SCIENCE

Ways to reduce atmospheric air pollution from transport in the city of Kutaisi through an artificial intelligence – driven method

  • 1 Akaki Tsereteli State University, Kutaisi, Georgia

Abstract

The article discusses the ecological state of atmospheric air in the city of Kutaisi and ways to reduce pollution from one of the main factors – transport, through the method of optimizing the urban road traffic system. This method integrates environmental responsibility with the goal of ensuring efficient, safe, and accessible mobility. It will also help urban planners and environmentalists, through an artificial intelligence-driven method, to reduce the negative environmental impact of transport and maintain the efficiency of transport systems.

Keywords

References

  1. M. Miftah, D.I. Desrianti, N. Septiani, A.Y. Fauzi, C. Williams. Big data analytics for smart cities: optimizing urban traffic management using real-time data processingJ. Comput. Sci. Technol. Appl., 2 (1) (2025), pp. 14-23Google Scholar
  2. I. Moumen, J. Abouchabaka, N. RafaliaEnhancing urban mobility: integration of IoT road traffic data and artificial intelligence in smart city environmentIndones. J. Electr. Eng. Comput. Sci., 32 (2) (2023), pp. 985-993View at publisherCrossrefView in ScopusGoogle Scholar
  3. H. Xu, A. Berres, S.B. Yoginath, H. Sorensen, P.J. Nugent, J. Se verino, J. SanyalSmart mobility in the cloud: enabling real-time situational awareness and cyber-physical control through a digital twin for trafficIEEE Trans. Intell. Transp. Syst., 24 (3) (2023), pp. 3145-3156View at publisherCrossrefView in ScopusGoogle Scholar
  4. Balasubramanian, A. AI-Driven Optimization of Urban Mobility: Integrating Autonomous Vehicles with Real-Time Traffic and Infrastructure Analytics. traffic, 5(5).Google Scholar
  5. A.A. Taiwo, C.C. Nzeanorue, S.A. Olanrewaju, Q.O. Ajiboye, A .A. Idowu, S. Hakeem, R.A. OlusolaIntelligent transportation system leveraging Internet of things (IoT) technology for optimized traffic flow and smart urban mobility managementWorld J. Adv. Res. Rev., 22 (3) (2024), pp. 1509-1517Google Scholar
  6. R. Kumar, N. Kori, V.K. ChaurasiyaReal-time data sharing, path planning and route optimization in urban traffic managementMultimed. Tools Appl., 82 (23) (2023), pp. 36343- 36361View at publisherCrossrefView in ScopusGoogle Scholar
  7. M. Anedda, M. Fadda, R. Girau, G. Pau, D. GiustoA social smart city for public and private mobility: a real case studyComput. Netw., 220 (2023), Article 109464View PDFView articleView in ScopusGoogle Scholar
  8. C. Gheorghe, A. SoicaRevolutionizing urban mobility: a systematic review of AI, IoT, and predictive analytics in adaptive traffic control systems for road networksElectronics (20799292), 14 (4) (2025)Google Scholar
  9. G. Chen, J. wan ZhangIntelligent transportation systems: machine learning approaches for urban mobility in smart citiesSustain. Cities Soc., 107 (2024), Article 105369View PDFView articleView in ScopusGoogle Scholar
  10. S. Dikshit, A. Atiq, M. Shahid, V. Dwivedi, A. ThusuThe use of artificial intelligence to optimize the routing of vehicles and reduce traffic congestion in urban areasEAI Endorsed Trans. Energy Web, 10 (2023), pp. 1-13View in ScopusGoogle Scholar
  11. E. Faliagka, E. Christopoulou, D. Ringas, T. Politi, N. Kostis, D . Leonardos, N. VorosTrends in digital twin framework architectures for smart cities: a case study in smart mobilitySensors, 24 (5) (2024), p. 1665View at publisherCrossrefView in ScopusGoogle Scholar
  12. Dai, S., Pu, M., Liu, C., Ning, X., Wang, Q., Makantasis, K., & Cheng, L. Towards Energy-Efficient and Low-Carbon Transportation Systems Via Llm-Assisted Multi-Agent Reinforcement Learning. Available at SSRN 5207038.Google Scholar
  13. L. Da, K. Liou, T. Chen, X. Zhou, X. Luo, Y. Yang, H. WeiOpe n-ti: open traffic intelligence with augmented language modelInt. J. Mach. Learn. Cybern., 15 (10) (2024), pp. 4761-4786View at publisherCrossrefView in ScopusGoogle Scholar
  14. S. Choi, Y. LimOptimizing traffic signal control using LLM-Driven reward weight adjustment in reinforcement learning J. Inf. Process. Syst., 21 (1) (2025), pp. 43-51View in ScopusGoogle Scholar
  15. Arvind R. Singh , Muhammad Wasim Abbas Ashraf , Rajkumar Singh Rathore , Bin Li M.S. Sujatha.Real-time traffic flow optimization using large language models and reinforcement learning for smart urban mobility https://doi.org/10.1016/j.asoc.2025.113917Get rights and content

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