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

Statistical traffic data analysis is an important topic in traffic management and control. Current research focuses on analysing traffic flows of individual links or local regions in a transportation network. Less attention is paid to the global view of traffic states over the entire network, which is important for modelling large-scale traffic scenes. The aim of this study is 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 the 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 longterm forecasting.

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  • English

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  • Accession Number: 01496544
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 27 2013 11:30AM