Integrating macro and micro level crash frequency models considering spatial heterogeneity and random effects

Abstract

Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops an integrated model system that allows for the influence of independent variables at the microscopic level to be incorporated within the macroscopic propensity estimation. The empirical analysis is based on the data drawn from 300 traffic analysis zones, 1818 roadway segments, and 4184 intersections from the City of Orlando, Florida for the years 2018 and 2019. The study considers a host of exogenous variables including roadway and traffic factors, land-use, built environment, and sociodemographic characteristics for the model estimation. The proposed model system can also accommodate for hierarchical correlations such as correlation between all segments or intersections in a zone. The study findings highlight the presence of common spatial unobserved factors influencing crash frequency across segment level and intersection level as well as presence of significant parameter variability across both micro and macro level in the crash frequency. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated micro–macro model relative to the non-integrated macro model. The overall model fit measures and interpretations encourage the application of the proposed model for crash frequency analysis.