## 1 Introduction  
One of the most crucial operations of Winter Road Maintenance (WRM) in cold regions is the spreading of chemicals (such as salt) on the road surface. Spreading chemicals on the road for both anti-icing and de-icing prevents slippery conditions and provides road users with safe transportation. However, it demands equipped trucks, truck drivers, and materials, which can be extremely expensive. In addition, chemicals damage the environment, vehicles, and road infrastructure [1]. In fact, the quality of WRM depends on three major goals: (i) road safety, (ii) the more efficient use of resources, and (iii) environmental protection [2]. In order to achieve these three goals, it is necessary to use an optimal amount of chemicals. One solution to providing decision-makers with future insights for optimal chemical usage is to utilize a data-driven approach and prediction model. Many studies have suggested different methods to achieve increased road safety, lower WRM costs, and a reduced environmental impact. For instance, [PERSON] et al. [3] developed a statistical approach to model road surface friction/grip by using observations from an optical sensor during two winter seasons. [PERSON] [4] suggested different methods to estimate road weather using infrared thermometry and image analysis. [PERSON] et al. [5] conducted laboratory experiments to design a time-dependent model to predict salt solution temperature. [PERSON] et al. [6] established a pavement temperature prediction model in winter by improving backpropagation neural networks. [PERSON] et al. [7] published a review paper to evaluate the pros and cons of using organic deicers. [PERSON] [8] applied multivariate regression, and Monto Carlo simulation to analyze WRM costs. [PERSON] and [PERSON] [9] used visual analytics to explore the relationship between WRM operations and traffic safety. [PERSON] and [PERSON] [10] evaluated three different machine learning classification algorithms to monitor road surface conditions. [PERSON] et al. [11] proposed a time-series prediction model using a recurrent neural network to predict road surface friction. [PERSON] et al. [12] utilized regression machine learning algorithms to present a methodology to predict salt quantity on street segments every hour. In fact, road salt application has negative impacts on the environment, vehicles, water, and road infrastructures (such as groundwater, bumpers, bridges, brake linings, and frames. [PERSON] and [PERSON] [13] carried out research an analysis of the infrastructure and environmental costs of applying salt to the road surface. In their research, they stated how winter conditions and road salt application negatively impact steel corrosion and concrete structures. [PERSON] et al. [14] used soil samples along the highway in Vienne, Austria. They analyzed the soils to investigate road salt residues and other contaminations. The results indicated that the pollution of roadside soil was high. [PERSON] and [PERSON] [15] showed the importance of soil for road maintenance and construction. [PERSON] et al. [16] stated that road maintenance and construction needs a considerable budget, and cold climatic conditions have negative effects on the durability of road infrastructure due to anti-icing and de-icing [17]. [PERSON] et al. [18] examined the potential extension of chloride in groundwater and its impact on private wells. The results showed that road salt application damages private and public drinking water infrastructure. Although different techniques in various disciplines have been developed to cover various gaps in WRM to obtain three major objectives (safety, cost, and environment), there is no study to use a data-driven approach to analyze the influencing factors and predict the amount of chemicals on the wheel track. As a further explanation, winter roads need to be protected from snow and ice, especially in cold climate countries, where the winter weather condition is adverse and the winter period is long.