MAIN PROJECT2

ii

CERTIFICATION

This is to certify that this research work is an original work undertaken by IGE, AYODELE

DAMILOLA (CVE/08/3305) under the supervision of Engr. O.J Oyedepo and has been

prepared in accordance with the regulations governing the research work in the department of

Civil Engineering, Federal University of Technology, Akure. This project has been read and

approved by:

……………………………….. …………………………..

Engr. Oyedepo O.J Date

(Project Supervisor)

…………………………….. ……………………………

Prof. Babatola J.O Date

(Head of Department)

iii

DEDICATION

This project is gratefully dedicated to God, the fear of Israel, for his steadfast love and

faithfulness. Endless is the list of his blessings over my life and family, Him alone deserves

the glory.

Finally, I dedicate this project to my late uncle, Mr. Joel Ige.

iv

ACKNOWLEDGEMENT

Being an ingrate would have branded me a fool before my God. All the way through this

journey, you never deserted me. Your mighty hands lead me through every step I take and

every stone I turn. How great thou art!

I am grateful to my supervisor, Engr. O.J Oyedepo, for the knowledge imparted into me and

his time that he expended in guiding my green knowledge in transportation engineering to

carry out this project and also, vetting this report. To the entire staff of the department of

Civil Engineering I say thank you.

I would like to acknowledge the guiding hand, constructive recommendations and support of

Mr. Seun Oluwajana.

I acknowledge the staff of Federal Road Safety Commission (FRSC) Akure Command for

granting the request made to access their database for vehicle registration data to expand the

precision of this project and every one that participated in the survey, without you this project

would be impossible

I acknowledge the tremendous sacrifices that my parents, Elder and Mrs. R. Ige, make to

ensure that I had an excellent education. For this and much more, I am forever in their debt.

To my siblings, Toluwalope, Oluwaseyifunmi, Oluwafunmike, and Olamide, for love, care,

advice and support before, during and after this project. I could not have asked God for better

siblings. To Oluwadamilola for been there.

I extend my acknowledgement to my great Uncles and lovely Aunts for their prayers, advice

and support financially. To my lovely nephew, Oluwasemilore and my frabjous cousins,

Ayodele, Joshua, Emmanuel, Peace, Philip, Mercy, IBK, Samuel, Moyinoluwa, Oluwabunmi,

Oluwatobilola, Kehinde, Temitope e.t.c for being a ‘wunnerful’ part of my life. May the good

Lord grant you the grace to keep the light shining brighter. I believe in your future.

To my project mate, Emmanuel Ogunmoriti, for his resilient efforts throughout this project. I

gratefully acknowledge the friendship of Eniolufe, Oladotun, Stanley, Charles, Damilola,

Nelson, Sehinde, Ephraim, Michael, Joseph, Diekololami, Fisayo, Frederick and other

beautiful friends I could not mention here, you all hold a special place in my heart.

v

TABLE OF CONTENTS

CERTIFICATION ..................................................................................................................... ii

DEDICATION ...........................................................................................................................iii

ACKNOWLEDGEMENT ........................................................................................................ iv

TABLE OF CONTENTS........................................................................................................... v

LIST OF FIGURES ..................................................................................................................vii

LIST OF TABLES ................................................................................................................... viii

ABSTRACT.............................................................................................................................. ix

CHAPTER ONE ........................................................................................................................ 1

INTRODUCTION ...................................................................................................................... 1

1.1 GENERAL............................................................................................................... 1

1.2 STATEMENT OF THE PROBLEM ....................................................................... 2

1.3 AIM AND OBJECTIVES OF THE STUDY .............................................................. 2

1.4 SIGNIFICANCE OF THE STUDY ........................................................................ 3

1.5 DESCRIPTION OF THE STUDY AREA .............................................................. 3

1.6 SCOPE AND LIMITATION OF STUDY. ............................................................. 5

CHAPTER TWO ....................................................................................................................... 6

LITERATURE REVIEW ........................................................................................................... 6

2.1 REVIEW OF PREVIOUS STUDIES .......................................................................... 6

2.2 VEHICLE MODEL TYPES ........................................................................................ 8

2.3 DISCRETE CHOICE MODEL ............................................................................... 9

2.3.1 DISAGGREGATE APPROACH TO CAR OWNERSHIP LOGIT MODEL 9

2.3.2 AGGREGATE TREND EXTRAPOLATION MODEL .................................... 10

2.3.3 PARTIALLY DISAGGREGATE MODEL ....................................................... 10

2.4 MULTIPLE LINEAR REGRESSION VEHICLE OWNERSHIP MODEL......... 11

2.5 S-CURVE MODELS ............................................................................................. 13

2.6 FACTORS AFFECTING VEHICLE OWNERSHIP MODEL............................. 14

CHAPTER THREE ................................................................................................................. 16

RESEARCH METHODOLOGY.............................................................................................. 16

3.1 RESEARCH APPROACH .................................................................................... 16

3.2 RESEARCH POPULATION ................................................................................ 16

3.2.1 LOW DENSITY AREAS............................................................................... 17

3.2.2 MEDIUM DENSITY AREAS ....................................................................... 17

vi

3.2.3 HIGH DENSITY AREAS.............................................................................. 17

3.3 METHOD OF DATA COLLECTION .................................................................. 17

3.3.1 SAMPLE SIZE ................................................................................................... 18

3.3.2 SAMPLING TECHNIQUE............................................................................ 18

3.4 METHOD OF DATA ANALYSIS ....................................................................... 19

CHAPTER FOUR.................................................................................................................... 21

RESULTS AND DISCUSSION ............................................................................................... 21

4.1 DATA COLLATION................................................................................................ 21

4.2COMPARISON OF ZONES IN THE STUDY AREA .............................................. 21

4.3 ANALYSIS OF VEHICLE REGISTRATION IN AKURE ..................................... 23

4.4 DEVELOPMENT OF VEHICLE OWNERSHIP MODEL FOR AKURE .......... 24

4.4.1 LOW DENSITY ZONE VEHICLE OWNERSHIP MODEL ....................... 25

4.4.2 MEDIUM DENSITY ZONE VEHICLE OWNERSHIP MODEL................ 26

4.4.3 HIGH DENSITY ZONE VEHICLE OWNERSHIP MODEL ...................... 27

4.4.4 MODEL FOR AKURE METROPOLIS ........................................................ 28

4. 5 ANALYSIS OF VEHICLE OWNERSHIP PER ZONE ......................................... 30

4.5.1 LOW DENSITY POPULATION ZONE............................................................ 30

4.5.2 MEDIUM DENSITY POPULATION ZONE ............................................... 34

4.5.3 HIGH DENSITY POPULATION ZONE ...................................................... 38

CHAPTER FIVE ................................................................................................................... 44

CONCLUSIONS AND RECOMMENDATIONS ................................................................... 44

5.1 CONCLUSIONS ................................................................................................... 44

5.2 RECOMMENDATIONS....................................................................................... 45

REFERENCES ......................................................................................................................... 47

APPENDIX A .......................................................................................................................... 51

APPENDIX B .......................................................................................................................... 52

APPENDIX C .......................................................................................................................... 53

vii

LIST OF FIGURES

Figure 1.0: The Map of Akure Metropolis …...………….........................................................4

Figure 1.1: Major road Networks in Akure……………………...……………………….........4

Figure 4.1: Vehicle-Type Distributions………………………………………………………22

Figure 4.2: Vehicle Registration Trend between Years 2009 – April, 2013…………………24

Figure 4.3: Educational Status of Household Heads for Low Density Area…………………30

Figure 4.4: Occupational Status of Household Heads for Low Density Area…………….....31

Figure 4.5: Numbers of Student per Household for Low Density Area……………………..32

Figure 4.6: Income Earning Range of Household Heads for Low Density Area……………33

Figure 4.7: Income of Household Heads by the Occupation for Low Density Area………...33

Figure 4.8: Income of Household Heads by the Educational Status for Low Density Area…34

Figure 4.9: Educational Status of Household Heads for Medium Density Area..…………...35

Figure 4.10: Occupational Status of Household Heads for Medium Density……………......36

Figure 4.11: Numbers of Student per Household for Medium Density Area………………..37

Figure 4.12: Income Earning Range of Household Heads for Medium Density Area………37

Figure 4.13: Income of Household Heads by the Occupation of Household Heads for Medium

