable environmental consequences. When renter households relocate from station areas and are replaced by households originating outside these areas, the resulting spatial redistribution is associated with higher estimated VMT and CO₂ emissions, reflecting differences in built environment and accessibility characteristics at the TAZ level. Our findings highlight the interdependence between housing market dynamics and transportation outcomes. Absent coordinated housing and land-use interventions, transit investments alone may be insufficient to sustain density, retain low-income renters, or maximize environmental benefits. Integrating affordable housing preservation with inclusive densification strategies may therefore be essential to advancing both sustainability and transportation efficiency objectives in light rail station areas. Declarations Acknowledgement We would like to express our gratitude to the Wasatch Front Regional Council (WFRC). In particular, we sincerely thank Bert Granberg, Analytics Director, for his coordination and for providing us with the household travel survey data. We also extend our appreciation to Kip Billings, Senior Transportation Engineer and Air Quality Analyst, for his assistance in coordinating and providing information on the MOVES4 model and emission rates. We thank Justyna Kaniewska, Doctoral Candidate in City and Metropolitan Planning, for assistance with accessing and processing Data Axle. We also acknowledge the limited use of large language models to improve the clarity and readability of the manuscript. We confirm that AI was not used in any kind of idea generation, data analysis, graph preparation, or writing any part of this paper. Author Contributions The authors confirm contribution to the paper as follows: conceptualization: Faria Afrin Zinia, Andy Hong, Reid Ewing; data curation: Faria Afrin Zinia, Andy Hong; formal analysis: Faria Afrin Zinia, Andy Hong; funding acquisition: Andy Hong, Reid Ewing; methodology: Faria Afrin Zinia, Andy Hong; project administration: Andy Hong, Reid Ewing; supervision: Andy Hong, Reid Ewing; validation: Faria Afrin Zinia, Andy Hong, Reid Ewing; visualization: Faria Afrin Zinia, Andy Hong; writing – original draft: Faria Afrin Zinia; writing – review and editing: Faria Afrin Zinia, Andy Hong, Reid Ewing. All authors reviewed the results and approved the final version of the manuscript. Declaration of Competing Interests The author(s) have disclosed no conflicts of interest regarding this article's research, authorship, and/or publication. 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Soc. 16 , 131–142 (2019). https://doi.org/10.1016/j.tbs.2019.05.003 Zhang, L., Hong, J., Nasri, A., Shen, Q.: How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities. J. Transp. Land. Use. 5 (3), 40–52 (2012). https://doi.org/10.5198/jtlu.v5i3.266 Zhang, M., Zhang, W.: When Context Meets Self-Selection: The Built Environment–Travel Connection Revisited. J. Plann. Educ. Res. 40 (3), 304–319 (2020). https://doi.org/10.1177/0739456X18755495 Zhao, P., Zhang, Y.: Travel behaviour and life course: Examining changes in car use after residential relocation in Beijing. J. Transp. Geogr. 73 , 41–53 (2018). https://doi.org/10.1016/j.jtrangeo.2018.10.003 Additional Declarations No competing interests reported. Supplementary Files SupplementarymaterialsZiniaetal.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 16 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8897368","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623206555,"identity":"44fcbd76-d7a2-4ede-9f27-3f3fd0fdeb7d","order_by":0,"name":"Faria Afrin Zinia","email":"","orcid":"","institution":"University of Utah","correspondingAuthor":false,"prefix":"","firstName":"Faria","middleName":"Afrin","lastName":"Zinia","suffix":""},{"id":623206556,"identity":"bb03a993-810c-4d00-b432-d7fc0373e268","order_by":1,"name":"Andy Hong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYFACHhBhA2WwEa8lDUiRqOUwCVr4G3gPPi74dT5xP/vZAwwfyg4T1iJxgC/ZeGbf7cQenrwExhnniNDCcIDHTJq3B6iFIceAmbeNCC3yEC3nEnv43xgw/yVGiwFIC8+PA4k9EkBbGInRYniYx9iYtyHZuOfGG4ODPefSCWuRO95j+Jjnj51se3+O4YMfZdaEtTAwAzFjG4R9gAj1MPCHBLWjYBSMglEw8gAA7EE3CN5R1xIAAAAASUVORK5CYII=","orcid":"","institution":"University of Utah","correspondingAuthor":true,"prefix":"","firstName":"Andy","middleName":"","lastName":"Hong","suffix":""},{"id":623206557,"identity":"a7cf14c3-179f-4545-a1c1-25bbc5744f9f","order_by":2,"name":"Reid Ewing","email":"","orcid":"","institution":"University of Utah","correspondingAuthor":false,"prefix":"","firstName":"Reid","middleName":"","lastName":"Ewing","suffix":""}],"badges":[],"createdAt":"2026-02-17 04:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8897368/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8897368/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107706239,"identity":"5092db52-636c-4ad5-a746-2dad6a492db5","added_by":"auto","created_at":"2026-04-24 09:17:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":482564,"visible":true,"origin":"","legend":"\u003cp\u003eMap of study area (Station areas as TAZs with quarter-mile buffers around light rail stations)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8897368/v1/7d34e36fa1488a4207ba20b0.png"},{"id":107568239,"identity":"4bc99bc9-2485-4ddd-b681-a6cb3159a4b9","added_by":"auto","created_at":"2026-04-22 17:30:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25114,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of low, very low, and extremely low-income households moved in and out of station areas (2012 to 2022)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8897368/v1/9b959e3a24d84a1f724e60e6.png"},{"id":107568242,"identity":"ca5b0d2d-6216-4aaa-a091-e064cf4bc9c8","added_by":"auto","created_at":"2026-04-22 17:30:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93988,"visible":true,"origin":"","legend":"\u003cp\u003eYear-wise number and percentage of displaced from station areas\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8897368/v1/15927e9bf7e2099dbbb45136.png"},{"id":107568240,"identity":"89e6affa-7000-4048-8c60-5df3e25a8ff6","added_by":"auto","created_at":"2026-04-22 17:30:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172065,"visible":true,"origin":"","legend":"\u003cp\u003eYear-wise estimated VMT for origin and destination TAZs of (a) all renter households moving out, (b) displaced renter households moving out, and (c) all renter households moving into station areas.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8897368/v1/0b284c9427a8ecd8cb0e94b6.png"},{"id":107706387,"identity":"d92f2721-5bec-4cd0-a0a9-6ff2720369b3","added_by":"auto","created_at":"2026-04-24 09:17:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":115179,"visible":true,"origin":"","legend":"\u003cp\u003eYear wise estimated CO\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;for origin and destination\u0026nbsp; TAZs of (a) all renter households moving out, (b) displaced renter households moving out, and (c) all renter households moving into station areas.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8897368/v1/367771984e0f845d0c77b672.png"},{"id":107709078,"identity":"f530a9ab-0215-4240-ad37-5af93012aaac","added_by":"auto","created_at":"2026-04-24 09:34:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1279831,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8897368/v1/5168007d-2254-49e3-9b0b-141417bd0fe9.pdf"},{"id":107568237,"identity":"4ff7a21d-791f-4a79-b368-b22ee8c7bfb2","added_by":"auto","created_at":"2026-04-22 17:30:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2108030,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialsZiniaetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-8897368/v1/76521f9045bff644bdc2a994.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eResidential-Relocation, Displacement, and CO\u003csub\u003e2\u003c/sub\u003e Emissions Near Light-Rail: A 10-Year Microdata Study\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTransportation is a leading source of CO\u003csub\u003e2\u003c/sub\u003e emissions in the United States, due to high automobile dependency (EPA, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Transportation Statistics, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To address this, urban planners have increasingly adopted strategies such as new urbanism and smart growth to create more sustainable and livable communities. These approaches focus on reshaping the built environment to reduce VMT by bringing destinations closer together and promoting low-emission transportation options such as public transit, walking, and biking(Choi \u0026amp; Zhang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Resultantly, over the past 25 years, significant investments in LRT and mixed-use, transit-oriented developments have been made to reduce car use and lower emissions (Boarnet et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Finio, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). While these investments often improve transportation accessibility, they also affect real estate markets by increasing housing premiums which in turn attracts further investment in urban infrastructure, stimulating additional development (Ibraeva et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kaniewska et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This higher housing cost in transit-served areas may increase displacement risk of low-income and vulnerable populations, and may inadvertently displace them to car-dependent neighborhoods, thus undermining CO\u003csub\u003e2\u003c/sub\u003e reduction goals (Atkinson, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Chatman et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Leung \u0026amp; Yiu, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile extensive research has examined how transit investments influence residential relocation and displacement, their downstream impact on travel behavior and CO\u003csub\u003e2\u003c/sub\u003e emissions resulting from changes in the built environment remains understudied (Boarnet, Bostic, Rodnyansky, et al., 2017; Cervero et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Delmelle \u0026amp; Nilsson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Doucet, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Finio, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Song \u0026amp; Chapple, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tehrani et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To our knowledge, no study has yet used longitudinal population data around light rail station areas to directly assess how residential relocation and potential displacement affect household travel behavior and vehicular CO₂ emissions. To address this gap, our research analyzes residential relocation and displacement surrounding light rail station areas using household-level data from 2012 to 2022. We then estimate the likely impact of these moves on VMT and emissions by considering how changes in the built environment associated with these moves influence household travel behavior.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 LRT, residential relocation, and displacement\u003c/h2\u003e \u003cp\u003eDespite the remarkable expansion of LRT and its potential influence on residential relocation, research on transit-induced residential relocation and travel behavior remains limited compared to studies on property values and neighborhood change, largely due to data constraints (Delmelle, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While LRT can improve access, attract redevelopment, and influence residential relocation, it may also lead to the displacement of low-income households from transit-served areas during this process. According to Grier and Grier (1978), \u0026ldquo;residential displacement\u0026rdquo; is defined as a situation where a household is forced to leave its home due to uncontrollable conditions that make continued residence impossible, hazardous, or unaffordable. This occurs even when the household meets all occupancy requirements. While the term \u0026ldquo;forced\u0026rdquo; displacement is not always clearly involuntary\u0026mdash;many moves stem from subtle pressures. For example, steep rent hikes may leave low-income households with no real choice, blurring the line between voluntary and involuntary relocation (Newman \u0026amp; Owen, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1982\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile many studies examined gentrification and displacement linked to transit investments, studies on residential mobility near transit stations have been limited compared to studies examining housing price or neighborhood outcomes. Among the limited research,Cervero et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) found that station-area residents in the Bay Area were typically younger, lower-income, more diverse, and had lower car ownership.Delmelle \u0026amp; Nilsson (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found no national-scale evidence of increased displacement among lower-income residents near transit, though they did exhibit higher mobility. Their follow-up study (Nilsson \u0026amp; Delmelle, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed unequal sorting: higher-income households tended to move to wealthier areas, while lower-income households did not experience similar upward mobility.\u003c/p\u003e \u003cp\u003eA key barrier to studying transit-area residential relocation and displacement is limited household-level data (Bardaka, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Decennial censuses miss interim moves, so early studies inferred displacement from demographic trends (Atkinson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; McKinnish et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A notable exception to not compromising the interim residential move is Boarnet et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who used Los Angeles tax data, found that the number of low-income households (\u0026lt;\u0026thinsp;30% AMI) decreased post-rail, while higher-income households (30\u0026ndash;50% AMI) were more likely to remain. Given the data limitation,Baker et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) emphasized the potential of longitudinal consumer data for studying residential relocation near transit, an approach we adopt in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Residential relocation, VMT, and CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/h2\u003e \u003cp\u003eHousehold relocation is closely linked to changes in travel behavior. After moving, individuals often adjust their transportation choices, including mode choice, car ownership, and travel frequency (Xue \u0026amp; Yao, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Often, this change in travel behavior is driven by shifts in the built environment. Research has long explored how urban form and built environment can influence travel behavior and reduce VMT, which is a key measure of land use and transportation system performance (Lee \u0026amp; Lee, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). VMT is often used to assess the impact of light rail-induced household relocation on travel behavior. In this regard, a central framework to study VMT and built environment is the \"five Ds\"\u0026mdash;density, diversity, design, distance to transit, and destination accessibility\u0026mdash;which summarize key built environment factors influencing travel behavior (Cervero \u0026amp; Kockelman, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Ewing \u0026amp; Cervero, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Handy, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Empirical studies, including meta-analyses by Ewing and Cervero (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), found that these factors\u0026mdash;especially density, diversity, and design\u0026mdash;are significantly associated with lower household VMT, even if individual elasticities are modest. More recent studies incorporating all five Ds show that compact, mixed-use, and transit-accessible neighborhoods effectively reduce car use, both in the U.S.(Lee \u0026amp; Lee, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; L. Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and internationally (Thao \u0026amp; Ohnmacht, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of LRT-induced residential relocation, the built environment and potential travel behavior can be affected in two ways: for households moving into transit-served areas and for those moving out, including displaced households. Several studies have shown that moving into transit-accessible neighborhoods can reduce VMT. For example, Boarnet et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found greater VMT reductions in dense, job-rich areas with light rail access. Adhikari et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted that people preferring mixed-use environments tend to drive less. Bailey et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that moving one mile closer to rail transit reduces household VMT by 10.9 miles daily\u0026mdash;5.76 miles from increased transit use and 5.19 miles from land use changes like higher density and mixed-use development. Spears et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed a 10-mile daily VMT drop among households near LA\u0026rsquo;s Expo Line, driven by tripling transit use. Cao (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also found that neighborhoods served by the Hiawatha LRT in the Twin Cities saw about 20% less driving than comparable areas without LRT. While much research focuses on households relocating into transit-served areas, fewer studies examine travel behavior after moving out. Kamruzzaman et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) examined modal shifts through \u0026ldquo;residential dissonance,\u0026rdquo; a mismatch between preferred and actual neighborhood types\u0026mdash;but did not distinguish displacement, and found that pro-transit individuals often switched to car use over time due to inadequate infrastructure, highlighting the built environment\u0026rsquo;s influence. Chatman et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found little impact of displacement by higher-income households on VMT or vehicular CO₂ emissions using tract-level data, indicating a gap in the detailed understanding of travel and environmental impact of LRT-induced residential relocation at the micro scale.\u003c/p\u003e \u003cp\u003eGiven the limited insights on how residential relocation influences driving behavior at the micro scale (household level) in both directions\u0026mdash;moving into and out of transit-served areas\u0026mdash;we estimate changes in VMT associated with such residential relocation surrounding LRT station areas. Leveraging the widely adopted \"five Ds\" framework, we model VMT as a function of built environment characteristics and socioeconomic factors. Finally, recognizing the strong link between VMT and transportation-related CO\u003csub\u003e2\u003c/sub\u003e emissions, we also estimate the resulting changes in vehicular CO₂ emissions linked to these residential relocations.