Parameters of the genetic algorithm.

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，进而导致跟车行为发生变化。雨天时，路面形成的水膜会降低轮胎抓地力，增加水滑现象及交通事故的发生概率。尽管当前已存在多种不同天气条件下的跟驰模型，但针对微气象因子具体影响的相关研究仍较为匮乏。为实现智能交通的精细化管理与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对应模型至关重要。本文以智能驾驶模型（Intelligent Driver Model, IDM）与Wiedemann99模型为研究对象，纳入微气象条件的影响因素。通过引入驾驶员判断系数λ，对IDM与Wiedemann99模型进行改进，分别得到改进后的新型模型：I-IDM与I-Wiedemann99。本文采用仿真验证方法，选取车速与跟驰距离作为I-IDM及I-Wiedemann99模型的参数标定性能指标，并以均方根百分比误差（Root Mean Square Percentage Error, RMSPE）之和作为拟合优度函数。通过仿真对参数标定前后跟驰车辆的行驶轨迹、车速及加速度进行对比分析与验证。研究结论如下：改进后的I-IDM模型的平均误差与标准差均小于I-Wiedemann99模型；其中I-IDM模型参数标定的均方根百分比误差最大值为0.4568，最小值为0.1324。而I-Wiedemann99模型的RMSPE最大值为0.4613，最小值为0.1376。相较于I-IDM模型，I-Wiedemann99模型的参数标定结果分布更为分散，这表明I-IDM模型对跟驰行为的模拟效果优于I-Wiedemann99模型。本研究成果可为进一步完善跟驰行为理论提供参考，并为雨天场景下的精细化交通管理提供理论依据与物联网（Internet of Things, IoT）技术支撑。