E3S Web of Conferences (Jan 2024)

Determination of the influence degree of technologies for issuing train traffic safety warnings

  • Aripov Nazirjon,
  • Kamaletdinov Shokhrukh,
  • Toxirov Nosirjon,
  • Eshmetova Dilbar,
  • Buriyev Shukhrat

DOI
https://doi.org/10.1051/e3sconf/202453102007
Journal volume & issue
Vol. 531
p. 02007

Abstract

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This article focuses on assessing the quality of information based on the Bayes network to justify the effectiveness of internet technology in providing traffic safety in rail transport, while issuing speed reduction alerts. To achieve this goal, the following tasks were completed: technological schemes of data transmission and reception systems “P”, “Sh”, “E” “L”, “D” of rail transport network enterprise were presented; a Bayes network was formed, the probabilities of events were determined; conditional probabilities of control phenomena were determined. An existing and proposed system of issuing and cancelling warnings on Uzbekistan Railways has been studied. An evaluation based on the Bayes network has been carried out to justify efficiency in terms of data quality. One plot with a train serving intermediate stations was chosen conditionally. The transmission of alerts to these trains was carried out with two different systems. Technological operations are reduced to probabilistic relations. The model identifies a control event in four for comparison. The results showed the reliability of existing monitoring system data lower than suggested. This is due to the connection of the existing transmission system with each other, which allows the OBAUT system to control that all data is simultaneously transmitted and the transmitted data is received, regardless of the previous data. In addition, the level of subjective interventions for information transmission is also significantly reduced. OBAUT capabilities are considered much more efficient in terms of data quality, reliability and timely transmission. The widespread use of Information Technology in the network helps to improve the quality of data, and the addition allows you to make quick decisions in the organization of train traffic.