TY  - EJOU
AU  - Musa, Sagvan Y. 
AU  - Ameen, Zanyar A. 
AU  - Alagal, Wafa 
AU  - Asaad, Baravan A. 

TI  - Urban Transportation Strategy Selection for Multi-Criteria Group Decision-Making Using Pythagorean Fuzzy N-Bipolar Soft Expert Sets
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 3
SN  - 1526-1506

AB  - Urban transportation planning involves evaluating multiple conflicting criteria such as accessibility, cost-effectiveness, and environmental impact, often under uncertainty and incomplete information. These complex decisions require input from various stakeholders, including planners, policymakers, engineers, and community representatives, whose opinions may differ or contradict. Traditional decision-making approaches struggle to effectively handle such bipolar and multivalued expert evaluations. To address these challenges, we propose a novel decision-making framework based on Pythagorean fuzzy N-bipolar soft expert sets. This model allows experts to express both positive and negative opinions on a multinary scale, capturing nuanced judgments with higher accuracy. It introduces algebraic operations and a structured aggregation algorithm to systematically integrate and resolve conflicting expert inputs. Applied to a real-world case study, the framework evaluated five urban transport strategies based on key criteria, producing final scores as follows: improving public transit (−0.70), optimizing traffic signal timing (1.86), enhancing pedestrian infrastructure (3.10), expanding bike lanes (0.59), and implementing congestion pricing (0.77). The results clearly identify enhancing pedestrian infrastructure as the most suitable option, having obtained the highest final score of 3.10. Comparative analysis demonstrates the framework’s superior capability in modeling expert consensus, managing uncertainty, and supporting transparent multi-criteria group decision-making.
KW  - Pythagorean fuzzy N-bipolar soft expert sets; N-soft sets; pythagorean fuzzy sets; MCGDM; urban transportation

DO  - 10.32604/cmes.2025.070019