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基于碰撞序列的摩托车碰撞风险矩阵。

Crash sequence based risk matrix for motorcycle crashes.

机构信息

Department of Transportation and Logistics Management, National Chiao Tung University, Taiwan.

School of Mathematical and Statistical Sciences, University of Texas-Rio Grande Valley, Edinburg, Texas, USA.

出版信息

Accid Anal Prev. 2018 Aug;117:21-31. doi: 10.1016/j.aap.2018.03.022. Epub 2018 Apr 6.

Abstract

Considerable research has been conducted related to motorcycle and other powered-two-wheeler (PTW) crashes; however, it always has been controversial among practitioners concerning with types of crashes should be first targeted and how to prioritize resources for the implementation of mitigating actions. Therefore, there is a need to identify types of motorcycle crashes that constitute the greatest safety risk to riders - most frequent and most severe crashes. This pilot study seeks exhibit the efficacy of a new approach for prioritizing PTW crash causation sequences as they relate to injury severity to better inform the application of mitigating countermeasures. To accomplish this, the present study constructed a crash sequence-based risk matrix to identify most frequent and most severe motorcycle crashes in an attempt to better connect causes and countermeasures of PTW crashes. Although the frequency of each crash sequence can be computed from crash data, a crash severity model is needed to compare the levels of crash severity among different crash sequences, while controlling for other factors that also have effects on crash severity such drivers' age, use of helmet, etc. The construction of risk matrix based on crash sequences involve two tasks: formulation of crash sequence and the estimation of a mixed-effects (ME) model to adjust the levels of severities for each crash sequence to account for other crash contributing factors that would have an effect on the maximum level of crash severity in a crash. Three data elements from the National Automotive Sampling System - General Estimating System (NASS-GES) data were utilized to form a crash sequence: critical event, crash types, and sequence of events. A mixed-effects model was constructed to model the severity levels for each crash sequence while accounting for the effects of those crash contributing factors on crash severity. A total of 8039 crashes involving 8208 motorcycles occurred during 2011 and 2013 were included in this study, weighted to represent 338,655 motorcyclists involved in traffic crashes in three years (2011-2013)(NHTSA, 2013). The top five most frequent and severe types of crash sequences were identified, accounting for 23 percent of all the motorcycle crashes included in the study, and they are (1) run-off-road crashes on the right, and hitting roadside objects, (2) cross-median crashes, and rollover, (3) left-turn oncoming crashes, and head-on, (4) crossing over (passing through) or turning into opposite direction at intersections, and (5) side-impacted. In addition to crash sequences, several other factors were also identified to have effects on crash severity: use of helmet, presence of horizontal curves, alcohol consumption, road surface condition, roadway functional class, and nighttime condition.

摘要

已经有相当多的研究涉及摩托车和其他两轮机动车(PTW)事故;然而,对于应该首先针对哪种类型的事故以及如何为实施缓解措施分配资源,从业者之间一直存在争议。因此,有必要确定构成骑手最大安全风险的摩托车事故类型——最频繁和最严重的事故。这项试点研究旨在展示一种新方法在优先考虑与伤害严重程度相关的 PTW 碰撞因果序列方面的功效,以便更好地为减轻对策的应用提供信息。为此,本研究构建了一个基于碰撞序列的风险矩阵,以确定最频繁和最严重的摩托车碰撞事故,试图更好地将两轮机动车碰撞的原因和对策联系起来。虽然可以从碰撞数据中计算出每个碰撞序列的频率,但需要一个碰撞严重程度模型来比较不同碰撞序列之间的碰撞严重程度水平,同时控制对碰撞严重程度有影响的其他因素,例如驾驶员的年龄、使用头盔等。基于碰撞序列构建风险矩阵涉及两个任务:碰撞序列的制定和混合效应(ME)模型的估计,以调整每个碰撞序列的严重程度,以说明可能对碰撞中最大严重程度产生影响的其他碰撞促成因素。从国家汽车抽样系统-一般估算系统(NASS-GES)数据中利用了三个数据元素来形成碰撞序列:关键事件、碰撞类型和事件序列。构建了一个混合效应模型来对每个碰撞序列的严重程度水平进行建模,同时考虑了这些碰撞促成因素对碰撞严重程度的影响。共有 8039 起涉及 8208 辆摩托车的事故发生在 2011 年至 2013 年期间,经加权后代表了三年内(2011-2013 年)涉及交通碰撞的 338655 名摩托车手(NHTSA,2013 年)。确定了前五种最频繁和最严重的碰撞序列类型,占研究中所有摩托车碰撞的 23%,它们是:(1)右侧驶离道路并撞击路边物体的碰撞,(2)中线交叉和翻车,(3)迎面而来的左转碰撞和正面碰撞,(4)在交叉路口穿越(通过)或转向相反方向的碰撞,(5)侧面碰撞。除了碰撞序列之外,还确定了其他几个因素对碰撞严重程度也有影响:头盔的使用、水平曲线的存在、酒精的消耗、路面状况、道路功能等级和夜间条件。

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