1. Incident Factor Classification System and Signals Passed at Danger Huw Gibson, Ann Mills, Dan Basacik, Chris Harrison

2. Overview Introduction to the Incident Factor Classification System database Signals Passed at Danger (SPAD) study using IFCS data SPAD probabilities and the Human Reliability Assessment Fatigue study (see paper) 2

3. IFCS Database The Incident Factor Classification System (IFCS) Event details: What, when, where Event causes: How, why Human Error/ Violation Classification Ten incident factor classification SPAD Investigation Reports 3 Safety management information system (SMIS)

4. 4 Equipment Environment Knowledge, skills and experience Communication

5. 5 Information Practices and Processes Personal Factors Supervision and Management Workload Teamwork

6. Ten Incident Factors – Alstom Prompt Card 6 The 10 incident factors

7. SPAD Data Collected 257 SPAD Incident Investigation Reports Reviewed 924 Causal/Contributory Factors, average four per incident 197 Passenger, 54 Freight, 184 Network Rail By Year: 7 2011201220132014 70621169

8. SPAD 10 Incident Factor causes 8

9. The ‘TOP 5’ things to deal with 9 NoPassengerFreightNetwork Rail 1Personal12% Knowledge, skills and experience 22%Equipment15% 2 Knowledge, skills and experience 9%Equipment17%Communication7% 3 Supervision/ Management 9%Communication13% Practices/ Processes 5% 4Communication8%Personal13% Supervision/ Management 5% 5Workload5% Supervision/ Management 13% Work environment 2%

10. SPAD Workshops 9 cross-company workshops Separate front line staff and manager workshops 60 participants: 9 freight companies 13 passenger companies Network Rail Challenges to existing management approaches for key areas: Route knowledge (knowledge, skills and experience), safety critical communications (communications), signal design and layouts (equipment), fatigue and health (personal) Good alignment between ten factor data and driver views Positive interest in seeing ten incident factor data for their company 10

11. What is going to change at railway companies? 1.Re-balance the approach to SPAD investigations to more reliably identify underlying causes, as they can currently have a bias towards considering driver performance rather than underlying factors. 2.Identify trends in underlying causes across SPAD incidents, in addition to managing each incident through recommendations and local actions, with the aim of focusing SPAD management on key underlying causes. 3.Include front line staff (particularly drivers), managers and directors in the review of underlying causes across SPAD incidents. The reviews to have the objective of identifying and prioritising improvements to company processes for managing SPADs. 11

12. SPAD Likelihood Generally a driver error, slip/lapse at the front line Often normalised by train miles Best normaliser is number of times drivers are required to stop at red aspects University of Huddersfield and RSSB project ongoing to collect normaliser data from UK national data feeds 7,500,000 red lights where drivers need to stop estimated per annum 300 SPADs per year Annual human error probability: 0.00004; 1 in 25,000 Lowest Railway Action Reliability Assessment value: 0.00002; 1 in 50,000 Lots of non-optimal signal designs out there 12

13. SPAD likelihoods – a little deeper (Nikandros and Tombs, 2007) Australian data Signal which is stopped at rarely, mostly green (red 1 in 1000 approaches) SPAD probability 0.001 – 1 in 1,000 Signal which is stopped at often, mostly red (red 990 times in 1000 approaches) SPAD probability is 0.000006 – 1 in 166,667 166 times worse 13

14. HRA Society http://hrasociety.org/ “The Human Reliability Analysis (HRA) Society gathers HRA professionals (practitioners, developers, and researchers) with the goal to improve safety in our society through its contributions to risk assessment and, in particular, to enhance qualitative and quantitative human performance prediction in safety analyses.” “Glue between HF and Risk/Engineering” 14

15. Conclusions Incident Factor Classification System project ongoing: understanding human performance and underlying causes across incidents. Data on SPADs, orienting to underlying causes. Data on fatigue and its contribution to railway incidents. Leading to national changes in safety reporting (SMIS+). Guidance to support investigators in capturing and classifying 10 incident factors and errors/violations delivered in 2016. Future: data understood and acted on at a company rather than a national level. Cross-company coordination still required to manage specific issues. 15

16. Any questions?