Safety Policies in Commercial Motorcycle Operation in Nigeria A PhD - - PowerPoint PPT Presentation

Institute for Transport Studies

FACULTY 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

• f 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

• btain

mental pictures

• f

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

• bviously 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 of drivers awareness

• f high job

returns ease of joining trade competition for passengers target income working period hire purchse and rent drivers' income fatigue high job returns violations alcohol and drug use available free time level of training risky and dangerous drivers + + + + + +

• Drivers'

populatio n loop Drivers' population loop

+

• +

+ + + + +

competition reduce drivers' population trade is strenuous loop expensive

• wnership
• ptions loop

+ + + + + + deterrence effect of sanction

• work

capacity + +

• +

willingness to give time for training + enforcement coverage corrupt practices in regulation and enforcement political influence dodging arrest prosecution rate probability of detection arrests leading to prosecution + + +

• +

+ + + national corruption index +

deterrence loop detection loop

+

• fficers'

benefit from rent paid

• +

rent to

• fficers'

loop time for training loop

+ earning pressure + +

...not a lifetime trade

thrift saving + + <enforcement coverage> fine and bribe paid + + <fine and bribe paid>

• experience

SD Submodels (Modules)

Stock and Flow model

Additional Officers' Support drafting

• fficers

initial workforce excess support removal Target Perception time for removal change in target time to reduce target Enforcement Workforce net hire total legal enforcement time to raise target time to draft more hands initial target <public perception about risk in operation> <public perception about risk in

• peration>

<public perception about risk in operation> <initial attention> rest <equivalence of number of

• fficers motivated for
• vertime service>

enforcement size. 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 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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 1 1 1 1 1 1 1 1 1 1

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 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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

• wnership 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 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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

• ptions 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 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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

• f expensive ownership
• ptions:
• 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 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

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

• wnership 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