Improving measurements accuracy in weight-in-motion systems using dynamic neural networks

Abstract

The work is devoted to the problem of weighing vehicles in motion as part of modern information technologies and automated intelligent systems of managing urban resources and infrastructure. The purpose of this work is to improve the measurements accuracy of weight-in-motion systems under heavy traffic conditions by developing an innovative weighting system based on dynamic neural networks as an integral part of intelligent urban infrastructure management systems, thereby contributing to the efficiency and sustainability of urban processes. Scientific novelty consists in the use of models in the form of time delay neural networks to process data from weighing sensors. The application of this approach allows increasing the accuracy of mass measurement in weight-in-motion systems in conditions of heavy traffic by taking into account the dynamic and nonlinear properties of the weighing process. The practical usefulness of the developed method lies in the development of new innovative weighing systems as part of modern information technologies and automated intelligent systems for managing urban resources and infrastructure. The application of dynamic neural networks for determining the mass of a vehicle in motion is a promising approach that allows to significantly increasing the speed of vehicle while maintaining the accuracy and reliability of mass determination