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.
Xu X, Liu J, Li H, et al. Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study. Transportation Research Part E: logistics and Transportation Review, 2016, 87:130-148.
Thorhauge M, Haustein S, Cherchi E. Accounting for the Theory of Planned Behaviour in departure time choice. Transportation Research Part F: Traffic Psychology and Behaviour, 2016, 38:94-105.
Habib, K. M. N., Day, N., & Miller, E. J. An investigation of commuting trip timing and mode choice in the greater Toronto area: application of a joint discrete-continuous model. Transportation Research Part A: Policy and Practice, 2009, 43(7), 639-653.
Hess S, Daly A, Rohr C, et al. On the development of time period and mode choice models for use in large scale modelling forecasting systems. Transportation Research Part A: Policy and Practice, 2007, 41(9): 802-826.
Bajwa S U, Bekhor S, Kuwahara M, et al. DISCRETE CHOICE MODELING OF COMBINED MODE AND DEPARTURE TIME. Transportmetrica, 2008, 4(2): 155-177.
Jong G D, Daly A, Pieters M, et al. A model for time of day and mode choice using error components logit. Transportation Research Part E: Logistics and Transportation Review, 2003, 39(3):245-268.
Sasic A, Habib K N. Modelling departure time choices by a Heteroskedastic Generalized Logit (Het-GenL) model: An investigation on home-based commuting trips in the Greater Toronto and Hamilton Area (GTHA). Transportation Research Part A: Policy and Practice, 2013, 50(2):15-32.
Jou R. MODELING THE IMPACT OF PRE-TRIP INFORMATION ON COMMUTER DEPARTURE TIME AND ROUTE CHOICE. Transportation Research Part B: Methodological, 2001, 35(10): 887-902.
Schwanen T, Ettema D. Coping with unreliable transportation when collecting children: Examining parents' behavior with cumulative prospect theory. Transportation Research Part A: policy and Practice, 2009, 43(5): 511-525.
Chorus C G, Arentze T, Timmermans H, et al. A random regret minimization model of travel choice. Transportation Research Part B: Methodological, 2008, 42(1): 1-18.
Zhu Z, Chen X, Xiong C, et al. A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice. Transportation, 2017:1-24.
Tao, C. C., & Fan, C. C. A Modified Decomposed Theory of Planned Behaviour Model to Analyze User Intention towards Distance-Based Electronic Toll Collection Services. PROMET-Traffic&Transportation, 2017: 29(1), 85-97.
Garcia T R, Cancelas N G, Solerflores F, et al. Setting the Port Planning Parameters In Container Terminals through Bayesian Networks. Promet-traffic & Transportation, 2015, 27(5): 395-403.
Zhang K, Taylor M A P. Effective arterial road incident detection: A Bayesian network based algorithm. Transportation Research Part C: Emerging Technologies, 2006, 14(6):403-417.
Zhu Z, Chen X, Xiong C, et al. A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice. Transportation, 2017:1-24.
Nozick L K, Xie C, Wang H. Modeling Travel Mode Choice Behavior by Bayesian Network. Transportation Research Board 85th Annual Meeting. 2006.
Gao J X, Zhi-Cai J, An-Ning N I. Modeling and Applications of Traveler Destination Choice Behavior based on Bayesian Network. Journal of Systems & Management, 2015, 108(2):289-295.
Huber J, Zwerina K. The Importance of Utility Balance in Efficient Choice Designs. Journal of Marketing Research, 1996, 33(3):307-317.
Cooper G F, Herskovits E H. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 1992, 9(4): 309-347.
Murphy K P. The Bayes Net Toolbox for Matlab. Computing Science & Statistics, 2001, 33:2001.
Saleh W, Farrell S. Implications of congestion charging for departure time choice: Work and non-work schedule flexibility. Transportation Research Part A: Policy and Practice, 2005, 39(7–9):773-791.
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
The mission of our journal is to provide high quality communication of research results, which is crucial for the effectiveness of scientific research, the promotion of scientific information, new ideas, and original research in the field of traffic and transportation engineering.
1848-4069
0353-5320
Proud publisher
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal
