A countermeasure-based approach for identifying and treating accident prone locations

Abstract

Accident prone locations are traditionally defined as locations that exhibit higher accident occurrence (frequency and severity) than an established norm. This definition represents the foundation on which Black Spot programs for safety improvements are established and executed. This paper extends the definition of accident prone locations by examining not only accident frequency and severity but also their patterns. It is argued that a location with a given number of accidents with well-defined patterns can be treated more effectively than a location with a larger number of accidents with poorly defined patterns. Traditional approaches start with a problem (high accident occurrence) and attempt to find solutions (countermeasures). The approach presented in this paper reverses the traditional process by first identifying main accident patterns that can be targeted by specific countermeasures and then searching for locations that have overrepresentation of these patterns. The approach uses an empirical Bayes technique for the identification process. The approach is not intended as an alternative to traditional methods, but represents a complementary way of targeting safety problems in the sense that it is implemented in parallel with other methods in order to identify locations that exhibit an overrepresentation of specific accident patterns. The approach is discussed and its usefulness is demonstrated using case studies. It was found that many locations identified in the countermeasure-based program because of their well-defined accident patterns were not accident prone according to the traditional identification approach. This indicates the usefulness of the countermeasure-based approach in identifying locations that have a high chance of being cost effectively treated and which can be missed by the traditional approach.