The method of deep reinforcement learning based on deep deterministic policy gradient is employed to solve the proposed model with a row of continuous control variables. That helps to remove the speed difference between the vehicles from on-ramp and mainline flooding into the merging zone, and to sustain actual traffic density close to critical density to counteract capacity drop as indicated with macroscopic fundamental diagram. The paper proposes an adaptive coordinated variable speed limit model to manage highway speed of on-ramp and mainline continuous sections without priority to mainline. Highway merging bottleneck is challenged with serious traffic conflicts between on-ramp and mainline vehicles, causing significant capacity drop and drastic speed changes.
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