Citation

Li J, Fang S, Guo J, Fu T, Qiu M. Accid. Anal. Prev. 2020; 151: e105953.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.aap.2020.105953

PMID

unavailable

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.

FINDINGS of the study provide specific and insightful countermeasures targeting at the contributing factors of motorcycle crashes.

Language: en

Keywords

Injury severity; Latent Class Clustering; Motorcycle crash