1

The Islamic University-Gaza

Higher Education Deanship

Faculty of Engineering

Civil Engineering Department

Development of Mode Choice Model for

Gaza City

(نموذج الختيار وسائل النقل في مدينة غزة تطوير)

Sadi I. S. Alraee

Supervised by

Dr. Essam Almasri

A thesis submitted in partial fulfillment of the requirements for the

Degree of Master of Science in Civil Engineering- Infrastructure

The Islamic University of Gaza- Palestine

June, 2012

I

بسم اهلل الرمحن الرحيم

درجات و يرفع اهلل الذين امنوا منكم و الذين أوتوا العلم

اهلل مبا تعملون خبري

صدق اهلل العظيم

(11)سورة اجملادلة

II

DEDICATION

To the soul of my mother God rest her soul; to my loving father who supported me all the way; to my wife for her unlimited support and encouragement; to my son whose innocent energy was and still a source of inspiration; to all my friends who stood beside me with great commitment; I dedicated this work hopping that I made all of them proud.

Sadi Alraee

III

ACKNOWLEDGMENT

First and foremost I would like to thank God for giving me inspiration, ability, and

discipline to make it through.

I would like to extent my sincere thanks to my supervisor Dr. Essam Almasri for all

his support and guidance during my thesis. His valuable suggestion and comments

always served me as a source of inspiration and encouragement

I would like to express my gratitude to the higher education division at faculty of

engineering for their administration and academic support.

My special thanks to all my friends and colleagues for their unlimited support and

encouragement.

Finally, I would like to thank my family for their support, love and for tolerating the

time I spend working with my research.

IV

ABSTRACT

Gaza city is considered one of the most densely populated areas in the world and it is

the most densely city in Gaza strip. The lack of efficient application of transportation

planning process leads to deficiency in adopting the suitable transport policies to

mitigate the transportation problems resulting from urbanization and rapid increase of

population. The mode choice model is probably the most important element in

transportation planning and policy making.

The aim of this study is to develop mode choice model for work trips in Gaza city and

therefore investigating the factors that affect the employed people’s choice for

transport modes. The revealed and stated preference mode choice models were

developed using about 2/3rd

of 552 questionnaires distributed for this purpose. The

rest 1/3rd

of questionnaires were used to validate the chosen models.

The results of this research show that the factors that significantly affect the choice of

transport modes for revealed model are: total travel time, total cost divided by

personal income, ownership of means of transport, distance, age, and average family

monthly income. The results also indicated that the travel time, fare divided by

personal income, frequency of service, age, average family monthly income and

distance are the factors that affect the mode choice for stated preference model. Both

revealed and stated preference models as illustrated in the results are able to predict

the choice behavior of employed people in Gaza city as the two models are valid at

95% confidence level.

This study can be used by transportation planners to predict the employed people’s

behavior and travel demand analysis in addition to study the possibility and feasibility

of introducing the bus services to the transport system in Gaza city. The developed

models can be used for predicting the future modal split by inputting predicted future

value of exploratory variables.

Key wards: Gaza City, Mode Choice, Transportation Planning.

V

ملخص البحث

قطاع مدنتعتبر مدينة غزة واحدًة من أعلى المناطق السكنية كثافًة في العالم كما أنها األعلى كثافًة من بين و لقد أدى الضعف في التطبيق الفعال آلليات و أسس تخطيط النقل و المواصالت الى القصور في تبني .غزة

و يعد . عداد السكانأسياسات نقل مناسبة للحد من المشاكل المرورية المرتبطة بالتمدن و الزيادة المطردة في

لية التخطيط و صنع السياسات في مجال النقل و نموذج اختيار وسائل النقل واحدًا من العناصر المهمة في عم .المواصالت

و يتمثل الهدف الرئيسي لهذه الدراسة في بناء نموذج رياضي الختيار وسائل النقل لرحالت العمل في مدينة غزة مقد تلهذا الغرض ف و. المستخدمة لوسائل النقلالعامين العوامل التي تؤثر في اختيار ما يترتب عليه من تحديد

باستخدام و ذلكفي مدينة غزة لرحالت العمل وسائل النقل رالختيا خر افتراضياواقعي و نموذج بناء

بينما استبيان 555و البالغ عددها التي تم توزيعها لهذا الغرض تاالستبياناالمعلومات التي تم جمعها من ثلثي . بناؤهاتم تيذج الااستخدم الثلث المتبقي في اختبار صحة النم

بشكل معنوي وضحت نتائج الدراسة المتعلقة بالنموذج الواقعي الختيار وسائل النقل أن العوامل التي تؤثرأو لقد الكلية للرحلة مقسومة على التكلفة الزمن الكلي للرحلة،: لوسائل النقل في مدينة غزة هي العاملين في اختيار

امتالك وسيلة نقل، المسافة،العمر و متوسط الدخل الشهري األسرة،متوسط الدخل الشهري للفرد الواحد في التي تؤثر وضحت النتائج أن العوامل أما فيما يتعلق بالنموذج االفتراضي الختيار وسائل النقل فقد أ. للعائلة

لكل فرد زمن الرحلة ، التعرفة مقسومة على الدخل الشهري : بشكل معنوي في اختيار العاملين لوسيلة النقل هي ن كال أو قد بينت النتائج . من العائلة ، التكرار ، العمر ، متوسط الدخل الشهري للعائلة و طول الرحلة

لوسائل النقل المستخدمة في القوى العاملةالنموذجين الواقعي و االفتراضي لديهما القدرة على التنبؤ باختيارات

%.55حيث انهما صحيحان عند مستوى ثقة مدينة غزة

من قبل مخططي النقل و المواصالت التي تم بناؤها وصت الدراسة بان يتم استخدام النماذج أو في النهاية باإلضافة الى استخدامها في دراسة امكانية و جدوى إدخال خدمة في اختيار وسائل النقل العاملينللتنبؤ بسلوك

كما يمكن استخدام نتائج هذه الدراسة للتنبؤ المستقبلي . الى نظام المواصالت في مدينة غزة النقل بالباصات . بأنماط وسائل النقل و ذلك من خالل ادخال القيم المستقبلية المتوقعة للمتغيرات االستكشافية

VI

TABLE OF CONTENTS

Detecation ............................................................................................................ 1

Aknoledgement .............................................................................................. I1

Abstract .............................................................................................................. III

Abstract (arabic) ........................................................................................... V

table of contents ........................................................................................ VI

list of abbreviations ................................................................................... X

List of figures .................................................................................................. XI

list of Tables ................................................................................................. XIII

Chapter 1: Introduction ........................................................................... 1

1.1Background ............................................................................................................... 1

1.2 Problem statement .................................................................................................... 3

1.3 Research objectives .................................................................................................. 4

1.4 Research Significance .............................................................................................. 5

1.5Research Scope: ........................................................................................................ 5

1.6 Research methodology ............................................................................................. 5

1.7 Thesis organization .................................................................................................. 6

Chapter 2: Literature Review ................................................................ 7

2.1 Introduction .............................................................................................................. 7

2.2 Background .............................................................................................................. 7

2.3Urban transportation planning process ................................................................... 10

2.3.1Classical Four-Step Model ............................................................................... 11

2.3.1.1 Trip Generation ........................................................................................ 11

2.3.1.2 Trip Distribution Model ........................................................................... 13

2.3.1.3 Modal Split............................................................................................... 14

2.3.1.4 Trip Assignment....................................................................................... 14

VII

2.4 Mode choice model ................................................................................................ 15

2.4.1 Overview and historical development of the mode choice model .................. 15

2.4.2 Factors influencing mode choice .................................................................... 16

2.4.3 Aggregate mode choice models ...................................................................... 17

2.4.3.1 Trip-End modal split models ................................................................... 17

2.4.3.2 Trip-Interchange modal split models ....................................................... 17

2.4.4 Disaggregate (Discrete) mode choice models ................................................. 18

2.4.5 Theoretical Framework for disaggregate mode choice models ...................... 19

2.4.6 Types of Mode Choice Models ....................................................................... 21

2.4.6.1 Logit Model ............................................................................................. 22

2.4.6.1.1 Binary Logit Models ............................................................................. 23

2.4.6..1.2 Multinomial Logit Models ................................................................... 26

2.4.6.2 Probit Model ............................................................................................ 27

2.4.6.3 General Extreme Value Model ................................................................ 29

2.4.7 Comparison of Modal Split Models ................................................................ 29

2.4.8 Model Estimation Techniques ........................................................................ 31

2.4.8.1 Maximum Likelihood Method ................................................................. 31

2.4.8.2 Least Squares Method .............................................................................. 33

2.5 Sampling and Data Collection ............................................................................... 33

2.5.1 Travel Survey Types ....................................................................................... 33

2.5.1.1 Household Travel Surveys ....................................................................... 34

2.5.1.2 Workplace Surveys .................................................................................. 34

2.5.1.3 Destination Survey ................................................................................... 34

2.5.1.4 Intercept Survey ....................................................................................... 35

2.5.2 Sampling Generation Methods ....................................................................... 35

2.5.2.1 Simple Random Sampling ....................................................................... 35

2.5.2.2 Stratified Random Sampling .................................................................... 36

VIII

2.5.2.3 Multi-stage Sampling ............................................................................... 37

2.5.2.4 Cluster Sampling ...................................................................................... 38

2.5.2.5 Systematic Sampling ................................................................................ 38

2.5.3 Revealed and stated preference survey ........................................................... 39

2.6 Previous case studies of mode choice modeling .................................................... 41

2.7 Summary ................................................................................................................ 44

Chapter 3: Research Methodology ................................................. 46

3.1Stages of the Study.................................................................................................. 46

3.2Study area…………………………………………………………………………50

3.3Target group ............................................................................................................ 50

3.4Design of Questionnaire ......................................................................................... 51

3.5 Sample Size Determination.................................................................................... 53

3.6 Pilot Study .............................................................................................................. 54

3.7 Preliminary analysis of questionnaire .................................................................... 54

3.8 Model Calibration and Comparison ....................................................................... 55

3.9Model Validation .................................................................................................... 56

Chapter 4: Results &Analysis ............................................................... 58

4.1 Introduction ............................................................................................................ 58

4.2 General Analysis of Data ....................................................................................... 58

4.2.1 Gender of respondents .................................................................................... 58

4.2.2 Status of respondents ...................................................................................... 59

4.2.3 Jobs of respondents ......................................................................................... 60

4.2.4 Age of respondents ......................................................................................... 61

4.2.5 Monthly income of respondents...................................................................... 62

4.2.6 Family size of respondents.............................................................................. 63

4.2.7 Ownership of transport modes ........................................................................ 64

4.2.8 Trip length ....................................................................................................... 65

IX

4.2.9 The means of transport usually used by the respondentsUsually used by the

respondents ..................................................................................................... 66

4.3 Relation between the mode of transport and socioeconomic characteristics ......... 68

4.3.1 Relation between the mode of transport and gender ....................................... 68

4.3.2 Relation between the mode of transport and marital status ............................ 70

4.3.3 Relation between the mode of transport and age ............................................ 72

4.3.4 Relation between the mode of transport and family size ................................ 74

4.3.5 Relation between the mode of transport and the monthly income.................. 76

4.3.6 Relation between the mode of transport and the Job ...................................... 78

4.3.7 Relation between the mode of transport and the ownership of means of

transport .......................................................................................................... 80

4.3.8 Relation between the mode of transport and the length of trip ....................... 82

4.4 Relation between the captive ridership and socioeconomic characteristics .......... 85

4.5 hypothetical questions ............................................................................................ 86

4.6 Importance of factors that affect mode choice ....................................................... 88

4.7 Calibration of revealed model ................................................................................ 89

4.8 Validation for revealed model ............................................................................. 106

4.9 Calibration of stated preference model ................................................................ 107

4.10 Validation of stated preference model ............................................................... 114

Chapter 5: Conclusions & Recommendations ........................ 115

5.1 Summary .............................................................................................................. 115

5.2 Conclusions .......................................................................................................... 116

5.2 Recommendations ................................................................................................ 118

References .................................................................................................................. 119

Annex1: Questionnaire in Arabic…………….………………...…123

ANNEX2: Questionaaire in english ……….…...………………….128

X

LIST OF ABBREVIATIONS

UTMS Urban Transport Model System

O-D Origin –Destination

SPME Single Path Matrix Estimation

MPME Multiple Path Matrix Estimation

AON All or Nothing

DM Discrete Choice Models

IIA Independent of Irrelevant Alternative

OSL Ordinary Least squares

SPSS Statistical Package for Social Science

RII Relative Important Index

ELM Easy Logit Model

LRTS Likelihood Ratio Test

PCBS Palestinian Central Bureau of Statistics

SP Stated Preference

CAPI Computer Assisted Personal Interviewing

XI

LIST OF FIGURES

Figure (2.1): Role of transport modeling in policy making ........................................... 8

Figure (2.2): Classical Four-Step Model ....................................................................... 9

Figure (2.3): Urban transportation planning process ................................................... 10

Figure (2.4): Example of a Simple Binary Logit Model .............................................. 23

Figure (2.5): Example of a nested Binary Logit Model ............................................... 25

Figure (2.6): Example of a simple multinomial Logit Model ...................................... 26

Figure (2.7): Example of a nested multinomial Logit Model ...................................... 27

Figure (2.8): Classification of mode choice models .................................................... 30

Figure (2.9): Example of Multistage Sampling Process .............................................. 37

Figure (3.1): Flow chart for research methodology ..................................................... 49

Figure (3.2): Study Area (Gaza city) ........................................................................... 50

Figure (4.1): Respondent’s gender ............................................................................... 59

Figure (4.2): Respondent’s status ................................................................................ 59

Figure (4.3): Respondent’s job .................................................................................... 60

Figure (4.4): Respondent’s age .................................................................................... 61

Figure (4.5): Respondent’s monthly income ............................................................... 62

Figure (4.6): Respondent’s family size ........................................................................ 63

Figure (4.7): Respondent’s ownership of transport means .......................................... 64

Figure (4.8): Trip length .............................................................................................. 65

Figure (4.9): The percent of different modes usually used by the respondents ........... 66

Figure (4.10): The number of captive and choice riders for different modes .............. 67

Figure (4.11): The percent of male and female riders for different modes .................. 69

Figure (4.12): Distribution of transport modes for marital status ................................ 71

Figure (4.13): Distribution of transport modes for age ................................................ 73

Figure (4.14): Distribution of transport modes over family size ................................. 75

Figure (4.15): Distribution of transport modes over monthly income ......................... 77

XII

Figure (4.16): Distribution of transport modes over job .............................................. 79

Figure (4.17): Distribution of transport modes over ownership of transport means ... 81

Figure (4.18): Distribution of transport modes over trip length .................................. 83

Figure (4.19): Distribution of riders’ choice for different levels (total sample) .......... 87

XIII

LIST OF TABLES

Table (2.1): Comparison of Common Mode Choice Models (Khan 2007) ................. 30

Table (3.1): Different levels of the hypothetical questions.......................................... 52

Table (4.1): Frequency table for respondent’s gender ................................................. 58

Table (4.2): Frequency table for respondent’s status ................................................... 59

Table (4.3): Frequency table for respondent’s job ....................................................... 60

Table (4.4): Frequency table for respondent’s age ...................................................... 61

Table (4.5): Frequency table for respondent’s monthly income .................................. 62

Table (4.6): Frequency table for respondent’s family size .......................................... 63

Table (4.7): Frequency table for respondent’s ownership of means of transport ........ 64

Table (4.8): Frequency table for trip length ................................................................. 65

Table (4.9): Frequency table for the modes of transport thatUsually used by the

respondents .................................................................................................................. 66

Table (4.10): Frequency table for the choice and captive riders ................................. 67

Table (4.11): Cross tabulation between the mode of transport and gender ................. 69

Table (4.12): Chi-square test for mode-gender relationship ........................................ 70

Table (4.13): Cramer’s V statistics for mode-gender relationship .............................. 70

Table (4.14): Cross tabulation between the mode of transport and marital status....... 71

Table (4.15): Chi-square test for mode-marital status relationship ............................. 72

Table (4.16): Cramer’s V statistics for mode-marital status relationship .................... 72

Table (4.17): Cross tabulation between the mode of transport and age ...................... 73

Table (4.18): Chi-square test for mode-age relationship ............................................. 74

Table (4.19): Cramer’s V test for mode-age relationship ............................................ 74

Table (4.20): Cross tabulation between the mode of transport and family size .......... 75

Table (4.21): Chi-square test for mode-family size relationship ................................. 76

Table (4.22): Cramer’s V test for mode-family size relationship ................................ 76

Table (4.23): Cross tabulation between the mode of transport and monthly income .. 77

XIV

Table (4.24): Chi-square test for mode-monthly income relationship......................... 78

Table (4.25): Cramer’s V test for mode-monthly income relationship ....................... 78

Table (4.26): Cross tabulation between the mode of transport and job ....................... 79

Table (4.27) :Chi-Square Tests for mode-job relationship .......................................... 80

Table (4.28): Cramer’s V test for mode-job relationship ............................................ 80

Table (4.29): Cross tabulation between the mode of transport and ownership of means

of transport ................................................................................................................... 81

