Intelligent transportation systems (ITS) have developed rapidly for urban road networks in recent years. Accurate and efficient short-term traffic flow speed prediction is key to the realization of ITS. Traffic flow data usually perform stochastic and nonlinear characteristics, making short-term forecasting of large-scale urban road networks challenging. To extract the spatial and temporal correlations between traffic flows, we propose a novel short-term speed forecasting of large-scale urban road network based on the deep learning algorithm Transformer used in the field of natural language processing. We test the model using a real floating car dataset collected on a large-scale urban road network. It is found that the Transformer model shows high prediction accuracy and efficiency performance and outperforms the benchmark models.


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    Titel :

    Short-Term Speed Forecasting of Large-Scale Urban Road Network Based on Transformer


    Beteiligte:
    Zhang, Jiawei (Autor:in) / Geng, Maosi (Autor:in) / Gu, Jiangsa (Autor:in) / Chen, Xiqun (Michael) (Autor:in)

    Kongress:

    21st COTA International Conference of Transportation Professionals ; 2021 ; Xi’an, China


    Erschienen in:

    CICTP 2021 ; 609-618


    Erscheinungsdatum :

    2021-12-14




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Short-Term Speed Forecasting of Large-Scale Urban Road Network Based on Transformer

    Zhang, Jiawei / Geng, Maosi / Gu, Jiangsa et al. | TIBKAT | 2021


    Short-term speed predictions exploiting big data on large urban road networks

    Fusco, Gaetano / Colombaroni, Chiara / Isaenko, Natalia | Elsevier | 2016