Does gender really matter? A structural equation model to explain risky and positive cycling behaviors

While the use of bicycles as mean of transport is growing worldwide, the increasing rates of traffic crashes involving cyclists have turned into a relevant scientific, public health, and road safety concern. According to several studies, and despite the fact that some countries are taking part in preventive actions, the data indicate that the problem of cycling injuries implies high costs for the community welfare, for the economy, and for healthcare systems, thus proving a clear need for solutions. In this regard, and considering the available empirical evidence, risky and positive riding behaviors have gained significant weight in terms of explaining, intervening in, and preventing traffic crashes of cyclists, and some evidence suggests that gender may influence the road behavior of users.The objective of this study was to examine the effect of gender on cyclists' risky and positive riding behavior, considering a set of demographic, psychosocial and bike-use-related variables as potential predictors.For this cross-sectional study, data from 1064 cyclists (61.2% males and 38.8% females, aged between 17 and 80) from 20 countries, responding an electronic survey, were analyzed through a multi-group structural equation modeling approach.Although hourly intensity, psychological distress and level of knowledge of traffic rules similarly predict the risky road behaviors of both genders, age and risk perception are significant behavioral predictors only in the case of male cyclists. On the other hand, positive behaviors of men are predicted by cycling intensity, knowledge of traffic rules and risk perception, while in the case of women psychological distress predicts -to a significant extent- positive behaviors. Age had no significant effect on the explanation of positive behaviors.The findings of this study support the influence of gender in the statistical explanation of risky and protective behaviors, and they also reveal differentiating variables predicting the riding behavior of male and female cyclists.