ISSN: 2490-3477

E-ISSN: 2490-3485

TTTP

Traffic and Transport Theory and Practice

Journal for Traffic and Transport Research and Application

Vol. 1 No. 1 (2016): TTTP - APEIRON

Ivan Račić

Estimation of External Costs of Transport in Canton Sarajevo for 2014

Original scientific paper

DOI: 10.7251/JTTTP1601015P

Abstract

The main aim of this paper is to raise awareness of the necessity to estimate the external costs of transport, and in particular in urban area of Canton Sarajevo. It does not provide full extent of the costs as it focuses only on two components, air pollution and accidents. It focuses on the concise methodology for estimation of external expenses of air pollution from road and air transport and road traffic accidents, using official statistical data for modeling emissions (COPERT 4, Copert Street Level, IPPC Tier 3A methodology) and for accounting road traffic accidents. Statistical significance of correla- tion between traffic flow and measured concentration of pollutant at Otoka location is determined by Pearson’s correlation coefficient. Air pollution and traffic accidents are monetized according to the Synapse Energy Economics cost estimation of metric ton of CO2, and Nicholas School of the Environ- ment, Duke University, for other pollutants, while estimation methodology for both air pollution and traffic accidents is done in line with Handbook on External Costs of Transport, European Commission.

Keywords: air pollution, external expenses, traffic accidents.

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.