Spatial and Temporal Analysis of Traffic States on Large Scale Networks

International audienceWe 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

Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013