CityMindX:AI-Based Mobility and Infrastructure Optimisation in Smart Cities

Abstract

Nowadays, artificial intelligence (AI) plays an increasingly important role in addressing sustainability challenges. There is a lack of comprehensive smart city solutions that integrate both technological and social dimensions. To fill this gap, this research focuses on developing the CityMindX framework: an integrated, three-layer AI architecture aimed at the holistic optimisation of urban mobility and infrastructure systems by combining social science and data science methodologies. The research encompasses the theoretical framework of the CityMindX concept, the analysis of the functioning of the architecture by processing four case studies, and the empirical validity of the concept is tested by statistical analysis of a quantitative survey (mixed methodological). The novelty of the proposed approach lies in the fact that it is the first to integrate technological (edge–fog–cloud layering, federated learning) and social elements (e.g., public trust, inclusive governance) within a triple AI framework, complemented by the application of explainable and trustworthy AI principles in a smart city model. The findings indicate that the application of CityMindX can bring systematic improvements to the coordination of urban subsystems (e.g., transportation, energy), reducing parallel burdens (e.g., environmental impacts from simultaneous peak traffic) and making city operations more adaptive. The research confirmed that technological advancement directly and indirectly enhances urban well-being, while public trust and social inclusion play a key mediating role in achieving these positive effects. The results highlight that CityMindX can facilitate sustainable and inclusive smart city development, offering policymakers a practical tool to improve urban services and residents’ quality of life.