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study area\u003c/h2\u003e \u003cp\u003eWe focus our analysis on Salt Lake County, Utah\u0026mdash;a region that has received substantial investment in LRT infrastructure since the late 1990s. Operated by the Utah Transit Authority (UTA), the TRAX system currently includes the Blue, Red, and Green lines, spanning 42.5 miles and serving 50 stations across key residential, commercial, and transit corridors (Utah Transit Authority (UTA), \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As of 2024, TRAX recorded approximately 13.5\u0026nbsp;million annual boardings\u0026mdash;a 26.5% increase from the previous year\u0026mdash;reflecting a strong post-pandemic recovery, with ridership reaching over 91% of pre-pandemic levels (Utah Transit Authority, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Since the opening of the Blue Line in 1999, followed by the Red Line in 2001 and the Green Line in 2011, TRAX has become a core component of a broader multimodal network that includes bus, bus rapid transit (BRT), streetcar, and commuter rail services (Deseret News., 2019). More than just transit stops, TRAX stations are designed to support walking, biking, and transit-oriented development (TOD). Their role is further reinforced by the Housing and Transit Reinvestment Zone Act (HTRZ), which promotes affordable housing development within a 1/3-mile radius of transit stations.\u003c/p\u003e \u003cp\u003eTo study the effects of transit proximity on residential sorting and travel behavior, researchers often use 1/2-mile or 1/4-mile buffers around light rail stations (Boarnet, Wang, et al., 2017; Cervero et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hess \u0026amp; Almeida, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tehrani et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Werner et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In our case, the HTRZ policy sets 1/3 mile as the maximum eligible distance for reinvestment, so we conducted a sensitivity analysis using both 1/3-mile and 1/4-mile buffers (Please see Appendix A from supplementary materials). Observing similar residential relocation patterns in both buffer sizes, we defined station areas as Traffic Analysis Zones (TAZs) within a 1/4-mile buffer to enable a more focused analysis consistent with established literature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data and variables\u003c/h2\u003e \u003cp\u003eTo analyze residential relocation, we utilized a relatively novel, proprietary household-level dataset called \u0026ldquo;Data Axle\u0026rdquo;. The dataset is available on a yearly basis and is compiled from public records (e.g., property and voter registration), commercial sources (e.g., utility connections, purchase behavior), and over 100 licensed third-party providers, validated using the U.S. Postal Service\u0026rsquo;s standards and proprietary matching algorithms (Data Axle, n.d.). This data provides household locations (X and Y coordinates, which were geocoded to track households\u0026rsquo; moves). The dataset also provides household-level socioeconomic and housing characteristics, including annual household income, housing tenure (likelihood of homeownership), ethnicity, household wealth, and types of housing. Data Axle provides an estimated housing tenure score ranging from 1 (most likely renter) to 9 (most likely homeowner). Following prior research byChapple et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), we classify households with tenure scores of 6 or below as renters and those with scores of 7\u0026ndash;9 as homeowners, as this approach yields distributions comparable to U.S. Census data. In \u0026ldquo;Data Axle\u0026rdquo;, Ethnicity is provided using 168 country-of-origin values, and we recoded these values into standard Census race categories\u0026mdash;White, Black or African American, American Indian or Alaska Native, Asian, and Native Hawaiian or Other Pacific Islander\u0026mdash;based on the country of origin of the household head. We found the recoded values from Data Axle 2022 align with the 2022 ACS 5-year estimates.\u003c/p\u003e \u003cp\u003eTo obtain the travel, built environment, and socioeconomic characteristics that we used to estimate the travel impacts of household relocation, we compiled data from multiple sources (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details). Household-level VMT data were drawn from the 2023 Utah Household Travel Survey (UHTS 2023). Since such surveys are conducted only once per decade, year-specific VMT and related indicators are unavailable, necessitating reliance on UHTS 2023. However, to ensure the accuracy of our estimates, we validated the results of our VMT models against actual VMT data from the household travel surveys from two distinct time periods (2012 and 2023). The validation analysis \u003cb\u003e(\u003c/b\u003ePlease see Appendix B from supplementary materials) confirmed that our estimated VMT data were consistent with the actual VMT measurements from both time periods.\u003c/p\u003e \u003cp\u003eThe socioeconomic variables were drawn from Data Axle and the US Census, including population, household income, homeownership, vehicle ownership, employment, education, and race. The built environment data consists of the common D variables, such as density (population, employment, and activity density), diversity (land use mix from parcel-level data), design (intersection and road density, four-way intersections, and street connectivity), distance to transit (proximity to rail stations and bus stops, and hourly transit frequency), and destination accessibility (jobs accessible within 30 minutes by car and transit, and distances to downtown and the nearest city center). These variables were compiled from a number of sources, including Longitudinal Employer\u0026ndash;Household Dynamics Origin\u0026ndash;Destination Employment Statistics (LODES), the Utah Geospatial Resource Center (UGRC), Salt Lake County Parcel Data, the Wasatch Front Regional Council (WFRC), and the General Transit Feed Specification (GTFS), as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We also included mode-preference variables derived from the bike_more/transit_more variables from the 2023 Utah Household Travel Survey, reflecting attitudes favoring biking and transit use. These variables were included to account for potential issues of self-selection, which is known to influence travel behavior (Cao et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\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\u003eVariable definitions and descriptive statistics (unit: TAZ)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \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\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVMT variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeighed household vehicle miles traveled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUHTS 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnVMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog of weighed household vehicle miles traveled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUHTS 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSocioeconomic and household characteristic variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage Household size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% renter households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of renter households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% owner households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent of owner households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian household income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53776.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35459.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of black population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% college degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of population having some college education (25 years and over)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS 5 years 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of population employed (population 16 years and over)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS 5 years 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% unemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of population unemployed (population 16 years and over)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS 5 years 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% household with no vehicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of households with no vehicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS 5 years 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% household with 1 vehicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of households with 1 vehicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS 5 years 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% household with 2 or more vehicle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of households with 2 or more vehicles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACS 5 years 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBuilt environment and travel behavior variables\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e972.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e653.