Residential Self-selection Effects Research Articles

Complex associations between multidimensional land use mix and walking: Walk more or farther?

Land Use Mix (LUM) has attracted attention for its potential to promote walking; however, the LUM-walking association was not universally positive. Existing research, potentially confounded by diverse LUM connotations and walking metrics, prompted our effort to establish a comprehensive framework. This framework aims to clarify LUM’s influence on walking, including various LUM indicators (mix in quantity, distance, function), multiple walking indicators (frequency, time, distance), distinct geographical background (residence, workplace), and travel types (weekly travel, weekday-exclusive travel). Besides, China’s unique context provide an opportunity to mitigate residential self-selection effects. We found that the proximity (LUM in distance) is the sole environmental factor driving increased walking frequency. While the LUM in quantity or function could enhance resident convenience by diversifying services, it occasionally diminishes walking distance and duration, potentially conflicting with public health considerations. Conversely, although exposure to high-functional mixed, low-compatibility environments may increase walking time and distance, it is more likely a compensatory behavior aimed at avoiding the negative externalities such as pollution and noises. Additionally, the placement of public transit stops will not only help ease traffic congestion but also encourage walking.

The varying effects of residential built environment on travel behavior of internal migrants and locals

The built environment impacts multiple dimensions of travel behavior of urban residents. However, limited research has explicitly unveiled how these impacts vary between diverse socioeconomic groups, especially internal migrants versus locals, notwithstanding hundreds of millions of internal migrants are living in cities in developing countries. Hence, employing a large-scale travel behavior survey dataset collected in Xiamen (China) in 2015 and applying the propensity score matching method, this study comparatively examines the differentiations in responsiveness of such two groups to the built environment. The results demonstrate that (1) the effects of the built environment on travel behavior differ between internal migrants and locals in some dimensions (e.g., active travel frequency and duration); (2) the built environment has larger effects on all the travel dimensions of locals than on those of internal migrants, implying the existence of “transportation assimilation” and a temporal lag effect; (3) the internal migrants are less impacted by residential self-selection effects. These findings can enlighten decision-makers about more nuanced, targeted, and socially inclusive intervention strategies, thus effectively accommodating the travel demands of both locals and internal migrants.

Quantifying residential self-selection effects on commuting mode choice: A natural experiment

Though the impact of residential self-selection on travel behavior has been widely discussed in transport studies, few studies have examined its effect on commuting mode choice, particularly when considering the mediating effects of commuting distance and car ownership. By using survey data from 19 large cities in China and taking advantage of the largely exogenous residential locations of social housing residents, we employed a natural experiment framework that combines propensity score weighting and simultaneous equation models to investigate the effects of the built environment and residential self-selection on commuting mode choice. The results show that most direct and indirect effects through commuting distance and car ownership are significant. The total effects of the built environment and residential self-selection have the same coefficient signs for car and regular bike modes, while they have opposite coefficient signs for public transit and e-bike modes.

Does the built environment shape commuting? The case of Lyon (France)

Is built environment the most influential factor on travel behavior when compared to individual socioeconomic characteristics? This paper extends the empirical knowledge by providing and comparing quantitative estimates of these various effects on both commuting distance and mode choice in a European city spatial context, while using up-to-date and novel methodology. Eight indicators of built and social environment are identified in order to characterize clusters of residential locations, giving a rich view of spatial and social diversity of locations. To disentangle the causal effects of residential self-selection and built environment, both sample selection and specific matching preprocessing (“coarsened exact matching”, a novel approach in the field) are implemented. Regarding commuting distance, the true effect of built and social environment appears modest with an increase of distance in the range of 10-20%. It comes behind individual socioeconomic characteristics such as car availability and skill. Regarding commuting mode choice, again the true effect of built and social environment is modest, with a near 20%pt increase of car share and around 10%pt decrease or public transport share for the most prominent effects, and it comes behind car availability. These results suggest the primary importance of directly influencing car use, if not car ownership, in the European context, while trying to modify the built environment would provide only limited results.

