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

Marko Subotić, Dunja Radović, Edis Softić

Methodology of Calculating Heavy Vehicle Equivalents

Original scientific paper

DOI: 10.7251/JTTTP1901021S

Abstract

Passenger car equivalents (PCE) present a very important parameter for capacity calculation and road service level as well as a planning segment of road capacity. There are many ways of calculat- ing PCE and most of them are based on Greenshield’s basic method. This paper studies the PCE calcula- tion methodology and conditions under which it is applied. The first part of the paper is about role of PCE in analyzing traffic flow, and the rest of the paper is presenting methodologies for computation of PCE. Example of the latest method for determining PCE according to HCM-2010 is given in this paper. The goal of the research is presented by structural, parameter and functional analysis of methods. Fur- ther research directions of PCE are shown as well.

Keywords : passenger car equivalents (PCE), analysis, heavy vehicle, flow.

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.