Jiangsu Key Laboratory of Urban ITS, Southeast University, Nanjing 211189, China; Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, China; School of Transportation, Southeast University, Nanjing 211189, China.
School of Transportation, Southeast University, Nanjing 211189, China; Nanyang Technological University 639798, Singapore.
Accid Anal Prev. 2024 Oct;206:107709. doi: 10.1016/j.aap.2024.107709. Epub 2024 Jul 9.
Driving behaviors are important cause of expressway crash. In this study, method based on modified time-to-collision (MTTC) to identify risky driving behaviors on an expressway diverge area is proposed, thus investigating the impact of velocity and acceleration features of risky driving behavior. Firstly, MTTC is applied to judge whether the behavior is risky. Then, the relationships between velocity, acceleration and different driving behavior on the expressway diverge area were fit by binary logistic regression models (BLR) with L2 regularization and random forests (RF) models, and the models were interpreted by feature importance plots and partial dependency plots. The results show that the AUC metric of 4 RF models for 4 types of driving behaviors, namely, left lane change, right lane change, acceleration and deceleration, are 0.932, 0.845, 0.846 and 0.860 separately. The interpretation of models demonstrates that velocity and absolute value of acceleration greatly affect the risk of the driving behaviors. Different driving behaviors with a certain acceleration have a range of safety speed range. The range will get narrower with the growth of maximum absolute value of acceleration rate, and will be nearly non-exist when the acceleration is over 5 m/s. In conclusion, this study provided a methodology to measure the risk of driving behaviors and establish a model to recognize of risky driving behaviors. The results can lay the foundation for making countermeasures to prevent risky driving behaviors by managing the vehicle speed.
驾驶行为是高速公路碰撞事故的重要原因。本研究提出了一种基于改进碰撞时间（MTTC）的方法，用于识别高速公路出口区域的危险驾驶行为，从而研究了速度和加速度特征对危险驾驶行为的影响。首先，应用 MTTC 判断行为是否危险。然后，通过具有 L2 正则化和随机森林（RF）模型的二元逻辑回归模型（BLR）拟合速度、加速度与高速公路出口区域不同驾驶行为之间的关系，并通过特征重要性图和部分依赖图对模型进行解释。结果表明，4 种驾驶行为（左车道变换、右车道变换、加速和减速）的 4 个 RF 模型的 AUC 度量值分别为 0.932、0.845、0.846 和 0.860。模型的解释表明，速度和加速度的绝对值极大地影响驾驶行为的风险。具有一定加速度的不同驾驶行为具有一定的安全速度范围。随着最大绝对加速度的增长，范围会变窄，当加速度超过 5m/s 时，范围几乎不存在。总之，本研究提供了一种衡量驾驶行为风险的方法，并建立了识别危险驾驶行为的模型。研究结果为通过管理车辆速度来制定预防危险驾驶行为的对策奠定了基础。
