10.57647/ijm2c.2026.1603.21

Smart and Sustainable Transportation Based on Artificial Intelligence in Big Cities : A Case Study of Isfahan, Iran

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  • Hourivash Ghaderi1,
  • Ahmad Biyabani Dehkordi*2,
  1. Department of Psychiatry, Clinical Research Development Unit, Hajar Hospital, Shahrekord University of Medical Sciences, Shahrekord, Iran
  2. Department of Mathematics, Isf.C., Islamic Azad University, Isfahan, Iran

Received: 13-09-2025

Revised: 07-02-2026

Accepted: 12-02-2026

Published in Issue 30-09-2026

Published Online: 15-02-2026

Copyright (c) 2025 Hourivash Ghaderi, Ahmad Biyabani Dehkordi (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Ghaderi, H., & Biyabani Dehkordi, A. (2026). Smart and Sustainable Transportation Based on Artificial Intelligence in Big Cities : A Case Study of Isfahan, Iran. International Journal of Mathematical Modelling & Computations, 16(3), 261-276. https://doi.org/10.57647/ijm2c.2026.1603.21
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Abstract

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.

Keywords

  • Smart,
  • Sustainable Transportation,
  • Artificial Intelligence,
  • Big Cities,
  • Isfahan,
  • Iran
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References

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