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

1. 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

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

3. Signals Passed At Danger Southall Ladbroke Grove

4. 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

5. 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

6. Organisational Accidents • Operator errors have ‘organisational’ causes • gradual relaxation of alertness • pressure to increase efficiency Increasing Resistance Increasing Vulnerability Currents acting within the Safety Space

7. 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 …’

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

9. Driver Management Driver Training Driver Signal Route Organisational Model • Actors in the organisation (idea from Rasmussen’s AcciMap) • Responsibilities of actors • Interactions between actors

10. assessment Driver Management Driver Training route knowledge previous signal Driver Signal Route BN Variables from Attributes • Actors and interactions can have attributes pressure quality experience alertness visibility curve traffic

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

12. SPAD Scenario Influence Event

13. 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!

14. 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?

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