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 Signals Passed at Danger (SPADs) Organisational Accidents Bayesian Networks Building a BN for SPADs Conclusions

Signals Passed At Danger SouthallLadbroke Grove

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

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 Operator errors have ‘organisational’ causes gradual relaxation of alertness pressure to increase efficiency Currents acting within the Safety Space Increasing ResistanceIncreasing Vulnerability

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 …’

Bayesian Network Misinterpretation Brakes not applied Signal not located Sighting obstruct. Distraction Late sighting Read across SPAD Read across at proceed Phantom proceed Late brake application Variable Cause Table of Conditional Probabilities

Organisational Model Responsibilities of actors Interactions between actors Driver Management Driver Training Driver SignalRoute Actors in the organisation (idea from Rasmussen’s AcciMap)

BN Variables from Attributes Actors and interactions can have attributes Driver Management Driver Training Driver SignalRoute qualitypressure experience alertness visibility curve traffic route knowledge previous signal assessment

SPAD Scenarios Each SPAD scenario modelled as a BN events influences: attributes of driver, infrastructure, … Scenario model merged

SPAD Scenario Event Influence

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!

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?

Summary Integrated causal model of SPADs Organisational influences Event sequence Bayesian networks Generalise other probabilistic modelling Future challenges Use