自动驾驶专用车道影响下的CACC车流管理策略

自动驾驶专用车道对混合交通流的作用与协同自适应巡航控制(Cooperative Adaptive Cruise Control,CACC)车流大小相关.为分析已存在自动驾驶专用车道场景下CACC车流在各车道上的分布情况对交通流的影响,利用已有的人工驾驶车辆(Human-driving Vehicle,HV)和CACC跟...

Management Methods for Cooperative Adaptive Cruise Control Vehicles Flow Considering Dedicated Lanes

The influence of dedicated lanes for connected and automated vehicles on a mixed traffic flow can be analyzed using cooperative adaptive cruise control (CACC) vehicles. The aim of this study is to analyze the influence of CACC flow distribution on traffic flow when dedicated lanes exist. The existing human-driving vehicle and CACC models were used to derive an average time-headway formula in a stable traffic flow. Scenarios of CACC degrading into adaptive cruise control (ACC) and the gap errors of ACC vehicles caused by communication failures were considered in deriving the formula. The capacity difference between a mixed-flow lane and a dedicated lane was analyzed based on the formula. Next, two CACC flow management techniques were developed for a two-lane with dedicated lanes, considering the traffic demands, CACC penetration rates, and capacity. The first case was a fixed approach in which a CACC flow was preferentially allocated to a dedicated lane. The other case was a variable approach in which CACC flow was preferentially allocated to a mixed-flow lane. An optimal CACC flow management method was verified by comparing traffic volumes based on different traffic demands and CACC penetration rates. Furthermore, the analysis was extended to a multilane scenario. Finally, two CACC flow management models were developed to verify the theoretical results through numerical analyses. The results show that the fixed management approach can maximize the traffic volume, owing to the restriction of the capacity of dedicated lanes. However, no restriction is observed for the capacity of the dedicated lane when the CACC flow is preferentially allocated to the mixed-flow lane in the variable management approach. The proportion of CACC demand allocations for this approach is variable. Therefore, the traffic volume for this approach is lower than or equal to that of the fixed approach. A simulation experiment was used to verify the theoretical analysis results. The results show that the traffic flow is in a nonequilibrium state and cannot reach the theoretical equilibrium state with the influence of vehicle disturbance. Therefore, the roadway capacity of the dedicated and mixed-flow lanes are lower than the theoretical value. Under this influence, the maximum traffic volume of the optimal management technique obtained through simulations is lower than the theoretical value, and the optimal allocation ratio obtained by simulation is slightly higher than the theoretical value. Furthermore, sensitivity analyses of the parameters indicate that, the traffic volume can be improved by increasing the penetration rate, increasing the average speed, and decreasing the time gap. However, the traffic volume is insensitive to changes in the gap error.