Time-dependent pricing strategies for metro lines considering peak avoidance behaviour of commuters

References

• Ben-Elia, Eran, and Dick Ettema. 2011a. “Changing Commuters’ Behavior Using Rewards: A Study of Rush-Hour Avoidance.” Transportation Research Part F: Traffic Psychology and Behaviour 14 (5): 354–368. doi:10.1016/j.trf.2011.04.003.
   Google Scholar
• Ben-Elia, Eran, and Dick Ettema. 2011b. “Rewarding Rush-Hour Avoidance: A Study of Commuters’ Travel Behavior.” Transportation Research Part A: Policy and Practice 45 (7): 567–582. doi:10.1016/j.tra.2011.03.003.
   Google Scholar
• Gong, Huawei, and Wenzhou Jin. 2014. “Analysis of Urban Public Transit Pricing Adjustment Program Evaluation Based on Trilateral Game.” Procedia - Social and Behavioral Sciences 138: 332–339. doi:10.1016/j.sbspro.2014.07.211.
   Google Scholar
• Graham, Daniel J., Daniel Hörcher, Richard J. Anderson, and Prateek Bansal. 2020. “Quantifying the Ex-Post Causal Impact of Differential Pricing on Commuter Trip Scheduling in Hong Kong.” Transportation Research Part A: Policy and Practice 141: 16–34. doi:10.1016/j.tra.2020.08.011.
   Google Scholar
• Habib, Nurul, M. Khandker, Nicholas Day, and Eric J. Miller. 2009. “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 43 (7): 639–653. doi:10.1016/j.tra.2009.05.001.
   Google Scholar
• Hamdouch, Younes, and Siriphong Lawphongpanich. 2010. “Congestion Pricing for Schedule-Based Transit Networks.” Transportation Science 44 (3): 350–366. doi:10.1287/trsc.1090.0312.
   Google Scholar
• Hess, Stephane, Andrew Daly, Charlene Rohr, and Geoff Hyman. 2007. “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 41 (9 SPEC. ISS.): 802–826. doi:10.1016/j.tra.2007.04.001.
   Google Scholar
• Ho, Chinh Q., David A. Hensher, and Shangbo Wang. 2020. “Joint Estimation of Mode and Time of Day Choice Accounting for Arrival Time Flexibility, Travel Time Reliability and Crowding on Public Transport.” Journal of Transport Geography 87.
   Google Scholar
• Kou, Weibin, Xumei Chen, Lei Yu, Yi Qi, and Ying Wang. 2017. “Urban Commuters’ Valuation of Travel Time Reliability Based on Stated Preference Survey: A Case Study of Beijing.” Transportation Research Part A: Policy and Practice 95: 372–380. doi:10.1016/j.tra.2016.10.008.
   Google Scholar
• Kouwenhoven, Marco, and Gerard de Jong. 2018. “Value of Travel Time as a Function of Comfort.” Journal of Choice Modelling 28: 97–107. doi:10.1016/j.jocm.2018.04.002.
   Google Scholar
• Lemp, Jason D., Kara M. Kockelman, and Paul Damien. 2010. “The Continuous Cross-Nested Logit Model: Formulation and Application for Departure Time Choice.” Transportation Research Part B: Methodological 44 (5): 646–661. doi:10.1016/j.trb.2010.03.001.
   Google Scholar
• Li, Haiying, Xian Li, Xinyue Xu, Jun Liu, and Bin Ran. 2018. “Modeling Departure Time Choice of Metro Passengers with a Smart Corrected Mixed Logit Model - A Case Study in Beijing.” Transport Policy 69: 106–121. doi:10.1016/j.tranpol.2018.06.005.
   Google Scholar
• Lin, Xiaoyan, and Ying Feng. 2013. “Rail Transit Ticket Price Elasticity in Beijing Based on Reference-Dependent Principle.” China Industrial Economics 7: 108–120.
   Google Scholar
• Liu, Shixu, Qiyuan Chen, Hao Yan, Lidan Guo, Yingnuo Tang, and Hai Yan. 2018. “Multimodal Stochastic User Equilibrium Model Based on Nested Logit.” Journal of Chang’an University(Natural Science Edition) 38 (5): 114–122.
   Google Scholar
• Liu, Henry X., Xiaozheng He, and Bingsheng He. 2009. “Method of Successive Weighted Averages (MSWA) and Self-Regulated Averaging Schemes for Solving Stochastic User Equilibrium Problem.” Networks and Spatial Economics 9 (4): 485–503. doi:10.1007/s11067-007-9023-x.
   Google Scholar
• Liu, Meiyin, and Jiangwei Wang. 2017. “Pricing Method of Urban Rail Transit Considering the Optimization of Passenger Transport Structure.” Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology 17 (3): 53–59.
