Md Julfiker Hossain, J. Ivan, Shanshan Zhao, Kai Wang, Sadia Sharmin, N. Ravishanker, Eric D. Jackson
{"title":"考虑其他涉及司机的人口统计数据,以预测加州农村双车道道路上多车碰撞中最高的司机伤害严重程度","authors":"Md Julfiker Hossain, J. Ivan, Shanshan Zhao, Kai Wang, Sadia Sharmin, N. Ravishanker, Eric D. Jackson","doi":"10.1080/19439962.2022.2033899","DOIUrl":null,"url":null,"abstract":"Abstract The injury severity of a driver in a crash is significantly related to the driver’s age and gender and vehicle characteristics. Previous studies have used only information about the most severely injured driver to represent the crash severity, ignoring other drivers involved in the crash, which can also be important to explain the crash severity. This study uses demographic information of all drivers involved in a multi-vehicle crash to predict the injury severity of the most severely injured driver using a partial proportional odds model. Models incorporating demographic information and vehicle characteristics of all drivers and vehicles involved in a crash were compared with models considering only information about the most severely injured driver in terms of significance of factors and prediction accuracy. The results indicate that although young drivers are likely to have lower levels of injury severity compared to working-age drivers, injury severity increases if the proportion of young drivers increases in a multi-vehicle crash. Drivers indicated to be not at fault frequently were more severely injured than drivers at fault. Finally, the inclusion of all drivers’ demographic information shows an improvement in the prediction accuracy of crash severity of the most severely injured driver.","PeriodicalId":46672,"journal":{"name":"Journal of Transportation Safety & Security","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Considering demographics of other involved drivers in predicting the highest driver injury severity in multi-vehicle crashes on rural two-lane roads in California\",\"authors\":\"Md Julfiker Hossain, J. Ivan, Shanshan Zhao, Kai Wang, Sadia Sharmin, N. Ravishanker, Eric D. Jackson\",\"doi\":\"10.1080/19439962.2022.2033899\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract The injury severity of a driver in a crash is significantly related to the driver’s age and gender and vehicle characteristics. Previous studies have used only information about the most severely injured driver to represent the crash severity, ignoring other drivers involved in the crash, which can also be important to explain the crash severity. This study uses demographic information of all drivers involved in a multi-vehicle crash to predict the injury severity of the most severely injured driver using a partial proportional odds model. Models incorporating demographic information and vehicle characteristics of all drivers and vehicles involved in a crash were compared with models considering only information about the most severely injured driver in terms of significance of factors and prediction accuracy. The results indicate that although young drivers are likely to have lower levels of injury severity compared to working-age drivers, injury severity increases if the proportion of young drivers increases in a multi-vehicle crash. Drivers indicated to be not at fault frequently were more severely injured than drivers at fault. Finally, the inclusion of all drivers’ demographic information shows an improvement in the prediction accuracy of crash severity of the most severely injured driver.\",\"PeriodicalId\":46672,\"journal\":{\"name\":\"Journal of Transportation Safety & Security\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.4000,\"publicationDate\":\"2022-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Transportation Safety & Security\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/19439962.2022.2033899\",\"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/10.1080/19439962.2022.2033899","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Considering demographics of other involved drivers in predicting the highest driver injury severity in multi-vehicle crashes on rural two-lane roads in California
Abstract The injury severity of a driver in a crash is significantly related to the driver’s age and gender and vehicle characteristics. Previous studies have used only information about the most severely injured driver to represent the crash severity, ignoring other drivers involved in the crash, which can also be important to explain the crash severity. This study uses demographic information of all drivers involved in a multi-vehicle crash to predict the injury severity of the most severely injured driver using a partial proportional odds model. Models incorporating demographic information and vehicle characteristics of all drivers and vehicles involved in a crash were compared with models considering only information about the most severely injured driver in terms of significance of factors and prediction accuracy. The results indicate that although young drivers are likely to have lower levels of injury severity compared to working-age drivers, injury severity increases if the proportion of young drivers increases in a multi-vehicle crash. Drivers indicated to be not at fault frequently were more severely injured than drivers at fault. Finally, the inclusion of all drivers’ demographic information shows an improvement in the prediction accuracy of crash severity of the most severely injured driver.