Statistical traffic state analysis in large‐scale transportation networks using locality‐preserving non‐negative matrix factorisation

Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analysing 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 modelling large‐scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatiotemporal traffic patterns, ultimately for modelling large‐scale traffic dynamics, and long‐term traffic forecasting. The authors attack this issue by utilising locality‐preserving non‐negative matrix factorisation (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. The authors 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.