Using Deep Reinforcement Learning For Adaptive Traffic Control in Four-Way Intersections

The consequences of traffic congestion include increased travel time, fuel consumption, and the number of crashes. Studies suggest that most traffic delays are due to nonrecurring traffic congestion. Adaptive traffic control using real-time data is effective in dealing with nonrecurring traffic congestion. Many adaptive traffic control algorithms used today are deterministic and prone to human error and limitation. Reinforcement learning allows the development of an optimal traffic control policy in an unsupervised manner. We have implemented a reinforcement learning algorithm that only requires information about the number of vehicles and the mean speed of each incoming road to streamline traffic in a four-way intersection. The reinforcement learning algorithm is evaluated against a deterministic algorithm and a fixed-time control schedule. Furthermore, it was tested whether reinforcement learning can be trained to prioritize emergency vehicles while maintaining good traffic flow. The reinforcement learning algorithm obtains a lower average time in the system than the deterministic algorithm in eight out of nine experiments. Moreover, the reinforcement learning algorithm achieves a lower average time in the system than the fixed-time schedule in all experiments. At best, the reinforcement learning algorithm performs 13% better than the deterministic algorithm and 39% better than the fixed-time schedule. Moreover, the reinforcement learning algorithm could prioritize emergency vehicles while maintaining good traffic flow