Urban railway transit is of great significance in the daily lives of Metropolitan residents. Therefore, forecasting rail- way traffic is fundamental to urban management. However, very few research has been focused on collectively forecast railway transit in a citywide scale. With the development of location based service, the huge volume of GPS trajectory data make it possible for a citywide prediction of railway traffic. In this paper, we propose a deep-learning-based system named DeepRailway to predict and simulate rail- way traffic through heterogeneous data sources. Our data sources include huge volume of trajectory data and rail- way network. In our system, we firstly match the trajectory points to the railway network. And then the patterns of these trajectories are found using a network-based kernel density estimation (KDE), which converts the forecasting task into a sequence prediction problem. An LSTM recurrent neu- ral network model is built to predict the densities through- out the whole network. We evaluate our system in differ- ent timespan and prediction steps to verify its performance against other prediction methods.


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    Titel :

    DeepRailway: A Deep Learning System for Forecasting Railway Traffic


    Beteiligte:
    Xia, Tianqi (Autor:in) / Song, Xuan (Autor:in) / Fan, Zipei (Autor:in) / Kanasugi, Hiroshi (Autor:in) / Chen, QuanJun (Autor:in) / Jiang, Renhe (Autor:in) / Shibasaki, Ryosuke (Autor:in)


    Erscheinungsdatum :

    01.04.2018


    Format / Umfang :

    2425719 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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