E. Adanu, W. Agyemang, Riffat Islam, Steven L. Jones
{"title":"影响阿拉巴马州州际公路碰撞严重程度的因素的综合分析","authors":"E. Adanu, W. Agyemang, Riffat Islam, Steven L. Jones","doi":"10.1080/19439962.2021.1949414","DOIUrl":null,"url":null,"abstract":"Abstract This paper identifies factors that influence the severity of interstate crash outcomes and how they vary depending on the location and manner of collision. Four separate injury severity models were developed to explore the differences and similarities in crash factors between single-and multi-vehicle crashes that occurred in rural and urban areas of the state. Random parameters multinomial logit with heterogeneity in means and variances modeling approach was used to account for unobserved heterogeneity in the crash data. The model estimation results show that some driver behavioral factors such as speeding, aggressive driving, failure to use seatbelt, and driving without a valid license were found to significantly contribute to some form of injury outcome. The influence of roadway features such as type of opposing lane separation, collision type, temporal and lighting conditions on crash outcomes were also explored. Some differences and similarities in the associations between these factors and crash injury severity based on the manner and location of crash were unraveled. These findings are expected to guide the implementation of crash countermeasures on interstates. The findings of this study further support the evidence for the analysis of subsets of crash data to unravel underlying complex relationships within factors that influence crash injury severity.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":"84 1","pages":"1552 - 1576"},"PeriodicalIF":2.4000,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"A comprehensive analysis of factors that influence interstate highway crash severity in Alabama\",\"authors\":\"E. Adanu, W. Agyemang, Riffat Islam, Steven L. Jones\",\"doi\":\"10.1080/19439962.2021.1949414\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract This paper identifies factors that influence the severity of interstate crash outcomes and how they vary depending on the location and manner of collision. Four separate injury severity models were developed to explore the differences and similarities in crash factors between single-and multi-vehicle crashes that occurred in rural and urban areas of the state. Random parameters multinomial logit with heterogeneity in means and variances modeling approach was used to account for unobserved heterogeneity in the crash data. The model estimation results show that some driver behavioral factors such as speeding, aggressive driving, failure to use seatbelt, and driving without a valid license were found to significantly contribute to some form of injury outcome. The influence of roadway features such as type of opposing lane separation, collision type, temporal and lighting conditions on crash outcomes were also explored. Some differences and similarities in the associations between these factors and crash injury severity based on the manner and location of crash were unraveled. These findings are expected to guide the implementation of crash countermeasures on interstates. The findings of this study further support the evidence for the analysis of subsets of crash data to unravel underlying complex relationships within factors that influence crash injury severity.\",\"PeriodicalId\":46672,\"journal\":{\"name\":\"Journal of Transportation Safety & Security\",\"volume\":\"84 1\",\"pages\":\"1552 - 1576\"},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2021-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Transportation Safety & Security\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/19439962.2021.1949414\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Transportation Safety & Security","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/19439962.2021.1949414","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
A comprehensive analysis of factors that influence interstate highway crash severity in Alabama
Abstract This paper identifies factors that influence the severity of interstate crash outcomes and how they vary depending on the location and manner of collision. Four separate injury severity models were developed to explore the differences and similarities in crash factors between single-and multi-vehicle crashes that occurred in rural and urban areas of the state. Random parameters multinomial logit with heterogeneity in means and variances modeling approach was used to account for unobserved heterogeneity in the crash data. The model estimation results show that some driver behavioral factors such as speeding, aggressive driving, failure to use seatbelt, and driving without a valid license were found to significantly contribute to some form of injury outcome. The influence of roadway features such as type of opposing lane separation, collision type, temporal and lighting conditions on crash outcomes were also explored. Some differences and similarities in the associations between these factors and crash injury severity based on the manner and location of crash were unraveled. These findings are expected to guide the implementation of crash countermeasures on interstates. The findings of this study further support the evidence for the analysis of subsets of crash data to unravel underlying complex relationships within factors that influence crash injury severity.