2012/12/20 by Yufei Han, Han, Yufei, Fabien Moutarde +1
Computer Science · Engineering · Social Sciences · #Computational Engineering #FOS: Computer and information sciences #Finance #Human Mobility and Location-Based Analysis #Traffic Prediction and Management Techniques #Transportation Planning and Optimization #and Science (cs.CE) #cs.CE
paper · pdf · doi:10.48550/arxiv.1212.5264
IET Intelligent Transport Systems (2013)
arxiv created 2012/12/20 · openalex publication_date 2012/12/20 · arxiv updated 2012/12/24 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.