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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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An AI-Enabled Predictive Framework for Traffic Flow Optimization in Smart Cities

Sethuraman E
PG Student, Department of MCA, New Prince Shri Bhavani College of Engineering and Technology, India.
Adhi Rajan S
IV Year Student, Department of IT, New Prince Shri Bhavani College of Engineering and Technology, India.

Keywords: Smart Cities, Traffic Flow Prediction, Artificial Intelligence, Deep Learning, Traffic Optimization, Intelligent Transportation Systems, IoT Sensors, Real-Time Prediction, Urban Mobility, Traffic Signal Control, Congestion Management, Multimodal Transportation, Sustainability, Data Fusion, Predictive Modelling

Abstract

In the age of fast-growing urbanization, the efficient traffic flow management is essential for the development of sustainable smart cities. Traffic prediction models in the literature have some shortages, including lack of suitable adaptability to changeable circumstances, disregard for myriad urban data sources, inability to optimize to the most extensive extent, or testing problems when used in a larger urban network. To overcome these limitations, this paper presents an AI-Enabled Predictive Framework for Traffic Flow Optimization in Smart Cities, that leverages advanced deep learning models together with real-time multi-source data, e.g., IoT sensor networks, GPS trajectories, weather forecasts, and social event indicators. The architecture improves traffic flow prediction accuracy, and it also introduces optimization algorithms that adapt traffic signals, routing policies and congestion control strategies on-the-fly. Comprehensive experimental validations show that the framework can prominently save travel time, fuel, and environmental emissions, optimize commuter experience and system resilience even under changing traffic conditions. This paper proposed a scalable, adaptive and intelligent approach to current urban traffic problems, further workflows of a fully integrated smart transportation ecosystem.
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Details
Published
2024-03-25
Pages
1-13
Issue
Vol. 3 No. 1 (2024): IJRTTE - 03 - 01
Section
Articles

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