Efficiency and equity based freeway traffic network flow control

A distributed approach in multi-objective optimization problem of traffic flow control and dynamic route guidance is presented. The problem domain, a freeway integration control application considers the efficiency and equity 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 return 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 large traffic demand. Using the DRL approach, the Gini coefficient of the network has been reduced by 28.99% compared to traditional method.