在公路-铁路平交道口碰撞伤害严重程度分析中考虑未观察到的异质性和空间不稳定性:均值和方差方法中具有异质性的随机参数

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Analytic Methods in Accident Research Pub Date : 2023-03-01 DOI:10.1016/j.amar.2022.100250
Sheikh Shahriar Ahmed , Francesco Corman , Panagiotis Ch. Anastasopoulos
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引用次数: 11

摘要

与其他类型的碰撞相比,公路铁路平交道口的碰撞往往导致车辆乘员受伤和死亡的比例更高,需要深入调查以确定其原因。在本研究中,使用德克萨斯州和加利福尼亚州的碰撞数据分析了公路-铁路平交道口碰撞的伤害严重程度结果,这两个州是美国最脆弱的州,就公路-铁路平交道口碰撞发生率而言。这些数据是从联邦铁路管理局(FRA)安全分析办公室收集的,涵盖了2012年至2020年的时间。由于成本和可用资源的限制,这类数据往往存在过时或信息缺失的问题,这不可避免地会导致在数据的各个方面系统地变化未观察到的特征。未观察到的异质性是一个重要的错误规范问题,这反过来又引入了建模偏差。针对这些局限性,本文采用均值和方差均存在异质性的随机参数多项logit建模框架进行计量分析,有效地解释了多层未观测异质性。研究了不同损伤严重程度影响因素的空间不稳定性。结果表明,这些因子在德克萨斯州和加利福尼亚州的空间上并不稳定,导致两种不同的州特有模型的估计。两种特定状态模型的估计结果有助于识别几种影响不同伤害严重程度结果的车辆,火车,车辆驾驶员,天气和交叉特定因素。此外,结果还表明,在两个州,识别的因素对伤害严重程度的影响程度不同,表明存在空间不稳定性。本研究的结果强调了考虑未观察到的异质性和空间不稳定性的重要性,以避免关键的方法问题和从大多数计量经济学分析中使用的简单汇总中产生的误导性推论。
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Accounting for unobserved heterogeneity and spatial instability in the analysis of crash injury-severity at highway-rail grade crossings: A random parameters with heterogeneity in the means and variances approach

Crashes at highway-rail grade crossings often result in higher proportion of injury and fatality of the vehicle occupants as compared to other crash types, necessitating in-depth investigation to identify their causal factors. In this study, injury-severity outcomes from highway-rail grade crossing crashes are analyzed using crash data from Texas and California, which are the most vulnerable states in the United States, in terms of highway-rail grade crossing crash occurrences. The data are collected from the Federal Railroad Administration’s (FRA) Office of Safety Analysis, covering a period between 2012 and 2020. Such data often suffer from out-of-date or missing information due to cost and available resources limitations, which inevitably may lead to unobserved characteristics varying systematically across various aspects of the data. Unobserved heterogeneity is an important misspecification issue, that in turn introduces modeling bias. To address these limitations, the random parameters multinomial logit modeling framework with heterogeneity in the means and variances is employed for the econometric analysis in this paper, which effectively accounts for multilayered unobserved heterogeneity. Spatial instability of the factors affecting different injury-severity levels is investigated as well. The results indicate that the factors are not spatially stable across Texas and California, leading to the estimation of two separate state-specific models. The estimation results of the two state-specific models help identify several vehicle-, train-, vehicle driver-, weather- and crossing-specific factors affecting different injury severity outcomes. Moreover, the results also demonstrate the varying magnitude of the identified factors on injury-severity across the two states, indicating the presence of spatial instability. The findings of this study highlight the importance of accounting for unobserved heterogeneity and spatial instability to avert critical methodological issues and misleading inferences from the simple aggregation used in most econometric analysis of highway-rail grade crossing crashes.

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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.
期刊最新文献
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