Abstract
The paper presents the approach to train movement simulation based on the application of real statistical data. A comparison of machine learning methods is presented for solving the problem of predicting the acceleration of a train depending on its mass, speed, percentage of traction force used, and track gradient. The possibility of increasing the throughput capacity of the station bottleneck by increasing the percentage of the locomotive traction force while trains departure is shown.
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