Abstract The linearity and heteroscedasticity are the important characteristics of short‐term traffic flow data. Generally, the autoregressive integrated moving average (ARIMA) model and the generalized autoregressive conditional heteroscedasticity (GARCH) model are respectively used to explain these characteristics. However, the prerequisite for the use of ARIMA is that the training residuals should follow the standard Gaussian distribution, which is hard to be satisfied in practice. Meanwhile, the variance in the GARCH model usually neglects the time‐varying characteristic. To address these problems, this paper proposes an innovative method based on the combination of ARIMA, maximum correntropy criterion (MCC), conditional kernel density estimation (CKDE), and GARCH. Specifically, the MCC method is first employed to estimate the coefficients of the ARIMA model (i.e. ARIMA‐MCC), by which the linear prediction is conducted. Then, the CKDE model is established to describe the training residuals obtained by ARIMA‐MCC and estimate the time‐varying variance in the GARCH model (i.e. GARCH‐CKDE). Case studies based on four groups of data measured in the urban road are used to evaluate the performance of the proposed method. Compared with the traditional ARIMA model, the improvement by the proposed method reaches 14.45% in terms of the evaluation criterion of mean absolute percentage error.


    Access

    Download


    Export, share and cite



    Title :

    A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction


    Contributors:
    Leina Zhao (author) / Xinyu Wen (author) / Yanpeng Wang (author) / Yiming Shao (author)


    Publication date :

    2022




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    A novel hybrid model of ARIMA‐MCC and CKDE‐GARCH for urban short‐term traffic flow prediction

    Zhao, Leina / Wen, Xinyu / Wang, Yanpeng et al. | Wiley | 2022

    Free access

    Short-time traffic flow prediction with ARIMA-GARCH model

    Chenyi Chen, / Jianming Hu, / Qiang Meng, et al. | IEEE | 2011


    Short-Time Traffic Flow Prediction with ARIMA-GARCH Model

    Chen, C. / Hu, J. / Meng, Q. et al. | British Library Conference Proceedings | 2011