Bowtie Analysis without Expert Acquisition for Safety Effect Assessments of Cooperative Intelligent Transport Systems

Abstract

Estimating the safety effects of emerging or future technology based on expert acquisitions is challenging because the accumulated judgment is at risk to be biased and imprecise. Therefore, this semiquantitative study is proposing and demonstrating an upgraded bowtie analysis for safety effect assessments that can be performed without the need for expert acquisition. While bowtie analysis is commonly used in, for example, process engineering, it is novel in road traffic safety. Four crash case studies are completed using bowtie analysis, letting the input parameters sequentially vary over the entire range of possible expert opinions. The results suggest that only proactive safety measures estimated to decrease the probability of specific crash risk factors to at least “very improbable” can perceptibly decrease crash probability. Further, the success probability of a reactive measure must be at least “moderately probable” to reduce the probability of a serious or fatal crash by half or more. This upgraded bowtie approach allows the identification of (1) the sensitivity of the probability of a crash and its consequences to expert judgment used in the bowtie model and (2) the necessary effectiveness of a chosen safety measure allowing adequate changes in the probability of a crash and its consequences.