scholarly journals How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area

2019 ◽  
Vol 11 (12) ◽  
pp. 3403
Author(s):  
Arefeh Nasri ◽  
Lei Zhang
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Urban Form ◽  
Mode Choice ◽  
Discrete Choice Models ◽  
Sustainable Transportation ◽  
Lower Probability ◽  
Transit Oriented Development ◽  
Travel Mode ◽  
Travel Mode Choice

Understanding travel behavior and its relationship with built environment is crucial for sustainable transportation and land-use policy-making. This study provides additional insights into the linkage between the built environment and travel mode choice by looking at the built environment characteristics at both the trip origin and destination in the context of transit-oriented development (TOD). The objective of this research is to provide a better understanding of how travel mode choice is influenced by the built environment surrounding both trip end locations. Specifically, it investigates the effect of transit-oriented development policy and the way it affects people’s mode choice decisions. This is accomplished by developing discrete choice models and consideration of urban form characteristics at both trip ends. Our findings not only confirmed the important role the built environment plays in influencing mode choice, but also highlighted the influence of policies, such as TOD, at both trip end locations. Results suggest that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. However, the magnitude of this TOD effect is larger at trip origin compared to destination. Higher residential and employment densities at both trips ends are also associated with lower probability of auto and higher probability of transit and non-motorized mode choices.

scholarly journals Assessing Impact of Urban Form Measures on Nonwork Trip Mode Choice After Controlling for Demographic and Level-of-Service Effects

2003 ◽  
Vol 1831 (1) ◽  
pp. 158-165 ◽  
Author(s):  
Jayanthi Rajamani ◽  
Chandra R. Bhat ◽  
Susan Handy ◽  
Gerritt Knaap ◽  
Yan Song
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Urban Form ◽  
Choice Model ◽  
Mode Choice ◽  
Explanatory Power ◽  
Walking Behavior ◽  
Travel Mode Choice ◽  
Activity Survey ◽  
The Relationship

The relationship between travel behavior and the local built environment remains far from entirely resolved, despite several research efforts in the area. The significance and explanatory power of a variety of urban form measures on nonwork activity travel mode choice have been investigated. The travel data used for analysis are from the 1995 Portland Metropolitan Activity Survey conducted by Portland Metro. The database on the local built environment was developed by Song in 2002 and includes a more extensive set of variables than previous studies that have examined the relationship between travel behavior and the local built environment by using the Portland data. The results of the multinomial logit mode choice model indicate that mixed uses promote walking behavior for nonwork activities.

scholarly journals Exploring the Influence of Attitudes to Walking and Cycling on Commute Mode Choice Using a Hybrid Choice Model

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Chuan Ding ◽  
Yu Chen ◽  
Jinxiao Duan ◽  
Yingrong Lu ◽  
Jianxun Cui
Keyword(s):  
Travel Behavior ◽  
Traffic Congestion ◽  
Structural Equation ◽  
Choice Model ◽  
Mode Choice ◽  
Influential Factors ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
The Impact ◽  
Hybrid Choice Model

Transport-related problems, such as automobile dependence, traffic congestion, and greenhouse emissions, lead to a great burden on the environment. In developing countries like China, in order to improve the air quality, promoting sustainable travel modes to reduce the automobile usage is gradually recognized as an emerging national concern. Though there are many studies related to the physically active modes (e.g., walking and cycling), the research on the influence of attitudes to active modes on travel behavior is limited, especially in China. To fill up this gap, this paper focuses on examining the impact of attitudes to walking and cycling on commute mode choice. Using the survey data collected in China cities, an integrated discrete choice model and the structural equation model are proposed. By applying the hybrid choice model, not only the role of the latent attitude played in travel mode choice, but also the indirect effects of social factors on travel mode choice are obtained. The comparison indicates that the hybrid choice model outperforms the traditional model. This study is expected to provide a better understanding for urban planners on the influential factors of green travel modes.

scholarly journals Understanding Attitudes towards Proenvironmental Travel: An Empirical Study from Tangshan City in China

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Xiaoping Fang ◽  
Yajing Xu ◽  
Weiya Chen
Keyword(s):  
Travel Behavior ◽  
Mode Choice ◽  
Revealed Preference ◽  
Individual Characteristics ◽  
Survey Method ◽  
Model Parameters ◽  
Travel Mode ◽  
Preference Theory ◽  
Travel Mode Choice ◽  
Revealed Preference Theory

Understanding people’s attitudes towards proenvironmental travel will help to encourage people to adopt proenvironmental travel behavior. Revealed preference theory assumes that the consumption preference of consumers can be revealed by their consumption behavior. In order to investigate the influences on citizens’ travel decision and analyze the difficulties of promoting proenvironmental travel behavior in medium-sized cities in China, based on revealed preference theory, this paper uses the RP survey method and disaggregate model to analyze how individual characteristics, situational factors, and trip features influence the travel mode choice. The field investigation was conducted in Tangshan City to obtain the RP data. An MNL model was built to deal with the travel mode choice. SPSS software was used to calibrate the model parameters. The goodness-of-fit tests and the predicted outcome demonstrate the validation of the parameter setting. The results show that gender, occupation, trip purpose, and distance have an obvious influence on the travel mode choice. In particular, the male gender, high income, and business travel show a high correlation with carbon-intensive travel, while the female gender and a medium income scored higher in terms of proenvironmental travel modes, such as walking, cycling, and public transport.

