Simulator- Based Driver Training - University of Birmingham

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Systems Thinking in Simulator-Based Driver Training
Systems Thinking in Simulator-
Based Driver Training
Liam Moloney
Department of Civil Engineering, University of Birmingham Slide No: 1
Systems Thinking in Simulator-Based Driver Training
Background
• Graduated from college over 30 years ago
• Returned to education in early 1990s and took
degrees in management and engineering
• All of my working life was spent within the railway
in either engineering or management roles
• Project work (rail freight activities)
• Heavy plant (specification, procurement and maintenance)
• Railway operations (service delivery)
• Training of operating staff (drivers, guards, signalmen)
• Multiple perspectives led me to conclude that the
effectiveness of the training process for railway
operating staff could be improved by simulation
Department of Civil Engineering, University of Birmingham Slide No: 2
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Systems Thinking in Simulator-Based Driver Training
Core research objectives
• To inform the author of the strategic issues when
writing a functional specification. This specification
was used to procure a driver training simulator
• Benefits
• Shortcomings
• Usability issues
• To assess the effectiveness of simulation as an
training tool for traction drivers. Criteria include:
• Users reactions
• Learning outcomes
• Behavioural change
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• Financial
Department of Civil Engineering, University of Birmingham Slide No: 3
Systems Thinking in Simulator-Based Driver Training
Overview 1/3
• Motivation
• Key to business success
• Costs of developing capability
• Operating context of I.E.
• Understanding the enterprise - IDEFØ modelling
• Operator training – differences between a car and a
train
• Nature and complexity of train driving
Department of Civil Engineering, University of Birmingham Slide No: 4
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Systems Thinking in Simulator-Based Driver Training
Overview 2/3
• Drivers’ cues
• Review of literature (cognitive psychology etc)
• Review of rail-specific literature
• Review of road-specific literature
• Review of air-transport specific literature
• Hypothesis
• What is a simulator?
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Department of Civil Engineering, University of Birmingham Slide No: 5
Systems Thinking in Simulator-Based Driver Training
Overview 3/3
• Uses and shortcomings of simulation
• Types of simulators
• The 3 environments
• Fidelity and realism – how much?
• Effectiveness evaluation criteria
• Longevity of training benefits
• Questions and clarifications
Department of Civil Engineering, University of Birmingham Slide No: 6
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Systems Thinking in Simulator-Based Driver Training
Motivation
• Vocational interest
• Academic interest
• Interest in wellbeing of trainees and organisation
• Period of phenomenal growth
• Low accident rate
• Results of previous accident inquiries
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Department of Civil Engineering, University of Birmingham Slide No: 7
• Margin
• Paths to
SCA
• Drivers of
value
• Enablers
Systems Thinking in Simulator-Based Driver Training
Key to business success
Resources
Distinctive
Competencies
Capabilities
Superior:
•Efficiency
•Quality
•Innovation
•Customer
Responsiveness
Differentiation
Low Cost
Value
Creation
Department of Civil Engineering, University of Birmingham Slide No: 8
Higher
Profits
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Systems Thinking in Simulator-Based Driver Training
Costs of developing capability
• Knowledge assets – Arthur Andersen (n=386) –
intellectual capital reporting
• Expensive
• 3.91% of payroll 2006 – IBEC
• 3.94 % of payroll 2006 – I.E.
• 2·28% of payroll 2006 – Proglio
• 1·75% of payroll 2004 - RENFE
• Uncertain outcome & always requires top-up
• Always focus of post-incident inquiries
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Department of Civil Engineering, University of Birmingham Slide No: 9
Systems Thinking in Simulator-Based Driver Training
Operating context of I.E.
• The Celtic tiger - overall demand for transport +
• Demand for rail services +5·52% o/a (’03 -’06)
• DART sales +25% Vs population +2·3%. (’96 - ’02)
• Ramifications of enhanced service delivery -
increasing number of drivers, experiential profile,
• New driver training programme - GDL
• Buoyant state of the stakeholder’s finances; new
traction, signalling, station improvements
• External influences account for 75% of incidents
Department of Civil Engineering, University of Birmingham Slide No: 10
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Input:
Transport
demand
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Systems Thinking in Simulator-Based Driver Training
IDEFØ – L0
Constraints:
Resource availability, Rules, Standards,
Political, Environmental, Social, Technical
Transformation or
process:
Move people and goods
System railway
Mechanisms:
Staff, Finance, Power, Equipment, Infrastructure,
Organisational Capability, Knowledge
Ouput: Transport
product and
quality
Department of Civil Engineering, University of Birmingham Slide No: 11
Transport
demand
Political
direction &
finance
Plan system
requirements
Railway capacity
demand ?