Table (4.30) :Chi-Square Tests for mode-ownership of transport means relationship 82

Table (4.31): Cramer’s V test for mode-ownership of transport means relationship .. 82

Table (4.32): Cross tabulation between the mode of transport and length of trip ....... 83

Table (4.33):Chi-Square Tests for mode-trip length relationship ................................ 84

Table (4.34): Cramer’s V test for mode-trip length relationship ................................. 84

Table (4.35): Test of relationship between the mode choice and travel socioeconomic

variables ....................................................................................................................... 85

Table (4.36): Test of relationship between the captive ridership and travel

socioeconomic variables .............................................................................................. 86

Table (4.37): Distribution of riders’ choice for different levels .................................. 86

Table (4.38): Relative Importance Index and Rank of the factors that affect mode

choice ........................................................................................................................... 88

Table (4.39): Abbreviation and description of explanatory variables ......................... 89

Table (4.40): Estimation results of model_1 ................................................................ 91

Table (4.41): Estimation results of model_2 ................................................................ 92

Table (4.42): Estimation results of model_3 ................................................................ 94

Table (4.43): Estimation results of model_4 ................................................................ 95

Table (4.44): Estimation results of model_5 ................................................................ 97

Table (4.45): Estimation results of model_6 ................................................................ 99

Table (4.46): Estimation results of model_7 .............................................................. 100

Table (4.47): Estimation results of model_8 .............................................................. 102

XV

Table (4.48): Estimation results of model_9 .............................................................. 104

Table (4.49): Abbreviation and description of explanatory variable used in stated

preference model ........................................................................................................ 107

Table (4.50): Estimation results of model_S1 ........................................................... 108

Table (4.51): Estimation results of model_S2 ........................................................... 109

Table (4.52): Estimation results of model_S3 ........................................................... 110

Table (4.53): Estimation results of model_S4 ........................................................... 111

Table (4.54): Estimation results of model_S5 ........................................................... 112

1

Chapter 1: Introduction

1.1 Background

Gaza strip is located at the southern part of Palestine with area of 365 km2. It

composed of five governorates which are; Gaza, Middle, Northern, Khanyounis, and

Rafah governorate. According to the census conducted by the Palestinian central

bureau of statistics (PCBS) ; the total number of populations of Gaza strip at the mid

2011 is 1.59 millions. The percent of males is about 50.6% and the females represent

about 49.4% of the populations. According to these figures Gaza strip is considered

one of the most densely populated area in the world with 4356 inhabitants/km2. The

population pyramid for Gaza strip shows that the Palestinian community is a young

society where the percent of population in the range between 0-14 years is about

44.1% and the populations between 15-29 years is about 29.7% while the percent of

populations over 65 years represents about 2.4% of the populations. The percent

populations within the work age (over than 15 years) represents about 51.7%. The

participation of labor force is 38.1% of the populations within the work age. The

percent of unemployment is about 40.6% of the peoples within the labor force.

Gaza is the densely populated governorate in Gaza strip with a density of 7.5

inhabitants/dun. The area of Gaza is 72593 dounms and the number of population at

mid 2011 is 552,000 persons Gaza city is composed of eleven districts. Gaza has one

of the most highly rate of populations increase with a rate of 4% annually. The

participation of labor force in Gaza is about 36.4% of the peoples within the work age

and the percent of unemployment is about 38.3%.

The construction of modern paved roads in Gaza began in the last century. The road

network which was planned and constructed between 1936-1945 aimed at serving the

British security and logistics requirements in the second war. During the period

between 1947-1967 there is no significant improvement of the road networks. Only

minor improvement and construction of few roads leading to population centers

during the Egyptian period which ended in 1967. During the Israeli occupation for

Gaza strip, only minor improvements of road network have taken place. The roads

were constructed and improved for serving the Israeli settlements. During this period

a Minor improvements for the roads that constructed during the Egyptian period for

serving the Palestinian. In May 1994 after signing the Interim Agreement in Cairo, the

2

Palestinian National Authority takes its role in constructing Gaza strip. During this

period a highly improvement of roads construction has occurred but this integrated

with highly increasing of population due to coming back high number of refugees to

Gaza strip. During this period the development in roads construction does not

integrated with improvement in transportation planning process and policies.

Currently Gaza city is facing a congestion and transportation problems resulting from

a rapid increase in population. In order to solve the transportation problems relating to

congestion problem a good understanding for the travelers’ behavior is needed to help

in adopting the suitable transport policies for mitigating these problems.

The transport system in Gaza city depends on land transport which can be categorized

into private and public transport. Due to the limited income levels in Gaza city, a

public transport service is playing a major role in satisfying the mobility of the

population. The public transport is served by three different modes in Gaza city which

are Shared taxi, taxi and buses. The buses in Gaza city are classified into public and

private buses. The registered and licensed public buses served the regional connection

between Gaza city and the other governorates in Gaza strip. Private buses don’t have

fixed lines but most of its work is directed towards school and university students. As

far as private buses are concerned they don’t have stations nor terminals and they

regularly don’t enter the city center. Shared taxis are the mean mode of public

transport services in Gaza city which operate to provide short haul services within the

Gaza city. Taxis are available in Gaza city for point to point transport.

Because of the growing and complex problems of congestion and air pollution in

recent years, urban policymakers have begun to ask for more sophisticated decision

tools including models to forecast travel demand and its effect under various

circumstances (Abdel-Aty and Abdelwahab, 2001). In urban and regional areas

transport models are gaining more and more importance both for traffic and transport

planning, design and operation in modern-information guidance and management

system.

Travel demand forecasting is a process used in transportation planning to predict

future demand of a transportation facility. The transportation planning process has

four basic steps that are trip generation, trip distribution, mode choice, and travel

assignment. Trip generation estimates the number of potential trips starting or ending

in a given area. Trip distribution associates origin and destination to each trip

3

generated. Mode choice analyzes how the trips are split between available modes of

transportation based on the attractiveness of each mode. Finally, travel assignment

estimates volumes on different links of the transportation network (Richardson, 2003).

The choice of transport mode is probably one of the most important classic models in

transport planning. This is because of the key role played by public transport in policy

making. Almost without exception public transport modes make use of road space

more efficiently than the private car. Moreover, if some drivers could be persuaded to

use public transport instead of cars, the rest of the car users would benefit from

improved levels of service. It is unlikely that all car owners wishing to use their cars

could be accommodated in urban areas without sacrificing large parts of the fabric to

roads and parking space (Ortuzar and Willumsen, 2002).

Mode choice process is needed when there are two or more alternatives. For example,

an individual going to work might choose among driving (if a vehicle is available),

ride public transit, or walk. Decision for taking an alternative are usually based on

complex factors. For example, a parent might decide to let his/her child walk to

school after evaluating the distance or travel time between home and school, the status

of sidewalks along the route, the pedestrian safety along the route, and neighborhood

safety issues. Another parent might allow his/her child to walk to school because

other children in their neighborhood are also walking to school (Koppelman and Bhat,

2006).

Discrete choice models are widely used in transportation modeling during the last 25

years and they have played an important role in transportation modeling. The reason

towards the wide using of these models is its ability to provide a detailed

representation of the complex aspects of transportation demand, based on strong

theoretical justifications. The art of finding the appropriate model for a particular

application requires from the analyst both a close familiarity with the reality under

interest and a strong understanding of the methodological and theoretical background

of the model (Abdel-Aty and Abdelwahab, 2001).

1.2 Problem statement

Since the Israeli occupation of Gaza Strip in 1967, the transport sector has suffered

from deterioration in terms of quality and quantity. The occupation neglected the

construction of infrastructure projects that can improve the transport sector. After

4

signing the Oslo Agreement between Israelis and Palestinians and establishment of

Palestinian National Authority in 1994 there was a dramatic improvement in the

construction of road networks. But from another side there was a highly increase in

population and vehicles due to coming back of a lot of refugees to Gaza strip. The

transport policies that were adopted by transport planners were not sufficient for

solving the transport problems resulting from the increase of travel demands.

Gaza city is currently facing urbanization and economic growth, with this, demand for

private and public transport have been increasing. To meet the increasing of travel

demand without increasing the congestion problem there is a need for increasing the

use of high occupancy modes in addition to encourage the use of non-motorized

modes (walking and biking). This could not be done without understanding the

travelers’ needs and preference of using the modes.

In orders to adopt a suitable transport policies for solving the expected congestion

problem resulting from urbanization and economic growth, there is a need for

improving the transport planning process in Gaza Strip. One step should be improved

is mode choice modeling, which is considered very essential for predicting the future

growth for each mode in addition to specifying the factors that contribute the use of

each mode and shifting from one mode to another one.

Developing countries including Gaza Strip often use the mode choice models that are

developed by the developed countries. These models are not suitable to be used as the

original form because of the different conditions and circumstances in developing

countries. Therefore, there is a need to develop mode choice model for Gaza in order

to help in predicting the future demand for each mode of transport and adopting the

suitable transport policies to solve the congestion problem.

1.3 Research objectives

The main aim of this study is to develop a mode choice model for work trips in Gaza

city that can be used to simulate the behavior of individuals towards motorized and

non-motorized modes.

This main aim includes the following objectives:

1. To provide a quantitative explanation of the choices of travel modes for work

trips in Gaza city.

2. To study the factors affecting the mode choice.

5

3. To specify the most significant factors which affect mode choice.

4. To study the various types of mode choice models.

5. To choose the most suitable model.

6. To calibrate and estimate the chosen mode choice model.

7. To validate the developed models.

1.4 Research Significance

The main aim of this study is to develop mode choice model for work trips in Gaza

city. In addition to its application in transport modeling process as a travel demand

forecasting tool, this mode choice model can be used in:

1. The analysis of probable market share of motorized and non- motorized

modes.

2. The computation of modal choice elasticity.

3. Determination of time value for Gaza city residents.

1.5 Research Scope:

The scope of this study will be limited for work trips in only Gaza city. The reason for

this limitation is the time and financial constraints.

1.6 Research methodology

This study comprises six main phases of work as follows:

First phase:

The first phase is the literature review on mode choice modeling. The concentration

will be on discrete mode choice models as they are more efficient than conventional

models. The literature review should seek for case studies applied in cities of

developing countries especially in the cities that have similar conditions. Based on the

literature review, the transportation planning process that is appropriate to Gaza City

must be decided.

Second phase:

This phase relates to the process of selection of the travel attributes. It involves,

designing of initial (pilot) survey form and analysis of the survey data. This process is

important to determine the attributes which are most relevant to the travelers in the

study area. The resulting attributes will be included in the main survey.

6

Third phase:

This phase involves designing the final survey form and conducting the survey from

start to finish including selection of level of attributes, implementation of the survey,

the collection and analysis of data.

Fourth phase:

This phase includes calibrating and estimating of the utility functions for the Model.

Fifth phase:

This phase is preliminary concerned with the model validation.

Sixth phase:

This phase summarizes the main findings and conclusions from the study.

1.7 Thesis organization

This thesis will be organized into six chapters:

Chapter one presents the introduction chapter which includes background,

problem definition, objectives, scope of the study, significance of the study

and research methodology.

Chapter two reviews briefly the literature related to discreet choice behavior in

different field of research including the current literature on transportation

planning models, and aggregate and disaggregate mode choice models.

Chapter three describes the methodology and approach for the analysis and

evaluation of the results. It also describes the explanation of theoretical

foundation of the proposed mode choice methodology.

Chapter four describes the results of the descriptive analysis of the survey as

well as development of the mode choice model for work trips in Gaza city. It

begins with the estimation procedure, calibration and validation of the model.

Chapter five concludes the study with main findings from this behavioral

experiments and how the objectives of this study have been addressed. This

chapter includes conclusions and recommendations in addition to some

thoughts of future researches.

7

Chapter 2: Literature Review

2.1 Introduction

As the mode choice model is a part of transport planning process and the third step in

4-step travel demand forecasting, this chapter presents a state of the art literature

review on passenger mode choice model. The literature reviewed in section 2.2

includes background on transportation planning process. Section 2.3 discusses the

transportation planning process in some details with a brief description of the classical

4-step model. Section 2.4 illustrates the factors that affect the choice of transportation

modes and the approaches for modeling the mode choice. This section also presents

the types and estimation techniques for various types of mode choice models along

with selecting a particular discrete mode choice model in order to forecast the travel

demand behavior for this research. Section 2.5 presents the types of travel survey and

sampling generation methods. In addition, the difference between the revealed and

stated preference survey is discussed in this section. Section 2.6 presents for the

experience of some countries in the field of modeling the mode choice behavior for

various types of trips. Finally section 2.8 summarizes the main findings from the

literature review revealing the research framework design to forecast the travel

behavior of the study area.

2.2 Background

Modeling is one important part of the most decision making process. It is concerned

with the methods, be they quantitative or qualitative which allows us to study the

relationships that underlie the decision making. (Hensher and Button, 2000)

A model is defined as a simplified representation of the real world which concentrated

on certain elements considered important for its analysis from a particular point of

view (Qrtuzar and Willumsen, 2002).

A transport models can be defined as a simplified representation of the real world

usually implemented in computer software which describe the impact of transport

decisions. Transport models can cover whole countries, cities, areas or simply

individual junctions (European commission, 1996).

Transport models allow alternative solutions to problem to be tested before resources

are committed to implementing them. Models can also be used to:

8

Create traffic control systems which response to changing transport solutions,

allowing changing to control to be made automatically every few minutes.

Help understanding the full range of impacts which may result from transport

scheme.

Evaluate the costs and benefits of transport investments and so to prioritize

investments.

Transportation is very important for sustainable development of economics. Large

investments have been made in transportation planning and policy making in order to

forecast the future demand of travel. The forecasting needs to integrate between the

designing of existing transport system and the behavior of residents in the study area.

(Khan, 2007).Transportation modeling plays an important role in supporting

transportation planning and policy making as illustrated in Figure 2.1.

Figure (2.1): Role of transport modeling in policy making (Richardson, 2003)

PROBLEM

DEFINTION

System

Resources Objectives

TRANSPORT

MODELS

Criteria Consequences

Evaluation

Selection

Implementation Monitoring

Constraints

Alternatives

Data

Collection

9

The fundamentals of transport modeling were developed in the united stated during

the 1950s' and imported in the UK in the early of the 1960s'. Thereafter the following

20 years saw important theoretical development in the field of transport modeling

leading to further work in specific sub-areas. A contemporary dimension was the

development of transport mode choice models representing the behavior of travelers

of the study area. Since then the interest of this field as well as the growing

complexity has led to further development of various travel demand models. However

the most of these models trace their origin back to classical transport demand models,

the four-step models because of its overcharging framework and logical appeal. The

basic structure of the model is illustrated in Figure 2.2

Figure (2.2): Classical Four-Step Model (MCNally, 2000)

One of the most important aspects of the transportation modeling is to predict the

travel choice behavior which is the most frequently modeled travel decisions. It

involves specific aspects of human behavior dedicated to choice decisions.

Traditionally aggregate models are used in dealing with the travel choice behavior of

individual travelers; however the aggregate models have the limitation of forecasting

and estimating of travel choice with aggregated zonal data.

Disaggregate behavioral demand models which became popular during the 1980's

offer substantial advantages over the aggregate counterparts. Disaggregate behavioral

Trip Generation

Trip Distribution

Modal Split

Trip Assignment

11

models are based on the observed choices behavior of individual travelers. These

models considered that the demand is the result of several decisions of each individual

traveler. A discrete choice analysis is the methodology used to analyze and predict the

traveler decisions. The discrete choice model is mathematical functions which

estimate the probability of selecting individual travel choice based on the utility

maximization principle or relative attractiveness of competing alternatives (Qrtuzar

and Willumsen, 2002).

Revealed and stated preference survey data which contains data sets of individual

decisions, characteristics of the individuals and the alternative choices of the trip is

used to develop the discrete choice model (Qrtuzar and Willumsen, 2002).

2.3Urban transportation planning process

Transportation planning is undertaken at many levels from strategic planning to

project level planning at different geographic scales in any urban area. The urban

transport planning process can be classified into three phases as shown in Figure (2.3)

(Hanson, 1995).

Figure (2.3): Urban transportation planning process (Hanson, 1995)

11

The pre- analysis phase concerns with defining the current situation and problems

associated with the mode for trips. This is followed by the proposed solutions of

introducing new modes. The second part of this phase includes the data collection to

be used in technical phase and evaluation. Review of both secondary sources from

available reports and documents and the primary data collection is made as required.

The technical analysis phase concerns with using the mathematical description of

travel and related behavior to predict consequences of each scenario of transport

planning that is to be evaluated.

The post analysis phase comprises of predictions of the impacts of alternative plans

and policies. The purpose of these predictions is to inform decision making. The post

analysis phase of urban transportation planning includes evaluating the impacts of

alternatives, selecting the alternatives to be implemented and future programs

associated with in.

2.3.1Classical Four-Step Model

The Urban Transport Model System (UTMS) often referred as the 4-step model is

commonly used to predict the flows on the links of a particular transportation network

as a function of the land-use activity system that generates the travel (Hanson, 1995).

The model comprises of four sub-models as shown in Figure (2.2) that are employed

in sequential process: Trip generation, Trip distribution, Mode choice (or modal split)

and trip assignment.

