Using Deep Learning to Predict the Amount of Chemicals Applied on the Wheel Track for Winter Road Maintenance

• Published in: Applied sciences 2022, Vol.12 (7), p.3508
• Main Authors: Hatamzad, Mahshid, Polanco Pinerez, Geanette Cleotilde, Casselgren, Johan
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Back propagation; Chemicals; Cold; Costs; Data analysis; Datasets; Deep learning; deep neural network/deep learning; Deicing; Experimental Mechanics; Experimentell mekanik; Friction; Ice; Infrastructure; intelligent road transportation; Machine learning; Neural networks; Precipitation; prediction model; Prediction models; Road maintenance; Roads; Roads & highways; Salt; salting; Salts; Sensors; Stochasticity; System effectiveness; Traffic accidents & safety; Variables; Weather; Winter; winter road maintenance
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12073508

The decade of big data has emerged in recent years, which has led to entering the era of intelligent transportation. One of the main challenges to deploying intelligent transportation is dealing with winter roads in cold climate countries. Different operations can be used to protect the road from ice and snow, such as spreading chemicals (here salt) on the road surface. Using salt for de-icing and anti-icing increases road safety. However, the excess use of salt must be avoided since it is not cost-efficient and has negative impacts on the environment. Therefore, the accurate and timely prediction of salt quantity for winter road maintenance helps decision support systems to achieve effective and efficient winter road maintenance. Thus, this paper performs exploratory data analysis to determine the relationships among variables to find the best prediction model for this problem. Due to the stochastic nature of variables regarding weather and roads, a deep neural network/deep learning is selected to predict the amount of salt on the wheel track, using historical data measured by sensors and road weather stations. The results show that the proposed model performs perfectly to learn and predict the amount of salt on the wheel track, based on different metrics, including the loss function, scatter plot, mean absolute error, and explained variance.