Tensor Robust Principal Component Analysis with Continuum Modeling of Traffic Flow: Application to Abnormal Traffic Pattern Extraction in Large Transportation Networks

The study addresses the needs of detection and description of abnormal traffic patterns in large transportation networks formed due to the presence of unexpected disruptions, such as natural or manmade disasters. In order to take into account complex spatiotemporal structure of traffic dynamics and preserve multi-mode correlations, tensor-based traffic data representation is put forward. Further, with the reasonable assumptions on normal or expected traffic dynamics to exhibit similar periodic structure, the problem of abnormal or unexpected traffic patterns detection is treated as a low-rank modeling problem. More precisely, tensor robust principal component analysis is applied for the purpose of discovering distinctive normal and abnormal traffic patterns. For the validation purposes, continuum modeling approach is employed to emulate traffic dynamics, with consideration of the effect of aforementioned disruptions. The results suggested the applicability of proposed approach in order to extract abnormal traffic patterns in large transportation networks.

• Record URL:
  https://doi.org/10.1016/j.trpro.2018.11.031
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S235214651830320X
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23521465
• Supplemental Notes:
  • © 2018 Stanislav Lykov and Yasuo Asakura. Published by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Lykov, Stanislav
  • Asakura, Yasuo
• Conference:
  • International Symposium of Transport Simulation (ISTS’18) and the International Workshop on Traffic Data Collection and its Standardization (IWTDCS’18)
  • Location: Matsuyama , Japan
  • Date: 2018-8-6 to 2018-8-8
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 187-194
• Serial:
  • Transportation Research Procedia
  • Volume: 34
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2352-1465
  • Serial URL: http://www.sciencedirect.com/science/journal/23521465/
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Disasters; Traffic flow
• Uncontrolled Terms: Anomaly detection; Continuum modeling; Spatiotemporal traffic dynamics; Tensor robust principal component analysis; Traffic network disruption; Traffic patterns; Transportation networks
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01689692
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 24 2018 2:56PM