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Driving style recognition and comparisons among driving tasks based on driver behavior in the online car-hailing industry

机译：基于在线车载行业的驾驶员行为的驾驶任务中的风格识别与比较

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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.
机译：作为共同经济的产品，可以有效地使用在线车载平台，以帮助最大限度地提高资源和减轻交通拥堵。司机的行为是他或她的驾驶风格的特点，并在交通安全中发挥着重要作用。本文提出了一种小说框架，以基于在线车载处理数据对驾驶风格（定义为激进，正常和谨慎）来调查驱动程序在执行各种驾驶任务时的不同特性（定义为巡航，乘坐请求和丢弃 - 关闭）并进行某些机动（定义为转动，加速和减速）。所提出的模型基于使用基于阈值的端点检测方法，主成分分析和K-means聚类的检测和分类来构建驱动操纵。基于分类的动作，比较和分析驾驶员展示的驾驶员表现出不同的驾驶任务。中国南京的经验结果表明，所提出的框架可以检测驾驶机动，准确地分类驱动风格。此外，根据该框架，驱动任务导致驱动风格的变化，并且在不同的驱动任务期间驱动风格的变化显着不同地对转动，加速和减速机动进行了显着的影响。

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