Coordinated multi-agent reinforcement learning transit signal priority reduces simulated bus travel time by 27% across two intersections, outperforming independent agents (22%) while keeping side street delay increases small.
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment
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abstract
Model free reinforcement learning (RL) provides a potential alternative to earlier formulations of adaptive transit signal priority (TSP) algorithms based on mathematical programming that require complex and nonlinear objective functions. This study extends RL - based traffic control to include TSP. Using a microscopic simulation environment and connected vehicle data, the study develops and tests a TSP event-based RL agent that assumes control from another developed RL - based general traffic signal controller. The TSP agent assumes control when transit buses enter the dedicated short-range communication (DSRC) zone of the intersection. This agent is shown to reduce the bus travel time by about 21%, with marginal impacts to general traffic at a saturation rate of 0.95. The TSP agent also shows slightly better bus travel time compared to actuated signal control with TSP. The architecture of the agent and simulation is selected considering the need to improve simulation run time efficiency.
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cs.AI 1years
2024 1verdicts
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Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Control
Coordinated multi-agent reinforcement learning transit signal priority reduces simulated bus travel time by 27% across two intersections, outperforming independent agents (22%) while keeping side street delay increases small.