Density Area…………………………………………………………………………………38

Figure 4.14: Income of Household Heads by the Educational Status of Household Heads for

Medium Density Area………………………………………………………………………..39

Figure 4.15: Educational Status of Household Heads for High Density

Area…………………………………………………………………………………………..40

Figure 4.16: Occupational Status of Household Heads for High Density

Area………………………………………………………………………………..…………41

Figure 4.17: Numbers of Student per Household for High Density

Area………………………………………………………………………………………….42

Figure 4.18: Income Earning Range of Household Heads for High Density

Area………………………………………………………………………………………….43

Figure 4.19: Income of Household Heads by the Occupation for High Density

Area………………………………………………………………………………………….43

Figure 4.20: Income of Household Heads by the Educational Status for High Density

Area….....................................................................................................................................44

viii

LIST OF TABLES

Table 2.1: Multivariate Regression Results ………………………………………………….13

Table 2.1: Attributes of Explanatory Variables …………………….……………………......15

Table 3.1: Area Selected for Study with their Population Densities ……………………..… 16

Table 4.1: Numbers of Administered Questionnaire for the Locations …………………..…21

Table 4.2: Vehicles Registered in Akure between January, 2009 and April, 2013………..…23

Table 4.3: Model Summary for Low Density Zone ……………………………………...….26

Table 4.4: Analysis of Variance for Low Density Zone……………………………………..26

Table 4.5: Model Summary Medium Density Zone……………………………………….....27

Table 4.6: Analysis of Variance Medium Density Zone…………………………….…….…27

Table 4.7: Model Summary High Density Zone………………………………………......…28

Table 4.8: Analysis of Variance High Density Zone ………………………………………..28

Table 4.9: Model Summary …………………..…………………….…………………......…29

Table 4.10: Analysis of Variance ………..…………………………………………………..29

ix

ABSTRACT

This project examines the influence of explanatory variables like: Income of Households;

License holders in the household, Educational level of the house-head; and other socio-

economic details of the households for the study area on the numbers of vehicle owned by a

household. For the purpose of our analysis, we develop a multiple linear regression models

using data obtained through a house-to-house survey of the selected zones in the study area.

The model results indicate the important effects of household socioeconomic information,

vehicle type, and other important factors on vehicle type ownership. The model developed

from this research can be applied to predict the impact of socioeconomic information on the

numbers of vehicle owned per household. Such predictions are important at a time like this

when the demand for transportation infrastructure is greatly increasing in Nigeria. Also,

vehicle distribution analysis was carried out to determine the vehicle type driven most in

Akure.

In this report, SPSS 17 and MS Excel software were used in the analysis of the data while

Multiple linear regression models were used in modeling of the vehicle ownership of

households in the study area. Data for the analysis were drawn from administered

questionnaires used for the Household Interview Survey (HIS) in some selected areas of

Akure metropolis. These selected areas were based on 3 categories; low, medium and high

density population areas.

This report is organized as following. Chapter 1 introduces the project topic. Chapter 2

discusses literature related to vehicle ownership models. Methods usually used for vehicle

ownership modeling are described in Chapter 3. Analysis and discussion of results are

presented in Chapter 4. Finally, conclusion deduced from the model and future works are

discussed in Chapter 5.

1

CHAPTER ONE

INTRODUCTION

1.1 General

The study of vehicle ownership is a classic topic in the area of transportation. The decision to

own a vehicle(s) is one of the most significant decisions made by individuals. Not only is it one

of the most significant financial investments for many people, but it also represents a dramatic

increase in mobility and is often viewed as a status symbol (de Jong et al., 2004). Vehicle

ownership influences city structure, public investment priorities including roadway

infrastructure, and the patterns of daily life. In developed countries, vehicle ownership study

already has been advanced to the household level and currently is being refined to the daily

usage level.

While Nigerian cities experience high level of vehicle ownership and reliance, there is an

absence of studies on the factors influencing the numbers of vehicles per households. According

to Awoyemi et al (2012), the study of vehicle ownership in developing countries can forecast the

future level of vehicle ownership and help transportation officials in road system planning,

policies and design. While developed countries are confronting serious auto-oriented problems,

motorization in most developing countries is still in its infancy. However, economic expansion

and improvement in quality of life in recent years have generated a high demand for private

mobility which is reflected in national vehicle registrations. Vehicle ownership is an important

determinant of household travel behaviour and it is fundamentally interconnected with

residential location and decision making regarding motorized trips (Scott and Axhausen, 2005).

High levels of vehicle ownership are associated with urban sprawl, increasing levels of vehicle

travel and the resulting air quality, noise pollution as well as health problem (Handy et al., 2005).

Understanding of how households choose the number of vehicles to own, based on where they

live and other explanatory variables is of vital importance to urban planners, transportation

engineers, national government and judgment makers (De Jong et al, 2004). Developed countries

like Canada and Netherlands have made several efforts in the study of vehicle demands and

2

ownership. Vehicle ownership was examined in Akure by Okoko E. in his research: Modelling

Car Ownership in a Developing Country: Empirical Data from Akure, the factors that influences

car ownership were studied. Poisson distributions were used to determine the probability and the

numbers of family that will own a certain number of cars. The Model predicted a reduction in the

number of families without cars and an increase in the number of vehicle owning families.

1.2 Statement of the Problem

Cities, like Akure, are locations having a high level of accumulation and concentration of

economic activities. The most important transport problems are often related to urban areas and

take place when the transport systems can not satisfy the demand of the urban mobility

(Rodrigue, 2013).

Endless is the list of the transportation infrastructure problems, though these problems can be

general, but there are some that are related to the vehicle acquisition rate. The general problems

that are associated with increase in vehicular movement in urban areas are: Traffic congestion

and parking difficulties; longer commuting; public transport inadequacy; loss of public spaces;

environmental impact and energy consumption; and land consumption to transportation facilities

(Rodrigue, 2013).

1.3 Aim and Objectives of the Study

The aim of this study is to develop vehicle ownership model for Akure metropolis. The

objectives of this research are to:

a) determine the rate of vehicle acquisition in Akure Metropolis.

b) examine varying factors responsible for vehicular growth in the study area;

c) use the factors in (a) above to formulate a simple model of vehicle ownership in the study

area.

3

1.4 Significance of the Study

The impact of this study on the economy of the study area is enormous. National governments

(notably the Ministries of Finance) make use of vehicle ownership models for forecasting tax

revenues and the regulatory impact of changes in the level of taxation. National, regional and

local governments (particularly traffic and environment departments) use vehicle ownership

models to forecast transport demand, energy consumption and emission levels, as well as the

likely impact on this of policy measures. Vehicle manufacturers apply models on the consumer

valuation of attributes of vehicles that are not yet on the market. (de Jong et al., 2004).

1.5 Description of the Study Area

The study area is Akure, the administrative seat of Ondo State. Akure is located on latitude 70 15′

north and longitude 50 05′ east. Its population was estimated at 353, 211 according to 2006

census. This consisted of 175,495 (49.68%) males and 177,716 (50.32%) females who are

mainly civil servants, traders and peasant farmers (Owolabi, 2010).The area is characterized by a

tropical wet and dry climate. The raining season starts from early April to late October, while the

dry season starts from November to March. The rainfall in the study area is between 1200mm to

1700mm, the humidity is about 85% and the area experiences wet season for roughly 8 to 9

months. The upgrading of the area into a business hub has led to increase in population with a

corresponding increase in vehicular ownership. Furthermore, Akure is one of the Millennium

Development Goals (MDG) cities (Millennium Development Goals city and World Bank

assisted programme). The area serves as a significant corridor to Abuja, the capital city of

Nigeria. The development of the area into a world class city had induced people from different

part of the suburb to migrate to this city; this in turn has resulted in traffic and vehicular

movement with inherent traffic congestion (Awoyemi et al, 2012).The various activities,

performed in Akure and the rapid increase of vehicle acquisition by its populace influence the

desire to construct new roads and rehabilitate the old ones to take care of the envisaged new roles

and status of the city. Figure 1.0 and 1.1 shows the map of Akure Metropolis and the location of

the study area in Akure Metropolis respectively

4

Figure 1.1: The Map of Akure Metropolis.

Source: Produced by the Author on Arc GIS, 2013

Figure 1.2: The Locations of the Study Area.

