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At the limit? Using operational data to estimate train driver human reliability.

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Area of Science:

  • Rail operations safety
  • Human reliability analysis
  • Big data applications

Background:

  • Human reliability analysis is crucial for rail safety.
  • Operational data availability is increasing.
  • Understanding train driver reliability is key to enhancing rail safety.

Purpose of the Study:

  • To derive human reliability data for specific train driving tasks.
  • To assess train driver reliability using big data techniques.
  • To compare reliability data between different driver types and tasks.

Main Methods:

  • Development of a tool using big data to estimate red signal approaches on the Great Britain (GB) rail network.
  • Analysis of millions of operational data points from live feeds.
  • Comparison of derived reliability data with other train driving tasks.

Main Results:

  • Human reliability for analyzed tasks approaches current performance limits.
  • Freight train drivers exhibit higher error rates than passenger train drivers for stopping at red signals and controlling speed.
  • Big data analysis provides insights into task-specific performance limits.

Conclusions:

  • Understanding task-specific performance limits is essential for improving human reliability in rail operations.
  • Big data offers a practical approach to system error management and real-time safety indicators.
  • The study demonstrates the value of big data in assessing and enhancing rail safety performance.