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

Andres Ladino 1 Alain Y. Kibangou 1 Carlos Canudas de Wit 1 Hassen Fourati 1
1 NECS - Networked Controlled Systems
Inria Grenoble - Rhône-Alpes, GIPSA-DA - Département Automatique
Abstract : 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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Article dans une revue
Transportation research. Part C, Emerging technologies, Elsevier, 2017, 80 (July), pp.216-238. 〈10.1016/j.trc.2017.05.002〉
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Andres Ladino, Alain Y. Kibangou, Carlos Canudas de Wit, Hassen Fourati. A real time forecasting tool for dynamic travel time from clustered time series. Transportation research. Part C, Emerging technologies, Elsevier, 2017, 80 (July), pp.216-238. 〈10.1016/j.trc.2017.05.002〉. 〈hal-01521723〉

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