A data-driven technique for risk allocation in the micromobility sector using q-rung orthopair fuzzy rough numbers

Risk allocation in the micromobility sector is challenged by multi-stakeholder participation, interdependent criteria, and high uncertainty in expert judgments, which limits the effectiveness of conventional multi-attribute group decision-making (MAGDM) approaches. This study proposes a data-driven technique for evaluating risk-prevention alternatives in the micromobility sector using a q-rung orthopair fuzzy rough number (q-ROFRN). The primary objective of this research is to develop an integrated CRITIC-REGIME model by combining the Criteria Importance Through Intercriteria Correlation (CRITIC) method and the Rangements Et Gestion Intégrée des MElanges (REGIME) technique to address MAGDM problems. The proposed approach is applied to a real-world micromobility case study involving multiple governance and operational alternatives evaluated across several dimensions and 18 sub-criteria. The results indicate that state-private sector partnerships represent the most effective strategy for managing risks in micromobility systems. The findings further demonstrate that the proposed framework improves decision-making accuracy by effectively integrating multiple influential factors and accounting for uncertainty in expert assessments. Comparative and sensitivity analyses confirm that the integrated q-ROFRN-based CRITIC-REGIME model outperforms existing decision-making approaches in terms of precision, robustness, and adaptability under uncertain conditions.