@article{ref1,
title="A motorcyclist-injury severity analysis: a comparison of single-, two-, and multi-vehicle crashes using latent class ordered probit model",
journal="Accident analysis and prevention",
year="2020",
author="Li, Jing and Fang, Shouen and Guo, Jingqiu and Fu, Ting and Qiu, Min",
volume="151",
number="",
pages="e105953-e105953",
abstract="Motorcycle crashes increasingly become a high proportion of the overall motorized vehicle fatalities. However, limited research has been conducted to compare the  injury severity of single-, two- and multi-vehicle crashes involving a motorcycle. This study aims to investigate the effects of rider characteristics, road  conditions, pre-crash situations, and crash features on motorcycle severities with  respect to different numbers of vehicles involved. The crash data used was obtained  through a comprehensive Motorcycle Crash Causation Study (MCCS) by the Federal  Highway Administration. An anatomic injury severity indicator, the New Injury  Severity Score (NISS), is utilized to calculate a total score as the sum of squared  the abbreviated injury scale scores of each of the rider's three most severe  injuries. A hybrid approach integrating Latent Class Clustering (LCC) and Ordered  Probit (OP) models was used to uncover the unobserved heterogeneity and to explore  the major factors which significantly affect the injury severities resulting from  single-, two- and multi-vehicle crashes involving a motorcycle. The results show  that the significant differences in severity exist between different numbers of  vehicles involved. More importantly, they also indicate dividing motorcycle crashes  into homogeneous classes before modelling helps to discover insightful information. Pre-speed of the motorcycle is found to be a main factor associated with serious and  critical injuries in most types of crashes. <br><br>FINDINGS of the study provide specific  and insightful countermeasures targeting at the contributing factors of motorcycle  crashes.<p /> <p>Language: en</p>",
language="en",
issn="0001-4575",
doi="10.1016/j.aap.2020.105953",
url="http://dx.doi.org/10.1016/j.aap.2020.105953"
}