How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities

Abstract

Mixed findings have been reported in previous research regarding the impact of built environment on travel behavior-i.e., statistically and practically significant effects found in a number of empirical studies and insignificant correlations shown in many other studies. It is not clear why the estimated impact is stronger or weaker in certain urban areas and how effective a proposed land use change/policy will be in changing certain travel behavior. This knowledge gap has made it difficult for decision makers to evaluate land use plans and policies according to their impact on vehicle miles traveled (VMT), and consequently, their impact on congestion mitigation, energy conservation, and pollution and greenhouse gas emission reduction.This research has several objectives: (1) re-examine the effects of built-environment factors on travel behavior, in particular, VMT in five US metropolitan areas grouped into four case study areas; (2) develop consistent models in all case study areas with the same model specification and datasets to enable direct comparisons; (3) identify factors such as existing land use characteristics and land use policy decision-making processes that may explain the different impacts of built environment on VMT in different urban areas; and (4) provide a prototype tool for government agencies and decision makers to estimate the impact of proposed land use changes on VMT.The four case study areas include Seattle, WA; Richmond-Petersburg and Norfolk-Virginia Beach, VA; Baltimore, MD; and Washington, DC. Our empirical analysis employs Bayesian multilevel modeling method with various person-level socioeconomic and demographic variables, and five built-environment factors including residential density, employment density, entropy (measuring level of mixed-use development), average block size (measuring transit/walking friendliness), and distance to city center (measuring decentralization and level of infill development).Our findings show that promoting compact, mixed-use, small-block, and infill developments can be effective in reducing VMT per person in all four case study areas. However, the effectiveness of land use plans and policies encouraging these types of land development is different both across case study areas and within the same case study area. We have identified several factors that potentially influence the connection between built environment shifts and VMT changes including urban area size, existing built environment characteristics, transit service coverage and quality, and land use decision-making processes.

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

“…And it is usually quantified by distance to the central business district (CBD). Many researchers confirm that the location of residence has a significant influence on VMT and the influence is always negative [40][41][42].…”

A joint count-continuous model of travel behavior with selection based on a multinomial probit residential density choice model

“…The net impact on overall VMT patterns will depend on the aggregation across the effects on individual travel dimensions. However, most earlier studies on the effect of BE measures on travel, while considering residential self-selection, focus directly (and solely) on the effect on vehicle miles of travel (see Zhang et al, 2012, Salon et al, 2012, which are but a few recent examples). There have also been studies that consider residential self-selection and focus on BE effects on specific travel dimensions, such as auto ownership, vehicle type, trip frequencies, bicycle ownership, activity durations, and mode choice, though these have been relatively few and have focused on each dimension individually (see Bhat andKrizek, 2012 for detailed reviews).…”

“…Indeed, the literature on the subject is now vast and growing rapidly, with a substantial amount of quantitative and qualitative research having been undertaken in the past decade (see, for example, Bhat and Guo, 2007, Transportation Research Board, 2009, Ewing and Cervero, 2012, Pinjari et al, 2011, Zhang et al, 2012, Handy and Krizek, 2012). …”

The Effects of Access and Accessibility on Public Transport Users’ Attitudes

“…Residents of mixed-use urban areas were seen to make shorter journeys. Zhang et al (2012) also suggested that compact, mixed-use developments are effective in reducing vehicles miles traveled (VMT) per person through shorter journeys.…”