Staff,
Finance,
Power,
Equipment
Develop
railway
system
Systems Thinking in Simulator-Based Driver Training
IDEFØ – L1
Rules,
principles &
standards
Develop line
capacity
Plan railway
operation
Staff,
Finance,
Power,
Equipment
Allocate finance ?
Topography, equipment,
availability
Staff,
Finance,
Power,
Equipment
Service pattern
Staff levels
Operate railway
system
Staff,
Finance,
Power,
Equipment
Passengers
& Cargo
Transport
product and
quality
Department of Civil Engineering, University of Birmingham Slide No: 12
Rules
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Planned
availability
Service
pattern
Railway
capacity
demand
Operations
Maintenance
procedures
Staff
levels
Maintain
system
Equipment,
staff, power
and finance
Equipment types
and quantities
Actual
availability
Systems Thinking in Simulator-Based Driver Training
IDEFØ – L2
Finance
allocation
Infrastructure
Control
service
Equipment,
staff, power
and finance
Staff
levels
Demand movement
Equipment,
staff, power
and finance
Driving methodology &
policy
Operate
service
Cargo &
passengers
Transport
product
Department of Civil Engineering, University of Birmingham Slide No: 13
Department of Civil Engineering, University of Birmingham Slide No: 14
Rules
Systems Thinking in Simulator-Based Driver Training
IDEFØ – L3 – context of research
Demand train
movement
Demand for
drivers
Diversity of
train type
Assign
train
Traction equipment and
rolling stock
Rules and standards for
normal, degraded and
emergency operating modes
Training
resources
Provide crew
- driver
Driving
philosophy
Licensing
authority
Create route
reservation
Train and crew
diagrams
Trainees Permanent way, Cargo and
signalling and train
control systems
passengers
Provide safe and
effective transport
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Operator training - differences
between a car and a train
• Contextual differences
• Block working, interactions, timetable
• Human factors
• Accidents, licensing, stimulation, mileage
• Physical differences
• Energies, friction, manoeuvrability
• Different MMIs
• Layout of controls, heterogeneity
• Task complexity
• Knowledge domains, judgemental skills
Department of Civil Engineering, University of Birmingham Slide No: 15
Systems Thinking in Simulator-Based Driver Training
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Nature and complexity of train driving
• The history and context of train driver training
• Task complexity
• Heterogeneity (H), interdependence (H), variability (H)
and unanalysability (L)
• Is it possible to re-design the job of the train driver?
• rotation, engineering, enlargement, enrichment and sociotechnical
systems
• MMIs (out)
• Information processing model (out to SA)
Department of Civil Engineering, University of Birmingham Slide No: 16
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• Visual
• Aural
• Tactile
• Aromatic
• Combination
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Systems Thinking in Simulator-Based Driver Training
Drivers’ cues
Department of Civil Engineering, University of Birmingham Slide No: 17
Systems Thinking in Simulator-Based Driver Training
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Review of literature - cognitive
psychology
• Cognitive psychology and training – theories
• Kolb, Rogers, Gutherie, Carroll, Gagne, Leave, Bandura,
mental models, psychomotor tasks
• Risk taking, homeostasis and calibration
• Train Driver Training Needs Analysis (HTA and
CTA)
• Deliver the correct content and amount of training
using the correct media
• Ironies of Automation
Department of Civil Engineering, University of Birmingham Slide No: 18
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Systems Thinking in Simulator-Based Driver Training
Review of rail-specific literature
• Safety performance of rail Vs road (John Lauber,
Torahiko Terada )
• Information paucity
• Individual safety performance - experience Vs age
• SPADs – shunting – LE movements
• SPAD responsibility – attitudes (Pocock & Boath)
• Risk levels (Fletcher)
• Multitasking (Waters)
• Train drivers’ eye movements (McKnight, Young)
Operations
Department of Civil Engineering, University of Birmingham Slide No: 19
Systems Thinking in Simulator-Based Driver Training
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Review of rail-specific literature contd.
