Engineering Applications of Artificial Intelligence· 2026Q1
Evaluating and promoting urban public transportation systems: An intuitionistic fuzzy triangular divergence measure-based group decision-making analytics
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- Q1SCImago
- 2026year
Short summary
A new decision-making model using intuitionistic fuzzy sets (IFSs) and a novel divergence measure effectively ranks urban public transport systems, identifying commuter trains as the top choice in Ankara, Turkiye.
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Key points
- A new intuitionistic fuzzy set (IFS) based model is proposed for multi-criteria group decision-making in public transport selection.
- A novel divergence measure is introduced to improve the discrimination and reliability of intuitionistic fuzzy information for criteria weighting.
- The framework integrates expert evaluation, combined objective-subjective criteria weighting (CRITIC-RCC), and an improved CORASO ranking approach.
- The model was applied to Ankara, Turkiye, identifying 'Commuter trains' as the most suitable public transport system.
- Sensitivity and comparative analyses confirm the proposed framework's steadiness and robustness.
AI-generated from the title and abstract; the full text is not read.
Abstract
The selection of the right public transport system is crucial in shaping environmental accessibility, economic prospects, and quality of life in urban cities. It is considered a multi-criteria group decision-making problem because of the presence of multiple criteria, experts, and uncertain information. Accordingly, this study aims to propose a decision support model on intuitionistic fuzzy sets (IFSs) for identifying and evaluating the public transit systems. The proposed method evaluates the significance degrees of involved experts using the intuitionistic fuzzy coefficient of variation model. Next, the proposed framework presents the modified criteria importance through intercriteria correlation (CRITIC)-relative closeness coefficient (RCC)-based combined objective-subjective criteria weighting model considering a new divergence measure on IFSs. The proposed divergence measure is used to effectively quantify the discrimination between intuitionistic fuzzy information, which improves the reliability of obtained criteria weights. This framework further presents an improved compromise ranking from alternative solutions (CORASO) approach for ranking the options under an IFSs context. To reveal the applicability of the developed framework, it is implemented in a case study of urban public transportation systems’ assessment of Ankara, Turkiye. The results indicate that the option "Commuter trains" is the most suitable urban public transport system among the considered alternatives in Ankara, Turkiye. At last, this study conducts sensitivity and comparative analysis, confirming the steadiness and robustness of the proposed framework. The findings of this work help urban planners to select suitable public transport systems for metropolitan cities.
The authors' abstract, as published at the source. Engineering Applications of Artificial Intelligence, 2026 · DOI ↗
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Field: Management Science and Operations Research
Management Science and Operations ResearchDecision Sciences