Safety performance models are commonly used to correlate explanatory variables in the form of geometric, operational, environmental and human characteristics to estimate road accident frequency and severity. Generally these models employ multivariate relationships between variables requiring local calibration. The selection of independent variables normally follows traditional tests of significance but lacks a real test of the ability of each factor to explain the observed response and the nature of the variance-covariance structure. This paper uses principal components analysis and correlation matrices to identify and retain significant factors, find linear codependences and cluster similar explanatory factors. Nonlinear regression methods for safety severity were examined by looking at their ability to develop safety performance functions for a case study of regional highways in New Brunswick. Linear dependency between shoulder and lane width was founded. Lighting and road surface condition were not relevant in explaining accident severity. It was found that vertical alignment, vehicles running of the road and colliding with obstacles and AADT were of intermediate relevance, of high importance; speed, percentage of trucks, intensity of intersections per kilometer, horizontal alignment and type of facility (divided or undivided). A clear cluster of geometric characteristics and another of operational characteristics was observed. The analysis aims to serve as a guide for practitioners in need to develop locally calibrated safety performance functions able to explain locally observed road accidents by severity.
|Published - 26 May 2013
|23rd Canadian Multidisciplinary Road Safety Conference - Montreal, Canada
Duration: 26 May 2013 → 29 May 2013
|23rd Canadian Multidisciplinary Road Safety Conference
|26/05/13 → 29/05/13