Big data is becoming a research focus recently. Urban rail transit systems produce large amounts of data, such as real time train speed and position, passenger origin-destination (OD) information, etc. With the support of big data analytics, the rail transit operators will be able to improve the operation efficiency of rail transit systems. In this paper, we obtain the historical passenger OD data from the automatic fare collection system (AFC), and process these data to get the passenger arrival rate and passenger alighting proportion using Hadoop big data platform. A multi-objective model is proposed to optimize train schedule time table. The model consists of two submodel components, namely, train operation model and passenger demand model. We propose a parallel genetic algorithm (GA) using an adaptive crossover operator and mutation operator to obtain the optimal solution. The proposed model and solution method are evaluated using real-life data. The obtained results demonstrate the efficiency and accuracy of the proposed method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Leveraging Big Data Analytics for Train Schedule Optimization in Urban Rail Transit Systems


    Contributors:
    Wang, Yige (author) / Zhu, Li (author) / Lin, Qingqing (author) / Zhang, Lin (author)


    Publication date :

    2018-11-01


    Size :

    191817 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    URBAN RAIL TRANSIT TRAIN OPERATION METHOD

    WANG ZHE | European Patent Office | 2021

    Free access

    URBAN RAIL TRANSIT TRAIN OPERATION METHOD

    WANG ZHE | European Patent Office | 2021

    Free access

    Optimization Method for Train Plan of Urban Rail Transit

    Deng, Lianbo / Zeng, Qiang / Gao, Wei et al. | ASCE | 2011