Peculiarities of algorithms for monitoring vehicle performance

Abstract

Efficient operation of vehicles and systems is crucial for smooth transportation of passengers and cargo. However, the increasing complexity and size of transportation networks create problems related to vehicle operation. Challenges faced by advanced algorithms for monitoring vehicle performance include analyzing large amounts of data, unstable real-time indicators, and the need for accurate and automated methods to predict the technical condition of vehicles. This article reviews modern approaches to monitoring, identifying factors that affect the technical condition of vehicles, and implementing advanced analysis and forecasting methods in modern information and analytical systems. Thus, this article aims to examine the characteristics of algorithms used to monitor vehicle performance indicators and identify ways to improve their efficiency and accuracy. This can be achieved by utilizing the latest methods of data analysis and forecasting. This article investigates algorithms for monitoring vehicle operation indicators and aims to develop algorithms for an information system to monitor vehicle performance. The article discusses different methods for monitoring technical conditions of vehicles, such as time series analysis, forecasting, and fault detection. It describes the process of creating models and using them to predict the condition of vehicles. The article concludes by evaluating the effectiveness of current monitoring methods and suggesting areas for further research. The study's results have practical applications and can improve vehicle monitoring systems, increasing their safety and efficiency. The authors are confident that the results of the study will help improve monitoring systems and increase the overall level of safety and efficiency of vehicles and transport systems