Modeling offenses among motorcyclists involved in crashes in Spain

• The study of motorcyclist offenses is useful to prevent crash severity and crash risk.
• A logit regression is applied to explain being offender or not.
• Being offender is explained by individual and environmental factors.
• Interaction effects between gender × age and road condition × road layout have statistical significant effects on being offender.

In relative terms, Spanish motorcyclists are more likely to be involved in crashes than other drivers and this tendency is constantly increasing. The objective of this study is to identify the factors that are related to being an offender in motorcycle accidents. A binary logit model is used to differentiate between offender and non-offender motorcyclists. A motorcyclist was considered to be offender when s/he had committed at least one traffic offense at the moment previous to the crash. The analysis is based on the official accident database of the Spanish general directorate of traffic (DGT) for the 2003–2008 time period. A number of explanatory variables including motorcyclist characteristics and environmental factors have been evaluated. The results suggest that inexperienced, older females, not using helmets, absent-minded and non-fatigued riders are more likely to be offenders. Moreover, riding during the night, on weekends, for leisure purposes and along roads in perfect condition, mainly on curves, predict offenses among motorcyclists. The findings of this study are expected to be useful in developing traffic policy decisions in order to improve motorcyclist safety.