AbstractOne of the most critical functions of an intelligent transportation system (ITS) is to provide accurate and real-time prediction of traffic condition. This paper develops a short-term traffic condition prediction model based on the k-nearest neighbor algorithm. In the prediction model, the time-varying and continuous characteristic of traffic flow is considered, and the multi-time-step prediction model is proposed based on the single-time-step model. To test the accuracy of the proposed multi-time-step prediction model, GPS data of taxis in Foshan city, China, are used. The results show that the multi-time-step prediction model with spatial-temporal parameters provides a good performance compared with the support vector machine (SVM) model, artificial neural network (ANN) model, real-time-data model, and history-data model. The results also appear to indicate that the proposed k-nearest neighbor model is an effective approach in predicting the short-term traffic condition.


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    Titel :

    k-Nearest Neighbor Model for Multiple-Time-Step Prediction of Short-Term Traffic Condition




    Erscheinungsdatum :

    2016




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Print


    Sprache :

    Englisch


    Schlagwörter :

    Klassifikation :

    BKL:    56.24 Straßenbau / 74.75 / 56.24 / 55.84 / 74.75 Verkehrsplanung, Verkehrspolitik / 55.84 Straßenverkehr
    Lokalklassifikation TIB:    770/7000