Driving style recognition and comparisons among driving tasks based on driver behavior in the online car-hailing industry. (May 2021)

• Record Type: Journal Article
• Authors: Ma, Yongfeng; Li, Wenlu; Tang, Kun; Zhang, Ziyu; Chen, Shuyan
• Is Part Of: Accident analysis and prevention. Volume 154(2021)
• Publication Date: 2021-05
• Subjects: Driver behavior -- Driving style -- Driving maneuver detection -- Driving tasks -- k-means clustering -- Principal component analysis (PCA); Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2021.106096
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Proposing a novel framework to classify driving styles based on large-scale online car-hailing data. Dividing the driving tasks into three categories (defined as cruising, ride requests, and drop-off) according to the tasks of professional drivers of online-hailed vehicles. Analyzing driving style based on maneuver detection (defined as turning, acceleration, deceleration). Analyzing variations in driving style during turning, acceleration, and deceleration maneuvers among the three driving tasks. The proposed framework is evaluated by a real case in Nanjing, China. Abstract: 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.