Causes and effects between attitudes, the built environment and car kilometres: A longitudinal analysis

Travel-related attitudes are believed to affect the connections between the built environment and travel behaviour. Previous studies found supporting evidence for the residential self-selection hypothesis which suggests that the impact of the built environment on travel behaviour could be overestimated when attitudes are not accounted for. However, this hypothesis is under scrutiny as the reverse causality hypothesis, which implies a reverse direction of influence from the built environment towards attitudes, is receiving increased attention in recent research. This study tests both directions of influence by means of cross-sectional and longitudinal structural equation models. GPS tracking is used to assess changes in travel behaviour in terms of car kilometres travelled. The outcomes show stronger reverse causality effects than residential self-selection effects and that land-use policies significantly reduce car kilometres travelled. Moreover, the longitudinal models show that the built environment characteristics provide a better explanation for changes in car kilometres travelled than the travel-related attitudes. This contradicts the cross-sectional analysis where associations between car kilometres travelled and travel-related attitudes were stronger. This highlights the need for more longitudinal studies in this field.

Determinants of Residential Preferences Related to Built and Social Environments and Concordance between Neighborhood Characteristics and Preferences.

We explored associations between residential preferences and sociodemographic characteristics, the concordance between current neighborhood characteristics and residential preferences, and heterogeneity in concordance by income and race/ethnicity. Data came from a cross-sectional phone and mail survey of 3668 residents of New York City, Baltimore, Chicago, Los Angeles, St. Paul, and Winston Salem in 2011-12. Scales characterized residential preferences and neighborhood characteristics. Stronger preferences were associated with being older, female, non-White/non-Hispanic, and lower education. There was significant positive but weak concordance between current neighborhood characteristics and residential preferences (after controlling sociodemographic characteristics). Concordance was stronger for persons with higher income and for Whites, suggesting that residential self-selection effects are strongest for populations that are more advantaged.

Why do they live so far from work? Determinants of long-distance commuting in California

The determinants of long-distance commuting (trips longer than 50 miles one-way) in the U.S. appear to be poorly understood even though long-distance commuting likely has substantial environmental, social, and economic impacts. A review of the literature shows that that few papers have considered how housing costs influence long-distance commuting. Moreover, residential self-selection has rarely been accounted for in commuting studies. To start addressing these gaps, we analyze the long-distance travel component of the 2012 California Household Travel Survey via a generalized structural equation model. In our model, land use and housing costs are explained by household and head of household characteristics; together with these characteristics, land use and housing costs influence long-distance commuting. We find that the probability of a household commuting long-distance decreases with an increase in the job-housing ratio (OR = 0.914***) and most importantly, with the median home value of the census tract where a household resides (OR = 0.488***). Conversely, this probability increases with the median census tract home values where household members work (OR = 1.765***). Finally, our model results confirm the presence of small residential self-selection effects. These results highlight the importance of providing more affordable housing and mixed development options to reduce long-distance commuting and its associated environmental impacts.

Quantifying the relative contribution of factors to household vehicle miles of travel

Household vehicle miles of travel (VMT) has been exhibiting a steady growth in post-recession years in the United States and has reached record levels in 2017. With transportation accounting for 27 percent of greenhouse gas emissions, planning professionals are increasingly seeking ways to curb vehicular travel to advance sustainable, vibrant, and healthy communities. Although there is considerable understanding of the various factors that influence household vehicular travel, there is little knowledge of their relative contribution to explaining variance in household VMT. This paper presents a holistic analysis to identify the relative contribution of socio-economic and demographic characteristics, built environment attributes, residential self-selection effects, and social and spatial dependency effects in explaining household VMT production. The modeling framework employs a simultaneous equations model of residential location (density) choice and household VMT generation. The analysis is performed using household travel survey data from the New York metropolitan region. Model results showed insignificant spatial dependency effects, with socio-demographic variables explaining 33 percent, density (as a key measure of built environment attributes) explaining 12 percent, and self-selection effects explaining 11 percent of the total variance in the logarithm of household VMT. The remaining 44 percent remains unexplained and attributable to omitted variables and unobserved idiosyncratic factors, calling for further research in this domain to better understand the relative contribution of various drivers of household VMT.

When Context Meets Self-Selection: The Built Environment–Travel Connection Revisited

Existing studies on the built environment (BE)–travel connection tend to underestimate the potential of BE-based mobility strategies due to these studies’ limitations in conceptual and analytical frames. This study conceptualized the combined direct and indirect effects of BE on travel as contextual effects around three features of BE: multiplicity, interaction, and scalability. The conceptual framework was operationalized through multilevel modeling. The empirical analysis from Austin, Texas, verified the multiplicity of BE as it affected VMT, confirmed the complementary effects of BE and residential self-selection on VMT, and demonstrated practical procedures to deal with the analytical challenges of BE scalability.

