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ABSTRACT

Internal Volume Forecasting Technique is one of the several methods of Quick
Response Travel Demand technique. This thesis reviewed the various techniques used
in travel demand modelling and demonstrated the application of the Internal Volume
Forecasting technique towards deriving a travel demand model for Zaria. The study
successfully validated the simplified travel demand model for application in the study
area and by extension, others with similar characteristics. The method was applied to
four zones (Zaria city, Sabon Gari, Tudun Wada and Samaru) all in Zaria Urban Area.
The data used in the model include demographic and socio-economic data. These are
population, employment, recreation and commercial activities. Appropriate
combinations of production and attraction which was population and recreation only for
the zones of study produced a final model of trip generation and distribution as;
􀢂 = 􀫚􀫜􀫚􀫟.􀫠􀫝􀫙+􀫙.􀫙􀡼􀫙􀡾. 􀫢Th×e 􀡲travel time exponent of 2.0 found in this study
conform to the study conducted by the U. S. Department of Transportation in the year
2000. The result of the model can be used in future forecasting of traffic in Zaria after a
further slight improvement to the model to carry out the assignment stage. A Microsoft
excel sheet probability factors.xls has been created in the course of this work to ease the
difficulty of determining the trip probability factors.

 

 

TABLE OF CONTENTS

Title page i
Dedication ii
Declaration iii
Certification iv
Acknowledgements v
Abstract vi
Table of contents vii
List of tables x
List of Figures xii
Chapter One: Introduction 1
1.1 Preamble 1
1.2 Statement of the Problem 3
1.3 Aim and Objectives 4
1.4 Study Area 4
Chapter Two: Literature Review 8
2.1 Travel Demand and Forecasting 8
2.2 Units for Measuring Travel Demand 12
2.3 Measuring Existing Travel 13
2.4 Time Frame for Travel Survey 12
2.5 Traffic Volume and Passenger Counts 15
2.6 Forecasting Future Travel on Road Segments and / or Transit
Lines
16
2.7 Direct Estimation of Traffic Volume by Trend Analysis 16
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2.8 Stepwise / Sequential Procedure 16
2.9 Data Required for Travel Demand Model 17
2.10 Land Use Models 20
2.11 A Brief History of Land – Use and Travel Demand Modelling 21
2.12 Development of Travel Demand Models 22
2.13 The Need for Land Use Models 25
2.14 NCHRP Report 365 28
2.15 The Lowry Model and Derivatives 30
2.16 Model Calibration 32
2.17 Regression Analysis 33
2.18 Trip Generation Model: Aggregate Approach 33
2.19 Trip Generation Model: Disaggregate Approach 35
2.20 Applications of Travel Models 40
2.21 Theoretical Framework and Concepts 41
Chapter Three: Research Methodology 44
3.1 Introduction 44
3.2 Types of Data Required 44
3.3 Sources of Data 44
3.4 Data Collection Strategy and Methodology 40
3.5 Data Analysis 45
3.6 Model Development of IVF Technique 46
Chapter Four: Analysis and Discussion of Results 49
4.1 Introduction 49
4.2 Employment Status 49
4.3 Inter – Regional Traffic by Road 49
ix
4.4 Road Network of Study Area 49
4.5 Traffic Analysis Zone 50
Chapter Five: Summary, Conclusion and Recommendations 69
5.1 Summary 69
5.2 Conclusion 70
5.3 Recommendations 70
References 72
Appendix I: Regression Analysis Result, All Land Uses (n=2) 80
Appendix II: Regression Analysis Result, Population and recreation
only (n=2)
82
Appendix III: Regression Analysis Result, All Land Uses (n=1.5) 84
Appendix IV: Regression Analysis Result, Population and
Recreation (n=1.5)
86
Appendix V: Regression Analysis Result, All Land Uses (n=2.5) 88
Appendix VI: Regression Analysis Result, All Land Uses (n=2.5) 90
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Project Topics

 

