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The Potential for Using Big Data
Analytics to Predict Safety Risks
by Analysing Rail Accidents.
Dr H. J. Parkinson and Dr G. Bamford
Digital Rail Limited, Lancaster, England
NTTX Advisory Limited, Warrington, England
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 1
The Third International Conference on Railway Technology: Research, Development and Maintenance
Cagliari, Sardinia, Italy 5-8 April 2016
Paper 0123456789
Stream: RW2016
Reference: RW2016/2015/00096
Session: RW2016-S09: Accidents Analysis and R&D of Safety Technologies
Focus of Paper
1. What is Big Data and why it’s useful
2. Data Taxonomy
3. Causes, Hazards and Accidents
4. Accident Analysis and links to data
5. “BDness” of accidents
6. A new approaching using the “ELBowtie”
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 2
A new dawn?
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 3
The Uptake in New Technology (Gartner 2014
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 4
1. Real time a. Remote monitoring
b. Traffic flows
c. Incident data
d. Close Calls/ Alerts/CIRAS
e. Emergency services communications
f. CCTV
2. Asset a. Maintenance
b. Risk based inspection and maintenance data
c. Integrity data, safety, security, environmental
d. Design Hazard Data (residual risks)
3. BI - business related a. Finance
b. HR related
c. Quality management
d. Safety management
e. Project management/Business Risk Assessment
4. Operational a. Complex unstructured data, reports, spreadsheets etc.
b. Staffing levels, etc.
c. Manning schedules
d. Service related, timetables, etc.,
e. Operational Risk Assessment
5. Social a. Twitter
b. LinkedIn
c. Facebook
d. Other e.g. news items
6. External a. Supplier data
b. Map related data, works location etc.
c. Environmental trends, weather etc.
7. Personal a. Location history
b. Health related
c. Education related
Accident Causation
Management Problems Operator Errors
Time and Financial Constraints Lack of training
Lack of understanding of
operation
Carelessness
Engineering Problems Tiredness or drug impairment
Design defects Problems from the working
culture
Material defects Environmental Effects
Manufacturing process errors Weather and ambient
conditions
Poor quality maintenance Plant layout interactions
Inadequate analysis of
experience
Potentially dangerous
equipment or materials.
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 5
Neale,M , (2007). The Causes of Accidents, IMechE 2007, Published: 16 January 2007.
Santiago de Compostela (24/7/2013)
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 6
Hatfield (17/10/2000)
• No speed control transition,
only the driver
• Driver distraction,
• Train stability
• Passenger survivability
• Risks of gauge corner cracking not
understood
• Lack of controls and proper
inspection regime
• Poor contractor and project
management
• Not acting on audit reports
Platja de Castelldefels in Spain [15].
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 7
Crowding on platform station. High
spirits and subsequent trespassing
onto the railway line instead of
using the underpass.
5.a/c/ social media Texting the
party locations, twitter
1.f Remote Monitoring (Vision
System CCTV). Station vision system
data. Could detect behavioural
norms, crowd density and
passengers moving over platform
edge.
7.a. Location History GPS. For
crowding
Station design, members of public
claim that it was not clear where
the exit was.
2.d Station design 1.d Close Calls
5.a/c/ social media 3.d safety
management
Time-table non-adherence allowed
high speed train to pass through
station at same time. Stopping train
was 10 minutes late
4.d timetable data
Police were present as it was known
that there would be a large crowd
1.e. Emergency Services. Police
communications
It was known either officially or to
the public that the crossing of the
track as a short cut was quite
common.
1.d Close Calls (From Diver or
station staff)
7.a Location Data, GPS. Will be used
for level crossing safety in USA.
Could regular crossing of line be
indicated by phone GPS more
generally
BDNess?
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 8
Platja data
source
Variety Volume Velocity Veracity
5.a/c/ social media
Texting the party
locations, twitter
H H L
1.f. Remote Monitoring
(Vision System CCTV).
