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Analysing and modelling train driver performance.

Ronald W McLeod1, Guy H Walker, Neville Moray

  • 1Nickleby HFE Ltd., 16, Glasgow, G77 6TJ, UK. ron@nickleby.com

Applied Ergonomics
|August 13, 2005
PubMed
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Understanding train driver performance requires considering context. This study analyzes Automatic Warning System (AWS) use, proposing a situational model to assess driver cognition in specific contexts.

Area of Science:

  • Human Factors
  • Cognitive Psychology
  • Transportation Safety

Background:

  • Contextual factors significantly influence human performance, particularly in complex operational environments like process control.
  • Previous research highlights the importance of applied psychological constructs in understanding specialized tasks.

Purpose of the Study:

  • To apply arguments for contextual factors to the train driving task.
  • To analyze train driver performance using data from the Automatic Warning System (AWS).
  • To propose a framework for investigating driver performance.

Main Methods:

  • Extensive analysis of train driver performance data.
  • Review of applied psychological research relevant to driver cognition.
  • Development of a 'situational model' as an analytical framework.

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Main Results:

  • Driver performance is heavily influenced by situational and contextual elements.
  • The Automatic Warning System (AWS) data provides insights into driver cognition.
  • A situational model can effectively frame the investigation of driver performance.

Conclusions:

  • Contextual factors are crucial for understanding train driver performance.
  • A 'situational model' offers a valuable framework for analyzing driver cognition.
  • Understanding the driver's cognitive state ('Now') within specific situations is key.