  In this paper, we present our work on clustering and prediction of temporal
dynamics 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 whole
network. In our work, we aim at describing the typical temporal dynamic
patterns of this network-level traffic state and achieving long-term prediction
of the large-scale traffic dynamics, in a unified data-mining framework. To
this end, we formulate this joint task using Non-negative Tensor Factorization
(NTF), which has been shown to be a useful decomposition tools for multivariate
data sequences. Clustering and prediction are performed based on the compact
tensor factorization results. Experiments on large-scale simulated data
illustrate the interest of our method with promising results for long-term
forecast of traffic evolution.
