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Research Reports |
| Title: | Connected-Autonomous Traffic Control Algorithms for Trucks and Fleet Vehicles |
| Authors: | Milan Zlatkovic, Mohamed Ahmed, Zorica Cvijovic, and Sara Bashir |
| University: | University of Wyoming |
| Publication Date: | Jul 2022 |
| Report #: | MPC-22-470 |
| Project #: | MPC-599 |
| TRID #: | 01857402 |
| Keywords: | algorithms, autonomous vehicles, connected vehicles, traffic signal control systems, truck traffic, vehicle fleets |
| Type: | Research Report – MPC Publications |
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 test-case 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.
Zlatkovic, Milan, Mohamed Ahmed, Zorica Cvijovic, and Sara Bashir. Connected-Autonomous Traffic Control Algorithms for Trucks and Fleet Vehicles, MPC-22-470. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2022.
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