Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in Iran
Objectives: This study aimed at examining the hidden patterns of motorcycle crashes and riders' injury severity at the national level in Iran. Methods: Hierarchical clustering (HC) and latent class clustering (LCC) techniques were used in combination to analyze riders' injury pattern in 66...
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2017-01-01
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doaj-b86d32966fcc41d9903d3e64a4c212802020-12-04T09:17:25ZengWolters Kluwer Medknow PublicationsArchives of Trauma Research2251-953X2017-01-0164879310.4103/atr.atr_38_17Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in IranAli Tavakoli KashaniAhmad MohammadianMohammad Mehdi BesharatiObjectives: This study aimed at examining the hidden patterns of motorcycle crashes and riders' injury severity at the national level in Iran. Methods: Hierarchical clustering (HC) and latent class clustering (LCC) techniques were used in combination to analyze riders' injury pattern in 6638 motorcycle crashes occurred in Iran during 2009–2012. First, the HC was performed to classify the provinces into homogeneous groups, based on the percentage of different crash factors in each province and a new variable called “province group” was added to the crash database as the output of the HC analysis. Next, the LCC was conducted to cluster the crash data and to investigate the riders' injury pattern across the country. Results: Among the six crash clusters identified by the LCC, Clusters 1 and 5, in which, respectively, 91% and 84%, of the riders were under 30 as well as Cluster 2, in which 65% of the riders were above 30 years had the highest percentages of injured motorcyclists (86%, 84%, and 88%, respectively). Cluster 5 had also the lowest percentage of helmet usage (about 5%) and licensed riders (5%). Moreover, Cluster 6 had the highest fatality rate among the six clusters. In this cluster, 73% of the crashes were occurred in nonresidential/agricultural land uses, and 94% were occurred in rural areas. Conclusions: Since a significant share of crashes in Cluster 5 was occurred in province Groups C and E; this might be regarded as an indication of weak law enforcement over helmet usage and licensure in these provinces. In addition, as the pattern of helmet usage was different among province clusters, future studies might be conducted regarding motorcyclists' helmet-wearing intentions among several provinces. Moreover, crashes occurred in rural roads, particularly in the vicinity of nonresidential or agricultural land uses were more severe and need special future attention.http://www.archtrauma.com/article.asp?issn=2251-953X;year=2017;volume=6;issue=4;spage=87;epage=93;aulast=Kashanicluster analysisdata mininginjuries and traumamotorcycle accidents |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ali Tavakoli Kashani Ahmad Mohammadian Mohammad Mehdi Besharati |
spellingShingle |
Ali Tavakoli Kashani Ahmad Mohammadian Mohammad Mehdi Besharati Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in Iran Archives of Trauma Research cluster analysis data mining injuries and trauma motorcycle accidents |
author_facet |
Ali Tavakoli Kashani Ahmad Mohammadian Mohammad Mehdi Besharati |
author_sort |
Ali Tavakoli Kashani |
title |
Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in Iran |
title_short |
Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in Iran |
title_full |
Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in Iran |
title_fullStr |
Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in Iran |
title_full_unstemmed |
Explorative Analysis of Motorcyclists' Injury Severity Pattern at a National Level in Iran |
title_sort |
explorative analysis of motorcyclists' injury severity pattern at a national level in iran |
publisher |
Wolters Kluwer Medknow Publications |
series |
Archives of Trauma Research |
issn |
2251-953X |
publishDate |
2017-01-01 |
description |
Objectives: This study aimed at examining the hidden patterns of motorcycle crashes and riders' injury severity at the national level in Iran. Methods: Hierarchical clustering (HC) and latent class clustering (LCC) techniques were used in combination to analyze riders' injury pattern in 6638 motorcycle crashes occurred in Iran during 2009–2012. First, the HC was performed to classify the provinces into homogeneous groups, based on the percentage of different crash factors in each province and a new variable called “province group” was added to the crash database as the output of the HC analysis. Next, the LCC was conducted to cluster the crash data and to investigate the riders' injury pattern across the country. Results: Among the six crash clusters identified by the LCC, Clusters 1 and 5, in which, respectively, 91% and 84%, of the riders were under 30 as well as Cluster 2, in which 65% of the riders were above 30 years had the highest percentages of injured motorcyclists (86%, 84%, and 88%, respectively). Cluster 5 had also the lowest percentage of helmet usage (about 5%) and licensed riders (5%). Moreover, Cluster 6 had the highest fatality rate among the six clusters. In this cluster, 73% of the crashes were occurred in nonresidential/agricultural land uses, and 94% were occurred in rural areas. Conclusions: Since a significant share of crashes in Cluster 5 was occurred in province Groups C and E; this might be regarded as an indication of weak law enforcement over helmet usage and licensure in these provinces. In addition, as the pattern of helmet usage was different among province clusters, future studies might be conducted regarding motorcyclists' helmet-wearing intentions among several provinces. Moreover, crashes occurred in rural roads, particularly in the vicinity of nonresidential or agricultural land uses were more severe and need special future attention. |
topic |
cluster analysis data mining injuries and trauma motorcycle accidents |
url |
http://www.archtrauma.com/article.asp?issn=2251-953X;year=2017;volume=6;issue=4;spage=87;epage=93;aulast=Kashani |
work_keys_str_mv |
AT alitavakolikashani explorativeanalysisofmotorcyclistsinjuryseveritypatternatanationalleveliniran AT ahmadmohammadian explorativeanalysisofmotorcyclistsinjuryseveritypatternatanationalleveliniran AT mohammadmehdibesharati explorativeanalysisofmotorcyclistsinjuryseveritypatternatanationalleveliniran |
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