Vehicle kilometers traveled reduction impacts of Transit-Oriented Development: Evidence from Shanghai City

The role of residential self-selection has become a major subject in the debate over the relationships between the built environment and travel behavior. Numerous previous empirical studies on this subject have provided valuable insights into the associations between the built environment and travel behavior. However, the vast majority of the studies were conducted in North American and European cities; yet this research is still in its infancy in most developing countries, including China, where residential and transport choices are likely to be more constrained and travel-related attitudes quite different from those in the developed world. Using the data collected from 2038 residents currently living in TOD neighborhoods and non-TOD neighborhoods in Shanghai City, this paper aims to partly fill the gaps by investigating the causal relationship between the built environment and travel behavior in the Chinese context. More specifically, this paper employs Heckman’s sample selection model to examine the reduction impacts of TOD on personal vehicle kilometers traveled (VKT), controlling for self-selection. The results show that whilst the effects of residential self-selection are apparent; the built environment exhibits the most significant impacts on travel behavior, playing the dominant role. These findings produce a sound basis for local policymakers to better understand the nature and magnitude toward the impacts of the built environment on travel behavior. Providing the government department with reassurance that effective interventions and policies on land use aimed toward altering the built environment would actually lead to meaningful changes in travel behavior.

Examining the effects of residential self-selection on internal and external validity: an interaction moderation analysis using structural equation modeling

Empirical studies on the land use–travel relationship have begun to consider the confounding or moderating effect of residential self-selection on the relationship. The studies were mainly focused on how to control for selection bias in relation to its threat to internal validity, but the bias also harms external validity through its interaction with explanatory variables. This study further considers this interaction moderation using partial least squares structural equation modeling, which compared to its covariance-based counterpart, can easily specify a large number of interaction indicators and include both reflective (compact land use) and formative (life situation inclined to automobile travel) constructs. Tested in Seoul, South Korea, structural equation models find that a considerable proportion of the land use effect on travel time is attributed to the self-selection. Regarding the interaction moderation, it turns out to significantly affect external validity: in a study area with more automobile-inclined residents than Seoul, compact land use may exert a stronger effect on automobile travel reductions and transit travel increases.

Travel behaviour and health: A conceptual model and research agenda

Objectives This paper proposes a conceptual model of the complex relationships between travel behaviour and health. In addition it gives a research agenda providing an overview of challenges for future research. Methods We review the relevant literature in the areas of public health, land use and transportation that address issues related to health and travel and their underlying mechanisms. We do not aim to give a full review of the literature but to underpin the conceptual model. Results and conclusions We conclude that research can easily come to the ‘wrong’ conclusions if the complex causal relationships that exist between relevant factors are overlooked. In particular, ignoring contradictory effects for specific socio-demographic groups, (residential) self-selection effects, substitutions of different forms of activity, and reverse causalities may lead to overestimation of the effect of policies. For example, travel-related physical activity might interact with other physical activity, self-selection effects may influence the complex relationships between travel behaviour and health, and people׳s health may influence their walking or cycling behaviour. Based on the conceptual model we present a research agenda. A first research challenge is to explore the combined effect of travel behaviour related determinants for health effects (physical activity, air pollution intake, injuries, and subjective well-being) on health. A second challenge is exploring the interactions between travel-related physical activity and other physical activity. Thirdly, the importance of attitudes and attitude formation, specifically health-related attitudes and self-selection processes related to travel behaviour, is an important research topic. Fourthly, it is important to explore the relationship between cycling levels and injury risks, because risks seem to be correlated with cycling levels: the more people cycle, the lower the injury risks. Fifthly, we think it is important to study the relevance of walking and cycling related self-selection effects. A sixth challenge relates to transport innovations.

Incorporating a multiple discrete-continuous outcome in the generalized heterogeneous data model: Application to residential self-selection effects analysis in an activity time-use behavior model

This paper makes both a methodological contribution as well as an empirical contribution. From a methodological perspective, we propose a new econometric approach for the estimation of joint mixed models that include a multiple discrete choice outcome and a nominal discrete outcome, in addition to the count, binary/ordinal outcomes, and continuous outcomes considered in traditional structural equation models. These outcomes are modeled together by specifying latent underlying unobserved individual lifestyle, personality, and attitudinal factors that impact the many outcomes, and generate the jointness among the outcomes. From an empirical perspective, we analyze residential location choice, household vehicle ownership choice, as well as time-use choices, and investigate the extent of association versus causality in the effects of residential density on activity participation and mobility choices. The sample for the empirical application is drawn from a travel survey conducted in the Puget Sound Region in 2014. The results show that residential density effects on activity participation and motorized auto ownership are both associative as well as causal, emphasizing that accounting for residential self-selection effects are not simply esoteric econometric pursuits, but can have important implications for land-use policy measures that focus on neo-urbanist design.

