Highlights We propose a time–space threshold vector error correction model to predict traffic. Cointegration theory and methodology are employed for traffic state prediction. We linked error correction model with the transformed Fundamental Traffic Diagrams. Regime switching framework is used to capture structural change in time series. Spatial cross-correlated information is incorporated with piecewise linear model.

    Abstract We propose Time–Space Threshold Vector Error Correction (TS-TVEC) model for short term (hourly) traffic state prediction. The theory and method of cointegration with error correction mechanism is employed in the general design of the new statistical model TS-TVEC. An inherent connection between mathematical form of error correction model and traffic flow theory is revealed through the transformation of the well-known Fundamental Traffic Diagrams. A threshold regime switching framework is implemented to overcome any unknown structural changes in traffic time series. Spatial cross correlated information is incorporated with a piecewise linear vector error correction model. A Neural Network model is also constructed in parallel to comparatively test the effectiveness and robustness of the new statistical model. Our empirical study shows that the TS-TVEC model is an effective tool that is capable of modeling the complexity of stochastic traffic flow processes and potentially applicable to real time traffic state prediction.


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    Titel :

    Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction


    Beteiligte:
    Ma, Tao (Autor:in) / Zhou, Zhou (Autor:in) / Abdulhai, Baher (Autor:in)


    Erscheinungsdatum :

    2015-02-22


    Format / Umfang :

    21 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch