手动-自动驾驶混合交通流元胞传输模型

为了分析自动驾驶车辆对交通流宏观特性的影响, 以手动驾驶车辆与自动驾驶车辆构成的混合交通流为研究对象, 提出了不同自动驾驶车辆比例下的混合交通流元胞传输模型(CTM); 应用Newell跟驰模型作为手动驾驶车辆跟驰模型, 应用PATH实验室真车测试标定的模型作为自动驾驶车辆跟驰模型; 计算了手动驾驶与自动驾驶车辆跟驰模型在均衡态的车头间距-速度函数关系式, 推导了不同自动驾驶车辆比例下的混合交通流基本图模型, 计算了混合交通流在不同自动驾驶车辆比例下的最大通行能力、最大拥挤密度以及反向波速等特征量, 依据同质交通流CTM理论建立了不同自动驾驶车辆比例下的混合交通流CTM; 选取移动瓶颈问题进行算例分析, 应用混合交通流CTM计算了不同自动驾驶车辆比例下的移动瓶颈影响时间, 应用跟驰模型对移动瓶颈问题进行微观数值仿真, 分析了混合交通流CTM计算结果与跟驰模型微观仿真结果之间的误差, 验证了混合交通流CTM的准确性。研究结果表明: 混合交通流CTM能够有效计算移动瓶颈的影响时间, 在不同自动驾驶车辆比例下, 混合交通流CTM计算结果与跟驰模型微观仿真结果的误差均在52 s以下, 相对误差均小于10%, 表明了混合交通流CTM在实际应用中的准确性; 混合交通流CTM体现了从微观到宏观的研究思路, 基于微观跟驰模型与目前逐步开展的小规模自动驾驶真车试验之间的关联性, 混合交通流CTM能够较真实地反映未来不同自动驾驶车辆比例下单车道混合交通流演化过程, 增加了模型研究的应用价值。 

Cell transmission model of mixed traffic flow of manual-automated driving

In order to analyze the impacts of automated driving vehicles on the macroscopic traffic flow characteristics, the mixed traffic flow with manual driving vehicles and automated driving vehicles was considered as the study objective, and the cell transmission model(CTM) of mixed traffic flow under different proportions of automated driving vehicles was proposed.The car-following model proposed by Newell was used for the car-following model of manual driving vehicles, while the model calibrated by PATH program used the real vehicle experiments was employed for the car-following model of automated driving vehicles.The function relation of equilibrium space headway-speed was calculated according to the car-following models of manual and automated driving vehicles.The fundamental diagram model of mixed traffic flow was derived under different proportions of automated driving vehicles. In addition, the characteristic quantities such as the maximum capacity, the maximum jam density, and backward wave speed were calculated for the mixed traffic flow under different proportions of automated driving vehicles. Based on the CTM theory of homogenous traffic flow, the CTM of mixed traffic flow was proposed under different proportions of automated driving vehicles. The moving bottleneck problem was selected for example analysis, the influence times of moving bottleneck under different proportions of automated driving vehicles were calculated by using the mixed traffic flow CTM. The car-following models were used for the microcosmic numerical simulation on the moving bottleneck problem. The errors between the calculation results of the mixed traffic flow CTM and the microcosmic simulation results of car-following models were analyzed. The accuracy of mixed traffic flow CTM was validated. Research result shows that the proposed mixed traffic flow CTM can effectively calculate the influence time of moving bottleneck. Under different proportions of automated driving vehicles, the errors between the calculation results of the mixed traffic flow CTM and the microcosmic simulation results of car-following models are all below 52 s, and the relative errors are all below 10%, which indicates the accuracy of the proposed mixed traffic flow CTM in actual application. The mixed traffic flow CTM reflects the study idea from microcosmic to macroscopic. There are relationships between the microcosmic car-following models and the small-scale automated driving vehicle experiments being gradually implemented. The mixed traffic flow CTM can truthfully reflect the evolutionary process of mixed traffic flow on single lane in the background of automated driving under different proportions in the future, which enhances the application value of the model research.