Prediction of high-risk bus drivers characterized by aggressive driving behavior

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Eunsol Cho, Yunjong Kim, Seolyoung Lee, Cheol Oh
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Abstract

AbstractIdentification of driving behavior is a fundamental to developing effective treatments to address various traffic-related problems. In particular, the driving behavior of city bus drivers is of great interest because the crash severity can become much higher than any other vehicle types due to the larger number of passengers on board. However, there is a lack of effective policy preparation to prevent crashes because of limitations associated with identifying intrinsic factors underlying the cause of traffic crashes based on driving behavior analysis. This study aims to develop a methodology to predict high-risk bus drivers, which can be a baseline in establishing effective bus safety policies. An in-depth questionnaire survey was conducted to collect wellness data to represent intrinsic characteristics used for inputs of the proposed prediction methodology in addition to the aggressive driving behavior data obtained from in-vehicle data recorders. Bus drivers were classified into two groups, normal drivers and risky drivers, based on aggressive driving behavior. The priority of intrinsic factors was determined by a gradient boosting method and further utilized to derive input features of the proposed method. Deep-learning-based neural network models were evaluated to predict risky bus drivers in this study. A model with variables up to 11th priority as inputs was selected as the best model. A classification accuracy of 85% was achievable with the proposed model. The outcome of this study would be valuable in supporting policymaking activities to prevent aggressive driving behavior.Keywords: aggressive driving behaviorartificial neural networkbus driver wellnessgradient boosting methodtraffic safety Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThis research was supported by a grant from Transportation and Logistics Research Program funded by Ministry of Land, Infrastructure and Transport of the Korean government (21TLRP-B148683-04).
具有攻击性驾驶行为的高危公交司机预测
摘要:识别驾驶行为是开发有效治疗各种交通相关问题的基础。特别是,城市公交车司机的驾驶行为引起了人们的极大兴趣,因为由于乘客人数较多,碰撞的严重程度可能比其他任何车辆都要高得多。然而,由于基于驾驶行为分析识别交通事故原因的内在因素存在局限性,因此缺乏有效的政策准备来防止交通事故。本研究旨在发展一种预测高风险巴士司机的方法,这可以作为制定有效巴士安全政策的基线。除了从车载数据记录仪获得的攻击性驾驶行为数据外,还进行了一项深入的问卷调查,以收集健康数据来表示用于所提出的预测方法输入的内在特征。根据攻击性驾驶行为,将公交车司机分为正常司机和危险司机两组。采用梯度增强法确定了内禀因子的优先级,并进一步推导了该方法的输入特征。本研究评估了基于深度学习的神经网络模型对危险巴士司机的预测。输入变量优先级最高为11的模型被选为最佳模型。该模型的分类准确率达到85%。这项研究的结果将为政策制定活动提供有价值的支持,以防止攻击性驾驶行为。关键词:攻击性驾驶行为人工神经网络公交车司机健康梯度增强方法交通安全披露声明作者未报道潜在利益冲突。本研究由韩国政府土地、基础设施和运输部资助的运输和物流研究计划(21TLRP-B148683-04)资助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.00
自引率
15.40%
发文量
38
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