Variable speed limit control method in work zone area of eight-lane highway considering effects of connected automated vehicles
To improve the traffic operation efficiency and safety of highway work zone areas under the Internet of vehicles,a variable speed limit(VSL)control method based on reinforcement learning was proposed.The intelligent driving model and the model based on real vehicle experiments were selected to model the car-fol-lowing behaviors of human-driven vehicles and connected automated vehicles(CAVs),respectively.A com-posite reward was constructed with the traffic throughput of bottleneck downstream segment(TTBDS)as an efficiency indicator and the standard deviation of bottleneck segment speed(SDBSS)as a safety indicator.The deep deterministic policy gradient algorithm was utilized to dynamically yield the optimal speed limit val-ues for each lane.The simulation results show that the proposed VSL control method can effectively improve traffic flow efficiency and safety under different penetration rates(PRs)of CAVs.The improvement is more obvious under lower PRs of CAVs.When the PR of the CAVs is 1.0,the TTBDS is increased by 10.1％and the mean of SDBSS is decreased by 68.9％.When the PR of the CAVs is 0,the TTBDS is increased by 20.7％and the mean of SDBSS is decreased by 78.1％.The introduction of CAVs can increase the TTBDS by up to 52.0％.
variable speed limit controldeep deterministic policy gradient algorithmeight-lane highway work zone areaconnected automated vehiclescooperative adaptive cruise control
万方数据
维普
