ISSN: 2490-3477

E-ISSN: 2490-3485

TTTP

Traffic and Transport Theory and Practice

Journal for Traffic and Transport Research and Application

Vol. 2 No. 1-2 (2017): TTTP - APEIRON

Snježana Rajilić

Public Service Obligation System Principles

Original scientific paper

DOI: 10.7251/JTTTP1701008R

Abstract

This paper tries to model the public passenger transport system in the railroad traffic from the aspect of the PSO obligation – the Public Transport Obligations, defined by a Regulation enacted by the state level of authority, in accordance with the EU Parliament Regulation 1370/2007/EC. The paper focuses on the aims of the Regulation which regulate the PSO principles, system financing, compensa- tion and subsidies systems. It also establishes the conditions and trends in EU countries. There is also stress about the structure of expenses and income in the exploitation process in relation to the need for subsidies by local authorities for the transport of passengers.

Keywords : Regulation, PSO- Public Transport Obligation, trends, expenses, income.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.