A review of accident prediction models for road intersections

Abstract

The objective of this report is to review accident prediction models for intersections used in literature to identify which variables have a significant effect on accident occurrence so that we can have a starting point for future research. Several models have been reviewed including multiple logistic regression, multiple linear regression, Poisson models, negative binomial models, random effects models and, classification and regression trees (CART) technique. The data, methodology and results of several studies are described. The direction of the effect of several significant explanatory variables is discussed and recommendations are made. Different APMs for different intersection types and accident types have been developed in the literature. It is recommended that fitting separate models for different intersection types and accident types gives a better fit and description of the data than one model for all intersection types. Provided data on intersection and accident types are available, it is recommended to fit disaggregated models rather than aggregated models (Reurings et al., 2005; Turner and Nicholson, 1998).