Integrating macro- and micro-level safety analyses: a Bayesian approach incorporating spatial interaction

ABSTRACT

Traditionally, crash frequency analyses have been undertaken at the macro- and micro-levels, independently. This study proposes a Bayesian integrated spatial crash frequency model, which links the crash counts of macro- and micro-levels based on the spatial interaction. In addition, the proposed model considers the spatial autocorrelation of the different types of road entities (i.e. segments and intersections) at the micro-level with a joint structure. The modelling results indicated that the integrated model can provide better model performance for estimating macro- and micro-level crash counts, which validates the concept of integrating the models for the two levels. Also, the integrated model could simultaneously identify both macro- and micro-level factors contributing to the crash occurrence. Subsequently, a novel hotspot identification method was suggested, which enables us to detect hotspots for both macro- and micro-levels with comprehensive information from the two levels.

KEYWORDS:

• Integrated model
• macro- and micro-level crash frequency
• spatial interaction
• hotspot identification
• Bayesian modeling

Acknowledgement

The opinions, findings and conclusions expressed in this paper are those of the authors and not necessarily those of the Florida Department of Transportation.

Disclosure statement

No potential conflict of interest was reported by the authors.