Evidence of sample selectivity in highway injury-severity models: The case of risky driving during COVID-19

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Mouyid Islam , Asim Alogaili , Fred Mannering , Michael Maness
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引用次数: 10

Abstract

Research in highway safety continues to struggle to address two potentially important issues; the role that unobserved factors may play on resulting crash and injury-severity likelihoods, and the issue of identification in safety modeling caused by the self-selective sampling inherent in commonly used safety data (the fact that drivers in observed crashes are not a random sample of the driving population, with riskier drivers being over-represented in crash data bases). This paper addresses unobserved heterogeneity using mixing distributions and attempts to provide insight into the potential sample-selection problem by considering data before and during the COVID-19 pandemic. Based on a survey of vehicle usage (vehicle miles traveled) and subsequent statistical modeling, there is evidence that riskier drivers likely made up a larger proportion of vehicle miles traveled during the pandemic than before, suggesting that the increase in injury severities observed during COVID-19 could potentially be due to the over-representation of riskier drivers in observed crash data. However, by exploring Florida crash data before and during the pandemic (and focusing on crashes where risky behaviors were observed), the empirical analysis of observed crash data suggests (using random parameters multinomial logit models of driver-injury severities with heterogeneity in means and variances) that the observed increase in injury severity during the COVID-19 pandemic (calendar year 2020) was likely due largely to fundamental changes in driver behavior and less to changes in the sample selectivity of observed crash data. The findings of this paper provide some initial guidance to future work that can begin to more rigorously explore and assess the role of selectivity and resulting identification issues that may be present when using observed crash data.

高速公路伤害严重程度模型中样本选择性的证据:新冠肺炎期间危险驾驶的案例
公路安全研究仍在努力解决两个潜在的重要问题;未观察到的因素可能在导致碰撞和伤害严重程度的可能性中发挥作用,以及由常用安全数据固有的自选择抽样引起的安全建模识别问题(观察到的碰撞中的驾驶员不是驾驶人口的随机样本,风险较高的驾驶员在碰撞数据库中被过度代表)。本文使用混合分布解决了未观察到的异质性,并试图通过考虑COVID-19大流行之前和期间的数据来深入了解潜在的样本选择问题。根据对车辆使用情况(车辆行驶里程)的调查和随后的统计建模,有证据表明,在大流行期间,风险较高的司机在车辆行驶里程中所占的比例可能比以前更大,这表明在COVID-19期间观察到的受伤严重程度的增加可能是由于观察到的碰撞数据中风险较高的司机比例过高。然而,通过在大流行之前和期间探索佛罗里达州的车祸数据(并关注观察到危险行为的车祸),对观察到的碰撞数据的实证分析表明(使用均值和方差均具有异质性的驾驶员伤害严重程度随机参数多项logit模型),在2019冠状病毒病大流行(2020日历年)期间观察到的伤害严重程度的增加可能主要是由于驾驶员行为的根本变化,而不是观察到的碰撞数据的样本选择性的变化。本文的发现为未来的工作提供了一些初步的指导,这些工作可以开始更严格地探索和评估选择性的作用,以及在使用观察到的碰撞数据时可能出现的识别问题。
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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