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Journal ArticleDOI

Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance

TLDR
In this paper, the authors investigated the relationship between the built environment and travel mode choice behavior by using integrated structural equation model (SEM) and discrete choice model (DCM).
Abstract
Though there is a growing literature on the connection between the built environment and travel behavior, limited efforts have been made to consider the intermediary nature of car ownership and travel distance simultaneously while modeling the relationship between the built environment and travel mode choice behavior. The mediating effects from car ownership and travel distance, as an important piece, are not sufficiently investigated. To fill this gap, in this study the relationships among travel mode choice, car ownership and travel distance were described using a framework of integrated structural equation model (SEM) and discrete choice model (DCM). Drawing on a rich dataset of National Household Travel Survey (NHTS) and numerous built environment measurements in Baltimore metropolitan area, this research applied the integrated SEM and DCM approach to investigate how the built environment affects travel mode choice through influencing car ownership and travel distance. Therefore, the direct and indirect effects of built environment on travel mode choice were revealed. This study hopes to give transportation planners a better understanding on how the built environment influences travel mode choice, and consequently develop effective and targeted countermeasures to reduce car use.

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Citations
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Journal ArticleDOI

Applying a random forest method approach to model travel mode choice behavior

TL;DR: A robust random forest method is proposed to analyze travel mode choices for examining the prediction capability and model interpretability of people’s travel behavior and results show that the random Forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost.
Journal ArticleDOI

A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership

TL;DR: Transit authorities can develop transit planning and traffic demand management policies with improved accuracy by utilizing the enhanced precision and spatiotemporal modeling of GTWR to alleviate urban traffic problems.
Journal ArticleDOI

Synergistic effects of the built environment and commuting programs on commute mode choice

TL;DR: In this paper, the authors apply a gradient boosting logit model to examine the influences of BE characteristics at both residential and workplace locations and commuting programs (transit/vanpooling subsidies and parking provision) on commute mode choice.
Journal ArticleDOI

Trends in onroad transportation energy and emissions.

TL;DR: Fossil fuels are likely to continue to be the major source of on-road vehicle energy consumption and fuel economy and emission standards are generally effective in achieving reductions per unit of vehicle activity, however, the number of vehicles and miles traveled will increase.
Journal ArticleDOI

Sustainable station-level planning: An integrated transport and land use design model for transit-oriented development

TL;DR: A multi-objective programming model that integrates transport and land use design for station-level TOD planning and an improved immune-genetic based algorithm is designed to obtain the optimal solutions under alternative land use schemes are proposed.
References
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Book

Principles and Practice of Structural Equation Modeling

TL;DR: The book aims to provide the skills necessary to begin to use SEM in research and to interpret and critique the use of method by others.
Journal ArticleDOI

Travel and the Built Environment

TL;DR: A meta-analysis of the built environment-travel literature existing at the end of 2009 is conducted in order to draw generalizable conclusions for practice, and finds that vehicle miles traveled is most strongly related to measures of accessibility to destinations and secondarily to street network design variables.
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Structural equation modeling for travel behavior research

TL;DR: This review of SEM is intended to provide an introduction to the field for those who have not used the method, and a compendium of applications for Those who wish to compare experiences and avoid the pitfall of reinventing previous research.
Journal ArticleDOI

Correlation or causality between the built environment and travel behavior? Evidence from Northern California

TL;DR: The authors investigated the relationship between neighborhood characteristics and travel behavior while taking into account the role of travel preferences and neighborhood preferences in explaining this relationship, and found that differences in travel behavior between suburban and traditional neighborhoods are largely explained by attitudes.
Journal ArticleDOI

Built environments and mode choice: toward a normative framework

TL;DR: This paper frames the study of mode choice in Montgomery County, Maryland around a normative model that weighs the influences of not only three core dimensions of built environments – density, diversity, and design – but factors related to generalized cost and socio-economic attributes of travelers as well.
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