INTEGRATION OF CLOUD COMPUTING AND ARTIFICIAL INTELLIGENCE FOR SCALABLE URBAN TRAFFIC OPTIMIZATION IN INTELLIGENT TRANSPORTATION SYSTEMS

Abstract

Urban traffic congestion poses significant challenges to city infrastructure, environmental sustainability, and public safety. The integration of Cloud Computing (CC) and Artificial Intelligence (AI) offers scalable solutions for optimizing traffic flow within Intelligent Transportation Systems (ITS). This paper explores the synergistic potential of CC and AI in enhancing urban traffic management. It delves into existing literature, proposes a layered architecture for AI-Cloud integration, and presents case studies demonstrating improved traffic efficiency. Visual tools such as mind maps, flowcharts, and sequence diagrams are employed to elucidate complex processes and system interactions. The findings underscore the transformative impact of CC and AI integration on urban mobility and provide a roadmap for future implementations.