Traffic Flow Optimization Using AI

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Author(s): Rajesh Kanna Rajendran (Christ University, India), N. R. Wilfred Blessing (University of Technology and Applied Sciences, Ibri, Oman)and T. Mohana Priya (Christ University, India)
Copyright: 2025
Pages: 22
Source title: Recent Trends in Geospatial AI
Source Author(s)/Editor(s): Dina Darwish (Ahram Canadian University, Egypt)and Houssem Chemingui (Brest Business School, France & Centre de Recherche en Informatique, Panthéon Sorbonne University, France )
DOI: 10.4018/979-8-3693-8054-3.ch008

Keywords: Artificial Intelligence / Computer Science & IT / Engineering Science Reference / Geographic Information Systems

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Abstract

Traffic flow optimization is a critical challenge in urban planning and transportation management, aimed at reducing congestion, improving travel times, and enhancing overall roadway efficiency. This paper explores the application of artificial intelligence (AI) techniques to address these challenges. Leveraging machine learning algorithms, neural networks, and advanced data analytics, AI-driven systems can dynamically adjust traffic signals, predict traffic patterns, and optimize routing in real-time. This approach utilizes historical traffic data, real-time sensors, and predictive modeling to make data-driven decisions that enhance traffic flow and reduce delays. Integrating AI with existing traffic management infrastructure, cities can achieve more responsive and adaptive traffic control and improved quality of life for commuters. This Chapter presents a review of current AI applications in traffic optimization, evaluates their effectiveness through case studies, and discusses potential future developments in this evolving field.

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