2.3.1.1 Trip Generation

The trip generation stage of the classical transport model aims at predicting the total

number of trips generated by and attracted to each zone of the study area. Since, it

essentially defines the total travel in the study area, it is after trip generation analysis

that the transportation planner comes up with the vital figures about the total number

of trips generated and attracted by each zone, purposes of these trips, and the

travelling modes generally used for these trips.

Ortuzar and Willumsen (2002) have demonstrated common trip generation patterns on

the basis of following standard trip purposes,

• Work trips;

• Educational trips;

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• Shopping trips; and

• Other trips (social, recreational, medical, bureaucratic trips etc.).

The most commonly used analytical technique to develop the trip generation patterns

of a study area is multiple linear regressions. In this technique, the dependent output

variable is assumed to have a linear dependence on the independent input variables,

which may or may not influence the trip generation, as shown in Equation 2.1.

EXXXY KK .......11110 (2.1)

Where,

,.....,2,1,0 K are coefficients of regression;

,.....,2,1,0 KX are independent input variables;

Y is dependent input variable;

E is error in estimating the output variable.

Definitions of the input and output variables vary with the type of linear regression

approach used in the research. Generally, two types of regression techniques are

applied in multi-modal transportation planning namely,

• Zonal-based Multiple Linear Regression; and

• Household-based Multiple Linear Regression.

The main difference between the two techniques is that the first is used to generate the

travel patterns on zonal basis, while the second does it at a household level.

Therefore, for zonal-based regression, Y is generally taken as the number of trips

generated for and attracted by each zone in the study area, while various independent

variables can be considered and tested for estimation purposes such as,

• Employment density of a zone1 (for work trips);

• School / university enrolment of a zone (for education trips); and

• shopping areas in a zone (for shopping, work, other trips).

Similarly, household-based regression tends to utilize various parameters associated

with a household, in order to estimate the regression coefficients, such as,

• Household size;

13

• Number of vehicles in a household;

• Number of adults in a household; and

• Number of workers and students in a household.

2.3.1.2 Trip Distribution Model

The trip distribution stage of the four-step model tends to provide a standard pattern

of trip making by linking the trip ends with the origins. The trip distribution model is

essentially a destination choice model and generates a trip table, for each trip purpose

utilized in the model as a function of activity-system attributes and network attributes.

This trip table, also commonly known as Origin-Destination Matrix (O-D Matrix),

provides a comprehensive illustration of the number of trips generated between

different zones of the study area. There are different traffic distribution algorithms for

forecasting the future O-D matrix which are: i) growth factor methods, ii) gravity

model iii) the entropy-maximizing approach, and iv) the proportional approach

(Qrtuzar and Willumsen, 2002).

A number of efforts have been made by transport researchers for developing efficient

and adaptive algorithms in order to optimize the O-D Matrix for achieving realistic

results. Nielsen (1994) presented two new methods for trip matrix estimation; namely

Single Path Matrix Estimation (SPME) and Multiple Path Matrix Estimation

(MPME), and demonstrated that the traffic models can be easily and cheaply

estimated using them. Three different approaches to O-D Matrix estimation were

reviewed and compared, in the context of transport planning, by Abrahamsson (1996)

who attempted to use the trip assignment parameters to calibrate the O-D matrix of

the study area. Later, Abrahamsson (1998) illustrated an O-D matrix for Stockholm,

Sweden that can reproduce the traffic counts, in terms of the number of trips

generated and attracted, using the previous distribution approaches improving the

accuracy of forecasting of O-D Matrices. Various computationally efficient

algorithms for estimating the trip distribution matrices were developed by Safwat and

Magnanti (2003) by using a simultaneous approach to develop a four-step model

rather than the conventional sequential method. Further, Ber-Gera and Boyce (2003)

developed a trip origin based algorithm for transportation forecasting models that

combine travel demand and network assignment variables in order to improve the

existing O-D flow models. Sherali et al. (2003) developed a non-linear approach to

14

estimate the O-D trip matrices by implicitly determining the path decomposition of a

network flow using a sequential linear programming approach.

2.3.1.3 Modal Split

Mode choice predicts the number of trips from each origin to each destination that

will use each mode of transportation. Clearly modal split has considerable

implications for transportation policy, particularly in large metropolitan areas (Qrtuzar

and Willumsen, 2002).

The issue of selecting the most appropriate travelling mode has always been a critical

issue in travel behavioral modeling, since it tells an individual about the most efficient

travelling mode available. Therefore, it is vital to develop and use models that are

receptive to those attributes of travel that influence a certain individual’s choice of

mode. The quantification of this interaction in terms of mathematical relationships is

known as modal split and the travel demand models are referred to as modal split or

mode choice models. Hence, the modal split assists a transport planner to assess the

impact of each urban element on mode choice and permits testing and evaluation of

various transportation schemes. This will be discussed in details in the next section

2.4

2.3.1.4 Trip Assignment

Trip assignment is the last stage of the four-step model, dealing with the allocation of

a given set of trip interchanges to a specific transport network. Its main objective is to

estimate the traffic volumes and the corresponding travel times or costs on each link

of the transportation system by the help of inter-zonal or intra-zonal trip movements

(determined by trip generation and distribution) and the travel behavior of the

individuals (determined by modal split).

The proportion of vehicles using each route between a particular origin-destination

pair depends upon a number of attributes and the alternative routes including travel

time, distance, number of stops / signals, aesthetic appeal etc. But travel time is the

attribute most commonly considered in network assignment models. There are

different traffic assignment algorithms which are: i) All-or-Nothing assignment

(AON), ii) Wardrop’s user Equilibrium assignment, iii) Method of successive

averages, iv) Stochastic user-equilibrium assignment (Qrtuzar and Willumsen, 2002).

15

Patriksson (1994) has presented a list of useful purposes of trip assignment in context

with transport planning namely,

• Assessing the deficiencies in the existing transportation system of the study area;

• Evaluating the effects of limited improvements and extensions to the existing

transportation systems;

• Developing construction priorities for the existing transportation system of the study

area; and

• Testing alternative transportation system proposals.

2.4 Mode choice model

2.4.1 Overview and historical development of the mode choice model

The model developed by Adam (1959) is one of the first modal split models to be

advised. Since mid 1960's many mode choice models for intercity travelers were

calibrated and used for prediction in various environments. Traditionally aggregate

models are used in dealing with the travel choice behavior of individual travelers;

however the aggregate models have the limitation of forecasting and estimating of

travel choice with aggregated zonal data.

The inability of aggregate data to explain the travelers’ behavior led to propose

another group of models called disaggregate models. These models require the data

that describes the behavior of an individual's characteristics and attitudes towards the

travel services provided by each mode.

Disaggregate travel demand models represent a recent innovation in travel forecasting

procedure. The earliest research into disaggregate mode choice models was done by

Warner (1962). The pioneering efforts were made during the period 1967-1969 used a

binary mode choice modeling with automobile as a base mode. Pioneers in this early

age of disaggregate modeling include, Quarmby (1967) [used discriminate analysis] ,

Elisco (1967) [used Probit analysis], Stopher (1969) [ used regression and

subsequently Logit analysis].

However it was found from the market research that besides the socioeconomic and

mode related characteristics and individual evaluates his choice depending upon the

level of service provided by the alternatives. This led to incorporation of factors such

as comfort, convenience, privacy and other mode related attitudinal indicators in the

16

models. One of the earliest efforts in this field was done by Ackoff (1965). His effort

was pioneering in considering psychological factors in mode choice. With the

development of transportation system the technology in various parts of the world,

attempts were made by prominent researcher in transportation planning to incorporate

the attitude of travelers in mode choice models.

2.4.2 Factors influencing mode choice

The factors influencing mode choice may be classified into three groups and a good

mode choice model should include the most important of these factors. These factors

are presented in Ortuzar, Willumsen, (2002) as follows:

a) Characteristics of the trip maker.

The following features are generally believed to be important:

• Car availability and/or ownership;

• Possession of a driving license;

• Household structure (young couple, couples with children, retired, singles, etc.),

• Income;

• Decisions made elsewhere, for example the need to use a car at work, take children

to school, etc;

• Residential density

b) Characteristics of the journey.

Mode choice is strongly influenced by:

• The trip purpose; for example, the journey to work is normally easier to undertake

by public transport than other journeys because of its regularity and the adjustment

possible in the long run;

• Time of the day when the journey is undertaken. Late trips are more difficult to

accommodate by public transport.

c) Characteristics of the transport facility.

These can be divided into two categories. Firstly, quantitative factors such as:

• Relative travel time: in-vehicle, waiting and walking times by each mode;

17

• Relative monetary costs (fares, fuel and direct costs);

• Availability and cost of parking.

Secondly, qualitative factors which are less easy to measure, such as:

• Comfort and convenience;

• Reliability and regularity;

• Protection, security.

2.4.3 Aggregate mode choice models

There are two basic ways of modeling aggregate behavior namely, aggregate and

disaggregate approaches. The aggregate approach directly models the aggregate share

of all or a segment of decision makers choosing each alternative as a function of the

characteristics of the alternatives and socio-demographic attributes of the group.

There are two types of aggregate mode choice mode namely, trip-end modal split

models and Trip-interchange modal split models

2.4.3.1 Trip-End modal split models

Traditionally, the objective of transportation planning was to forecast the growth in

demand for car trips so that investment could be planned to meet the demand. When

personal characteristics were thought to be the most important determinants of mode

choice, attempts were made to apply modal-split models immediately after trip

generation. Such a model is called trip-end modal split model. In this way different

characteristics of the person could be preserved and used to estimate modal split. The

modal split models of this time related the choice of mode only to features like

income, residential density and car ownership. The advantage is that these models

could be very accurate in the short run, if public transport is available and there is

little congestion. Limitation is that they are insensitive to policy decisions as example:

Improving public transport, restricting parking etc. would have no effect on modal

split according to these trip-end models (Ortuzar, Willumsen, 2002).

2.4.3.2 Trip-Interchange modal split models

This is the post-distribution model; that is modal split is applied after the distribution

stage. This has the advantage that it is possible to include the characteristics of the

journey and that of the alternative modes available to undertake them However, they

18

make it more difficult to include the characteristics of the trip maker as they may have

already been aggregated in the trip matrix (or matrices). It is also possible to include

policy decisions. This is beneficial for long term modeling. (Ortuzar, Willumsen,

2002).

The important limitation of these models is that they can only be used for trip

matrices of travelers who have a choice available to them. This often means the

matrices of car available persons, although modal split can also be applied to the

choice between different public transport modes (Ortuzar and Willumsen, 2002).

The models have little theoretical basis and therefore their forecasting ability must be

in doubt. They also ignore a number of policy sensitive variables like fares, parking

charges and so on. Further, as the models are aggregate they are unlikely to model

correctly the constraints and the characteristics of the modes available to individual

households (Ortuzar and Willumsen, 2002).

2.4.4 Disaggregate (Discrete) mode choice models

The disaggregate approach is to recognize that aggregate behavior is the result of

numerous individual decisions and to model individual choice responses as a function

of the characteristics of the alternatives available to and socio-demographic attributes

of each individual. Disaggregate mode choice models have substantial advantages

over the aggregate models for predicting the consequences of transportation policy

measures that affect mode choice. The advantages and useful proprieties of

disaggregate models have presented by (Ortuzar and Willumsen, 2002, Koppel and

Bhat, 2006, and Siddiqui, 1999) as follows:

1. The disaggregate approach explains why an individual makes a particular

choice given her/his circumstances and is, therefore, better able to reflect

changes in choice behavior due to changes in individual characteristics and

attributes of alternatives. The aggregate approach, on the other hand, rests

primarily on statistical associations among relevant variables at a level other

than that of the decision maker; as a result, it is unable to provide accurate and

reliable estimates of the change in choice behavior due changes in service or

in the population.

2. Disaggregate models avoid biases inherent in aggregate models.

19

3. Disaggregate models more efficient than aggregate one in terms of data and

computational requirements.

4. Disaggregate models can be developed using data less than one tenth of that

required by that aggregate models

5. The disaggregate approach, because of its causal nature, is likely to be more

transferable to a different point in time and to a different geographic context, a

critical requirement for prediction.

6. Discrete choice models are being increasingly used to understand behavior so

that the behavior may be changed in a proactive manner through carefully

designed strategies that modify the attributes of alternatives which are

important to individual decision makers. The disaggregate approach is more

suited for proactive policy analysis since it is causal, less tied to the estimation

data and more likely to include a range of relevant policy variables.

7. .DM models allow for a more flexible representation of the policy variables

considered relevant for the study.

8. The coefficients of the explanatory variables have a direct marginal utility

interpretation (i.e. they reflect the relative importance of each attribute).

2.4.5 Theoretical Framework for disaggregate mode choice models

A proposed framework for the choice process is that an individual first determines the

available alternatives; next, evaluates the attributes of each alternative relevant to the

choice under consideration; and then, uses a decision rule to select an alternative from

among the available alternatives (Koppel and Bhat, 2006)

An individual is visualized as selecting a mode which maximizes his or her utility

(Khan, 2007). The utility of a travelling mode is defined as an attraction associated to

by an individual for a specific trip. Therefore, the individual is visualized to select the

mode having the maximum attraction, due to various attributes such as in-vehicle

travel time, access time to the transit point, waiting time for the mode to arrive at the

access point, interchange time, travelling fares, parking fees etc. This hypothesis is

known as utility maximization.

As a matter of computational convenience, the utility is generally represented as a

linear function of the attributes of the journey weighted by the coefficients which

attempt to represent their relative importance as perceived by the traveler. A possible

21

mathematical representation of a utility function of a mode m is shown in Equation

(2.2) as,

(2.2)

Where,

is the net utility function for mode m for individual i;

, …, are k number of attributes of mode m for individual i; and

, ………, are k number of coefficients (or weights attached to each

attribute) which need to be inferred from the survey data.

As with deterministic choice theory, the individual is assumed to choose an

alternative if its utility is greater than that of any other alternative. The probability

prediction of the analyst results from differences between the estimated utility values

and the utility values used by the traveler

The choice behavior can be modeled using the random utility model which treats the

utility as a random variable, i.e. comprising of two distinctly separable components: a

measurable conditioning component and an error component. Therefore

(2.3)

Where,

is the systematic component (observed) of utility of mode m for individual i;

is the error component (unobserved) of utility of mode m for individual i.

For equation 2.3 to be correct, certain homogeneity is needed within the population

under study. In principle, it is required that all the individuals share a universal set of

alternatives and face the same constraints. Furthermore, in practical modeling work,

the difference between the socioeconomic characteristics of similar groups of

individuals is usually ignored (Ortuzar and Willumsen, 2002). Although this approach

makes the whole process simple overall, there is still a possibility of occurrence of

severe differences among various groups of people. This can be handled by

segmenting the entire set of individuals into separate utility functions for each group

of more similar individuals so that individual characteristics could be omitted from

the utility function.

21

By ignoring the attributes of the decision maker, the systematic component of the

utility can be treated as a function of attributes of available modes only. Therefore, a

single utility function can be visualized to exist for all individuals. Similarly, the error

component of the utility can also be considered independent of socioeconomic

characteristics for the same reason. Assuming that the error component has zero mean

and an extreme value distribution (Khan, 2007). The net utility function can be given

as:

(2.4)

Thus, if there are M number of total travelling modes available, the probability of an

individual selecting mode m, such that m Є M, is based on its associated utility

function Um, such that,

(2.5)

Where,

Um represents utility of travelling alternative m; and

Ui represents utility of any travelling alternative in the set of available

travelling modes.

Summarizing the theory of utility maximization presented in Equation 2.5, every

alternative associates a certain utility with itself determined by its various attributes

and an individual is supposed to select the alternative possessing the highest utility.

However, it is impractical to assume that the effects of all the variables in an

individual’s decision regarding the selection of a travel mode are perfectly

understood. The beauty of a random utility model is that it possesses the power to

estimate the effects of the observed variables without fully concerning that of the

unobserved ones incorporating all of them into the error component of the model, as

shown in Equation 2.4.

2.4.6 Types of Mode Choice Models

As mentioned above, Random Utility are the most used discrete choice models for

transportation applications. They have three different families of models depends

upon the functional form of the error term distribution:

1. Logit model

22

2. Probit Model

3. General Extreme Value Model.

2.4.6.1 Logit Model

Logit models are the most commonly used modal split models in the area of

transportation planning, since they possess the ability to model complex travel

behaviors of any population with simple mathematical techniques. The mathematical

framework of logit models is based on the theory of utility maximization (Ben-Akiva

and Lerman, 1985). There are three basic types of logit models depend on whether the

data or coefficients are chooser-specific or choice-specific .Multinomial logit model

has chooser-specific data where coefficients vary over the choices. Conditional logit

model has choice-specific data where the coefficients are equal for all choices. Mixed

logit model involves both types of data and coefficients (Siddiqui, 1999)

Briefly presenting the framework, the probability of an individual i selecting a mode

n, out of M number of total available modes, is given as,

Where,

Vin is the utility function of mode n for individual i;

Vim is the utility function of any mode m in the choice set for an individual i;

Pin is the probability of individual i selecting mode n; and

M is the total number of available travelling modes in the choice set for

individual i.

