학술논문
面向网联自动驾驶混合交通流的高速公路流量控制方法 / A Control Method for Mixed Traffic Flows with CAVs and HDVs on Freeways
Document Type
Academic Journal
Author
Source
交通信息与安全 / Journal of Transport Information and Safety. 41(5):74-82
Subject
Language
Chinese
ISSN
1674-4861
Abstract
由网联自动驾驶车辆(connected and autonomous vehicles,CAVs)与人类驾驶车辆(human-driven vehi-cles,HDVs)组成的新型混合交通流,是未来交通发展趋势.利用CAVs精准可控的优势提升交通管控能力是重要的研究方向之一.通过控制上游路段的CAVs目标巡航速度,间接影响HDVs的车速,实现上游交通需求的精准调控.考虑混合交通流具有时变特性以及平顺性需求,基于模型预测控制,以CAVs速度为控制量,建立流量控制偏差和CAVs速度变化幅度最小为目标的混合交通流量控制模型,实现控制过程优化;并设计控制模型的分布式求解算法,提高模型求解速度.基于VISSIM仿真结果表明:流量控制模型在不同CAVs渗透率、需求水平、目标需求下降率和控制更新时间间隔下均表现良好,流量控制精度均在80%以上;控制策略求解时间小于0.1 s,能够满足CAVs实时控制需求,从而更快调节流量到目标值,避免下游拥堵;模型可实现上游需求流量最高可下降40%,能够应对高速公路需求大幅波动情况,最大程度预防高速公路瓶颈拥堵.该方法对于预防高速公路拥堵、提高通行效率具有借鉴意义,为基于CAVs的主动交通管控方法开发提供参考.
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.
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.