Effective targeting of road safety budgets is dependent on reliable estimates of the accidents which would be expected without remedial treatment. While such estimates can be achieved by combining observed accidents and accident model predictions using an empirical bayes (eb) approach, there are a number of obstacles to the widespread adoption of the method. This paper concentrates on problems associated with the available predictive models. Of particular concern is the effect on model predictions of accident trends over time resulting from, for instance, traffic growth or national road safety programmes. Since accident models invariably include traffic flow as an explanatory variable the effects of flow changes can be included provided that account is taken of the non-linear relationship between accidents and exposure. It is, however, common to assume that accident risk per unit of exposure (qb.) is constant over time whereas national data imply that accident risk is declining. Also, there is a need in practice to rank and evaluate remedial sites in terms of the specific accident types or severities which might be targeted by treatment (for example, wet road accidents in the case of surface treatment), this then raises the question of whether the proportion of accidents of various types varies with traffic flow or over time. The paper presents models (for accident totals and accidents disaggregated by severity, road surface condition and lighting condition) which allow for the possibility of accident risk varying over time and demonstrates the importance of allowing for the effects of trend in accident models.
Abstract