Abstract

Train Driver workload is an under-researched area. Operator workload has been extensively studied in the automotive, aeronautical and other domains using performance, subjective and physiological measures. In this exploratory study, we combine subjective self-report measures with a task-based measure of workload and physiological measures. Heart Rate and Galvanic Skin Response are collected from train drivers over the course of their journey. These signals are analysed with respect to subjective and task-based measures of workload, but no reliable correlations were found between the physiological and other workload measures. However, the results show that peaks in both the Heart Rate and GSR data are associated with particular locations or events and changes in GSR data reflect anticipatory events and are inline with subjective driver commentary. This suggests that further research on physiological measures for train drivers is warranted

Highlights

  • Train driving is a highly specialised skill requiring detailed knowledge of traction characteristics, local geography, and railway procedures

  • Negative correlations were often found between heart rate and initiating and changing braking, meaning that heart rate tended to go down during these activities

  • Galvanic Skin Response Data The GSR data collected in this study showed more relevance to the subjectively experienced workload than the heart rate (HR) data

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Summary

Introduction

Train driving is a highly specialised skill requiring detailed knowledge of traction characteristics, local geography, and railway procedures. The safety critical nature of the task means that it has been the subject of human factors research as far back as 1979 [1] with a particular focus on the train driving task and high-risk events such as signals passed at danger (SPADs). More recent research, such as the studies conducted by Naweed The focus on SPADs has led to several studies using eye-tracking to study train driver visual behaviours The data was collected as part of a study of train driver workload, and this paper will discuss the collection, interpretation, and analysis of the data in this context as well as more generally

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