PSAM 16 - Paper Overview

PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Lead Author: Franciszek Restel Co-author(s): Lukasz Wolniewicz, lukasz.wolniewicz@pwr.edu.pl

Identification of safety relevant activities of train crews using the Functional Resonance Analysis Method (FRAM)
During training the classical traing process of train crews are used real vehicles. There are two disadvantages of this approach. The first, vehicles are out of order to perform commercial tasks. This factor cerates high costs and, to minimze them, the tarining time is kept as short as possible. Secondly, there is no possibilty to trainee dangerous sitations, as for example fire on board of a train. Thus, using of Virtual Reality in the traning process is a key undertaking to improve safety and efficiency of railway operation processes. The problem occurrs how to choose safety relevant situations for implenetation as scenarios in the Virtual Reality environment. The paper proposes a method for determining train crew activities based on activity execution variability. The variability of activity execution is characterized by precision and timeliness. The accuracy and timeliness of train crew activity performance were estimated mainly based on a survey of train crews, as well operation data from the Polish Railway Network Manager. The research problem is focussed on selection of the most impertant activities and scenarios that can be carried out by train crews. The research method has the following form. The structure of typical and atypical train crew activities and their interactions is modelled using the Functional Resonance Analysis Method (FRAM). Functions of the FRAM model represent activities of the train crews under their duty. For individual functions, the variability is determined taking into account precision, and time correctness of execution. Activities and scenarios requiring training are selected from the activities with the highest variability and the activities with the highest variability chains.

Identification of safety relevant activities of train crews using the Functional Resonance Analysis Method (FRAM)

During training the classical traing process of train crews are used real vehicles. There are two disadvantages of this approach. The first, vehicles are out of order to perform commercial tasks. This factor cerates high costs and, to minimze them, the tarining time is kept as short as possible. Secondly, there is no possibilty to trainee dangerous sitations, as for example fire on board of a train. Thus, using of Virtual Reality in the traning process is a key undertaking to improve safety and efficiency of railway operation processes. The problem occurrs how to choose safety relevant situations for implenetation as scenarios in the Virtual Reality environment. The paper proposes a method for determining train crew activities based on activity execution variability. The variability of activity execution is characterized by precision and timeliness. The accuracy and timeliness of train crew activity performance were estimated mainly based on a survey of train crews, as well operation data from the Polish Railway Network Manager. The research problem is focussed on selection of the most impertant activities and scenarios that can be carried out by train crews. The research method has the following form. The structure of typical and atypical train crew activities and their interactions is modelled using the Functional Resonance Analysis Method (FRAM). Functions of the FRAM model represent activities of the train crews under their duty. For individual functions, the variability is determined taking into account precision, and time correctness of execution. Activities and scenarios requiring training are selected from the activities with the highest variability and the activities with the highest variability chains.

Paper FR133 Preview

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Lead Author Name: Franciszek Restel (franciszek.restel@pwr.edu.pl)

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PSAM 16 Conference Paper Overview

Welcome to the PSAM 16 Conference paper and speaker overview page.

Paper FR133 Preview

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