Identification of Optimal Locations of Adaptive Traffic Signal Control Using Heuristic Methods

Adaptive Traffic Signal Control (ATSC) adjusts signal timings to real-time traffic measurements, increasing operational efficiency within a network. However, ATSC is both expensive to install and operate making it infeasible to deploy at all signalized intersections within a network. This study presents a bi-level optimization framework that applies heuristic methods to identify a limited set of locations for ATSC deployment within an urban network. At the upper-level, the Population Based Incremental Learning (PBIL) algorithm is employed to generate, evaluate, learn, and update different ATSC configurations. The lower-level uses the delay-based Max-Pressure algorithm to simulate the ATSC configuration within a microsimulation platform. The study proposes improvements to the PBIL algorithm by considering constraints on the maximum number of intersections for ATSC deployment and incorporates prior information about the intersection performance (i.e., informed search). Simulation results on the traffic network of State College, PA reveal that the proposed PBIL algorithm consistently outperforms baseline methods that select locations only based on queue-lengths or delays in terms of reducing overall network travel times. The study also reveals that intersections experiencing the highest delays or longest queues are not always the best candidates for ATSC. Moreover, applying ATSC at all intersections does not always provide the best performance; in fact, ATSC applied to some locations could increase travel times by contributing additional congestion downstream. Additionally, the modified PBIL algorithm with the informed search strategy is more efficient at identifying promising solutions suggesting it can be readily applied to more generalized optimization problems.

• Record URL:
  • https://doi.org/10.1016/j.ijtst.2023.12.003
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2046043023001089
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20460430
• Supplemental Notes:
  • © 2023 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Ahmed, Tanveer
  • Liu, Hao
  • Gayah, Vikash V
• Publication Date: 2024-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Maps; References;
• Pagination: pp 122-136
• Serial:
  • International Journal of Transportation Science and Technology
  • Volume: 13
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2046-0430
  • Serial URL: http://www.sciencedirect.com/science/journal/20460430

Subject/Index Terms

• TRT Terms: Adaptive control; Heuristic methods; Location; Optimization; Traffic signal control systems
• Geographic Terms: State College (Pennsylvania)
• Subject Areas: Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01905087
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 19 2024 4:35PM