Constructing a network fundamental diagram using a sample of connected vehicles and stationary sensors

The Network Fundamental Diagram (NFD can be used to monitor and alleviate traffic congestion. Theoretically, one needs the trajectories of all vehicles in order to accurately estimate the NFD, which is not feasible. One solution is to construct the NFD using a sample of vehicles serving as probes together with an estimated market penetration rate (MPR). In a previous paper, the authors built a model to first estimate the MPR and then incorporated it in the NFD estimation. That model used data from probe vehicles (trip travel time, origin, destination, and route) and a few traffic count sensors on a hypothetical grid network. The results indicated that sensor locations have a significant impact on the accuracy of the estimated MPR and NFD. In this paper, the authors extend the model by adding more spatial criteria and test the approach on a real-world calibrated network, namely downtown Los Angeles. The authors examine the effect of selecting different probe vehicle samples on the model performance. After identifying the spatial rules for selecting sensor locations and the criteria to select probe vehicles, the authors present a methodology for estimating the NFD producing an acceptable NFD root mean square error. The significance of this study is that first, the model is validated using a real-world calibrated network; second, the data needed for the estimation are highly available, given that with connectivity the location and speed of a sample of vehicles is available; and third, the computational load to select detector locations and probe vehicles is minimum.

• Record URL:
  https://doi.org/10.1016/j.trip.2021.100471
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2590198221001767
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/25901982
• Supplemental Notes:
  • © 2021 Jianhe Du and Hesham A. Rakha. Published by Elsevier Ltd. Abstract reprinted with permission of Elsevier.
• Authors:
  • Du, Jianhe
  • Rakha, Hesham A
  • 0000-0002-5845-2929
• Publication Date: 2021-12

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • Transportation Research Interdisciplinary Perspectives
  • Volume: 12
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2590-1982
  • Serial URL: https://www.journals.elsevier.com/transportation-research-interdisciplinary-perspectives
• Publication flags:

  Open Access (libre)

Subject/Index Terms

• TRT Terms: Connected vehicles; Traffic congestion; Traffic models; Vehicle trajectories
• Geographic Terms: Los Angeles (California)
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01783311
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 28 2021 11:30AM