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density per square mile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,623.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,777.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of employees per square mile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLODES 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,783.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,076.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation\u0026thinsp;+\u0026thinsp;employment per square mile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Axle 2022 and LODES 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,406.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9,190.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand use mix (entropy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;[single-family share * ln(single-family share) + multifamily share * ln(multifamily share) + commercial share*ln(commercial share) + industrial share*ln(industrial share)+public share*ln(public share)] / ln(5), where ln is the natural logarithm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSalt Lake County Parcel Data 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntersection density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of intersections per square mile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUGRC 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% 4-way intersection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of 4-way intersections\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUGRC 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStreet connectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta index, β = ⅇ ∕ v, ⅇ: number of links, v: number of nodes (intersections\u0026thinsp;+\u0026thinsp;dead ends)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTFS 2023, WFRC 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoad density in terms of link miles per square mile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTFS 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to nearest rail stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraight line distance from TAZ centroid to nearest rail stop in miles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUGRC 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to nearest bus stop\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraight line distance from TAZ centroid to the nearest bus stop in miles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUGRC 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransit frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic transit frequency per hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGTFS 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to jobs by transit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of regional jobs within 30 minutes by transit (applies a distance decay function to weight job accessibility based on proximity to the TAZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWFRC 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43,580.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27,710.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to jobs by auto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of regional jobs within 30 minutes by auto (applies a distance decay function to weight job accessibility based on proximity to the TAZ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWFRC 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e437,435.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76,157.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to downtown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraight line distance from TAZ centroid to downtown in miles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComputed as straight-line distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to the nearest city center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStraight line distance from TAZ centroid to the nearest city center in miles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComputed as straight-line distance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModal preference to bicycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividual preference to using more bikes (derived from bike_more variables indicating factors to encourage households to bike more)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUHTS 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModal preference to transit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividual preference to using more transit (derived from transit_more variables indicating factors to increase transit use)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUHTS 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: All the variables are calculated at the TAZ level; ACS and LODES data was interpolated at the TAZ level; UHTS: Utah Household Travel Survey; LODES: Longitudinal Employer Household-Dynamics; WFRC: Wasatch Front Regional Council; UGRC: Utah Geospatial Resource Center; GTFS: Google Transit Feed Specification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Analytical approach\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Residential relocation and displacement assessment\u003c/h2\u003e \u003cp\u003eAlthough residential relocation occurs among both homeowners and renters, renters are considerably more mobile and face a higher risk of relocation; therefore, our analysis focuses exclusively on renter households, examining those who moved out of and into station areas. Studies show that renter status, low income, and minority (non-White) racial identity are associated with a higher risk of residential displacement (Bates et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; OECD, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Also, most often, relocated low-income renters moved to more suburban, car-dependent, low-density areas, which is another recognized form of displacement, as per the literature on the suburbanization of poverty (Blumenberg \u0026amp; King, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Blumenberg \u0026amp; Wander, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Building on this literature, we conceptualize displaced households as the subset of moved-out households composed of low-income, racial minority renters who relocate to non-transit-served areas. We treated residential displacement as a distinct, one-way process in which low-income, racial minority renter households move out of station areas. A household is classified as displaced if it meets four criteria: (1) it relocates from a station area TAZ to a non-station area TAZ, (2) it falls into one of three income categories\u0026mdash;low, very low, or extremely low income, (3) it identifies as non-White (racial-minorities), and (4) it is a renter both before and after the move. Data on these criteria\u0026mdash;income, race, and housing tenure\u0026mdash;were derived from the Data Axle. Household income was categorized based on county-level Area Median Income (AMI) as: moderate income (80\u0026ndash;120% of AMI), low income (50\u0026ndash;80%), very low income (30\u0026ndash;50%), and extremely low income (below 30%) was inflation-adjusted using Bureau of Economic Analysis indices, followingU.S. Department of Transportation, (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) to maintain consistency over the study period. Using this definition, we analyzed displacement by longitudinally tracking renter households who moved out of station areas from 2012 to 2022. We also examined which household moves into station areas to capture potential exclusionary displacement dynamics.\u003c/p\u003e \u003cp\u003eAlthough we used decade-long (2012\u0026ndash;2022) data from Data Axle, our analysis of residential relocation and displacement was conducted on a consecutive two-year basis (e.g., 2012\u0026ndash;2013, 2013\u0026ndash;2014). This approach was adopted to capture short-term household dynamics, as households frequently form and dissolve within the Data Axle records (Acolin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chapple \u0026amp; Song, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Estimating VMT changes over time\u003c/h2\u003e \u003cp\u003eTo estimate the impact of household relocation and displacement on travel, we modeled household-level VMT as a function of socioeconomic characteristics, built environment features, and mode-choice preferences. Early studies primarily used statistical models like ordinary least squares (OLS), hierarchical linear modeling, Multilevel Bayesian Regression, and structural equation modeling (SEM) to model VMT (Choi \u0026amp; Zhang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Currans et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schneider et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Thao \u0026amp; Ohnmacht, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb; Zhang et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang \u0026amp; Zhang, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These models are easy to understand and have a straightforward structure, but they don't account for the intricate, non-linear relationships between VMT and built-environment characteristics. In recent studies, Machine Learning (ML) models, especially tree-based models, are being used by researchers to address this concern and improve model performance. These tree-based ML models are flexible with the ability to handle mixed data types