To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather


    Beteiligte:
    Yang Yang (Autor:in) / Xin Shao (Autor:in) / Yuting Zhu (Autor:in) / Enjian Yao (Autor:in) / Dongmei Liu (Autor:in) / Feng Zhao (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Forecasting usage and bike distribution of dockless bike-sharing using journey data

    Hua, Mingzhuang / Chen, Jingxu / Chen, Xuewu et al. | IET | 2020

    Freier Zugriff

    The Impact of Dockless Bike-Sharing and Built Environment on Ride-Sourcing Trips

    Boqing Wang / Xiaoyu Xue / Min Yang et al. | DOAJ | 2024

    Freier Zugriff


    Dockless Bike-Sharing Rebalancing Problem with Simultaneous Faulty Bike Recycling

    Usama, Muhammad / Zahoor, Onaira / Bao, Qiong et al. | ASCE | 2019


    Dockless bike-sharing systems: what are the implications?

    Chen, Zheyan / van Lierop, Dea / Ettema, Dick | Taylor & Francis Verlag | 2020

    Freier Zugriff