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Dr. J. Santhosh is an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at Sri Krishna Adithya College of Arts and Science, Coimbatore, India. His research interests include Artificial Intelligence, Machine Learning, intelligent transportation systems, cloud computing, predictive analytics, and data science. He is actively engaged in research, teaching, and innovation in intelligent computing technologies.
Accelerated urban development, rising automobile ownership, and the proliferation of road infrastructure have markedly exacerbated traffic congestion, travel delays, fuel usage, and ecological degradation. Conventional traffic management systems depend mostly on stationary traffic signals, past traffic assessments, and manual oversight, rendering them insufficient for managing rapidly changing urban transportation contexts. The amalgamation of Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Edge Computing, Big Data Analytics, and Intelligent Transportation Systems (ITS) has revolutionised traffic management through real-time traffic surveillance, predictive analytics, intelligent route optimisation, and adaptive traffic regulation. IoT-integrated sensors, surveillance cameras, connected automobiles, GPS technologies, and roadside infrastructure perpetually produce traffic data that are scrutinised through Machine Learning (ML) and Deep Learning (DL) algorithms to precisely predict traffic patterns, identify congestion, assess travel duration, and enhance traffic signal management. This chapter delineates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues pertaining to intelligent traffic flow forecasting utilising IoT, cloud computing, and artificial intelligence.