Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, No. 2, Southeast University Road, Jiangning District, Nanjing, 211189, China.
School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, China.
Accid Anal Prev. 2021 May;154:106096. doi: 10.1016/j.aap.2021.106096. Epub 2021 Mar 23.
As a product of the shared economy, online car-hailing platforms can be used effectively to help maximize resources and alleviate traffic congestion. The driver's behavior is characterized by his or her driving style and plays an important role in traffic safety. This paper proposes a novel framework to classify driving styles (defined as aggressive, normal, and cautious) based on online car-hailing data to investigate the distinct characteristics of drivers when performing various driving tasks (defined as cruising, ride requests, and drop-off) and undergoing certain maneuvers (defined as turning, acceleration, and deceleration). The proposed model is constructed based on the detection and classification of driving maneuvers using a threshold-based endpoint detection approach, principal component analysis, and k-means clustering. The driving styles that the driver exhibits for the different driving tasks are compared and analyzed based on the classified maneuvers. The empirical results for Nanjing, China demonstrate that the proposed framework can detect driving maneuvers and classify driving styles accurately. Moreover, according to this framework, driving tasks lead to variations in driving style, and the variations in driving style during the different driving tasks differ significantly for turning, acceleration, and deceleration maneuvers.
作为共享经济的产物，网约车平台可以有效地利用，以最大化资源并缓解交通拥堵。驾驶员的行为特点是驾驶风格，在交通安全中起着重要作用。本文提出了一种基于网约车数据的驾驶风格分类的新框架，以研究驾驶员在执行各种驾驶任务（巡航、接驾请求和下车）和进行特定操作（转弯、加速和减速）时的不同特征。该模型基于使用基于阈值的终点检测方法、主成分分析和 K 均值聚类来检测和分类驾驶操作而构建。根据分类操作，对不同驾驶任务下驾驶员表现出的驾驶风格进行比较和分析。中国南京的实证结果表明，所提出的框架可以准确地检测驾驶操作并分类驾驶风格。此外，根据该框架，驾驶任务导致驾驶风格的变化，并且在转弯、加速和减速操作期间，不同驾驶任务期间的驾驶风格变化差异显著。
