Highlights A scenario-based train timetabling framework is constructed to divide demand input methods into multi-scenario method (MM), average-scenario method (AM) and one-scenario method (OM). A MM-based bi-objective mixed-integer linear programming model is formulated to design an adaptable and stable train timetable at network level from perspectives of enterprises and passengers, in which multi-scenario small-granularity passenger demand follows actual distribution. Advanced and Adaptive NSGA-II (AANSGA-II) with advanced population sorting and adaptive genetic operation is proposed to solve the high-complexity bi-objective problem.

    Abstract It is critical to design an adaptable and stable train timetable for long-term use in rail transit network that not only meets the dynamicity of passenger demand in different hours within one day, but also meets the uncertainty of passenger demand in different days. In this study, a scenario-based train timetabling framework is constructed to classify the possibilities of passenger demand in multiple days into a set of scenarios based on profile and volume of passenger demand. On this basis, multi-scenario demand input method (MM) is introduced to deal with the uncertainty of passenger demand, which is different from one-scenario method (OM) and average-scenario method (AM). A MM-based mixed-integer linear programming model is formulated for the bi-objective train timetabling problem under uncertain and dynamic demand at acyclic network level, in which multi-scenario small-granularity passenger demand follows actual distribution processed from historical data. The two objectives are to minimize train service cost and penalized passenger waiting time from perspectives of enterprises and passengers. Advanced and Adaptive NSGA II (AANSGA-II) is proposed to cope with the high-complexity bi-objective problem, which applies advanced population sorting based on neighborhood distance, adaptive genetic operation based on scoring mechanism and improved population initialization based on boundary individuals. The model and algorithm are testified by a small-scale numerical experiment on a virtual line and a large-scale real-world instance in Shenyang Metro network. As a result, MM-based train timetables are generally better than AM-based and OM-based train timetables in reducing generalized cost and raising robustness. Besides, AANSGA-II is more applicable than NSGA-II and CPLEX in shortening computation time at the same time of improving computation result.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Train timetabling in rail transit network under uncertain and dynamic demand using Advanced and Adaptive NSGA-II


    Contributors:
    Han, Zhenyu (author) / Han, Baoming (author) / Li, Dewei (author) / Ning, Shangbin (author) / Yang, Ruixia (author) / Yin, Yonghao (author)


    Publication date :

    2021-10-01


    Size :

    35 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Single-line rail rapid transit timetabling under dynamic passenger demand

    Barrena, Eva / Canca, David / Coelho, Leandro C. et al. | Elsevier | 2014


    Demand-driven integrated train timetabling and rolling stock scheduling on urban rail transit line

    Zhuo, Siyu / Miao, Jianrui / Meng, Lingyun et al. | Taylor & Francis Verlag | 2024



    Demand-driven train timetabling for air and intercity high-speed rail synchronization service

    Jiang, Yangsheng / Chen, Shuiwang / an, Wenyao et al. | Taylor & Francis Verlag | 2023