THE CONVERGENCE OF MACHINE LEARNING AND TRANSPORTATION INFRASTRUCTURE: CRITICAL PERSPECTIVES ON AUTONOMOUS VEHICLES AND SMART TRAFFIC MANAGEMENT - Madhukar Dharavath (INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 2024)
The integration of artificial intelligence and data engineering in transportation systems represents a transformative shift in modern mobility infrastructure. This comprehensive article examines the technological frameworks, implementation challenges, and societal implications of AI-driven transportation solutions, with particular emphasis on autonomous vehicles and smart traffic management systems. The article analyzes the complex interplay between machine learning models, sensor networks, and data processing pipelines that enable real-time decision-making in autonomous vehicles while exploring the architectural requirements for effective smart traffic management. Critical attention is given to safety protocols, ethical considerations, and bias mitigation strategies essential for reliable system deployment. The article further investigates the socioeconomic implications of widespread AI adoption in transportation, including workforce transformation, economic impacts, and emerging industry paradigms. Through systematic analysis of current implementations and future projections, this article provides actionable insights for technical development, policy formulation, and strategic planning. The findings highlight the need for balanced integration of AI technologies while addressing safety concerns, ethical considerations, and societal readiness. This article contributes to the growing body of knowledge on intelligent transportation systems by offering a comprehensive framework for sustainable AI integration in transportation infrastructure, accompanied by specific recommendations for stakeholders across technical, regulatory, and policy domains.