Multi-objective optimization of freeway traffic flow via a fuzzy reinforcement learning method

A fuzzy approach to reinforcement learning in multi-objective optimization problem of freeway traffic flow control and dynamic route guidance is presented. The problem domain, a freeway network integration management application considers the efficiency and equity of system, is formulated as a distributed fuzzy reinforcement learning problem. The Gini coefficient is adopted in this study as an indicator of equity. The DFRL 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 large traffic demand. Using the DFRL approach, Compared with traditional methods, the network's Gini coefficient has fallen by 30% or more.