{"title":"北达科他州单车道偏离事故热点分析","authors":"Ihsan Ullah Khan , Kimberly Vachal , Sajad Ebrahimi , Satpal Singh Wadhwa","doi":"10.1016/j.iatssr.2022.12.003","DOIUrl":null,"url":null,"abstract":"<div><p>According to the North Dakota Department of Transportation (NDDOT), 90% of the state's fatal lane departure crashes between 2015 and 2019 occurred on rural roads. Of these, 77% were single-vehicle events. The objective here was to identify relatively high-risk areas on the rural road system. Spatial analysis techniques were explored as a beneficial tool in resource allocations aimed at single-vehicle crash prevention. Hotspot identification techniques, including Global Moran's I, local Moran's I, network kernel density estimation (NetKDE), and emerging hotspot analysis were employed. While the Global Moran's I index indicated the existence of crash clustering, the local Moran's I statistic revealed hot and cold spots in the state. The NetKDE approach was used to quantify crash clusters and prioritize locations. Results from NetKDE defined boundaries for each cluster in terms of density values embedded in the roadway. Emerging hotspot analysis evaluated the hot and cold spots with respect to time. This study will provide valuable insight and help decision makers to make more informed decisions with respect to education, enforcement and infrastructure strategies aimed at preventing single-vehicle lane departure crashes. Although limited to a narrow crash type in one state, this approach can inform other jurisdictions seeking to empirically visualize hotspots and more effectively deploy traffic safety strategies.</p></div>","PeriodicalId":47059,"journal":{"name":"IATSS Research","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Hotspot analysis of single-vehicle lane departure crashes in North Dakota\",\"authors\":\"Ihsan Ullah Khan , Kimberly Vachal , Sajad Ebrahimi , Satpal Singh Wadhwa\",\"doi\":\"10.1016/j.iatssr.2022.12.003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>According to the North Dakota Department of Transportation (NDDOT), 90% of the state's fatal lane departure crashes between 2015 and 2019 occurred on rural roads. Of these, 77% were single-vehicle events. The objective here was to identify relatively high-risk areas on the rural road system. Spatial analysis techniques were explored as a beneficial tool in resource allocations aimed at single-vehicle crash prevention. Hotspot identification techniques, including Global Moran's I, local Moran's I, network kernel density estimation (NetKDE), and emerging hotspot analysis were employed. While the Global Moran's I index indicated the existence of crash clustering, the local Moran's I statistic revealed hot and cold spots in the state. The NetKDE approach was used to quantify crash clusters and prioritize locations. Results from NetKDE defined boundaries for each cluster in terms of density values embedded in the roadway. Emerging hotspot analysis evaluated the hot and cold spots with respect to time. This study will provide valuable insight and help decision makers to make more informed decisions with respect to education, enforcement and infrastructure strategies aimed at preventing single-vehicle lane departure crashes. Although limited to a narrow crash type in one state, this approach can inform other jurisdictions seeking to empirically visualize hotspots and more effectively deploy traffic safety strategies.</p></div>\",\"PeriodicalId\":47059,\"journal\":{\"name\":\"IATSS Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IATSS Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0386111222000632\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IATSS Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0386111222000632","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION","Score":null,"Total":0}
Hotspot analysis of single-vehicle lane departure crashes in North Dakota
According to the North Dakota Department of Transportation (NDDOT), 90% of the state's fatal lane departure crashes between 2015 and 2019 occurred on rural roads. Of these, 77% were single-vehicle events. The objective here was to identify relatively high-risk areas on the rural road system. Spatial analysis techniques were explored as a beneficial tool in resource allocations aimed at single-vehicle crash prevention. Hotspot identification techniques, including Global Moran's I, local Moran's I, network kernel density estimation (NetKDE), and emerging hotspot analysis were employed. While the Global Moran's I index indicated the existence of crash clustering, the local Moran's I statistic revealed hot and cold spots in the state. The NetKDE approach was used to quantify crash clusters and prioritize locations. Results from NetKDE defined boundaries for each cluster in terms of density values embedded in the roadway. Emerging hotspot analysis evaluated the hot and cold spots with respect to time. This study will provide valuable insight and help decision makers to make more informed decisions with respect to education, enforcement and infrastructure strategies aimed at preventing single-vehicle lane departure crashes. Although limited to a narrow crash type in one state, this approach can inform other jurisdictions seeking to empirically visualize hotspots and more effectively deploy traffic safety strategies.
期刊介绍:
First published in 1977 as an international journal sponsored by the International Association of Traffic and Safety Sciences, IATSS Research has contributed to the dissemination of interdisciplinary wisdom on ideal mobility, particularly in Asia. IATSS Research is an international refereed journal providing a platform for the exchange of scientific findings on transportation and safety across a wide range of academic fields, with particular emphasis on the links between scientific findings and practice in society and cultural contexts. IATSS Research welcomes submission of original research articles and reviews that satisfy the following conditions: 1.Relevant to transportation and safety, and the multiple impacts of transportation systems on security, human health, and the environment. 2.Contains important policy and practical implications based on scientific evidence in the applicable academic field. In addition to welcoming general submissions, IATSS Research occasionally plans and publishes special feature sections and special issues composed of invited articles addressing specific topics.