Study Analyzed Impact of Connected Vehicle Technology, Actuated Control, and a Genetic Algorithm on Transit Signal Priority Performance for a Simulated Intersection in Charlotte, North Carolina.
Charlotte, North Carolina, United States
Impact of Connected and Autonomous Vehicles on Signalized Intersections with Transit Signal Priority
Summary Information
Transit signal priority (TSP) is a strategy which aims to improve the performance of transit vehicles by adjusting traffic signals to give priority for transit vehicles at intersections, on arterials, or in networks. Researchers in North Carolina used a microscopic simulation-based approach to study the potential for combining TSP with Connected Vehicle technology (TSPCV) to enhance mobility and safety. The traffic performance of two TSPCV control strategies, actuated TSP with Connected Vehicle (CV), and TSP with CV with genetic algorithm (GA) optimization, were compared with two conventional control strategies, actuated signal control with and without TSP. The four-approach intersection of Central Avenue and Eastway Drive in Charlotte, North Carolina was selected as the evaluation location in the simulation.
Methodology
Simulation of the selected intersection was used to compare the traffic performance of proposed control strategies under conditions with different CV penetration rates, traffic demand, and bus arrival frequency. The car following model used in the simulation was the intelligent driver model (IDM), a model widely used to simulate CVs. Four basic simulation environments were established using the Simulation of Urban Mobility (SUMO) platform which enabled assessment of the following control strategies:
- Baseline scenario – Actuated signal control without TSP (NTSP)
- Actuated signal control with TSP (ATSP)
- Actuated signal with TSP using CV (ATSP-CV)
- Optimized signal control with TSP using GA (TSP-GA)
The average vehicle delay was used as the primary measure to assess traffic performance in each simulation scenario.
Findings
- Compared to the baseline scenario, under peak period conditions, ATSP decreased bus delays by 54.31 percent, and ATSP-CV technology reduced bus delays by 63.36 percent. In these two scenarios, average delay for cars increased by 7.54 percent and 2.46 percent, respectively.
- TSP-GA was observed to perform better in peak hours than in off-peak periods. TSP-GA in peak conditions decreased bus delays by 24.50 percent compared to baseline while increasing average car delays by 2.58 percent. During off-peak hours, average bus delay was reduced by 23.50 percent and average car delay was increased by 8.88 percent.
- Under high levels of traffic demand, the TSP-GA control strategy provided substantial reduction of delay to buses while minimizing the negative impact on other traffic. Under low traffic demand conditions, ATSP-CV performed the best in terms of overall delay impacts.
