Bike sharing, as a rapidly emerging transportation mode, can provide reliable data support with ample travel information. Bike sharing’s ordering data has highly application value for obtaining bike share origin-destination (OD) volumes. This paper, considers the ordering data generated by Beijing’s Mobike users as the research object, and proposes a deep-learning-based method that considers both temporal and spatial correlations. Two deep learning structures, including recurrent neural network (RNN) and long short-term memory (LSTM) network, are proposed to forecast OD volumes. Experiment results show that the LSTM network has a relatively small prediction error, with 8.72% mean absolute percentage error (MAPE) for 15-minute time intervals, and thus, able to accurately estimate bike sharing’s OD volume.


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    Title :

    OD Demand Forecasting for the Large-Scale Dockless Sharing Bike System: A Deep Learning Approach


    Contributors:
    Liu, Shaoweihua (author) / Hou, Jun (author) / Liu, Yang (author) / Khadka, Anish (author) / Liu, Zhiyuan (author)

    Conference:

    18th COTA International Conference of Transportation Professionals ; 2018 ; Beijing, China


    Published in:

    CICTP 2018 ; 1683-1692


    Publication date :

    2018-07-02




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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