The theoretical framework of logit models is based on three main assumptions

regarding the error term , as shown in Equation 2.4. The assumptions are listed as

follows,

• is Gumbel distributed;

• is independently distributed; and

23

• is identically distributed.

All these three assumptions serve as the main postulates of the structure of logit

models. The first assumption of the random component being Gumbel distributed

indicates that all the utilities associated with the travelling modes should be

considered as a linear sum of attributes and has the same scale parameter (Ben- Akiva

and Lerman, 1985). The last two assumptions are normally grouped together to be

referred to as a property of Independence of Irrelevant Alternatives (IIA property),

simply meaning that all the travel modes used in modeling the travel behavior are

independent of each other.

Logit models are generally classified into two main categories namely binary and

multinomial logit models. Binary choice models are capable of modeling with two

discrete choices only, i.e. the individual having only two possible alternatives for

selection, where as the multinomial logit models imply a larger set of alternatives.

2.4.6.1.1 Binary Logit Models

The mathematical framework of a binary logit model can be represented by

simplifying of Equation 2.6 with the total number of available alternatives limited to

two, i.e. M = 2. An example of a binary logit model is shown in Figure 2.4 where the

choice set contains car and public transport as two competing alternatives.

Fig (2.4): Example of a Simple Binary Logit Model

24

Simplifying Equation 2.6, the probability of individual i selecting the mode m out of

two available travelling modes m and n is given as,

Or,

Where,

Vim is the utility function associated to alternative m for individual i;

Vin is the utility function associated to alternative n for individual i;

Pim is the probability that alternative m will be selected by individual i; and

Pin is the probability that alternative n will be selected by individual i.

The principle limitation of binary logit model mentioned above is that it relies on the

random or unexplained elements for each mode being independent (not correlated).

When there are groups of more similar or correlated modes, the assumption of having

an independent and identical error term across all the modes does not always remain

valid.

Nested (hierarchical) logit model can be used to relax the constraints of a simple logit

model by allowing correlation among utilities of the alternatives in a common group.

The nested logit model is constructed by grouping similar modes into hierarchical or

nests. Each nest, in turn, is represented by a composite alternative which competes

with the others available to the individual. The common example the choice between

the private car, bus and rail which can be represented as shown in Figure 2.5

25

Figure (2.5): Example of a nested Binary Logit Model

The theoretical framework of the nested logit model is based on the same assumptions

as the multinomial logit model, except that the correlation of error terms is assumed to

exist among various modes. Due to the tree structure of these models, Equation 2.6 is

reassessed, for trees having two levels, as,

(2.10)

(2.12)

(2.13)

26

Where,

C (i) is a set of lower-level alternatives that each form part of the higher-level

alternative i;

R is the set of higher-level alternatives;

Xj|I is the measured attractiveness of alternative j conditional on i;

Xi is the measured attractiveness of alternative i; and

Hi is the scale parameter.

The nested binary logit model has disadvantages over the simple binary logit model ,

mainly due to calibration difficulties but with increased the computing power theses

issues have been largely overcome. The choice of the most appropriate nesting

structure could, in theory, be a problem but practitioners have derived workable

nesting arrangement through time.

2.4.6..1.2 Multinomial Logit Models

The multinomial logit model can be derived from binary logit model to deal with to

deal with more than two modes. The multinomial logit model is categorized into

simple and nested multinomial logit models based on the characteristics of the

available travelling alternatives in the choice set. The examples of simple and nested

multinomial logit models are presented in Figures 2.6 and 2.7 respectively

Figure (2.6): Example of a simple multinomial Logit Model

27

Figure (2.7): Example of a nested multinomial Logit Model (Khan, 2007)

The multinomial logit models use the same mathematical framework as shown in

Equations 2.2 to 2.14 and are generally estimated using maximum likelihood method.

2.4.6.2 Probit Model

In certain cases where the utilities of some alternatives are correlated in a complex

way, the using of multinomial logit models can make incorrect forecasts regarding the

probabilities of mode shares when the attributes associated to one or more travelling

alternatives are varied. In these cases the probit can be used as one of the possible

methods to overcome this problem. The essential difference between the probit model

and the logit model is that the cost function coefficients in a probit model are random

(about normal distribution) compared to mean values in a logit model. The

multinomial probit model can address some of the problems associated with assuming

constant costs across individuals.

Similar to logit models, the probit model is based on random utility theory,

representing the utility function as the sum of the systematic component and an error

component. The model follows normal distribution for error terms and does not work

under the strict assumptions as that of logit models The standard equation for the

utility and the probability of an alternative i has the form (Horowitz, 1991 cited in

Khan, 2007) as shown in Equation 2.15 and 2.16,

28

(2.15)

And,

(2.16)

Where the covariance matrix of the Normal distribution associated to this latter model

has the form:

(2.17)

Where,

Ui is the utility of alternative i;

V is the systematic (observed) component of the utility function;

ε is the error (unobserved) component of the utility function;

xi is the vector of observed attributes of alternative i; and

s is the vector of observed characteristics of the individuals of the study area.

is the variance

The main disadvantage of the probit model is that it is very difficult to specify

mathematically especially for cases with more than three alternatives are available.

Due to this complexity the transport planners generally prefer using logit models as

they possess simple mathematical framework and can accurately model the travel

behavior of a study area. Ghareib (1996) estimated the travel behavior for different

cities of Saudi Arabia using logit and probit models and concluded that the logit

models are superior to their probit counterparts in terms of their goodness-of-fit

measures and tractable calibration. Dow and Endersby (2004) later supported his

findings by concluding that the logit models should always be preferred over probit

29

models and the latter should only be utilized if the travel behavior of the targeted

population to be determined is observed to be complexly correlated

2.4.6.3 Generalized Extreme Value Model

Generalized extreme value (GEV) models were developed as an important

simplification of multinomial logit models based on the stochastic utility

maximization. Although there exist a limitless number of possible models within this

class, only a few have been truly explored. This model is based on a function G(y1,

y2, …, yJn), for y1, y2, …, yJn ≥ 0, that has to satisfy certain conditions discussed in

detail in Ben-Akiva and Lerman (1985). The basic equation of the model is given as,

Where,

V is the systematic (observed) component of the utility function;

is the degree of homogeneity; and

Pn(i) is the probability of individual n selecting alternative i.

In addition to the three modal split models discussed above, there are a few discrete

choice models which can be referred as the generalizations of logit models, namely

Random Coefficient Logit, Tobit and Ordered Logistic models. Due to the occurrence

of high limitations in the specifications and estimation complexities of these models,

they are rarely put into practice by transport planners. A detailed mathematical

framework of these models is presented in Ben-Akiva and Lerman (1985).

2.4.7 Comparison of Modal Split Models

The generation of travel profile of the study area and determination of choice sets is

the first step in modal split modeling. The determination of choice sets size play an

important role in selecting the appropriate mode choice model in order to forecast the

travel behavior of the study area. If the choice set consists of two travelling modes, or

two sets of travelling modes, a binary modal split model can be applied. Contrarily,

31

multinomial modal split models can be selected for bigger choice sets. This

classification of the discrete mode choice models on the basis of the choice set is

illustrated in Figure 2.8

Figure (2.8): Classification of mode choice models (Khan, 2007)

The main difference among the three most common mode choice models namely,

Logit, probit, and general extreme value models are shown in Table (2.1) identifying

the main distinguishing factors among the specifications and applications of these

models.

Table (2.1): Comparison of Common Mode Choice Models (Khan, 2007)

Item Logit Model Probit Model General Extreme

value model

Basic Hypothesis Extreme value

distribution Normal distribution

Multivariate

extreme value

distribution

Major constraints

Error term should

be identically and

independently

distributed

Error term need not

be identically and

independently

distributed

Error terms need

not necessarily be

identically and

independently

distributed

31

Model

Formulation Simple Complex Complex

Model Estimation Simple Complex Complex

Introduction of

Access Modes

Model formulation

and calibration

becomes complex

to a small degree

Model formulation

and calibration

becomes highly

complex

Model formulation

and calibration

becomes highly

complex

Applications High Limited Limited

Accuracy High Low Low

The table mentioned above illustrates the reasons stand behind the using Logit models

among transportation planners for estimation and forecasting of travel behavior

although the specifications developed for logit models associate certain limitations

due to the IIA property. The main reasons for choosing them are their simple model

formulation and estimation techniques. Other mode choice models such as probit and

general extreme value models have relaxed the IIA restriction at the cost of

possessing highly complex mathematical structure and computational estimation.

Therefore, the logit models continue to remain dominant in the transport modeling

field (khan, 2007).

2.4.8 Model Estimation Techniques

Generally the most estimation techniques that are used for estimating the discrete

mode choice models, namely the maximum likelihood and least squares method.

These methods are used in order to infer the values of the unknown coefficients ,

…, shown in Equation 2.2,. Brief discussions of these methods are presented in the

next Sections. A detailed literature of the theoretical framework, applications and

limitations of these models is presented in (Ortuzar and Willumsen, 2002).

2.4.8.1 Maximum Likelihood Method

The method of maximum likelihood is the most common procedure used for

determining the estimators in simple and nested logit models. Stated simply as,

32

"The maximum likelihood estimators are the values of the parameters for which

the observed sample is most likely to have occurred" Ben-Akiva and Lerman (1985).

The procedure for maximum likelihood estimation involves two important steps:

1. developing a joint probability density function of the observed sample, called the

likelihood function, and

2. Estimating parameter values which maximize the likelihood function.

The likelihood function for a sample of ‘I’ individuals, each with ‘M’ alternatives are

defined as follows:

Where,

L is the likelihood the model assigns to the vector of available alternatives;

is the probability that individual i chooses alternative m.

is chosen indicator (=1 if j is chosen by individual i and 0, otherwise)

The values of the parameters which maximize the likelihood function are obtained by

finding the first derivative of the likelihood function and equating it to zero. The most

widely used approach is to maximize the logarithm of L rather than L itself. It does

not change the values of the parameter estimates since the logarithmic function is

strictly monotonically increasing. Thus, the likelihood function is transformed to a

log-likelihood function and is given as,

The first derivative of the logarithm of likelihood function can be represented as

shown in Equation 2.21

33

The maximum likelihood is obtained by setting Equation 2.21 equal to zero and

solving for the best values of the parameter vector, . to insure this is the solution

for a maximum value provided that the second derivative is negative definite.

Given the mode choice data, most existing estimation computer programs estimate the

coefficients that best explain the observed choices in the sense of making them most

likely to have occurred. Standard commercial packages such as ALOGIT are

generally implied for estimating logit models, mostly due to their capability of

handling complex nested logit structures, both linear and non-linear.

2.4.8.2 Least Squares Method

The method of least squares is generally stated as,

"The least square estimators are the values that minimize the sum of squared

differences between the observed and expected values of the observations". (Ben-

Akiva and Lerman, 1985)

The coefficients of regression are estimated by the basic objective function F which

is given by Equation (2.1),

The desired coefficients are estimated by taking (k+1) derivatives of equation 2.22

and solving for (k+1) unknowns. This method is usually called the Ordinary Least-

Squares (OLS). Generally, the least-squares estimators are unbiased under general

assumptions. However, it should be noted that the least-square method works

consistently and efficiently for linear models only, and can surmise erroneous

coefficients’ values in case of complex model specifications. Therefore, due to its

higher applications, the maximum likelihood method is generally preferred over the

least square method by the transport statisticians and planners.

2.5 Sampling and Data Collection

2.5.1 Travel Survey Types

The estimation of mode choice models requires collecting of travel and trip related

(including the actual mode choice of the traveler). These data are generally obtained

34

by surveying a sample of travelers from the population of interest. The most common

types of surveying methods are household, workplace and intercept surveys.

Koppelman. and Bhat, 2006).

2.5.1.1 Household Travel Surveys

The household travel surveys involve contacting respondents in their home and

collecting information regarding their household characteristics (e.g., number of

members in household, automobile ownership, etc.), their personal characteristics

(such as income, work status, etc.) and the travel decisions made in the recent past

(e.g., number of trips, mode of travel for each trip, etc.). Historically, most household

traveler surveys were conducted through personal interviews in the respondent’s

home. Currently, most household travel surveys are conducted using telephone or

mail-back surveys, or a combination of both. It is common practice to include travel

diaries as a part of the household travel survey. Travel diaries are a daily log of all

trips (including information about trip origin and destination, start and end time, mode

of travel, purpose at the origin and destination, etc.) made by each household member

during a specified time period. This information is used to develop trip generation,

trip distribution, and mode choice models for various trip purposes. Recently, travel

diaries have been extended to include detailed information about the activities

engaged in at each stop location and at home to provide a better understanding of the

motivation for each trip and to associate trips of different purposes with different

members of the household. Also, in some cases, diaries have been collected

repeatedly from the same ‘panel’ of respondents to understand changes in their

behavior over time.

2.5.1.2 Workplace Surveys

The workplace surveys involve contacting respondents at their workplace. The

information collected is similar to that for household surveys but focuses exclusively

on the traveler working at that location and on his/her work and work-related trips.

Such surveys are of particular interest in understanding work commute patterns of

individuals and in designing alternative commuter services.

2.5.1.3 Destination Survey

Destination Surveys involve contacting respondents at their destinations. Similar

information is collected as for workplace surveys but the objective is to learn more

35

about travel to other types of destinations and possibly to develop transportation

services which better serve such destinations.

2.5.1.4 Intercept Survey

Intercept Surveys “intercept” potential respondents during their travel. The emphasis

of the survey is on collecting information about the specific trip being undertaken by

the traveler. Intercept surveys are commonly used for intercity travel studies due to

the high cost of identifying intercity travelers through home-based or work-based

surveys. In intercept surveys, travelers are intercepted at a roadside rest area for

highway travel and on board carriers (or at carrier terminals) for other modes of

travel. The traveler is usually given a brief survey (paper or interview) for immediate

completion or future response and/or recruited for a future phone survey. A variant of

highway intercept surveys is to record the license plate of vehicles and subsequently

contact the owners of a sample of vehicles to obtain information on the trip that was

observed. Intercept surveys can be used to cover all available modes or they can be

used to enrich a household or workplace survey sample by providing additional

observations for users of infrequently used modes since few such users are likely to be

identified through household or workplace surveys.

2.5.2 Sampling Generation Methods

Sample generation is regarded as a vital step in travel demand modeling since the

modal split models are generally estimated using the data collected by surveying a

sample of respondents from the targeted population. Therefore, it is essential that the

sample generated for the research is representative of the characteristics of the

population of the study area. Inappropriate sample generation can lead to erroneous

modeling results involving biased estimated coefficients and non-representative travel

behavior forecasts.

2.5.2.1 Simple Random Sampling

Simple random sampling is the simplest approach out of all sample generation

techniques and is the basis of all other random sampling methods. In this method, a

totally random sample is chosen from the target population, using a sampling frame

with the units numbered. Since the sampling is totally random, every member of the

target population set has an equal probability of being selected. Therefore, if the set of

36

target population contains N number of members, and the sample is supposed to have

n members, provided that n ε N, the probability to generate the sample in n number of

draws, using simple random sampling, is presented in Equation 2.23 as,

Where,

NPn is the probability to select n number of members from a set of N members,

such that n ε N.

This method is also known as random sampling without replacement .Although this

method is simple; it becomes highly impractical for larger sample sizes. Ampt and

Ortuzar (2004) proved that the method often produces highly variable results from

repeated applications for high sample sizes. Therefore, the method is only applicable

for generating small sample sizes and is limited to simple sampling approaches.

2.5.2.2 Stratified Random Sampling

In stratified random sampling, the targeted population is split into distinct

subpopulations, known as strata. These strata are classified on the basis of various

factors of relevant interest to the survey and obtained by the simple random sampling

within each stratum. For example, for a mode choice survey, the strata can be

categorized on the basis of the users of various travelling modes, i.e. the individuals

using private cars and public transport. Similarly, the classification can also be done

on the basis of various socioeconomic conditions of the households such as structure,

age groups and income-levels. Chang and Wen (1994) explain that if the entire

population contains N units, then stratified random sampling can be done by dividing

it into L number of nonoverlapping strata such that,

Where,

N1,2, … , L are the number of units in each strata L.

37

Whilst stratified sampling is useful, in general, to ensure that the correct proportions

of each stratum are obtained in the sample, it becomes highly significant in

identifying relatively small sub-groups within the population. Therefore, it

enormously increases the precision of the estimates of attributes of the targeted

population of a study area. However, considerable prior information regarding the

attributes of the population should be known before generating the sample.

2.5.2.3 Multi-stage Sampling

Multi-stage sampling is a random sampling technique for study areas with large

populations. It is based on the process of selecting a sample in two or more successive

contingent stages. It proceeds by defining aggregates of the units that are subjects of

the survey, where a list of the aggregates is easily available or can be readily created.

