Volume 41 Issue 5
Oct.  2023
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GAO Jinyong, LUO Sheng, WANG Xinyuan, ZHOU Cheng, AN Lianhua. A Control Method for Mixed Traffic Flows with CAVs and HDVs on Freeways[J]. Journal of Transport Information and Safety, 2023, 41(5): 74-82. doi: 10.3963/j.jssn.1674-4861.2023.05.008
Citation: GAO Jinyong, LUO Sheng, WANG Xinyuan, ZHOU Cheng, AN Lianhua. A Control Method for Mixed Traffic Flows with CAVs and HDVs on Freeways[J]. Journal of Transport Information and Safety, 2023, 41(5): 74-82. doi: 10.3963/j.jssn.1674-4861.2023.05.008

A Control Method for Mixed Traffic Flows with CAVs and HDVs on Freeways

doi: 10.3963/j.jssn.1674-4861.2023.05.008
  • Received Date: 2022-03-29
    Available Online: 2024-01-18
  • The mixed traffic with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) is an ongoing trend. Improving traffic control capabilities through CAVs' precision and control advantages is a key focus area. By regulating the desired cruising speed of CAVs on the upstream segment, it indirectly influences HD-Vs'speeds, enabling fine-tuning control of traffic demand upstream. Considering the time-varying nature of traffic flow and the need for comfortable driving, a model predictive control approach is used. This model uses CAVs' speed as the controlling factor, creating a traffic control model. It aims to minimize deviations in flow control and changes in CAVs' speeds for optimized control processes. A distributed solution algorithm for the control model is designed. The solution algorithm enhances the model's speed of resolution. The effectiveness of the proposed control model is verified through VISSIM simulation. It shows that the control accuracy exceeds 80% across different CAVs penetration rates, demand levels, target demand drop rates, and update time intervals. The control strategy has a solu-tion time of less than 0.1 seconds. It enables real-time control requirements for CAVs, thereby efficiently reducing traffic flow towards the target to avoid congestion downstream. The model can potentially decrease the upstream de-mand flow by up to 40%, enabling it to effectively manage significant fluctuations in highway demand and reduce highway bottleneck congestion. This method has reference significance for preventing highway congestion and im-proving traffic efficiency. It also provides a reference for the development of active traffic control methods based on CAVs.

     

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