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.
A real time forecasting tool for dynamic travel time from clustered time series
2017
Article (Journal)
English
BKL: | 55.84 Straßenverkehr |
Online Contents | 1996
|Transportation Research Record | 1996
|British Library Conference Proceedings | 1995
|British Library Conference Proceedings | 1996
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