混有CACC和ACC车辆的连续型元胞自动机交通流模型

Continuous Cellular Automata Model of Traffic Flow Mixed with Cooperative Adaptive Cruise Control Vehicles and Adaptive Cruise Control Vehicles

现有的混合交通流元胞自动机模型中自动驾驶车辆与手动驾驶车辆在跟驰模型上大多仅存在反应时间上的差别,并不能体现自动驾驶上层控制系统实时调节加速度保持车速稳定的特点,基于Gipps模型和Path实验室标定的自适应巡航控制(adaptive cruise control,ACC)和协同自适应巡航控制(cooperative adaptive cruise control,CACC)跟驰模型提出了更符合自动驾驶机理的连续型元胞自动机模型。通过计算机数值仿真分别从速度、流量、拥堵比例以及期望车间时距方面对不同渗透率下的混合交通流进行分析。结果表明,智能网联车辆能有效提高道路通行能力,当渗透率为40%~60%时,道路通行能力比纯人工驾驶车辆时提升14%~30%,当道路上全为智能网联车时,其通行能力约为纯人工驾驶车辆的1.9倍;同时在相同智能网联车渗透率下,道路通行能力随智能网联车辆期望车间时距减小而增大。

In the existing cellular automata model of mixed traffic flow,most of the auto-driving vehicles and manual-driving vehicles have only the difference in reaction time in the car-following model,which does not reflect the characteristics of the auto-driving upper control system real-time adjustment of acceleration to keep the vehicle speed stable.Based on the Gipps model and the adaptive cruise control(ACC)and cooperative adaptive cruise control(CACC)car-following models calibrated by the Path laboratory,a continuous cellular automaton model that was more in line with the automatic driving mechanism was proposed.Through computer numerical simulation,the mixed traffic flow under different permeability was analyzed from the aspects of speed,flow,congestion ratio and expected time between workshops.The results show that intelligent networked vehicles can effectively improve road capacity.When the penetration rate is 40%~60%,the road capacity is increased by 14%~30%compared with purely manual driving vehicles.When the roads are all intelligent networked vehicles,its traffic capacity is about 1.9 times that of purely manually driven vehicles;At the same time,under the same intelligent networked vehicle penetration rate,the road traffic capacity increases as the expected time between intelligent networked vehicles decreases.