Incident Factor Classification System and Signals Passed at DangerHuw Gibson, Ann Mills, Dan Basacik, Chris Harrison

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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)

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IFCSDatabase

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

Safety management information system (SMIS)

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EquipmentEquipment

EnvironmentEnvironment

Knowledge, skills and experience

Communication

atceg.WAV

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InformationInformation

Practices and ProcessesPractices and Processes

Personal Factors

Supervision and Management

Workload

Teamwork

Ten Incident Factors – Alstom Prompt Card

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The 10 incident factors

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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:

2011 2012 2013 201470 62 116 9

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SPAD 10 Incident Factor causes

Communications

Equipment

Informati

on

Know./Skil

ls/Exp

.

Personal

Practice

s/Pro

cesse

s

Supervi

sion/M

ana..

.

Teamwork

Work

environment

Workl

oad0%

5%

10%

15%

20%

25% Passenger (n=197 incidents)Freight (n=54 in-cidents)

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The ‘TOP 5’ things to deal with

No Passenger Freight Network Rail

1 Personal 12%Knowledge, skills and experience

22% Equipment 15%

2Knowledge, skills and experience

9% Equipment 17% Communication 7%

3 Supervision/Management 9% Communication 13% Practices/

Processes 5%

4 Communication 8% Personal 13% Supervision/ Management 5%

5 Workload 5% Supervision/ Management 13% Work

environment 2%

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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

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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.

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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

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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

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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”

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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.

Any questions?