论文标题
基于车辆类，速度和道路几何形状的驾驶员行为识别和分类
Vehicle Class, Speed, and Roadway Geometry Based Driver Behavior Identification and Classification
论文作者
论文摘要
本文重点是研究车辆阶级尤其是重型车辆的影响，这是对以下车辆行为的造成的，特别是在以下车辆和领先的车辆之间的保险杠距离（GAP）方面。这是通过从下一代仿真（NGSIM）数据集中提取和分析与美国加利福尼亚州埃默里维尔市80号州际公路（I 80）的下一代模拟（NGSIM）数据集的不同汽车遵循的发作来完成的。将统计分析的结果与有关该主题进行的研究工作的综合文献的结果进行了比较，然后通过使用自然主义驾驶数据校准的Gazis-Herman-Rothery（GHR）CAR遵循模型进一步评估了使用不同的行为群集。我们评估了同一车辆类别驱动程序之间的相似性和差异，从而验证了统计分析的结果，并强调了未来可能的实现，以改善微观模拟中的建模。
This paper focuses on the study of the impact that the class of the vehicle, leading heavy vehicles in particular, causes on the following vehicle's behavior, specifically in terms of the bumper-to-bumper distance (gap) between the following and leading vehicles. This was done by extracting and analyzing different car-following episodes from the Next Generation Simulation (NGSIM) dataset for Interstate 80 (I 80) in Emeryville, California, USA. The results of the statistical analysis are compared to that of the synthesized literature of research efforts that have been conducted on the topic, then are further assessed utilizing different behavioral clusters for the Gazis-Herman-Rothery (GHR) car-following model calibrated from naturalistic driving data. We assess the similarities and differences in car-following behavior between drivers of the same vehicle class, validating the results of the statistical analysis and highlighting possible future implementations for improved modeling in microscopic simulation.