Richardson et al. (1995) explained the process of multi-stage sampling within

Australian context by splitting it into five distinct stages as shown in Figure 2.9

Figure (2.9): Example of Multistage Sampling Process (Richardson et al., 1995)

38

The major disadvantage of multi-stage sampling is its low level of accuracy of the

parameter estimates for a given sample size as compared to that estimated using a

simple random sample for the same study area. However, the reduction in accuracy is

often traded off against the reduction in costs and efficiency in administration of the

sampling process that the multi-stage sampling associate. Hossain et al. (2003) proved

this argument by presenting various population models based on different sampling

techniques, out of which the most efficient method, in terms of application and

economy, was found to be multi-stage sampling.

2.5.2.4 Cluster Sampling

Cluster sampling is a slight variation of multi-stage sampling where the targeted

population is first divided into clusters of sampling units, and then sampled randomly.

The units within the cluster are either selected in total or else sampled at a very high

rate. Detailed literature on the theoretical framework of the method, along with some

useful examples, is presented in Stehman (1997).Similar to multi-stage sampling;

cluster sampling can also be highly economical and administratively efficient as

compared to simple random sampling, especially for study areas with large

populations. Additionally, if the study areas are well-defined, a transport modeler can

easily manage to have a high degree of quality control on the conduct of the

interviews. However, the main disadvantage, like multi-stage sampling, continues to

be the less accuracy in estimating the coefficients for any given sample size as

compared to that estimated using simple random sampling.

2.5.2.5 Systematic Sampling

Systematic sampling is perhaps the most widely known non-random sampling

technique among the transport modelers. The method involves selecting each kth

member of the targeted population. The first member is chosen randomly and then,

after every kth interval, another member is selected to be part of the sample. For

example, if the targeted population contains N members and the desired sample size is

n, then after selecting the first member randomly, the other members are selected

every N/nth interval. However, this constraint does not need to be strictly enforced

and can be modified by the modeler according to the level of model complexity. In

study areas where the size of the targeted population is very large or almost infinite,

Stopher (2000) suggested that every twentieth member of the set should be selected as

39

part of the sample. Although systematic sampling is the easiest and simplest sampling

method known, it possesses various limitations. First, and most importantly, the

sample set generated using systematic sampling generally contains various biases

because the targeted populations sometimes exhibit a periodicity with respect to the

parameter being measured. This causes the resulting sampling set to be significantly

biased towards that certain parameter. The second limitation is the scenario in which

the resulting sample set may not effectively represent the users of a certain travelling

mode. This situation generally occurs in enormously populous study areas where there

is assorted practice of travelling modes and the transport modelers unconsciously

ignore these users, causing bias in the sample set.

2.5.3 Revealed and stated preference survey

Many researchers have attempted to model travel demand and travelers’ behavior

using revealed preference and/or stated preference survey data. These two techniques

are used a complementary tools to elicit the preferences of the decision. The revealed

preference and stated preference techniques conveniently provide data for the

development of disaggregate travel forecasting models..

Revealed Choice data describes current observed travel patterns and costs and hence

is a very accurate picture of current modal choice.

The stated preference techniques allow the deficiencies of revealed preference data to

be overcome by testing hypothetical transport alternatives in interviews. This

technique becomes an attractive option in transportation modeling since it presents the

decision-makers choice and behavioral pattern under different hypothetical scenarios.

Many researchers use this technique to understand the unpredictable behavior of

decision-makers under conditions that are new or hypothetical.

Hensher (1994) states that there are three types of questionnaire that can be used

instated preference studies. These are: ranking, choice or rating .In a choice

questionnaire, the task is simpler for the respondent. The respondent simply chooses

the hypothetical combination of attributes that is most favor able to him or her and the

researcher has an actual prediction of the respondent’s choice in a hypothetical

situation. In a ranking questionnaire, respondents must order the hypothetical

situations in order of preference. In a rating questionnaire, the task becomes more

complicated as respondents must be able to order their responses in order of

41

preference but they must also be able to indicate how much they prefer one alternative

over others.

Revealed preference and stated preference studies each have their advantages and

disadvantages .According to Swait et al.(1994) the main advantage of using a revealed

preference study is that it can represent current market situations better than stated

preference studies. In revealed preference studies, the choices that are made by

respondents are known outcomes, although they are dependent on the respondent’s

perceptions of Attribute levels, which may or may not be accurate (Hensher,1994).

Stated preference studies are less constrained than revealed preference studies and

allow us to look at potential changes(Swait e tal.,1994).Stated preference studies

allow us to examine how decision-making varies as different types of attribute

profiles and levels are considered(Hensher,1994).Stated preference techniques were

originally popularized by the work of Louviere and Hensher (1983) and Davidson

(1973) in the1970s and 1980s who demonstrated how researchers could examine trip

makers answers to hypothetical combinations of attribute levels for travel modes. In

stated preference studies, outcomes are potential outcomes (Hensher, 1994).

According to Wang et al.(2000),stated choice and stated preference methods have

limits, however .They are limited by a respondent’s ability to understand the

hypothetical situations with which they are presented and to provide reliable answers.

Wang et al. (2000) argue that if hypothetical situations are far removed from the

respondent’s daily experience, the stated preference study will result in poor models

and inaccurate results. Therefore, stated preference studies should have some relation

to the real world. Wangetal (2000) also make recommendations regarding

strengthening stated preference models by some type of fusion with revealed

preference models.

The stated preference technique has become a convenient tool for researchers to

analyze data especially for the non-existing scenarios. Ortuzar and Willumsen (2002)

have summarized the main features of stated preference technique as follows:

i. It is based on the elicitation of respondents’ statements of how they would

respond to different hypothetical (travel) alternatives;

ii. Each option is represented as a ‘package’ of different attributes like travel

time, price, headways, reliability and so on;

41

iii. The researcher constructs these hypothetical alternatives so that individual

effect of each attribute can be estimated; this is achieved using experimental

design techniques that ensure the variations in the attributes in each package

are statistically independent from one another;

iv. The researcher has to make sure that interviewees are given hypothetical

alternatives they can understand, appear plausible and realistic, and relate to

their current level of experience;

v. The respondents state their preferences towards each option by either ranking

them in order of attractiveness, rating them on a scale indicating strength of

preference or simply choosing the most preferred option from a pair or group

of them;

vi. The responses given by individuals are analyzed to provide quantitative

measures of the relative importance of each attribute.

2.6 Previous case studies of mode choice modeling

Many discrete case studies have been applied around the world in the area of mode

choice modeling. The studies aimed at developing a discrete choice model that fits

with the study area. In this research we present some studies in cities of developed

and developing countries in order to benefit in developing a suitable mode choice for

our country.

Al Ahmadi (2006) developed intercity mode choice models for Saudi Arabia. These

models indicated that in-vehicle travel time, out of pocket cost, number of family

members travelling together, monthly income, travel distance, nationality of traveler,

and number of cars owned by family played the major role in decision related to

intercity mode choice.

Khan (2007) estimated various nested logit models for different trip length and trip

purpose using data from stated preference (SP) survey. A unique computer assisted

personal interviewing (CAPI) instruments was designed using motorized and non-

motorized travelling modes in the SP choice set. Additionally a unique set of access

modes for bus on bus way was also generating containing hypothetical modes such as

secure park and ride facilities and kiss and ride drop-off zones. He found from the

final model estimation that the travel behavior forecasted for regional trip makers is

considering different from that for local trip makers. The regional travelers for work

42

were found not to perceive the non- motorized modes as valid alternatives to car,

possibly due to longer trip length. The value of time (VoT) determined for local work

trip makers was 16.5 A$/hr was also found to be higher than that of regional work trip

makers (11.7 A$/hr).

Siddiqui (1999) modeled the non-motorized modes (walk and bike) along with the

traditional motorized (auto and transit). A nested multinomial modal split models

were developed for simulating the p.m. peak period home based work, home based

school, and other trips purposes for the national capital region. Model and individual

characteristics considered important in mode choice were identified and testing using

sensitivity analysis. The results indicated that travel cost, travel time, and travel

vehicle ownership are important factors in motorized modes where as travel time,

gender, member of bikes, and population density are significant variables for non-

motorized modes.

Adjaka (2009) analyzed factors that influence parents’ decision in choosing

transportation modes for schoolchildren in district of Colombia. Multinomial logistic

regression was used to predict the share between choice modes. The factors that found

to be most significant in predicting mode choice included distance home to school,

student's grade, school's encouragement of walking and biking and walking fun for

schoolchildren.

Abdelwahab and Abdel-Aty (2001) developed mode choice models for Florida,

USA. The mode choice model was estimated as three level nested Logit structure. The

overall model utilized full information maximum likelihood estimation. Among the

significant variables that entered into model are: transit access time, transit waiting

time, number of transfer, in-vehicle travel time, fare and household car ownership.

Ewing, Schroeer and Greene (2004) analyzed the relationship between school

modes and factors that influence the choice of a given mode. The data used for their

study was from travel diary surveys of students in grades K-12 from Gainesville,

Florida. The mode choice was developed using multinomial Logit model. The

following factors have significant impacts on school mode choice: distance home-

school, built-environment between home and school and household incomes. Students

who lived closer to their schools were more likely to walk and bike to and from

school. The presence of sidewalks and crosswalks encouraged walking, but did not

43

affect the biking decision. The authors also found that vehicle availability, which is

related to income, made walking and biking less attractive compared to the other

modes. Other factors such as school size, school enrolment, and land use density were

not significant in predicting the modes.

Mc Donald (2008) studied the impact of distance in school mode choice using

elementary and middle school students’ data from the US Department of

Transportation 2001 National Household Survey. The findings suggested that among

factors that influence mode choice, distance between 14 school and home had the

strongest impact. Children were likely to walk when the distance is less than one mile.

Gender and ethnicity had minor influence on the mode choice. The results also

indicated that density around school zone and neighborhood increases walking and

biking to and from school. The study concluded that a better integration of land use,

transportation, and school planning can encourage walking and biking to and from

school.

Yarlagadda and Srinivasan (2008) used the San Francisco Bay area Travel Survey

data of 2000 to model travel behavior of children, and the interdependency between

parents and children in mode choice. The targeted populations of students were under

eighteen year olds. The authors modeled school mode choice using multinomial logit

model. The results showed that characteristics of students such as age, gender, and

ethnicity and characteristics of their parents such as employment and flexibility in

working hours have strong impacts on the mode choice. The research found distance

between home and school as the primary barrier to the choice of walking to or from

school. The authors also found that the significance of the explanatory variables were

not the same for trip to school and trip from school.

Kweon, Shin, Folzenlogen and Kim (2006) investigated environmental factors that

encourage walking and biking to school. The researcher used household surveys from

College Station, Texas. The authors limited their research to 2-mile radius around

each school, areas defined as walk zones. The results indicated that students walk

more in neighborhoods where there are mature trees and bike more in neighborhoods

with sidewalks. The average walking distance was 0.71 miles. The average biking

distance was 0.93 miles. The majority of children living beyond one mile from their

schools used motorized modes.

44

Rhoulac (2005) analyzed factors that affect K-8 children school mode in Wake

County, North Carolina. The data was from household surveys, and the alternative

modes considered in the study were school bus vs. private automobile. The results

suggested that mode choice for children’s school trips were influenced mainly by

factors such as number of students in a household, student’s grade, parents’ perceived

safe mode, and the convenience of driving

Almasri (2011) investigated the factors that affect travel choice of shared taxi versus

bus for Palestinian university student trips. The results of this study indicated that

factors that are significantly affecting the mode choice of students are: family income

divided by family size, weighted travel time, out of vehicle travel time divided by

distance and cost divided by natural logarithm of income. The results also show that

the age and gender variables are statistically insignificant and it could be dropped

from the model.

2.7 Summary

The mode choice model is the third step of the classical four-step model and it plays

an important role in travel demand forecasting. There are different factors that affect

the choice of transportation modes which can be categorized into three groups

namely; factors related to the characteristics of trip maker such as car availability and

possessing of driving license, factors related to the characteristics of journey such as,

time of day and types of trips, and factors related to characteristics of transport

facilities such as cost, travel time and waiting time. This chapter illustrated that there

are two approaches for modeling the choice of transportation modes which are

aggregate and disaggregate approaches. Disaggregate (discrete) approach are widely

used because of its advantages which overcome the problems facing the aggregate

one. There are three types of disaggregate mode choice models namely; logit model.

Probit model and general extreme value model. Among these types the logit model is

the most widely used for calibration the mode choice because it is simple in terms of

formulation and estimation of the model in addition to its accuracy compared with the

other types. The estimation techniques that are used for estimation the mode choice

model are maximum likelihood and least squares methods. The maximum likelihood

is the most common technique used in determining the estimators for simple and

nested logit model so this technique was adopted in this research for estimating the

mode choice model. Among the four methods of travel survey which are household

45

survey, workplace survey, destination survey, and intercept survey; the workplace

survey method was used for collecting the data in this study. The reviewing of

literature show there are five methods of sample generation. These methods are:

simple random method stratified random method, cluster sampling method, multi

stage sample method and systematic sample method. Among these methods the

simple random method was used for sample generation in this research. The

experiences of some countries in modeling the mode choice for different types of trips

which were reviewed in this chapter illustrated that the factors related to transport

policies and the factors related to the characteristics of trip maker are the most

important factors in guiding the choice behavior of travelers.

46

Chapter 3: Research Methodology

3.1 Stages of the Study

In order to achieve the main aim and objectives of this research the work is divided

into six main phases as can be seen in Figure 3.1

First phase:

The first phase is the literature review on mode choice modeling. The concentration

will be on discrete mode choice models as they are more efficient than conventional

models. The literature review should seek for case studies applied in cities of

developing countries especially in the cities that have similar conditions. Based on the

literature review, the transportation planning process that is appropriate to Gaza City

must be decided.

Second phase:

This phase relates to the process of selection of the travel attributes. It involves,

designing of initial (pilot) survey form and analysis of the survey data. This process is

important to determine the attributes which are most relevant to the travelers in the

study area. The resulting attributes will be included in the main survey.

Third phase:

This phase involves designing the final survey form and conducting the survey from

start to finish including selection of level of attributes, determine the sample size and

sample space, implementation of the survey, the collection and analysis of data.

Fourth phase:

This phase includes calibrating and estimating of the utility functions for the Model

and chooses the best model by comparing the models with regard to the coefficient

estimates of the variables and their overall fit.

Fifth phase:

This phase is preliminary concerned with the model validation.

Sixth phase:

This phase summarizes the main findings and conclusions from the study.

47

FIRST STAGE:

REVIEW THE LITERATURE

SECOND STAGE:

DESIGN OF INITIAL SURVEY QUESTIONNAIRE

Transportation

Planning Process

(Four Step Model)

Types of Mode

Choice Models

Model Estimation

Techniques

Sampling and

Data Collection

Design of initial

survey questionnaire

Pilot Study

Analysis of pilot

study

Contd..

.

48

THIRD STAGE:

DESIGN OF FINAL SURVEY QUESTIONNAIRE AND DATA COLLECTION

FOURTH STAGE:

CALIBRATION OF MODEL

Design of Final

survey questionnaire

Determination of

Sample Size

Distributing and

collecting of

questionnaire

Analysis of Data

Calibration

Model 1

Calibration

Model 2

Calibration

Model N

Comparison of

Models in terms of

a. Coeff- Estimators

b. t – Statistics

c. Stnd error

d. Overall fit

Contd..

.

49

FIFTH STAGE:

VALIDATION OF MODEL

SIXTH STAGE:

CONCLUSION AND RECOMMENDATION

Figure (3.1): Flow chart for research methodology

Validation of the

chosen Model

a. Likelihood Ratio test LRTS Calculation

b. Estimation of Prediction Ratio

Comparison of

LRTS with

Critical Chi-

Square Value @

95% confidence

level

Conclude the main Findings

Recommendations

51

3.2 Study area

The study are in this research is Gaza city which represents the largest part of Gaza

governorate. Gaza is the largest governorate after Khanyounis with area of 72593

dounms. The number of population in Gaza at mid 2011 is about 552,000 inhabitants.

This put Gaza as the most densely governorate with a density of 7.5 cap/dun (PCBS,

2007). Gaza city is composed of eleven districts as shown in Figure 3.2. These

districts are old city, Rimal, Zeiton, Shujaiyya, Alsabra, Aldarag, Alnasser, Tuffah,

Sheikh Radwan, Sheikh Ajlin, Tel Alhawa, Alshatia camp, and ALawda.

Figure (3.2): Study Area (Gaza city) (MoG, Planning department, 2005)

Gaza city is suffering from congestion problem resulting from urbanization and the

rapid increase of population where the population is growing about 4% a year. This

put the transportation planners toward a big challenge to adopt efficient transport

policies contribute in solving this problem.

3.3 Target group

This research targeted the employed people they live and work in Gaza city.

According to the statistics of PCBS the number of population within the work age

(>15 year) in Gaza strip at mid 2008 is 744,000 inhabitants represents about 51.7% of

the total number of population in Gaza strip. The labor force participation in Gaza

strip is about 38.1% of the population within the work age while the rest percent

which represents about 61.9% is outside the labor force. The outside labor force

51

population includes housewives (46.7%), students and trainee (37.9%), old/ill

(10.5%), and neither working or looking for work (4.9%). The percent of employed

people from the population within the labor force in Gaza strip is about 59.4%, of

which about 53% is full employed and 6.4% is underemployed. The percent of

unemployment in Gaza strip represents about 40.6% of the population within the

labor force

The census of PCBS referred that the participation of labor force in Gaza represents

about 36.4 % from the population within the work age; 63.4% for males and the rest

for females. The percent of employed people represents about 61.7% of the

population within the labor force, of which, about 57% is full employed and 4.7% is

underemployed. The percent of unemployment represents about 38.3% of the

population within the labor force. The labor forces in Gaza are divided into

governmental employee, UN employee, private sector employee, wage workers, self

works and business men. .

