Yamini N. Deshvena | International Journal of Transportation Engineering and Traffic System | Vol 11, Issue 01 | pp. 32-36 | ISSN: 2456-2343
Abstract
Abstract
Urban traffic congestion is a persistent challenge, resulting in economic setbacks, extended travel
durations, excessive fuel consumption, and environmental harm. The rapid urbanization and increasing
number of vehicles have intensified this issue, highlighting the need for smarter and more efficient
traffic management solutions. With advancements in artificial intelligence (AI), innovative traffic
control strategies can be implemented to optimize urban mobility and alleviate congestion. This paper
explores AI-driven traffic management solutions, focusing on machine learning-based adaptive traffic
signal control, predictive congestion analysis, and vehicle-to-infrastructure (V2I) communication.
These advanced technologies enable real-time data processing, dynamic traffic flow adjustments, and
predictive analytics to anticipate and mitigate congestion before it occurs. By leveraging AI, traffic
systems can adapt to varying conditions, enhance road network efficiency, and minimize delays. A case
study is conducted in a densely populated metropolitan city to evaluate the impact of AI-powered traffic
management solutions. Key performance indicators, such as traffic efficiency, vehicle delays, fuel
consumption, and overall road network performance are analyzed. The results demonstrate that AI
driven traffic control significantly enhances urban mobility by reducing congestion, improving travel
time reliability, and lowering emissions. Additionally, these solutions contribute to the development of
sustainable and smart city infrastructures, fostering a more efficient and eco-friendly transportation
system This study underscores the potential of AI in revolutionizing urban traffic management and
highlights its role in shaping the future of smart cities. The findings suggest that the integration of AI-
based traffic control systems can serve as a viable long-term solution for addressing the growing
challenges of urbanization, ultimately enhancing the quality of life for city residents.
Keywords: Traffic congestion, artificial intelligence, smart cities, machine learning, adaptive traffic
control, predictive congestion analysis, vehicle-to-infrastructure (V2I) communication, urban mobility,
traffic optimization, sustainable transportation
References
1. Olugbade S, Ojo S, Imoize AL, Isabona J, Alaba MO. A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transp Systems. Math Comput Appl. 2022;27(5):77. 2. Kumar R, Singh A. Sustainable Traffic Management Using AI and IoT: Challenges and Opportunities. Smart Cities J. 2023;18(2):56–72. 3. Chen B, Wang L. AI-Based Traffic Prediction Models for Smart Cities. J Urban Comput. 2021;10(4):231–248. 4. Johnson M, Patel S. Reinforcement Learning for Adaptive Traffic Signal Control: A Comparative Study. IEEE Trans Smart Transp. 2022;12(6):1321–1335. 5. Green D, Anderson P. Big Data in Traffic Management: Applications and Challenges. J Intell Transp Syst. 2020;25(1):45–62. 6. Lee K, Chang H. AI and Smart Mobility: Enhancing Urban Traffic Systems. Smart Transp Rev. 2023;14(3):102–118. 7. Garcia L, Thompson R. The Role of AI in Reducing Traffic Congestion in Megacities. Urban Mobil J. 2022;9(5):301–320. 8. Adams J, Nguyen T. Machine Learning Approaches to Traffic Forecasting: A Systematic Review. J AI Transp. 2021;7(2):87–104. 9. Karray F. A Review of AI-Driven Traffic Flow Management in Smart Cities. IEEE Conf Publ. 2023;135:103–120. 10. Smith R, Brown T. Integrating IoT and AI for Smart Traffic Solutions. J Transp Eng. 2020;146(3):04020014. 11. Jamebozorg M, Hami M, Deh Jani S. Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images. arXiv preprint arXiv:2405.13256. 2024 May 22. 12. Yan H, Li Y. A Survey of Generative AI for Intelligent Transportation Systems. arXiv preprint arXiv:2312.08248. 2023 Dec 13. 13. Wang M, Pang A, Kan Y, Pun MO, Chen CS, Huang B. LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments. arXiv preprint arXiv:2403.08337. 2024 Mar 13. 14. Shoaib MR, Emara HM, Zhao J. A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems. arXiv preprint arXiv:2401.06831. 2024 Jan 12. 15. Pillai AS. Traffic Management: Implementing AI to Optimize Traffic Flow and Reduce Congestion. J Emerg Technol Innov Res. 2024;11(7):272–278. 16. Yan H, Li Y. A Survey of Generative AI for Intelligent Transportation Systems. arXiv preprint arXiv:2312.08248. 2023 Dec 13. 17. Jamebozorg M, Hami M, Deh Jani S. Traffic Control Using Intelligent Timing of Traffic Lights with Reinforcement Learning Technique and Real-Time Processing of Surveillance Camera Images. arXiv preprint arXiv:2405.13256. 2024 May 22. 18. Shoaib MR, Emara HM, Zhao J. A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems. arXiv preprint arXiv:2401.06831. 2024 Jan 12. 19. Wang M, Pang A, Kan Y, Pun MO, Chen CS, Huang B. LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments. arXiv preprint arXiv:2403.08337. 2024 Mar 13. 20. Abdulhai B, Porwal H, Recker W. Short-Term Traffic Flow Prediction Using Neuro-Genetic Algorithms. J Intell Transp Syst. 2002;7(1):3–14. 21. Olia A, Abdelgawad H, Abdulhai B, Razavi SN. Assessing the Potential Impacts of Connected Vehicles: Mobility, Environmental, and Safety Perspectives. J Intell Transp Systems. 2016;20(3):229–243. 22. Ma T, Abdulhai B. Genetic Algorithm-Based Optimization Approach and Generic Tool for Calibrating Traffic Microscopic Simulation Parameters. Transp Res Record: J Transp Res Board. 2002;1800(1):6–15. 23. Abdulhai B, Ritchie SG. Enhancing the Universality and Transferability of Freeway Incident Detection Using a Bayesian-Based Neural Network. Transp Res Part C: Emerg Technol. 1999;7(5):261–280. 24. Othman K, Shalaby A, Abdulhai B. Dynamic Bus Lanes Versus Exclusive Bus Lanes: Comprehensive Comparative Analysis of Urban Corridor Performance. Transp Res Record: J Transp Res Board. 2023;2677(1):1–12.
