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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    Title :

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


    Contributors:

    Conference:

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


    Published in:

    CICTP 2021 ; 609-618


    Publication date :

    2021-12-14




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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