Black ice detection and road closure control system for Oklahoma.

Liu, Tieming; Wang, Ning; Yu, Hongbo; Basara, Jeffrey; Hong, Yang; Bukkapatnam, Satish · 2014 · ROSA P / Oklahoma. Dept. of Transportation. Planning and Research Division

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Summary

This report details the development of a prototype Decision Support System (DSS) for detecting black ice and controlling road closures in Oklahoma. The research was motivated by the high hazard posed by black ice, which is transparent, difficult to see, and responsible for nearly 200,000 auto crashes annually in the U.S. Existing static warning signs are ineffective, and current specialized ice sensors are prohibitively expensive (over $1,000 per unit) for widespread deployment. The project aimed to create an economically viable system using low-cost sensors (<$100) and predictive modeling to assist the Oklahoma Department of Transportation (ODOT) and the Oklahoma Department of Emergency Management (OEM) in making prompt decisions to reduce accidents. The study employed a multi-faceted approach involving predictive modeling, geographic information systems (GIS), sensor development, and remote control integration. First, the researchers developed a black ice prediction model utilizing data from the Oklahoma Mesonet, ASOS/AWOS networks, and the National Digital Forecast Database (NDFD). Since Oklahoma lacks Road Weather Information Systems (RWiS) to measure road surface temperatures directly, the model relied on meteorological parameterizations for hoar frost, freezing fog, and frozen precipitation. The model performed both diagnostic risk analysis using real-time observational data and prognostic forecasting using gridded NDFD data interpolated via Delaunay triangulation. Second, a GIS database and interface were created to visualize risk, manage road closure data, and support spatial analysis. Third, the team developed and tested two types of low-cost sensors: an electrical-conductivity sensor and a piezoelectric sensor system. The piezoelectric system used feature extraction and classifiers, such as K-Nearest Neighbor and Gaussian Mixture Models, to distinguish between air, water, and ice states. Finally, a remote control module was designed to wirelessly activate warning lights and lane-closure signals based on sensor inputs and model predictions. The findings demonstrate the feasibility of the integrated system. The prediction model successfully parameterized black ice formation mechanisms using available meteorological data, providing risk indices for various regions. The GIS tools enabled the visualization of black ice risk and the management of emergency response data. Laboratory and performance tests confirmed that the selected electrical-conductivity and piezoelectric sensors could effectively detect ice formation and differentiate it from other states. The piezoelectric system achieved real-time implementation results, validating its ability to classify surface conditions. The remote control module successfully demonstrated the capability to turn on warning lights and manage site statuses via a central server interface. The significance of this work lies in providing a cost-effective, comprehensive solution for black ice management in regions lacking expensive RWiS infrastructure. By combining predictive meteorological modeling with low-cost sensing and automated warning systems, the DSS offers a practical tool for reducing traffic accidents and fatalities associated with icy pavements. The system enables authorities to pinpoint dangerous road sections and activate dynamic warnings, addressing the limitations of static signage and high-cost sensor networks. This approach supports safer transportation infrastructure and more effective emergency management responses in Oklahoma and potentially other regions with similar constraints.

Key finding

The piezoelectric sensor system successfully distinguished ice from water and air states in real-time testing, providing a viable low-cost alternative to expensive commercial black ice sensors.

Methodology

mixed_methods

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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