Signal Control of Urban Expressway Ramp Based on Reinforcement Learning

With economic development and the increasing number of vehicles in cities, urban transport systems have become an important issue in urban development. Efficient traffic signal control is a key part of achieving intelligent transport. Reinforcement learning methods show great potential in solving complex traffic signal control problems with multidimensional states and actions. Most of the existing work has applied reinforcement learning algorithms to intelligently control traffic signals. In this paper, we investigate the agent-based reinforcement learning approach for the intelligent control of ramp entrances and exits of urban arterial roads, and propose the Proximal Policy Optimization (PPO) algorithm for traffic signal control. We compare the method controlled by the improved PPO algorithm with the no-control method. The simulation experiments used the open-source simulator SUMO, and the results showed that the reinforcement learning control ramp technique increases the average speed by 7% and reduces the lane occupancy rate by 15% compared to the no-control method, which proves the effectiveness of the proposed method.