Selection of train traffic managers activities in training process using functional resonance analysis method

Abstract

In training process of rail traffic manager (controller) using virtual reality technology, selection of activities among those assigned to a workplace and scenarios that should be taken in training is an important issue. The selection method that is based on performance variability of her/his activities has been proposed in the paper. This variability has been characterized by timing and precision. The traditional reliability and safety analysis methods are not sufficient when building the training program for traffic managers. In the paper the train controller work has been modelled using Functional Resonance Analysis Method (FRAM) that is system oriented approach. Scales of values of timing and precision that are train transport driven have been presented. They are different when comparing with typical timing and precision scales given in FRAM literature. In the paper the estimation of prob-abilities of occurring of values of timing and precision scales for these activities has been calculated as the mean from the values obtained by questionnaire done in traffic manager community or using Analytic Hierarchy Process (AHP) method. In FRAM with AHP approaches presented in literature, AHP pairwise comparison is executed using natural numbers and their reciprocals what is typical in AHP method. In our paper the AHP is used for estimating the probabilities, so in pairwise comparing the rational numbers are applied, because natural numbers and their reciprocals would limit the set of values of probabilities. The activities and scenarios that the training should be concentrated on are selected from those with the greatest variability.

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“…Due to this potential to map the complexity of human, technological, and organizational functions, FRAM is often used to identify critical processes that escape linear analysis [35,63,64], to offer social and organizational understandings [65][66][67], and to design leading and lagging Key Performance Indicators [68]. Aviation, maritime, road transportation, and railways sectors have also undergone similar analyses [69][70][71][72][73][74], as well as construction [75,76], and other critical infrastructures [77][78][79].…”