[Amended upload] Presented by PhD student Segun Aluko at UTSG2014. www.its.leeds.ac.uk/people/s.aluko www.utsg.net/web/uploads/UTSG%202014%20Newcastle%20Programme.pdf

1. Institute for Transport Studies
FACULTY OF ENVIRONMENT
Improving the Understanding about the
Safety Performance of Commercial
motorcycles in Nigeria: The Use of a
System Dynamics Model
O O Aluko
Astrid Guehnemann; Paul Timms
ITS, University of Leeds, Leeds

2. Presentation Outline
• Study background
• Methodology
• Simulation result
• Conclusions
• Questions

3. Background information
What
commercial
motorcycle
is
Commercial
motorcycle
problems
Research
concept

4. Background (contd)
Carries
passengers
for a fare
What
commercial
motorcycle
is:
A major
employer in
the informal
sector
Provides
basic mobility
in a low
motorisation
level state

5. Background (contd)
Serious safety
problem
Commercial
motorcycle
problem:
Policy
interventions
have not
been very
successful
No clear way
forward yet
about how
the mode
should
operate

6. Background (contd)
Previous studies
assess operating
characteristics
independently
Research
concept:
To consider
mode’s
operation as a
system of
interacting
components
Policy interventions is
responded to by the
entire system rather
than a sub-component;
thus unintended
consequences
Develop a tool
that
dynamically
evaluates
interactions; to
test proposed
interventions

7. Case-study peculiarities
Unavailability of data
Opposing views about the benefit
of the mode

8. Why System Dynamics Model (SDM)?
A comparison of SDM and statistical model
A)Statistical models
Substantial data requirement
Does not consider feedback effect
B) SDM
Less data demanding
Considers feedback effect

9. System Dynamics
• Principle: The structure of a system is responsible for its
behaviour

10. System Dynamics
• Principle: The structure of a system is responsible for its
behaviour.
• Adopts the following concepts in modelling:
– Stock and flow
– Time delay
– Feedback effect
Stock and flow illustration

11. Stock and Flow illustration
Savings
reinforcing
loop
interest
Savings
interest
interest
rate

12. Stock and flow model
• Requires both qualitative and quantitative data
– Quantitative are important for parameter specification and initial
conditions
– Qualitative data: required to determine the system’s structure
• stakeholders in the system have rich mental data about the
system structure
– Thus the need for qualitative data collection

13. Group list and data collection
method
Stakeholder Group Respondent
number
Stakeholder
classification
Data Collection
Method
1 Federal Road Safety Commission 4 enforcer Interview
2 Nigeria Police Force 1 enforcer Interview
3 Vehicle Inspection Officer
(Ministry of Works/ Transport)
1 enforcer Interview
4 Hospital staff 1 (A&E Unit Head) expert Interview
5 Academia 3 expert Interview
6 Transport Safety related
government agencies
3 enforcer Interview
7 Commercial motorcycle riders
and association
13 in two groups
of 6 and 7
rider Focus Discussion
Group
[1

14. Research design:
process towards model development
Stock
inflow outflow
Survey
• Field
• Desk
Data analysis
Model development

15. Fieldwork survey
Interviews
Group discussion
Quantitative data extraction
These two are required
to obtain the mental
picture of stakeholders
about how the system
is operating.
Lead Question: The
cause of… the cause of
accident is what?
This helps to provide
reference modes, initial
conditions, and
constants

16. Data analysis
Nvivo Data
analysis
Used to code themes and
linkages
Quantitatively assess the
strength of model
parameters from
stakeholders’ perspective
Nvivo: a tool that helps in
organising themes identified
in qualitative data

17. System CLD developed from
Nvivo analysis
no of riders
Accident
actual income
income
<violation>
shortfall/repayment
pressure
additional
work capacity
requirement
alcohol/
drug use
arrest prosecution
available
spare
time
willingness to give
time for training
contributory
savings
target
income
corruption
dodging
arrest
risky/dangerous experience
riders
violation
police road
block
probability of
detection
ignorance/
free/easy
entry
spending
aversion/cutting
corner
licensing and
particulars
(violation)
maintenance
(violation)
competition
between riders
cmcycle
on rent
work
capacity
participation
in training
losses from
accident
Other road
users
risky road
environment
inclement
weather
deterrence
peer
influence
cost of operation/huge
one-off cost
high job
returns
political
influence
speeding
overloading
A CLD is a map of
cause-and-effect
This map helps to show links
between related items and how
they relate, i.e., one increases or
decreases the other.