Source: Akure South Local Govt. Secretariat (Updated) 2004. (Reproduced by the

Author on AutoCAD 2010)

5

1.6 Scope and Limitation of Study.

This research was limited to vehicles and covered some households in six selected

neighborhoods of Akure Metropolis based on their population densities. The analysis carried out

considers explanatory variables such as the household socio-demographics and household

residential location variables among others.

6

CHAPTER TWO

LITERATURE REVIEW

2.1 Review of Previous Studies

According to De Jong (1996), estimation of the duration models for vehicle holding duration

until replacement, as well as a vehicle type choice model conditional on replacement and

regression equations for annual kilometrage and fuel efficiency. Together these sub-models form

a prototype version of a dynamic model system for vehicle holdings and use. The prototype

model system, as estimated on wave 1 of the car panel has been applied to forecast autonomous

changes between wave 1 and wave 2 of the car panel, which gave quite satisfactory results. The

model gave slightly less vehicle transactions than occurred in reality, whereas predicted vehicle

type changes were mostly somewhat more pronounced than those observed. The model has also

been used to simulate the impact of a number of possible policy measures and income growth.

One disadvantage of the duration models is that there is no variation over time in the individual

characteristics. Another limitation of the present prototype version is that, although they are

linked through the time-varying logsum variable, the duration and the type choice models are not

estimated as a joint model. Both limitations can to some extent be removed by estimation on data

for several waves and by using more sophisticated duration models.

For the vehicle quantity model, Bhat and Pulugurta (1998) compared two alternative behavioral

choice mechanisms for household car ownership decisions. First, they presented the underlying

theoretical structures and identified their advantages and disadvantages. Then, they compared the

ordered-response mechanism (represented by the ordered-response logit model) and the

unordered-response mechanism (represented by the multinomial logit model) empirically using

several data sets. This comparative analysis provided strong evidence that the appropriate choice

mechanism is the unordered-response structure.

Also, Hanly et al (2000) evaluated car ownership in Great Britain using a Panel Data. A car

ownership model is set up to examine whether owning a car in the previous year(s) has a

significant effect on the current state. The main purpose is to test the dynamics within the model

by applying advanced econometric estimation methods. A panel analysis is carried out using data

7

from the British Household Panel Survey. Data of four years (1993-1996) are used to estimate

the model. The dependent variable is the number of cars owned by the households in each of the

four years. This is a discrete variable, which can take the values 0, 1, 2, and 3 or more. The

dependence on past experience is incorporated by introducing lagged endogenous variables. The

model specification is an ordered Probit model. With four choices this results in a quaternary,

ordered choice latent regression model. Three types of models are estimated: a model without a

lagged dependent variable, a model with a lagged dependent variable and a model with dummies

for the number of cars in the last year (0, 1, 2, 3 or more cars). For each of the three models an

additional model is estimated with a household specific, time invariant error-component to

compensate for household heterogeneity. The explanatory variables are household income and

household socio-demographic variables, such as number of adults of driving age, number of

children, number of adults in employment and a dummy variable indicating whether the head of

the household is of pension age. Five location dummies are included reflecting urbanization and

the population density. The results of the model focus on the issue of state dependence, meaning

the state of car ownership a household was in last year compared with the state it is in this year.

The results support the hypothesis that last year’s car ownership influences the current car

ownership significantly at a 95% confidence level.

Choo (2004) identified travel attitude, personality, lifestyle, and mobility factors that individual’s

vehicle type choices, using data from a 1998 mail-out/mail-back survey of 1904 residents in the

three neighborhoods in the San Francisco Bay Area. Vehicle type was classified into nine

categories based on make, model and vintage of a vehicle, small, compact, mid-size, large,

luxury, sports, minivan/van, pickup, and SUV. The study developed a multinomial logit model

for vehicle type choice to estimate the joint effect of the key variables on the probability of

choosing each vehicle type

Furthermore, Whelan (2007) predicted the household’s decision to own zero, one, two or three or

more vehicles as a function of income (modified by eight household categories and five area

types), license holding, employment, the provision of company vehicles, and purchase and use

costs. The models were applied using a methodology known as prototypical sampling. This

method allowed the application of disaggregate models to 1203 zones to the year 2031 taking

into consideration changes in the demographic characteristics of each forecast area. The models

8

were successfully validated at the household level and the model forecasts compared favorably

with actual ownership information extracted from the 2001 Census.

2.2 Vehicle Model Types

There are different types of model types and these model types have been compared on a number

of criteria: inclusion of demand and supply side of the car market, level of aggregation, dynamic

or static model, long-run or short-run forecasts, theoretical background, inclusion of car use, data

requirements, treatment of business cars, car type segmentation, inclusion of income, of fixed

and/or variable car cost, of car quality aspects, of license holding, of socio-demographic

variables and of attitudinal variables, and treatment of scrap page.

In 2004, a Dutch researcher, De Jong, published a comprehensive review of car ownership

models. In his report, the models were classified into nine types based on the criteria listed

above: (1) aggregate time series models, (2) aggregate cohort models, (3) aggregate car market

models, (4) heuristic simulation method, (5) static disaggregate car ownership models, (6)

indirect utility car ownership and use models (joint discrete-continuous models), (7) static

disaggregate car type choice models, (8) (Pseudo)-panel methods, and (9) Dynamic car

transaction models with vehicle type conditional on transaction.

According to this comparison, aggregate time series, cohort models and aggregate car market

models do not appear very promising for the development of a full-fledged car fleet model, since

they lack vehicle types and policy variables. They could only be used to predict a total number of

cars in the future year, which would then be used as a starting point in other more detailed

models. Heuristic simulation models of car ownership do not offer extensive possibilities for

including many car types either. On the other hand they can fruitfully be used for predicting the

total number of cars with some policy sensitivities. The static car ownership models and the

discrete car type choice models with many car types are less suitable for short-run and medium-

run predictions, due to the assumptions of an optimal household fleet in every period. For such

time horizons it is much better to predict only the changes in the car fleet, instead of predicting

the size and composition of the entire car fleet in each period. For a long term prediction of the

9

number of cars and car type static models are well suited, though cohort effects on total car

ownership might not be well represented. Discrete car type choice models can be integrated with

panel models to account for the transition between car ownership states. Panel models could then

be used to study the evolution of the fleet, starting from the present fleet. For medium and long

term forecasts, this can only be carried out if there also is a mechanism for predicting changes in

the size and composition of the population. Pseudo-panels offer an attractive way to get short and

long-run policy-sensitive forecasts of the total number of cars (including the cohort effects), but

cannot take over the role of a choice-based model for the number of cars and car type. Dynamic

transaction models include duration models for the changes in the car ownership states of the

households, and in this respect are a continuous time alternative of the discrete time panel model

(de Jong et al., 2004).

2.3 Discrete Choice Model

In recent years the emphasis in econometrics has shifted from aggregate models to disaggregate

models (Train, 1986). There are several reasons for this shift: First, economically relevant

behaviour is necessarily at the individual level. Microeconomic theory provides methods to

analyze the actions of individual decision making units; these methods are based on strong

mathematical and statistical foundations. Individual behavior can be explained by disaggregate

econometric models to a degree that is not possible to achieve with aggregate models. Second,

survey data on households and individual behavior are becoming more and more available,

making it easier to estimate disaggregate models in situation that would before have been

impossible to estimate at the individual level.

2.3.1 Disaggregate Approach to Car Ownership Logit Model

Ui, n= Vi, n + ei, n (2.1)

Ui, n = the true utility of household (or individual) n for ownership Level i (i = 1, 2, I),

Vi, n = a deterministic component and a function of exogenous variable

Vin = ai + ßXin (3.2)

ai = constant specific to the alternative i, ß= vector of parameters to be estimated,

10

and Xin = vector of attributes for the individual n and the alternative i.e. i, n = a random

component/error term

The model used was of the multinomial (MNL) form written as:

Pn(i) = exp (Vin ) / exp(Vjn ) (3.3)

2.3.2 Aggregate Trend Extrapolation Model

In Britain, the very early car ownership forecasts were on the whole unconditional, i.e. they were

single-valued estimate without considering the influences of economic and policy variables

(Tanner, 1978). The first formal car ownership forecasting model for Britain is Tanner (1958),

which is an aggregate model of trend extrapolation. When applying the extrapolation techniques,

it has been recognized that car ownership rates should not increase indefinitely in time due to

saturation effects. For this reason, Tanner (1958) pioneered a logistic model that relates car

ownership rate (cars per capita Ct) with a time trend t:

Where C0 is the average number of cars per capita in the base year; g0 is the marginal growth of

average number of cars per capita in the base year

(calculated by evaluated at t0); S is the saturation level.

