AI Driven Framework for Sustainable Energy and Traffic Management

Prasad S R; Asha A; Hemashree S; Kirana P; Namratha Gudigar

doi:10.55524/ijirem.2025.12.6.13

Abstract

Over the past few years with the booming technological growth, there has been a new challenge to urban lifestyle, yet there are new problems that have risen, particularly in terms of the control of energy consumption and traffic jams. The conventional city planning systems do not always succeed to meet the growing sophistication of cities. The paper introduces an AI-based model that will support the increase of sustainability in the city by resolving these two important concerns by means of intelligent data processing and real-time decision-making. On the transport front, the system employs GPS tracking and OpenMap API, in analyzing traffic trends and also recommend the best routes to be taken on a day-to-day basis with less congestion. This assists in saving time, fuel and carbon dioxide emission. Under the energy management approach, machine learning models have been used to trace and forecast energy consumption, identify wastage, and offer practical recommendations on how to optimize the use of resources. There are also interactive dashboards and feedback tools, which promote the involvement of the citizens in streamlining the city resources. The suggested framework combines AI in the area of traffic and energy, showing a practical way to make urban ecosystems smarter, cleaner, and more efficient, which will help to develop a sustainable city and experience higher quality of life in the highly urbanized areas.

Keywords

Artificial Intelligence (AI), Machine Learning (ML), Urban Sustainability, Traffic Congestion Energy Management, Smart Cities and, Real-Time Monitoring.

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Cites this article as

P. S. R, A. A, H. S, K. P, N. Gudigar, "AI Driven Framework for Sustainable Energy and Traffic Management", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-12, Issue-6, Page No-73-80, 2025. Available from: https://doi.org/10.55524/ijirem.2025.12.6.13