Freeway network traffic management based on distributed reinforcement learning

A distributed machine learning approach in traffic flow control and dynamic route guidance is presented. The problem domain, a freeway network traffic flow integration control application considers multiple objectives of system, is formulated as a distributed reinforcement learning problem. The Gini coefficient is adopted in this study as an indicator of equity. The DRL approach was implemented via a multi-agent control architecture where the decision agent was assigned to each of the on-ramp or VMS. The reward of each agent is simultaneously updating a single shared policy. The control strategy´s effect is demonstrated through its application to the simple freeway network. Analyses of simulation results using this approach show the equity of the system have a significant improvement over traditional control, especially for the case of traffic peak hour. Using the DRL approach, the Gini coefficient of the network has been reduced by 28.99% compared to traditional method.