Traffic forecasting is an important part of the smart transportation system, and accurate traffic forecasting is crucial for urban traffic scheduling and public travel planning. The traffic forecasting problem is greatly affected by the time dimension, and it is of great significance to investigate and summarize the related methods of time series traffic forecasting. Aiming at the problem of time series traffic forecasting, this paper focuses on the existing time series traffic forecasting models based on deep learning, and studies and analyzes the application fields and structural characteristics of different forecasting models. Finally, the current mainstream traffic prediction datasets are introduced, and the main challenges and solutions in the current traffic prediction field are discussed, which provides a reference for solving the problem of intelligent traffic prediction.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Review of Time Series Traffic Forecasting Methods


    Contributors:
    Wang, Linkai (author) / Chen, Jing (author) / Wang, Wei (author) / Song, Ruizhuo (author) / Zhang, Zhaochong (author) / Yang, Guowei (author)


    Publication date :

    2022-12-02


    Size :

    378838 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Short term traffic forecasting using time series methods

    Moorthy, C. K. / Ratcliffe, B. G. | Taylor & Francis Verlag | 1988


    Bayesian Time-Series Model for Short-Term Traffic Flow Forecasting

    Ghosh, B. / Basu, B. / O Mahony, M. | British Library Online Contents | 2007




    Time-Series Modeling for Forecasting Vehicular Traffic Flow in Dublin

    National Research Council (U.S.) | British Library Conference Proceedings | 2005