Enhancing Winter Maintenance Decision Making through Deep Learning-Based Road Surface Temperature Estimation
Publication: Construction Research Congress 2024
ABSTRACT
State and local highway agencies spend substantial budgets repairing infrastructure damages caused by snow and ice every winter season. These infrastructure damages can be prevented by establishing effective winter maintenance strategies which require monitoring road surface temperatures (RST). Road weather information systems (RWIS) are a common source for obtaining data on both current and predicted RST. However, RWIS does not exist in many locations, making it challenging to monitor road surface temperature in locations where RWIS is not available. The objective of this research is to assess the applicability of deep learning models in estimating RST using weather forecasts from the National Weather Service which are provided in grid cells over the continental United States. The deep learning models were developed based on collected data on actual RST and associated weather forecasts from roadways in North Texas for two winter seasons. The accuracy metrics of the deep learning models were computed using mean absolute error, root mean square error, and R-squared. According to the results, the developed deep learning model can predict RST with a root mean square error of 1.71°C, mean absolute error of 1.35°C, and an R-squared value of 0.92. The proposed models can be incorporated into a winter maintenance decision support system, enabling the monitoring of the RST without dependence on RWIS.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Abediniangerabi, B., A. Makhmalbaf, and M. Shahandashti. 2021. “Deep learning for estimating energy savings of early-stage facade design decisions.” Energy AI. 5, 100077.
Achieng, K. O. 2019. Modelling of soil moisture retention curve using machine learning techniques: Artificial & deep neural networks vs support vector regression models. Comput. Geosci. 133, 104320.
Ameen, W., and M. Shahandashti. 2021. Surveying the Effectiveness of Methods for Enhancing the Visibility of Snowplows in Texas. In Proc. International Conference on Transportation and Development, held virtually: ASCE, 309–320.
Ameen, W., F. Farooghi, M. Shahandashti, and S. Mattingly. 2022. Visibility of Winter Operations Vehicles: The State of Practice in the United States. J. Cold Reg. Eng. 36 (2), 06022003.
Baral, A., P. Darghiasi, and M. Shahandashti. 2023. Risk-Averse Maintenance Decision Framework for Roadside Slopes along Highway Corridors. In Proc. International Conference on Transportation and Development, Austin, TX: ASCE 383–392.
Baral, A., M. Sadegh Nasr, P. Darghiasi, B. Abediniangerabi, and M. Shahandashti. 2022. Detection and Classification of Vegetation for Roadside Vegetation Inspection and Rehabilitation Using Deep Learning Techniques. In Proc. International Conference on Transportation and Development, Seattle, WA: ASCE 143–152.
Crevier, L. P., and Y. Delage. 2001. METRo: A new model for road-condition forecasting in Canada. J. Appl. Meteorol. 40 (11), 2026–2037.
Dai, B., W. Yang, X. Ji, F. Zhu, R. Fang, and L. Zhou. 2023. An Ensemble Deep Learning Model for Short-Term Road Surface Temperature Prediction. J. Transp. Eng. Part B. Pavements, 149 (1), 04022067.
Darghiasi, P. 2023d. Digital Twin Enabled Winter Operations Management Through the Integration of Artificial Intelligence, Sensory Level Data, and Publicly Available Data. Doctoral dissertation, Arlington, TX: The University of Texas at Arlington.
Darghiasi, P., A. Baral, and M. Shahandashti. 2023a. Developing a Cost-Effective Mobile-Based System for Collecting On-Demand Road Condition Images for Snowplow Operations Management. In Proc. of International Conference on Transportation and Development, Austin, TX: ASCE 127–137.
Darghiasi, P., A. Baral, B. Abediniangerabi, and M. Shahandashti. 2022. A Multi-Purpose All-in-One Mobile Data Collection System for Snowplow Operation Management. In Proc. International Conference on Transportation and Development, Seattle, WA: ASCE 39–50.
Darghiasi, P., A. Baral, B. Abediniangerabi, A. Makhmalbaf, and M. Shahandashti. 2023c. Multilevel optimization of UHP-FRC sandwich panels for building façade systems. In Artificial Intelligence in Performance-Driven Design. Wiley.
Darghiasi, P., A. Baral, S. Mattingly, and M. Shahandashti. 2023b. Estimation of Road Surface Temperature Using NOAA Gridded Forecast Weather Data for Snowplow Operations Management. J. Cold Reg. Eng. 37 (4), 04023018.
Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, and S. Lautenbach. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 27–46.
Feng, T., and S. Feng. 2012. A numerical model for predicting road surface temperature in the highway. Procedia Eng. 37, 137–142.
FHWA (Federal Highway Administration). 2020. Office of Operations Road Weather Management Program, “How do Weather Events Impact Roads?.” Accessed on November 1,2021.https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm.
