Sustainable Urban Logistics Evaluation in Smart Cities: A Multi-Criteria Group Decision-Making Approach Using the Hesitant Fuzzy Linguistic ARAS Method
in: Fuzzy Sets and Triangular Norms: Aggregation in Decision-Aided Intelligent Systems, CRC Press, pp.57-78, 2026
- Publication Type: Book Chapter / Chapter Research Book
- Publication Date: 2026
- Doi Number: 10.1201/9781003529088-3
- Publisher: CRC Press
- Page Numbers: pp.57-78
- Karadeniz Technical University Affiliated: Yes
Abstract
Smart cities have emerged as a critical response to the challenges of urbanization, environmental concerns, and population growth. Within smart cities, efficient urban mobility is essential to achieving sustainability goals, reducing congestion and emissions, and enhancing the quality of life for citizens. This study applies a group decision-making approach to evaluate and rank various urban mobility alternatives, including self-driving vehicles, electric public transport, smart bike-sharing systems, and intelligent traffic management systems, within the context of smart cities. To address the uncertainties and complexities inherent in decision-making, the Hesitant Fuzzy Linguistic Term Sets (HFLTS) approach is integrated with the Additive Ratio Assessment (ARAS) method, forming a comprehensive HFL-ARAS framework. This methodology enables the systematic assessment of alternatives based on multiple criteria, such as sustainability, cost-effectiveness, and user satisfaction. The results indicate that Smart Bike-Sharing Systems emerged as the most suitable alternative, achieving the highest benefit score due to its strong performance in sustainability and user satisfaction. Car-Sharing Systems and Electric Public Transport ranked second and third, respectively, demonstrating significant potential in terms of environmental benefits and ease of integration within existing urban infrastructure. The findings suggest that prioritizing sustainability, affordability, and user acceptance is crucial when designing urban mobility strategies for smart cities.