H H H
7.a. Location History
GPS. For crowding
H H L
2.d Station design 1.d
Close Calls
L L L
3.d safety management L L H
4.d timetable data L H H
1.e. Emergency
Services. Police
communications
H H H
1.d Close Calls (From
Driver or station staff)
L L H
7.a Location Data, GPS. H H H
Value, BD Quotient, (Number of Hs as
percentage of total)
67 %
The ELBowtie©
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 9
Flagging Heightened Risk
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 10
The All Seeing Eye: Knowledge and
Visualisation
Conclusions
• “BD Ness” depends upon complexity
• Intelligent algorithms will change both blue and
white collar work, including safety management!
• The genie is out of the bottle!
• The models will just get better and better with time!
• www.elbowtie.com
06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 11
Thanks for listening

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Sardinia pres 04

  • 1. The Potential for Using Big Data Analytics to Predict Safety Risks by Analysing Rail Accidents. Dr H. J. Parkinson and Dr G. Bamford Digital Rail Limited, Lancaster, England NTTX Advisory Limited, Warrington, England 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 1 The Third International Conference on Railway Technology: Research, Development and Maintenance Cagliari, Sardinia, Italy 5-8 April 2016 Paper 0123456789 Stream: RW2016 Reference: RW2016/2015/00096 Session: RW2016-S09: Accidents Analysis and R&D of Safety Technologies
  • 2. Focus of Paper 1. What is Big Data and why it’s useful 2. Data Taxonomy 3. Causes, Hazards and Accidents 4. Accident Analysis and links to data 5. “BDness” of accidents 6. A new approaching using the “ELBowtie” 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 2
  • 3. A new dawn? 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 3 The Uptake in New Technology (Gartner 2014
  • 4. 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 4 1. Real time a. Remote monitoring b. Traffic flows c. Incident data d. Close Calls/ Alerts/CIRAS e. Emergency services communications f. CCTV 2. Asset a. Maintenance b. Risk based inspection and maintenance data c. Integrity data, safety, security, environmental d. Design Hazard Data (residual risks) 3. BI - business related a. Finance b. HR related c. Quality management d. Safety management e. Project management/Business Risk Assessment 4. Operational a. Complex unstructured data, reports, spreadsheets etc. b. Staffing levels, etc. c. Manning schedules d. Service related, timetables, etc., e. Operational Risk Assessment 5. Social a. Twitter b. LinkedIn c. Facebook d. Other e.g. news items 6. External a. Supplier data b. Map related data, works location etc. c. Environmental trends, weather etc. 7. Personal a. Location history b. Health related c. Education related
  • 5. Accident Causation Management Problems Operator Errors Time and Financial Constraints Lack of training Lack of understanding of operation Carelessness Engineering Problems Tiredness or drug impairment Design defects Problems from the working culture Material defects Environmental Effects Manufacturing process errors Weather and ambient conditions Poor quality maintenance Plant layout interactions Inadequate analysis of experience Potentially dangerous equipment or materials. 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 5 Neale,M , (2007). The Causes of Accidents, IMechE 2007, Published: 16 January 2007.
