A Freeway Traffic Flow Control Model Based on Distributed Reinforcement Learning

A control-oriented macroscopic dynamic traffic flow discrete model applicable to the nonlinear,uncertain,fuzzy system of freeway traffic was proposed and discussed.Distributed Reinforcement Learning(DRL) was introduced to control and guide the traffic system.The traditional freeway traffic model Metanet was upgraded to an improved Metanet-OD within which the origins and destinations of freeway traffic was taken into account.The DRL was used in ramp metering and VMS guidance for freeway network.The actions space of DRL was designed and the DRL algorithm was presented.The control efficiency of the proposed model was also verified with simulation.