Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models

• Pedestrian and bicycle crashes based on traffic analysis zones have been analyzed.
• The viability of dual-state models for macro-level crash analysis is evaluated.
• Spatial independent variables are introduced for spatial spillover effects.
• Many variables in targeted and neighboring traffic analysis zones are significant.
• Marginal effects are computed to assess the effect of significant factors.

This study attempts to explore the viability of dual-state models (i.e., zero-inflated and hurdle models) for traffic analysis zones (TAZs) based pedestrian and bicycle crash frequency analysis. Additionally, spatial spillover effects are explored in the models by employing exogenous variables from neighboring zones. The dual-state models such as zero-inflated negative binomial and hurdle negative binomial models (with and without spatial effects) are compared with the conventional single-state model (i.e., negative binomial). The model comparison for pedestrian and bicycle crashes revealed that the models that considered observed spatial effects perform better than the models that did not consider the observed spatial effects. Across the models with spatial spillover effects, the dual-state models especially zero-inflated negative binomial model offered better performance compared to single-state models. Moreover, the model results clearly highlighted the importance of various traffic, roadway, and sociodemographic characteristics of the TAZ as well as neighboring TAZs on pedestrian and bicycle crash frequency.