Accurate and timely short‐term traffic flow forecasting is a critical component for intelligent transportation systems. However, it is quite challenging to develop an efficient and robust forecasting model due to complex non‐linear data pattern of traffic flow. Support vector regression (SVR) has been widely employed in non‐linear regression and time series prediction problems. However, the lack of knowledge of the choice of hyper‐parameters in the SVR model leads to poor forecasting accuracy. In this study, the authors propose a hybrid traffic flow forecasting model combining gravitational search algorithm (GSA) and the SVR model. The GSA is employed to search optimal SVR parameters. Extensive experiments have been conducted to demonstrate the superior performance of the proposal.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    SVRGSA: a hybrid learning based model for short‐term traffic flow forecasting


    Beteiligte:
    Cai, Lingru (Autor:in) / Chen, Qian (Autor:in) / Cai, Weihong (Autor:in) / Xu, Xuemiao (Autor:in) / Zhou, Teng (Autor:in) / Qin, Jing (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.09.2019


    Format / Umfang :

    8 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    SVRGSA: a hybrid learning based model for short-term traffic flow forecasting

    Cai, Lingru / Chen, Qian / Cai, Weihong et al. | IET | 2019

    Freier Zugriff

    GSA‐ELM: A hybrid learning model for short‐term traffic flow forecasting

    Cui, Zhihan / Huang, Boyu / Dou, Haowen et al. | Wiley | 2022

    Freier Zugriff

    GSA‐ELM: A hybrid learning model for short‐term traffic flow forecasting

    Zhihan Cui / Boyu Huang / Haowen Dou et al. | DOAJ | 2022

    Freier Zugriff

    Hybrid dual Kalman filtering model for short‐term traffic flow forecasting

    Zhou, Teng / Jiang, Dazhi / Lin, Zhizhe et al. | Wiley | 2019

    Freier Zugriff

    Hybrid dual Kalman filtering model for short-term traffic flow forecasting

    Zhou, Teng / Jiang, Dazhi / Lin, Zhizhe et al. | IET | 2019

    Freier Zugriff