# Applying Machine Learning to Preselective Weighing of Moving Vehicles
## 1 Introduction and Status of the Issue
In practice, based on the accuracy of measuring total vehicle weight and axle pressures, three classes of systems can be distinguished:  
* WIM-S (WIM-Statistics)--systems with the lowest accuracy, recording vehicle parameters for statistical purposes. These systems are used to evaluate traffic parameters, including traffic volume, traffic structure, and vehicle speed, mainly for road design purposes and assessing traffic distribution in the road network;
* WIM-P (WIM-Preselection)--systems based on which vehicles initially identified as overloaded are subjected to thorough weighing at static stations by authorized inspection authorities, using legalized scales;
* WIM-E (WIM-Enforcement)--systems with the highest accuracy, designed for automatic detection and enforcement of violations. In this case, a penalty can be imposed on an overloaded vehicle without the need to stop and reweigh it. All the above-described systems are equipped with pressure sensors (typically 2-4 rows of sensors for each wheel, depending on the required measurement accuracy), induction loops for vehicle detection and classification, cameras for documenting the passage and recognizing the registration number, and (optionally) lidars to improve the accuracy of vehicle dimensional measurements. In Europe, these systems are classified by measurement accuracy according to the COST 323 [8] and OIML R134 [9] specifications, with the measurement class determined separately for total weight and vehicle axle load. The main source of measurement error is the vertical oscillation of a vehicle passing through a WIM location. WIM systems must also use other technologies and cooperate with other systems, such as inductive loop systems (speed measurement and vehicle axle identification), vision systems (recording vehicle and license plate images and reading them), GPS systems (location and time synchronization), or GSM LTE data transmission systems (remote data access and dispatch). Sensors and inductive loops are placed on the road surface, while the rest of the system elements are placed on the road gantry and partially in a teletechnical box protecting the devices from weather conditions. If technical conditions permit, a fixed link is brought to the WIM installation site to provide broadband Internet access, preferably using a fiber-optic medium. In the case of technical constraints, it is possible to use a radio link or GSM LTE system, which, however, carries additional limitations and problems. The basic schematic of a preselection WIM system is shown in Figure 1.  
The basic element of the preselection system is a preselection weighing station for vehicles in traffic with B+(7) accuracy according to COST323. Two main zones can be distinguished in the system. The first is the weighing and traffic measurement zone, and the second is the video recording and height measurement zone. Induction loops are used to measure traffic parameters and classify vehicles (e.g., categorization and subcategorization). These loops are usually made of 2.5 mm\({}^{2}\) electrical wire and placed at a distance of 4 m in each lane, with two loops per lane (although different manufacturers may allow alternative solutions). The use of two detectors enables not only the measurement of the number and structure of vehicles but also their speed and length. Using cameras from the ANPR (Automatic License Plate Recognition) system, the passage of each vehicle is documented with a photograph that includes the license plate. An innovative example of using cameras for vehicle traffic analysis is presented in [10], along with an analysis of vehicle traffic volume versus infrastructure development using the example of Warsaw [11]. Information systems on highways and expressways often employ SMS services [12], which can also be effectively used to inform relevant authorities in the preselection of vehicles in traffic. The installation of weighing sensors is carried out per the manufacturer's instructions.