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

Physics-Informed Deep Learning and Governance Framework for Traffic Applications with Sparse Sensor Networks

This research develops a physics-informed deep learning (PIDL) framework to address critical data blind spots in transportation networks caused by limited sensor coverage, which undermines infrastructure planning and investment decisions required for federal Highway Performance Monitoring System reporting. The study combines classical traffic estimation models with data-driven deep learning to provide accurate traffic state estimation in sensor-sparse regions, helping State Departments of Transportation overcome prohibitive sensing infrastructure costs. The methodology integrates novel Fourier feature embedding algorithms to capture spatiotemporal variations, location-based trainable adjustment parameters for localized flow disruptions, and targeted collocation sampling near critical network features. The research addresses shortcomings of existing approaches where traditional physics-based models struggle with network complexity while deep learning methods require extensive data unavailable in sparse sensor environments. A collaborative pilot study with Delaware Department of Transportation will test the framework in real-world conditions with limited sensor coverage. The interdisciplinary approach brings together transportation engineers, network scientists, and public policy experts to develop both technical solutions and governance frameworks that align transportation management ecosystems with enhanced data collection capabilities for improved decision-making and infrastructure investments.

    Language

    • English

    Project

    Subject/Index Terms

    Filing Info

    • Accession Number: 01967857
    • Record Type: Research project
    • Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
    • Files: UTC, RIP
    • Created Date: Oct 2 2025 3:16PM