Accurate and real-time taxi demand prediction can help managers pre-allocate taxi resources in cities, which assists drivers quickly finding passengers and reduce passengers’ waiting time. Most of the existing studies focus on mining spatial-temporal characteristics of taxi demand distributions, while lacking in modeling the correlations between taxi pick-up demand and the drop-off demand from the perspective of multi-task learning. In this article, we propose a multi-task learning model containing three parallel LSTM layers to co-predict taxi pick-up and drop-off demands, and compare the performance of single demand prediction methodology and that of two demands’ co-prediction methodology. Experimental results on real-world datasets demonstrate that the pick-up demand and the drop-off demand do depend on each other, and the effectiveness of the proposed co-prediction methods.


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    Titel :

    Taxi Demand Prediction Using Parallel Multi-Task Learning Model


    Beteiligte:
    Zhang, Chizhan (Autor:in) / Zhu, Fenghua (Autor:in) / Wang, Xiao (Autor:in) / Sun, Leilei (Autor:in) / Tang, Haina (Autor:in) / Lv, Yisheng (Autor:in)


    Erscheinungsdatum :

    2022-02-01


    Format / Umfang :

    3812723 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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