• Abnormal and degraded working - issues
• Human factors study of railway worker information
requirements – Ireland Vs Australia
• Mixed passenger and freight operations (Astin)
• Impact of new systems on driver workload, e.g.,
European Rail Traffic Management System
(ERTMS)
• Competence assessment process – reliability
(Nichols & Cobb)
Department of Civil Engineering, University of Birmingham Slide No: 20
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Systems Thinking in Simulator-Based Driver Training
Review of road-specific literature
• Novice driver accidents and the driving test
• Crash rates and lack of experience
• Effects of driving experience on visual search for
hazardous driving situations
• Understanding driver performance, variability and
perception of risk
• Young novice drivers, driver education and training
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Department of Civil Engineering, University of Birmingham Slide No: 21
Systems Thinking in Simulator-Based Driver Training
Review of air-transport specific
literature
• Situational awareness
• Routine and non-routine situations
• Orientation on situation awareness and crisis
management and experience
Department of Civil Engineering, University of Birmingham Slide No: 22
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Systems Thinking in Simulator-Based Driver Training
Hypothesis
• Driver training and assessment process is
insufficient Improvement areas
• Why is this?
• Not opportune (e.g. fog);
• Not practical (e.g. 4 training trains on line);
• Not safe (e.g. purposely derail);
• Not cost effective (e.g. emergency brake application)
To use the real world training environment
• Complimentary not alternative
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Department of Civil Engineering, University of Birmingham Slide No: 23
Systems Thinking in Simulator-Based Driver Training
What is a simulator (1/2)?
• A simulator is a model of real activity created for
training purposes or to solve a problem RSSB
• A situation or environment which is reproduced but
not necessarily by a machine HSE
• Parameters are changeable
• It is not reality
• The development of simulators
Department of Civil Engineering, University of Birmingham Slide No: 24
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Systems Thinking in Simulator-Based Driver Training
What is a simulator (2/2)?
• Accident Inquiry Reports Cannon Street, Southall,
(Ladbroke Grove), Glenbroke, Waterfall
• Previous scientific studies by users
• Locomotive simulators – what US users have to
say!
• General commentary on safety by trade journalists
• The logic applied to simulator acquisition
• Value of simulation to ERTMS/GSMR project
• Computer based training (CBT)
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Department of Civil Engineering, University of Birmingham Slide No: 25
Systems Thinking in Simulator-Based Driver Training
Operations
Uses and shortcomings of simulation
• Uses and shortcomings of simulation and virtual
reality
• Model a system in advance of actualisation
• Train repeatedly for rare events
• Not dependent on context (day/night)
• Standardisation - controlled environment
• Sickness
• Capital costs (equipment, data, accommodation,)
• Maintenance and upgrades
• Danger - throwing technology at a problem (Pennant)
Department of Civil Engineering, University of Birmingham Slide No: 26
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Systems Thinking in Simulator-Based Driver Training
Types of simulators
Department of Civil Engineering, University of Birmingham Slide No: 27
Systems Thinking in Simulator-Based Driver Training
Department of Civil Engineering, University of Birmingham Slide No: 28
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Drivers’ environment
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Video camera
and microphone
Sound system
Simulator cab
Systems Thinking in Simulator-Based Driver Training
The 3 environments
Instructors’ environment
Instructor Station
Reports
Scenario builder
Electronic Interface
(Cab hardware to software)
“Signal man”
Post Run
Analyser
Monitors
Observer station
Monitors
Projection
Image
Databases
System
Generator
Infrastructure
Traction
Track images CBT Suite
Department of Civil Engineering, University of Birmingham Slide No: 29
Systems Thinking in Simulator-Based Driver Training
Fidelity and realism – how much?
• Fidelity
• realism of the simulation in the broadest sense
de Winter
• Physical fidelity: degree to which the simulated
environment looks like the real environment Eichinger
• Functional fidelity: degree to which the simulated
environment behaves like the real environment Eichinger
• Essential realism:
• what is essential to the particular training requirements
under consideration; rather than face validity Parkes
• Pardillo & Troglauer, Howells, RSSB, Dodshon,
Reed & Green Vs Tichon & Mildred, Groeger (cited
by SWOV), Boer, NTSB
Department of Civil Engineering, University of Birmingham Slide No: 30
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Effectiveness evaluation criteria
• (1) Reactions of trainees/trainers
• Happy sheets and their evaluation
• (2) Learning
• How much was learned – written/oral tests
• (3) Behaviour change
• Did the trainees perform differently back on the job -
feedback from the business unit manager
• (4) The overall objective
• Improvements in efficiency, service delivery, safety
• Cost benefit analysis at a business level (do the benefits
exceed costs?)
