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Dr. A. Thangam is an Assistant Professor in the Department of Mathematics at Pondicherry University–Community College, Puducherry, India. Her research interests include applied mathematics, computational techniques, Artificial Intelligence, optimization methods, and mathematical modeling for intelligent systems. She actively contributes to interdisciplinary research involving mathematics and emerging technologies.
The swift expansion of urban areas, rising automobile ownership, and the development of transportation infrastructures have posed considerable difficulties in traffic regulation, route efficiency, and urban mobility. Traditional shortest path algorithms typically depend on fixed road networks and neglect to account for dynamic elements like traffic congestion, road closures, weather conditions, accidents, construction projects, and variations in travel demand. The amalgamation of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), Global Positioning System (GPS), Cloud Computing, Edge Computing, and Intelligent Transportation Systems (ITS) has revolutionised route planning through the facilitation of real-time shortest path forecasting and smart navigation. AI-powered routing systems perpetually assess traffic dynamics, vehicular motion, roadway conditions, ecological information, and past travel trends to suggest the most optimal paths, thereby reducing travel duration, fuel usage, and congestion. This chapter elucidates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues pertaining to AI-enhanced shortest path forecasting for Intelligent Transportation Systems.