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Ticket Allocation Optimization of Fuxing Train Based on Overcrowding Control: An Empirical Study from China

Author

Listed:
  • Yu Wang

    (Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Xinghua Shan

    (Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Hongye Wang

    (Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Junfeng Zhang

    (Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Xiaoyan Lv

    (Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

  • Jinfei Wu

    (Institute of Computing Technology, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China)

Abstract

At the peak of passenger flow, some passengers extend travel sections, which will be likely to lead to overcrowding of high-speed railway (HSR) trains. Therefore, the problem of train overcrowding control needs to be considered in ticket allocation. Firstly, by simulating the passenger demand function and utility function, an optimization model of ticket allocation for multiple trains and multiple stops with the goal of maximizing revenue is constructed. Secondly, the concepts of the travel extension coefficient and risk coefficient are introduced, the number of passengers is estimated under the risk coefficient as the probability, and the total number of passengers on the train arriving at any station is obtained. Thus, preventing the number of passengers on the train from exceeding the train capacity is introduced to the ticket allocation optimization model of multiple trains and multiple stops as a constraint. Finally, this model is solved by the particle swarm optimization algorithm (PSO). The research results show that the idea of controlling passenger numbers so as not to exceed train capacity based on ticket allocation proposed in this paper has strong practical feasibility. By reasonably and accurately allocating the tickets to the departure terminal section and long-distance terminal sections, it can ensure that, even if there are some passengers extending their travel section, the train will not be overcrowded under a certain probability, improving the train safety and passenger travel experiences.

Suggested Citation

  • Yu Wang & Xinghua Shan & Hongye Wang & Junfeng Zhang & Xiaoyan Lv & Jinfei Wu, 2022. "Ticket Allocation Optimization of Fuxing Train Based on Overcrowding Control: An Empirical Study from China," Sustainability, MDPI, vol. 14(12), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7055-:d:834782
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    References listed on IDEAS

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    1. A. Ciancimino & G. Inzerillo & S. Lucidi & L. Palagi, 1999. "A Mathematical Programming Approach for the Solution of the Railway Yield Management Problem," Transportation Science, INFORMS, vol. 33(2), pages 168-181, May.
    2. Guillermo Gallego & Garrett van Ryzin, 1994. "Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons," Management Science, INFORMS, vol. 40(8), pages 999-1020, August.
    3. Jin Qin & Wenxuan Qu & Xuanke Wu & Yijia Zeng, 2019. "Differential Pricing Strategies of High Speed Railway Based on Prospect Theory: An Empirical Study from China," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    4. Hetrakul, Pratt & Cirillo, Cinzia, 2014. "A latent class choice based model system for railway optimal pricing and seat allocation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 68-83.
    5. Lawrence R. Weatherford, 1997. "Using Prices More Realistically as Decision Variables in Perishable-Asset Revenue Management Problems," Journal of Combinatorial Optimization, Springer, vol. 1(3), pages 277-304, October.
    6. Wang, Xinchang & Wang, Hua & Zhang, Xiaoning, 2016. "Stochastic seat allocation models for passenger rail transportation under customer choice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 95-112.
    7. Raja Gopalakrishnan & Narayan Rangaraj, 2010. "Capacity Management on Long-Distance Passenger Trains of Indian Railways," Interfaces, INFORMS, vol. 40(4), pages 291-302, August.
    8. You, Peng-Sheng, 2008. "An efficient computational approach for railway booking problems," European Journal of Operational Research, Elsevier, vol. 185(2), pages 811-824, March.
    9. Joonhyeong Joseph Kim & Insin Kim, 2018. "Entrepreneurial Marketing and Airline-Cause Sponsorship Congruence: Passenger Sponsorship Response to US-Based Full-Service Airlines," Sustainability, MDPI, vol. 10(7), pages 1-20, July.
    10. Jinzi Zheng & Jun Liu & David B. Clarke, 2017. "Ticket Fare Optimization for China’s High-Speed Railway Based on Passenger Choice Behavior," Discrete Dynamics in Nature and Society, Hindawi, vol. 2017, pages 1-6, February.
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    Cited by:

    1. Yu Wang & Jiafa Zhu, 2023. "Pricing Analysis for Railway Multi-Ride Tickets: An Optimization Approach for Uncertain Demand within an Agreed Time Limit," Mathematics, MDPI, vol. 11(23), pages 1-21, November.
    2. Jinfei Wu & Xinghua Shan & Jingxia Sun & Shengyuan Weng & Shuo Zhao, 2023. "Daily Line Planning Optimization for High-Speed Railway Lines," Sustainability, MDPI, vol. 15(4), pages 1-20, February.

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