Viewpoint: Quantifying residential self-selection effects: A review of methods and findings from applications of propensity score and sample selection approaches

The phenomenon whereby individuals self-select into their residential environment based on previously determined preferences for how to travel is known as residential self-selection (RSS). Numerous studies have investigated the influence of RSS on the estimated effect of the built environment on travel behavior. However, surprisingly few have actually quantified its effect in terms of partitioning the total influence of the built environment (BE) on travel behavior into a component attributable to RSS and one attributable to the built environment itself. This paper reviews 10 analyses (found in seven studies) that have quantified the proportion of the total influence of the built environment that is due to the BE itself (which we call the BEP), using either propensity-score or sample-selection approaches to control for RSS. After first outlining the basics of each approach, we then explain the various methods used to compute the BEP, followed by a discussion of the empirical results. The estimated BEPs vary widely, ranging from 34 percent to 98 percent. A number of reasons for these disparities are suggested, but there is considerable divergence in estimates even when many of these factors are held constant. Additional research is called for to better understand the circumstances under which the BEP is higher or lower.

Overcoming barriers to cycling: understanding frequency of cycling in a University setting and the factors preventing commuters from cycling on a regular basis

Much local and regional transport policy is attempting to increase cycling as an everyday mode of travel through infrastructure changes, education initiatives, and safety campaigns. While considerable research has examined the influence of the built form on cycling, less research has examined the barriers that prevent people who wish to cycle more (as part of their routine) from doing so. This study examines several factors influencing the frequency by which people do (and do not) cycle in a campus setting in a large metropolitan area. Mixed methods reveal differences between barriers to cycling as well as the relative strength of these barriers across categories of age, sex, and current mode used. A multinomial logit model, which controls for residential self-selection effects, predicts whether and how often a respondent cycles based on socio-demographic and trip characteristics. The presence of cycle paths is found to be strongly associated with a higher frequency of cycling commutes. Additionally, an analysis of stated barriers reveals effort and a lack of safety as the most important barriers to potential cyclists. Finally, a qualitative analysis of respondents’ open-ended responses confirms the influence of bicycle paths, but reveals other factors such as the importance of improved interactions among various street users. Findings from this research can be of benefit to transportation engineers and planners who are aiming to increase the use of cycling among various groups of commuters.

Travel Plans for New Residential Developments: Measuring Self-Selection Effects to Better Understand Travel Behavior Impacts

Travel plans can be required for new residential developments as part of the land use planning and approvals process. The aim is to manage car use of residents by providing a package of tailored initiatives and facilities that support the use of more sustainable forms of transport. However, there is a limited understanding of the extent to which such packages have influenced travel behavior or simply attracted residents who were already making travel choices to reduce car use; the latter effect is commonly referred to as residential self-selection. In this study, a survey of residents in Melbourne, Victoria, Australia, was conducted at developments with and without travel plans. Propensity score matching (PSM)—based on a set of attitudinal, preference, and demographic variables—was then used to explore how residential self-selection could be analyzed in the context of travel plans. Results suggest that residents living in developments with travel plans have lower car ownership and higher levels of walking and cycling compared with residents living in developments without travel plans. PSM was deemed to be a suitable method for analyzing residential self-selection effects, yet a sufficient sample size is required to appropriately quantify these effects.

Has Mexico City’s shift to commercially produced housing increased car ownership and car use?

Mexico City’s principal form of housing production has shifted over the past two decades. More households now purchase houses in large commercially built housing developments than move into informal settlements. Looking at 1500 households in two suburban municipalities from a 2007 metropolitan travel survey, this study is the first to quantify differences in car ownership and car use across households in informal settlements and commercial housing developments. Accounting for income, proxies for wealth, household composition, and geography, households living in commercial housing developments are likely to own more cars and drive more than similar households in neighboring informal settlements. A test for residential self-selection finds no unobserved correlations across households that own cars and live in commercial housing developments, suggesting that the included controls do a good job of capturing the effects of residential self-selection or that the effects are limited. Something about the local land use and design of new commercial housing developments appears conducive to car ownership and use. Differences between the two settlement types, including more parking, wider streets, less-connected street-grids, and less accessible transit stops in commercial settlements, likely play a role.

