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A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system

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  • Yang, Songpo
  • Liao, Feixiong
  • Wu, Jianjun
  • Timmermans, Harry J.P.
  • Sun, Huijun
  • Gao, Ziyou

Abstract

Complex passenger demand and electricity transmission processes in metro systems cause difficulties in formulating optimal timetables and train speed profiles, often leading to inefficiency in energy consumption and passenger service. Based on energy-regenerative technologies and smart-card data, this study formulates an optimization model incorporating energy allocation and passenger assignment to balance energy use and passenger travel time. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is applied and the core components are redesigned to obtain an efficient Pareto frontier of irregular timetables for maximizing the use of regenerative energy and minimizing total travel time. Particularly, a parallelogram-based method is developed to generate random feasible timetables; crossover and local-search-driven mutation operators are proposed relying on the graphic representations of the domain knowledge. The suggested approach is illustrated using real-world data of a bi-directional metro line in Beijing. The results show that the approach significantly improves regenerative energy use and reduces total travel time compared to the fixed regular timetable.

Suggested Citation

  • Yang, Songpo & Liao, Feixiong & Wu, Jianjun & Timmermans, Harry J.P. & Sun, Huijun & Gao, Ziyou, 2020. "A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 85-113.
    • Handle: RePEc:eee:transb:v:133:y:2020:i:c:p:85-113
    DOI: 10.1016/j.trb.2020.01.001
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    Cited by:

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    5. Kang, Liujiang & Li, Hao & Sun, Huijun & Wu, Jianjun & Cao, Zhiguang & Buhigiro, Nsabimana, 2021. "First train timetabling and bus service bridging in intermodal bus-and-train transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 443-462.
    6. Huang, Yu & Zhou, Wenliang & Qin, Jin & Deng, Lianbo, 2023. "Optimization of energy-efficiency train schedule considering passenger demand and rolling stock circulation plan of subway line," Energy, Elsevier, vol. 275(C).
    7. Yang, Songpo & Chen, Yanyan & Dong, Zhurong & Wu, Jianjun, 2023. "A collaborative operation mode of energy storage system and train operation system in power supply network," Energy, Elsevier, vol. 276(C).
    8. Huang, Kang & Wu, Jianjun & Sun, Huijun & Yang, Xin & Gao, Ziyou & Feng, Xujie, 2022. "Timetable synchronization optimization in a subway–bus network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    9. Almaksour, Khaled & Krim, Youssef & Kouassi, N’guessan & Navarro, Nicolas & François, Bruno & Letrouvé, Tony & Saudemont, Christophe & Taunay, Lionel & Robyns, Benoit, 2021. "Comparison of dynamic models for a DC railway electrical network including an AC/DC bi-directional power station," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 184(C), pages 244-266.
    10. 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).
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    13. Han, Zhenyu & Han, Baoming & Li, Dewei & Ning, Shangbin & Yang, Ruixia & Yin, Yonghao, 2021. "Train timetabling in rail transit network under uncertain and dynamic demand using Advanced and Adaptive NSGA-II," Transportation Research Part B: Methodological, Elsevier, vol. 154(C), pages 65-99.
    14. Wu, Zhibei & Sun, Jitao & Xu, Ruihua, 2021. "Consensus-based connected vehicles platoon control via impulsive control method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    15. 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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