Abstract
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.
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