Toward More Sustainable Behavior: A Systematic Review of the Impacts of Involuntary Workplace Relocation on Travel Mode Choice

2018 ◽  
Vol 34 (1) ◽  
pp. 38-58 ◽  
Author(s):  
Z. Zarabi ◽  
S. Lord
Keyword(s):  
Systematic Review ◽  
Travel Behavior ◽  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Home Work ◽  
Habitual Behavior ◽  
Sustainable Behavior ◽  
Transport Modes ◽  
Daily Travel

Daily home–work travel is a habitual behavior that can be disrupted when the location of work, as one of the behavioral contexts, changes. It is then likely that individuals will reconsider their travel behavior more intentionally and choose alternative transport modes. To identify motivations and barriers to incorporating the use of sustainable modes into the individual’s daily travel, this article systematically reviews the literature on the impacts of involuntary workplace relocation on commuting behavior. Effective measures that incentivize sustainable commuting behavior are also discussed. This study on involuntary workplace relocation informs considerations of changes in travel behavior related to other contextual changes.

scholarly journals Deep Neural Network Design for Modeling Individual-Level Travel Mode Choice Behavior

2020 ◽  
Vol 12 (18) ◽  
pp. 7481
Author(s):  
Daisik Nam ◽  
Jaewoo Cho
Keyword(s):  
Neural Network ◽  
Discrete Choice ◽  
Deep Neural Network ◽  
Choice Behavior ◽  
Mode Choice ◽  
Choice Models ◽  
Discrete Choice Models ◽  
Travel Mode ◽  
Individual Level ◽  
Travel Mode Choice

Individual-level modeling is an essential requirement for effective deployment of smart urban mobility applications. Mode choice behavior is also a core feature in transportation planning models, which are used for analyzing future policies and sustainable plans such as greenhouse gas emissions reduction plans. Specifically, an agent-based model requires an individual level choice behavior, mode choice being one such example. However, traditional utility-based discrete choice models, such as logit models, are limited to aggregated behavior analysis. This paper develops a model employing a deep neural network structure that is applicable to the travel mode choice problem. This paper uses deep learning algorithms to highlight an individual-level mode choice behavior model, which leads us to take into account the inherent characteristics of choice models that all individuals have different choice options, an aspect not considered in the neural network models of the past that have led to poorer performance. Comparative analysis with existing behavior models indicates that the proposed model outperforms traditional discrete choice models in terms of prediction accuracy for both individual and aggregated behavior.

scholarly journals Joint Analysis of the Commuting Departure Time and Travel Mode Choice: Role of the Built Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Xiaolei Ma ◽  
Jie Yang ◽  
Chuan Ding ◽  
Jianfeng Liu ◽  
Quan Zhu
Keyword(s):  
Built Environment ◽  
Socioeconomic Factors ◽  
Public Transit ◽  
Mode Choice ◽  
Joint Analysis ◽  
Mobile Source ◽  
Travel Mode ◽  
Study Region ◽  
Travel Mode Choice ◽  
Departure Time

This paper aims to conduct an empirical study to evaluate the influence of built environment features and socioeconomic factors on commuters’ simultaneous choice of departure time and travel mode. Using Kunming, China, as the study region, the 2015 Regional Household Travel Survey and 2016 Point of Interest data are used in the analysis. The results show that, in addition to socioeconomic factors, built environment, such as the density of residential building, employment, and service facility are correlated with joint choice behavior. Moreover, there exist differences regarding the influence of built environment and socioeconomic factors on departure time and travel mode choice. The dissimilarity parameters show that commuters prefer to shift travel mode than departure time generally when travel condition alters. In order to examine the policy measures’ potential performance, the paper conducts simulation tests based on the Monte Carlo method. The simulation results show that congestion pricing of car travel during peak hours can reduce the number of commuting trips, and reducing travel time of public transit would be a better strategy to attract more passengers during peak hours. Moreover, reasonable land use planning, such as building more bus stops around commuters’ home location, would be a long term and fundamental approach to reduce mobile-source emissions and attract more public transit passengers.

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