Microscopic Traffic Simulation Study Emulated the Impacts of Multiple CV Applications in Wyoming and Utah Using Field Data and Advanced Signal Controller Specifications.
Statewide, Wyoming, United States
Salt Lake City, Utah, United States
Connected-Autonomous Traffic Signal Control Algorithms for Trucks and Fleet Vehicles
Summary Information
Connected and Automated Vehicles (CAV) technologies create opportunities to develop traffic signal control strategies that have the potential to improve the operations and safety of signalized intersections. This study developed and tested traffic signal control algorithms in which CAV-equipped heavy trucks can communicate with traffic signals. Several applications are studied; Intelligent Traffic Signals (ISIG), Freight Signal Priority (FSP), Queue Warning (Q-WARN), and Speed Harmonization (SPD-HARM). The application, testing and analysis were performed through microscopic traffic simulation with real-world traffic control software through Software-in-the-Loop (SIL) implementation. Vehicles were tested at six signalized intersections adjacent to I-80, in Wyoming. The study was also expanded to assess CAV-based Transit Signal Priority (TSP) using a test-network in Salt Lake City, Utah.
METHODOLOGY
This project used a communication process which uses latitude/longitude coordinates of CAVs and intersections to define the detection zone and enables information sharing. Once the communication was established, CAV application was called and implemented as needed. . This study allows only trucks and busses to send a Signal Control Priority (SCP) request to the signal. Finally, intersection performance for the six major intersections on the I-80 corridor was assessed through the average vehicle delays per vehicle type for various model scenarios. For the freight and transit signal CV-based priority strategies, in addition to the build-in SCP functions in the traffic controller, the study developed a 3-level customized conditional TSP that uses bus schedule adherence and real-time ridership to determine the level of TSP for each approaching vehicle (no TSP, low TSP or high TSP). The TSP tests were performed on a 10-intersection congested urban corridor in Salt Lake City, UT, using 2015 traffic data and 2025 traffic projections.
FINDINGS
- FSP application could reduce the intersection delay of CAV-equipped trucks between 10 and 70 percent, depending on location. The implemented FSP generally increased the average network-wide delay, especially on other vehicles and non-FSP signal phases, but the negative impacts were not significant in most cases. Overall, the most benefits of the FSP implementation were recorded for CV truck rates between 50 and 75 percent.
- The CAV-based Q-WARN application were effective in reducing truck delays by an average of two to five percent and increasing vehicle spacing in the vicinity of the intersections (up to 134 percent). The larger spacing was due to the earlier start of deceleration and lower lane speeds and could potentially prevent rear-end conflicts to improve intersection safety.
- SPD-HARM can reduce intersection delays for trucks between four and 82 percent, without impacts on other traffic.
- The implementation of unconditional SCP (all CVs would send a request and receive priority) provided significant delay savings for trucks, up to 40 percent, but it also caused 35 percent increase in delay for other vehicles.
- When schedule adherence and real-time ridership was used to determine the level of granted TSP for BRT, the introduced TSP strategies reduced transit delays by about six percent, without significant impacts on other traffic.
- BRT implementation without TSP is found to benefit all modes, reducing delays by more than 20 percent and improving speeds by 15 percent for general-purpose traffic. About a 50 percent decrease in delay for buses was also observed. Cars and trucks experienced a 21 percent delay reduction.
