Disentangling the influence of neighborhood type and self-selection on driving behavior: an application of sample selection model

The causality issue has become one of the key questions in the debate over the relationships between the built environment and travel behavior. Although previous studies have tested statistical and/or practical significance of the built environment on travel behavior, few have quantified the relative roles of the built environment and residential self-selection in influencing travel behavior. Using 1,479 residents living in four traditional and four suburban neighborhoods in Northern California, this study explores the causal effect of neighborhood type on driving behavior and its relative contribution to the total influence of neighborhood type. Specifically, this study applied Heckman’s sample selection model to separate the effect of the built environment itself and the effect of self-selection. The results showed that, on average, the effect of neighborhood type itself on driving distance was 25.8 miles per week, which accounted for more than three quarters of the total influence of neighborhood type and 16% of individuals’ overall vehicle miles driven. These results suggest that the effect of the built environment on driving behavior outweighs that of self-selection. This paper also discussed the advantages and weaknesses of applying the Heckman’s model to address the self-selection issue.

Scrutinizing individuals’ leisure-shopping travel decisions to appraise activity-based models of travel demand

Activity-based models for modeling individuals’ travel demand have come to a new era in addressing individuals’ and households’ travel behavior on a disaggregate level. Quantitative data are mainly used in this domain to enable a realistic representation of individual choices and a true assessment of the impact of different Travel Demand Management measures. However, qualitative approaches in data collection are believed to be able to capture aspects of individuals’ travel behavior that cannot be obtained using quantitative studies, such as detailed decision making process information. Therefore, qualitative methods may deepen the insight into human’s travel behavior from an agent-based perspective. This paper reports on the application of a qualitative semi-structured interview method, namely the Causal Network Elicitation Technique (CNET), for eliciting individuals’ thoughts regarding fun-shopping related travel decisions, i.e. timing, shopping location and transport mode choices. The CNET protocol encourages participants to think aloud about their considerations when making decisions. These different elicited aspects are linked with causal relationships and thus, individuals’ mental representations of the task at hand are recorded. This protocol is tested in the city centre of Hasselt in Belgium, using 26 young adults as respondents. Response data are used to apply the Association Rules, a fairly common technique in machine learning. Results highlight different interrelated contexts, instruments and values considered when planning a trip. These findings can give feedback to current AB models to raise their behavioral realism and to improve modeling accuracy.     

The relationship between the built environment and nonwork travel: A case study of Northern California

Many studies have found that residents living in suburban neighborhoods drive more and walk less than their counterparts in traditional neighborhoods. This evidence supports the advocacy of smart growth strategies to alter individuals’ travel behavior. However, the observed differences in travel behavior may be more of a residential choice than a travel choice. Applying the seemingly unrelated regression approach to a sample from Northern California, we explored the relationship between the residential environment and nonwork travel frequencies by auto, transit, and walk/bicycle modes, controlling for residential self-selection. We found that residential preferences and travel attitudes (self-selection) significantly influenced tripmaking by all three modes, and also that neighborhood characteristics (the built environment and its perception) retained a separate influence on behavior after controlling for self-selection. Both preferences/attitudes and the built environment itself played a more prominent role in explaining the variation in non-motorized travel than for auto and transit travel. Taken together, our results suggest that if cities use land use policies to offer options to drive less and use transit and non-motorized modes more, many residents will tend to do so.     

How perceptions mediate the effects of the built environment on travel behavior?

This study provides a better understand the mechanism underlying the built environment-behavior connection by systematically exploring the relationships between the objective (actual) environment and people’s perceptions of the environment, and their relative effects on travel behavior using the Stimuli-Organism-Response framework. Based on data for the Twin Cities, this study explores (1) How do perceptions mediate the effects of the objective environment on travel behavior? (2) How do travel attitudes influence the effects of perceptions on travel behavior? Among the eight empirical models tested here, six are consistent with the framework: objective built environment affects travel behavior through its influence on perceptions. Moreover, the framework fits walking and bicycling behavior better than transit and driving behavior. Furthermore, travel attitudes greatly moderate the influences of perceptions on travel behavior.

