驾驶模拟器
加速度
毒物控制
聚类分析
工程类
计算机科学
驱动因素
模拟
运输工程
汽车工程
人工智能
中国
物理
环境卫生
法学
经典力学
医学
政治学
作者
Yongfeng Ma,Wenlu Li,Kun Tang,Ziyu Zhang,Shuyan Chen
标识
DOI:10.1016/j.aap.2021.106096
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI