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一种分层多项逻辑模型,用于按碰撞运动配置检查信号策略对右转碰撞风险的影响。

A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration.

作者信息

Islam Sheikh Manirul, Washington Simon, Kim Jiwon, Haque Md Mazharul

机构信息

School of Civil Engineering, Faculty of Engineering, Architecture, and Information Tech., The University of Queensland, St Lucia 4072, Australia.

Advanced Mobility Analytics Group Pty Ltd, Australia.

出版信息

Accid Anal Prev. 2023 May;184:106993. doi: 10.1016/j.aap.2023.106993. Epub 2023 Feb 15.

Abstract

Crash risk models relying on total crash counts are limited in their ability to extract meaningful insights regarding the context of crashes and to identify effective remedial measures. In addition to the typical classification of collisions noted in the literature (e.g., angle, head-on and rear-end), crashes can also be categorised according to vehicle movement configurations (Definitions for Coding Accidents or DCA codes in Australia). This classification presents an opportunity to extract useful insights into road traffic collision causes and contributing factors that are highly contextual. With this aim, this study develops crash-type models by DCA crash movement, with a focus on right-turn crashes (equivalent to left-turn crashes for right-hand traffic) at signalised intersections using a novel approach for linking crashes with signal control strategies. The modelling approach with contextual data enables quantification of the effect of signal control strategies on right-turn crashes, offering potentially unique and novel insights into right-turn crash causes and contributing factors. Crash-type models are estimated with the crash data of 218 signalised intersections in Queensland from 2012 to 2018. Multilevel (Hierarchical) Multinomial Logit Models with random intercepts are employed to capture the hierarchical influence of factors on crashes and unobserved heterogeneities. These models capture upper-level influences on crashes from intersection characteristics and lower-level influences from individual crash characteristics. The models specified in this way account for the correlation among crashes within intersections and influences on crashes across spatial scales. The model results reveal that the probabilities of the opposite approach crash type are significantly higher than the same direction and adjacent approach crash types for all right-turn signal control strategies at intersections except the split approach, for which the opposite is true. The results also suggest that the number of right-turning lanes and occupancy in conflicting lanes are positively associated with the likelihood of crashes for the same direction crash type.

摘要

依赖总碰撞次数的碰撞风险模型在提取有关碰撞背景的有意义见解以及识别有效补救措施方面能力有限。除了文献中提到的典型碰撞分类(例如,角度碰撞、正面碰撞和追尾碰撞)之外,碰撞还可以根据车辆运动配置进行分类(澳大利亚的事故编码定义或DCA代码)。这种分类为深入了解道路交通碰撞原因和高度依赖具体情境的促成因素提供了机会。出于这个目的,本研究通过DCA碰撞运动开发碰撞类型模型,重点关注信号交叉口的右转碰撞(对于右侧交通相当于左转碰撞),采用一种将碰撞与信号控制策略相联系的新方法。使用情境数据的建模方法能够量化信号控制策略对右转碰撞的影响,为右转碰撞原因和促成因素提供潜在独特而新颖的见解。利用昆士兰州218个信号交叉口2012年至2018年的碰撞数据估计碰撞类型模型。采用具有随机截距的多级(分层)多项逻辑回归模型来捕捉因素对碰撞的分层影响以及未观察到的异质性。这些模型捕捉交叉口特征对碰撞的上层影响以及单个碰撞特征的下层影响。以这种方式指定的模型考虑了交叉口内碰撞之间的相关性以及不同空间尺度对碰撞的影响。模型结果表明,在除分流方法外的所有交叉口右转信号控制策略中,对向碰撞类型的概率显著高于同向和相邻向碰撞类型,而分流方法则相反。结果还表明,右转车道数量和冲突车道的占有率与同向碰撞类型的碰撞可能性呈正相关。

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