     

A joint flexible econometric model system of household residential location and vehicle fleet composition/usage choices

Modeling the interaction between the built environment and travel behavior is of much interest to transportation planning professionals due to the desire to curb vehicular travel demand through modifications to built environment attributes. However, such models need to take into account self-selection effects in residential location choice, wherein households choose to reside in neighborhoods and built environments that are conducive to their lifestyle preferences and attitudes. This phenomenon, well-recognized in the literature, calls for the specification and estimation of joint models of multi-dimensional land use and travel choice processes. However, the estimation of such model systems that explicitly account for the presence of unobserved factors that jointly impact multiple choice dimensions is extremely complex and computationally intensive. This paper presents a joint GEV-based logit regression model of residential location choice, vehicle count by type choice, and vehicle usage (vehicle miles of travel) using a copula-based framework that facilitates the estimation of joint equations systems with error dependence structures within a simple and flexible closed-form analytic framework. The model system is estimated on a sample derived from the 2000 San Francisco Bay Area Household Travel Survey. Estimation results show that there is significant dependency among the choice dimensions and that self-selection effects cannot be ignored when modeling land use-travel behavior interactions.     

Flexible spatial dependence structures for unordered multinomial choice models: formulation and application to teenagers’ activity participation

The current article proposes an approach to accommodate flexible spatial dependency structures in discrete choice models in general, and in unordered multinomial choice models in particular. The approach is applied to examine teenagers’ participation in social and recreational activity episodes, a subject of considerable interest in the transportation, sociology, psychology, and adolescence development fields. The sample for the analysis is drawn from the 2000 San Francisco Bay Area Travel Survey (BATS) as well as other supplementary data sources. The analysis considers the effects of a variety of built environment and demographic variables on teenagers’ activity behavior. In addition, spatial dependence effects (due to common unobserved residential neighborhood characteristics as well as diffusion/interaction effects) are accommodated. The variable effects indicate that parents’ physical activity participation constitutes the most important factor influencing teenagers’ physical activity participation levels, In addition, part-time student status, gender, and seasonal effects are also important determinants of teenagers’ social-recreational activity participation. The analysis also finds strong spatial correlation effects in teenagers’ activity participation behaviors.     

Modeling residential sorting effects to understand the impact of the built environment on commute mode choice

This paper presents an examination of the significance of residential sorting or self selection effects in understanding the impacts of the built environment on travel choices. Land use and transportation system attributes are often treated as exogenous variables in models of travel behavior. Such models ignore the potential self selection processes that may be at play wherein households and individuals choose to locate in areas or built environments that are consistent with their lifestyle and transportation preferences, attitudes, and values. In this paper, a simultaneous model of residential location choice and commute mode choice that accounts for both observed and unobserved taste variations that may contribute to residential self selection is estimated on a survey sample extracted from the 2000 San Francisco Bay Area household travel survey. Model results show that both observed and unobserved residential self selection effects do exist; however, even after accounting for these effects, it is found that built environment attributes can indeed significantly impact commute mode choice behavior. The paper concludes with a discussion of the implications of the model findings for policy planning.

Exploring the propensity to perform social activities: a social network approach

Conceptual and empirical models of the propensity to perform social activity–travel behavior are described, which incorporate the influence of individuals’ social context, namely their social networks. More explicitly, the conceptual model develops the concepts of egocentric social networks, social activities, and social episodes, and defines the three sets of aspects that influence the propensity to perform social activities: individuals’ personal attributes, social network composition, and information and communication technology interaction with social network members. Using the structural equation modeling (SEM) technique and data recently collected in Toronto, the empirical model tests the effect of these three aspects on the propensity to perform social activities. Results suggest that the social networks framework provides useful insights into the role of physical space, social activity types, communication and information technology use, and the importance of “with whom” the activity was performed with. Overall, explicitly incorporating social networks into the activity–travel behavior modeling framework provides a promising framework to understand social activities and key aspects of the underlying behavioral process. Juan Antonio Carrasco a PhD candidate in Civil Engineering at the University of Toronto, holds a MSc degree in Transportation Engineering from the Pontificia Universidad Católica de Chile. His doctoral research explores the relationships between social networks, activity–travel behavior, and ICTs. His research interests also include microsimulation, land use-transportation, and econometric modeling. Eric J. Miller is Bahen-Tanenbaum Professor of Civil Engineering at the University of Toronto where he is also Director of the Joint Program in Transportation. His research interests include integrated land-use/transportation modeling, activity-based travel modeling, microsimulation and sustainable transportation planning.     

