  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.
