Reasonable bus timetable can reduce the operating costs of bus company and improve the quality of bus services. A data-driven method is proposed to optimize bus timetable in this study. Firstly, a bi-objective optimization model is constructed considering minimize the total waiting time of passengers and the departure times of bus company. Then, Global Positioning System (GPS) trajectories of buses and passenger information collected from Smart Card are fused and applied to calculate the key parameters or variables in optimization model, including time-dependent travel time, bus dwell time and passenger volume. Finally, by adopting a specific coding scheme, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is designed to quickly search Pareto optimal solutions. Furthermore, an experiment is conducted in Beijing city from one bus line to validate the effectiveness of the proposed method. Comparing with empirical scheduling method and traditional single-objective optimization base on GA, the results show that the proposed model could quickly provide high-quality and reasonable timetable schemes for the administrator in urban transit system.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Data-Driven Timetable Optimization of Urban Bus Line Based on Multi-Objective Genetic Algorithm


    Contributors:
    Tang, Jinjun (author) / Yang, Yifan (author) / Hao, Wei (author) / Liu, Fang (author) / Wang, Yinhai (author)


    Publication date :

    2021-04-01


    Size :

    2444610 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Optimization of Subway Departure Timetable by Genetic Algorithm

    He, Junxiang / Zeng, Xiaoqing / Ying, Peiran et al. | TIBKAT | 2019


    Optimization of Subway Departure Timetable by Genetic Algorithm

    He, Junxiang / Zeng, Xiaoqing / Ying, Peiran et al. | Springer Verlag | 2019


    Demand-driven timetable and stop pattern cooperative optimization on an urban rail transit line

    Shang, Pan / Li, Ruimin / Yang, Liya | Taylor & Francis Verlag | 2020