Robust Traffic Control Using a First Order Macroscopic Traffic Flow Model

Traffic control is at the core of research in transportation engineering because it is one of the best practices for reducing traffic congestion. It has been shown in recent years that the traffic control problem involving Lighthill-Whitham-Richards (LWR) model can be formulated as a Linear Programming (LP) problem given that the corresponding initial conditions and the model parameters in the fundamental diagram are fixed. However, the initial conditions can be uncertain when studying actual control problems. This paper presents a stochastic programming formulation of the boundary control problem involving chance constraints, to capture the uncertainty in the initial conditions. Different objective functions are explored using this framework, and the proposed model is validated by conducting case studies for both a single highway link and a highway network. In addition, the accuracy of relaxed optimal results is proved using Monte Carlo simulation.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3075225
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Liu, Hao
  • Claudel, Christian
  • Machemehl, Randy
• Publication Date: 2022-7

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 8048-8062
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 7
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Highway traffic control; Traffic congestion; Uncertainty
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01883415
• Record Type: Publication
• Files: TRIS
• Created Date: May 25 2023 1:31PM