Spatial and temporal analysis of traffic states on large scale networks

We propose a set of methods aiming at extracting large scale features of road traffic, both spatial and temporal, based on local traffic indexes computed either from fixed sensors or floating car data. The approach relies on traditional data mining techniques like clustering or statistical analysis and is demonstrated on data artificially generated by the mesoscopic traffic simulator Metropolis. Results are compared to the output of another approach that we propose, based on the belief-propagation (BP) algorithm and an approximate Markov random field (MRF) encoding on the data. In particular, traffic patterns identified in the clustering analysis correspond in some sense to the fixed points obtained in the BP approach. The identification of latent macroscopic variables and their dynamical behavior is also obtained and the way to incorporate these in the MRF is discussed as well as the setting of a general approach for traffic reconstruction and prediction based on floating car data.