Macro-level vulnerable road users crash analysis: A Bayesian joint modeling approach of frequency and proportion

This study aims at contributing to the literature on pedestrian and bicyclist safety by building on the conventional count regression models to explore exogenous factors affecting pedestrian and bicyclist crashes at the macroscopic level. In the traditional count models, effects of exogenous factors on non-motorist crashes were investigated directly. However, the vulnerable road users' crashes are collisions between vehicles and non-motorists. Thus, the exogenous factors can affect the non-motorist crashes through the non-motorists and vehicle drivers. To accommodate for the potentially different impact of exogenous factors we convert the non-motorist crash counts as the product of total crash counts and proportion of non-motorist crashes and formulate a joint model of the negative binomial (NB) model and the logit model to deal with the two parts, respectively. The formulated joint model is estimated using non-motorist crash data based on the Traffic Analysis Districts (TADs) in Florida. Meanwhile, the traditional NB model is also estimated and compared with the joint model. The result indicates that the joint model provides better data fit and can identify more significant variables. Subsequently, a novel joint screening method is suggested based on the proposed model to identify hot zones for non-motorist crashes. The hot zones of non-motorist crashes are identified and divided into three types: hot zones with more dangerous driving environment only, hot zones with more hazardous walking and cycling conditions only, and hot zones with both. It is expected that the joint model and screening method can help decision makers, transportation officials, and community planners to make more efficient treatments to proactively improve pedestrian and bicyclist safety.