This paper addresses the problem of dynamic travel time (DT T) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DT T are provided. DT T is dynamically clustered using a K-means algorithm and then information on the level and the trend of the centroid of the clusters is used to devise a predictor computationally simple to be implemented. To take into account the lack of information in the cluster assignment for the new predicted values, a weighted average fusion based on a similarity measurement is proposed to combine the predictions of each model. The algorithm is deployed in a real time application and the performance is evaluated using real traffic data from the South Ring of the Grenoble city in France.


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    Title :

    A real time forecasting tool for dynamic travel time from clustered time series




    Publication date :

    2017




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English



    Classification :

    BKL:    55.84 Straßenverkehr



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