System for determining the capacity of a railway section

Abstract

The proposed utility model relates to railway automation and can be used in hardware and software systems for dispatch control and train traffic monitoring to optimize the throughput of railway sections under conditions of high uncertainty through the use of modern adaptive control technologies and machine learning methods. The essence of the proposed utility model is that, without a significant increase in the computational complexity of the system, an increase in the stability of the management of the capacity of a railway section is achieved through the use of adaptive algorithms based on the prediction of disturbances using machine learning methods, an assessment of the stability of the schedule and its automatic adjustment based on monitoring data. The objective of the proposed utility model is to improve the forecasting accuracy, resilience, and adaptability of railway section capacity management. This is achieved through automated real-time rescheduling and the integration of intelligent modules that minimize the impact of external factors, reduce delays, and improve infrastructure utilization while ensuring data security. The beneficial effect of the proposed utility model is that, while maintaining the advantages of the known system, it ensures sustainable and adaptive management of the capacity of a railway section by using machine learning for forecasting under conditions of uncertainty on the railway.