Traffic prediction is an important research issue for solving the traffic congestion problems in an Intelligent Transportation System (ITS). In urban areas, traffic congestion has increasingly become a difficult problem. In recent years, abundant traffic data and powerful GPU computing have led to improved accuracy in traffic data analysis via deep learning approaches. In this paper, we propose a long short-term memory recurrent neural network for urban traffic prediction in a case study of Seoul, Korea. The proposed method combines various kinds of time-series data into a model and we conduct comparative analysis using synthetic and real datasets. Our model confirms the proposed method can achieve better accuracy.


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    Titel :

    Long Short-Term Memory Recurrent Neural Network for Urban Traffic Prediction: A Case Study of Seoul


    Beteiligte:
    Lee, Yong-Ju (Autor:in) / Min, OkGee (Autor:in)


    Erscheinungsdatum :

    01.11.2018


    Format / Umfang :

    2263820 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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