Transcript Using Bayesian Networks to Model Accident Causation in the UK Railway Industry

Using Bayesian Networks
to Model Accident
Causation in the UK
Railway Industry
William Marsh
Risk Assessment and Decision Analysis Group
Department of Computer Science
Queen Mary, University of London
George Bearfield
Transport Safety and Reliability
Atkins Rail, London
Outline
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Signals Passed at Danger (SPADs)
Organisational Accidents
Bayesian Networks
Building a BN for SPADs
Conclusions
Signals Passed At Danger
700
600
Numberof SPADs
500
400
300
200
100
0
1997
Southall
1998
1999
2000
Ladbroke Grove
2001
2002
Signals Passed At Danger
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‘Train has passed a stop signal without
authority’
Incident on 27/3/03 at Southampton
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360 yard overrun
affected by low sunlight
driver read adjacent signal
signal is approached on a curve
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wrong signal into the driver’s direct line of sight
for a short time
Waterloo
Southampton
From: Railway Safety
Assessment of Railtrack’s Response to
Improvement Notice I/RIS/991007/2
Covering the ‘Top 22’ Signals Passed
Most Often at Danger
HSE, 2002
Organisational Accidents
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Operator errors have ‘organisational’
causes
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gradual relaxation of alertness
pressure to increase efficiency
Increasing Resistance
Increasing Vulnerability
Currents acting
within the Safety
Space
Organisational Causes of SPADs
‘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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Infrastructure: multi-SPAD signals
Driver training and timetable pressure
Bayesian Network
Variable
Misinterpretation
Signal not located
Brakes not applied
Read across
at proceed
Sighting obstruct.
SPAD
Read across
Phantom proceed
Distraction
Late sighting
Cause
Late brake
application
Table of Conditional
Probabilities
Organisational Model
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Actors in the organisation (idea from
Rasmussen’s AcciMap)
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Driver
Management
Driver
Training
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Driver
Signal
Route
Responsibilities
of actors
Interactions
between actors
BN Variables from Attributes
pressure
Driver
Management
quality
Driver
Training
assessment
experience
alertness
visibility
curve
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route
knowledge
Driver
Signal
previous
signal
Route
traffic
Actors and interactions can have attributes
SPAD Scenarios
Pressure
Traffic
Route Knowldge
Alertness
Infrastructure
Late Brakes
Read Across
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No Brakes
SPAD
Each SPAD scenario modelled as a BN
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RA at Proceed
events
influences: attributes of driver, infrastructure, …
Scenario model merged
SPAD Scenario
Influence
Event
Expert Judgement
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Strength of probabilistic influences
judged by experts
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Aggregated data
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Modify network structure
Build probability tables
SPAD frequencies
Used to validate judgements
Status
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Not yet completed!
Using the Causal Model
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Assess frequency / risk
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Monitor organisational changes
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Where are SPADs likely?
Use audit results
Select interventions
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How can the frequency of SPADs be
reduced?
Summary
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Integrated causal model of SPADs
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Bayesian networks
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Organisational influences
Event sequence
Generalise other probabilistic modelling
Future challenges
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Use