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Incorporating accident liability into crash risk analysis: A multidimensional risk source approach
Accident Analysis & Prevention ( IF 5.7 ) Pub Date : 2021-02-16 , DOI: 10.1016/j.aap.2021.106035
Xin Wang , Zhaowei Qu , Xianmin Song , Qiaowen Bai , Zhaotian Pan , Haitao Li

In the field of traffic safety, the occurrence of accidents remains a cause of concern for road regulators as well as users. Exploring risk factors inducing the accidents and quantifying the accident risk will not only benefit the prevention and control of traffic accidents but also assist in developing effective risk propagation model for road accidents. This study uses detailed accident record data to mine the risk factors affecting the occurrence of accidents, and quantify the accident risk under the combination of risk factors. First, by reviewing relevant literature and analyzing historical accident, we construct a multi-dimension characterization framework of risk factors with bi-level structure. The Human Factors Analysis and Classification System (HFACS) is applied to supplement and improve the framework. Next, under this framework, we identify the risk factors in traffic accident record, and analyze the statistical characteristics from the level of risk sources and risk characteristics. Then, the concept of accident liability weight is proposed to measure the impact of risk factors on accident occurrence. Through the liability affirmation of risk factors, the accident probability are updated. Last, we establish an accident risk quantify model (ARQM) based on the mean mutual information to compare the likelihood of accidents in different scenarios. In addition, we compare the accident probability and risk under equivalent liability and liability affirmation, as well as give some fundamental ideas regarding how to effectively prevent accidents.



中文翻译:

将事故责任纳入碰撞风险分析:多维风险源方法

在交通安全领域,事故的发生仍然是道路管理者和使用者关注的原因。探索诱发事故的风险因素,量化事故风险,不仅有利于交通事故的预防和控制,而且有助于建立有效的道路事故风险传播模型。本研究使用详细的事故记录数据来挖掘影响事故发生的风险因素,并在风险因素组合下量化事故风险。首先,通过回顾相关文献并分析历史事故,构建了具有双层结构的风险因素多维表征框架。人为因素分析和分类系统(HFACS)用于补充和改进该框架。接下来,在此框架下,我们识别交通事故记录中的风险因素,并从风险来源和风险特征的层次分析统计特征。然后,提出了事故责任权重的概念,以衡量风险因素对事故发生的影响。通过风险因素的责任确认,事故概率得以更新。最后,我们基于平均互信息建立事故风险量化模型(ARQM),以比较不同情况下事故的可能性。此外,我们比较了等效责任和责任认定下的事故概率和风险,并给出了有关如何有效预防事故的一些基本思路。并从风险来源和风险特征的层次分析统计特征。然后,提出了事故责任权重的概念,以衡量风险因素对事故发生的影响。通过风险因素的责任确认,事故概率得以更新。最后,我们基于平均互信息建立事故风险量化模型(ARQM),以比较不同情况下事故的可能性。此外,我们比较了等效责任和责任认定下的事故概率和风险,并给出了有关如何有效预防事故的一些基本思路。并从风险来源和风险特征的层次分析统计特征。然后,提出了事故责任权重的概念,以衡量风险因素对事故发生的影响。通过风险因素的责任确认,事故概率得以更新。最后,我们基于平均互信息建立事故风险量化模型(ARQM),以比较不同情况下事故的可能性。此外,我们比较了等效责任和责任认定下的事故概率和风险,并给出了有关如何有效预防事故的一些基本思路。更新事故概率。最后,我们基于平均互信息建立事故风险量化模型(ARQM),以比较不同情况下事故的可能性。此外,我们比较了等效责任和责任认定下的事故概率和风险,并给出了有关如何有效预防事故的一些基本思路。更新事故概率。最后,我们基于平均互信息建立事故风险量化模型(ARQM),以比较不同情况下事故的可能性。此外,我们比较了等效责任和责任认定下的事故概率和风险,并给出了有关如何有效预防事故的一些基本思路。

更新日期:2021-02-16
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