Passenger Travel Behavior Analysis under Unplanned Metro Service Disruption: Using Stated Preference Data in Guangzhou, China
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 2
Abstract
Passengers may change their original travel plans when unplanned metro service disruption occurs, which causes excessive crowding and endangers the safety of passengers. However, it’s not clear about the mechanism of passengers’ behaviors under unplanned service disruption and what factors affect mode shift and travel plan choice behavior. Based on the stated preference data in Guangzhou, China, this paper analyzes the travel plan choice behavior under unplanned service disruption by using a nested logit model. The nested structure consists of two levels: the upper level represents the mode shift choice, and the lower level shows travel plan choice corresponding to the mode shift or not. The results indicate that service disruption attributes and individual attributes are significant predictors to the mode shift. Furthermore, passengers are more sensitive to intermode transfers compared with interline transfers when disruption occurs. The proposed model is also applied to forecast passenger flow volume of metro stations under disruption, and the prediction result indicates that the proposed model well captures passenger mode shift and travel plan choice behavior under unplanned service disruption.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.
Since the questionnaire survey was conducted in cooperation with Guangzhou Metro Group Co., Ltd., the original questionnaire data is not completely owned by the authors, and we can only provide with the sample data.
The historical data of passenger flow volume used for the case study is confidential data, so the data can only be provided after anonymization.
Acknowledgments
The authors would like to thank Guangzhou Metro Group Co., Ltd. for their assistance in the survey. This study is supported by Beijing Natural Science Foundation (8171003). The work presented in this study remains the sole responsibility of the authors.
References
Currie, G., and C. Muir. 2017. “Understanding passenger perceptions and behaviors during unplanned rail disruptions.” Transp. Res. Procedia 25 (Jan): 4392–4402. https://doi.org/10.1016/j.trpro.2017.05.322.
Dai, X. 2015. “Choice behavior of passengers in metro emergency evacuation: Using stated preference data in Shanghai, China.” In Proc., Transport Research Board, 94th Annual Meeting. Madison, WI: Omnipress.
Dai, X., H. Tu, and L. Sun. 2016. “A multi-modal evacuation model for metro disruptions: Based on automatic fare collection data in Shanghai, China.” In Proc., Transport Research Board, 94th Annual Meeting. Madison, WI: Omnipress.
dell’Olio, L., A. Ibeas, R. Barreda, and R. Sañudo. 2013. “Passenger behavior in trains during emergency situations.” J. Saf. Res. 46 (Sep): 157–166. https://doi.org/10.1016/j.jsr.2013.05.005.
De-Los-Santos, A., G. Laporte, J. A. Mesa, and F. Perea. 2012. “Evaluating passenger robustness in a rail transit network.” Transp. Res. Part C: Emerg. Technol. 20 (1): 34–46. https://doi.org/10.1016/j.trc.2010.09.002.
Gao, Y., L. Kroon, M. Schmidt, and L. Yang. 2016. “Rescheduling a metro line in an over-crowded situation after disruptions.” Transp. Res. Part B: Methodol. 93 (Nov): 425–449. https://doi.org/10.1016/j.trb.2016.08.011.
Giorgetta, C. et al. 2012. “Reduced risk-taking behavior as a trait feature of anxiety.” Emotion 12 (6): 1373. https://doi.org/10.1037/a0029119.
Gu, W., J. Yu, Y. Ji, Y. Zheng, and H. M. Zhang. 2018. “Plan-based flexible bus bridging operation strategy.” Transp. Res. Part C: Emerg. Technol. 91 (Jun): 209–229. https://doi.org/10.1016/j.trc.2018.03.015.
Jin, J. G., L. Lu, L. Sun, and J. Yin. 2015a. “Optimal allocation of protective resources in urban rail transit networks against intentional attacks.” Transp. Res. Part E: Logist. Transp. Rev. 84 (Dec): 73–87. https://doi.org/10.1016/j.tre.2015.10.008.
Jin, J. G., L. C. Tang, L. Sun, and D. Lee. 2014. “Enhancing metro network resilience via localized integration with bus services.” Transp. Res. Part E: Logist. Transp. Rev. 63 (Mar): 17–30. https://doi.org/10.1016/j.tre.2014.01.002.
Jin, J. G., K. M. Teo, and A. R. Odoni. 2015b. “Optimizing bus bridging services in response to disruptions of urban transit rail networks.” Transp. Sci. 50 (3): 790–804. https://doi.org/10.1287/trsc.2014.0577.
Koppelman, F. S., and C. H. Wen. 1998. “Alternative nested logit models: Structure, properties and estimation.” Transp. Res. Part B: Methodol. 32 (5): 289–298. https://doi.org/10.1016/S0191-2615(98)00003-4.
Nguyen-Phuoc, D. Q., G. Currie, C. De Gruyter, and W. Young. 2018a. “Transit user reactions to major service withdrawal: A behavioural study.” Transp. Policy 64 (May): 29–37. https://doi.org/10.1016/j.tranpol.2018.01.004.
Nguyen-Phuoc, D. Q., G. Currie, C. De Gruyter, and W. Young. 2018b. “Exploring the impact of public transport strikes on travel behavior and traffic congestion.” Int. J. Sustain. Transp. 12 (8): 613–623. https://doi.org/10.1080/15568318.2017.1419322.
Pender, B., G. Currie, A. Delbosc, and N. Shiwakoti. 2014. “Social media use during unplanned transit network disruptions: A review of literature.” Transp. Rev. 34 (4): 501–521. https://doi.org/10.1080/01441647.2014.915442.
Pender, B., G. Currie, A. Delbosc, and Y. Wang. 2012. “Proactive recovery from rail disruptions through provision of track crossovers and bus bridging.” J. Transp. Res. Board 2275 (1): 68–76. https://doi.org/10.3141/2275-08.
Pnevmatikou, A. M., M. G. Karlaftis, and K. Kepaptsoglou. 2015. “Metro service disruptions: How do people choose to travel?” Transport 42 (6): 933–949. https://doi.org/10.1007/s11116-015-9656-4.
Saadatseresht, M., A. Mansourian, and M. Taleai. 2009. “Evacuation planning using multiobjective evolutionary optimization approach.” Eur. J. Oper. Res. 198 (1): 305–314. https://doi.org/10.1016/j.ejor.2008.07.032.
Sun, H., J. Wu, L. Wu, X. Yan, and Z. Gao. 2016. “Estimating the influence of common disruptions on urban rail transit networks.” Transp. Res. Part A: Policy Pract. 94 (Dec): 62–75. https://doi.org/10.1016/j.tra.2016.09.006.
Van Exel, N. J. A., and P. Rietveld. 2001. “Public transport strikes and traveler behaviour.” Transp. Policy 8 (4): 237–246. https://doi.org/10.1016/S0967-070X(01)00022-1.
Van Exel, N. J. A., and P. Rietveld. 2009. “When strike comes to town…anticipated and actual behavioural reactions to a one-day, pre-announced, complete rail strike in the Netherlands.” Transp. Res. Part A: Policy Pract. 43 (5): 526–535. https://doi.org/10.1016/j.tra.2009.01.003.
Wang, Y., J. Guo, G. Currie, A. Ceder, W. Dong, and B. Pender. 2014. “Bus bridging disruption in rail services with frustrated and impatient passengers.” IEEE Trans. Intell. Transp. Syst. 15 (5): 2014–2023. https://doi.org/10.1109/TITS.2014.2307859.
Yang, X., A. Chen, B. Ning, and T. Tang. 2017. “Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty.” Transp. Res. Part E: Logist. Transp. Rev. 97 (Jan): 22–37. https://doi.org/10.1016/j.tre.2016.10.012.
Zhang, S., H. K. Lo, and K. An. 2015. “Substitute bus service network design after a metro failure.” In Proc., 6th Int. Symp. of Transportation Network Reliability. Kyoto, Japan: Kyoto Univ.
Zhu, S., H. Masud, C. Xiong, Z. Yang, Y. Pan, and L. Zhang. 2017. “Travel behavior reactions to transit service disruptions.” J. Transp. Res. Board 2649 (1): 79–88. https://doi.org/10.3141/2649-09.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 146 • Issue 2 • February 2020
Copyright
©2019 American Society of Civil Engineers.
History
Received: Mar 24, 2019
Accepted: Jul 10, 2019
Published online: Dec 6, 2019
Published in print: Feb 1, 2020
Discussion open until: May 6, 2020
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