As an emerging force in the travel industry, online car-hailing (OCH) has been relying on digital and intelligent technologies for business innovation since its birth. Three hybrid deep learning models combining multi-factor external and internal features are proposed to predict the OCH demand. Convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and convolutional LSTM (ConvLSTM) are selected to extract features. Attention mechanisms are used to combine the global parts so that the importance of feature sequences at different times can be distinguished. The effectiveness of the proposed models considering all factors is proved by comparative experiments. Then, ablation experiments are performed to analyze the effects of attention module, external and internal factors. The results showed that the hybrid models performed better than the existing models under different factors. Various factors had different impacts on the departure and arrival flows and the hybrid models.


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    Titel :

    Hybrid deep learning models for short-term demand forecasting of online car-hailing considering multiple factors


    Beteiligte:
    Li, Siteng (Autor:in) / Yang, Hang (Autor:in) / Cheng, Rongjun (Autor:in) / Ge, Hongxia (Autor:in)

    Erschienen in:

    Transportation Letters ; 16 , 3 ; 218-233


    Erscheinungsdatum :

    2024-03-15


    Format / Umfang :

    16 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch