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Statistical traffic data analysis is a hot topic in traffic management andcontrol. In this field, current research progresses focus on analyzing trafficflows of individual links or local regions in a transportation network. Lessattention are paid to the global view of traffic states over the entirenetwork, which is important for modeling large-scale traffic scenes. Our aim isprecisely to propose a new methodology for extracting spatio-temporal trafficpatterns, ultimately for modeling large-scale traffic dynamics, and long-termtraffic forecasting. We attack this issue by utilizing Locality-PreservingNon-negative Matrix Factorization (LPNMF) to derive low-dimensionalrepresentation of network-level traffic states. Clustering is performed on thecompact LPNMF projections to unveil typical spatial patterns and temporaldynamics of network-level traffic states. We have tested the proposed method onsimulated traffic data generated for a large-scale road network, and reportedexperimental results validate the ability of our approach for extractingmeaningful large-scale space-time traffic patterns. Furthermore, the derivedclustering results provide an intuitive understanding of spatial-temporalcharacteristics of traffic flows in the large-scale network, and a basis forpotential long-term forecasting.

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