3.4 Design of Questionnaire

Questionnaire was developed in order to collect the data required for calibrating the

mode model for work trips in Gaza city. The questionnaire was divided into four parts

as can be seen below:

1. Part one: which includes the social and economical information about the

respondents such as (gender, age, job, income, family size, ownership of private

car, ownership of motorcycle, ownership of bicycle……etc).

2. Part two: This focuses on the factors that affect the mode choice. The respondents

were asked to indicate their perception on the importance of twelve well organized

factors that affect the choice of transportation mode for work trips in Gaza city.

These variables are: age, gender, average monthly income, travel cost, travel time,

waiting time, weather conditions, privacy, comfort, health status, and trip length.

A five point Likert scale ranging from (1: very low important to 5: very high

important) was adopted to analyze the importance of factors that affect the choice

of transportation mode.

3. Part three: This focuses on the trip characteristics. For the purpose of this study

the daily trips which constitute home-work trips have been included. In this part

the information covered is relating to travel behavior of individual for his/her daily

52

trips such as (the mode usually used, travel time, travel cost, monthly fuel

consumption, license and maintenance cost ….. etc.).

4. Part Four: this concern on the hypothetical choice of the respondents. The

respondents were asked to rate his/her preferred modes among three different

modes (shared taxi, mini bus, and bus) according to three different levels of

attributes as shown in the Table 3.1

Table (3.1): Different levels of the hypothetical questions

Level Attribute/mode Bus Minibus Shared taxi

Level 1

Travel time 40% more than

shared taxi

30% more than

shred taxi -

Travel cost 50% less than

shared taxi

25% less than

shared taxi --

Frequency Every 40

minutes

Every 20

minutes

Every 5

minutes

Level 2

Travel time 30% more than

shared taxi

15% more than

shred taxi -

Travel cost 40% less than

shared taxi

15% less than

shared taxi --

Frequency Every 30

minutes

Every 15

minutes

Every 5

minutes

Level 3

Travel time 20% more than

shared taxi

10% more than

shred taxi -

Travel cost 30% less than

shared taxi

10% less than

shared taxi --

Frequency Every 15

minutes

Every 8

minutes

Every 5

minutes

53

3.5 Sample Size Determination

Survey is intended to estimate the true value of one or more population

characteristics. In order to draw inference from a sample that will accurately reflect

the population careful attention must be given to determining the needed sample size.

Many efforts were done to determine the minimum sample size that accurately

reflects the population characteristics. The central limit theorem is on the heart of

these efforts. Kish (1995) showed that the minimum sample size can be calculated

using the following equation

Where,

is a total number of population

is a sample size from finite population

is a sample size from infinite population

The Sample size from infinite population can be calculated using the following

equation

Where,

is the variance of population elements

is the standard error of sampling population

For 95% confidence level and 10% error the sample size can be calculated as a

function of coefficient of variation cv

As the value of is very small comparing with N thus the ratio of

is very small

thus the sample size of finite population n can be taken as the same value of . For

cv=1 the minimum sample size is 384.

54

Green (1991) recommended minimum sample size N for Multinomial logistic

regression with N>50+8m where m is the number of predictors (factors). For twelve

factors that affect the mode choice model in Gaza city, the minimum sample size for

this study is 146.

For the purpose of this study 700 questionnaires were distributed for work trips. The

random sample method was adopted in this study. Of the 700 questionnaires were

distributed 552 questionnaires are valid.2/3rd

of theses questionnaires were used for

calibration of model and the rest were used for the validation process.

3.6 Pilot Study

A pilot survey is a complete run through of the actual survey done over a small set of

population in order to the level of credibility of instrument, data coding, and data

recording. In this study 20 questionnaires were distributed to experts in transportation

field and for a chosen sample of population. The objective of pilot study was to verify

the completeness of questionnaire. The following items are a summary of major

observations based on pilot study:

1. Some questions were added to the different parts of questionnaire such as:

The fuel consumption per month for private car (Part III)

The number of kilometers that the private car cut per 1 liter of fuel (Part

III).

The distance factor was added to the factors list (part II).

The engine type of private car (part I).

The ownership of motorcycle and bicycle (part I).

2. One question was omitted from questionnaire as suggested by the respondents.

These questions were considered impractical or unrealistic which is:

The distance between home and work (part III).

3. Some questions were rearranged in order to give more suitable and considered

meaning such as questions 1,2,3 in part III and the question in part IV

3.7 Preliminary analysis of questionnaire

This is the fourth phase in the present study. This focuses on the determination of

choice and captive riders for various travel and socioeconomic characteristics. The

statistical analysis software (SPSS) was used to perform the analysis. The important

55

travel and socioeconomic characteristics of the captive travelers affecting mode

choice were also determined with the help of chi-square and Cramer’s test.

The relative important index (RII) was used to determine the relative importance of

various factors that affect the mode choice. The (RII) can be calculated using the

following equation

Where,

W is the weight given to each factor by the respondents and range from 1-5

A is the highest weight =5

N is the total number of respondents

The RII was used to rank the different factors that affect the mode choice in order to

cross-compare the relative importance of the factors as perceived by the respondents.

3.8 Model Calibration and Comparison

This is the fifth phase in the present study. The concept of utility theory is used for

calibration process. The basic approach in this theory is that the individual select an

alternative that maximizing his/her utility. The mathematical details of this theory as

well as the available procedures for model calibration were explained in chapter two.

A multinomial logit model which relates the utility of the alternatives to the

probability of choice is used to calibrate the model. The EASY LOGIT software

package was used in this study to calibrate the desired models. The package used the

maximum likelihood technique to calibrate logit model. The outputs of this package

include various statistical performance indicators which are:

1. t-test and associated significance of parameters estimators.

2. Log of likelihood function value LL (β) at its maximum.

3. Log of likelihood function value LL (0) when all parameters are zero. In other

words all alternatives have equal probability of being chosen.

4. Goodness of fit index roh-square (ρ2) that measures the fraction of an initial

likelihood value explained by the model, which can be calculated as

56

5. Corrected goodness of fit index roh-bar square similar to (ρ2) but corrected

for the number of parameters estimated which is calculated as follows

Where, K is the number of parameters estimated in the model.

The signs of parameters are also checked. The signs of parameters have to be logical

for instance if travel time and travel cost coefficients have positive signs then a

decrease in these variables will decrease the demand and vise versa which is illogical.

Mode choice models were calibrated on 2/3rd

of the data and the remaining 1/3rd

is

reserved for validation. The calibrated models were compared with respect to

statistical performance indicators. Among the most important one that are studied are

the signs of coefficient, the goodness of fit, the adjusted goodness of fit, and t- test.

3.9 Model Validation

After the calibration process is completed and the models have been compared,

validation of mode choice model is checked. Approximately 1/3rd

of the reserved data

sets were used for this purpose. The validity of the model was tested by LRTS

(likelihood ratio test) and estimation of prediction ratio.

The null hypothesis formulated for the purpose is as follows:

H0: there is no difference between the observed and predicted behavior i.e. there is

no difference between the parameter vectors obtained from calibration data

and the validation data

H0: βi = βj , where

βi , βj are the estimated parameter vectors of the model obtained from calibration and

validation data ( same specification is needed for this test)

To obtain the LRTS value the coefficient of variables of particular model will be

restricted and the ELM program is executed with validation data. The program

outputs two log-likelihood values. The first value is the one computed by restricting

the coefficient of the calibrated model while the second is the one when the

parameters are unrestricted for validation data. The LRTS value can be obtained by

the following

57

Where,

represents the likelihood ratio test statistics which restricts the

parameters estimated from data j to be used to predict mode share in

data i for same specifications

is log likelihood ratio value when the parameters are restricting in data

j

is log likelihood ratio value when the parameters are unrestricted in

data j

The LRTS tests discussed is distributed as chi-square with k degrees of freedom

where k is the number of model parameters. If LRTS value is less than critical chi-

square value @ 95% confidence level and degree of freedom equal to k then for that

particular case the null hypothesis can’t be rejected otherwise it is rejected.

58

Chapter 4: Results &Analysis

4.1 Introduction

This chapter describes the results of the descriptive analysis of the survey as well as

the calibration and validation for revealed and stated preference models. Section 4.1

presents the results of general analysis of data. Section 4.2 discusses the relation

between the mode choice and socioeconomic variables. Section 4.3 discusses the

relation between the captive ridership and socioeconomic variables. Section 4.4

presents the results of the travelers’ choice for the hypothetical questions. Section 4.5

involves the calculation of the relative importance index and ranking the factors that

affect the mode choice. Sections 4.6 and 4.7 discuss the calibration and validation for

revealed mode choice model. Finally sections 4.8 and 4.9 describe the calibration and

validation for stated preference model.

4.2 General Analysis of Data

Initially frequency tables were obtained on a whole datasets to determine the

distribution of travelers for various travel and socioeconomic characteristics. The

results of this analysis are summarized in the form of frequency tables and pie charts.

4.2.1 Gender of respondents

The distribution of travelers for their gender can be seen in Table and Figure (4.1). As

can be seen in the table about 68.5% of the respondents are male and 31.5% are

female.

Table (4.1): Frequency table for respondent’s gender

Frequency Percent Valid

Percent

Cumulative

Percent

Valid Male 378 68.5 68.5 68.5

Female 174 31.5 31.5 100.0

Total 552 100.0 100.0

59

Figure (4.1): Respondents’ gender

4.2.2 Marital status of respondents

Table and Figure (4.2) presented the distribution of travelers for their status .As can

be seen in the table about 20.8% of the respondents is single and 79.2% are married.

Table (4.2): Frequency table for respondent’s status

Frequency Percent Valid

Percent

Cumulative

Percent

Valid Single 115 20.8 20.8 20.8

Married 437 79.2 79.2 100.0

Total 552 100.0 100.0

Figure (4.2): Respondents’ marital status

61

4.2.3 Jobs of respondents

The distribution of respondents’ job can be seen in Table and Figure (4.3). the results

reported in the table show that about 40.2% of the respondents are governmental

employee, 25% are private sector employee,13.6% are UN employee , 3.8% are

business man, 15.8 % are waged workers and 1.6 works on others job.

Table (4.3): Frequency table for respondent’s job

Frequency Percent Valid

Percent

Cumulative

Percent

Valid Governmental employee 222 40.2 40.2 40.2

Private sector employee 138 25.0 25.0 65.2

UN employee 75 13.6 13.6 78.8

Business man or special

works 21 3.8 3.8 82.6

waged worker 87 15.8 15.8 98.4

Others 9 1.6 1.6 100.0

Total 552 100.0 100.0

Figure (4.3): Respondents’ job

61

4.2.4 Age of respondents

The distribution of respondents’ age was presented in Table and Figure (4.4). As can

be seen in the table the large percent of workers lies in the age category from 25-45

years which represents about 60% of the whole sample.

Table (4.4): Frequency table for respondent’s age

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid 18-25 38 6.9 6.9 6.9

26-30 107 19.4 19.4 26.3

31-35 114 20.7 20.7 47

36-40 130 23.6 23.6 70.6

41-45 85 15.3 15.3 86

46-50 55 9.9 9.9 95.9

51-55 17 3.0 3.0 98.9

>55 6 1.1 1.1 100.0

Total 552 100.0 100.0

Figure (4.4): Respondents’ age

62

4.2.5 Average family monthly income of respondents

The distribution of respondents’ average family monthly income can be seen in Table

and Figure (4.5). The results reported in the table show the majority of workers have a

monthly income between 1500-4000 NIS which represents about 73% of the whole

sample.

Table (4.5): Frequency table for respondent’s monthly income

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid less than 1000 NIS 16 2.9 2.9 2.9

1001-1500 NIS 56 10.1 10.1 13.0

1501-2000 NIS 111 20.1 20.1 33.2

2001-3000 NIS 181 32.8 32.8 65.9

3001- 4000 NIS 115 20.8 20.8 86.8

4001-5000 NIS 46 8.3 8.3 95.1

more than 5000 27 4.9 4.9 100.0

Total 552 100.0 100.0

Figure (4.5): Respondents’ monthly income

63

4.2.6 Family size of respondents

The distribution of respondents’ family size was presented in Table and Figure

(4.6).the collected data was categorized into three categories. The results show that

the majority of employed people have a family size between 1-6 persons which

represents about 69.6% of the whole sample. While the employed people with a

family size bigger than 10 persons represents about 1.5 % of the sample.

Table (4.6): Frequency table for respondent’s family size

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid 1-6 386 69.9 69.9 69.6

7-10 158 28.6 28.6 98.5

>10 8 1.5 1.5 100.0

Total 552 100.0 100.0

Figure (4.6): Respondents’ family size

64

4.2.7 Ownership of transport modes

Table and Figure (4.7) presented the distribution of transport means owned by the

respondents. As can be seen in the table about 16.5% of the respondents have a

private car, 20.3% of the respondents have a motorcycle, 4% have a bicycle and the

rest have no means of transport.

Table (4.7): Frequency table for respondent’s ownership of means of transport

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid Private car 91 16.5 16.5 16.5

motorcycle 112 20.3 20.3 36.8

Bicycle 22 4.0 4.0 40.8

No means 327 59.2 59.2 100.0

Total 552 100.0 100.0

Figure (4.7): Respondents’ ownership of transport means

65

4.2.8 Trip length

The distribution of trip length can be seen in Table and Figure (4.8). The result

shown in the table below illustrate that the majority of the sample has a trip length lies

between 0.3-4.0 km which represents about 75.2% of the whole sample. While the

trips which have a length more than 6.0 km represents about 2% of the sample.

Table (4.8): Frequency table for trip length

Frequency Percent Valid

Percent

Cumulative

Percent

Valid 0.30 -1.0KM 37 6.7 6.7 6.7

1.10 - 2.0 KM 121 21.9 21.9 28.65

2.10 -3.0 KM 164 29.7 29.7 58.3

3.10 – 4.0 KM 93 16.9 16.9 75.2

4.10 – 5.0 KM 77 13.9 13.9 89.1

5.10 – 6.0 KM 49 8.9 8.9 98.0

> 6.0 Km 11 2.0 2.0 100.0

Total 552 100.0 100.0

Figure (4.8): Trip length

66

4.2.9 The means of transport usually used by the respondents

The distribution of the various modes that are usually used by the respondents was

reported in Table and Figure (4.9).

Table (4.9): Frequency table for the modes of transport that

Usually used by the respondents

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid private car 76 13.8 13.8 13.8

shared taxi 245 44.4 44.4 58.2

Taxi 39 7.1 7.1 65.2

motorcycle 90 16.3 16.3 81.5

Bicycle 13 2.4 2.4 83.9

Walking 89 16.1 16.1 100.0

Total 552 100.0 100.0

Figure (4.9): The percent of different modes usually used by the respondents

As can be seen from the table and chart above the large percent of the respondents

usually use the shared taxi to go their work by 44.4% of the sample while the using of

67

bicycle comes on the last order by 2.4%. The percents for different modes shown in

the table and chart above include the people of one choice “captive”. In order to study

the effect of these people on the model, the questionnaire form includes a question

about the captive riders as follows: “ Would you considered using modes of

transport other than the one you usually used to go to your work?” if the answer

with no , then the respondent considered as “ captive rider”. The Table and Chart 4.10

show the frequency and percent of captive and choice riders.

Table (4.10): Frequency table for the choice and captive riders

CAPTIVE

/CHOICE Total

Choice Captive

MODES Private Car 63 13 76

Shared Taxi 141 104 245

Taxi 28 11 39

Motorcycle 81 9 90

Bicycle 13 0 13

Walking 70 19 89

Total 396 156 552

Figure (4.10): The number of captive and choice riders for different modes

68

The results reported in the table and chart above show that the percent of the captive

riders is about 28.2% of the respondents while the rest 71.8% considered as a choice

riders. The above table and chart also illustrate that the percent of captive riders

increases in the Shared Taxi users in comparing with the other modes of transport.

The percent of captive riders in the users of this mode represents about 42.4% . This

result seems to be realistic because there is a high percent of shared taxi users don’t

have their own means of transport ( private car, motorcycle, bicycle) and in certain

circumstances the other modes of transport that can be available for them such as

(taxi, and walking) are not be suitable for them to use. As example if the traveler does

not have his own mode of transport and the distance between the home and work is

long and the traveler’s income is low so he has no choice other than Shared Taxi

because the fare of taxi is very high comparing with his income and the distance is

very long to walk.

