Abstract
This paper presents an empirical inquiry into the predictive modeling of crashes involving pedestrians and motorized traffic on roadways. Empirical models based on the negative binomial distribution and mixing distributions, such as the zero-inflated Poisson distribution, are presented and discussed in terms of their applicability to pedestrian crash phenomena. Key modeling issues relating to the presence of excess zeros as well as unobserved heterogeneity in pedestrian crash distributions are addressed. The empirical results show that zero-inflated count distributions, such as the zero-inflated Poisson, are promising methodologies for providing explanatory insights into the causality behind pedestrian-traffic crashes.
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