2.3.3 Partially Disaggregate Model

In the 1970s, the shortcomings of the aggregate trend extrapolation models were increasingly

recognized. The response was to introduce a “partially disaggregate” cross-sectional models

while handling the time trend separately using time series approach. Two prime examples are

Regional Highway Traffic Model (RHTM, described in Bates, et al. 1978 and cited in Ortuzar

and Willumsen, 2001) and 1989 National Road Traffic Forecasts (NRTF).

11

In RHTM, car ownership was defined as a function of real income deflated by a real car price

index, and separate models have been estimated for percentage of households with one plus car

(P1+) and percentage of households with two plus cars (P2+):

In NTRF (1997), a fully disaggregate model, two binary models were calibrated for each

household type: a P1+ model to predict the probability of the household owning at least one car,

and a P2+|1+ model, defining the conditional probability of the household owning two or more

cars, given that they own at least one car:

2.4 Multiple Linear Regression Vehicle Ownership Model

From Xin Deng research on Private Car Ownership in China, it was found that income effect is

strong at both national levels and within regions in China. However, even if per capita income

may still be an important factor to explain car ownership difference across the regions, its

explanatory power dropped considerably. Further discussion revealed that charges and fees

imposed on private car owners by authorities at different levels may have a strong influence on

car ownership level. Among other models developed by Deng from his findings was a Multiple

Linear Regression Model. He used data from China Statistical year book to generate this model

He attempted to model private passenger ownership by per capita income at both national and

provincial levels to test the significance of income effect. Then he introduced other variables to

(2)

(3)

(5)

(4)

12

evaluate the impact of costs and quality private car trips and trips via public transport. As

suggested by economic theory, income is the most important factor to affect private car

ownership. Apart from that, other main explanatory variables for private car ownership may

include costs of private car, and costs and quality of its substitutes: public transport or taxi. Other

variables such as the size of family, and real interest rate may also influence car ownership.

Given the number of factors that may influence the car ownership decisions, it may be useful to

identify factors contributing to different income elasticity across the regions in China.

Since there was no official data on cost of car ownership and use in China, Deng used price

index of transport services as a proxy. As a result, the following model is formed.

PPV=f (PCY, AR, PT, TX)

PPV: Private Passenger Vehicles Per 10000 people

PCY: Per capita Disposable Income of Urban Households

PRT: Price Index of Transport Services

AR: Area of Paved Road per 10000 people in urban areas

PT: Number of Public Transport Vehicles per 10000 people

TX: Number of Taxis Per 1000 people

The outcome of this model is shown in the table 2.1

Table 2.1: Multivariate Regression Results (Deng, 2006)

At first glance, the overall model fit is acceptable. The adjusted R-squared is 0.61, and income,

public transport and the number of taxis turned out to be statistically significant. It is not surprising

to find out that “price index of transport services” is not statistically significant, as it covers all

13

types of transport services, and is not a good proxy of private car ownership or use. However, the

interesting findings are that the variable “number of taxis per 1000 people” has more explanatory

power (Beta 0.6) than per capita income (Beta: 0.25), and the sign for taxi variable is positive.

None of them makes economic sense. A plausible explanation is that collinearity exists between

the two independent variables: per capita income and number of taxis. This study found strong

correlation between income and private car ownership both at national level and within each

region, but was unable to confirm all of factors that may affect private car ownership across the

regions in China.

2.5 S-Curve Models

This model was used by Kemal Selcuk Ogut to forecast the car ownership in turkey. In the

developed models, the Car Ownership (CO) consists of the number of the private cars. Trial cars,

as taxis and the cars owned by the government are excluded from the total car population. CO

values have been expressed as the number of private car per 1000 persons. Thus, the starting year

of the CO model was chosen as 1970 and all models were developed with the data between 1970

- 2002. At the S-curve models, the Gross National Product per capita (GNPPC) was used as the

user income variable. In this study, three S-curve models such as, the logistic, power growth and

Gompertz curves were developed.

The logistic Curve formula of the developed model is:

where CO is the car ownership per capita at time t, S is the saturation level of CO, t is year and

“a ”and “b ”are model coefficients.

The coefficients a, b and c are calculated as 1.9091×1079, 0.0837 and 0.9308 respectively.

The used power growth curve is expressed as:

(6)

14

where n is the additional coefficient.

The model coefficients a, T, c and n are found as 9.0274×10−3, 1939, 6.191×10−8 and 4.7,

Gompertz curve, another extrapolatory curve, was also used for determining CO in Netherlands,

and in the developing countries. The form of Gompertz curve is:

Where, x is the major independent variable that effects. The model coefficients a, b and c are

9.8903×1021, 2.4789×10−2, and 1.8486×10−7, respectively.

The logistic curve model overestimated the CO between 1996 - 2002 and it gives the biggest CO

level upon the forecast. The forecasts of the power growth and Gompertz curves models are

similar to each other. There is about 10% difference between two model outputs for the

projection year 2020.

2.6 Factors Affecting Vehicle Ownership Model

There are various indices that determine the look of any model, but for vehicle ownership model,

the variables that determines the dependent variable is the number of cars per household. The

variable indicates the average number of cars for that particular study group(Kanaroglou and

Potoglou, 2006). The explanatory variables are socio-economic characteristics of the household:

income, the number of adults, and the number of children, metropolitan and rural areas and a

generation effect for the head of the household. Price indices for car purchase costs, car running

costs and public transport fares are added to the set of explanatory variables (Cirillo et al, 2010).

The table 2.2 shows the attributes of the explanatory variables. In terms of land use information,

population density, and regional variable (urban, suburban, and rural). Estimation results indicate

that households in the area with large density or in urban area own more vehicles. A few studies

(7)

(8)

15

included the accessibility of transit in the attributes; however the measurement is not quantitative

enough. The other variables include the parking space, motoring cost and the influence of

company car (Cirillo et al, 2010).

Table 2.2: Attributes of Explanatory Variables(Cirillo et al, 2010)

16

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Research Approach

This area explains the approaches to data collection that was adopted in carrying out the research

which include data collection from all the study zones using self-administered questionnaire, the

data collected were converted into numerical form so that statistical calculations can be made;

and data analysis and modeling using statistical package.

3.2 Research Population

The research population is the entire group of individuals or team that is selected for Statistical

measurement of this research. For the purpose of this research, the group included household

heads in some selected locality of Akure metropolis. The selected areas were classified into Low,

Medium and High density. Table 3.1 shows the selected density areas.

Table 3.1: Area Selected for Study with their Population Densities

S/NO Location/Area Population Density

1 FUTA Staff Quarters

Low

2 Ijapo Estate

3 Obele Estate

Medium

4 Shagari Estate

5 Isolo Area

High

6 Oke-Aro Area

17

3.2.1 Low Density Areas

The low density areas selected for this survey in the Akure Metropolis are The Federal

University of Technology, Akure, Staff Quarters (FUTA Staff Quarters) and Ijapo Estate. FUTA

Staff Quarters is an educational area located in the institution under the Akure South local

government jurisdiction. From the survey conducted, the household heads are majorly

lecturers/workers of the university, with university educational level for the household heads. On

the other hand, Ijapo Estate is a residential area with some educational facilities under the Akure

South Local Government. It is an area composed of majorly civil servants and

businessmen/traders and with university educational level household heads. Ijapo Estate is a

centralized, civilized, highly advanced, organized and one of the oldest estate in the Akure

Metropolis.

3.2.2 Medium Density Areas

Obele Estate and Shagari Village are the medium density areas considered in this study. Obele

Estate is more of a residential area located under Akure South Local Government. It is a

moderately organized area and composed mostly of civil servant household heads with university

educational level. Shagari village on the other hand, is also found under the Akure South Local

Government jurisdiction. It is majorly a residential, civil servants and mixed educational level

household heads area.

