A Bayesian Network Modeling for Departure Time Choice: A Case Study of Beijing Subway

Keywords: departure time choice, Bayesian network, congestion, subway passengers

Abstract

Departure time choice is critical for subway passengers to avoid congestion during morning peak hours. In this study, we propose a Bayesian network (BN) model to capture departure time choice based on data learning. Factors such as travel time saving, crowding, subway fare, and departure time change are considered in this model. K2 algorithm is then employed to learn the BN structure, and maximum likelihood estimation (MLE) is adopted to estimate model parameters, according to the data obtained by a stated preference (SP) survey. A real-world case study of Beijing subway is illustrated, which proves that the proposed model has higher prediction accuracy than typical discrete choice models. Another key finding indicates that subway fare discount higher than 20% will motivate some passengers to depart 15 to 20 minutes earlier and release the pressure of crowding during morning peak hours.

Author Biographiesaaa replica rolex repwatches replica rolex watches for men replica iwc watch

Xian Li, Beijing Jiaotong University

Xian Li is a Master candidate in Transportation Planning and Management at School of Traffic and Transportation in Beijing Jiaotong University, China. His major research interests are travel choice behavior modeling with an expertise in crowding for subways.

Haiying Li, Beijing Jiaotong University

Haiying Li received her PhD degree in Transportation Planning and Management from Beijing Jiaotong University in 2009. She is a professor at State Key Lab of Rail Traffic Control and Safety in Beijing Jiaotong University, China. Her major research interests focus on the simulation of the railway system, modeling travel behavior for railway passengers, and capacity analysis.

Xinyue Xu, Beijing Jiaotong University
Xinyue Xu received his PhD degree of Transportation Planning and Management from Beijing Jiaotong University in 2015. He is a Lecturer at State Key Lab of Rail Traffic Control and Safety in Beijing Jiaotong University, and serves as reviewer of Transportation Research Part C and Transportation Research Part E. His research focus is behavioral modeling and capacity analysis of subway stations with an expertise in passenger flow control at crowding stations. He works to improve the models that are used for transportation policy and operations

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Published
2018-11-08
How to Cite
1.
Li X, Li H, Xu X. A Bayesian Network Modeling for Departure Time Choice: A Case Study of Beijing Subway. Promet [Internet]. 2018Nov.8 [cited 2024Mar.19];30(5):579-87. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/2644
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Articles