Institute for Transport StudiesFACULTY 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 TimmsITS, University of Leeds, Leeds

Presentation Outline

• Study background

• Methodology

• Simulation result

• Conclusions

• Questions

Background information

What commercial motorcycle

is

Commercial motorcycle problems

Research concept

Background (contd)

What commercial motorcycle

is:

Carries passengers

for a fare

A major employer in the informal

sector

Provides basic mobility

in a low motorisation level state

Background (contd)

Commercial motorcycle problem:

Serious safety problem

Policy interventions

have not been very successful

No clear way forward yet

about how the mode should

operate

Background (contd)

Research concept:

Previous studies assess operating

characteristics independently

To consider mode’s

operation as a system of interacting

componentsPolicy 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

Case-study peculiarities

Unavailability of data

Opposing views about the benefit of the mode

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

System Dynamics

• Principle: The structure of a system is responsible for its behaviour

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

Stock and Flow illustration

Savings

interest

reinforcingloop

Savingsinterest

interestrate

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

  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

Group list and data collection method

Research design: process towards model development

Stockoutflowinflow

Survey• Field• Desk

Data analysis

Model development

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

Data analysis

Nvivo Data analysisUsed 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

System CLD developed from Nvivo analysis

Accident

actual income

incomeshortfall/repayment

pressure

additionalwork capacityrequirement

alcohol/drug use

arrest prosecution

availablesparetime

willingness to givetime for training

contributorysavings

targetincome

corruption

dodgingarrest

experiencerisky/dangerousriders

violation

police roadblock

probability ofdetection

no of riders

ignorance/free/easy

entry

spendingaversion/cutting

corner

licensing andparticulars(violation)

maintenance(violation)

competitionbetween riders

cmcycleon rent

workcapacity

participationin training

losses fromaccident

Other roadusers

risky roadenvironment

inclementweather

deterrencepeer

influence

<violation>

cost of operation/hugeone-off cost

high jobreturns

politicalinfluence

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.

Causal Loop diagram of enforcement sub-model

Causal loop diagram

Accident

arrest

prosecution

corruption

dodging arrest

violation

police roadblock

probability ofdetection

++

+

-

+

-

+

-

+

+losses from

accident

+

-

risky roadenvironment

+

+

deterrence+

-

+

deterrenceloop

detectionloop

prosecutionloop

accidentlosses loop

Stock and Flow model

EffectiveDeterrence

effect ofsanction

averagepaymentby rider

fine

sanction+

+

+

- risk takingculture

probabilityof detectionprosecution

rate

+

cost frombribery

-

violatingpopulation

+

productivity

fulldeterrence

-+

mcyclefocus

deterrenceloss

deterrencegain

perceptionabout risk in

operation

time to formperception

enforcementcoverage

trend ofcoverage

benefitfrom

violation

productivitychange

violationutility

enforcementsize

enforcementcapacity

totalviolations

violationprevalence

effect ofviolationbenefit

detectableviolatoions

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)

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)

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 60Time (Quarter)

dete

rred

/rid

er

Effective Deterrence : Current

Effective Deterrence

0.8

0.6

0.4

0.2

0

0 6 12 18 24 30 36 42 48 54 60Time (Quarter)

dete

rred

/rid

er

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 60Time (Quarter)

dete

rred

/rid

er

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

Figure e: Violating Population

risk taking culture

1

0.75

0.5

0.25

0

0 6 12 18 24 30 36 42 48 54 60Time (Quarter)

unde

terr

ed/r

ider

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 60Time (Quarter)

unde

terr

edviolating population : Current

Figure d: Risk taking Culture

Results (contd)

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

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

Questions