3.2.3 High Density Areas

Oke-Aro and Isolo the two locations share almost the same features, in that they are both in

Akure South Local Government, more of residential than commercial in view and made of

mostly artisans and businessmen/traders household heads. They are locations with less

organizations and unplanned structures.

3.3 Method of Data Collection

Two data-set were considered majorly, these are primary and secondary data.

a) Primary Data Collection Method: Primary data for this research was obtained from

households in Akure Metropolis by administering questionnaires. The questionnaire

18

focused on socio-economic and demographic situation of the households, also,

information on the household residential location, vehicle attributes and other questions

that will proffer solutions to the questions raised in the research questions. Appendix A-1

shows the sample of the questionnaire. There are typically two underlying methods for

conducting survey; self-administered and interview administered. A self-administered

survey method, being the more adaptable in some respects, was employed, because the

respondents will have little doubt about it. Also, the questionnaire approach was used

because of its accuracy, speed and wide range of consultation.

b) Secondary Data collection Method: Secondary data used in this study was obtained

from the database of the Federal Road Safety Commission, Akure Command. It

comprises of data of motor vehicle registered in Akure from January 2009 till April 2013.

3.3.1 Sample Size

This constituted the number of households that responded from the sampling frame (Olajide,

2007). It focuses on the percentage of every household considered e.g 1 in every 10 households.

3.3.2 Sampling Technique

There are various sampling techniques employed in the collection of research data and these

include the simple random, systematic, stratified, purposive, and cluster sampling method.

i. Simple Random Sampling: This is usually based on the principle of

randomization, giving every element of the population an equal and sometimes a

known chance of being selected.

ii. Systematic Sampling: This is used where a large population exists, it is most

convenient. An inverse sampling fraction (K) is calculated using the formula:

K = N / n

Where, N is size of population and n is the size of sampling.

iii. Stratified Sampling: The population is divided into strata selected by the

researcher as relevant to his purpose e.g. Age-groups, Marital Status, e.t.c This

sampling method guarantee that all sectors of the population are in the final

sample.

19

iv. Cluster Sampling: This involves selection of group rather than individuals. This

is common where the population is large and widely spread.

v. Purposive Sampling: This method involves the researcher hand-picking the

sample of the research work based on his judgment about the relevance of the

chosen sample to his research work.

And since it is impossible to collect data from all households within the study area, it is therefore

desirable to adopt a sampling process that will be suitable for the target population, which is

purposive. Purposive sampling technique was employed in this study by making use of Home

Interview Survey System (HISS) otherwise called Household to Household Survey (HTHS).

However sampling was done during work free days (weekends and public holidays) and

occasionally on Friday evening in some areas.

3.4 Method of Data Analysis

Data analysis could be defined as the process of using more than one technique to facilitate the

ease of communicating the results while at the same time improving its validity (Ajayi, 1990).

In analyzing the data gathered from questionnaires administered in order to achieve the aim of

this study, the following parameters were evaluated using SPSS version17:

i. Adjusted R-Squared: A version of R-Squared that has been adjusted for the number

of predictors in the model. It can be calculated using the formula below:

R2adj = 1 –[((1 – R2)(n – 1))/(n – k – 1)]

Where, k is the number of coefficients/predictors and R is multiple regression

coefficient.

ii. Coefficient of Multiple Determinations (R2): The percent of the variance in the

dependent variable that can be explained by the independent variable. It can be

calculated using the formula below:

R2= 1 – (SSerror/SStotal)

Where, SSerror is the explained variation and SStotal is the total variation.

20

iii. Multiple Correlation Coefficient (R): A measure of the amount of correlation

between more than two variables. It can be calculated using the formula below:

R = 1/(n -1) ∑[(x1–x)(y – y)/Sx - Sy]

iv. Analysis of Variance Table (ANOVA): This table comprises of: the Mean of

Squares, which is calculated by dividing the sum of squares by the degree of

freedom (Df); Degree of Freedom (Df) are associated with the three sources of

variance – Total, Model and Residual; F-Statistics, which is used to test the equality

of two population variance, it is usually the ratio of the Mean Square for the effect of

interest and Mean Square Error; and P-value is compared to show alpha level in

testing the null hypothesis that all model coefficient.

21

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 Data Collation

In the totality of the surveying, 643 questionnaires were administered based on the population

density of these selected areas as shown in Table 4.1.

Table 4.1: The Result of the Administered Questionnaire for the Locations

CLASS OF

LOCATION

LOCATIONS NUMBER OT

RESPONDENTS

TOTAL

LOW DENSITY

AREA

FUTA-STAFF QUARTERS 57

129 IJAPO ESTATE 72

MEDIUM

DENSITY AREA

OBELE ESTATE 109

227 SHAGARI VILLAGE 118

HIGH DENSITY

AREA

ISOLO 127

287 OKE-ARO 160

TOTAL 643

4.2Comparison of Zones in the Study Area

The comparison of the three zones considered (low, medium, and high density areas), were done

based on the vehicle-type distributions of the areas. The vehicle-type distributions were

categorized into Salon, Wagon, SUV/Jeep, Mini-Van, Pick-up, Bus, and others.

4.2.1 Vehicle Distribution of Zones

The vehicle-type distribution of the low density population, as shown in Figure 4.1(a), revealed

that: salon has the highest percentage of 53%; SUV/Jeep came second with 22%; wagon 12%;

22

and min-van has 9%. Similarly on this distribution pick up occupied 4% while bus is 0% in the

locations vehicular distribution.

(a) (b)

(c)

Figure 4.1: Vehicle-Type Distributions

Similarly, Figure 4.1(b) shown that the medium density vehicle-type distribution has Salon with

the highest percentage of 56%, Wagon 19%, SUV/Jeep 11%, Min-Van 9%, Pick-up 4% and Bus

with the lowest of 1% with other 0%.

56%

19% 11%

9%

4%

1%

0%

5%

salon wagon suv mini van

pick up bus other

48%

19%

6%

5%

10%

12%

0%

salon

wagon

suv

mini van

pick up

bus

other

53%

12%

22%

9%4% 0%

salon wagon suv mini van pick up bus

23

On the other hand, the vehicle-type distribution of the high density population as shown in

Figure 4.1(c) above, Salon as well has the highest percentage of 48%. As shown in this Figure

Wagon came second with 19% vehicular distribution and Bus 12%, Min-Van 5%, SUV/Jeep 6%.

And Pick-up occupied 10% while other vehicle type has 0% in the distribution.

4.3 Analysis of Vehicle Registration in Akure

The information on the vehicles registration in Akure metropolis was obtained from the Federal

Road Safety Commission (FRSC), Akure Command. The information gathered covers the

vehicles registration between 2009 and April 2013.

Table 4.2: Vehicles Registered in Akure between January, 2009 and April, 2013.

Source: FRSC, Akure Command (2013)

From Table 4.2 above, the classes of vehicle registered include: Motorcycle (Private (PTE) and

Commercial (COM)), Cars (Private (PTE) and Commercial (COM)), Buses (Private (PTE) and

Commercial (COM), Jeeps/SUV, Pick-ups, Articulated vehicles (ARTLD), EME/Agric (these

are heavy duty vehicles that are either used for construction or Agriculture purposes), and

Others(vehicle types that are not in the classes listed). Figure 4.1 shows the trend of vehicle

Registration in Akure metropolis between the years 2009 – April, 2013.

PERIOD

M/CYCLE CARS

JEEP

BUSES PICK

UPS ARTLD

EME/

AGRIC

OTHER TOTAL

PTE COM PTE COM PTE COM

2009 50 60 65 45 55 23 30 35 10 15 45 433

2010 65 40 61 45 28 44 33 31 12 5 35 399

2011 40 30 55 30 16 45 46 33 10 4 25 334

2012 35 22 56 100 36 50 150 30 15 10 20 524

2013

(JAN-

APRIL)

25 15 40 50 30 40 45 30 10 2 15 302

TOTAL 215 167 277 270 165 202 304 159 57 36 140 1992

24

From Figure 4.2, the numbers of vehicle registered in the year 2012 had the highest count of 524

vehicles, while 2009 had 433 vehicles.

There was a sudden drop in the number of vehicle acquired in 2010 and further fall in 2011. In

general, there was a drop of 65.7% in the number of vehicle imported in Nigeria in 2012

(www.tradingeconomics.com/nigeria/imports) compare to the previous year, due, mainly, to the

general election conducted in 2011 and the gubernatorial election of Ondo state in 2012

Figure 4.2: Vehicle Registration Trend between Years 2009 – April, 2013.

