Cluster analysis and multi-level modeling for evaluating the impact of rain on aggressive lane-changing characteristics utilizing naturalistic driving data

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2022-05-01 DOI:10.1080/19439962.2022.2069896
Anik Das, Md Nasim Khan, Mohamed M. Ahmed, S. Wulff
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引用次数: 6

Abstract

Abstract This study investigated lane-changing characteristics with regard to drivers’ aggressiveness in rain and clear weather utilizing the SHRP2 Naturalistic Driving Study (NDS) dataset. An innovative methodology was developed to identify lane-changing events and extract corresponding parameters from the SHRP2 NDS database. Initially, K-means and K-medoids clustering methods were examined to classify drivers into non-aggressive and aggressive categories considering six features related to driving behavior, and K-means clustering was adopted based on the average silhouette width method (ASWM). Two-level mixed-effects linear regression models were calibrated to assess the contributing factors that affect lane-changing durations, which revealed that different vehicle kinematics, traffic, driver, and roadway characteristics, as well as weather conditions combined with other factors, were significant in the calibrated models for both driver types. The results revealed that the lane-changing duration associated with heavy rain decreased with a higher speed limit for aggressive drivers. Furthermore, the lane-changing duration associated with light/moderate rain decreased with the number of lanes for non-aggressive drivers. The study findings could be leveraged to incorporate drivers’ aggressiveness into microsimulation lane-changing model calibration and validation as well as could have significant implications in improving safety in Connected and Autonomous Vehicles (CAV).
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利用自然驾驶数据,聚类分析和多级建模来评估降雨对侵略性变道特性的影响
摘要本研究利用SHRP2自然驾驶研究(NDS)数据集,研究了雨天和晴朗天气下驾驶员攻击性变道特征。开发了一种创新的方法来识别变道事件并从SHRP2 NDS数据库中提取相应的参数。首先,结合驾驶行为的6个特征,研究了K-means聚类和K-medoids聚类方法,将驾驶员分为非攻击性和攻击性两类,并基于平均轮廓宽度方法(ASWM)采用K-means聚类。对两级混合效应线性回归模型进行了校准,以评估影响变道时间的因素,结果表明,不同的车辆运动学、交通、驾驶员和道路特征,以及天气条件和其他因素在校准模型中对两种驾驶员类型都有显著影响。结果显示,对于侵略性司机来说,暴雨时变道所需的时间随着车速限制的提高而减少。此外,在小雨或中雨的情况下,非攻击性司机的变道时间随着车道数的增加而减少。研究结果可用于将驾驶员的攻击性纳入微模拟变道模型校准和验证中,并可能对提高联网和自动驾驶汽车(CAV)的安全性产生重大影响。
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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