Clustering and Modeling of Network level Traffic States based on Locality Preservative Non-negative Matrix Factorization

Abstract : In this paper, we propose to cluster and model network-level traffic states based on a geometrical weighted similarity measure of network-level traffic states and locality preservative non-negative matrix factorization. The geometrical weighted similarity measure makes use of correlation between neighboring roads to describe spatial configurations of global traffic patterns. Based on it, we project original high-dimensional network-level traffic information into a feature space of much less dimensionality through the matrix factorization method. With the obtained low-dimensional representation of global traffic information, we can describe global traffic patterns and the evolution of global traffic states in a flexible way. The experiments prove validity of our method for the case of large-scale traffic network.
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Communication dans un congrès
8th Intelligent Transport Systems (ITS) European Congress, Jun 2011, Lyon, France. 2011
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Yufei Han, Fabien Moutarde. Clustering and Modeling of Network level Traffic States based on Locality Preservative Non-negative Matrix Factorization. 8th Intelligent Transport Systems (ITS) European Congress, Jun 2011, Lyon, France. 2011. 〈hal-00638074〉

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