4.4 Development of Vehicle Ownership Model for Akure

The vehicle ownership data collected during the survey were modeled using multiple linear

regression models. In this modeling, the independent variables used were cross-correlated to

know their degree of influence on the dependent variable, see Table 1 of the Appendix B. The

number of vehicles owned per household (NV) is a function of:

NV = f(ESH, IR, LH, TW)

Where, Educational Status of the house hold head (ESH); Income earning range of the household

head (IR); License holders in the household (LH); and Mode of transportation to work (TW)

0

100

200

300

400

500

600

F

R

E

Q

U

E

N

C

Y

YEAR OF REGISTRATION

NO. OF VEHICLE REGD.

2 per. Mov. Avg. (NO. OF

VEHICLE REGD.)

25

These independent variables were coded thus for easy computation and analysis:

a) Educational Status of the Household Head(ESH):

None = 0, Primary = 1, Secondary = 2, College = 3, Polytechnic = 4, University = 5

b) Income Earning Range of the Household Head (IR):

#10,000 - #50,000 = 1, #51,000 - #100,000 = 2, #101,000 - #150,000 = 3, #151,000 -

above = 4

c) License Holders in the Household (LH):

No License = 0, One License = 1, Two Licenses = 2, greater than two License = 3

d) Mode of Transportation to Work (TW):

Private Car = 1, Walk = 2, Taxi = 3, Bus = 4, an Motorcycle = 5

See APPENDIX C for the coding table for the dependent variable and independent variable used

in developing these models.

However, the following values were used for the degree of vehicle ownership:

i. Households with no vehicle, NV = 0;

ii. Households with one vehicle, NV = 1;

iii. Households with two vehicles, NV = 2;

iv. Households with three vehicles, NV = 3; and

v. Households with more than three vehicles, NV = 4.

4.4.1 Low Density Zone Vehicle Ownership Model

The model obtained using Statistical Package for Social Sciences (SPSS) is:

NV = 0.200IR + 0.342LH – 0.46 ESH – 0.141TW + 0.688………..(1)

26

4.3: Model Summary for Low Density Zone

Model R

R

Square

Adjusted

R Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .574a .330 .307 .760 .330 14.748 4 120 .000

a. Predictors: (Constant), No. of License Holders, Educational Status of Household Heads,

Income of Household Head, Mode of Transportation to Work

Table 4.4: Analysis of Variance for Low Density Zone

ANOVAb

Model

Sum of

Squares Df Mean Square F Sig.

1 Regression 34.055 4 8.514 14.748 .000a

Residual 69.273 120 .577

Total 103.328 124

a. Predictors: (Constant), No. of License Holders, Educational Status of Household

Heads, Income of Household, Mode of Transportation to Work

b. Dependent Variable: Numbers of Vehicles Owned

Prediction Ability of the Model: " Coefficient of Correlation “R”, R = 0.574 which means that

there is 57.4% linear relationship between the dependent and independent variables, while "

Coefficient of Determination R2”, R2 = 0.330 which means that 33.0% of the dependent variable

is explained by the independent variables.

4.4.2 Medium Density Zone Vehicle Ownership Model


NV = 0.273IR + 0.328LH – 0.021 ESH – 0.136TW + 0.400………..(2)

27

Table 4.5: Model Summary for Medium Density Zone

Model R

R

Square

Adjusted R

Square

Std. Error

of the

Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .765a .586 .578 .603 .586 74.548 4 211 .000

a. Predictors: (Constant), Mode of Transportation to Work, Educational Status of the Household

Head, Income of Earning Range of Household Heads, License Holders

Table 4.6: Analysis of Variance for Medium Density Zone

ANOVAb

Model Sum of Squares Df Mean Square F Sig.

1 Regression 108.491 4 27.123 74.548 .000a

Residual 76.768 211 .364

Total 185.259 215

a. Predictors: (Constant), Mode of Transportation to Work, Educational Status of the

Household head, Income of Earning Range of Household Heads, License holders

b. Dependent Variable: Numbers of Vehicle Owned


there is 76.5% linear relationship between the dependent and independent variables, while "

Coefficient of Determination R2”, R2 = 0.586 which means that 58.6% of the dependent variable


4.4.3 High Density Zone Vehicle Ownership Model


NV = 0.184IR + 0.455LH – 0.021ESH – 0.071TW + 0.191………..(3)

28

Table 4.7: Model Summary for High Density Zone

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change F Change df1 df2

Sig. F

Change

1 .789a .623 .617 .408 .623 108.491 4 263 .000

a. Predictors: (Constant), Mode of Transportation to Work, Educational Status of the Household Head,

Income of Earning Range of Household Heads, License holders

Table 4.8: Analysis of Variance for High Density Zone

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 72.097 4 18.024 108.491 .000a

Residual 43.694 263 .166

Total 115.791 267

a. Predictors: (Constant), Mode of Transportation to Work, Educational Status of the

Household Head, Income of Earning Range of Household Heads, License Holders

b. Dependent Variable: Numbers of Vehicle Owned.


there is 78.9% linear relationship between the dependent and independent variables, while

"Coefficient of Determination R2”, R2 = 0.623 which means that 62.3% of the dependent variable


4.4.4 Model for Akure Metropolis

This model is developed using the combination of all the data collected from all the areas.


NV = 0.239IR + 0.386LH – 0.044ESH – 0.108TW + 0.381………..(4)

29

Table 4.9: Model Summary

Model R

R

Square

Adjusted R

Square

Std. Error of

the Estimate

Change Statistics

R Square

Change

F

Change df1 df2

Sig. F

Change

1 .802a .643 .641 .565 .643 272.254 4 604 .000

a. Predictors: (Constant), Mode of Transportation to Work, Income of Earning Range of Household

Heads, Educational Status of the Household Head, License Holders

Table 4.10: Analysis of Variance (ANOVA)

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 348.060 4 87.015 272.254 .000a

Residual 193.044 604 .320

Total 541.103 608

a. Predictors: (Constant),Mode of Transportation to Work, Income Earning Range of

Household Heads, Educational Status of the Household Head, License Holders

b. Dependent Variable: Numbers of Vehicle Owned


there is 80.2% linear relationship between the dependent and independent variables, while

"Coefficient of Determination R2”, R2 = 0.643 which means that 64.3% of the dependent variable


General Remarks:

From these model equations, the positive sign in the coefficient of IR and LH indicate that an

increase in their numbers is also associated with an increase in vehicle ownership degree.

However, the negative sign in the coefficients of Mode of Transportation to work (TW.)

Educational Status of the Household Head ESH, indicate the occurrence of multi-colinearity in

the independent variables; is not statistically significant, as it covers all types of educational level

and transport services.

30

4. 5 Analysis of Vehicle Ownership Per Zone

4.5.1 Low Density Population Zone

The survey on the low density areas (FUTA Staff Quarters and Ijapo Estate) gave total

administered questionnaires of 129 (57 and 72 for FUTA Staff Quarters and Ijapo Estate

respectively). As shown in Figure 4.3 the percentage of household heads with university

educational status had the highest percentage of vehicle ownership followed by the colleges and

polytechnic, this is because educational status boost the ego of an individual to own exotic and

different cars while the illiterates may not see the need to own a car despite the fact that some of

them can afford it. Some prefer to own motorcycle and divert the remaining to building and other

projects. The secondary and primary had the lowest percentage of vehicle ownership.

Figure 4.3: Educational Status of Household Heads for Low Density Area

In these locations, the occupations range from artisan to teaching in which no teacher/lecturer

was without vehicle and they were found to have the highest percentage of vehicle ownership

followed by the civil servants and traders/businessmen as shown in Figure 4.4.

31

Figure 4.4: Occupational Status of Household Heads for Low Density Area

Figure 4.5: Numbers of Student per Household for Low Density Area

32

The relationship between the numbers of vehicle owned by households is not directly related to

the numbers of student in the households, because this depends on other factors like the level of

education of the children, the nearness of their house to public transportation or School buses

route e.t.c. The numbers of student per household had little or no influence on the numbers of the

vehicle own in this zone with different and varying percentage values, as shown in Figure 4.5.

