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

Dragan Obradović

Detecting Drug Abuse and Misuse in Road Traffic

Original scientific paper

DOI: 10.7251/JTTTP1901034O

Abstract

Every road user understands what driving under the influence (DUI) of alcohol means. Equally, many of those who use psychoactive substances and who are drivers know what driving under the influence of psychoactive substances implies. A small number of drivers across all categories who daily use drugs based on prescribed medication know what drug abuse means in traffic. The danger of drug misuse while driving is significantly higher when there is a traffic accident due to driving under the influ- ence of drugs. In this paper, we have pointed out the importance of investigating a road traffic accident, primarily from the criminal aspect, when one of the parties involved in a traffic accident has driven a vehicle under the influence of drugs. The regulations from the Law on Traffic Safety from the Republic of Serbia can be of use to the officials of the police and judiciary of the Republic of Srpska, as well as Bosnia and Herzegovina. These regulations are very relevant for each local community or its relevant authorities.

Keywords : Law on Traffic Safety, Traffic Accident, Drugs, Medications, Criminal Procedure, Investigation.

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.