and missing values, and the capacity to capture nonlinear relationships (Hu et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tian et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the limited application of ML methods in VMT research, this study employs both traditional statistical models and ML-based approaches to compare their performance and contribute to the emerging literature on ML applications in VMT analysis. Statistical models we tested include the ordinary least squares (OLS), and tree-based ML models include Random Forest, AdaBoost, and XGBoost regressor. In all cases, we modeled VMT as a function of socioeconomic and built environment variables mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. We selected the most appropriate variables using feature engineering techniques, including correlation tests and variance inflation factor (VIF) analysis. It should be noted that due to the skewed distribution of VMT data, we followed the same method from prior research(Ewing et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and applied a logarithmic transformation to address this issue. Initially, we included all variables in the model and then applied feature selection using importance scores (\u0026gt;\u0026thinsp;0.02), correlation analysis, and VIF (\u0026gt;\u0026thinsp;10). As ML models do not produce coefficients, we interpreted results using relative variable influence, scaled to 100 for better interpretability.\u003c/p\u003e \u003cp\u003eFrom all models, final VMT model was selected based on common evaluation metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R\u0026sup2;. RMSE and MAE capture average prediction errors, while R\u0026sup2; reflects model fit. Model robustness was ensured through 5-fold cross-validation, in which the dataset was divided into five subsets for iterative training and testing. We then used the best-performing VMT model to assign mean household VMT to household-level residential relocation at the TAZ level. This allowed us to assess the potential impact of residential relocation and displacement on VMT, which was subsequently linked to CO₂ estimation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Estimating changes in CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/h2\u003e \u003cp\u003eVMT is typically used as a proxy for estimating vehicle GHG emissions (Bailey Patricia Mokhtarian \u0026amp; Little, 2008; Boarnet, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Glaeser \u0026amp; Kahn, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tuffour et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent studies used advanced models like the Motor Vehicle Emission Simulator (MOVES) developed by the U.S. Environmental Protection Agency (EPA) to estimate GHG emissions from VMT (Choi \u0026amp; Zhang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we followed the same method of using passenger car CO₂ emission rates based on the EPA\u0026rsquo;s MOVES4 model developed by the Wasatch Front Regional Council (WFRC). MOVES4 incorporates \"Vehicle Activity\" data from WFRC\u0026rsquo;s four-step regional travel demand model. MOVES4 also uses recent county-level vehicle registration data by fuel type (gasoline, diesel, CNG, electric, and E85) for passenger vehicle types 21, 31, and 32. Using the WFRC-provided total grams per mile CO2 emission rate, the household-level CO\u003csub\u003e2\u003c/sub\u003e was estimated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{CO}_{2}=gpm\\:{CO}_{2}\\text{*}Household\\:VMT$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSince CO₂ emission rates were only available for 2012, 2017, and 2022 from WFRC, the impact of residential relocation and displacement on vehicular CO₂ emissions was estimated specifically for those years using pre- and post-move VMT. To illustrate the broader trend over the entire study period, the mean VMT and CO₂ emissions across all years were also calculated.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Residential relocation and displacement patterns\u003c/h2\u003e \u003cp\u003eFor the relocation analysis, we tracked 435,024 renter households over the study period 2012\u0026ndash;2022, averaging around 43,500 renters per year. Among them, the economic and racial composition of moved households among all households across the period is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The vast majority of these moved households\u0026mdash;ranging from 81% to 96% among those moving out, and 72% to 93% among those moving in\u0026mdash;belong to low, very low, and extremely low-income categories (0\u0026ndash;80% of the county\u0026rsquo;s median income). In contrast, only a small proportion of movers fall into the moderate- or above-moderate-income groups (above 80% of the county\u0026rsquo;s median income). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the year-by-year distribution of moved households within these dominant income categories, providing a clear visualization of these trends over time.\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\u003eYear-wise Adjusted Household Income and Racial Composition of Households Moving into and out of LRT Station Areas\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnalysis Year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMoving out of Station Areas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMoving into Station Areas\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Households (% of County)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHouseholds' Income-Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Non-white Households\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of Households (% of County)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHouseholds' Income-Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% Non-white Households\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u0026ndash;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e311 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e14815 (\u003cspan\u003e$\u003c/span\u003e15544)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e228 (0.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e24024 (\u003cspan\u003e$\u003c/span\u003e19041)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u0026ndash;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e444 (1.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e16304 (\u003cspan\u003e$\u003c/span\u003e15625)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e266 (0.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e21523 (\u003cspan\u003e$\u003c/span\u003e17152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u0026ndash;2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e432 (1.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e21643 (\u003cspan\u003e$\u003c/span\u003e14856)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e259 (0.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e30849 (\u003cspan\u003e$\u003c/span\u003e23026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026ndash;2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e483 (1.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e19252 (\u003cspan\u003e$\u003c/span\u003e14832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e255 (0.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e26435 (\u003cspan\u003e$\u003c/span\u003e20937)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026ndash;2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410 (1.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e27992 (\u003cspan\u003e$\u003c/span\u003e18684)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e201 (0.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e39152 (\u003cspan\u003e$\u003c/span\u003e30048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u0026ndash;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e692 (1.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e28011 (\u003cspan\u003e$\u003c/span\u003e20134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e458 (1.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e38100 (\u003cspan\u003e$\u003c/span\u003e30058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026ndash;2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e666 (1.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e32538 (\u003cspan\u003e$\u003c/span\u003e29399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e509 (1.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e38969 (\u003cspan\u003e$\u003c/span\u003e26042)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026ndash;2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e568 (0.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e34938 (\u003cspan\u003e$\u003c/span\u003e22729)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e587 (1.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e38485 (\u003cspan\u003e$\u003c/span\u003e24087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e808 (1.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e28850 (\u003cspan\u003e$\u003c/span\u003e20502)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e625 (1.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e43857 (\u003cspan\u003e$\u003c/span\u003e37647)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u0026ndash;2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e944 (1.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e32425 (\u003cspan\u003e$\u003c/span\u003e22064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e395 (0.