In Figure 4.6, the income ranged is from #10,000 - #50,000 to #151,000 - above. It was resulted

that #51, 000 - #100,000 range had the highest percentage for one vehicle ownership (16.28%)

followed by #101, 000 - #150,000 (10.08%) while the #151,000- above had the highest

percentage (13.95%) for two and three vehicle ownership.

Figure 4.6: Income Earning Range of Household Heads for Low Density Area

As shown in Figure 4.7, lecturing had the highest percentage in #101,000 - #150,000 and

#151,000 - above followed by the civil servants and the businessmen/traders.

33

Figure 4.7: Income of Household Heads by the Occupation for Low Density Area

The educational status of the household heads plays a significant role on the income

earning state of the households. The households which their heads were degree holders

had the highest income in all the income range as shown in Figure 4.8.

Figure 4.8: Income of Household Heads by the Educational Status for Low Density Area

34

4.5.2 Medium Density Population Zone

The survey on the medium density areas (Obele Estate and Shagari village) gave a total

(returned) administered questionnaires of 227 with 109 and 118 for Obele Estate and Shagari

village respectively (see Tables 11-19 in the Appendix B for the Frequency Tables).

Vehicle ownership increased with the level of educational attainment of the household head,

principally, level of education is affected by the level of income. As shown in Figure 4.9 the

percentage of household heads whose educational level are degree holders had the highest

percentage of vehicle ownership and most of them owned at least one vehicle, though there were

few of them with no vehicle. Only 6.61% of university graduates owned three vehicles with

other levels of education having 0%.

Figure 4.9: Educational Status of Household Heads for Medium Density Area

The numbers of vehicle own is highly determined by the occupation of the household heads

since this has much to do with the income earned. As shown in Figure 4.10, the percentage of

people that are employed and do not own at least a vehicle is 17.75%, while 0.3% of the

35

employed individuals own more than three vehicles, 32.66% of civil servants owned at least a

vehicle.

Figure 4.10: Occupational Status of Household Heads for Medium Density

The numbers of student per household had a slight influence on the numbers of the

vehicle owned, but it did not increase with an increase in the numbers of vehicle owned in

the various households. As the highest percentage of households with vehicles occurred at

3 and 2 students, households with 0, 1, 4, 5, 6, 7, 8, 9, and 10 students have low, different

and varying percentage of vehicle ownership as shown in Figure 4.11.

36

Figure 4.11: Numbers of Student per Household for Medium Density Area

Figure 4.12: Income Earning Range of Household Heads for Medium Density Area

37

As revealed by the low population zone, where vehicle ownership increased directly with

income, medium population zone results agreed to this relationship between these parameters. As

shown in Figure 4.12, the income range is from #10,000- #50,000, #51,000 – #100,000,

#101,000 - #150,000, and #151,000 - above. In this result, it was seen that #51,000 - #100,000

range had the highest percentage (18.06%) for one vehicle ownership, #101,000 - #150,000

(10.57%) for the household with two vehicles while the #151,000 - above had the highest percent

(4.41%) for three vehicles ownership and 0.88% for more than three vehicle owned households.

In this zone, the occupation of the household heads varied directly with the income of the

households. As shown in Figure 4.13, civil servant had the highest percentage in #101,000 -

#150,000 and #151,000 – above range, followed by households which their heads were

businessmen/traders. Artisans had the highest percentage for #10,000 - #50,000 income earning

range.

Figure 4.13: Income of Household Heads by the Occupation of Household Heads for

Medium Density Area

38

Figure 4.14: Income of Household Heads by the Educational Status of Household

Heads for Medium Density Area

The educational status of the household heads played a significant role on the income of the

households. The households which heads were degree holders had the highest income in all the

income ranges as shown in Figure 4.14.

4.5.3 High Density Population Zone

The survey on the high density areas (Oke-Aro and Isolo Area) gave the total administered

questionnaires to be 287 with 127 and 160 for Oke-Aro and Isolo area respectively. (see Tables

20-28 in the Appendix B for the Frequency Tables).

Vehicle ownership increased with the level of educational attainment of the household head. As

shown in Figure 4.13, 39.30% of household heads whose educational level is secondary level

had no vehicles. Only 8.77% of secondary school certificate holders owned one vehicle. About

7% of both university and polytechnic graduate had one vehicle. The households which heads

39

are secondary school certificate holders have the highest percentage of vehicle ownership in total

(see Figure 4.15).

Figure 4.15: Educational Status of Household Heads for High Density Area

The numbers of vehicle owned is highly determined by the occupation of the household heads

since this has much to do with the income earned. In these locations, the occupations ranged

from unemployed to civil servants in which low percentage of teachers/lecturers group is

represented. As shown in Figure 4.16, the percentage of people that were employed and did not

own at least a vehicle is 59.32%, while about 3% of the employed individuals owned more than

two vehicles, 32.1% of businessmen/traders owned at least a vehicle. Households which

occupation of the household heads was artisans and traders dominated the chart.

40

Figure 4.16: Occupational Status of Household Heads for High Density Area

The numbers of student per household did not vary directly with the numbers of the vehicle

owned, as the highest percentage occurred at 3 and 2 students while 0, 1, 4, 5, 6, and 7 students

have low, different and varying percentage across the vehicle quantity owned range as shown in

Figure 4.17. Households with three vehicles ownership level have a percentage of 0.5% and

0.5% for households that have 5 and 4 students respectively.

41

Figure 4.17: Numbers of Student per Household for High Density Area

As shown in Figure 4.18, the income ranged from #10,000- #50,000, #51,000 – #100,000,

#101,000 - #150,000, and #151,000 - above. In this result, #51,000 - #100,000 range had the

highest count of households (40 out of 287 households) for one vehicle, #101,000 - #150,000 (16

out of 287 households) for the household at least a vehicle while the households that their

income earning range were #101,000 - #150,000 and #151,000 - above had a total count of two

(2 out of 287 households) for three vehicles ownership.

In another view as shown in Figure 4.19, traders/businessmen had the highest percentage in

#10,000 - #50,000 (19.78%) and #51,000 – #100,000 (9.89%) income earning range.

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Figure 4.18: Income Earning Range of Household Heads for High Density Area

Figure 4.19: Income of Household Heads by the Occupation for High Density Area

43

As shown in Figure 4.20, the educational status of the household heads played a significant role

on the income of the households. In this zone, the balance shifted towards the households that

have their household heads to be primary school and secondary school certificate holders to have

the largest percentage, though secondary school certificate holder household heads have the

highest percentage for the #10,000 - #50,000 (36.53%) and #51,000 – #100,000 (12.92%)

income earning range. Degree holders and diploma certificate holders dominated the high

income earning range but they have low percentages.

Figure 4.20: Income of Household Heads by the Educational Status for High Density Area

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CHAPTER FIVE

CONCLUSIONS AND RECOMMENDATIONS

5.1 Conclusions

Environmental concerns and the need for comprehensive planning strategies for sustainable

urban transportation systems require refined components of transportation and land-use. One of

the most critical is household vehicle ownership because it is a key determinant of household

travel behaviour and major contributor to air pollution and traffic congestion.

The aim of this project has been to develop vehicle ownership model for Akure Metropolis. We

approached this task by studying vehicle ownership at the household level using a Questionnaire-

Approach Survey (QAS). Multiple Linear Regression Models allowed us to determine the effects

of the aforementioned variables to household decisions to own zero, one, two, three or more

vehicles.

For the low and medium density population zones, the effects of the independent variables on the

dependent variable are similar, though differs in terms of values (coefficients) as it was shown in

Chapter Four of this report. Mode of Transportation to Work (TW) has the lowest influence on

the Numbers of Vehicle Owned per Households. From the survey carried out, it was evident that

the occupation has a great influence on the income earned per household, therefore, Income of

the Household (IR) has an upper influence than the Educational Level of the House-Head (ESH),

while License Holders (LH) coefficient is larger than that of ESH and IR.

In case of high population density zone, it was revealed that Income earned per Households (IR)

has a low influence on the dependent variable after Mode of Transportation to Work (TW). This

shows the high level of poverty rate in this zone of the study area.

In general, the average vehicle-type distribution for the study area shows that, Salon is the most

driven vehicle in the study area with a value of 52.33%, followed by Wagon 16.67%. In this

distribution, Mini-Van came forth with 7.67% after SUV/Jeep 13%. Bus has the lowest with

4.33% after Pick-Up 6% while others is 0%. Other important findings from the analysis are as

follows:

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1. Households located in densely populated neighborhoods have a disinclination for pickup

trucks.

