Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment

TL;DR

This paper introduces a deep reinforcement learning-based adaptive transit signal priority system that leverages connected vehicle data to reduce bus travel time with minimal impact on general traffic in a microsimulation environment.

Contribution

It develops and tests a novel RL-based TSP agent integrated with a general traffic signal controller using connected vehicle data in a microsimulation environment.

Findings

Bus travel time reduced by about 21%.

Marginal impact on general traffic at high saturation.

Slightly better than actuated control with TSP.

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.