Revisiting the traffic flow observability problem: A matrix-based model for traffic networks with or without centroid nodes

This study introduces a graph theory-based model that addresses the link flow observability problem in traffic networks by optimizing passive sensor deployment. The model aims to determine the minimal number of sensors and their optimal placement. It constructs a virtual network and uses isomorphic graph theory to map between the original and virtual networks, ensuring consistency in nodes, links, and link directions. Two formulas are proposed to calculate the minimum number of observable links required across different networks, factoring in links, ordinary nodes, centroid nodes, and added links. Key concepts such as chords, cut sets, and loops, along with their matrices, are analyzed. A matrix-based framework is developed to consider flow conservation conditions. Results show that solving the full link flow observability problem using node flow conservation equations yields a fixed number of sensors with non-unique deployment schemes, Additionally, a resource-constrained sensor network optimization (RSNO) model is presented, employing null space projection (NSP) as an objective function to quantify the impact of budget constraints particularly under the condition if all the link flows cannot be observed. Numerical examples demonstrate the RSNO model's applications.

• Record URL:
  • https://doi.org/10.1016/j.trb.2024.103099
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0191261524002236
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhuo, Yue
  • Shao, Hu
  • 0000-0001-7816-0707
  • Lam, William H K
  • Tam, Mei Lam
  • 0000-0002-0149-6402
  • Cao, Shuhan
• Publication Date: 2024-12

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: 103099
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 190
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Network links; Network nodes; Optimization; Sensors; Traffic flow; Virtual reality
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01937036
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 15 2024 4:05PM