main project2
TRANSCRIPT
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.
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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.
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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
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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
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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
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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
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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.
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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.
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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
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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)
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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.
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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
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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
The model obtained using Statistical Package for Social Sciences (SPSS) is:
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
Prediction Ability of the Model: " Coefficient of Correlation “R”, R = 0.765 which means that
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
is explained by the independent variables.
4.4.3 High Density Zone Vehicle Ownership Model
The model obtained using Statistical Package for Social Sciences (SPSS) is:
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.
Prediction Ability of the Model: " Coefficient of Correlation “R”, R = 0.789 which means that
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
is explained by the independent variables.
4.4.4 Model for Akure Metropolis
This model is developed using the combination of all the data collected from all the areas.
The model obtained using Statistical Package for Social Sciences (SPSS) is:
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
Prediction Ability of the Model: " Coefficient of Correlation “R”, R = 0.802 which means that
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
is explained by the independent variables.
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.
42
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
44
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:
45
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.
47
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APPENDIX A
52
APPENDIX B
53
APPENDIX C