  • 6. Santiago de Compostela (24/7/2013) 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 6 Hatfield (17/10/2000) • No speed control transition, only the driver • Driver distraction, • Train stability • Passenger survivability • Risks of gauge corner cracking not understood • Lack of controls and proper inspection regime • Poor contractor and project management • Not acting on audit reports
  • 7. Platja de Castelldefels in Spain [15]. 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 7 Crowding on platform station. High spirits and subsequent trespassing onto the railway line instead of using the underpass. 5.a/c/ social media Texting the party locations, twitter 1.f Remote Monitoring (Vision System CCTV). Station vision system data. Could detect behavioural norms, crowd density and passengers moving over platform edge. 7.a. Location History GPS. For crowding Station design, members of public claim that it was not clear where the exit was. 2.d Station design 1.d Close Calls 5.a/c/ social media 3.d safety management Time-table non-adherence allowed high speed train to pass through station at same time. Stopping train was 10 minutes late 4.d timetable data Police were present as it was known that there would be a large crowd 1.e. Emergency Services. Police communications It was known either officially or to the public that the crossing of the track as a short cut was quite common. 1.d Close Calls (From Diver or station staff) 7.a Location Data, GPS. Will be used for level crossing safety in USA. Could regular crossing of line be indicated by phone GPS more generally
  • 8. BDNess? 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 8 Platja data source Variety Volume Velocity Veracity 5.a/c/ social media Texting the party locations, twitter H H L 1.f. Remote Monitoring (Vision System CCTV). H H H 7.a. Location History GPS. For crowding H H L 2.d Station design 1.d Close Calls L L L 3.d safety management L L H 4.d timetable data L H H 1.e. Emergency Services. Police communications H H H 1.d Close Calls (From Driver or station staff) L L H 7.a Location Data, GPS. H H H Value, BD Quotient, (Number of Hs as percentage of total) 67 %
  • 9. The ELBowtie© 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 9
  • 10. Flagging Heightened Risk 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 10 The All Seeing Eye: Knowledge and Visualisation
  • 11. Conclusions • “BD Ness” depends upon complexity • Intelligent algorithms will change both blue and white collar work, including safety management! • The genie is out of the bottle! • The models will just get better and better with time! • www.elbowtie.com 06/04/2016 Copyright © Digital Rail Ltd and NTTX Ltd 11 Thanks for listening

Editor's Notes

  1. Good morning ladies and gentlemen. Great to be here and thank you for attending this special session on Accidents Analysis and R&D of Safety Technologies.
  2. What is Big Data and why it’s useful Data Taxonomy Causes, Hazards and Accidents Accident Analysis and links to data “BDness” of accidents A new approaching using the “ELBowtie”
  3. Big Data, massive amounts of data, structure and unstructured, real time and historical. Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone – IBM Source. Zetta byte 1000to the power 7: 1000kB kilobyte10002MB megabyte10003GB gigabyte10004TB terabyte10005PB petabyte10006EB exabyte10007ZBzettabyte10008YBy ottabyte IOT, massive interconnectivity of assets. Intelligent monitoring using cheap electronics, sensors and connectivety, for exam[le GPRS 4G etc. Long battery lives so can be remote. Could festoon the rail network with this technology creating and internet of rail. We can have thero couples, accelometers, strain gauges, etc etc. Lot of hype about big data. Number of conference papers maps the exponential growth in data. Gartnet cycle. We did Neural Nets back in the last centrury. Alos involved in an AI in design project. These were followed by what has been described as an AI winter.We are now climbing out of the trough of disillutionment, up the slope of enlightenment and onto the heady uplans of the Plateau of productivity. Volume (amount), •Velocity (speed of capture or change), •Variety (number of sources), •Veracity (quality), and •Value (here the safety related value). I this paper we have used these Big Data attributes to try to categorise the analysis of accidents and the associated data.
  4. What data is available in the railway and related areas? condition based monitoring information from sensors, either analogue or digital, that would provide digital information, including vibration (accelerometers), machine vision, heat, displacement, strain, humidity, particle ingress, etc. would be classified as ‘Real Time, remote monitoring’, which is already an accepted means of classifying this type of data. Other data types are less well defined, for example, data from industry reports, staff morale, organisational culture, but can be equally as important in safety evaluations.
  5. What are the main causes of railway accidents? Accident Causes. A Study Based Upon research of 43 accidents [THE CAUSES OF ACCIDENTS IMechE 2007] From a study it can bee seen that there are always several causes for each accident. One of the causes in isolation would probably not resluted in an accident, but the combination of that cause with others has the potential for disaster. These have a big overlap or mapping to our Data Taxonomy.
