Explorative Analysis of Motorcyclist's Injury Severity Pattern at a National Level in Iran

This study aimed at examining the hidden patterns of motorcycle crashes and rider's injury severity at the national level in Iran.
Hierarchical clustering (HC) and latent class clustering (LCC) techniques were used in combination to analyze rider's 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 rider's injury pattern across the country.
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.
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 motorcyclist's 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.