Abstract: Connected vehicle (CV) has been proven to effectively improve traffic safety under fog weather conditions in microscopic driving behavior analysis. A microscopic car-following model is important for simulating the trajectory of CV in fog weather. Based on the traffic information perception mode and car-following behavior characteristics of CV in fog weather, this paper proposes a fog-related intelligent driver model of connected vehicle (FIDMCV) considering factors such as time headway, weighting, and compliance, based on the fog-related intelligent driver model. To evaluate the effectiveness of the FIDMCV model and assess the traffic impact of CV in fog weather, the cumulative reciprocal of Time-to-collision (1/TTC) and throughput were selected as analysis indicators, and numerical simulation scenarios with different CV penetration rates and decelerations of the leading vehicle were established. Before conducting numerical simulations, sensitivity analyses were performed on key parameters of time headway and compliance. The simulation results show that with the increase in the penetration rate of CV, mixed traffic flow more effectively improved traffic safety in fog weather. However, it also led to an increase in car-following distances of vehicles, thereby reducing road throughput and decreasing traffic efficiency. The proportion of reduction in cumulative 1/TTC values for CV in a high risk scenario (deceleration of 6 m⋅s²) is 14.3%, and in medium-low risk scenarios (decelerations of 4 m ⋅ s² and 2 m ⋅ s²) is 5.6% and 6.3%, respectively, indicating that the improvement of traffic safety for CV is more significant in the high risk scenario. The proposed FIDMCV model can effectively reflect the traffic safety improvement effect and car-following distance increase characteristics of CV in fog weather conditions, and can be used as a microscopic simulation tool for CV.

Key words: traffic engineering, car-following model, numerical simulation, connected vehicle(CV), fog weather

摘要： 网联车辆(ConnectedVehicle, CV)已从微观驾驶行为方面被证实其能有效改善雾天交通安全，但鲜有建立微观跟驰模型来模拟CV车辆在雾天的跟驰轨迹。本文根据雾天CV车辆的交通信息获取模式和跟驰行为特征，在雾天智能驾驶人模型的基础上，构建考虑车头时距因子、遵守因子和权重因子的雾天网联车辆智能驾驶人模型(Fog-related Intelligent Driver Model of Connected Vehicle, FIDMCV)。为评价FIDMCV模型的有效性及评估CV车辆在雾天的交通影响，选取累计碰撞时间倒数和交通量作为分析指标，并建立不同CV车辆渗透率和领车减速度的数值仿真场景。在进行数值仿真前，针对关键参数遵守因子和车头时距因子的取值进行敏感性分析。仿真结果表明：随着CV车辆渗透率的增加，混合交通流能更有效地改善雾天交通安全，但会导致雾天车辆跟驰间距增加，从而减少道路交通量，降低交通效率。CV车辆在高风险场景下(6 m·s-2减速度)的碰撞时间倒数值减少比例为14.3%，在中低风险(4m·s-2和2m·s-2减速度)场景中为5.6%和6.3%，因此CV车辆在高风险场景下的交通安全改善作用更显著。本文提出的FIDMCV模型能有效再现雾天CV车辆的交通安全改善作用和跟驰间距增加特征，可用作雾天CV车辆的微观仿真工具。

关键词: 交通工程, 跟驰模型, 数值仿真, 网联车辆, 雾天