CHAPTER ONE

INTRODUCTION
1.1 Preamble
Many urban activities are not situated in one place and there is an interesting need for
people to travel to work, school, for shopping and to visit other places in the city, so as
to satisfy their daily needs. To overcome the impedance from their activity they require
particular means of mobility and those without personal vehicles must make use of
public transport for such journeys in cities. Workers make trips daily from the
residential areas to their workplaces generally characterizes the traffic. Traders too carry
their wares from one part of the city to another, while others go to schools and business
centres.
The demand for transport is inevitable within the context of socio-economic interaction
in the society. For instance, it enhances city logistics relatively to land use, traffic and
delivery characteristics. However, the Growth in Demand for urban transport in
Nigerian cities like Zaria is largely influenced by the following factors;
i. Large increase in urban population, leading to a proportional increase in
motorised trips.
ii. Spread of urban areas – longer journeys and more fuel consumption
iii. Greater availability of motorized transport, resulting in more motorized trips and
increase in fuel consumption;
iv. Increase in household incomes, creating a greater propensity for travel
v. Increases in commercial and industrial activities; leading to increased volumes
of service vehicles and freight traffic.
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A thorough understanding of existing travel pattern is necessary for identifying and
analyzing existing traffic related problems.
Detailed data on current travel pattern and traffic volumes are needed also for
developing travel forecasting/prediction models. The prediction of future travel demand
is an essential task of the long-range transportation planning process for determining
strategies for accommodating future needs.
Travel demand models use current traffic and transit ridership characteristics along with
population and employment forecasts to predict future travel demand in terms of mode
choice, destination, temporal distribution, route choice, etc. Travel demand models were
originally developed (Alan, 1993) to determine the impacts of major highway projects.
Traditional travel demand models employ an approach known as the Urban
Transportation Modelling System (UTMS), which is also called the four-step planning
model (Hogberg, 1976). The four-step planning model involves predicting the number
of trips produced by and attracted to each zone (trip generation), creating origindestination
(O-D) matrices which link origins and destinations (trip distribution),
determining the portion of travellers that will use each available mode (mode split), and
assigning each trip to a particular route (trip assignment).
One of the most expensive and time-consuming tasks in the travel – demand modelling
process is the collection of data on household, land use, and trip characteristics for
calibration of the Urban Travel Demand (UTD) models. In most urban areas it is
necessary to conduct a special home-interview survey to obtain the required data. Even
xv
for a small city the data collection process can take more than a year at a cost of many
thousands of dollars (Smith et al., 1978).
A travel demand model, the Internal Volume Forecasting model, which uses traffic
counts rather than home – interview survey data for model calibration has been found to
simplify the travel demand model process (Smith et al., 1978). The aggregate approach
provided by this method makes estimation of zonal trip frequency quite convenient with
a few regression variables. The method is also economical in terms of data collection,
calibration and operation.
1.2 Statement of the Problem
Due to the growth in land development and population, residents of Zaria are faced with
the problems of delay in accomplishing intra-city transport related tasks.
Prolonged routes and longer trip times in many cases have become unavoidable due to
inadequacy of fundamental requirements such as:
 Effective regulatory control of transport operations.
 Transport infrastructure and facilities as alternative accessibilities between
certain areas of the town such as a bypass to convey traffic from Sokoto,
Zamfara, Katsina, Jos and Kaduna away from Kwangila.
 Adequate research data for planning and projection of the current and future
transport demand.
These problems have resulted in increased frequency of accidents, traffic congestion
during peak hours and the accompanying negative externalities.
xvi
Consequently, this research intends to focus on developing a travel demand model for
Zaria through the use of a simplified travel demand analysis.
1.3 Aim and Objectives
1.3.1 Aim
The broad aim of this study is to develop a travel demand model of Zaria with a view to
providing a platform for forecasting travel pattern of the area.
1.3.2 Objectives
The study has the following objectives
1. To delineate the study area into compact traffic zones and determine the
population, employment, recreational and commercial uses in each of the
identified traffic zones as well as the travel times.
2. To develop a model that will provide the basis for forecasting future
development of the transport system in Zaria using simplified forecasting
techniques.
1.4 Study Area
The modern urban area of Zaria consists of five nuclei of settlement centres all arranged
successively along a major highway.
The urban area of Zaria is made up of four districts namely the old walled city, the
colonial township (Tudun Wada and GRA), the Ahmadu Bello University (ABU)
Samaru village area and Sabon Gari. Each of these units has characteristics associated
with age in which it was built and with the culture of its builders and occupations.
xvii
Fig. 1.1 Map of Zaria Urban Area Showing Study Zones (Source: Field survey and satellite imagery, 2011)
1.4.1 Birnin Zaria
The original Birnin (walled – city) retains much of physical character that matured
during its evaluation. Briefly described, it is a complex maze of mud-walled
compounds tightly arranged into groupings which are separated by – narrow footpaths
that cut their ways through the city leading to the market areas, the Emir’s Palace and
adjacent Friday mosque which forms the focal point of the city. The population of the
walled city was about 1300 people in 1918. The National Population Commission gave
an estimated population of about 30,000 people in 1991.
xviii
1.4.2 Tudun Wada
The Tudun Wada area situated immediately north of the main gate of Zaria City that
opens towards the North West is also being occupied by residential quarters and
educational institute (tertiary institute and secondary school). The main market is
located at the main road i.e. at the centre of the area. One of the main road networks
include Wusasa to Congo via Gaskiya, in addition to the main road of Zaria that divide
the area into two leading to Sabon Gari from Zaria City. Two other roads originate
from the area and lead to other towns in Kaduna and Jos.
1.4.3 The Colonial Township
The colonial township founded in the first decade of the twentieth century is about three
kilometres north of the city across the river Kubani from Tudun Wada. It serves as the
centre of provincial administration, the Nigerian Railway corporation workshop and
modern commercial activities and industries.
The Government Residential Area (GRA) which forms the largest part of the old
European location is composed of extensive planting of ornamental trees and shrubs
with a golf course separating the GRA from the residential compound.
1.4.4 Samaru
The nucleus of the fourth and most recent addition to Zaria urbanized areas was the
Agricultural Research station founded in Samaru in 1924. The station attracted the
School of Agriculture by 1945 which in turn precipitated the laying out of newly
planned Samaru Village with the development of better road and the increased use of
automobiles. Other government institutions were cited; these include the Zaria branch
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of Nigerian College of Arts, Sciences and Technology which is currently the main
campus of Ahmadu Bello University, the Industrial Development Centre, The Federal
Ministry of Works and Housing and the Northern Nigerian Housing Corporation
Housing Estate were cited near Samaru, while the Nigerian College of Aviation
Technology is the second largest institution complex located at the outskirts near
Samaru.
Ahmadu Bello University (ABU) is the first largest tertiary institution located in
Samaru. It was established in 1963 thereby creating sources of income and revenue for
both the people in the city and Government.
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