scholarly journals Simulation Modeling and Application of Travel Mode Choice Based on Bayesian Network

2014 ◽  
Vol 8 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Ruijing Chen
Keyword(s):  
Bayesian Network ◽  
Simulation Model ◽  
Travel Behavior ◽  
Structure Learning ◽  
Influence Factor ◽  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Travel Decision ◽  
Network Simulation Model

In this paper, we study the travel mode choice of residents to determine the set of factors which can influence travel mode choice of residents and analyze the influence factor characteristics. Using Bayesian theory, we analyze the travel decision-making data of the residents, discrete them, and use them in Bayesian network structure learning and parameter estimation by K2 algorithm. We establish a Bayesian network simulation model to analyze the dependence probability relationship between the parent nodes and child nodes. Validation test was carried out for the building simulation model of Bayesian network. Data analysis results showed that the Bayesian network has a high accuracy prediction for actual travel mode choice of residents. This paper studies the Bayesian structure and parameters learning method for the actual travel behavior, and this method which provides a new method for studying the travel mode choice of residents can reveal the relationship between the various attributes associated with travel mode choice through a new perspective.

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 A Bayesian Network Model on the Public Bicycle Choice Behavior of Residents: A Case Study of Xi’an

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Qiuping Wang ◽  
Hao Sun ◽  
Qi Zhang
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Expert Knowledge ◽  
Choice Model ◽  
Mode Choice ◽  
Bayesian Network Model ◽  
Travel Mode ◽  
The Public ◽  
Proposed Model ◽  
Travel Mode Choice

In order to study the main factors affecting the behaviors that city residents make regarding public bicycle choice and to further study the public bicycle user’s personal characteristics and travel characteristics, a travel mode choice model based on a Bayesian network was established. Taking residents of Xi’an as the research object, a K2 algorithm combined with mutual information and expert knowledge was proposed for Bayesian network structure learning. The Bayesian estimation method was used to estimate the parameters of the network, and a Bayesian network model was established to reflect the interactions among the public bicycle choice behaviors along with other major factors. The K-fold cross-validation method was used to validate the model performance, and the hit rate of each travel mode was more than 80%, indicating the precision of the proposed model. Experimental results also present the higher classification accuracy of the proposed model. Therefore, it may be concluded that the resident travel mode choice may be accurately predicted according to the Bayesian network model proposed in our study. Additionally, this model may be employed to analyze and discuss changes in the resident public bicycle choice and to note that they may possibly be influenced by different travelers’ characteristics and trip characteristics.

scholarly journals Predicting Travel Behavior by Analyzing Mobility Transactions

2020 ◽  
Vol 02 (02) ◽  
pp. 25-33
Author(s):  
J. Slik ◽  
S. Bhulai
Keyword(s):  
Urban Planning ◽  
Travel Behavior ◽  
Mode Choice ◽  
Considerable Influence ◽  
Detailed Data ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Mode Of Transportation ◽  
Travel Type ◽  
Unique Dataset

Urban planning can benefit tremendously from a better understanding of where, when, why, and how people travel. Through advances in technology, detailed data on the travel behavior of individuals has become available. This data can be leveraged to understand why one prefers one mode of transportation over another one. In this paper, we analyze a unique dataset through which we can address this question. We show that the travel behavior in our dataset is highly predictable, with an accuracy of 97%. The main predictors are reachability features, more so than specific travel times. Moreover, the travel type (commute or personal) has a considerable influence on travel mode choice.

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.

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