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e37797 (\u003cspan\u003e$\u003c/span\u003e32761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\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\u003eOver the decade, both the number and proportion of households moving out of station areas increased, rising from 311 households (0.92% of total movers) in 2012\u0026ndash;2013 to 944 households (1.89%) in 2021\u0026ndash;2022. These households typically had lower incomes, with average income increasing from \u003cspan\u003e$\u003c/span\u003e14,815 in 2012\u0026ndash;2013 to \u003cspan\u003e$\u003c/span\u003e32,425 in 2021\u0026ndash;2022, yet remaining below the average income of those moving into station areas. A large share of these movers consistently fell within the low, very low, and extremely low-income categories (0\u0026ndash;80% of the county\u0026rsquo;s median income), with extremely low-income households (below 30% of the county\u0026rsquo;s median income) comprising the largest proportion, up to 82% in some years. Households moving into station areas are also predominantly low-income brand, though they have relatively higher incomes than those moving out. Their average income increased from \u003cspan\u003e$\u003c/span\u003e24,024 in 2012\u0026ndash;2013 to a peak of \u003cspan\u003e$\u003c/span\u003e43,857 in 2020\u0026ndash;2021; however, these levels still fall within the low-income category. Despite this modest increase, low-income households continued to comprise a substantial share of in-movers, though their numbers remained lower than those of households moving out.\u003c/p\u003e \u003cp\u003eDisaggregating the low-income category into low-, very low-, and extremely low-income groups provides further insight into the income differences between households moving in and moving out. In each year, the percentage of very low and extremely low-income households was consistently higher among those moving out than among those moving in. Conversely, the proportion of low-income households (just below the moderate-income threshold) was consistently lower among those moving out but relatively higher among those moving in. This pattern might suggest a gradual demographic shift within station areas, where extremely low- and very low-income households are increasingly being replaced by households in slightly higher, though still low-income, brackets.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRacial composition data further support this trend (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While the percentage of non-white households moving into station areas remained relatively stable (21%\u0026ndash;28%), the proportion of non-white households among those moving out increased from 18% to 30% over the study period. As the absolute number of households moving out was higher than those moving in, the actual number of non-white households leaving station areas exceeded the number moving in, highlighting potential racial dynamics within these residential mobility patterns.\u003c/p\u003e \u003cp\u003eWe found limited evidence of displacement in station areas over the study period. Displaced households represent less than 0.25% of all renter households in any given year, though they account for approximately 8\u0026ndash;13% of households moving out of station areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Displaced households were predominantly extremely low-income, making up over 85% of the displaced households in the early years (2012\u0026ndash;2014), with some diversification later, but no clear shift indicating that the most economically vulnerable residents might be disproportionately impacted by displacement from station areas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Effect of residential relocation on household VMT\u003c/h2\u003e \u003cp\u003eAmong all models we tested, the XGBoost regressor performed best, achieving an R\u0026sup2; of 0.20, MAE of 0.67, and RMSE of 0.86\u0026mdash;closely matching the standard deviation of VMT (0.88) and indicating a strong relative fit (Please see Appendix C1 for model performance comparison). The relative variable importance for this model indicates that socioeconomic and built-environment characteristics contribute almost equally to shaping household travel behavior. Within the socioeconomic category, household size was the most influential predictor (suggesting that larger households have distinct travel patterns that strongly affect per capita VMT), followed by variables such as households with two or more vehicles, renter households, median household income, zero vehicle households, and owner households. On the built environment side, access to jobs by transit stands out as the top factor, followed by the share of 4-way intersections, distance to downtown, access to jobs by auto, and distance to the nearest rail stop (Please see Appendix C2 for details of the relative variable importance).\u003c/p\u003e \u003cp\u003eUsing the XGBoost regressor, we estimated mean household VMT at the TAZ level. We then plotted the estimated mean VMT for households\u0026rsquo; origin and destination TAZs to illustrate how changes in the built-environment and socioeconomic context associated with relocation may influence travel behavior. This analysis was conducted annually from 2012 to 2022 for the three previously defined groups: (1) all renter households moving out of station areas, (2) displaced renter households moving out, and (3) all renter households moving into station areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Vertical lines represent standard deviations, indicating within-group variation in estimated VMT.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a) illustrates that households moving out of station areas are associated with higher estimated VMT at their destination TAZs compared to their origin TAZs. For example, in 2012\u0026ndash;2013, the mean estimated VMT of a household in origin TAZs was slightly below 28 miles, whereas the corresponding estimate for destination TAZs was approximately 33 miles per household. This gap persists over time, with destination TAZ estimates reaching nearly 39 miles by 2021\u0026ndash;2022, compared to about 30 miles for origin TAZs. Aggregated across the study period, the difference between destination and origin TAZ estimates averages approximately 5 miles (SD\u0026thinsp;=\u0026thinsp;2 miles). This increase in potential VMT likely reflects differences in built-environment and accessibility characteristics between origin and destination TAZs. Between 2012 and 2022, households relocating from station areas moved consistently farther from TRAX stations\u0026mdash;by over 1 mile on average\u0026mdash;as well as moderately farther from bus and other transit stops, and slightly farther from city centers (Please see Appendix D from supplementary materials). Since most of these renter households were not displaced, the spatial patterns might suggest a shift toward increased automobile reliance and reduced transit accessibility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b), displaced households exhibit smaller differences in estimated VMT between origin and destination TAZs, along with greater variability, compared to all households relocating from station areas. In most years, estimated VMT per household in destination TAZs exceeded that of origin TAZs, though the magnitude of the difference was modest. The largest gap appears in 2021\u0026ndash;2022, when the mean estimated VMT per household was approximately 29 miles in origin TAZs and nearly 37 miles in destination TAZs. Aggregated across the study period, the average difference between destination and origin TAZ estimates is approximately 1 mile (SD\u0026thinsp;=\u0026thinsp;3 miles), indicating substantial heterogeneity in relocation contexts among displaced households. Given that these households were disproportionately composed of extremely low-, very low-, and low-income renters from racial minority backgrounds, it is plausible that relocation choices were shaped by both housing affordability constraints and the need to maintain some degree of transit accessibility. Supporting this interpretation, distance measures using GTFS stops indicate that while proximity to TRAX stations consistently declined, changes in access to bus and other transit stops and city centers were more variable (Appendix D). On average, distance to bus and other transit stops increased only slightly, by approximately 0.07 miles, with certain years registering small decreases. Similarly, distance to city centers fluctuated between \u0026minus;\u0026thinsp;0.13 and +\u0026thinsp;0.24 miles across the study period, yielding a negligible average increase of 0.03 miles. Collectively, these patterns suggest that although displaced households generally lost access to high-capacity rail, many resettled in areas that preserved, or in some cases modestly improved, access to bus transit and urban centers.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(c) indicates that households moving into station areas likely experience minimal differences in estimated VMT between origin and destination TAZs. In most years, estimated VMT in destination TAZs is comparable to\u0026mdash;or slightly lower than\u0026mdash;that of origin TAZs. Across the study period, the average difference between destination and origin TAZ estimates is only\u0026thinsp;+\u0026thinsp;0.12 miles (SD\u0026thinsp;=\u0026thinsp;1.6 miles), indicating limited variation in estimated travel intensity associated with relocation contexts. This limited change is likely attributable to modest improvements in the built environment. Access to TRAX stations improved, with average distance reductions ranging from 0.71 to 0.97 miles, while gains in proximity to bus and other transit stops were minimal, typically less than 0.1 miles. Distance to city centers also decreased only slightly, by between 0.02 and 0.24 miles (Appendix D). Although these renter households relocated to more transit-served areas, the incremental nature of these improvements\u0026mdash;combined with their pre-existing residential environments\u0026mdash;may not have been sufficient to substantially alter VMT.