AI-driven predictive models for traffic flow in IoT-driven smart cities

Authors

  • Srinjoy Mitra * Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha, India

https://doi.org/10.48313/uda.v1i2.38

Abstract

This paper presents an innovative approach to traffic flow management in IoT-driven smart cities by developing AI-driven predictive models. As urbanization intensifies, efficient traffic management becomes crucial for enhancing mobility and reducing congestion. Leveraging the vast data from IoT sensors, including traffic cameras, GPS devices, and environmental monitors, our predictive models utilize machine learning algorithms to analyze real-time traffic patterns and predict future congestion points. The study integrates historical traffic data with real-time inputs to create a dynamic model that adapts to changing conditions, enabling city planners and traffic management systems to make informed decisions. We evaluate the model's performance using prediction accuracy and response time metrics, demonstrating significant improvements over traditional traffic management systems. Additionally, the paper explores the implications of these models on urban planning and policy-making, highlighting how they can inform infrastructure development and enhance public transportation systems. Our findings contribute to the ongoing discourse on smart city innovations, offering a framework for implementing AI-driven solutions in urban traffic management, ultimately leading to more sustainable and efficient cities.

Keywords:

Artificial Intelligence, Internet of Things, Traffic patterns, Challenges, Algorithm

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Published

2024-12-08

Issue

Vol. 1 No. 2 (2024): Uncertainty Discourse and Applications

Section

Articles

How to Cite

AI-driven predictive models for traffic flow in IoT-driven smart cities. (2024). Uncertainty Discourse and Applications, 1(2), 170-178. https://doi.org/10.48313/uda.v1i2.38

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