1. Multi-Intersection Control with Deep Reinforcement Learning and Ring-and-Barrier Controllers
- Author
-
Guangyuan Pan, Matthew Muresan, and Liping Fu
- Subjects
050210 logistics & transportation ,Ring (mathematics) ,Computer science ,Mechanical Engineering ,05 social sciences ,Transferability ,02 engineering and technology ,Topology ,Intersection ,020204 information systems ,0502 economics and business ,0202 electrical engineering, electronic engineering, information engineering ,Reinforcement learning ,Control (linguistics) ,Civil and Structural Engineering - Abstract
This paper discusses a machine-learning traffic signal control method. A full-scale corridor is analyzed and the transferability of using a model pre-trained on a single intersection is examined. Two controller designs are explored, a simple two-phase design and a full ring-and-barrier style controller. The full ring-and-barrier controller adapts many of the key features present in traditional controllers, such as protected-permissive left turns, so that they can be used in the reinforcement learning (RL) paradigm. This study is the first to propose a method that uses deep reinforcement learning (DRL) to implement a full ring-and-barrier style controller. The study also examines the feasibility of using transfer learning to pre-train a model on a single intersection and then fine-tune it for application in a complete environment. Training is done on a simple four lane intersection and the pre-trained model is then transferred for fine-tuning to six controllers operating on a corridor modeled with field data obtained for University Avenue in Waterloo, Ontario, Canada. The performance of the fully trained model is then compared with the existing signal plans in relation to the average delay and average queue length. Application of the ring-and-barrier design to this corridor was found to reduce delays by at least 5% and average queue lengths at intersections by 27%.
- Published
- 2020