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.

• Record URL:
  https://doi.org/10.1049/iet-its.2011.0157
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/1751956X
• Authors:
  • Han, Yufei
  • Moutarde, Fabien
• Publication Date: 2013-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 283-295
• Serial:
  • IET Intelligent Transport Systems
  • Volume: 7
  • Issue Number: 3
  • Publisher: Institution of Engineering and Technology (IET)
  • ISSN: 1751-956X
  • EISSN: 1751-9578
  • Serial URL: http://digital-library.theiet.org/content/journals/iet-its

Subject/Index Terms

• TRT Terms: Factor analysis; Statistical analysis; Traffic data; Traffic flow theory; Traffic forecasting; Traffic models
• Uncontrolled Terms: Road networks
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting; I71: Traffic Theory;

Filing Info

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