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

Drago Pupavac

Accident Costs in Regard to the Length Of Motorway – Croatian Experience

Original scientific paper

DOI: 10.7251/JTTTP1601015P

Abstract

The basic objective of this research is to explore the contribution of development of motor- way networks to minimization of accident costs. Results of the study are based on Croatian experience. The possibility of a statistically negative correlation between gradation in the total motorway length and traffic accident costs will be investigated through observation of variations in the total length of motorway and the number and type of accidents that occured. Different scientific methods were ap- plied in the research, including the method of induction and deduction, the method of abstraction and the method of correlation and regression analysis. The resulting knowlegde may be of help to traffic authorities, both on micro and macro levels.

Keywords: road traffic, motorways, accidents, costs.

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.