Explorative Analysis of Motorcyclists’ Injury Severity Pattern at a National Level in Iran

Document Type: Original Article

Authors

1 Department of Transportation Engineering, School of Civil Engineering, Iran University of Science and Technology, 2Road Safety Research Center, Iran University of Science and Technology, Tehran, Iran

2 Department of Transportation Engineering, School of Civil Engineering, Iran University of Science and Technology

3 1Department of Transportation Engineering, School of Civil Engineering, Iran University of Science and Technology, 2Road Safety Research Center, Iran University of Science and Technology, Tehran, Iran

Abstract

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.

Keywords


1. Lin MR, Kraus JF. A review of risk factors and patterns of motorcycle
injuries. Accid Anal Prev 2009;41:710‑22.
2. Mahdian M, Sehat M, Fazel MR, Moraveji A, Mohammadzadeh M.
Epidemiology of urban traffic accident victims hospitalized more than
24 hours in a level III trauma center, Kashan County, Iran, during
2012‑2013. Arch Trauma Res 2015;4:e28465.
3. Vlahogianni EI, Yannis G, Golias JC. Overview of critical risk factors in
power‑two‑wheeler safety. Accid Anal Prev 2012;49:12‑22.
4. Savolainen P, Mannering F. Probabilistic models of motorcyclists’
injury severities in single‑ and multi‑vehicle crashes. Accid Anal Prev
2007;39:955‑63.
5. Sadeghi‑Bazargani H, Ayubi E, Azami‑Aghdash S, Abedi L,
Zemestani A, Amanati L, et al. Epidemiological patterns of road traffic
crashes during the last two decades in Iran: A Review of the literature
from 1996 to 2014. Arch Trauma Res 2016;5:e32985.
6. Tavakoli Kashani A, Rabieyan R, Besharati MM. A data mining
approach to investigate the factors influencing the crash severity of
motorcycle pillion passengers. J Safety Res 2014;51:93‑8.
7. Tavakoli Kashani A, Rabieyan R, Besharati MM. Modeling the effect of
operator and passenger characteristics on the fatality risk of motorcycle
crashes. J Inj Violence Res 2016;8:35‑42.
8. Kasantikul V, Ouellet JV, Smith T, Sirathranont J, Panichabhongse V.
The role of alcohol in Thailand motorcycle crashes. Accid Anal Prev
2005;37:357‑66.
9. Quddus MA, Noland RB, Chin HC. An analysis of motorcycle injury
and vehicle damage severity using ordered probit models. J Safety Res
2002;33:445‑62.
10. Tavakoli Kashani A, Besharati MM. An analysis of vehicle occupants’
injury severity in crashes occurred on rural freeways and multilane 
highways in Iran. Int J Transp Eng 2016;4:137‑46.
11. Kashani AT, Besharati MM. Fatality rate of pedestrians and fatal crash
involvement rate of drivers in pedestrian crashes: A case study of Iran.
Int J Inj Contr Saf Promot 2017;24:222‑31.
12. Besharati MM, Tavakoli Kashani A. Which set of factors contribute to
increase the likelihood of pedestrian fatality in road crashes? Int J Inj
Contr Saf Promot 2017;1-0. Doi: 10.1080/17457300.2017.1363781.
13. Depaire B, Wets G, Vanhoof K. Traffic accident segmentation by means
of latent class clustering. Accid Anal Prev 2008;40:1257‑66.
14. O’brien O, Cheshire J, Batty M. Mining bicycle sharing data for
generating insights into sustainable transport systems. J Transp Geogr
2014;34:262‑73.
15. Höppner F. Fuzzy Cluster Analysis: Methods for Classification, Data
Analysis and Image Recognition. Hoboken, New Jersey: John Wiley &
Sons; 1999.
16. Nylund KL, Asparouhov T, Muthén BO. Deciding on the number of
classes in latent class analysis and growth mixture modeling: A Monte
Carlo simulation study. Struct Equ Model 2007;14:535‑69.
17. Vermunt JK, Magidson J. Latent GOLD 4.0 User’s Guide; 2005.
18. de Oña J, López G, Mujalli R, Calvo FJ. Analysis of traffic accidents on
rural highways using Latent Class Clustering and Bayesian Networks.
Accid Anal Prev 2013;51:1‑0.