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

• Status: Active
• Funding: $391,989.00
• Sponsor Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Safety and Mobility Advancements Regional Transportation and Economics Research Center

  Morgan State University
  Baltimore, MD  United States 
• Performing Organizations:

  University of Delaware, Newark

  Department of Civil Engineering
  301 DuPont Hall
  Newark, DE  United States  19716
• Principal Investigators:

  Faghri, Ardeshir

  Nejad, Mark

  Barnes, Philip

  Pierce, Andrea

• Start Date: 20251001
• Expected Completion Date: 20260401
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Data collection; Deep learning; Traffic estimation
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

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