This invention discloses a collaborative control method for highway merging zones based on deep
reinforcement learning. A LiikeSim-Python co-
simulation environment is established, and loop detectors are set up in the
simulation environment to acquire
traffic flow data upstream and downstream of the highway merging zone. An EM
algorithm based on
Gaussian mixture distribution is used as a traffic state classifier, taking the
traffic flow data of the highway merging zone as input to classify the traffic state of the merging zone. A
state space, action space, and reward function are designed. Using the
state space of the highway merging zone as input and the actions of the variable
speed limit agent and the ramp metering agent as output, a multi-agent shared experience
network model under time-series characteristics is constructed. An independent experience
pool is set up for the variable
speed limit agent and the ramp metering agent, and the interaction experience between the agent and the
traffic simulation environment is collected at the control
cycle frequency. The agent model is trained using sampled data. The trained agent model is used to realize collaborative control of the highway merging zone. This invention can reduce travel delays in highway merging zones.