Urban taxi demand prediction plays an important role in reducing the taxi empty driving rate and alleviating road traffic congestion. However, due to the complex structure of urban road network, taxi flow is difficult to be accurately predicted. In order to capture the spatial features of taxi data and accurately predict the future demand changes of taxi, a new hybrid model — LSGCN model is proposed in this paper. This model combines graph convolutional neural network (GCN) and long short-term memory network (LSTM) to achieve simultaneous acquisition of spatial-temporal correlation. Finally, the taxi demand prediction experiment is conducted based on the real order data set of Haikou taxi-hailing platform to verify the prediction performance of the model proposed in this paper.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Urban Taxi Demand Forecast Based on Graph Convolutional Network


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Liang, Jianying (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Qin, Yong (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Wang, Yaguan (Autor:in) / Qin, Yong (Autor:in) / Guo, Jianyuan (Autor:in)

    Kongress:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Erscheinungsdatum :

    19.02.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Urban Taxi Demand Forecast Based on Graph Convolutional Network

    Wang, Yaguan / Qin, Yong / Guo, Jianyuan | TIBKAT | 2022


    Urban Taxi Demand Forecast Based on Graph Convolutional Network

    Wang, Yaguan / Qin, Yong / Guo, Jianyuan | British Library Conference Proceedings | 2022


    Graph Multi-Attention Network-based Taxi Demand Prediction

    Tang, Haifan / Wu, Youkai / Guo, Zhaoxia | IEEE | 2022


    Predictions of Taxi Demand Based on Neural Network Algorithms

    Lin, Chung-Yi / Tung, Shen-Lung / Lu, Po-Wen et al. | Springer Verlag | 2021