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Department of Civil Engineering, University of Birmingham Slide No: 31
Systems Thinking in Simulator-Based Driver Training
L1 – Users’ reactions
• Russell (n = 54 train drivers of varying ages)
• 47 agreed that they were easy to use
• 52 said that the experience was enjoyable
• 54 would be happy to use it again
• 52 thought that it supported their learning
• Mildred – virtual reality rail training centre
• N = 6000 interventions
• 43% greatly assisted; 57% assisted; 0% unsure; 0%
assisted little
• Medley– networked train simulator & CBT
• (L1) 85% user satisfaction rating
Department of Civil Engineering, University of Birmingham Slide No: 32
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L2 – Learning outcomes
• Vlakveld (car-driving in Netherlands)
• 6 subjects – never driven before
• 13 modules – 9 hours on simulator plus small amount of
real driving
• Undertook driving test (2 succeeded, 2 almost passed
and 1 failed to meet standard clearly)
• University of Southampton hp of novices (car)
• 2 year, n = 144, 17 - 25 y/o;
• Baseline measure;
• Behaviour and attitude questionnaire;
• Control group , classroom, class + on-road, on-road only;
• Performance of all groups better and Group 3 fared best
• Crundall, Underwood argue findings
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Department of Civil Engineering, University of Birmingham Slide No: 33
Systems Thinking in Simulator-Based Driver Training
L2 – Learning outcomes contd.
• Allen et al. – effect of repetition on performance
(car)
• “Incident” rate declined as the number of simulated runs
increased (33% to 50% over 6 simulated runs)
• novice driver (car) performance approached the level of
experienced drivers after six trials (2 blocks of 3 trials)
• Brock in TRB, eds (CBT 1) – end-of-course test
• Students, tutored individually, outperformed grouptrained
students by as much as two standard deviations,
raising the performance of students from the 50th
percentile to the 95th percentile.
Department of Civil Engineering, University of Birmingham Slide No: 34
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Systems Thinking in Simulator-Based Driver Training
L2 – Learning outcomes contd.
• Institute of Defense Analyses (IDA)
• N - 233 studies
• Effects of multimedia
• Intelligent tutoring system
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Department of Civil Engineering, University of Birmingham Slide No: 35
Systems Thinking in Simulator-Based Driver Training
L3 – Behavioural change
• Medley– networked simulator & CBT (1)
• 35% improvement in safety critical
communications
• Jensen (1) – phone interview
• Unquantified improvement in train crew interaction
(CRM training)
Department of Civil Engineering, University of Birmingham Slide No: 36
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Systems Thinking in Simulator-Based Driver Training
L4 – Output objectives
• de Winter et al. – training truck drivers
• a simulator can be used for about two thirds of the
training programme and that it is twice as time-efficient
as a real vehicle
• Welles and Holdsworth in Parkes – HP - police
drivers
• 74% reduction in accidents at intersections
• 24% reduction overall (in 6 month post-training period)
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Department of Civil Engineering, University of Birmingham Slide No: 37
Systems Thinking in Simulator-Based Driver Training
L4 - Output objectives – energy ŋ
• Reed and Parkes – longitudinal study commercial
drivers
• (n = 56), reduction of 7·1% in fuel consumption could be
achieved (cost saving of over £2,000 p.a. for a single
vehicle at 2005 prices)
• UK Government - “Safe and Fuel Efficient Driving
(SAFED) programme”
• improvement of 6% could be achieved.
Department of Civil Engineering, University of Birmingham Slide No: 38
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Systems Thinking in Simulator-Based Driver Training
L4 - Output objectives - energy ŋ
• Dolan et al. – truck drivers
• two-hour programme, (n = 40), fuel efficiency increased
by an average of 2·8% over the 6 month post training
period
• those with the poorest pre-training fuel efficient driving
characteristics benefited most (7%).
• UIC – “Energie Sparen” project – train drivers
• four-hour theoretical lesson, one hour of simulator
training and one-hour of practical on-train
training/coaching 14,000 drivers
• Estimated cost of training (14,000 X 6 X €25 = €2.1m)
resulted in saving of over €8m (€5.9 m net).
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Department of Civil Engineering, University of Birmingham Slide No: 39
Systems Thinking in Simulator-Based Driver Training
L4 – Output objectives
• Eichenger (Mackay sugar)
• Reduced fuel consumption and better management of intrain
forces
• Major train dynamic-induced derailments fell from 10 to
1 per year. (A train derailment can cost up to Aus.
$100,000.)
• Mackay purchased Aus $ 70,000 worth of time on
Queensland Rail’s simulator.
• Meta messages from management
• Jensen (2)
• Unintentional divides -16% YTD 2007 versus 2005
Department of Civil Engineering, University of Birmingham Slide No: 40
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Systems Thinking in Simulator-Based Driver Training
L4 – Output objectives
• McInerney (Glenbrook Inquiry) – CBA conjecture
• Cost about $2.5 million each
• Derailment at Cronulla cost about $1 million
• Derailment of a freight train in Queensland could cost
between $5 million and $10 million
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Department of Civil Engineering, University of Birmingham Slide No: 41
Systems Thinking in Simulator-Based Driver Training
L 4 – CBT
• Brock in TRB, eds (2)
• Reduce instructional costs by about 1/3rd and reduce the
time of instruction by 1/3rd or increase the effectiveness
by 1/3rd.
Department of Civil Engineering, University of Birmingham Slide No: 42
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Systems Thinking in Simulator-Based Driver Training
L 4 – noticeable but unquantified
• Mildred – virtual reality centre
• N = 1,000 guards
• Incidents of passenger entrapment in doors decreased
• Medley– networked simulator & CBT (2)
• Contributed to decrease in SPAD occurrences
• de Winter et al. and Pardillo and Troglauer
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Department of Civil Engineering, University of Birmingham Slide No: 43
Systems Thinking in Simulator-Based Driver Training
Longevity of training benefits
• Dolan et al. – energy efficiency
• the effects of training diminish over the 6-month posttraining
period.
• McKnight - safety of novice drivers
• reduction in motor accidents, attributable to training,
lasted only 6 months (critical time)
Department of Civil Engineering, University of Birmingham Slide No: 44
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Systems Thinking in Simulator-Based Driver Training
Thank you for your attention!
Questions and clarification?
Department of Civil Engineering, University of Birmingham Slide No: 45
Systems Thinking in Simulator-Based Driver Training
MMI of car
Department of Civil Engineering, University of Birmingham Slide No: 46
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Systems Thinking in Simulator-Based Driver Training
MMI of DMU
Department of Civil Engineering, University of Birmingham Slide No: 47
Systems Thinking in Simulator-Based Driver Training
Department of Civil Engineering, University of Birmingham Slide No: 48
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Importance of SA
• Endsley’s (2000) situation awareness (SA) focuses on the
information and cues (internal and external) environments that
underlie the decision-making process.
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External factors
Perception Comprehension
Projection
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Perception
Internal factors
Decision
making
� Feedback
Actions
� Feedback
Department of Civil Engineering, University of Birmingham Slide No: 49
Haptic cue – engine and
ground speeds not
synchronised;
Visual cue - ammeter
fluctuating;
Aural cue – grinding noise
from wheelsets;
Errors include:
External factors:
Driver never experienced this
before;
Driver does not look at the
instruments;
Driver does not hear the noise
because he is wearing hearing
defenders.
External cues – Did the driver receive advance warning of prevailing low adhesion
conditions and were the tell-tale signs evident at the time of occurrrence?
Internal cab environment – Is the driver’s MMI easy to interpret?
Were there distractors - other people, mobile phone etc.
Comprehension
“This is the area where wheelslip is
encountered regularly”;
“It mentions these symptoms in the
locomotive drivers’ manual”as being
related to wheelslip;
“It is autumn and there are a lot of
leaves on the line”;
Errors include:
Internal factors (driver):
Not being able to apply the written
material practically;
Not establishing the connectedness
between the seasons, seasonal effects
and the location;
Poor mental model of the mechanical
subsystem.
Systems Thinking in Simulator-Based Driver Training
Projection
“If I persist with this course of action, i.e.,
demanding tractive effort from the traction
unit, I will damage the wheelsets and burn
the railhead. This could lead to a rail
fracture with potentially dangerous
consequences. I will note the exact
location and inform the regulator to ensure
that the line is examined in case I have
already damaged the rail!
