ISSN: 2490-3477

E-ISSN: 2490-3485

TTTP

Traffic and Transport Theory and Practice

Journal for Traffic and Transport Research and Application

Vol. 4 No. 1-2 (2019): TTTP - APEIRON

Mirsad Kulović

Probability Analysis of Traffic Accident Occurrence

Original scientific paper

DOI: 10.7251/JTTTP1901046K

Abstract

The paper presents a methodology for analyzing a traffic accident and evaluating the exis- tence of a traffic accident in the conditions of incomplete and unreliable evidence. Namely, the exami- nation of the causes of a traffic accident is the usual activity of traffic experts who, with their knowl- edge, experience and skills, help the court and parties in the court proceedings to find out from the qualified, experienced and impartial person the reasons for the occurrence of a traffic accident, and in relation to that, mistakes and responsibilities participants in a traffic accident. In contrast, lately there are phenomena of traffic accidents in which individuals and/or groups try to improvise traffic accidents in order to achieve their various benefits and interests. This paper provides a methodological approach to such an expert evaluation of probability of traffic accident evaluation with examples in the cases of vehicle-vehicle and vehicle-pedestrian collision.

Keywords : Traffic Accident, Probability, Evidence

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.