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头盔使用对轻便摩托车事故中骑手伤害严重程度的影响:来自部分时间约束随机参数二元概率模型的见解。

Effects of helmet usage on moped riders' injury severity in moped-vehicle crashes: Insights from partially temporal constrained random parameters bivariate probit models.

机构信息

Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, United States.

Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, FL 32816, United States; School of System Science, Beijing Jiaotong University, Beijing 100044, PR China.

出版信息

Accid Anal Prev. 2024 Dec;208:107800. doi: 10.1016/j.aap.2024.107800. Epub 2024 Oct 1.

Abstract

Mopeds are small and move unpredictably, making them difficult for other drivers to perceive. This lack of visibility, coupled with the minimal protection that mopeds provide, can lead to serious crashes, particularly when the rider is not wearing a helmet. This paper explores the association between helmet usage and injury severity among moped riders involved in collisions with other vehicles. A series of joint bivariate probit models are employed, with injury severity and helmet usage serving as dependent variables. Data on two-vehicle moped crashes in Florida from 2019 to 2021 are collected and categorized into three periods: before, during, and after the COVID-19 pandemic. Crash involvement ratios are calculated to examine the safety risk elements of moped riders in various categories, while significant temporal shifts are also explored. The correlated joint random parameters bivariate probit models with heterogeneity in means demonstrate their superiority in capturing interactive unobserved heterogeneity, revealing how various variables significantly affect injury outcomes and helmet usage. Temporal instability related to the COVID-19 pandemic is validated through likelihood ratio tests, out-of-sample predictions, and calculations of marginal effects. Additionally, several parameters are noted to remain temporally stable across multiple periods, prompting the development of a partially temporally constrained modeling approach to provide insights from a long-term perspective. Specifically, it is found that male moped riders are less likely to wear helmets and are negatively associated with injury/fatality rates. Moped riders on two-lane roads are also less likely to wear helmets. Furthermore, moped riders face a lower risk of injury or fatality during daylight conditions, while angle crashes consistently lead to a higher risk of injuries and fatalities across the three periods. These findings provide valuable insights into helmet usage and injury severity among moped riders and offer guidance for developing countermeasures to protect them.

摘要

轻便摩托车体积小,行驶轨迹难以预测,这使得其他驾驶员很难察觉。这种不可见性,再加上轻便摩托车提供的最小保护,可能导致严重的碰撞事故,尤其是当骑手没有戴头盔时。本文探讨了在与其他车辆发生碰撞的轻便摩托车骑手的头盔使用与伤害严重程度之间的关联。采用了一系列联合双变量概率模型,以伤害严重程度和头盔使用为因变量。收集了 2019 年至 2021 年佛罗里达州两车轻便摩托车碰撞事故的数据,并将其分为三个时期:新冠疫情前、疫情期间和疫情后。计算碰撞参与率,以检查各个类别的轻便摩托车骑手的安全风险因素,同时还探讨了显著的时间变化。相关联合随机参数双变量概率模型具有均值异质性,证明了其在捕捉交互未观察到的异质性方面的优越性,揭示了各种变量如何显著影响伤害结果和头盔使用。通过似然比检验、样本外预测和边际效应计算,验证了与新冠疫情相关的时间不稳定性。此外,还注意到几个参数在多个时期内保持时间稳定,这促使开发了一种部分时间受限的建模方法,从长期角度提供见解。具体而言,研究发现男性轻便摩托车骑手不太可能戴头盔,并且与伤害/死亡率呈负相关。在双车道上行驶的轻便摩托车骑手也不太可能戴头盔。此外,轻便摩托车骑手在白天条件下受伤或死亡的风险较低,而角度碰撞在三个时期内始终导致受伤和死亡的风险较高。这些发现为了解轻便摩托车骑手的头盔使用和伤害严重程度提供了有价值的见解,并为制定保护他们的对策提供了指导。

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