Operational safety indicators using real train driving data
Rawia El Rashidy
University of Huddersfield
A research fellow at the institue of Railway research, University of Huddersfield.
Rawia joined the University of Huddersfield as a Research Fellow. Rawia obtained a PhD in Transport Network Resilience from the Institute of Transport Studies, University of Leeds and an MSc with Distinction in Energy and Environment from the Faculty of Environment, University of Leeds. Rawia’s first degree was a BSc (Hons) in Civil Engineering.
Rawia was awarded a number of awards as a recognition of her research quality and innovation during her PhD period as listed below:
A golden Medal winner in the Transport Research Arena Conference, Athens, 2012.The ITS Researcher of the Year, Institute of Transport Studies, University of Leeds, 2012.Received special commendation for outstanding research, The ten ‘Women of Achievement’ Event, University of Leeds, 2013.
Rawia has joined the University’s research activities as part of the Strategic Partnership between the Rail Safety and Standards Board (RSSB) and the Institute of Railway Research(IRR). Rawia works in On Train Data Recorders (OTDR) aiming to identify Driver Competency Performance Indicators (DCPIs) for behaviour of drivers from OTMR data and recording systems. She is also investigating the implementation of these DCPIs across passenger train operating companies.
Abstract
On Train Data Recorders (OTDR) are used within the GB Railways to collect data relating to train operations and the state of various train systems throughout a journey. These data includes power and brake controller position,... [ view full abstract ]
On Train Data Recorders (OTDR) are used within the GB Railways to collect data relating to train operations and the state of various train systems throughout a journey. These data includes power and brake controller position, driver acknowledgement of signalling system warnings, whether the doors are open and the operation of the brake system.
The OTDR data have a large potential to support safety management of the railway and the data have been used to support accident investigations; for example, RAIB (2007) used the data from OTDR to investigate events where two signals were passed at danger at Esher on 25 November 2005. In this instance, the OTDR data enabled the investigators to recognise the severe low adhesion conditions that the 1A12 train experienced. Despite this potential for safety management, the OTDR data are not used generally for day-to-day management of safety.
This paper explores how OTDR data can provide a valuable source of information on how the train driver performance may impact the safety of a train. The OTDR data were initially examined to identify safety-related elements such as the state of the Driver's Reminder Appliance (DRA), Emergency Bypass Switch (EBS) and Train Protection and Warning System (TPWS). Whilst the use of each of these systems by the driver is governed by the Railway Rule Book (GE/RT8000/TW5, 2014), there may be instances where drivers may switch off a safety system.
Using OTDR data to monitor safety systems has to potential to improve compliance with the Rule Book, especially if data can be collected and analysed in real-time. Furthermore, the data may allow for improved understanding of driver performance which in turn could allow the development of more effective safety management strategies.
Our work used R software to develop a number of algorithms to clean the raw OTDR data files and extract the required data. A case study has been provided to illustrate the application of OTDR data to identify safety-related indicators. The use of these indicators points to how OTDR data can be useful in the pre-incident investigation, for example, identify the deviation from recommended rules that may have safety implications.
Authors
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Rawia El Rashidy
(University of Huddersfield)
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Peter Hughes
(University of Huddersfield)
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Miguel Figueres-Esteban
(University of Huddersfield)
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Coen Van Gulijk
(University of Huddersfield)
Topic Area
Train driving models and performance
Session
TD-1 » Train Driving (13:50 - Monday, 6th November, Smile 1)
Paper
OperationalSafetyIndicatorsUsingRealTrainDrivingData_HumanFactorPaper_Final.pdf
