Ma Jian, Zhang Yuchen, Zhang Liyan, Gao Zongwei, Cao Keyi, Fu Qianlong, Qian Zheng
School of Civil Engineering, Suzhou University of Science and Technology, Suzhou, China.
School of Business, Suzhou University of Science and Technology, Suzhou, China.
PLoS One. 2025 Jul 7;20(7):e0326191. doi: 10.1371/journal.pone.0326191. eCollection 2025.
Different micro-meteorological conditions can affect a driver's judgment of road conditions, leading to changes in following behavior. On rainy days, water films on the road reduce traction, increasing the likelihood of hydroplaning and traffic accidents. While there are existing following models under various weather conditions, research on the specific impact of micro-meteorological factors is insufficient. To achieve fine management in intelligent transportation and real-time monitoring of vehicle states, it's essential to study following behavior under different micro-meteorological conditions and establish corresponding models. This paper focuses on the Intelligent Driver Model (IDM) and the Wiedemann99 model, considering the impact of micro-meteorological conditions. By incorporating a driver's judgment factor, λ, the IDM and Wiedemann99 models are improved, leading to the development of new models: I-IDM and I-Wiedemann99. Simulation validation is used to choose speed and following distance as performance indicators for parameter calibration of the I-IDM and I-Wiedemann99 models, with the sum of Root Mean Square Percentage Error (RMSPE) as the goodness-of-fit function. Comparisons are made between the driving paths, speeds, and accelerations of following vehicles before and after calibration, verified through simulations. The conclusions are as follows: the average error and standard deviation of the improved I-IDM model are smaller than those of the I-Wiedemann99 model, with the maximum Root Mean Square Percentage Error (RMSPE) for I-IDM model parameter calibration being 0.4568 and the minimum being 0.1324. For the I-Wiedemann99 model, the maximum RMSPE is 0.4613 and the minimum is 0.1376. The parameter calibration results of the I-Wiedemann99 model are more dispersed compared to those of the I-IDM model, indicating that the I-IDM model simulates following behavior more effectively than the I-Wiedemann99 model. The findings of this study can provide a reference for further improving the theory of following behavior, and offer a theoretical basis and IoT technology support for refined traffic management under rainy conditions.
不同的微气象条件会影响驾驶员对道路状况的判断，从而导致跟车行为发生变化。在雨天，道路上的水膜会降低摩擦力，增加车辆打滑和发生交通事故的可能性。虽然已有各种天气条件下的跟车模型，但对微气象因素的具体影响研究不足。为了实现智能交通的精细化管理和车辆状态的实时监测，研究不同微气象条件下的跟车行为并建立相应模型至关重要。本文聚焦于智能驾驶员模型（IDM）和维德曼99模型，考虑微气象条件的影响。通过引入驾驶员判断因子λ，对IDM和维德曼99模型进行改进，从而开发出新模型：I-IDM和I-维德曼99。利用仿真验证来选择速度和跟车距离作为I-IDM和I-维德曼99模型参数校准的性能指标，以均方根百分比误差（RMSPE）之和作为拟合优度函数。通过仿真验证，对校准前后跟车车辆的行驶路径、速度和加速度进行比较。结论如下：改进后的I-IDM模型的平均误差和标准差均小于I-维德曼99模型，I-IDM模型参数校准的最大均方根百分比误差（RMSPE）为0.4568，最小为0.1324。对于I-维德曼99模型，最大RMSPE为0.4613，最小为0.1376。与I-IDM模型相比，I-维德曼99模型的参数校准结果更为分散，这表明I-IDM模型比I-维德曼99模型更有效地模拟了跟车行为。本研究结果可为进一步完善跟车行为理论提供参考，并为雨天精细化交通管理提供理论依据和物联网技术支持。
