An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity

Venkataraman Shankar, Fred Mannering

Research output: Contribution to journalArticle

224 Citations (Scopus)

Abstract

Most previous research on motorcycle accident severity has focused on univariate relationships between severity and an explanatory variable of interest (e.g., helmet use). The potential ambiguity and bias that univariate analyses create in identifying the causality of severity has generated the need for multivariate analyses in which the effects of all factors that influence accident severity are considered. This paper attempts to address this need by presenting a multinomial logit formulation of motorcycle-rider accident severity in single-vehicle collisions. Five levels of severity are considered: (a) property damage only, (b) possible injury, (c) evident injury, (d) disabling injury, and (e) fatality. Using 5-year statewide data on single-vehicle motorcycle accidents from the state of Washington, we estimate a multivariate model of motorcycle-rider severity that considers environmental factors, roadway conditions, vehicle characteristics, and rider attributes. Our findings show that the multinomial logit formulation that we use is a promising approach to evaluate the determinants of motorcycle accident severity.

Original languageEnglish (US)
Pages (from-to)183-194
Number of pages12
JournalJournal of Safety Research
Volume27
Issue number3
DOIs
StatePublished - Jan 1 1996

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Motorcycles
Accidents

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality

Cite this

Shankar, Venkataraman ; Mannering, Fred. / An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. In: Journal of Safety Research. 1996 ; Vol. 27, No. 3. pp. 183-194.
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An exploratory multinomial logit analysis of single-vehicle motorcycle accident severity. / Shankar, Venkataraman; Mannering, Fred.

In: Journal of Safety Research, Vol. 27, No. 3, 01.01.1996, p. 183-194.

Research output: Contribution to journalArticle

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