18. Causal loop diagram
probability of
detection
+
+ deterrence
detection
loop
prosecution
loop
losses from
accident
+
deterrence
loop
+
Accident
arrest
police road
block
+
corruption
+
+
-
prosecution
+
+
dodging arrest
-
violation
-
+
accident
losses loop
+
-
-
risky road
environment
+
Causal Loop diagram of enforcement sub-model

19. Stock and Flow model
full
deterrence
Effective
Deterrence
effect of
sanction
average
payment
by rider
-
fine
sanction
+
+
+
deterrence
effect of
violation
benefit
- risk taking
culture
enforcement
capacity
probability
+
loss
prosecution of detection
rate
+
cost from
bribery
violating
population
+
productivity
-
mcycle
focus
deterrence
gain
perception
about risk in
operation
time to form
perception
enforcement
coverage
trend of
coverage
benefit
from
violation
productivity
change
violation
utility
enforcement
size
violation
prevalence
total
violations
detectable
violatoions

20. Quantitative data and data
sources
Variable Data used Source Comment
1 Riders 100 - 5000 Survey Estimated number of riders at
the start and end of simulation
period was obtained during the
survey
2 Productivity 0.2 – 0.9 Survey Survey indicated that the police
now tend to concentrate more
on riders for infractions.
3 Prosecution
rate
Corruption index
(0.275)
Online Obtained from transparency
International’s index of
corruption
4 Enforcement
workforce
25 – 85 personnel
plus support from
regular police
Survey Information provided by the
head of traffic unit of the police
5 Fine NGN2000 (NGN is
Nigerian naira and
is about $12)
Literature
and
survey
Information from riders during
survey and from literature
(Arosanyin et al, 2012)

21. Use of data in model
1) Equation for “Effective Deterrence”:
Effective Deterrence= INTEG (deterrence gain-deterrence
loss, initial deterrence)
2) Equation for “effect of sanction”:
net effect of sanction=MAX(0, MIN(1, (ZIDZ(average
payment by rider, average riders' income))))
3) Equation for “average payment by rider” due to violation:
average payment by rider = (payment as bribe + sanction)

22. Results
Figure a: Effective deterrence in
full prosecution scenario (no
corruption)
Figure b: Effective deterrence when
violation is beneficial (corruption is
high; prosecution rate = 27%)
Effective Deterrence
1
0.85
0.7
0.55
0.4
0 6 12 18 24 30 36 42 48 54 60
Time (Quarter)
deterred/rider
Effective Deterrence : Current
Effective Deterrence
0.8
0.6
0.4
0.2
0
0 6 12 18 24 30 36 42 48 54 60
Time (Quarter)
deterred/rider
Effective Deterrence : Current
Effective Deterrence
0.8
0.7
0.6
0.5
0.4
0 6 12 18 24 30 36 42 48 54 60
Time (Quarter)
deterred/rider
Effective Deterrence : Current
Figure c: Effective deterrence when violation is of little
benefit (prosecution rate = 27%; but benefit from violation is a
third of figure 4b case

23. Figure e: Violating Population
Results (contd)
risk taking culture
1
0.75
0.5
0.25
0
0 6 12 18 24 30 36 42 48 54 60
Time (Quarter)
undeterred/rider
risk taking culture : Current
violating population
4,000
3,000
2,000
1,000
0
0 6 12 18 24 30 36 42 48 54 60
Time (Quarter)
undeterred
violating population : Current
Figure d: Risk taking Culture

24. Summary of preliminary
findings
SDM can be used in modelling the
system, data limitation not
withstanding
It is shown that Deterrence level has
never been low even when the mode
was not known to be very risky
Increasing enforcement capacity does
not necessarily achieve target
deterrence level
This preliminary result is not validated

25. Future works
• Expanding model to reflect different types of violations
• Changing some constants into stocks and studying their
changing pattern
• Reviewing model with some of the stakeholders

26. Questions