{"title":"揭开交通安全的面纱:全面分析影响美国各州交通事故频率的因素","authors":"M. Obeidat, Rahma Mohammad Obeidat, F. Dweiri","doi":"10.1093/tse/tdae016","DOIUrl":null,"url":null,"abstract":"\n Traffic crashes are a prominent cause of fatalities around the world. This study focuses on the dynamics of traffic safety by analyzing factors influencing crash frequency during both nighttime and daytime conditions. Four years data derived from the Fatality Analysis Reporting System (FARS) database were analyzed statistically based on the negative binomial regression model, to identify the contributions of several factors affecting crash outcomes. The findings reveal significant related factors including the area type, roadway alignment, speeding-related factors, gender, number of lanes, grade, surface type, and weather conditions that contributed to the expected crash frequency during both daytime and nighttime conditions. Through quantitative analysis, the extent to which each factor contributes to the expected crash frequency was determined, offering effective insights for policymaking to boost roadway safety. The findings highlight the necessity of implementing targeted strategies to minimize the risk of crashes by creating safer road environments.","PeriodicalId":52804,"journal":{"name":"Transportation Safety and Environment","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unraveling the veil of traffic safety: A comprehensive analysis of factors influencing crash frequency across U.S. States\",\"authors\":\"M. Obeidat, Rahma Mohammad Obeidat, F. Dweiri\",\"doi\":\"10.1093/tse/tdae016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Traffic crashes are a prominent cause of fatalities around the world. This study focuses on the dynamics of traffic safety by analyzing factors influencing crash frequency during both nighttime and daytime conditions. Four years data derived from the Fatality Analysis Reporting System (FARS) database were analyzed statistically based on the negative binomial regression model, to identify the contributions of several factors affecting crash outcomes. The findings reveal significant related factors including the area type, roadway alignment, speeding-related factors, gender, number of lanes, grade, surface type, and weather conditions that contributed to the expected crash frequency during both daytime and nighttime conditions. Through quantitative analysis, the extent to which each factor contributes to the expected crash frequency was determined, offering effective insights for policymaking to boost roadway safety. The findings highlight the necessity of implementing targeted strategies to minimize the risk of crashes by creating safer road environments.\",\"PeriodicalId\":52804,\"journal\":{\"name\":\"Transportation Safety and Environment\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-04-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Safety and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1093/tse/tdae016\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Safety and Environment","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1093/tse/tdae016","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Unraveling the veil of traffic safety: A comprehensive analysis of factors influencing crash frequency across U.S. States
Traffic crashes are a prominent cause of fatalities around the world. This study focuses on the dynamics of traffic safety by analyzing factors influencing crash frequency during both nighttime and daytime conditions. Four years data derived from the Fatality Analysis Reporting System (FARS) database were analyzed statistically based on the negative binomial regression model, to identify the contributions of several factors affecting crash outcomes. The findings reveal significant related factors including the area type, roadway alignment, speeding-related factors, gender, number of lanes, grade, surface type, and weather conditions that contributed to the expected crash frequency during both daytime and nighttime conditions. Through quantitative analysis, the extent to which each factor contributes to the expected crash frequency was determined, offering effective insights for policymaking to boost roadway safety. The findings highlight the necessity of implementing targeted strategies to minimize the risk of crashes by creating safer road environments.