OICC Press International Journal of Mathematical Modelling & Computations 2228-6233 16 3 2026 09 30 Smart and Sustainable Transportation Based on Artificial Intelligence in Big Cities : A Case Study of Isfahan, Iran 261 276 10.57647/ijm2c.2026.1603.21 EN Hourivash Ghaderi Department of Psychiatry, Clinical Research Development Unit, Hajar Hospital, Shahrekord University of Medical Sciences, Shahrekord, Iran Ahmad Biyabani Dehkordi Department of Mathematics, Isf.C., Islamic Azad University, Isfahan, Iran https://orcid.org/0000-0002-5943-1134 Journal Article 2026 09 30 This study presents a comprehensive framework for deploying Artificial Intelligence (AI) to advance smart and sustainable urban transportation, using Isfahan, Iran, as a case study. The research designs and proposes a multi-model AI architecture, utilizing Graph Neural Networks (GNNs) with LSTM layers for high-accuracy (target >80%) short-term traffic prediction, Deep Reinforcement Learning for adaptive signal control that incorporates BRT priority, and XGBoost for passenger demand forecasting. A phased implementation plan is outlined, integrating these models with Isfahan's existing BRT data infrastructure through a microservices architecture. The projected environmental impact, calculated via a tailored emissions model, indicates targeted reductions of 20% in CO₂ emissions and 18% in fuel consumption. A socio-economic cost-benefit analysis forecasts a substantial benefit-cost ratio (BCR > 2.5) by optimizing travel time, safety, and operational costs. The study critically addresses implementation challenges, including data governance, computational demands, and algorithmic bias, providing a replicable blueprint for AI-driven urban mobility that balances efficiency, equity, and environmental sustainability.