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    Short-term prediction of traffic dynamics with real-time recurrent learning algorithms

    Short-term prediction of traffic dynamics with real-time recurrent learning algorithms


    Short-term prediction of dynamic traffic states remains critical in the field of advanced traffic management systems and related areas. In this article, a novel real-time recurrent learning (RTRL) algorithm is proposed to address the above issue. We dabble in comparing pair predictability of linear method versus RTRL algorithms and simple non-linear method versus RTRL algorithms individually using a first-order autoregressive time-series AR(1) and a deterministic function. A field study tested with flow, speed and occupancy series data collected directly from dual-loop detectors on a freeway is conducted. The numerical results reveal that the performance of RTRL algorithms in predicting short-term traffic dynamics is satisfactorily accepted. Furthermore, it is found that the dynamics of short-term traffic states characterised in different time intervals, collected in diverse time lags and times of day may have significant effects on the prediction accuracy of the proposed algorithms.

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    Titel :

    Short-term prediction of traffic dynamics with real-time recurrent learning algorithms

    Beteiligte:
    Sheu, Jiuh-Biing (Autor:in) / Lan, Lawrence W. (Autor:in) / Huang, Yi-San (Autor:in)
    Erschienen in:

    Transportmetrica ; 5 , 1 ; 59-83

    Erscheinungsdatum :

    2009-01-01

    Format / Umfang :

    25 pages

    ISSN :

    1812-8602 , 1944-0987

    DOI :

    https://doi.org/10.1080/18128600802591681

    Medientyp :

    Aufsatz (Zeitschrift)

    Format :

    Elektronische Ressource

    Sprache :

    Unbekannt

    Schlagwörter :

    real-time recurrent learning , traffic dynamics , stochastic , deterministic

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