AHP integrated TOPSIS and VIKOR methods with Pythagorean fuzzy sets to prioritize risks in self-driving vehicles

Abstract

Abstract Self-driving vehicles are of critical importance to a future sustainable transport system, which is expected to become widespread around the world. However a substantial amount of risk is associated with self-driving vehicles which must be considered by decision-makers effectively. Given that automated driving technology and how it will interact with the mobility system are substantially risky, the risks involved in self-driving vehicles need to be addressed appropriately. The identified knowledge gap of the pre-literature review is that an overview of the identification which completely considers all types of risks related to self-driving vehicles does not exist. In response to this knowledge gap, this study aims to prioritize the risks in self-driving vehicles. Risk prioritization is a complicated multi-criteria decision making (MCDM) problem that requires consideration of multiple feasible alternatives and conflicting tangible and intangible criteria. This study addresses the prioritization of risks involved with self-driving vehicles by proposing new hybrid MCDM methods based on the Analytic Hierarchy Process (AHP), the Technique for order preference by similarity to an ideal solution (TOPSIS) and Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) under Pythagorean fuzzy environment. This study addresses the prioritization of risks involved with self-driving vehicles by proposing new hybrid MCDM methods based on the Analytic Hierarchy Process (AHP), the Technique for order preference by similarity to an ideal solution (TOPSIS) and Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) under Pythagorean fuzzy environment. The result of the proposed model is validated by performing sensitivity analysis. The performance of proposed methodology with Pythagorean fuzzy sets is also compared with those with ordinary fuzzy sets and it is revealed that the proposed method produces reliable and informative outcomes better representing the impreciseness of decision making problems. The findings of this study will provide useful insight to the planners and policymakers for decision making in self-driving vehicles.