CRASH RISK RELATIONSHIPS FOR IMPROVED SAFETY MANAGEMENT OF

This paper presents the results of a first attempt to combine detailed information on road geometry (horizontal curvature, gradient and cross-fall), road surface condition (roughness, rut depth, texture depth and skid resistance), carriageway characteristics (region, urban/rural environment, and traffic flow) and crashes. Such a study was only made possible because of annual surveys of the entire 22,000 lane-km of New Zealand’s State Highway network made with SCRIM since 1997, which involves simultaneous measurement of road condition and road geometry. Four subsets of road crashes were investigated: all reported injury and fatal crashes; selected injury and fatal crashes covering loss of control events; reported injury and fatal crashes occurring in wet conditions; and selected injury and fatal crashes occurring in wet conditions. One and two-way tables and Poisson regression modelling were employed to identify critical variables and the form of their relationship with crash risk. The critical variables common to all crash types investigated were horizontal curvature, traffic flow, skid resistance and to a lesser extent lane roughness. The resulting Poisson regression model uses 2 or 3 order polynomial functions of these variables to allow for the observed non-linear responses. Therefore, the model can be incorporated in existing road asset management systems. A comparison of observed and predicted crash numbers for different segments of the State Highway network showed that the model can provide estimates of crash numbers that are sufficiently accurate for safety management purposes. For example, the predicted effect of increasing the level of skid resistance was in line with the results from a paired crash site analysis, which considered changes in the number of crashes and road surface skid resistance at two different points in time at specific crash sites.