Driver competence: supporting people and processes

Automating driver competence indicators – based on analysis of On-Train Monitoring Recorder (OTMR) data
Driver in a train simulator

The rail industry makes a significant investment in driver training. Driver managers undertake in-cab assessments of driver competence every six months and manually analyse OTMR data to monitor and investigate operational incidents such as signals passed at danger (SPADs), stop shorts, station overruns and wrong side door releases. However, this approach is resource intensive and doesn't make best used of the data available.

As part of RSSB's strategic partnership with the University of Huddersfield to develop engineering and safety models to inform decision making, we have conducted research into the relationship between OTMR data and competence indicators. The findings have been used to support the development of algorithms for the creation of Automated Driver Competency Indicators (ADCIs).

The proposed ADCIs - which focus on train handling, compliance with rules, vigilance and efficiency -can analyse OTMR data more rapidly and more efficiently than before, allowing for analysis to happen under a much wider range of driving conditions. The results provide a clearer and more comprehensive picture of driver behaviours and trends which can be used to enhance performance – through both self-review and management support.

We are piloting ADCIs with London North Eastern Railway and c2c to understand the challenges associated with implementing them on the network. The trial – which is due to be completed early in 2020 - will include software development, embedding indicators into the everyday use of competency management systems, and an assessment of their effectiveness.

Topics covered:

  • Driver training
  • Driver competence

Resources

spark bulb
Train Driver Performance Indicators for Safety from On Train Monitoring Recorder data (COF-UOH-18)
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Justin Willett
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