Black Ice Prediction Model for Road Pavement Using Weather Forecast Data and GIS Database




API, asphalt pavement, black ice, GIS, meteorological data, prediction model


Black ice is a thin coating of ice on the road surface, which strongly reduces friction at the tire-road surface, resulting in dangerous driving when it happens. An appropriate diagnostic of black ice could prevent traffic accidents as well as provide timely notice to drivers. Therefore, this study aims at developing a black ice prediction model to diagnose the probability of black ice formation. Several combinations that can form road ice have been considered, including freezing rain, hoar frost, freezing of wet roads. In addition, black ice risky index (BRI) has been computed to reflect the probability of black ice formation. To acquire a fast prediction and high accuracy, the existing Geographical Information System (GIS) database and meteorological data have been utilized. GIS database includes road geometry and location of automatic weather stations, while the meteoritical data consists of air temperature, wind speed, humidity, cloud cover. The model has been developed based on the Python programming language. A 5-km road condition was observed from 1 December to 31 December 2021 to determine the model accuracy. Based on the results from the prediction model, black ice formation has been verified when the BRI is higher than 0.8. The model may be useful to develop black ice diagnostic program.


Barszcz, A., Milbrandt, J. A., & Thériault, J. M. (2018). Improving the explicit prediction of freezing rain in a kilometer-scale numerical weather prediction model. Weather and Forecasting, 33(3), 767–782.

Gocheva, A. (1990). Statistical distribution of air temperature, relative humidity and wind velocity during rime-icing for the non-mountain part of the territory of Bulgaria. Proceedings of 5th International Youth School on Meteorology and Hydrology, 4, 84–89.

Gode, K., & Paeglitis, A. (2014). Concrete bridge deterioration caused by de-icing salts in high traffic volume road environment in Latvia. The Baltic Journal of Road and Bridge Engineering, 9(3).

Gustavsson, T. & Bogren, J. (1990). Road slipperiness during warm air advection. Meteorological Magazine, 119, 267–270.

Hong, S.-B., Lee, B.-W., Kim, C.-H., & Yun, H.-S. (2021). System dynamics modeling for estimating the locations of road Icing using GIS. Applied Sciences, 11(18), Article 8537.

Karlsson, M. (2001). Prediction of hoar-frost by use of a road weather information system. Meteorological Applications, 8(1), 95–105.

Kim Hyun-bin. (2019). Fears of black ice spreads nationwide. The Korea Times.

Korea Meteorological Administration. (2022). KMA.

Lim, H.-S., & Kim, S.-T. (2020). A study on road ice prediction by applying road freezing evaluation model. Journal of the Korean Applied Science and Technology, 37(6), 1507–1516.

Liu, T., Wang, N., Yu, H., Basara, J., Hong, Y. (Eric), & Bukkapatnam, S. (2014). Black ice detection and road closure control system for Oklahoma (Final Report FHWA-OK-14-08). Oklahoma Department of Transportation. https://l92018.

Mass, C. (n.d.). Roadway icing and weather: A tutorial. Washington Road and Weather Page.

Mass, C., & Steed, R. (n.d.). Roadway icing and weather: A tutorial. College of the Environment.

Minh Phan, T., Park, D.-W., & Ho Minh Le, T. (2021). Improvement on rheological property of asphalt binder using synthesized micro-encapsulation phase change material. Construction and Building Materials, 287, Article 123021.

NSDI. (2022). National Spatial Data Infrastructure Portal.

Public Data Portal. (2022). Public Data Portal. https://Www.Data.Go.Kr/.

Ramer, J. (1993). An empirical technique for diagnosing precipitation type from model output. 5th International Conference on Aviation Weather Systems, Vienna, 227–230.

Souayfane, F., Fardoun, F., & Biwole, P.-H. (2016). Phase change materials (PCM) for cooling applications in buildings: A review. Energy and Buildings, 129, 396–431.

Szklarek, S., Górecka, A., & Wojtal-Frankiewicz, A. (2022). The effects of road salt on freshwater ecosystems and solutions for mitigating chloride pollution – A review. Science of The Total Environment, 805, Article 150289.

Toms, B. A., Basara, J. B., & Hong, Y. (2017). Usage of existing meteorological data networks for parameterized road ice formation modeling. Journal of Applied Meteorology and Climatology, 56(7), 1959–1976.

Wikipedia. (2022). Geographic information system. Wikipedia. https://

Zilioniene, D., & Laurinavicius, A. (2007). De-icing experience in Lithuania. The Baltic Journal of Road and Bridge Engineering, 2(2), 73–79. https://




How to Cite

Phan, T. M., Jang, M.-S., & Park, D.-W. (2022). Black Ice Prediction Model for Road Pavement Using Weather Forecast Data and GIS Database. The Baltic Journal of Road and Bridge Engineering, 17(4), 63-79.