Investigating risk factors associated with injury severity in highway crashes: A hybrid approach integrating two-step cluster analysis and latent class ordered regression model with covariates

IF 5.7 1区 工程技术 Q1 ERGONOMICS Accident; analysis and prevention Pub Date : 2024-10-04 DOI:10.1016/j.aap.2024.107805
Siliang Luan , Zhongtai Jiang , Dayi qu , Xiaoxia Yang , Fanyun Meng
{"title":"Investigating risk factors associated with injury severity in highway crashes: A hybrid approach integrating two-step cluster analysis and latent class ordered regression model with covariates","authors":"Siliang Luan ,&nbsp;Zhongtai Jiang ,&nbsp;Dayi qu ,&nbsp;Xiaoxia Yang ,&nbsp;Fanyun Meng","doi":"10.1016/j.aap.2024.107805","DOIUrl":null,"url":null,"abstract":"<div><div>Highway crashes are responsible for a significant number of severe and fatal injuries drawing considerable attention from transportation authorities and safety researchers. This paper aims to investigate the unobserved heterogeneous effects of various risk factors, such as pre-crash circumstances, environmental and road conditions, vehicle-involved information, and driver attributes on injury severities. Our methodology uses a hybrid approach that combines two-step cluster analysis and latent class ordered regression model with covariates. The proposed approach extends traditional latent class model by elucidating potential relationships among predictors, covariates, and outcomes. A cross-sectional crash data covering a period of over five years (2011–2016) was obtained via the Dutch crash registration database for modeling injury severity outcomes. The results reveal substantial and statistically significant differences in injury severity between two latent classes. Moreover, we identify road lighting, time of crash, road surface conditions, weather, and season as covariates influencing class membership prediction. Factors such as high speed, alcohol involvement, frontal collision points, and older driver demographics increase the probability of serious injury and facility across all cases analyzed. Additionally, we observe notable heterogeneity effects between the two classes regarding temporal characteristics, the number and type of vehicles involved, as well as driver gender. Our findings provide specific and valuable insights into injury severity outcomes, which can inform the formulation of targeted safety countermeasures and regulatory strategies for traffic policies and relevant agencies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"208 ","pages":"Article 107805"},"PeriodicalIF":5.7000,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accident; analysis and prevention","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0001457524003506","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ERGONOMICS","Score":null,"Total":0}
引用次数: 0

Abstract

Highway crashes are responsible for a significant number of severe and fatal injuries drawing considerable attention from transportation authorities and safety researchers. This paper aims to investigate the unobserved heterogeneous effects of various risk factors, such as pre-crash circumstances, environmental and road conditions, vehicle-involved information, and driver attributes on injury severities. Our methodology uses a hybrid approach that combines two-step cluster analysis and latent class ordered regression model with covariates. The proposed approach extends traditional latent class model by elucidating potential relationships among predictors, covariates, and outcomes. A cross-sectional crash data covering a period of over five years (2011–2016) was obtained via the Dutch crash registration database for modeling injury severity outcomes. The results reveal substantial and statistically significant differences in injury severity between two latent classes. Moreover, we identify road lighting, time of crash, road surface conditions, weather, and season as covariates influencing class membership prediction. Factors such as high speed, alcohol involvement, frontal collision points, and older driver demographics increase the probability of serious injury and facility across all cases analyzed. Additionally, we observe notable heterogeneity effects between the two classes regarding temporal characteristics, the number and type of vehicles involved, as well as driver gender. Our findings provide specific and valuable insights into injury severity outcomes, which can inform the formulation of targeted safety countermeasures and regulatory strategies for traffic policies and relevant agencies.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
调查高速公路碰撞事故中与伤害严重程度相关的风险因素:将两步聚类分析与带有协变量的潜类有序回归模型相结合的混合方法。
高速公路撞车事故造成了大量严重和致命伤害,引起了交通管理部门和安全研究人员的极大关注。本文旨在研究各种风险因素(如碰撞前情况、环境和道路条件、车辆信息和驾驶员属性)对伤害严重程度的非观测异质性影响。我们的方法采用了一种混合方法,结合了两步聚类分析和带有协变量的潜类有序回归模型。所提出的方法通过阐明预测因素、协变量和结果之间的潜在关系,扩展了传统的潜类模型。通过荷兰碰撞登记数据库获得了五年多(2011-2016 年)的横截面碰撞数据,用于建立伤害严重程度结果模型。结果显示,两个潜在类别之间的伤害严重程度存在实质性差异,且具有统计学意义。此外,我们还发现道路照明、撞车时间、路面状况、天气和季节是影响类别成员预测的协变量。在分析的所有案例中,高速行驶、酗酒、正面碰撞点和年长驾驶员等因素会增加重伤和设施的概率。此外,我们还观察到两个类别之间在时间特征、涉及车辆的数量和类型以及驾驶员性别方面存在明显的异质性效应。我们的研究结果对伤害严重性结果提供了具体而有价值的见解,可为交通政策和相关机构制定有针对性的安全对策和监管策略提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.90
自引率
16.90%
发文量
264
审稿时长
48 days
期刊介绍: Accident Analysis & Prevention provides wide coverage of the general areas relating to accidental injury and damage, including the pre-injury and immediate post-injury phases. Published papers deal with medical, legal, economic, educational, behavioral, theoretical or empirical aspects of transportation accidents, as well as with accidents at other sites. Selected topics within the scope of the Journal may include: studies of human, environmental and vehicular factors influencing the occurrence, type and severity of accidents and injury; the design, implementation and evaluation of countermeasures; biomechanics of impact and human tolerance limits to injury; modelling and statistical analysis of accident data; policy, planning and decision-making in safety.
期刊最新文献
A comprehensive multi-objective framework for the estimation of crash frequency models. Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections. Do automation and digitalization distract drivers? A systematic review. Influence of road safety policies on the long-term trends in fatal Crashes: A Gaussian Copula-based time series count model with an autoregressive moving average process. Nudges may improve hazard perception in a contextual manner.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1