Errors include:
Not associating wheel slip with the
potential for a rail fracture under a
following train;
Not associating wheelslip with accelerated
wheel wear.
Decision making:
“I will close off the
power controller”;
“I will have the rail
head examined for
burns and damage”;
“I will obtain
assistance from
another train”
Errors include:
Limited search for
options;
Dissonance.
“I’m frustrated. This is the second time this month that my train has failed. If I wait for assistance, I will be late for my domestic
commitments”.
“The passengers are irate and are knocking on the cab door looking for information updates. I cannot divide my attention between
the operational issue at hand and my customer care duties”.
“Whenever possible, I should not stop the train at the foot of an incline – particularly during periods of low adhesion”.
It’s not my problem that the traction unit does not have effective WSP equipment. I cannot be held responsible for wheel damage”
� Feedback
Department of Civil Engineering, University of Birmingham Slide No: 50
Actions:
Apply brakes;
Lessen demand for
power;
Declare train a
failure;
Communicate.
Failure to act will
result in:
Lost time,
Wheel and rail
damage;
Possible accident;
Driver frustration.
� Feedback
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Systems Thinking in Simulator-Based Driver Training
Some visual cues
Department of Civil Engineering, University of Birmingham Slide No: 51
Systems Thinking in Simulator-Based Driver Training
Combination of cues
Up to 8 red warning lights
And an emergency brake application
Department of Civil Engineering, University of Birmingham Slide No: 52
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3) Planning
Choosing future action
2) Conceptualising
Operations
Making meaning
Systems Thinking in Simulator-Based Driver Training
Kolb
4) Doing
Action or experience
Observing,
thinking, feeling
1) Reflecting
Department of Civil Engineering, University of Birmingham Slide No: 53
Systems Thinking in Simulator-Based Driver Training
Learning by doing (Gutherie etc.)
Department of Civil Engineering, University of Birmingham Slide No: 54
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Systems Thinking in Simulator-Based Driver Training
Social learning w/o practice 1/2
Department of Civil Engineering, University of Birmingham Slide No: 55
Systems Thinking in Simulator-Based Driver Training
Social learning (Bandura) 2/2
Department of Civil Engineering, University of Birmingham Slide No: 56
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29
Department of Civil Engineering, University of Birmingham Slide No: 58
Motion platform
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Systems Thinking in Simulator-Based Driver Training
Train Control
Department of Civil Engineering, University of Birmingham Slide No: 57
Driving experience
Under 1 year
Between 1&2
Between 2&3
Between 3&4
Between 4&5
Between 5&6
Between 6&7
Between 7&8
Between 8&9
Between 9&10
Between 10&11
Between 11&12
Between 12&13
Between 13&14
Between 14&15
Between 15&16
Between 16&17
Between 17&18
Between 18&19
Between 19&20
Between 20&21
Between 21&22
Between 22&23
Between 23&24
Between 24&25
Between 25&26
Between 26&27
Between 27&28
Between 28&29
Between 29&30
Between 30&31
Between 31&32
Between 32&33
Between 33&34
Between 34&35
Between 35&36
Between 36&37
Between 37&38
0
0 0 0 0 0 0 0 0 0
0
0
0
1 0
1 1
1
1
1
2
2
2 2
3
3 3
4
4 4
4
5
6
7
8
10
10
Number of SPADs
13
14
15
18
20
25
27
30
SPAD propensity Vs experience
Infrastructure
Operations
Rolling Stock
Signalling
Systems Thinking in Simulator-Based Driver Training
Train Control

Train Control
Train Control
Signalling
Signalling
Rolling Stock
Rolling Stock
Infrastructure
Infrastructure
Operations
Operations
Systems Thinking in Simulator-Based Driver Training
Networked simulator driving desk
Department of Civil Engineering, University of Birmingham Slide No: 59
Systems Thinking in Simulator-Based Driver Training
OTW view
Department of Civil Engineering, University of Birmingham Slide No: 60
30

Train Control
Train Control
Signalling
Signalling
Rolling Stock
Rolling Stock
Infrastructure
Infrastructure
Operations
Operations
Systems Thinking in Simulator-Based Driver Training
Side view platform activity
View from
drivers’ cab
Platform CCTV
view
Department of Civil Engineering, University of Birmingham Slide No: 61
Systems Thinking in Simulator-Based Driver Training
2 instructor stations (6 simulators)
Department of Civil Engineering, University of Birmingham Slide No: 62
31

Train Control
Train Control
Signalling
Signalling
Rolling Stock
Rolling Stock
Infrastructure
Infrastructure
Operations
Operations
Systems Thinking in Simulator-Based Driver Training
Possible general layout
Department of Civil Engineering, University of Birmingham Slide No: 63
Systems Thinking in Simulator-Based Driver Training
Instructor and observer station
Department of Civil Engineering, University of Birmingham Slide No: 64
32

Train Control
Train Control
Signalling
Signalling
Rolling Stock
Rolling Stock
Infrastructure
Infrastructure
Operations
Operations
Systems Thinking in Simulator-Based Driver Training
Evaluation templates
Department of Civil Engineering, University of Birmingham Slide No: 65
Systems Thinking in Simulator-Based Driver Training
Some normal issues not adequately
trained/assessed (1/3)
• Application of emergency brake at speed (Mr.
