Evaluating safety in Dublin’s bike-sharing system using the concept of intuitionistic fuzzy rough power aggregation operators

Abstract

The multi-attribute group decision-making (MAGDM) process plays a pivotal role in identifying the most suitable solutions when faced with conflicting criteria. This study applies MAGDM to address safety concerns within Dublin’s bike-sharing system by incorporating innovative methods that effectively manage ambiguous and uncertain information. By utilizing Aczel-Alsina (AA) aggregation operators (AOs) within the intuitionistic fuzzy (IF) rough (IFR) framework, we mitigate information loss typically encountered during decision-making processes. The idea of the IFR set is a prestigious tool to express human thought in the shape of lower and upper approximation spaces. Where the ordinary intuitionistic fuzzy set failed to aggregat IFR information by inspiring the idea of IFR, the set introduces the IFR AA power-weighted averaging and geometric operators, both vital in aggregating uncertain and asymmetric data. These newly developed operators are used to evaluate safety improvements for Dublin’s bike-sharing system. Criteria such as infrastructure, user behaviour, maintenance, technology, and emergency response are assessed, with alternative solutions presented to ensure optimal safety improvements. Our results demonstrate the superiority of IFR AA-based AOs in addressing complex safety challenges in urban bike-sharing systems.