The AI Revolution in Transportation Asset Management: A Comprehensive Synthesis of Technologies, Methods, and State DOT Implementations

Transport infrastructure faces threats from rapid urbanization, effects from climate change, and decay of current infrastructure, thus forcing the implementation of efficient, precise, and scalable approaches to inventory and condition monitoring of assets. This paper provides an overview of best practices across a number of U.S. State Departments of Transportation (DOTs) regarding the application of artificial intelligence (AI) and emerging technological solutions for transportation asset management. The book routinely categorizes various transport infrastructure like pavement, bridges, traffic signals, sign supports, pavement markings, roadway lighting, and highway buildings with common as well as state-level approaches. There is a detailed examination of conventional manual and cutting-edge automated data collection processes with emphasis on LiDAR, drones (UAVs), mobile mapping systems, automated data collection vehicles (ADCVs), GIS platforms, photogrammetry, and remote sensing technology. Specific focus is on innovative AI applications such as automated sign detection, analysis of pavement distress, and predictive maintenance through deep learning algorithms. By leveraging learnings from successful implementations by Connecticut DOT, Utah DOT, Vermont Agency of Transportation, and many more, this overview explores how state-of-the-art AI-based solutions significantly enhance accuracy, reduce costs, and facilitate proactive infrastructure management. The synthesized findings summarize long-lasting patterns, ongoing difficulties, and future avenues while providing a systematic framework meant to inform state and global transport authorities to adopt more smart, sustainable, and resilient approaches to infrastructure asset maintenance.