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Journal Articles Transportation research. Part C, Emerging technologies Year : 2017

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

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Andres Ladino Lopez
Alain Kibangou
Hassen Fourati

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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Dates and versions

hal-01521723 , version 1 (12-05-2017)

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Andres Ladino Lopez, Alain 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, 2017, 80 (July), pp.216-238. ⟨10.1016/j.trc.2017.05.002⟩. ⟨hal-01521723⟩
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