网联背景下货运通道混合交通流安全性研究

Research on the Safety of Mixed Traffic Flow in Freight Channels under the Background of Network Connection

摘要: 随着智能网联技术的发展，自动驾驶车辆开始普及，但仍将长期处于智能网联车辆与人工驾驶车辆混合的交通流状态。为了探究网联车辆的不同渗透率以及重型卡车比例对交通尾部碰撞安全风险水平(TTC)的影响，本文采用Python执行模拟数值仿真实验，引入IDM模型、CACC模型来分别表征人工驾驶和自动驾驶跟驰行为，考虑CACC退化为ACC跟驰模式，量化汽车与重卡、人工驾驶与自动驾驶之间的参数差异，包含反应延迟、安全间距与最大加减速度等，分别建立不同车辆之间的跟驰模型。结果表明：当网联车渗透率大于10%，随着网联车渗透率的升高或重卡比例的下降，TIT不断下降，安全水平不断提高，说明货运重卡因本身重载的特性会提高危险系数，而网联车的引入可以通过提高车辆反应速度来改善安全水平；随着货运重卡比例的上升，网联车渗透率的增加所带来的TIT变化斜率变小，说明网联技术对汽车的改善幅度大于重卡。本研究根据实验结果评估了不同智能网联车辆渗透率、重卡比例下的碰撞风险指标。网联车辆的引入最高可以将通道内的整体安全水平提升53%。

Abstract: With the development of intelligent connected technologies, autonomous vehicles are increasingly prevalent; however, the traffic flow is expected to remain in a mixed state of intelligent connected vehicles and manually driven vehicles for the foreseeable future. To investigate the impact of varying penetration rates of connected vehicles and the proportion of heavy trucks on traffic rear-end collision safety risk levels (Time-to-Collision, TTC), this study employs Python to conduct numerical simulation experiments. The Intelligent Driver Model (IDM) and Cooperative Adaptive Cruise Control (CACC) model are introduced to represent the following behaviors of manually driven and autonomous vehicles, respectively. The study considers the degradation of CACC to an Adaptive Cruise Control (ACC) following mode, quantifying the parameter differences between cars and heavy trucks, as well as between manually driven and autonomous vehicles, which include reaction delays, safety gaps, and maximum acceleration and deceleration rates. Different following models between vehicles are established accordingly. The results indicate that when the penetration rate of connected vehicles exceeds 10%, both an increase in the penetration rate of connected vehicles and a decrease in the proportion of heavy trucks lead to a continuous reduction in the Time-to-Collision (TTC), thereby improving safety levels. This finding suggests that the inherent characteristics of heavy trucks, due to their loaded nature, increase the hazard factor, while the introduction of connected vehicles enhances safety levels by increasing vehicle reaction speeds. Furthermore, as the proportion of heavy trucks rises, the slope of the TTC variation due to an increase in connected vehicle penetration rate diminishes, indicating that the improvements brought about by connected technologies are more significant for passenger cars than for heavy trucks. This research assesses collision risk indicators under varying penetration rates of intelligent connected vehicles and proportions of heavy trucks based on the experimental results. The incorporation of connected vehicles can improve the overall safety level within a corridor by up to 53%.