2. The large numbers of student in households does not signify that the household will have any

vehicle at all.

3. The presence of mobility challenged individuals in some households does not mean the

household will own a vehicle or a particular vehicle-type.

4. Having more than one license holder in the household means that the number of drivers in

the household is more than one which could cause the household to acquire more vehicles.

5.2 Recommendations

To reduce the increase in the numbers of vehicle owned by various households, since income is

the only index that can be influenced by government and knowing that rising income will lead to

increase in vehicle ownership, as can be predicted by our models, government should ensure that

society pay for the external costs associated with motor vehicles one way or the other, and it is

more efficient to require the vehicle owners to pay for the external costs. Policy makers need to

be aware that current charges and fees on vehicle owners do not address the issue of

externalities. Instead, they are mainly used as a vehicle to collect extra revenue for government

at different levels. This policy should be incurred not only because people who can afford private

cars are relatively rich but also because government’s policy was to restrict private car

ownership. If new taxes and charges are to be introduced to replace current charges and fees,

both efficiency and external effect issues need to be addressed properly.

For future study on this research topic, the research should take into consideration other vehicle

ownership model parameters like vehicle information-cost of purchase and maintenance,

Purchase price, Operation cost, Fuel cost, Fuel efficiency, Seats, Luggage Space, Shoulder room,

Engine-size, Horse power, Acceleration time, Vehicle Age, Weight, Salvage value. Also, look

further into transportation infrastructure like availability of Parking Space, Public Transport

System and Land-Use acts of the study area.

The effect of rising oil prices in recent years merits further investigation. It would be interesting

to investigate whether vehicle owners would adapt to higher oil prices by owning fewer vehicles

46

or shifting to vehicles that run on alternative fuels. Future research which incorporates models of

vehicle type choice would be required to understand this. Another interesting line of further

inquiry is the effect of behavioural and attitudinal changes, e.g. towards walking and cycling,

environmental protection and climate change, as well as attitudes to health. These are areas for

further research.

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REFERENCES

Awoyemi, O. K., Ita, A. E., Oke, M. O., Abdulkarim, I. A. & Awotayo, G. P.(2012). An

Analysis of Trip Generation and Vehicular Traffic Pattern in Akure Metropolis Ondo

State, Nigeria, Journals of Social Science and Public Policy, Volume 4.

Bhat, C.R. & Pulugurta, V.(1998). A Comparison of Two Alternative Behavioural Choice

Mechanisms for Household Auto Ownership Decisions, Transportation Research Part B,

32(1), 61-75.

Bunch D.S. (2000). Automobile Demand and Type Choice: Handbook of Transport Modeling

(Eds, Hensher, D.A. and Button, K.J.), Pergamon, New York, pp. 463-479.

Chandra R.Bhat & Sudesshna Sen (2005). Household Vehicle Type Ownership and Usage: An

Application of the Multiple Discrete-Continuous Extreme Value (MDCEV) Model.

Cinzia Cirillo, Alice Liu & Michael Maness (2010).Modeling Vehicle Ownership Decisions

for the State of Maryland.

Dargay, J. (2002). Determinants of Car Ownership in Rural and Urban Areas: A Pseudo-Panel

Analysis, Transportation Research Part E: Logistics and Transportation Review Volume

38, Issue 5, September 2002, pp351-366

Dargay, J. and Vythoulkas, P. (1999). Estimation of a Dynamic Car Ownership Model, A

Pseudo-Panel Approach, Journal of Transport Economics and Policy, Vol. 33, Part 3,

Sept. 1999, pp 287-302

Dargay, J. and Gately, D. (1999). Income’s Effect on Car and Vehicle Ownership, Worldwide:

1960-2015, Transportation Research, Part A, Vol. 33, pp101-138

De Jong, G.C. (1996).A Disaggregate Model System of Vehicle Holding Duration, Type Choice

and Use, Transportation Research Part B: Methodological, 30(4), pp. 263–276.

48

De Jong, G.C., Fox, J., Daly, A., Pieters, M. & Smit, R. (2004). A Comparison of Car

Ownership Models; Transport Reviews, Vol. 24, No. 4, 379-408.

Gerard de Jong, James Fox, Marits Pieters, Liese Vonk & Andrew Daly (2002).Audit of Car

Ownership Prepared for the Transport Research Center (AW) of the Netherlands Ministry

of Transport, Public Works and Water Management.

Handy, S (1996).Methodologies for Exploring the Link between Urban Form and Travel

Behaviour Transportation Research D, 1(2), 151-165.

Jong, G.C. de (1996). A Disaggregate Model System of Vehicle Holding Duration, Type Choice

and Use. Transportation Research Part B (Methodological), 30-4,263-276.

Kanaroglou, P.S. & Scott, D.M. (2002). Integrated Urban Transportation and Land-Use Models

for Policy Analysis. In: Governing Cities on the Move: Functional and Management

Perspectives on Transformations of Urban Infrastructure in European Agglomerations.

(Eds, M. Dijst, W. Schenkel and Thomas, I.), Ashgate Publishing Ltd., Aldershot (UK),

pp. 42-72.

Mohammadian, A. & Miller, E.J. (2003).Empirical Investigation of Household Vehicle Type

Choice Decisions. Transportation Research Record: Journal of the Transportation

Research Board, No. 1854, TRB, National Research Council, Washington D.C. , 2003,

pp. 99-106.

Nobile, A., C. Bhat & E. Pas (1996). A Random Effects Multinomial Probit Model of Car

Ownership Choice, research paper of Duke University and University of Massachusetts at

Amherst.

Ogunbodede, E. F. (2007). Assessment of Traffic Congestions in Akure (Nigeria) using GIS

Approach: Lessons and Challenges for Urban Sustenance. Department of Geography &

Planning Sciences, Adekunle Ajasin University, Ondo State, Nigeria.

Olajide, O (2007).Introduction to Research Methodology, Niyak Print and Publication, Lagos.

49

Owolabi, A.O. (2010). Trip Patterns in Akure, Nigeria: A Land-Use Analytical Approach.

Journals of Transportation Management; fall 2010, Vol. 21 Issue 2A, p63

Oyedepo, O.J and Makinde, O.O (2010). Accident Prediction Models for Akure – Ondo

Carriageway, Ondo State Southwest Nigeria; Using Multiple Linear Regressions. African

Research Review: An International Multi-Disciplinary Journal, Ethiopia,Vol. 4 (2)

April, 2010, pp. 30-49.

RAND Europe (2002).Audit of Car Ownership Models, Rand Europe Report 01192, Jan. 2002,

Hague.

Rodrigue, J-P et al. (2013).The Geography of Transport Systems, Hofstra University,

Department of Global Studies & Geography. Retrieved from

http://www.people.hofstra.edu/geotrans.on 03/10/2013 at 12:00noon

Scott, D.M. & Axhausen, K.W. (2005). Household Mobility Tool Ownership: Modelling

Interactions between Cars and Season Tickets Transportation (forthcoming).

Tanner, J.C. (1958). An Analysis of Increases in Motor Vehicles in Great Britain, Research

Note RN/1631, Road Research Laboratory, Harmondsworth

Tanner, J.C. (1978). Long Term Forecasting of Vehicle Ownership and Road Traffic (with

discussion), Journal of the Royal Statistical Society, Series A, Volume 14, pp14-63

Tardiff, T.J. (1980). Vehicle Choice Models: Review of Previous Studies and Directions for

Further Research. Transportation Research Part A, 14A, 327-335.

The Nigeria Imports (n.d). Retrieved from www.tradingeconomics.com on 12/09/2013 at 1:34pm

Train, K. (1986).Qualitative Choice Analysis: Theory, Econometrics and an Application to

Automobile Demand (Cambridge, MA: MIT Press).

50

Whelan, G. (2001). Methodological Advances in Modelling and Forecasting Car Ownership in

Great Britain, paper presented to European Transport Conference, PTRC, and

Cambridge.

Whelan, G. (2007). Modelling Car Ownership in Great Britain, Transportation Research Part A

41 pp 205–219.

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APPENDIX A

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APPENDIX B

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APPENDIX C

MAIN PROJECT2

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