  6. The Hatfield rail crash was a railway accident on 17 October 2000, at Hatfield, Hertfordshire, UK. Although the accident killed fewer than other accidents, it exposed the major stewardship shortcomings of the privatised national railway infrastructure company Railtrack and the failings of the regulatory oversight which the company displayed in its initial years (principally a failure to ensure that the company had a sound knowledge of the condition of its assets) and ultimately triggered its partial renationalisation. A GNER InterCity 225 train bound for Leeds had left London King's Cross at 12:10, and was travelling at approximately 115 miles per hour (185 km/h) when it derailed south of Hatfield station at 12:23. The primary cause of the accident was later determined to be the left-hand rail fracturing as the train passed over it. The accident killed four passengers and injured a further seventy. The leading Class 91 locomotive (91023) and the first two coaches remained upright and on the rails. All of the following coaches, and the trailing Driving Van Trailer were derailed, and the train set separated into three sections. The restaurant coach, the eighth vehicle in the set, overturned onto its side and struck an overhead line gantry after derailing, resulting in severe damage to the vehicle. Crash investigators identified the integrity and strength of the British Rail-designed Mark 4 coaches for protecting occupants. Coincidentally, the locomotive in the crash was also involved in the Great Heck rail crash (where the leading Driving Van Trailer hit a road vehicle on the track) a few months later. A preliminary investigation found a rail had fragmented as trains passed and that the likely cause was "rolling contact fatigue" (defined as multiple surface-breaking cracks). Such cracks are caused by high loads where the wheels contact the rail.[2] Repeated loading causes fatigue cracks to grow. When they reach a critical size, the rail fails. Over 300 critical cracks were found in rails at Hatfield. The problem was known about before the accident, and replacement rails made available but never delivered to the correct location for installation. speed restrictions  Railtrack, got rid of the engineering knowledge of British Rail into maintenance contractors, had inadequate maintenance records and no accessible asset register. No knowledge of extent of problem and 1200 restriction imposed, the railway had a nervous breakdown. Road deaths increased The Santiago de Compostela derailment occurred on 24 July 2013, when an Alvia high-speed train travelling from Madrid to Ferrol, in the north-west of Spain, derailed at high speed on a bend about 4 kilometres (2.5 mi) outside of the railway station at Santiago de Compostela, Spain. Of the 222 people (218 passengers and 4 crew) aboard, around 140 were injured and 79 died.[2] The train's data recorder showed that it was travelling at about twice the posted speed limit of 80 kilometres per hour (50 mph) when it entered a bend in the line. The crash was recorded on a track-side camera which shows all thirteen vehicles derailing and four overturning. On 28 July 2013, the train's driver Francisco José Garzón Amo was charged with 79 counts of homicide by professional recklessness and an undetermined number of counts of causing injury by professional recklessness.[3]
  7. The Castelldefels train accident occurred on 23 June 2010 when a passenger train struck a group of people who were crossing the railway on the level at Platja de Castelldefels station to the southwest of Barcelona, in Catalonia, Spain. Twelve people were killed, and fourteen injured: all victims but one Romanian were of Latin American origin, with a majority from Ecuador.[1] The accident occurred on St. John's Eve,[2][3][4] a major celebration in Spain and in several other European countries. The victims were apparently trying to get to the beach less than 200 metres (660 ft) from the station, where a concert by Ecuadorian singer Rubén de Rey had been organized.[1] It was the worst railway accident in Spain since the Valencia Metro derailment in July 2006 killed 43 and injured 47 others.[5][6]
  8. Enetrprise data Linked Bowtie. Good simple of model of accident causation. Easy to understand and explain. There are many other models such as Swiss Cheese, STAMP, etc .
  9. H|ow will we build the model and what type of machine learning do we need. What does normal look like and what does heightened risk look like. We are starting to build up our analytics Neural Networks for predicting train derailment quotinets Analysis of close Call Information. Machine vision
  10. We have looked at 3 accidents and tried to define their Bdness in terms of the 5 V.