An agent-based evaluation of impacts of transport developments on the modal shift in Tehran, Iran

Changes in travel modes used by people, particularly reduction of the private car use, is an important determinant of effectiveness of transportation plans. Because of dependencies between the choices of residential location and travel mode, integrated modelling of these choices has been proposed by some researchers. In this paper, an agent-based microsimulation model has been developed to evaluate impacts of different transport development plans on choices of residential location and commuting mode of tenant households in Tehran, the capital of Iran. In the proposed model, households are considered as agents who select their desired residential location using a constrained NSGA-II algorithm and in a competition with other households. In addition, they choose their commuting mode by applying a multicriteria decision-making method. Afterwards, effects of development of a new highway, subway and bus rapid transit (BRT) line on their residential location and commuting mode choices are evaluated. Results show that despite the residential self-selection effects, these plans result in considerable changes in the commuting mode of different socio-economic categories of households. Development of the new subway line shows promising results by reducing the private car use among the all socio-economic categories of households. But the new highway development unsatisfactorily results in increase in the private car use. In addition, development of the new BRT line does not show significant effects on the commuting mode change, particularly on decrease in the private car use.

Understanding Travel Plan Effectiveness for New Residential Developments

Travel plans can be required for new or expanded buildings as part of the process of land use planning and approval. The purpose of these plans is to manage car use of future building occupants and support access by more sustainable forms of transport. Although the effectiveness of travel plans is generally well established at preexisting sites, there is very little evidence of the impacts when they are applied to new developments, particularly residential sites. In response to this situation, a series of multimodal person trip counts and parking surveys was undertaken at four case sites (residential developments with travel plans) and four control sites (similar residential developments without travel plans) in Melbourne, Australia. Results indicated that the average weekday (7:00 a.m. to 9:00 a.m.) mode share for car driver trips was 14 percentage points lower at the case sites than at the control sites. The findings also highlighted issues with relying solely on comparisons with secondary data sources, such as published vehicle trip generation rates and regional travel survey data, to assess travel plan effectiveness. Such reliance was shown to lead to an overestimation of travel plan impacts because of inconsistencies in the geographical locations and data collection periods of the secondary data sources. This finding highlights the importance of using suitable control sites to account for such factors. Further work is needed to strengthen the evidence base through evaluating travel plans at other residential developments and also accounting for residential self-selection effects.

Residential self-selection effects in an activity time-use behavior model

This study presents a joint model system of residential location and activity time-use choices that considers a comprehensive set of activity-travel environment (ATE) variables, as well as socio-demographic variables, as determinants of individual weekday activity time-use choices. The model system takes the form of a joint mixed Multinomial Logit–Multiple Discrete-Continuous Extreme Value (MNL–MDCEV) structure that (a) accommodates differential sensitivity to the ATE attributes due to both observed and unobserved individual-related attributes, and (b) controls for the self-selection of individuals into neighborhoods due to both observed and unobserved individual-related factors. The joint model system is estimated on a sample of 2793 households and individuals residing in Alameda County in the San Francisco Bay Area. The model results indicate the significant presence of residential self-selection effects due to both observed and unobserved individual-related factors. For instance, individuals from households with more bicycles are associated with a higher preference for out-of-home physically active pure recreational travel pursuits (such as bicycling around in the neighborhood). These same individuals locate into neighborhoods with good bicycling facilities. This leads to a non-causal association between individuals’ time investment in out-of-home physically active pure recreational travel and bicycling facilities in their residential neighborhoods. Thus, ignoring the effect of bicycle ownership in the time-use model, would lead to an inflated estimate of the effect of bicycling facility density on the time invested in physically active pure recreational travel. Similarly, there are significant unobserved individual factors that lead to a high preference for physically active recreational activities and also make individuals locate in areas with good bicycling facilities. When such unobserved factors were controlled by the proposed joint residential location and time-use model, the impact of bicycling facility density on out-of-home physically active recreational activities ceased to be statistically significant (from being statistically significant in the independent time-use model). These results highlight the need to control for residential self-selection effects when estimating the effects of the activity-travel environment on activity time-use choices.