Connected-Autonomous Traffic Signal Control Algorithms for Trucks and Fleet Vehicles
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2021-12-01
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Edition:Final Report (December 2018 – September 2021)
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Abstract: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), Queue Warning (Q-WARN), and Speed Harmonization (SPD-HARM) 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). Communication and information sharing was modeled using Python programming language, while signal control applications was programmed directly in the signal control software. The test-case networks included six signalized intersections adjacent to I-80, in Wyoming. Model scenarios included different rates of CAV-equipped trucks. Additionally, the study tested CAV-based Transit Signal Priority using a test-network in Salt Lake City, Utah. First, the study developed a communication process which uses latitude/longitude coordinates of CAVs and intersections to define the detection zone and enabled information sharing. Once the communication was established, CAV application can be called and implemented as needed. FSP provides extra time for trucks approaching a signalized intersection, therefore, minimizing delays. The implementation of FSP has the potential to reduce truck intersection delays between 10 and 70 percent, with minimal impacts on other traffic. Q-WARN can warn drivers on a downstream queue of the conditions and allow extra time to slow down. This application can reduce CAV truck delays 2 to 6 percent, with a significant increase in spacing between vehicles. SPD-HARM optimizes the speed of vehicles as they approach an intersection so that the delay is minimized. The results show that SPD-HARM can reduce intersection delays for trucks between 4 and 82 percent. TSP implementation allows extra time to transit vehicles at signalized intersections, and the developed algorithm has the potential to reduce transit delays up to six percent. The developed control programs can be directly implemented in the field, as they use the information which exists within CAV technology and the field-based signal control software.
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