Adaptive Traffic Signal Control Usign Reinforcement Learning

Abstract

Traffic demand increases day by day and leads to traffic congestion problem in big cities. One solution is to build new traffic infrastructure that might cause a huge burden on country’s economy. Another solution is to optimize existing traffic network by controlling traffic lights at intersections. The advancement in Reinforcement Learning technique has shown a potential to solve complex nature of traffic congestion problem. In this thesis research, reinforcement learning was implemented in context of traffic control system to investigate the improvement made in traffic flow at intersection. The two reinforcement learning agents, turn-based and time-based, were designed to optimize traffic flow at intersection. Turn-based agent opens the traffic signal for that side of intersection that has high traffic flow, in contrast to this time-based agent follows the fixed phase cycle and changes the phase duration on the basis of traffic. In both turn-based and time-based agents, the state is obtained by encoding scalar number of queue length. The agents were trained using deep Q-learning approach. In order to improve the agent’s learning experience four different traffic scenarios were generated. The simulation software SUMO was used to generate simulations for agent training and evaluation. The result shows that turn-based and time-based agents perform better than conventional traffic light control system in all traffic scenarios. Both agents are designed according to the need of practical application and experiment is conducted to evaluate them in simulation on real-world map of Islamabad city. This experiment includes four intersection on Kashmir Highway form G-11 to G-9. The result shows that reinforcement learning agents perform better than conventional light traffic control system at all four intersections