A Bayesian Network Modeling for Departure Time Choice: A Case Study of Beijing Subway
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.
 Jong GD, 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.
 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 KN, Day N, Miller EJ. 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 SU, Bekhor S, Kuwahara M, et al. Discrete choice modeling of combined mode and departure time. Transportmetrica; 2008;4(2): 155-177.
 Sasic A, Habib KN. 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 CG, 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.
 Zhang K, Taylor MAP. Effective arterial road incident detection: A Bayesian network based algorithm. Transportation Research Part C: Emerging Technologies. 2006;14(6): 403-417.
 Nozick LK, Xie C, Wang H. Modeling Travel Mode Choice Behavior by Bayesian Network. Transportation Research Board 85th Annual Meeting, 22-26 Jan 2006, Washington DC, USA; 2006.
 Gao JX, Zhi-Cai J, An-Ning NI. 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 GF, Herskovits EH. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning. 1992;9(4): 309-347.
 Murphy KP. The Bayes Net Toolbox for Matlab. Computing Science & Statistics; 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.
 Tirachini A, Hensher DA, Rose JM, et al. Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transportation Research Part A: Policy and Practice. 2013;53: 36-52.
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