   Google Scholar
• Lovrić, Milan, Sebastián Raveau, Muhammad Adnan, Francisco C. Pereira, Kakali Basak, Harish Loganathan, and Moshe Ben-Akiva. 2016. “Evaluating Off-Peak Pricing Strategies in Public Transportation with an Activity-Based Approach.” Transportation Research Record 2544: 10–19. doi:10.3141/2544-02.
   Google Scholar
• Peer, Stefanie, Jasper Knockaert, and Erik T. Verhoef. 2016. “Train Commuters’ Scheduling Preferences: Evidence from a Large-Scale Peak Avoidance Experiment.” Transportation Research Part B: Methodology 83: 314–333. doi:10.1016/j.trb.2015.11.017.
   Google Scholar
• Prashker, Joseph N., and Shlomo Bekhor. 2004. “Route Choice Models Used in the Stochastic User Equilibrium Problem: A Review.” Transport Reviews 24 (4): 437–463. doi:10.1080/0144164042000181707.
   Google Scholar
• Roby, Helen. 2014. A Supplementary Dictionary of Transport Studies. Oxford: Oxford University Press.
   Google Scholar
• Sabounchi, Nasim S., Konstantinos P. Triantis, Sudipta Sarangi, and Shiyong Liu. 2014. “Dynamic Simulation Modeling and Policy Analysis of an Area-Based Congestion Pricing Scheme for a Transportation Socioeconomic System.” Transportation Research Part A: Policy and Practice 59: 357–383. doi:10.1016/j.tra.2013.11.007.
   Google Scholar
• Tang, Chunyan, Ying En Ge, and William H.K. Lam. 2019. “Optimizing Limited-Stop Bus Services Along a Public Transit Corridor with a Differential Fare Structure.” Transport 34 (4): 476–489. doi:10.3846/transport.2019.11235.
   Google Scholar
• Tang, Yili, Yu Jiang, Hai Yang, and Otto Anker Nielsen. 2020a. “Modeling and Optimizing a Fare Incentive Strategy to Manage Queuing and Crowding in Mass Transit Systems: Modeling and Optimizing a Fare Incentive Strategy to Manage Queuing and Crowding in Mass Transit Systems.” Transportation Research Part B: Methodological 138: 247–267. doi:10.1016/j.trb.2020.05.006.
   Google Scholar
• Tang, Yili, Hai Yang, Bo Wang, Jianling Huang, and Yunyun Bai. 2020b. “A Pareto-Improving and Revenue-Neutral Scheme to Manage Mass Transit Congestion with Heterogeneous Commuters.” Transportation Research Part C: Emerging Technologies 113: 245–259. doi:10.1016/j.trc.2019.05.016.
   Google Scholar
• Wu, Xuanke, Jin Qin, Wenxuan Qu, Yijia Zeng, and Xia Yang. 2019. “Collaborative Optimization of Dynamic Pricing and Seat Allocation for High-Speed Railways: An Empirical Study from China.” IEEE Access 7: 139409–139419. doi:10.1109/ACCESS.2019.2943229.
   Google Scholar
• Yang, Hai, and Yili Tang. 2018. “Managing Rail Transit Peak-Hour Congestion with a Fare-Reward Scheme.” Transportation Research Part B: Methodological 110: 122–136. doi:10.1016/j.trb.2018.02.005.
   Google Scholar
• Zhang, Zheng, Hidemichi Fujii, and Shunsuke Managi. 2014. “How Does Commuting Behavior Change Due to Incentives? An Empirical Study of the Beijing Subway System.” Transportation Research Part F: Traffic Psychology and Behaviour 24: 17–26. doi:10.1016/j.trf.2014.02.009.
   Google Scholar
• Zhang, Rui, Enjian Yao, and Long Pan. 2019. “Optimizing EV-Based P&R Subsidy Policies for Commuting Corridor Based on Cross-Nested Logit Model.” International Journal of Sustainable Transportation 13 (7): 461–478. doi:10.1080/15568318.2018.1482032.
   Google Scholar
• Zheng, Nan, Guillaume Rérat, and Nikolas Geroliminis. 2016. “Time-Dependent Area-Based Pricing for Multimodal Systems with Heterogeneous Users in an Agent-Based Environment.” Transportation Research Part C: Emerging Technologies 62: 133–148. doi:10.1016/j.trc.2015.10.015.
   Google Scholar
• Zhou, Feng, Changfeng Li, Zhiyuan Huang, Ruihua Xu, and Wei David Fan. 2020. “Fare Incentive Strategies for Managing Peak-Hour Congestion in Urban Rail Transit Networks.” Transportmetrica A: Transport Science 1–22. doi:10.1080/23249935.2020.1793028.
   Google Scholar