AI-Powered Traffic Management and Optimization

Rapid urbanization and rising vehicle density have made conventional traffic management systems inadequate in addressing congestion, accidents, and environmental impacts. This chapter explores how Artificial Intelligence (AI) offers transformative solutions through predictive analytics, computer vision, and machine learning for dynamic traffic monitoring and optimization. Case studies from Los Angeles and Singapore demonstrate quantifiable improvements in travel time, delays, emissions, and public transport efficiency, emphasizing the adaptability of AI to diverse infrastructures. The discussion also highlights ethical and regulatory considerations, including privacy, transparency, and fairness, while acknowledging technical and social challenges such as infrastructure readiness and public trust. Finally, the chapter proposes a scalable AI-driven framework integrating IoT, edge computing, 5G, and V2I communication to build safer, efficient, and sustainable urban transport systems.

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