# Applying Machine Learning to Preselective Weighing of Moving Vehicles
## 1 Introduction and Status of the Issue  
Machine learning methods, along with data analytics approaches, are becoming a critical aspect in many areas of science and the economy. Machine learning (ML) is a branch of artificial intelligence that focuses on designing and studying algorithms capable of learning and making predictions from large datasets. Various models are fundamental in ML, applied depending on the data characteristics and task requirements. Among the most commonly used ML models are regression, decision trees, random forest, support vector machines (SVMs), and neural networks [1, 2, 3]. Machine learning algorithms have numerous applications in supporting systems operating on large datasets. One such system is a weigh-in-motion (WIM) system. WIM systems are advanced technological solutions that measure the total weight and axle loads of vehicles traveling on public roads under normal traffic conditions. The primary goal of these systems is to weigh vehicles automatically, without requiring them to stop, thereby enabling automatic penalties for overloaded vehicles, similar to fines for speeding offenses. Overloaded vehicles significantly accelerate the degradation of road infrastructure. This degradation depends mainlyon the type of pavement (asphal or concrete), its load-bearing capacity, axle load, and the degree of overloading. Pavement degradation is primarily influenced by the degree of axle overload rather than the vehicle's total weight. The wheels of an overloaded axle in contact with the road surface cause deflection, leading to premature fatigue wear of the pavement over time. This shortens the lifespan of road infrastructure and necessitates more frequent repairs. Vehicles with an axle load of no more than 115 kN (the maximum allowed on highways, expressways, and major roads) typically cause greater deformation in the wearing course (the top layer of the roadway) than in the substructure (the deeper layers) [4]. The substructure deforms more intensively for overloaded vehicles with axle loads exceeding 115 kN. The greater the degree of overloading, the more severe the degradation. For asphalt pavements, it is assumed that damage increases with the fourth power of the degree of overloading. The degradation is even more significant for concrete pavements, which are stiffer. Structural deformations in the deeper layers necessitate replacing all layers of the road, not just the wearing course, leading to substantially higher maintenance costs. Pavement degradation manifests through cracks, cavities, and deformations, such as ruts (in asphalt pavements). Degradation primarily reduces the bearing capacity of the pavement, negatively impacting driving comfort and traffic safety. Overloaded vehicles also have longer braking distances and reduced maneuverability (due to higher inertia and often a higher center of gravity), increasing traffic hazards. Deteriorating pavement amplifies vibrations transmitted to vehicles and cargo, raises noise levels, and heightens the risk of sudden mechanical failures. Studies in [5] indicate that the cost of maintaining road surfaces per overloaded vehicle is double that of a properly loaded vehicle. Another study [6] shows that reducing the share of overloaded vehicles in the traffic stream from 23% to 5% can lower road maintenance costs for a given section by up to 11%. Repair and renovation expenses are substantial considering the cost of maintaining the entire road infrastructure. According to the JASPERS Blue Book [7], the cost of maintaining one kilometer of road in 2022 was PLN 1.6 million for a two-lane highway and expressway and PLN 1.2 million for a two-lane main expressway. Additionally, roadworks reduce transportation efficiency on repaired sections, creating obstructions and safety hazards. Reducing road maintenance costs is achievable through vehicle weighing systems that eliminate overloaded vehicles from traffic. These systems weigh vehicles in motion without requiring them to stop.