An ensemble machine learning method for crash responsibility assignment in quasi-induced exposure theory

IF 2.4 3区 工程技术 Q3 TRANSPORTATION
Guopeng Zhang, Ying Cai, Xinguo Jiang, Yingfei Fan, Yue Zhou, Jun Qian
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引用次数: 2

Abstract

Abstract Quasi-induced exposure theory requires the clear-cut assignment of crash responsibility for individual crash-involved drivers. The assignment method based on the citation by police officers poses a concern that the citation would be issued due to the nonmoving violations rather than the driving actions that directly contribute to the crash. Thus, the objective of the study is to improve the accuracy of citation-based responsibility assignments. Binary logistic regression is employed to identify the factors affecting the citation decision of the police officers. An ensemble machine learning method that combines random forest, neural network, and extreme gradient boosting classifiers is established to allocate the crash responsibility. The findings include that (1) the police citation is closely related to the presence of hazardous driving behavior, but it can also be influenced by several factors such as driver age, drinking status, and the collision impact point of the vehicle; and (2) compared to the conventional models, the ensemble machine learning methods have better performance for crash responsibility assignment in terms of accuracy, Kappa coefficient, and area under the curve. The study serves to provide a reliable crash responsibility assignment approach to improve the accuracy of exposure estimation.
准诱导暴露理论中碰撞责任分配的集成机器学习方法
准诱导暴露理论要求对涉及碰撞的驾驶员个体进行明确的碰撞责任分配。基于警察传票的分配方法引起了人们的担忧,即传票将由于非移动违规而不是直接导致撞车的驾驶行为而发出。因此,本研究的目的是提高基于引文的责任分配的准确性。采用二元logistic回归分析方法对影响公安人员传讯决策的因素进行了分析。建立了一种结合随机森林、神经网络和极端梯度增强分类器的集成机器学习方法来分配事故责任。研究发现:(1)警察传讯与危险驾驶行为的存在密切相关,但也会受到驾驶员年龄、饮酒状况、车辆碰撞撞击点等因素的影响;(2)与传统模型相比,集成机器学习方法在准确率、Kappa系数和曲线下面积方面具有更好的碰撞责任分配性能。本研究提供了一种可靠的事故责任分配方法,以提高事故暴露估计的准确性。
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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