Built environment,travel behavior,and residential self-selection: a study based on panel data from Beijing,China

The influence of the built environment on travel behavior has been the subject of considerable research attention in recent decades. Scholars have debated the role of residential self-selection in explaining the associations between the built environment and travel behavior. The purpose of this study is to make a contribution to the literature by adopting the cross-lagged panel modeling approach to analyze a panel data, which scholars have recommended as the ideal design for studying the influence of the built environment on travel behavior accounting for the residential self-selection. To that objective, we collected activity-travel diary data from a sample of 229 households in Beijing before and after they moved from one residential location to another. We developed a two-wave structural equation model linking the residential built environment to travel behavior and taking into consideration travel-related attitudes before and after residential change. The modeling results show that individuals’ travel attitudes may change after a home relocation. We found no evidence of residential self-selection, but significant influence of the built environment on travel preference. Nevertheless, the direct influence of travel preference on travel behavior seems to be stronger than that of the built environment. As one of the very few studies to use panel data, this research presents new insights into the relationship between the built environment and travel behavior and the role of residential self-selection.

     

The built environment and travel behavior in urban China: A literature review

Interests in studying of the built environment impacts on travel behavior have proliferated from North America to other parts of the world including China. Until very recently, there has been very little research into travel behavior in China. However, during the last decade, there has been a fast growing interest in studying the built environment and travel behavior in Chinese cities, perhaps motivated by China’s unprecedented urbanization and rapid urban transport development. Case studies from China provide new insights into the impacts of built environment on travel behavior that can help to enrich existing scholarship. However, currently there is a generally poor understanding of the role played by Chinese research and how it has enriched the international literature. This paper aims to fill this gap by reviewing studies in and outside China by both Chinese and non-Chinese scholars. The focus is on the contribution of these studies to the international literature. We identify four areas of contribution: how the built environment has been developed and its implications for travel behavior; the importance of housing sources in defining residential built environment and explaining travel behavior; the unique Danwei (or work unit) perspective on jobs-housing relationships and commuting behavior; and the importance of neighborhood types in explaining travel behavior in Chinese cities. The findings from this review should be relevant for researchers interested in developing future studies that will further advance geographic knowledge of the built environment and travel behavior, specifically in China and with broader global contexts.     

The relationship between non-motorized mode choice and the local physical environment

By estimating multinomial choice models, this paper examines the relationship between travel mode choice and attributes of the local physical environment such as topography, sidewalk availability, residential density, and the presence of walking and cycling paths. Data for student and staff commuters to the University of North Carolina in Chapel Hill are used to illustrate the relationship between mode choice and the objectively measured environmental attributes, while accounting for typical modal characteristics such as travel time, access time, and out-of-pocket cost. Results suggest that jointly the four attributes of the local physical environment make significant marginal contributions to explaining travel mode choice. In particular, the estimates reveal that local topography and sidewalk availability are significantly associated with the attractiveness of non-motorized modes. Point elasticities are provided and recommendations given regarding the importance of incorporating non-motorized modes into local transportation planning and in the study of how the built environment influences travel behavior.     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice

Concerns over transportation energy consumption and emissions have prompted more studies into the impacts of built environment on driving-related behavior, especially on car ownership and travel mode choice. This study contributes to examine the impacts of the built environment on commuter’s driving behavior at both spatial zone and individual levels. The aim of this study is threefold. First, a multilevel integrated multinomial logit (MNL) and structural equation model (SEM) approach was employed to jointly explore the impacts of the built environment on car ownership and travel mode choice. Second, the spatial context in which individuals make the travel decisions was accommodated, and spatial heterogeneities of car ownership and travel mode choice across traffic analysis zones (TAZs) were recognized. Third, the indirect effects of the built environment on travel mode choice through the mediating variable car ownership were calculated, in other words, the intermediary nature of car ownership was considered. Using the Washington metropolitan area as the study case, the built environment measures were calculated for each TAZ, and the commuting trips were drawn from the household travel survey in this area. To estimate the model parameters, the robust maximum likelihood (MLR) method was used. Meanwhile, a comparison among different model structures was conducted. The model results suggest that application of the multilevel integrated MNL and SEM approach obtains significant improvements over other models. This study give transportation planners a better understanding on how the built environment influences car ownership and commuting mode choice, and consequently develop effective and targeted countermeasures.     