Cooksey, HMRI)
• Application of the defensive driving policy
• Economical energy use - accelerating/decelerating
and coasting
• Correct braking technique
• Correct use of WSP (Contd.)
Department of Civil Engineering, University of Birmingham Slide No: 66
33

Train Control
Train Control
Signalling
Signalling
Rolling Stock
Rolling Stock
Infrastructure
Operations
Systems Thinking in Simulator-Based Driver Training
Normal driving conditions (2/3)
• Non-verbal messages (visual, aural, tactile,
aromatic)
• Hazard perception and situational awareness
• Road traffic lights – confusion with railroad signals
• Slack management (starting and stopping train)
• Range of equipment faults on traction unit
• Dealing with prolonged wheel slip/slide
illumination (Contd.)
Infrastructure
Operations
Department of Civil Engineering, University of Birmingham Slide No: 67
Systems Thinking in Simulator-Based Driver Training
Normal driving conditions (3/3)
• Emergency brake applied in running
• Ground relay lamp illuminated (correct response to
prevent the automatic lock out of the protection
system)
• Stopping distance - varying with the trailing
load/train length/degraded conditions
• Failure of speedometer – (provide drivers with an
intuitive sense of speed without reference to
instrumentation) (Degraded conditions)
Department of Civil Engineering, University of Birmingham Slide No: 68
34

Train Control
Train Control
Signalling
Signalling
Rolling Stock
Rolling Stock
Infrastructure
Operations
Systems Thinking in Simulator-Based Driver Training
Degraded driving conditions (1/2)
• Mock setting up of temporary block working
• Authorised passing of signals at danger
• Permanent, temporary and emergency speed
restrictions
• Lineside fires, fog/adverse weather/night driving
• Effect of unbraked vehicles in rear.
• Abnormal downgrade/upgrade on CAWS
• Runaway train/“Out-of-control” sensation (Contd.)
Infrastructure
Operations
Department of Civil Engineering, University of Birmingham Slide No: 69
Systems Thinking in Simulator-Based Driver Training
Degraded driving conditions (2/2)
• Explosion of detonators on line
• Brakes dragging on train (to gauge the tactile
response)
• Faulty CAWS, deadmans, vigilance (to test rule
compliance)
• Traction motor (TM) or truck cut out
• SCE/CAWS – audible inputs, failure – defects &
reasons for isolation
• Train failure/DMU door failure (Emergency
Conditions)
Department of Civil Engineering, University of Birmingham Slide No: 70
35

Train Control
Train Control
Signalling
Signalling
Rolling Stock
Rolling Stock
Infrastructure
Systems Thinking in Simulator-Based Driver Training
Operations
Emergency driving conditions (1/2)
• Train radio responses (Emergency)
• Obstruction on line, e.g., car on crossing
• Signal reversions (category B SPAD)
• No correspondence between lineside and CAWS
aspect displays
• Train on fire on opposite line
• Train divided (Contd.)
Infrastructure
Department of Civil Engineering, University of Birmingham Slide No: 71
Systems Thinking in Simulator-Based Driver Training
Operations
Emergency driving conditions (2/2)
• Collision
• Prolonged slide indication
• Emergency handsignal responses
• Trespassers on line
• Points incorrectly set - shunting operation
• Signals from signal box / per way (Back)
Department of Civil Engineering, University of Birmingham Slide No: 72
36