A real time forecasting tool for dynamic travel time from clustered time series |
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| Affiliation: | 1. Clermont Université, Université Blaise Pascal, Institut Pascal, Clermont-Ferrand, France;2. CNRS, UMR 6602, IP, Aubière, France;3. CEREMA, Direction Territoriale Centre-Est, Département Laboratoire de Clermont-Ferrand, Clermont-Ferrand, France;1. Department of Mathematics, East China University of Science and Technology, Shanghai 200237, China;2. Rotterdam School of Management, Erasmus University, 3000 DR Rotterdam, The Netherlands;3. School of Management, Fudan University, Shanghai 200433, China;4. Warwick Business School, University of Warwick, Coventry CV4 7AL, UK;1. Department of Communications and Networking, Aalto University, Finland;2. University of California Berkeley, Ind. Eng. and Opns. Res. Dept., Berkeley, CA 94720-1777, United States |
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| Abstract: | This paper addresses the problem of dynamic travel time (DTT) forecasting within highway traffic networks using speed measurements. Definitions, computational details and properties in the construction of DTT are provided. DTT 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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| Keywords: | Traffic forecasting Travel time forecasting Sensor fusion Clustering Kalman filter |
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