Efficient and accurate prediction of traffic state plays a major role in the implementation of effective intelligent transportation systems. Therefore, traffic forecasting has been attracting a significant interest over the last decades striving to efficiently achieve highest accuracy of reliable prediction algorithms. In this paper, we present a novel prediction algorithm based on the Conditionally Gaussian Observed Markov Fuzzy Switching Model (CGOMFSM). The proposed scheme relies on a triplet representation of traffic encompassing traffic flow, speed and a switch process in order to infer parameters that govern the dynamics of traffic. This work investigates the impact of explicit incorporation of fuzzy switching processes on the accuracy of traffic data prediction. It aims to overcome the shortcomings of several existing prediction schemes in which the crisp modeling of traffic dynamics is hindering the effectiveness of prediction. The experimental study shows that the proposed algorithm yields satisfactory results for a prediction horizon up to 60 minutes for data collected at regular 15-minutes intervals.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic state prediction using conditionally Gaussian observed Markov fuzzy switching model


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2023-07-04


    Format / Umfang :

    20 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt





    SAR ATR performance using a conditionally Gaussian model

    O'Sullivan, J.A. / DeVore, M.D. / Kedia, V. et al. | IEEE | 2001




    Conditionally-Linear Filtering Using Conditionally-Orthogonal Projection

    Choukroun, D. / Speyer, J. | British Library Conference Proceedings | 2007