4.3 Relation between the mode of transport and socioeconomic characteristics

In order to better understand of the relation between the choice of the mode of

transport and the socioeconomic characteristics of the respondent, a cross tabulation

were performed between the modes of transport that are usually used by the

respondents and the different social and economical characteristics of the respondents

and the results were analyzed. A chi-square test was applied to cross tabulation

between the mode variable and socioeconomic variables. The null hypothesis stated

that there is no relationship between the mode of transport and the socioeconomic

variables. The null hypothesis will be rejected if the significance level is less than 5%

for 95% confidence interval. Cramer’s V statistics was used to give indication of the

strength of the relationship which range in value between 0-1 i.e. the higher is better.

4.3.1 Relation between the mode of transport and gender

The distribution of the transport modes that are usually used by the respondents

versus the gender of the respondents can be seen in Table and Figure 4.11.The table

and chart below illustrate that the using of motorcycle and bicycle modes are limited

to males because of the social habits of the Palestinian society. The table also shows

that the females are likely to use the taxi and shared taxi modes of transport more than

the males do as the percent of the taxi and shared taxi users from female is about

69

13.8% and 59.8 % respectively while this percent is about 4% and 37.3% of the male

users.

Table (4.11): Cross tabulation between the mode of transport and gender

Count Gender Total

Male Female

MODES private car 51 25 76

shared taxi 141 104 245

Taxi 15 24 39

motorcycle 90 0 90

Bicycle 13 0 13

walking 68 21 89

Total 378 174 552

Figure (4.11): The percent of male and female riders for different modes

The results of chi-square test and Cramer’s V statistics which show the relation

between the gender and the mode of transport that is usually used by the respondent

can be seen in Tables 4.12 and 4.13 respectively.

71

Table (4.12): Chi-square test for mode-gender relationship

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 79.901a 5 .000

Likelihood Ratio 108.487 5 .000

Linear-by-Linear

Association 23.243 1 .000

N of Valid Cases 552

Table (4.13): Cramer’s V statistics for mode-gender relationship

Value Approx.

Sig.

Nominal by

Nominal Phi .380 .000

Cramer's V .380 .000

N of Valid Cases 552

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

The results presented in the tables above show that the null hypothesis which stated

that there is no relationship between the gender and the mode of transport that is

usually used by the respondent can be rejected for 95% confidence level because the

significance level is less than 0.05. The Cramer’s V statistics is 0.38 as can be seen in

Table 4.13 which means that there is a weak relationship between the gender and the

mode of transport that are usually used by the respondent.

4.3.2 Relation between the mode of transport and marital status

The distribution of the transport modes that are usually used by the respondents

versus the marital status of the respondents was presented in Table 4.14 and Figure

4.12. As can be seen from the table and chart the percent of single users of taxi,

motorcycle, and walking modes is bigger than the percent of married users with a

percents of 10.43%, 22.61%, and 20.8% respectively. The table also illustrates that

the percent of married users of private car mode is bigger than the percent of single

users with a percent of 16.25 %. The result that can be concluded from the table and

71

chart shown below is that the effect of marital status will be significant on private car

and motorcycle modes more than the other modes.

Table (4.14): Cross tabulation between the mode of transport and marital status

Count Status Total

Single Married

MODES private car 5 71 76

shared taxi 48 197 245

taxi 12 27 39

motorcycle 26 64 90

bicycle 0 13 13

walking 24 65 89

Total 115 437 552

Figure (4.12): Distribution of transport modes for marital status

For testing the relationship between the marital status and the choice of transport

mode, a chi-square test was applies to cross tabulation between the transport mode

variable and marital status variable. The results of chi-square test and Cramer’s V

72

statistics which show the relation between the marital status and the mode of transport

that is usually used by the respondent were presented in Tables 4.15 and 4.16

respectively.

Table (4.15): Chi-square test for mode-marital status relationship

Value Df Asymp. Sig.

(2-sided)

Pearson Chi-Square 20.918a 5 .001

Likelihood Ratio 25.573 5 .000

Linear-by-Linear

Association 7.473 1 .006

N of Valid Cases 552

Table (4.16): Cramer’s V statistics for mode-marital status relationship

Value Approx.

Sig.

Nominal by

Nominal Phi .195 .001

Cramer's V .195 .001

N of Valid Cases 552

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

As can be seen from the tables above the null hypothesis which says that there is no

relationship between the marital status and the choice of transport mode can be

rejected for 95% confidence level because the significance level is less than 0.05. The

Cramer’s V statistics is 0.195 as can be seen in Table 4.16 which means that there is a

very weak relationship between the marital status and the choice of transport mode.

4.3.3 Relation between the mode of transport and age

The distribution of the transport modes that are usually used by the respondents over

the age of the respondents can be seen in Table 4.17 and Figure 4.13. As can be seen

from the figure, the percent of private care users increases as the age increases

because the old people prefer to have privacy and comfort in transport modes which

are available in private car. The relationship between the age and the choice of

73

transport mode can be tested by applying chi-square test to cross tabulation between

the transport mode variable and age variable. The results of chi-square test and

Cramer’s V statistics which show the relation between the age and the mode of

transport that is usually used by the respondent were presented in Tables 4.18 and

4.19 respectively.

Table (4.17): Cross tabulation between the mode of transport and age

Count Age Total

18-30 31-40 41-50 >50

MODES private car 3 29 33 11 76

shared taxi 70 108 60 7 245

Taxi 11 24 4 0 39

motorcycle 33 37 19 1 90

Bicycle 0 5 10 0 13

Walking 28 30 14 4 89

Total 145 233 140 23 552

Figure (4.13): Distribution of transport modes for age

74

Table (4.18): Chi-square test for mode-age relationship

Value Df Asymp. Sig.

(2-sided)

Pearson Chi-Square 331.141a 205 .000

Likelihood Ratio 294.428 205 .000

Linear-by-Linear

Association 17.025 1 .000

N of Valid Cases 552

Table (4.19): Cramer’s V test for mode-age relationship

Value

Approx.

Sig.

Nominal by

Nominal

Phi .775 .000

Cramer's V .346 .000

N of Valid Cases 552

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

The results of statistical tests show that the null hypothesis which stated that there is

no relationship between the age and the choice of transport mode can be rejected for

95% confidence level because the significance level is less than 0.05. The Cramer’s V

statistics is 0.346 as can be seen in Table 4.19 which means that there is weak

relationship between the age and the choice of transport mode.

4.3.4 Relation between the mode of transport and family size

The distribution of transport modes that are usually be used by the respondents over

the family size of the respondents can be seen in table 4.20 and chart 4.14. In order to

study the relationship between the family size and the choice of transport mode, a chi-

square test was applied to cross tabulation between the transport mode variable and

age variable. Tables 4.21 and 4.22 show the results of chi-square test and Cramer’s V

statistics.

75

Table (4.20): Cross tabulation between the mode of transport and family size

Count Family size Total

1-6 7-10 >10

MODES private car 37 39 0 76

shared taxi 182 60 3 245

Taxi 37 2 0 39

Motorcycle 61 27 2 90

Bicycle 1 10 2 13

Walking 68 20 1 89

Total 386 158 8 552

Figure (4.14): Distribution of transport modes over family size

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

50.00%

private car shared taxi taxi motorcycle bicycle walking

1-6 persons

7-10 persons

>10 persons

76

Table (4.21): Chi-square test for mode-family size relationship

Value df

Asymp. Sig.

(2-sided)

Pearson Chi-Square 139.101a 55 .000

Likelihood Ratio 118.196 55 .000

Linear-by-Linear

Association .717 1 .397

N of Valid Cases 552

Table (4.22): Cramer’s V test for mode-family size relationship

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

As can be seen from the tables above the null hypothesis stated that there is no

relationship between the family size and the choice of transport mode can be rejected

for 95% confidence level because the significance level is less than 0.05. The

Cramer’s V statistics is 0.224 as can be seen in table 4.19 which means that there is

weak relationship between the age and the choice of transport mode.

4.3.5 Relation between the mode of transport and the monthly income

The distribution of transport modes that are usually be used by the respondents over

the average family monthly income of the respondents was presented in Table 4.23

and Figure 4.15. As can be seen from the chart the percent of travelers using private

car and taxi increases as the monthly income increases and the percent of riders using

motorcycle and walking modes decreases as the monthly income increases. In order to

study the relationship between the monthly income and the choice of transport mode,

a chi-square test was applied to cross tabulation between the transport mode variable

Value

Approx.

Sig.

Nominal by

Nominal Phi .502 .000

Cramer's V .224 .000

N of Valid Cases 552

77

and the average family monthly income variable. The results of chi-square test and

Cramer’s V statistics can be seen in Tables 4.24 and 4.25 respectively.

Table (4.23): Cross tabulation between the mode of transport and monthly income

Figure (4.15): Distribution of transport modes over monthly income

Count Average monthly income Total

less than

1000

NIS

1001-

1500

NIS

1501-

2000

NIS

2001-

3000

NIS

3001-

4000

NIS

4001-

5000

NIS

more

than

5001

Modes private car 0 1 2 11 18 20 24 76

shared taxi 5 14 47 98 67 12 2 245

taxi 0 0 1 3 23 12 0 39

motorcycle 5 22 30 33 0 0 0 90

bicycle 0 0 7 6 0 0 0 13

walking 6 19 24 30 7 2 1 89

Total 16 56 111 181 115 46 27 552

78

Table (4.24): Chi-square test for mode-monthly income relationship

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 369.977a 30 .000

Likelihood Ratio 340.922 30 .000

Linear-by-Linear

Association 113.342 1 .000

N of Valid Cases 552

Table (4.25): Cramer’s V test for mode-monthly income relationship

Value

Approx.

Sig.

Nominal by

Nominal Phi .819 .000

Cramer's V .366 .000

N of Valid Cases 552

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

According to the results shown in the tables above, the null hypothesis stated that

there is no relationship between the average family monthly income and the choice of

transport mode can be rejected for 95% confidence level because the significance

level is less than 0.05. The Cramer’s V statistics is 0.366 as can be seen in Table 4.25

which means that there is weak relationship between the average family monthly

income and the choice of transport mode.

4.3.6 Relation between the mode of transport and the Job

The distribution of transport modes that are usually used by the respondents over the

job of the respondents can be seen in Table 4.26 and Chart 4.16. As can be seen from

the chart, the large percent of private car users locate in businessman category

(85.7%) which has a high income to enable them to own their private vehicles while

the large percent of motorcycle users is from the waged workers (44.8%) that have a

relatively low income so they tend to use a cheap means of transport. In order to study

the relationship between the job and the choice of transport mode, a chi-square test

79

was applied to cross tabulation between the transport mode variable and the job

variable. The results of chi-square test and Cramer’s V statistics can be seen in Tables

4.26 and 4.27 respectively.

Table (4.26): Cross tabulation between the mode of transport and job

Count Job Total

Governmental

employee

Private sector

employee

Un

employee

Business

man or

special

works

waged

worker Others

Modes private car 25 14 19 18 0 0 76

shared taxi 121 60 37 2 20 5 245

taxi 8 27 3 0 0 1 39

motorcycle 31 17 2 1 39 0 90

bicycle 6 1 0 0 6 0 13

walking 31 19 14 0 22 3 89

Total 222 138 75 21 87 9 552

Figure (4.16): Distribution of transport modes over job

81

Table (4.27) :Chi-Square Tests for mode-job relationship

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 245.437(a) 25 .000

Likelihood Ratio 212.538 25 .000

Linear-by-Linear

Association 17.410 1 .000

N of Valid Cases 552

Table (4.28): Cramer’s V test for mode-job relationship

Value Appro

x. Sig.

Nominal by

Nominal Phi .667 .000

Cramer's V .298 .000

N of Valid Cases 552

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

As can be seen from the tables above the null hypothesis which stated that there is no

relationship between the job and the choice of transport mode can be rejected for 95%

confidence level because the significance level is less than 0.05. The Cramer’s V

statistics is 0.298 as can be seen in Table 4.27 which means that there is weak

relationship between the job and the choice of transport mode.

4.3.7 Relation between the mode of transport and the ownership of means of

transport

The distribution of transport modes that are usually used by the respondents over the

ownership of transport means of the respondents can be seen in Table 4.29 and Chart

4.17. As can be seen from the chart, the large percent of shared taxi and walking users

don’t have their own mean of transport (PC, MC, BC) while the large percent of

private car, motorcycle and bicycle users have their own means of transport which are

usually used to go to work .In order to study the relationship between the ownership

of transport means and the choice of transport mode, a chi-square test was applied to

81

cross tabulation between the transport mode variable and the ownership of transport

means variable. The results of chi-square test and Cramer’s V statistics can be seen in

Tables 4.30 and 4.31 respectively.

Table (4.29): Cross tabulation between the mode of transport and ownership of means

of transport

Count availability of

private car

availability of

motorcycle

availability of

bicycle Total

Yes No Yes No Yes No

Modes private car 72 4 0 76 0 76 76

shared taxi 10 235 12 233 1 244 245

Taxi 4 35 0 39 0 39 39

motorcycle 2 88 90 0 4 86 90

Bicycle 0 13 1 12 12 1 13

Walking 3 86 9 80 5 84 89

Total 91 461 112 440 22 530 552

Figure (4.17): Distribution of transport modes over ownership of transport means

82

Table (4.30) :Chi-Square Tests for mode-ownership of transport means relationship

Transport Mean Private Car Motorcycle Bicycle

Value df

Asymp.

Sig. (2-

sided)

Value df

Asymp.

Sig. (2-

sided)

Value df

Asymp.

Sig. (2-

sided)

Pearson Chi-Square 393.474 5 .111 425.719 5 .111 278.647 5 .111

Likelihood Ratio 318.174 5 .111 395.713 5 .111 93.619 5 .111

Linear-by-Linear

Association 96.211 1 .111 49.921 1 .111 31.673 1 .111

N of Valid Cases 552 552 552

Table (4.31): Cramer’s V test for mode-ownership of transport means relationship

Private Car Motorcycle Bicycle

Value Appro

x. Sig. Value

Appro

x. Sig. Value

Appro

x. Sig.

Nominal by

Nominal Phi 8440. .111 8780. .111 7110. .111

Cramer's V 8440. .111 8780. .111 7110. .111

N of Valid Cases 552 552 552 552

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

As can be seen from the tables above the null hypothesis stated that there is no

relationship between the ownership of transport means and the choice of transport

mode can be rejected for 95% confidence level because the significance level is less

than 0.05. The Cramer’s V statistics is 0.844 , 0.878 , and 0.710 for private care,

motorcycle and bicycle respectively which means that there is strong relationship

between the ownership of transport means and the choice of transport mode.

4.3.8 Relation between the mode of transport and the length of trip

The distribution of transport modes that are usually used by the respondents over the

distance between the home and the work of the respondents can be seen in Table 4.32

and Chart 4.18. As can be seen from the table and chart below the using of non-auto

modes (walking and bicycle) increases as the length of trip decreases while the using

of auto modes ( private car, taxi, shared taxi, and motorcycle) increases as the length

83

of trip increases. In order to study the relationship between trip length and the choice

of transport mode, a chi-square test was applied to cross tabulation between the

transport mode variable and the distance between home and work variable. The

results of chi-square test and Cramer’s V statistics can be seen in Tables 4.33 and 4.34

respectively.

Table (4.32): Cross tabulation between the mode of transport and length of trip

Count Monthly income Total

0.3-1.00

KM

1.1-2.00

KM

2.1-3.00

KM

3.1-4.00

KM

4.1-5.00

KM

5.1-6.00

KM

more

than 6

KM

Modes private car 1 6 23 29 10 7 0 76

shared taxi 2 41 93 31 40 29 9 245

taxi 0 6 14 10 8 1 0 39

motorcycle 0 19 19 20 18 12 2 90

bicycle 1 3 6 2 1 0 0 13

walking 33 46 9 1 0 0 0 89

Total 37 121 164 93 77 49 11 552

Figure (4.18): Distribution of transport modes over trip length

84

Table (4.33):Chi-Square Tests for mode-trip length relationship

Value df Asymp. Sig.

(2-sided)

Pearson Chi-Square 527.932a 280 .000

Likelihood Ratio 455.502 280 .000

Linear-by-Linear

Association 99.438 1 .000

N of Valid Cases 552

Table (4.34): Cramer’s V test for mode-trip length relationship

Value

Approx.

Sig.

Nominal by

Nominal

Phi .978 .000

Cramer's V .437 .000

N of Valid Cases 552

a Not assuming the null hypothesis.

b Using the asymptotic standard error assuming the null hypothesis.

As can be seen from the tables above the null hypothesis stated that there is no

relationship between the trip length and the choice of transport mode can be rejected

for 95% confidence level because the significance level is less than 0.05. The

Cramer’s V statistics as can be seen in table 4.34 is 0.437 which means that there is

medium relationship between the trip length and the choice of transport mode.

The Table 4.35 shown below summarizes the chi square, Cramer’s V, and

significance of the factors that affect the mode choice. As can be seen in the table,

among the ten factors which affect the mode choice, the ownership of transport mean,

the distance between home and work, gender, age, and monthly income have the

strongest relationship as they have the highest Cramer’s V value while the marital

status, family size and job have weak relationship with the choice of transport mode

as they have low Cramer’s V value.

