对自行车手安全头盔在车辆/自行车碰撞事故中减轻受伤严重程度的效果进行时间统计评估

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Nawaf Alnawmasi , Asim Alogaili , Rakesh Rangaswamy , Oscar Oviedo-Trespalacios
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引用次数: 0

摘要

本研究估计了混合 Logit 模型,其中考虑到了均值和部分约束参数的异质性,以探索参数随时间推移可能发生的变化,从而研究影响自行车手受伤严重程度结果的因素。利用佛罗里达州为期三年的综合数据集,针对两种骑车人佩戴头盔的情况(头盔和非头盔)分别估算了统计模型,以评估 COVID-19 在 2019 年 1 月 1 日至 2021 年 12 月 31 日期间的影响。这项研究评估了影响戴头盔和不戴头盔骑车人受伤严重程度的几个因素,包括驾驶员和骑车人的属性、环境和天气、道路特征及其时间方面,以及不同类型的车辆。所进行的分析进一步增强了模型的稳健性,通过似然比检验评估了模型的时间稳定性和在不同情况下的可转移性,并通过边际效应分析深入研究了解释变量的时间一致性,确认了无头盔和有头盔骑车者模型之间的显著差异,并揭示了研究期间影响伤害严重程度的因素的时间变化。模型估计结果确定了几个重要变量,其参数估计值在不同年份之间保持一致。在非头盔模型中,停车标志、与车流同时骑车以及黑暗无光的环境会增加严重受伤的风险,而在头盔模型中,停车标志指标会持续降低严重受伤的风险。在不同年份和佩戴头盔的情况下,包括男性驾驶员指标在内的具有统计意义的随机参数对受伤严重程度的影响各不相同。样本外预测分析表明,头盔降低了严重伤害概率,但可能会增加轻微伤害,减少无伤害事故,这表明戴头盔的骑车人可能存在风险补偿行为。虽然头盔能保护骑车人免受严重伤害,但采取全面的安全方法也至关重要,尤其是考虑到在 COVID-19 爆发期间骑车人的人口结构不断变化。这就需要考虑骑车人和司机的行为、环境条件和基础设施改善等因素。政策制定者、道路安全专业人员和宣传团体应通力合作,制定整体战略,解决自行车碰撞严重程度的决定因素,并在不同的道路环境中加强对自行车骑行者的安全措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A temporal statistical assessment of the effectiveness of bicyclist safety helmets in mitigating injury severities in vehicle/bicyclist crashes

This study estimates mixed logit models taking into account heterogeneity in means and partially constrained parameters in order to explore possible shifts within parameters over time to study factors influencing bicyclist injury severity outcomes. Separate statistical models are estimated for two bicyclist helmet-wearing scenarios (helmet and non-helmet) using a comprehensive dataset from Florida covering a three-year period to assess COVID-19 effects from the 1st of January 2019 to the 31st of December 2021. This research evaluates several factors influencing helmeted and non-helmeted bicyclist injury severity, encompassing the attributes of drivers and cyclists, the environment and weather, the features of the roads and their temporal aspects, and the different types of vehicles. The performed analysis further enhances model robustness by assessing the temporal stability and transferability across different contexts through likelihood ratio tests, alongside an in-depth examination of the temporal consistency of explanatory variables via marginal effects analysis, confirming significant variations between non-helmeted and helmeted bicyclist models and revealing temporal shifts in factors affecting injury severity during the study period. Findings from the model estimations identify several significant variables with consistent parameter estimates across years. Stop signs, cycling with traffic, and dark, unlit conditions increase severe injury risk in non-helmet models, while the stop sign indicator consistently reduces severe injury risk in helmet models. Statistically significant random parameters are identified across different years and helmet-wearing scenarios, including the male driver indicator, which exhibits varying effects on injury severity. Out-of-sample prediction analysis suggests helmets reduce severe injury probability but may increase minor injuries and decrease no-injury accidents, indicating potential risk compensation behavior among helmeted bicyclists. Although helmets offer protection against severe injuries for bicyclists, it is crucial to adopt a comprehensive safety approach, particularly given the evolving demographics of bicyclists amid the COVID-19 outbreak. This entails considering factors like bicyclist and driver behavior, environmental conditions, and infrastructure enhancements. Policymakers, road safety professionals, and advocacy groups should collaborate to develop holistic strategies to address the determinants of bicycle crash severity outcomes and enhance safety measures for bicyclists across diverse road environments.

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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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