Recent development in ITS (Intelligent Transportation Systems) methods and technologies has moved traffic operating systems from passive to pro-active control and management, where real-time and accurate traffic flow information is critical to actual implementation. So far many algorithms have been proposed for traffic network flow forecasting, but problems in accuracy and timeliness still remain to be the major obstacle for their successful applications. For example, presumed human travel habit and vehicle turning probabilities at intersections have greatly limited the use of dynamic assignment algorithm. In order to improve the forecasting and real-time responsiveness, a new algorithm based on data mining which can do association rules mining and association analysis is proposed here for predicting traffic network flow. Simulation results using Corsim 5.0 have demonstrated effectiveness of the new algorithm in both accuracy and timeliness.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A data mining based algorithm for traffic network flow forecasting


    Beteiligte:
    Xiaoyan Gong, (Autor:in) / Xiaoming Liu, (Autor:in)


    Erscheinungsdatum :

    01.01.2003


    Format / Umfang :

    370259 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Data Mining Based Algorithm for Traffic Network Flow Forecasting

    Gong, X. / Liu, X. / IEEE | British Library Conference Proceedings | 2003



    Data Mining for Air Traffic Flow Forecasting: A Hybrid Model of Neural Network and Statistical Analysis

    Cheng, T. / Cui, D. / Cheng, P. et al. | British Library Conference Proceedings | 2003



    Application of Data Mining in Air Traffic Forecasting

    Busquets, Judit G. / Evans, Antony / Alonso, Eduardo | AIAA | 2015