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Institute for Transport StudiesFACULTY OF ENVIRONMENT
A Model for the Evaluation of Transport
Safety Policies in Commercial Motorcycle
Operation in Nigeria
A PhD research work undertaken by
Aluko O.O
Under the supervision of
Astrid Gühnemann; Paul Timms
ITS, University of Leeds
Research Background and
Objectives
• Commercial motorcycles play important role
in developing countries’ transport
– Accessibility
– Employment
• Lack of regulation and enforcement lead to
significant safety problems
Objectives:
A. Identify and understand factors
contributing to the safety problem and their
relationships (focus on violations)
B. Develop a dynamic model to understand
how driver behaviour develops and is
influenced by external conditions
Case-study peculiarities
Unavailability of data
Opposing views about the benefit of the mode
• Here: Nigeria
(Ado Ekiti)
• Similar issues
encountered
in most
developing
countries
• Safety often
analysed without
considering
possible
feedbacks
Fieldwork survey
Interviews
Quantitative data extraction
Interviews were
conducted to obtain
mental pictures of
stakeholders about how
the system is operating.
This helps to provide
reference modes, initial
conditions, and
constants
Data analysis
Nvivo Data analysis
Helped to identify themes and linkages
Helped to provide an audit trail of the analysis process
Data analysis (cont’d)
Development of causal
network Generation of a narrative
• … more violations (10) led
to more enforcement
capacity (1) which led to
reduced drivers’ income
(7)… This was because
violations (10) offered some
financial benefits too
(increased drivers’ income
(7)).
Generation of dynamic hypothesis
Excerpt from the narrative
“Whenever violations increased,
more officers were drafted to
increase enforcement capacity
(1) and match the problem. This
obviously would result in
increase in the probability of
detection (4) and violation would
go down…In this way,
increasing enforcement capacity
(1) could reduce the total
number of violation (10)”
Corresponding hypothesis
Officers enforce law by
detecting and arresting
violators.
Causal loop diagram
number ofdrivers
awarenessof high job
returns
ease ofjoiningtrade
competitionfor
passengers
targetincome working
period
hirepurchseand rent
drivers'income
fatigue
high jobreturns
violations
alcoholand drug
use
availablefree time
level oftraining
risky anddangerous
drivers
+
++ +
+
+
-
-
Drivers'
populatio
n loop
Drivers'population
loop
+
-
+
+
+
+
+
+
competitionreducedrivers'
population
trade is
strenuous
loop
expensive
ownership
options loop
+
+
+
+
+
+
deterrenceeffect ofsanction
-
workcapacity
+
+
--
+
willingness
to give time
for training
+enforcement
coverage
corrupt practicesin regulation and
enforcement
politicalinfluence
dodgingarrest prosecution
rate
probability ofdetection
arrests leadingto prosecution
+
+
+
-
-
-
+
+
+
+
nationalcorruption
index
+
deterrenceloop
detection
loop
+officers'benefitfrom
rent paid
-
+ rent toofficers'
loop
time fortraining
loop
+
earningpressure
+
+ ...not a
lifetime
trade
thriftsaving
+
+
<enforcement
coverage>
fine andbribe paid
++
<fine and bribe
paid>
-
experience-
SD Submodels (Modules)
Stock and Flow model
AdditionalOfficers'Support
draftingofficers
initialworkforce excess
supportremoval
TargetPerception
time forremoval
change intarget
time toreducetarget
EnforcementWorkforce
net hiretotal legal
enforcement
time toraisetarget
time to draftmore hands
initial target<public perception
about risk in operation>
<publicperception
about risk inoperation>
<public perception
about risk in operation>
<initialattention>
rest
<equivalence of number ofofficers motivated for
overtime service>
enforcementsize.
enforcement
coverage.
<Attention To
Mode>
Enforcement sub-model
Baseline Results Example
Result
0.6 .
2,000 .
40 .
0.3 .
1,000 .
20 .
0 .
0 .
0 .3
3
3
3
3 3 33
33
33
33
2
2
2
2
22
22 2 2
22
2 2
1
1
1
1 1 1 1 1 1 1 1 1 1 1 1
0 130 260 390 520 650 780 910 1040 1170 1300
Time (Week)
Tendency to Violate : .test_baseline .1 1 1 1 1 1 1 1 1
total violations : .test_baseline .2 2 2 2 2 2 2 2 2
enforcement coverage : .test_baseline .3 3 3 3 3 3 3 3
Baseline result
interpretation:
• Tendency to violate
• Total violations
• Enforcement coverage
• Total drivers
• Driver income
6,000 .
2,000 .
3,000 .
1,000 .
0 .
0 .
2
2 2 2 2 2 2 2 2 2 2 2 2 2
1
1
1
1
1
11
11
11
11
11
0 130 260 390 520 650 780 910 1040 1170 1300
Time (Week)
total drivers : .test_baseline .1 1 1 1 1 1 1 1 1 1
drivers' income : .test_baseline .2 2 2 2 2 2 2 2 2
Result
0.6 .
2,000 .
40 .
0.3 .
1,000 .
20 .
0 .
0 .
0 .3
3
3
3
3 3 33
3
3
3
3
3
3
2
2
2
2
22
22 2 2 2
22
2
1
1
1
1 1 1 1 1 1 1 1 1 1 1 1
0 130 260 390 520 650 780 910 1040 1170 1300
Time (Week)
Tendency to Violate : .test_double_recruitment .1 1 1 1 1 1 1 1
total violations : .test_double_recruitment .2 2 2 2 2 2 2 2 2
enforcement coverage : .test_double_recruitment .3 3 3 3 3 3 3
Double recruitment
rate:
• Insignificant changes to
tendency to violate
• Insignificant changes to
total violations
• Significant additional
enforcement coverage
Graph of doubled recruitment rate scenario
Responsiveness Testing
Responsiveness Testing
Remove expensive
ownership options:
• Minor changes to tendency to
violate
• Substantial reduction in total
violations
• Significant reduction in
enforcement coverage
Result
0.6 .
2,000 .
40 .
0.3 .
1,000 .
20 .
0 .
0 .
0 .3
3
3
3
3 3 33 3
3 33
33
2
2
2
2
22
22 2
2
22 2 2
1
1
1
1 1 1 1 1 1 1 1 1 1 1 1
0 130 260 390 520 650 780 910 1040 1170 1300
Time (Week)
Tendency to Violate : .test_ownership .1 1 1 1 1 1 1 1 1
total violations : .test_ownership .2 2 2 2 2 2 2 2 2
enforcement coverage : .test_ownership .3 3 3 3 3 3 3 3
Graph of removal of expensive ownership
options scenario
Scenario 3
Raise prosecution rate:
• Substantial changes to
tendency to violate
• Less than expected reduction
in total violations
• Significant reduction in
enforcement coverage
Result
0.6 .
2,000 .
40 .
0.3 .
1,000 .
20 .
0 .
0 .
0 .3
3
3
3
3 3 33 3
33
33
3
2
2
2
2
22
22 2
2 2 2 2 2
1
1
1
1 1 1 1 1 1
1
1 1 1 1 1
0 130 260 390 520 650 780 910 1040 1170 1300
Time (Week)
Tendency to Violate : .test_raise_prosecution .1 1 1 1 1 1 1
total violations : .test_raise_prosecution .2 2 2 2 2 2 2 2
enforcement coverage : .test_raise_prosecution .3 3 3 3 3 3 3
Graph of increase in prosecution rate
scenario
Responsiveness Testing
Combination of increased
prosecution and removal
of expensive ownership
options:
• Substantial reduction in
tendency to violate
• Substantial reduction in total
violations
• Significant reduction in
enforcement coverage
Result
0.6 .
2,000 .
40 .
0.3 .
1,000 .
20 .
0 .
0 .
0 .3
3
3
3
3 3 33 3
3 3 33
3
2
2
2
2
22
22 2
2
22 2 2
1
1
1
1 1 1 1 1 1
1
11 1 1 1
0 130 260 390 520 650 780 910 1040 1170 1300
Time (Week)
Tendency to Violate : ..test_ab .1 1 1 1 1 1 1 1 1 1
total violations : ..test_ab .2 2 2 2 2 2 2 2 2 2
enforcement coverage : ..test_ab .3 3 3 3 3 3 3 3 3
Graph of combination of increased
prosecution and removal of expensive
ownership options
Responsiveness Testing
Extracts from findings
SDM can be used in modelling the system
The entry method into the trade contributes to the system problem substantially
Improving sanction is not the same thing as increasing enforcement capacity
A leverage is achieved by a combination of measures