Optimizing Urban Mobility with AI-Based Traffic Management

Year : 2025 | Volume : 12 | 03 | Page :
    By

    D Arthi,

  • D Sudha,

  • Mohammed Aamir,

  • Mohammed Jeelani S M,

  • Hisham Feroz Pasha,

Abstract

Urban mobility is a pressing concern in modern cities, plagued by issues like traffic congestion and pollution. This research involves, “Optimising Urban Mobility with AI-based Traffic Management”, delves into the potential of Artificial Intelligence (AI) to revolutionize traffic management. Focusing on AI algorithms, data analytics, and sensor technologies, the research aims to enhance traffic flow, reduce congestion, and improve overall efficiency. Through statistical analysis and simulations, the research evaluates the effectiveness of AI-driven systems in real-world urban settings, considering metrics like travel time and environmental impact. By exploring scalability, regulatory aspects, and ethical considerations, it seeks to pave the way for responsible and equitable AI implementation in urban transportation. By leveraging machine learning and predictive modeling, the proposed framework aims to reduce congestion, optimize signal timing, enhance public transportation efficiency, and minimize environmental impact. Statistical analysis combined with computer-based simulations is employed to evaluate system performance using key indicators such as average travel time, fuel consumption, and emission levels. The research also highlights scalability across small, medium, and large urban environments, ensuring applicability to diverse city infrastructures Beyond technical innovation, the study investigates regulatory challenges, infrastructure readiness, and ethical considerations, including data privacy and equitable access. These aspects are crucial for responsible deployment and long-term acceptance of AI-driven mobility solutions. Ultimately, the research envisions AI-enabled traffic management as a vital component in building smart, sustainable, and livable cities, offering both immediate operational benefits and long-term societal advantages.

Keywords: Urban mobility, AI-based traffic management, traffic flow, congestion reduction, sensor technologies

How to cite this article:
D Arthi, D Sudha, Mohammed Aamir, Mohammed Jeelani S M, Hisham Feroz Pasha. Optimizing Urban Mobility with AI-Based Traffic Management. Trends in Transport Engineering and Applications. 2025; 12(03):-.
How to cite this URL:
D Arthi, D Sudha, Mohammed Aamir, Mohammed Jeelani S M, Hisham Feroz Pasha. Optimizing Urban Mobility with AI-Based Traffic Management. Trends in Transport Engineering and Applications. 2025; 12(03):-. Available from: https://journals.stmjournals.com/ttea/article=2025/view=234966


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Ahead of Print Subscription Original Research
Volume 12
03
Received 13/06/2025
Accepted 01/09/2025
Published 08/09/2025
Publication Time 87 Days
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