\u003c/p\u003e \u003cp\u003eOverall, households moving out of station areas are associated with higher estimated VMT at their destination TAZs, while in-movers show little difference. Given that out-movers exceed in-movers, this imbalance likely contributes to a net increase in aggregate estimated VMT, underscoring the broader implications of residential relocation for transportation-related environmental outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Effect of residential relocation on household vehicular CO\u003csub\u003e2\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates that household CO\u003csub\u003e2\u003c/sub\u003e emissions closely follow estimated VMT patterns as they are directly proportional to VMT estimates and vehicle emission rates.\u003c/p\u003e \u003cp\u003eHouseholds moving out of station areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) are associated with higher estimated CO₂ emissions in their destination TAZs compared to their origin TAZs. For example, estimated emissions increase from approximately 11,000 grams per housegold in origin TAZs to about 13,500 grams per household in destination TAZs in 2012\u0026ndash;2013, and from roughly 10,500 to 13,400 grams in 2021\u0026ndash;2022. Across the study period, the average difference between destination and origin TAZ estimates is 2,431 grams (SD\u0026thinsp;=\u0026thinsp;768), consistent with relocation to more car-dependent environments.\u003c/p\u003e \u003cp\u003eDisplaced households (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb) exhibit smaller but still positive differences between destination and origin TAZ emission estimates, averaging 1,362 grams (SD\u0026thinsp;=\u0026thinsp;1,373), with greater variability reflecting heterogeneous relocation contexts. In contrast, households moving into station areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) show minimal differences between origin and destination TAZ estimates, and in some years slightly lower emissions in destination TAZs\u0026mdash;for example, declining from approximately 10,500 to just under 10,000 grams in 2021\u0026ndash;2022. This group displays the smallest average difference (311 grams, SD\u0026thinsp;=\u0026thinsp;521) and the lowest variability.\u003c/p\u003e \u003cp\u003eOverall, these patterns suggest that when a renter household in a station area is replaced by one relocating from outside the station area, the resulting spatial redistribution is likely associated with higher vehicle-related CO₂ emissions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eOur findings indicate that residential relocation around station areas is overwhelmingly concentrated among low-, very low-, and extremely low-income renter households, who comprise the large majority of both out-movers and in-movers. However, the higher out-migration rate relative to in-migration suggests a subtle de-densification of station areas. This pattern aligns with findings byBoarnet et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who reported a higher out-mobility rate and a widening residential mobility gap between higher- and lower-income households.\u003c/p\u003e \u003cp\u003eAlthough we find limited evidence of displacement\u0026mdash;affecting less than 0.25% of renter households annually\u0026mdash;the disproportionately higher out-mobility of very low- and extremely low-income households suggests a subtle pattern of income- and race-based filtering within low-income groups. Households at the bottom of the income distribution, many of whom are non-White, appear more likely to move out and may be replaced by renters with slightly higher incomes, though still within the low-income bracket. These patterns are consistent with Song and Chapple (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who find that low-income households are more likely to leave transit-served, gentrifying neighborhoods and less likely to move into them than higher-income households. The higher out-mobility observed in our study also echoes concerns raised by Newman and Wyly (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), suggesting that lower in-migration rates among extremely low- and very low-income households may signal diminishing housing opportunities and potential exclusionary displacement pressures in station areas. While the absolute number of displaced households in our study is relatively small, even modest displacement rates may be policy-relevant when coupled with exclusionary dynamics that constrain low-income households\u0026rsquo; access to revitalizing neighborhoods. Addressing these pressures may require coordinated housing strategies. Federal programs, such as public housing, Housing Choice Vouchers (Section 8), and the Low-Income Housing Tax Credit, combined with local tools including rent regulation, inclusionary zoning, and city-funded affordable housing initiatives, can help moderate affordability pressures, as demonstrated in New York City (Whitehead \u0026amp; Goering, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, sustaining the density necessary to fully realize the social and environmental benefits of public transit investments will require proactive policies that expand housing opportunities for a diverse range of income groups within station areas.\u003c/p\u003e \u003cp\u003eOur VMT estimation based on origin and destination TAZs associated with the residential relocation indicate that these residential relocation patterns have a knock-on effect on VMT and CO\u003csub\u003e2\u003c/sub\u003e emissions. We found that daily household VMT increased when households relocated away from station areas. As shown by previous research, their residential relocation might significantly influence mode choices, with changes in the built environment, commute distance, and socio-economic factors shaping travel decisions; therefore, those who move farther from their workplace often tend to resort to private vehicle ownership and usage when public transportation becomes less accessible or convenient (Zarabi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao \u0026amp; Zhang, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In such cases, households that already own a car or have the financial capacity to purchase one may exhibit more stable post-relocation VMT patterns and those who cannot afford to buy a car might still rely on public transit.\u003c/p\u003e \u003cp\u003eFor households displaced from station areas, the estimated increase in mean household VMT at the destination TAZ was smaller but more variable than for all households that moved out of station areas. The higher variability suggests that, after being displaced from station areas, these households may have found housing with less access to essential services and public transit. Despite being displaced, some households may still seek housing near other transit options (e.g. bus), which could explain the higher variability in their destination TAZs' mean VMT. A study by Kamruzzaman et al., (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found that when households relocate due to displacement or affordability issues, their travel habits do not readily adapt to their new surroundings. This could mean that after being displaced, households might have experienced longer walks to existing transit stations, longer commutes in general, or switched to different types of public transit, all of which contribute to the increased variability in destination VMT.\u003c/p\u003e \u003cp\u003eIn contrast, we found households moving into station areas are expected to have little or no savings in their mean VMT. Our findings\u0026mdash;showing minimal VMT reduction among households relocating to station areas with a high share of low-income renters\u0026mdash;can be partly explained by the similarity between their previous and current built environments, as well as their socioeconomic status. These households may have already lived in transit-accessible neighborhoods, limiting the potential for further VMT reduction. This finding also aligns withBoarnet et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) who found that lower-income households tend to experience smaller VMT reductions than higher-income households after moving into transit-served areas. Together, these results suggest that while proximity to transit can support lower VMT, the magnitude of these benefits varies across income groups. As noted by Boarnet et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while equity concerns should get strong support for the inclusion of low-income housing near rail transit, strategies centered solely on either low- or high-income groups may be less effective than mixed-income approaches. Our results likewise underscore the importance of inclusive, mixed-income housing policies to advance both equity and environmental gains in station areas.