ISSN: 2490-3477

E-ISSN: 2490-3485

TTTP

Traffic and Transport Theory and Practice

Journal for Traffic and Transport Research and Application

Vol. 3 No. 1-2 (2018): TTTP - APEIRON

Ognjen Sančanin, Danislav Drašković, Demeter Prislan

Passive Road Safety Systems - Case Study Of Road Section Banja Luka - Prnjavor (M16, M16.1)

Original scientific paper

DOI: 10.7251/JTTTP1801048S

Abstract

In this paper, the authors will show the influence of roadside objects on road safety on the Banja Luka- Prnjavor section. Roadside objects have a major impact on the weight of a traffic accident because they represent direct obstacles to the wandering vehicle, which in most cases will be stopped by a collision in one of them in the immediate vicinity of the road. Roadside objects can be of different types and constructions, concrete poles, public lighting poles, trees, inadequately installed rebound fences and unprotected petrol stations are only some of them. Therefore, the essence of this paper is to spot possible roadside objects on the observed road section, categorize them, and make suggestions for short, medium and long term improvements.

Keywords : roadside objects types – RSI analysis – proposal of the solution for the observed road ac- cident type – short, medium and long term improvements.

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.