Connected-Autonomous Traffic Control Algorithms for Trucks and Fleet Vehicles

Connected and Autonomous Vehicle (CAV) technologies enable communication among vehicles, and vehicles and infrastructure, paving the way for multiple safety and operational applications. This research developed and tested traffic signal control algorithms and control programs, which utilized CAV-equipped heavy trucks and traffic signals. The focus of the study was on Intelligent Traffic Signals (ISIG), Freight Signal Priority (FSP), Transit Signal Priority (TSP), Queue Warning (Q-WARN), Speed Harmonization (SPD-HARM) and Emergency Preemption (PREEMPT) applications. The application, testing and analysis were performed through Traffic In Cities Simulation Model (VISSIM) microsimulation software, coupled with real-world traffic control software (Econolite ASC/3). The testcase networks included six signalized intersections adjacent to I-80 in Wyoming, and a busy urban corridor along State Street in Salt Lake City, Utah. The results showed significant improvements in operations and safety for CV-equipped vehicles. FSP can reduce intersection truck delays up to 70 percent, TSP can reduce transit delays six percent on average, SPD-HARM can reduce truck delays in excess of 80 percent, Q-WARN can significantly improve safety without impacts on operations and PREEMPT can reduce the intersection delay of emergency vehicles up to 35 percent and increase their speeds in excess of 50 percent.

• Record URL:
  https://www.ugpti.org/resources/reports/details.php?id=1089
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  University of Wyoming, Laramie

  Department of Civil & Architectural Engineering and Construction Management
  Laramie, WY  United States 

  Mountain-Plains Consortium

  North Dakota State University
  Fargo, ND  United States  58108

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Zlatkovic, Milan
  • Ahmed, Mohamed
  • Cvijovic, Zorica
  • Bashir, Sara
• Publication Date: 2022-7

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; References; Tables;
• Pagination: 70p

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Connected vehicles; Traffic signal control systems; Truck traffic; Vehicle fleets
• Identifier Terms: VISSIM (Computer model)
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01857402
• Record Type: Publication
• Report/Paper Numbers: MPC 22-470
• Contract Numbers: MPC-599
• Files: UTC, TRIS, ATRI, USDOT
• Created Date: Sep 12 2022 10:18AM