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. Sp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111222000632 |
| _version_ | 1827975318649962496 |
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| author | Ihsan Ullah Khan Kimberly Vachal Sajad Ebrahimi Satpal Singh Wadhwa |
| author_facet | Ihsan Ullah Khan Kimberly Vachal Sajad Ebrahimi Satpal Singh Wadhwa |
| author_sort | Ihsan Ullah Khan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T20:09:42Z |
| format | Article |
| id | doaj.art-4f4a747a3f394ba2ad5669855e2bcd14 |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-04-09T20:09:42Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-4f4a747a3f394ba2ad5669855e2bcd142023-04-01T08:42:38ZengElsevierIATSS Research0386-11122023-03-014712534Hotspot analysis of single-vehicle lane departure crashes in North DakotaIhsan Ullah Khan0Kimberly Vachal1Sajad Ebrahimi2Satpal Singh Wadhwa3North Dakota State University, United States of America; Corresponding author.North Dakota State University, United States of AmericaNorth Dakota State University, United States of America; Upper Great Plains Transportation Institute, North Dakota State University, United States of America; Nicolais School of Business, Wagner College, Staten Island, New York, United States of AmericaUpper Great Plains Transportation Institute, North Dakota State University, United States of AmericaAccording 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.http://www.sciencedirect.com/science/article/pii/S0386111222000632CrashesHotspotsMoran's ISpace-Time cubeNetKDE |
| spellingShingle | Ihsan Ullah Khan Kimberly Vachal Sajad Ebrahimi Satpal Singh Wadhwa Hotspot analysis of single-vehicle lane departure crashes in North Dakota IATSS Research Crashes Hotspots Moran's I Space-Time cube NetKDE |
| title | Hotspot analysis of single-vehicle lane departure crashes in North Dakota |
| title_full | Hotspot analysis of single-vehicle lane departure crashes in North Dakota |
| title_fullStr | Hotspot analysis of single-vehicle lane departure crashes in North Dakota |
| title_full_unstemmed | Hotspot analysis of single-vehicle lane departure crashes in North Dakota |
| title_short | Hotspot analysis of single-vehicle lane departure crashes in North Dakota |
| title_sort | hotspot analysis of single vehicle lane departure crashes in north dakota |
| topic | Crashes Hotspots Moran's I Space-Time cube NetKDE |
| url | http://www.sciencedirect.com/science/article/pii/S0386111222000632 |
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