Russian Federation
Purpose: Machine learning is one of the most popular technologies at the present time, which finds application in various industries. One such industry is rail transport, where machine learning can significantly improve the management of locomotives and electric trains. The article is devoted to the study of the possibility of using machine learning to analyze the technical characteristics and parameters of the rolling stock using electric trains operating on high-speed railway lines as an example. The article discusses machine learning mechanisms for data analysis, as well as offers practical recommendations for using machine learning for data analysis on railway transport. Methods: The k-means method is one of the most popular machine learning algorithms for clustering, which allows to split a dataset into k similar groups or clusters. The algorithm is based on finding the centroids (mean values) of each cluster and assigning objects to the cluster that has the closest centroid. Depending on the choice of the initial values of the centroids and the parameters of the algorithm, the partitioning of data into clusters can be different. In addition, k-means is a relatively easy-to-implement and efficient clustering algorithm that can be used as an initial estimate for more complex clustering algorithms; The linear regression method is a statistical algorithm used to determine the relationship between two continuous variables. The algorithm uses a linear function that describes the relationship between the dependent variable and one or more independent variables. Results: Algorithm for processing and primary analysis of the characteristics and parameters of high-speed rolling stock, the results of parameter clustering, a technique for predicting the technical characteristics of prospective high-speed rolling stock. Practical significance: The methods of application of machine learning for the analysis of technical characteristics and parameters of the operated rolling stock on high-speed railway lines are shown. The methods will allow to substantiate and predict the necessary characteristics when developing technical requirements for high-speed transport in Russia. Using machine learning, it is possible to improve the accuracy of calculating the technical and economic models of high-speed highways, and further reduce the cost of operating rolling stock.
Machine learning, rolling stock, predicting the technical characteristics of transport, data analysis, high-speed transport
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