Coordinated dual-objective transit signal priority: a deep reinforcement learning approach

Transit Signal Priority (TSP) has been widely used for reducing transit delays for decades. Since reliability is valued equally as travel time, a dual-objective coordinated (DC) TSP is developed to adaptively optimize transit headway adherence and travel time simultaneously over consecutive intersections. This is the first attempt at using a centralized agent deep reinforcement learning (RL) framework in solving a coordinated TSP optimization problem. Decentralized control algorithms using multi-agent RL are also developed as baseline scenarios. TSP algorithms are trained and tested in a stochastic microsimulation environment within Aimsun Next for a corridor segment in Toronto with a transit line experiencing high service variability. DC TSP demonstrates a clear promise in reducing headway variability and travel time at different traffic levels. It highlights the importance of coordinating TSP actions at consecutive intersections. It is also shown to be robust, providing effective control under various configurations of bus stop locations.