Effect of Train-Driving Simulator Practice in the European Rail Traffic Management System: An Experimental Study

Abstract

Many European train drivers face major changes in their work with the introduction of the new train-protection system, the European Rail Traffic Management System (ERTMS), as information retrieval shifts from outside to in-cab, and a new rulebook is introduced. Therefore, many train drivers have to be educated in a short time, to make the transition safe and efficient. The purpose was to find out how a successful ERTMS practice can be designed in a physically low-fidelity but highly functional train-driving simulator. An experimental design was used, with 16 drivers divided into two groups: one group practiced in a simulator, and the other in reality. Standard training methodology was used, and the learning outcome was assessed by both measuring driving errors and via instructor evaluation of a simulator test. The drivers also filled in a questionnaire to capture how different factors, such as repeated practice, experience, and self-estimated confidence, correlate with performance. Results show that the simulator group committed significantly fewer driving errors and received significantly higher scores from the instructor. In addition, the simulator group's better performance is mostly caused by the possibility of repeated training of different special cases. The findings also imply that several of the more common special cases on the ERTMS can hardly be provoked in real train driving. Furthermore, this work strengthens the theory that novices can hardly estimate their own ability. Therefore, we argue that this type of low-fidelity simulator is well suited for research purposes, for practicing special cases, and for train operation companies to assess drivers’ skills.

Train driver selection: The impact of cognitive ability on train driving performance

“…The results in the present study indicate a significant positive relationship between selective attention and train driving performance, as measured by stop accuracy ( r = 0.268, p < .01), yet the relationship between stop accuracy and train driving performance in the Hickey and Collins (2017) study was nonsignificant at the p < .05 level ( r = 0.233, p < .10). It could be possible that the stronger relationship reported in this study resulted from the use of a train simulator and standardized assessment (see Olsson et al, 2022), as opposed to on‐the‐job assessment which could have been affected by extraneous factors (e.g., network delays). Another possible reason for this stronger relationship could be the larger sample size obtained in this study ( N = 101) compared with the Hickey and Collins (2017) study ( N = 56).…”

“…First, it could be argued that using a train simulator to assess driving performance limits the findings of this study, as this is not the same as driving an actual train. However, it could also be argued that a train simulator provides a far more controllable and therefore standardized assessment task (e.g., Olsson et al, 2022; Tripathi & Borrion, 2016). This is achieved by reducing the variability from contextual factors in the real world that might otherwise influence an assessment of driving performance in uncontrolled and therefore inconsistent ways (e.g., late running, unpredictable weather).…”

“…theoretically derived combination of cognitive abilities influences train driving performance (e.g., Guo et al, 2019;Hani Tabai et al, 2018). Second, this study contributes to the growing number that measure driving performance in a train simulator (e.g., Olsson et al, 2022;Tripathi & Borrion, 2016), which provides a standardized high-fidelity assessment of driver performance while controlling for unexpected or unknown influences on train driving performance (e.g., train breakdowns, network disruptions).…”

“…First, it is among the first to examine how a theoretically derived combination of cognitive abilities influences train driving performance (e.g., Guo et al, 2019; Hani Tabai et al, 2018). Second, this study contributes to the growing number that measure driving performance in a train simulator (e.g., Olsson et al, 2022; Tripathi & Borrion, 2016), which provides a standardized high‐fidelity assessment of driver performance while controlling for unexpected or unknown influences on train driving performance (e.g., train breakdowns, network disruptions). Finally, the three cognitive ability tests used in this study are performance‐based and therefore impervious to response distortion or “faking good” by job candidates (Peterson et al, 2009; Tett & Simonet, 2011).…”

The Concept of a Universal Tram Driver Console with Interchangeable Panels for a Polish Tram Simulator

“…Increased safety is possible through the use of simulators, which create the right conditions to prepare for work and acquire the skills to cope with a changing environment [10]. The validity of using simulators as training tools has already been noted in the literature.…”