Accident cost saving and highway attributes

Two semi-logarithmic regression models are developed to estimate accident rates and accident costs, respectively, for rural non-interstate highways in the state of Iowa. Data on 21,224 accidents occurring between 1989 and 1991 on 17,767 road segments are used in the analysis. Seven road attributes of these road segments are included as predictor variables. Applying the resulting regression models to a rather typical highway upgrade situation, the present value of the accident cost saving is computed. The sensitivity of the estimated cost saving to values for fatal, personal injury, and property damage only accidents is tested.Because factors other than road characteristics greatly influence accident costs, the models developed in this research explain a limited amount of the variance in these costs among road segments. Results of the analysis indicate that the most important attribute associated with accident costs is average daily traffic per lane, followed by conditions requiring passing restrictions and the sharpness of curves. Varying the values for the three categories of accidents shows that results are far more sensitive to the value of personal injuries than fatalities. The feasibility of using predictive models of accident costs in benefit-cost analyses of highway investments is demonstrated.