Network-based model were developed to predict short term future traffic volume based on current traffic, historical average, and upstream traffic. It is presumed that upstream traffic volume can be used to predict the downstream traffic in a specific time period. Three models are developed for traffic flow prediction: a combination of historical average and upstream traffic, a combination of current traffic and upstream traffic, and a combination of all three variables. The three models were evaluated using regression analysis. The third model is found to provide the best prediction for the analyzed data. In order to balance the variables appropriately according to the present traffic condition, a heuristic adaptive weighting system is devised based on the relationships between the beginning period of prediction and the previous periods. The developed models were applied to 15-minute freeway data obtained by regular induction loop detectors. The prediction models were shown to be capable of producing reliable and accurate forecasts under congested traffic condition. The prediction systems perform better in the 15-minute range than in the ranges of 30- to 45-minute. It is also found that the combined models usually produce more consistent forecasts than the historical average.<>


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic-flow-prediction systems based on upstream traffic


    Beteiligte:
    Hobeika, A.G. (Autor:in) / Chang Kyun Kim (Autor:in)


    Erscheinungsdatum :

    01.01.1994


    Format / Umfang :

    500270 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Traffic-Flow-Prediction Systems Based on Upstream Traffic

    Hobeika, A. G. / Kim, C. / IEEE; Vehicular Technology Society | British Library Conference Proceedings | 1994




    TRAFFIC FLOW PREDICTION DEVICE AND TRAFFIC FLOW PREDICTION SYSTEM

    OKUDE MARIKO / NAGAI TORU / SAKAMOTO TOSHIYUKI et al. | Europäisches Patentamt | 2019

    Freier Zugriff