Modelling of crash types at signalized intersections based on random effect model

TL;DR

This paper develops Bayesian random effect models to analyze crash types at signalized intersections, accounting for spatial correlation among approaches and revealing diverse factor influences on different crash types.

Contribution

It introduces approach-level Bayesian models with random effects to capture intersection-specific variations and spatial correlations in crash data.

Findings

Different crash types are influenced by distinct factor groups.

Random effects confirm spatial correlation among approaches.

Models show diverse effects of factors on crash types.

Abstract

Approach-level models were developed to accommodate the diversity of approaches within the same intersection. A random effect term, which indicates the intersection-specific effect, was incorporated into each crash type model to deal with the spatial correlation between different approaches within the same intersection. The model parameters were estimated under the Bayesian framework. Results show that different crash types are correlated with different groups of factors, and each factor shows diverse effects on different crash types, which indicates the importance of crash type models. Besides, the significance of random effect term confirms the existence of spatial correlations among different approaches within the same intersection.