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In this paper, we present our work on clustering and prediction of temporaldynamics of global congestion configurations in large-scale road networks.Instead of looking into temporal traffic state variation of individual links,or of small areas, we focus on spatial congestion configurations of the wholenetwork. In our work, we aim at describing the typical temporal dynamicpatterns of this network-level traffic state and achieving long-term predictionof the large-scale traffic dynamics, in a unified data-mining framework. Tothis end, we formulate this joint task using Non-negative Tensor Factorization(NTF), which has been shown to be a useful decomposition tools for multivariatedata sequences. Clustering and prediction are performed based on the compacttensor factorization results. Experiments on large-scale simulated dataillustrate the interest of our method with promising results for long-termforecast of traffic evolution.

Analysis of Large-scale Traffic Dynamics using Non-negative Tensor Factorization