using bayesian networks to model accident causation in the uk railway industry william marsh risk...
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Using Bayesian Networks to Model Accident Causation in the UK Railway Industry
William Marsh Risk Assessment and Decision Analysis GroupDepartment of Computer ScienceQueen Mary, University of London
George Bearfield Transport Safety and ReliabilityAtkins Rail, London
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Outline
Signals Passed at Danger (SPADs) Organisational Accidents Bayesian Networks Building a BN for SPADs Conclusions
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1997 1998 1999 2000 2001 2002
Num
ber
of S
PA
Ds
Signals Passed At Danger
Southall Ladbroke Grove
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Signals Passed At Danger
‘Train has passed a stop signal without authority’
Incident on 27/3/03 at Southampton 360 yard overrun affected by low sunlight driver read adjacent signal signal is approached on a curve
wrong signal into the driver’s direct line of sight for a short time
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Waterloo
Southampton
From: Railway SafetyAssessment of Railtrack’s Response to Improvement Notice I/RIS/991007/2 Covering the ‘Top 22’ Signals Passed Most Often at DangerHSE, 2002
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Organisational Accidents
Operator errors have ‘organisational’ causes gradual relaxation of alertness pressure to increase efficiency
Currents acting within the Safety
Space
Increasing Resistance
Increasing Vulnerability
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Organisational Causes of SPADs
Infrastructure: multi-SPAD signals Driver training and timetable pressure
‘Within the workforce there is a perception that emphasis on performance has affected attitudes to safety.’
Ladbroke Grove report
‘the industry is generally poor at identifying organisational issues that may underpin SPAD incidents …’
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Bayesian NetworkMisinterpretation
Brakes not appliedSignal not located
Sighting obstruct.
Distraction
Late sighting
Read acrossSPAD
Read acrossat proceed
Phantom proceed
Late brakeapplication
Variable
Cause Table of ConditionalProbabilities
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Organisational Model
Responsibilities of actors
Interactions between actors
DriverManagement
DriverTraining
Driver
Signal Route
Actors in the organisation (idea from Rasmussen’s AcciMap)
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BN Variables from Attributes
Actors and interactions can have attributes
DriverManagement
DriverTraining
Driver
Signal Route
qualitypressure
experiencealertness
visibilitycurve
traffic
routeknowledge
previoussignal
assessment
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SPAD Scenarios
Each SPAD scenario modelled as a BN events influences: attributes of driver, infrastructure,
… Scenario model merged
Read Across RA at Proceed No Brakes SPAD
Late Brakes
Infrastructure
Route KnowldgePressure
Alertness
Traffic
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SPAD Scenario
Event
Influence
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Expert Judgement
Strength of probabilistic influences judged by experts Modify network structure Build probability tables
Aggregated data SPAD frequencies Used to validate judgements
Status Not yet completed!
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Using the Causal Model
Assess frequency / risk Where are SPADs likely?
Monitor organisational changes Use audit results
Select interventions How can the frequency of SPADs be
reduced?
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Summary
Integrated causal model of SPADs Organisational influences Event sequence
Bayesian networks Generalise other probabilistic modelling
Future challenges Use