Kršmanc, R., A. Š. Slak, and J. Demšar. 2013. Statistical approach for forecasting road surface temperature. Meteorol. Appl. 20 (4), 439–446.
LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521, 436–444.
Liu, B., S. Yan, H. You, Y. Dong, Y. Li, J. Lang, and R. Gu. 2018. Road surface temperature prediction based on gradient extreme learning machine boosting. Comput. Ind. 99, 294–302.
Lövqvist, L., R. Balieu, and N. Kringos. 2018. Freeze-thaw damage in asphalt: A set of simplified simulations. In Proc. 63rd Annual Conference of Canadian Technical Asphalt Association, Regina, SK, Canada: CTAA 11–14.
Milad, A. A., I. Adwan, S. A. Majeed, Z. A. Memon, M. Bilema, H. A. Omar, and N. I. M. Yusoff. 2021. Development of a Hybrid Machine Learning Model for Asphalt Pavement Temperature Prediction. IEEE Access. 9, 158041–158056.
Miller, T. R., and E. Zaloshnja. 2009. On a crash course: The dangers and health costs of deficient roadways. Accessed on November 1, 2021.https://www.infrastructureusa.org/wp-content/uploads/2009/10/pire_onacrashcourse.pdf.
Qiu, X., J. X. Xu, J. Q. Tao, and Q. Yang. 2018. Asphalt Pavement Icing Condition Criterion and SVM–Based Prediction Analysis. J. Highway Transp. Res. Dev. 12 (4), 1–9.
Raiko, T., H. Valpola, and Y. LeCun. 2012. Deep learning made easier by linear transformations in perceptron. In Proc. Artificial Intelligence and Statistics, La Palma, Canary Island: W&CP 924–932).
Shahandashti, M., S. Hossain, M. Zamanian, and M. A. Akhtar. 2021. “Advanced Geophysical Tools for Geotechnical Analysis”. Texas Department of Transportation. Research and Technology Implementation Office.
Shahandashti, M., S. Mattingly, W. Ameen, and F. Farooghi. 2019. Synthesis of Safety Applications in Winter Weather Operations. Texas Department of Transportation. Research and Technology Implementation Office.
Shahandashti, M., S. Mattingly, P. Darghiasi, A. Baral, and B. Abediniangerabi. 2022. “Snowplow Operations Management System”. Texas Department of Transportation. Research and Technology Implementation Office.
Thomas, A. J., M. Petridis, S. D. Walters, S. M. Gheytassi, and R. E. Morgan. 2017. Two hidden layers are usually better than one. In Proc. of Engineering Applications of Neural Networks, Athens, Greece: Springer International Publishing 279–290.
Toivonen, E., M. Hippi, H. Korhonen, A. Laaksonen, M. Kangas, and J. P. Pietikäinen. 2019. The road weather model RoadSurf (v6. 60b) driven by the regional climate model HCLIM38: evaluation over Finland. Geosci. Model Dev. 12 (8), 3481–3501.
Weng, L., H. Zhang, H. Chen, Z. Song, C. J. Hsieh, L. Daniel, and I. Dhillon. 2018. Towards fast computation of certified robustness for relu networks. In International Conference on Machine Learning, Stockholm, Sweden: 5276–5285.
Xu, B., N. Wang, T. Chen, and M. Li. 2015. Empirical evaluation of rectified activations in convolutional network. https://doi.org/10.48550/arXiv.1505.00853.
Yang, C. H., D. G. Yun, and J. G. Sung. 2012. Validation of a road surface temperature prediction model using real-time weather forecasts. KSCE J. Civ. Eng. 16 (7), 1289–1294.
Yin, Z., J. Hadzimustafic, A. Kann, and Y. Wang. 2019. On statistical nowcasting of road surface temperature. Meteorol. Appl. 26 (1), 1–13.
Zamanian, M., and M. Shahandashti. 2022. “Investigation of Relationship between Geotechnical Parameters and Electrical Resistivity of Sandy Soils.” In Proc., Construction Research Congress 2022, Washington, D.C.: ASCE, pp. 686–695.
Zamanian, M., N. Asfaw, P. Chavda, and M. Shahandashti. 2023b. “Classifying Soil Sulfate Concentration Using Electrical Resistivity Imaging and Random Forest Algorithm.” In Proc., Airfield and Highway Pavements, Austin, TX: ASCE, 204–213.
Zamanian, M., Y. A. Thorat, N. Asfaw, P. Chavda, and M. Shahandashti. 2023a. “Electrical Resistivity Imaging for Identifying Critical Sulfate Concentration Zones Along Highways.” Transp. Res. Rec., p.03611981231167162.
Information & Authors
Information
Published In
History
Published online: Mar 18, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.