Performance of Safety Indicators in Identification of Black Spots on Two-Lane Rural Roads

Abstract

Defining a black spot is not as perceptual as it may appear. Unusually high crash counts do not necessarily indicate a real safety problem. When black spots on two-lane rural roads with low and medium traffic volumes are being identified, a reduced number of observed crashes is a critical issue that can emphasize weak results obtained with various safety indicators and checks. It is generally assumed that an empirical Bayesian (EB) estimate represents the best approach for the identification of black spots, but it is difficult to define quantitatively the precision of estimates that are arrived at with different procedures. Because traffic volume, segment length, and crash observation period are critical issues in the identification of black spots and can emphasize the quality of the results obtained with safety indicators and checks, definition is needed of the limits and errors that can be expected when inadequate indicators and procedures are used to identify black spots on two-lane rural roads. A Monte Carlo simulation was used to produce theoretical crash data similar to empirical data, and the data were used to define a priori hazardous sites and, therefore, to assess whether a method could correctly identify such sites. The accuracy and efficiency of procedures are compared on the basis of observed frequency of crashes, crash rate, EB estimation, and the potential for safety improvement (PSI). As a general rule, even if some arrangements can mitigate the lower performance of some indicators, the best practice is to use the indicators based on the EB estimation PSI to identify true positives.