Passenger flow forecasting plays a critical role in intelligent transportation systems. Existing studies mainly focused on the accuracy of forecasting. In this paper, we propose a tensor-based framework combined with “priori modeling” and “posterior analysis”, which can forecast peak-hour passenger flow with both accuracy and interpretability. This framework consists of three parts: 1) construct 5-way tensor model based priori features of passenger flow (e.g. multiple periodicity, temporal variabilities and spatial information); 2) design a tensor completion algorithm to forecast peak-hour passenger flow; 3) qualitatively and quantitatively assess the reliability and importance of priori features. Our framework is evaluated on a real world large-scale passenger flow dataset in the largest city in China. Experimental results showed that our approach achieves 25%-40% improvement over baselines. We also provided in-depth analysis for traffic meanings of modes of tensor and the rank of importance of priori features.


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    Titel :

    Peak-Hour Subway Passenger Flow Forecasting: A Tensor Based Approach


    Beteiligte:
    Pan, Pingjun (Autor:in) / Wang, Haiyang (Autor:in) / Li, Longyuan (Autor:in) / Wang, Yongkun (Autor:in) / Jin, Yaohui (Autor:in)


    Erscheinungsdatum :

    2018-11-01


    Format / Umfang :

    287914 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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