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Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework

BACKGROUND: Fatigue and drowsiness accidents are more likely to cause serious injuries and fatalities than other accidents. Statistics revealed that 20 to 40 percent of traffic accidents in Iran are due to drivers' fatigue. This study identified the most important factors affecting driver injur...

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Autores principales: Tavakoli Kashani, Ali, Rakhshani Moghadam, Marzieh, Amirifar, Saeideh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Kermanshah University of Medical Sciences 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9115810/
https://www.ncbi.nlm.nih.gov/pubmed/35124683
http://dx.doi.org/10.5249/jivr.v14i1.1679
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author Tavakoli Kashani, Ali
Rakhshani Moghadam, Marzieh
Amirifar, Saeideh
author_facet Tavakoli Kashani, Ali
Rakhshani Moghadam, Marzieh
Amirifar, Saeideh
author_sort Tavakoli Kashani, Ali
collection PubMed
description BACKGROUND: Fatigue and drowsiness accidents are more likely to cause serious injuries and fatalities than other accidents. Statistics revealed that 20 to 40 percent of traffic accidents in Iran are due to drivers' fatigue. This study identified the most important factors affecting driver injuries in fatigue and drowsiness accidents. METHODS: The Classification and Regression Tree method (CART) was applied 11,392 drivers were in-volved in fatigue and drowsiness accidents in three provinces of Iran, over the 7 years from 2011-2018. A two-level target variable was used to increase the accuracy of the model. First, dataset in each of three provinces was classified into homogeneous clusters using a two-step clus-tering algorithm. Oversampling method was used for imbalanced accident severity datasets. Then, classification was improved by boosting method. RESULTS: The classification tree reveals that the month, time of day, collision type, and vehicle type were common factors. Also, driver's age was important in female drivers cluster; the geometry of the place and seat belt/helmet usage were important in urban roads cluster; and area type, road type, road direction, and vehicle factor were important in rural roads cluster. Also, the combination of the CART algorithm with oversampling and boosting increased the accuracy of the models. CONCLUSIONS: The analysis results revealed motorcycles, lack of using a helmet or seat belt, curvy roads, roads with two-way undivided and one-way movement direction increased the injury and death of drivers. Collision with fixed object, run-off-road, overturning, falling, and defective vehicles increased the severity of accidents. Female drivers older than 44 years old have a higher probability of fatality. Identifying the factors affecting the severity of driver injuries in such accidents in each province could assist in determining engineering countermeasures and training educational programs to mitigate these crash severities.
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spelling pubmed-91158102022-05-27 Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework Tavakoli Kashani, Ali Rakhshani Moghadam, Marzieh Amirifar, Saeideh J Inj Violence Res Injury &Violence BACKGROUND: Fatigue and drowsiness accidents are more likely to cause serious injuries and fatalities than other accidents. Statistics revealed that 20 to 40 percent of traffic accidents in Iran are due to drivers' fatigue. This study identified the most important factors affecting driver injuries in fatigue and drowsiness accidents. METHODS: The Classification and Regression Tree method (CART) was applied 11,392 drivers were in-volved in fatigue and drowsiness accidents in three provinces of Iran, over the 7 years from 2011-2018. A two-level target variable was used to increase the accuracy of the model. First, dataset in each of three provinces was classified into homogeneous clusters using a two-step clus-tering algorithm. Oversampling method was used for imbalanced accident severity datasets. Then, classification was improved by boosting method. RESULTS: The classification tree reveals that the month, time of day, collision type, and vehicle type were common factors. Also, driver's age was important in female drivers cluster; the geometry of the place and seat belt/helmet usage were important in urban roads cluster; and area type, road type, road direction, and vehicle factor were important in rural roads cluster. Also, the combination of the CART algorithm with oversampling and boosting increased the accuracy of the models. CONCLUSIONS: The analysis results revealed motorcycles, lack of using a helmet or seat belt, curvy roads, roads with two-way undivided and one-way movement direction increased the injury and death of drivers. Collision with fixed object, run-off-road, overturning, falling, and defective vehicles increased the severity of accidents. Female drivers older than 44 years old have a higher probability of fatality. Identifying the factors affecting the severity of driver injuries in such accidents in each province could assist in determining engineering countermeasures and training educational programs to mitigate these crash severities. Kermanshah University of Medical Sciences 2022-01 /pmc/articles/PMC9115810/ /pubmed/35124683 http://dx.doi.org/10.5249/jivr.v14i1.1679 Text en https://creativecommons.org/licenses/by/3.0/This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Injury &Violence
Tavakoli Kashani, Ali
Rakhshani Moghadam, Marzieh
Amirifar, Saeideh
Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework
title Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework
title_full Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework
title_fullStr Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework
title_full_unstemmed Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework
title_short Factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework
title_sort factors affecting driver injury severity in fatigue and drowsiness accidents: a data mining framework
topic Injury &Violence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9115810/
https://www.ncbi.nlm.nih.gov/pubmed/35124683
http://dx.doi.org/10.5249/jivr.v14i1.1679
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