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Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty

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  • Yang, Xin
  • Chen, Anthony
  • Ning, Bin
  • Tang, Tao

Abstract

For optimization of timetables in metro systems with regular cyclic operation, this paper develops a bi-objective programming approach addressed to minimization of net energy consumption and total travel time with provision for dwell time uncertainty. Firstly, we formulate the bi-objective timetable optimization problem as an expected value model with speed profile control. Secondly, we use the ɛ-constraint method within a genetic algorithm framework to determine the Pareto optimal solutions. Finally, numerical examples based on the real-life operation data from the Beijing Metro Yizhuang Line are presented in order to illustrate the practicability and effectiveness of the approach developed in the paper.

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  • Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2017. "Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 22-37.
    • Handle: RePEc:eee:transe:v:97:y:2017:i:c:p:22-37
    DOI: 10.1016/j.tre.2016.10.012
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    Cited by:

    1. Wenliang Zhou & Wenzhuang Fan & Xiaorong You & Lianbo Deng, 2019. "Demand-Oriented Train Timetabling Integrated with Passenger Train-Booking Decisions," Sustainability, MDPI, vol. 11(18), pages 1-34, September.
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    6. Ying, Cheng-shuo & Chow, Andy H.F. & Chin, Kwai-Sang, 2020. "An actor-critic deep reinforcement learning approach for metro train scheduling with rolling stock circulation under stochastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 210-235.
    7. Fei Dou & Huiru Zhang & Haodong Yin & Yun Wei & Yao Ning, 2022. "An Optimization Method of Urban Rail Train Operation Scheme Based on the Control of Load Factor," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    8. Canca, David & Barrena, Eva, 2018. "The integrated rolling stock circulation and depot location problem in railway rapid transit systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 115-138.
    9. Wang, Xuekai & Tang, Tao & Su, Shuai & Yin, Jiateng & Gao, Ziyou & Lv, Nan, 2021. "An integrated energy-efficient train operation approach based on the space-time-speed network methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    10. Yu, Chao & Li, Haiying & Xu, Xinyue & Liu, Jun, 2020. "Data-driven approach for solving the route choice problem with traveling backward behavior in congested metro systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    11. Pan, Hanchuan & Liu, Zhigang & Yang, Lixing & Liang, Zhe & Wu, Qiang & Li, Sijie, 2021. "A column generation-based approach for integrated vehicle and crew scheduling on a single metro line with the fully automatic operation system by partial supervision," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    12. Jiajie Yu & Yanjie Ji & Liangpeng Gao & Qi Gao, 2019. "Optimization of Metro Passenger Organizing of Alighting and Boarding Processes: Simulated Evidence from the Metro Station in Nanjing, China," Sustainability, MDPI, vol. 11(13), pages 1-20, July.
    13. Meloni, Carlo & Pranzo, Marco & Samà, Marcella, 2021. "Risk of delay evaluation in real-time train scheduling with uncertain dwell times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    14. Guo, Xin & Wang, David Z.W. & Wu, Jianjun & Sun, Huijun & Zhou, Li, 2020. "Mining commuting behavior of urban rail transit network by using association rules," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    15. Zhou, Wenliang & Huang, Yu & Deng, Lianbo & Qin, Jin, 2023. "Collaborative optimization of energy-efficient train schedule and train circulation plan for urban rail," Energy, Elsevier, vol. 263(PA).
    16. Li, Wenqing & Ni, Shaoquan, 2022. "Train timetabling with the general learning environment and multi-agent deep reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 230-251.
    17. Shi, Jungang & Yang, Jing & Yang, Lixing & Tao, Lefeng & Qiang, Shengjie & Di, Zhen & Guo, Junhua, 2023. "Safety-oriented train timetabling and stop planning with time-varying and elastic demand on overcrowded commuter metro lines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    18. Chen, Junlan & Pu, Ziyuan & Guo, Xiucheng & Cao, Jieyu & Zhang, Fang, 2023. "Multiperiod metro timetable optimization based on the complex network and dynamic travel demand," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 611(C).

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