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https://www.nationalacademies.org/trb/events

AI-Powered Infrastructure Monitoring and Decision Support for Transportation Safety

This research enhances transportation safety through large-scale deployment of intelligent video analysis and artificial intelligence techniques for real-time infrastructure monitoring, building on two years of previous development. The Year 3 initiative expands the proven framework beyond bridges to include urban infrastructure including traffic signals, light poles, pedestrian bridges, and other structural components essential for safe transportation operations. The methodology leverages existing surveillance infrastructure to capture displacement and vibration signals through computer vision techniques including feature tracking, optical flow, and motion magnification, eliminating the need for extensive physical instrumentation. Advanced signal processing methods integrate with video analytics to quantify dynamic behavior and detect structural changes over time. Inverse modeling algorithms estimate key structural parameters including stiffness, mass distribution, and damage locations from measured displacement histories. The research develops an AI-based decision support system combining analysis results with predictive capabilities to help agencies prioritize maintenance work based on risk assessment and structural condition evaluation. A pilot program implementation with District of Columbia Department of Transportation demonstrates real-world applicability in urban settings, serving as a platform for practical validation and stakeholder engagement.