基于低频轨迹数据的分时租赁驾驶人驾驶风格分析

Driving Style Analysis for Car-sharing Drivers with Low-frequency Trajectory Data

摘要:

基于上海某汽车分时租赁企业的运营数据，开展了基于低频轨迹数据的驾驶行为特征提取及驾驶风格分析.采用相对超速时间比例及其变异系数为驾驶风格特征指标，基于K-means聚类算法将驾驶人风格划分为谨慎、温和、激进三类，相应驾驶人比例分别为54.04%、36.60%和9.36%.对不同驾驶风格租赁用户的出行、运行及个体特征的比较发现,不同驾驶风格的用户在出行、运行速度及车辆能耗特征方面具有差异性，在年龄、性别及违章方面无统计上的显著差异.

Abstract:

Driving style analysis was conducted based on the operation data of a car-sharing project located in Shanghai. Rather than high resolution driving behavior data in most driving style studies, low-frequency trajectory data were utilized. The relative speeding time ratio and its coefficient of variation on urban expressways were used as feature variables. K-means clustering algorithm was used to classify driving styles. A total of three categories were concluded, which are calm, moderate and aggressive with the percentages of 54.04%, 36.60%, and 9.36% correspondingly. Then, for the purpose of understanding different driving styles, comparison analyses were further conducted from the aspects of trip characteristics, vehicle operation features, and personal information. The results show that drivers with distinct styles have substantial differences in their trip and vehicle operation characteristics. The aggressive drivers tend to drive faster, holding higher speeding tendency but better vehicle energy efficiency. Besides, no statistically significant differences in age, gender or violation between driving styles are identified.