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.
Universities Involved
University of Delaware
Principal Investigators
Arde Faghri,
Mark Nejad,
Philip Barnes,
Andrea Pierce
Expected Research Outcomes & Impacts
The application of this research will transform transportation data collection and network management by enabling accurate traffic estimation without costly dense sensing infrastructure. State Departments of Transportation will gain enhanced capabilities for Highway Performance Monitoring System reporting, supporting more effective federal fund allocation and infrastructure investment decisions. Transportation planners will benefit from improved future-state estimates enabling more efficient system design, while traffic operations managers will receive enhanced near-real-time traffic state estimation for implementing control strategies including tolling and ramp metering. The governance framework will bridge gaps between policy and practice, ensuring stakeholder interests are incorporated in technology deployment decisions. Long-term impacts include rebalanced cost-to-accuracy tradeoffs enabling high-fidelity modeling without extensive sensor networks, supporting better measurement of infrastructure needs and more informed governance decisions. The framework’s potential applications extend beyond transportation to disaster management and resilient infrastructure planning. Industry stakeholders including consulting firms and traffic system vendors will benefit from technology transfer opportunities and potential commercialization. The research will enable more comprehensive traffic network representation, supporting development of optimal routing, infrastructure design, and higher-accuracy transportation network modeling across diverse applications and geographic regions.
Subject Areas
Traffic Management, Public Policy, Artificial Intelligence