Short-term traffic flow forecasting is a fundamental and challenging task since it is required for the successful deployment of intelligent transportation systems and the traffic flow is dramatically changing through time. This study presents a novel hybrid dual Kalman filter (H-KF2) for accurate and timely short-term traffic flow forecasting. To achieve this, the H-KF2 first models the propagation of the discrepancy between the predictions of the traditional Kalman filter and the random walk model. By estimating the a posteriori state of the prediction errors of both models, the calibrated discrepancy is exploited to compensate the preliminary predictions. The H-KF2 works with competitive time and space to traditional Kalman filter. Four real-world datasets and various experiments are employed to evaluate the authors’ model. The experimental results demonstrate the H-KF2 outperforms the state-of-the-art parametric and non-parametric models.


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    Titel :

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


    Beteiligte:
    Zhou, Teng (Autor:in) / Jiang, Dazhi (Autor:in) / Lin, Zhizhe (Autor:in) / Han, Guoqiang (Autor:in) / Xu, Xuemiao (Autor:in) / Qin, Jing (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2019-02-12


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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