A Collaborative Reinforcement Learning Approach to Urban Traffic Control Optimization

The high growth rate of vehicles per capita now poses a real challenge to efficient urban traffic control (UTC). An efficient solution to UTC must be adaptive in order to deal with the highly-dynamic nature of urban traffic. In the near future, global positioning systems and vehicle-to-vehicle/infrastructure communication may provide a more detailed local view of the traffic situation that could be employed for better global UTC optimization. In this paper we describe the design of a next-generation UTC system that exploits such local knowledge about a junction's traffic in order to optimize traffic control. Global UTC optimization is achieved using a local adaptive round robin (ARR) phase switching model optimized using collaborative reinforcement learning (CRL). The design employs an ARR-CRL-based agent controller for each signalized junction that collaborates with neighbouring agents in order to learn appropriate phase timing based on the traffic pattern. We compare our approach to non-adaptive fixed-time UTC system and to a saturation balancing algorithm in a large-scale simulation of traffic in Dublin's inner city centre. We show that the ARR-CRL approach can provide significant improvement resulting in up to ~57% lower average waiting time per vehicle compared to the saturation balancing algorithm.