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 relationship between built environment and metro ridership at station-to-station level

Very few studies have examined the impact of built environment on urban rail transit ridership at the station-to-station (origin-destination) level. Moreover, most direct ridership models (DRMs) tend to involve simple a prior assumed linear or log-linear relationship in which the estimated parameters are assumed to hold across the entire data space of the explanatory variables. These models cannot detect any changes in the linear (or non-linear) effects across different values of the features of built environment on urban rail transit ridership, which possibly induces biased results and hides some non-negligible and detailed information. Based on these research gaps, this study develops a time-of-day origin-destination DRM that uses smart card data pertaining to the Nanjing metro system, China. It applies a gradient boosting regression trees model to provide a more refined data mining approach to investigate the non-linear associations between features of the built environment and station-to-station ridership. Data related to the built environment, station type, demographics, and travel impedance including a less used variable – detour, were collected and used in the analysis. The empirical results show that most independent variables are associated with station-to-station ridership in a discontinuous non-linear way, regardless of the time period. The built environment on the origin side has a larger effect on station-to-station ridership than the built environment on the destination side for the morning peak hours, while the opposite holds for the afternoon peak hours and night. The results also indicate that transfer times is more important variables than detour and route distance.     

Modeling interdependencies between vehicle transaction,residential relocation and job change

This paper introduces a vehicle transaction timing model which is conditional on household residential and job relocation timings. Further, the household residential location and members’ job relocation timing decisions are jointly estimated. Some researchers have modeled the household vehicle ownership decision jointly with other household decisions like vehicle type choice or VMT; however, these models were basically static and changes in household taste over time has been ignored in nearly all of these models. The proposed model is a dynamic joint model in which the effects of land-use, economy and disaggregate travel activity attributes on the major household decisions; residential location and members’ job relocation timing decisions for wife and husband of the household, are estimated. Each of these models is estimated using both the Weibull and log-logistic baseline hazard functions to assess the usefulness of a non-monotonic rather than monotonic baseline hazard function. The last three waves of the Puget Sound Panel Survey data and land-use, transportation, and built environment variables from the Seattle Metropolitan Area are used in this study as these waves include useful explanatory variables like household tenure that were not included in the previous waves.     

Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach

Understanding travel behavior and its relationship to urban form is vital for the sustainable planning strategies aimed at automobile dependency reduction. The objective of this study is twofold. First, this research provides additional insights to examine the effects of built environment factors measured at both home location and workplace on tour-based mode choice behavior. Second, a cross-classified multilevel probit model using Bayesian approach is employed to accommodate the spatial context in which individuals make travel decisions. Using Washington, D.C. as our study area, the home-based work (Home-work) tour in the AM peak hours is used as the analysis unit. The empirical data was gathered from the Washington-Baltimore Regional Household Travel Survey 2007–2008. For parameter estimation, Bayesian estimation method integrating Markov Chain Monte Carlo (MCMC) sampling is adopted. Our findings confirmed the important role that the built environment at both home location and work ends plays in affecting commuter mode choice behavior. Meanwhile, a comparison of different model results shows that the cross-classified multilevel probit model offers significant improvements over the traditional probit model. The results are expected to give a better understanding on the relationship between the built environment and commuter mode choice behavior.     

The Impact of Travel Time Information on Travelers’ Learning under Uncertainty

In this work, laboratory experiment was conducted in order to evaluate the effect of feedback on decision-making under uncertainty, with and without provided information about travel times. We discuss the prediction of travelers’ response to uncertainty in two route–choice situations. In the first situation travelers are faced with a route–choice problem in which travel times are uncertain but some external information about routes’ travel times is provided. The second situation takes place in a more uncertain environment in which external information about travel times is not provided, and the travelers’ only source of information is their own experience. Experimental results are in conflict with the paradigm about traveler information systems: As a consequence of information, the propensity of travelers to minimize expected travel time is not necessarily increased. Providing travelers with static information about expected travel times reveals an increase in the heterogeneity of travelers’ choices and reduces the maximization rate.     

Children’s mode choice for the school trip: the role of distance and school location in walking to school

Rising levels of childhood obesity in the United States and a 75% decline in the proportion of children walking to school in the past 30 years have focused attention on school travel. This paper uses data from the US Department of Transportation’s 2001 National Household Travel Survey to analyze the factors affecting mode choice for elementary and middle school children. The analysis shows that walk travel time is the most policy-relevant factor affecting the decision to walk to school with an estimated direct elasticity of −0.75. If policymakers want to increase walking rates, these findings suggest that current policies, such as Safe Routes to School, which do not affect the spatial distribution of schools and residences will not be enough to change travel behavior. The final part of the paper uses the mode choice model to test how a land use strategy—community schools—might affect walking to school. The results show that community schools have the potential to increase walking rates but would require large changes from current land use, school, and transportation planning practices.