\u003c/p\u003e \u003cp\u003eRegarding the CO₂ impacts of residential relocation and displacement, our findings indicate a net increase in household vehicle emissions, consistent with the observed VMT patterns. This aligns withBoarnet et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) who found that living near station areas can reduce vehicle CO₂ emissions. Our study extends this research by providing a more detailed picture of emissions associated with households moving in, moving out, and being displaced.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLimitations\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThere are several limitations in our study. First, we focused only on displacement using proxy indicators such as income, race, and tenure, and were geographically confined to Salt Lake County. The use of proxy measures and the lack of intra-regional analysis may obscure some voluntary moves. Additionally, due to data limitations, specifically the lack of information on housing rents, new housing production, and other market dynamics, we were unable to directly capture exclusionary displacement, where low-income renters are unable to move into station areas. However, by analyzing the composition of households moving into station areas, we offered indirect insight into this phenomenon. Second, we estimated mean household VMT at the TAZ level using socioeconomic and built environment factors, rather than direct household-level travel data. The VMT model was based on 2023 UHTS data and applied across the entire study period, even though our analysis spans multiple two-year intervals. Ideally, VMT would be estimated for each year, but household travel surveys\u0026mdash;on which such modeling depends\u0026mdash;are typically conducted only once every decade. Due to this limitation, we relied on 2023 data but validated our estimates against the actual 2012 VMT to ensure consistency in trends. While this approach captures general patterns, it may not reflect individual travel behavior and introduces the risk of the ecological fallacy by assigning TAZ-level estimates to the household level. However, key variables such as density and median income are generally more reliably assessed at the area level than at the household level. Third, CO₂ emissions were derived from WFRC\u0026rsquo;s MOVES model estimates for 2012, 2019, and 2022. We averaged these values to assess the environmental impact of driving behavior over the full period. While not ideal, this approach offers a reasonable approximation of longer-term trends.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn this study, we examine how light rail investments are associated with renter household relocation patterns in station areas and the resulting implications for vehicle-related CO₂ emissions. We document gradual de-densification of station areas and limited formal displacement, alongside a subtle pattern of income- and race-based sorting within low-income groups. These dynamics suggest a narrowing of housing access for extremely low- and very low-income renters in transit-served neighborhoods. We further show that this compositional shift has measurable environmental consequences. When renter households relocate from station areas and are replaced by households originating outside these areas, the resulting spatial redistribution is associated with higher estimated VMT and CO₂ emissions, reflecting differences in built environment and accessibility characteristics at the TAZ level.\u003c/p\u003e \u003cp\u003eOur findings highlight the interdependence between housing market dynamics and transportation outcomes. Absent coordinated housing and land-use interventions, transit investments alone may be insufficient to sustain density, retain low-income renters, or maximize environmental benefits. Integrating affordable housing preservation with inclusive densification strategies may therefore be essential to advancing both sustainability and transportation efficiency objectives in light rail station areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the Wasatch Front Regional Council (WFRC). In particular, we sincerely thank Bert Granberg, Analytics Director, for his coordination and for providing us with the household travel survey data. We also extend our appreciation to Kip Billings, Senior Transportation Engineer and Air Quality Analyst, for his assistance in coordinating and providing information on the MOVES4 model and emission rates. We thank Justyna Kaniewska, Doctoral Candidate in City and Metropolitan Planning, for assistance with accessing and processing Data Axle. We also acknowledge the limited use of large language models to improve the clarity and readability of the manuscript. We confirm that AI was not used in any kind of idea generation, data analysis, graph preparation, or writing any part of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm contribution to the paper as follows: conceptualization: Faria Afrin Zinia, Andy Hong, Reid Ewing; data curation: Faria Afrin Zinia, Andy Hong; formal analysis: Faria Afrin Zinia, Andy Hong; funding acquisition: Andy Hong, Reid Ewing; methodology: Faria Afrin Zinia, Andy Hong; project administration: Andy Hong, Reid Ewing; supervision: Andy Hong, Reid Ewing; validation: Faria Afrin Zinia, Andy Hong, Reid Ewing; visualization: Faria Afrin Zinia, Andy Hong; writing \u0026ndash; original draft: Faria Afrin Zinia; writing \u0026ndash; review and editing: Faria Afrin Zinia, Andy Hong, Reid Ewing. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) have disclosed no conflicts of interest regarding this article\u0026apos;s research, authorship, and/or publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC), the Center for Transit-Oriented Communities (CETOC) (Grant No. 69A3552348337).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDue to the proprietary nature of household microdata, we are not allowed to share this data publicly. 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Geogr. \u003cb\u003e73\u003c/b\u003e, 41\u0026ndash;53 (2018). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtrangeo.2018.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jtrangeo.2018.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Light Rail Transit, Station areas, Built-environment, Vehicle Miles Traveled, CO2 emission, Residential relocation","lastPublishedDoi":"10.21203/rs.3.rs-8897368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8897368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLight rail transit (LRT) is often promoted as a sustainable mobility strategy to reduce traffic emissions. Yet its impact on vehicular CO₂ emissions through residential relocation around its stations remains poorly understood. This gap stems from the lack of household-level data to track residential moves and the limited understanding of how built environment changes associated with those moves shape vehicle use. To address this, we used 10 years of Data Axle microdata to track individual household relocation patterns near LRT stations in Salt Lake County, Utah. Our findings indicate that station areas concentrate residential moves among low-income renter households, with net out-migration and a small degree of displacement, indicating subtle socioeconomic and racial filtering in which lower-income, non-white households are replaced by slightly higher-income renters near LRT stations. In our analysis of travel-related environmental outcomes, we found a net increase in estimated vehicle miles traveled (VMT) and CO₂ emissions, as potential VMT reductions associated with station-area built environments are offset by moves to more car-dependent areas and the de-densification of station areas. Our findings suggest that targeted housing policies to retain low-income renters and the densification of station areas through a broader mix of socioeconomic groups may help maximize the environmental benefits of public transit.\u003c/p\u003e","manuscriptTitle":"Residential-Relocation, Displacement, and CO2 Emissions Near Light-Rail: A 10-Year Microdata Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-22 17:30:06","doi":"10.21203/rs.3.rs-8897368/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"257949343595104380494429295224671380742","date":"2026-05-08T03:23:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209196706000991474150407504330007904173","date":"2026-05-06T22:32:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T21:35:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T19:32:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-17T08:38:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Transportation","date":"2026-02-17T04:15:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"transportation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"port","sideBox":"Learn more about [Transportation](http://link.springer.com/journal/11116)","snPcode":"11116","submissionUrl":"https://submission.nature.com/new-submission/11116/3","title":"Transportation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f11b958a-a013-498b-89a1-a8f49212fb02","owner":[],"postedDate":"April 22nd, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"257949343595104380494429295224671380742","date":"2026-05-08T03:23:03+00:00","index":11,"fulltext":""},{"type":"reviewerAgreed","content":"209196706000991474150407504330007904173","date":"2026-05-06T22:32:36+00:00","index":10,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-22T17:30:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-22 17:30:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8897368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8897368","identity":"rs-8897368","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}