Enhancing Smart City Management with AI: Analyzing Key Criteria and their Interrelationships using DEMATEL under Neutrosophic Numbers and MABAC for Optimal Development

Purpose: This paper explores the transformative role of AI across various domains of smart cities, including urban mobility, energy management, public safety, healthcare, environmental monitoring, economic development, and data management. It aims to develop a novel decision-making framework that integrates AI technologies with a hybrid approach to analyze the interrelationships among smart city components. Methodology: This paper employs a hybrid decision-making framework that combines the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method with single-valued trapezoidal neutrosophic numbers (STrNN). This approach is used to analyze the complex relationships among criteria and sub-criteria in smart city contexts, addressing uncertainty and incomplete information while providing a structured evaluation of alternatives. Additionally, the study incorporates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to assess and rank alternatives for smart city development. This integration of MABAC complements the insights derived from the STrNN-DEMATEL approach, offering a robust and nuanced evaluation of alternatives. By combining these methodologies for the first time, the research delivers a novel and effective decision-making framework tailored to the complexities of smart city development. Findings: The key findings of this paper highlight the pivotal role of economic development, Energy Management and Sustainability, and Urban Mobility and Transportation in shaping smart city ecosystems. The paper demonstrates the effectiveness of the proposed framework in identifying and prioritizing key drivers and understanding the causal relationships among various smart city components. Originality: This paper is pioneering in developing a decision-making framework that integrates AI technologies with a hybrid approach, combining DEMATEL and STrNN for the first time. The framework provides a comprehensive analysis of interrelationships among criteria and sub-criteria in smart city contexts, addressing ethical concerns related to AI and ensuring a balanced approach to technological innovation, and social, environmental, and economic considerations. Incorporating MABAC alongside STrNN-DEMATEL enhances the framework's robustness and effectiveness, offering new insights into smart city development.