Estimating crash risk and injury severity considering multiple traffic conflict and crash types: A bivariate extreme value approach

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Md Mohasin Howlader , Fred Mannering , Md Mazharul Haque
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引用次数: 0

Abstract

Traffic conflicts are generally considered independent events in existing extreme value theory models to estimate the risk of total or single types of crashes. However, traffic events at a road entity are not necessarily independent interactions and can lead to multiple traffic conflicts with shared common unobserved factors. A comprehensive estimation of crash risks in a road entity needs to consider the correlation of potential traffic conflicts to avoid possible bias in prediction performance and the problem of undetected deficiencies. This study proposes a Bayesian non-stationary bivariate generalised extreme value modelling framework to estimate the severe and non-severe crash risks accounting for the correlation between right-turn and rear-end conflicts at signalised intersections. A deep neural network-based computer vision technique was applied to extract the traffic conflicts from 77 h of video recordings over two right-turn approaches at two signalised intersections in Cairns, Australia. Post encroachment time and modified time to collision were used to characterise right-turn and rear-end conflicts, respectively, while an expected post-collision velocity difference was combined with post encroachment time and modified time to collision for crash risk estimation by injury severity levels. Several covariates were used to address the time-varying heterogeneity of traffic conflict extremes and to estimate the differential crash risks at signal cycles. Results showed a significant correlation between right-turn and rear-end crashes at signal cycle levels, indicating the importance of accounting for the dependency among traffic conflict types. Overall, the bivariate models considering the correlation among traffic conflict types were found to understandably perform better than their univariate counterparts. This study provides a demonstration of a correlated crash risk modelling framework that addresses issues related to the suitable traffic conflict measures, time varying risks (non-stationarity), heterogeneity, and injury severity levels of different crash types.

考虑多种交通冲突和碰撞类型,估算碰撞风险和伤害严重程度:双变量极值法
在现有的极值理论模型中,交通冲突通常被视为独立事件,用于估算总体或单一类型的碰撞风险。然而,道路实体中的交通事件并不一定是独立的相互作用,可能会导致具有共同的未观测因素的多重交通冲突。全面估算道路实体的碰撞风险需要考虑潜在交通冲突的相关性,以避免预测结果可能出现的偏差和未发现的缺陷问题。本研究提出了一种贝叶斯非稳态双变量广义极值建模框架,用于估算严重和非严重碰撞风险,其中考虑了信号灯控制交叉口右转和追尾冲突之间的相关性。应用基于深度神经网络的计算机视觉技术,从澳大利亚凯恩斯市两个信号灯控制交叉路口两个右转方向 77 小时的视频记录中提取交通冲突。侵占后时间和修改后碰撞时间分别用于描述右转和追尾冲突,而预期碰撞后速度差则与侵占后时间和修改后碰撞时间相结合,用于按伤害严重程度估算碰撞风险。针对交通冲突极端情况的时变异质性,使用了几个协变量来估算信号周期的不同碰撞风险。结果显示,在信号灯周期水平上,右转和追尾碰撞事故之间存在明显的相关性,这表明考虑交通冲突类型之间的依赖性非常重要。总体而言,考虑到交通冲突类型之间相关性的二元模型比单元模型的表现更好,这是可以理解的。本研究展示了一种相关碰撞风险建模框架,该框架可解决与合适的交通冲突措施、时间变化风险(非平稳性)、异质性和不同碰撞类型的伤害严重程度有关的问题。
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
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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