Safety Effect Assessment of Cooperative Intelligent Transport Systems - A Bowtie Analysis Approach
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With the rise of intelligent transportation systems (ITS) expectations are high that they are able to further enhance road traffic safety among other transportation challenges. Cooperative intelligent transport systems (C-ITS) are a subgroup of ITS and their direct safety effects are mostly unknown and associated with uncertainties, because these systems represent emerging technology involving a lack of statistically reliable empirical data. This PhD proposes a probabilistic and fuzzy based bowtie approach for estimating the safety effect of C-ITS, whose application seeks to prevent road traffic accidents or mitigate their consequences. While bowtie analysis is used in the field of for example process engineering, it is novel in road traffic safety. Under the assumption of the potential occurrence of a particular single vehicle accident, three case studies demonstrate the application of the bowtie analysis approach in road traffic safety. The approach utilizes exemplary expert estimates and knowledge from literature on the probability of the occurrence of accident risk factors and of the success of safety measures. Fuzzy set theory is applied to handle uncertainty in expert judgment. Though the use of expert estimates seems feasible at the current lack of statistically reliable data, accumulated expert judgment is at risk to be biased and imprecise. Furthermore, conducting expert surveys and workshops takes time and financial resources. Therefore, the bowtie analysis approach is enhanced to allow a semi-quantitative safety effect assessment independently from expert estimates. Four accident case studies are completed using bowtie analysis while letting the input parameters sequentially vary over the entire range of possible expert opinions. This enhanced bowtie approach allows the identification of: (a) the sensitivity of the probability of accident occurrence and its associated consequences to expert judgment used inside the bowtie model, and (b) the necessary effectiveness of a chosen safety measure allowing adequate changes in the probability of an accident and its consequences. Finally, the proposed and enhanced bowtie approach is implemented, using ex-ante accident statistics and simulated fuzzy expert judgment as input data, to semi-quantitatively estimate the safety effect of a specific cooperative warning system. The results are then compared to the results of a traditional approach, where the safety effect was estimated in expert workshops. The bowtie approach is able to simulate the results of the traditional approach in significantly less time. Thereby it demonstrates that the probabilistic bowtie analysis approach allows a practical and fast estimation of the safety effects of proactive cooperative intelligent transport systems without the need for acquiring expert estimates. Thus, a valuable investment decision support tool is developed and provided. Using this method, decision makers such as road authorities can identify the minimum safety effectiveness to be achieved by emerging technology, and they can then choose the best investments to support safety.