85

Table (4.35): Test of relationship between the mode choice and travel socioeconomic

variables

Factor Chi-square Cramer’s V Sig. ( 2-sided)

Gender 79.9 0.380 0.000

Marital Status 20.918 0.195 0.001

Age 331.101 0.346 0.001

Family Size 139.101 0.224 0.000

Monthly Income 369.977 0.366 0.000

Job 245.43 0.298 0.000

P.C. ownership 393.474 0.884 0.000

Motorcycle ownership 429.709 0.878 0.000

Bicycle ownership 278.647 0.710 0.000

Distance 527.932 0.437 0.000

4.4 Relation between the captive ridership and socioeconomic characteristics

To study the relation between socioeconomic variables and the captive ridership, the

chi-square test was used. This test was applied to cross tabulations between the

variable captive (which categorized into captive and choice) and the socioeconomic

variables. The null hypothesis to be tested stated that is that there is no relationship

between the captive and socioeconomic variable used. The null hypothesis will be

rejected if the significance level is less than 0.05 for 95% confidence interval.

Cramer’s V statistics was used to give indication of the strength of the relationship

which range from 0-1 i.e the higher the better. The Table 4.36 shown below

summarizes the chi-square, Cramer’s V statistics, and the significance of travel

socioeconomic variables which affect the captive ridership. As can be seen from the

table there are six variables which affect the captive ridership where the null

hypothesis can be rejected at a confidence level greater than 95%. These variables are

gender, job, P.C. ownership, motorcycle ownership, bicycle ownership and distance

(trip length). Among these variables the distance and gender variables seem to have

the strongest relationship as they have the highest Cramer’s V values.

86

Table (4.36): Test of relationship between the captive ridership and travel

socioeconomic variables

Factor Chi-square Cramer’s V Sig. ( 2-sided)

Gender 29.79 0.232 0.000

Marital Status 1.097 0.045 0.295

Age 30.668 0.236 0.881

Family Size 7.004 0.113 0.799

Average monthly Income 6.883 0.112 0.332

Job 18.047 0.181 0.003

P.C. ownership 10.497 0.138 0.001

Motorcycle ownership 28.35 0.227 0.000

Bicycle ownership 9.026 0.128 0.001

Distance 90.060 0.404 0.003

On the other hand, marital status, age, family size, and average family monthly

income were not found to have an effect on the captive ridership where the null

hypothesis can’t be rejected at a confidence level greater than 95%.

4.5 Hypothetical questions

The distribution of travelers’ choice for the hypothetical questions mentioned in the

questionnaire shown in [Appendix 1] can be seen in Table 4.37 and Figure 4.19

Table (4.37): Distribution of riders’ choice for different levels

87

Figure (4.19): Distribution of riders’ choice for different levels (total sample)

As can be seen from the table and chart above the large percent of travelers prefer to

use shared taxi so they put it as the first choice for different levels while the large

percent of travelers don’t prefer to use bus so they put it as the last choice in different

levels. The table and chart also show that the percent of travelers who put minibus as

their first choice increases as the frequency decreases although the reduction in cost

decreases. This result illustrates that the travelers are more sensitive to frequency

which give indication about the waiting time rather than the cost and in vehicle travel

time because the travel cost and in vehicle travel time using shared taxi is relatively

small so any reduction in cost or increasing of in vehicle travel time will not be

significant if the travel waiting time or frequency is large. This will illustrate the large

percent of travelers who put minibus in the third level as the first choice (which

represent about 33.2% of total sample) as the frequency for minibus is 8 minutes

which is relatively near to those of shared taxi although the reduction of cost is only

10 % less than the cost of travel by shared taxi.

88

4.6 Importance of factors that affect mode choice

The relative importance index and the rank of the factors that affect the mode choice

can be seen in Table 4.38

Table (4.38): Relative Importance Index and Rank of the factors that affect mode

choice

Factor RII Rank

Health status 0.884 1

Distance (trip length) 0.806 2

Waiting time 0.788 3

Weather conditions 0.786 4

Travel time (IVTT) 0.755 5

Travel cost 0.752 6

Monthly income 0.748 7

Age 0.703 8

Gender 0.703 8

Comfort 0.671 9

Privacy 0.666 10

The results shown in the table above show that health status, distance, waiting time,

weather condition, in vehicle travel time, and cost is the most important factors that

affect the mode choice so these factors have to be taken into account when calibrating

the model. Some factors like health status and weather condition will be taken into

account through adding constant for the utility functions of the modes to reflect the

unobserved conditions which are difficult to account. These results will be compared

with the results which will be obtained from the model to stand on the consistency

between the results obtained from direct question for respondents about the factors

that affect their mode choice and the results obtained from indirect question which

can be concluded through calibration process.

89

4.7 Calibration of revealed model

On the basis of descriptive analysis of the data, there are six modes to be considered

for modeling the mode choice for work trips in Gaza city which are private car, shared

taxi, taxi, motorcycle, bicycle, and walking. Different specifications have been

evaluated to determine which specifications which replicate the data for work trips in

Gaza city. The list of variables that have been used in this research include the

variables that have been found from the literature review such as in vehicle travel time

(IVTT), out of vehicle travel time (OVTT), total travel time (TT), total travel cost

include the depreciation for private car and motorcycle modes (TC), age of

respondent (AGE), Gender of respondent (GENDER), ownership of transport means

(OWTM), family income (FINC), and distance (DIST). Composition variable such as

family income over family size and total cost over family income also have been used

to modify the impact of pure level of service variables. The list of variables that have

been used in model calibration with their abbreviations are presented in Table 4.39

Table (4.39): Abbreviation and description of explanatory variables

Variable Description

IVTT In vehicle travel time in minutes (generic variable)

OVTT Out of vehicle travel time in minutes (generic variable)

TT Total travel time in minutes (generic variable)

TC Total travel cost in ILS (generic)

GENDER Gender of respondent ( I if female and 0 otherwise)

AGE Age of respondent in years

DIST Length of the trip in kilometers

OWTM Ownership of transport means ( 1 if have transport mean and 0

otherwise)

FINC Average family monthly income in ILS

PINC Average monthly income per person in ILS

TC/PINC Total cost over person income in ILS

Constant Mode specific constant

91

Different model specifications were tested based on prior experience in intercity mode

choice modeling and the impact of introducing additional explanatory variables. The

specifications of the model are formulated based on the following criteria:

Wrong sign coefficient variables were dropped from the model.

Variables with insignificant coefficients were dropped from the model except the

level of service variables (travel time and travel cost).

Some variables with insignificant coefficient were considered based on its

improving the statistics of the model.

The level of service variables were considered in different forms (strait forward as

cost and travel time) or in ration form such as cost over income.

Sets of variables with high correlation were considered.

Some of intuitively important variables which have been dropped from the model

were reconsidered.

The mode specific constants were considered in spite of the significance of

coefficients of the variables.

The first model which has been estimated includes total travel time (TT) and total

travel cost (TC) as a generic variables which means that an increase of one unit of

travel time or travel cost has the same impact on the modal utility for all modes. The

distance variable (DIST) considered as a specific variable for bicycle and walking

modes. The private car mode was considered as a base mode when adding constants

for the mode utilities. The utilities for different modes can be written as the following:

(4.1)

The results of estimation shown in Table 4.40 illustrate that the estimated coefficients

of cost and travel time variables have a negative sign as expected which means that

the utility of modes decreases as the travel time and travel cost increase.

91

Table (4.40): Estimation results of model_1

Parameters MODEL_1 (MNL)

Estimated Value t -statistics

Generic Parameters

TT -0.0915 -2.7645

TC -0.5148 -1.6013

Alternative Specific Parameters

CONSTANT S_Taxi -0.2971 -0.4490

CONSTANT Taxi -0.1575 -0.1235

CONSTANT Motorcycle 0.1316 0.1641

CONSTANT Bicycle 0.8295 0.3938

CONSTANT Walking 2.5356 2.3450

DIST Bicycle -0.2001 -0.2716

DIST Walking -1.6014 -3.8219

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -120.9370

Rho Squared w.r.t. Zero 0.3666

Rho Squared w.r.t Constants 0.1638

Adjusted Rho Squared w.r.t. Zero 0.3195

Adjusted Rho Squared w.r.t Constants 0.1316

Number of Cases 368

Number of iterations 41

Estimation status Converged, with contants, with zeros, valid lic.

The results of estimation also show that both travel time (TT) and distance for

walking (DIST) have a large absolute values of t-statistics of 2.764 and 3.82

respectively which are greater than critical t- value at 90% confidence level (1.65),

this result leads to reject the null hypothesis that these variables have no effect on

modes utilities. Although the t-statistics of travel cost variable is lower bit than critical

t-value at 90% confidence level it should be included in the model because it

considered as a policy variable (Qrtuzar and Willumsen 2002). The lack in

significance of the alternative specific constants for shared taxi, taxi, motorcycle and

bicycle is immaterial since the constants represent the average effect of all variables

not included in the model and always should retain in the model despite the fact they

don’t have well understood behavior interpretation (Koppelman and Bhat 2006). The

t-statistic on bicycle specific distance variable is less than critical t-value even at 90%

confidence level, suggestion that the effect of distance on bicycle utility may not

92

differentiate it from the reference mode (private car mode) so this variable is

considered to drop from the model.

The removing of this variable (DIST) from the utility of bicycle leads to model_2

where the utility of bicycle can be written as the following while the utilities for the

other modes still they are in model_1:

(4.2)

The results of estimation for model_2 can be shown in Table 4.41

Table (4.41): Estimation results of model_2

Parameters MODEL_2 (MNL)

Estimated Value t-statistics

Generic Parameters

TT -0.092 -2.7823

TC -0.5074 -1.5892

Alternative Specific Parameters

CONSTANT S_Taxi -0.2818 -0.4280

CONSTANT Taxi -0.1836 -0.1444

CONSTANT Motorcycle 0.1408 0.1761

CONSTANT Bicycle 0.3512 0.3161

CONSTANT Walking 2.5407 2.3520

DIST Walking -1.5937 -3.8141

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -120.9767

Rho Squared w.r.t. Zero 0.3665

Rho Squared w.r.t Constants 0.1636

Adjusted Rho Squared w.r.t. Zero 0.3246

Adjusted Rho Squared w.r.t Constants 0.1381

Number of Cases 368

Number of iterations 13

Estimation status converged, with constants, with zeros, valid lic.

The results for two models show that both have a good goodness of fit measures ρ2.

To compare the two models a likelihood test was applied in order to study the impact

of exclusion a distance specific variable from the utility of bicycle mode. The null

hypothesis to be tested stated that there is no impact of a distance specific variable on

the mode choice decision

93

(4.3)

The statistical test that a distance specific variable (DIST) for bicycle mode has no

effect on the choice decision has a chi square value of

(4.4)

Where,

LLR is the log likelihood of the restricted model

LLU is the log likelihood of the unrestricted model

With one degree of freedom ( one variable was constrained to zero), the null

hypotheses can’t be rejected even at low confidence level 90% where the chi square

value << critical chi square value at 90% confidence level (2.71) thus the distance

specific variable can be excluded from the bicycle utility.

In order to improve the statistics of model_2, new intuitive variables were added to

the model. Average monthly income for family variable (FINC) was added to the

utility function of taxi, bicycle, motorcycle and walking and the model was estimated

and labeled as model_3. The utility functions for different modes can be written as the

following

(4.5)

The results of estimation presented in Table 4.42 illustrate that both travel time and

travel cost variables have a correct negative sign of coefficients and they are

statistically significant at 90% confidence level where the t-statistics value are greater

than the critical one (1.645). The results also show that both average family monthly

income variables for taxi and bicycle are statistically insignificant at significance level

greater than 0.1 where the t-statistics value is less than the critical one at 90%

94

confidence level so the null hypothesis that these variables have no effect on choice

decision can’t be rejected even at 90% confidence level so these two variables are

suggested to drop from the mode.

Table (4.42): Estimation results of model_3

Parameters Model_3 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.1301 -3.4088

TC -0.6816 -1.8606

Alternative Specific Parameters

CONSTANT S_Taxi 0.0371 0.0513

CONSTANT Taxi -1.3403 -0.6259

CONSTANT Motorcycle 3.3519 1.8833

CONSTANT Bicycle -71.8416 -0.0073

CONSTANT Walking 5.9025 3.3668

DIST Walking -1.9979 -4.2545

FINC Taxi 0.0005 1.1787

FINC Motorcycle -0.0016 -2.2157

FINC Bicycle 0.0512 0.0065

FINC Walking -0.001 -2.7333

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -106.2369

Rho Squared w.r.t. Zero 0.4436

Rho Squared w.r.t Constants 0.2655

Adjusted Rho Squared w.r.t. Zero 0.3808

Adjusted Rho Squared w.r.t Constants 0.2098

Number of Cases 368

Number of iterations 16

Estimation status converged, with contants, with zeros, valid lic.

The dropping of average family monthly income variable (FINC) from the utility

functions of bicycle and taxi modes leads to new model labeled as model_4 where the

utilities of taxi and bicycle modes can be written as the following while the utilities

for the other modes still as they are in the previous one.

(4.6)

The results of estimation for the model mentioned above are reported in Table 4.43

95

Table (4.43): Estimation results of model_4

Parameters Model_4 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.1068 -2.9849

TC -0.4878 -1.5109

Alternative Specific Parameters

CONSTANT S_Taxi 0.032 0.047

CONSTANT Taxi -0.1777 -0.1378

CONSTANT Motorcycle 3.973 2.3196

CONSTANT Bicycle 1.2022 1.0167

CONSTANT Walking 6.3621 3.7658

DIST Walking -1.9967 -4.1707

FINC Motorcycle -0.0017 -2.3869

FINC Walking -0.0011 -3.1419

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -113.6093

Rho Squared w.r.t. Zero 0.405

Rho Squared w.r.t Constants 0.2145

Adjusted Rho Squared w.r.t. Zero 0.3526

Adjusted Rho Squared w.r.t Constants 0.1739

Number of Cases 368

Number of iterations 13

Estimation status converged, with contants, with zeros, valid lic.

The results reported in the table above show that both travel time and travel cost have

negative sign of coefficients as expected. The results also show that travel time (TT),

distance (DIST), average family monthly income for motorcycle (FINC motorcycle) , and

average family monthly income for walking (FINC walking) have absolute t-statistics

value of 2.98, 4.17,2.38, and 3.14 respectively which are greater than the critical t-

value at 95% confidence level so the null hypothesis stated that these variables has no

effect on choice decision can be rejected at significance level greater than 0.05. Even

though the travel cost variable (TC) has a low absolute t-statistics value of 1.51 which

is less than the critical t-value even at 90% confidence level it will retain in the model

because it is considered as level of service variable. In order to study the effect of

adding average family monthly income variable to both motorcycle and walking

utilities on the statistics measures of the model, this model was compared with

model_2. The comparison of the two models show that the goodness of fit measures

96

for this model ,

, and

were improved to 0.405, 0.2145, 0.3526, and 0.1739

compared with 0.3665, 0.1636, 0.3246, and 0.1381 for model_2.

The likelihood ratio test was applied to between modl_4 and mode_2 to test the

hypothesis that the average family monthly income variable (FINC) for motorcycle

and walking modes can be excluded from the model

(4.7)

The statistical test has a chi square value of - - - which

is greater than critical chi square value even at 99.9 % confidence level with two

degrees of freedom ( two variables were constrained to zero) (13.82). This result leads

to reject the null hypothesis stated that these variables have no effect on choice

decision and accordingly they could not be excluded from the model.

For further improvement the statistics of model_4 the ownership of transport means

variable (OWTM) was added to the utility of shared taxi and the new model was

labeled as model_5. The utilities for different modes can be written as the following:

(4.8)

Table 4.44 presents the estimation results of model. As can be seen from the table

both travel cost and travel time coefficients have a correct negative sign. The results

also show that the total travel time (TT), ownership of transport means (OWTM S_taxi),

distance (DIST walking) and average family monthly income for motorcycle and

walking modes (FINC Motorcycle, FINC Walking) variables have a large absolute t-

statistics values of 2.774, 1.828, , 3.98, 2.53, and 3.04 respectively which are greater

than the critical t-value at 90% confidence level (1.645) . This result leads to reject the

null hypothesis stated that these variables have no effect on choice decision. Although

total cost (TC) variable has absolute value of t-statistic (1.4138) which is lower bit

97

than critical t-value at 90% confidence level, it will retain in the model as it is

considered as a policy variable.

Table (4.44): Estimation results of model_5

Parameters Model_5 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.1039 -2.7774

TC -0.4579 -1.4138

Alternative Specific Parameters

CONSTANT S_Taxi 0.6291 0.8152

CONSTANT Taxi 0.1739 0.1332

CONSTANT Motorcycle 4.4475 2.4906

CONSTANT Bicycle 1.3541 1.1244

CONSTANT Walking 6.5117 3.8294

OWTM S_Taxi -0.9876 -1.8281

DIST Walking -1.8958 -3.9829

FINC Motorcycle -0.0019 -2.5321

FINC Walking -0.0011 -3.0413

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -111.9227

Rho Squared w.r.t. Zero 0.4138

Rho Squared w.r.t Constants 0.2262

Adjusted Rho Squared w.r.t. Zero 0.3562

Adjusted Rho Squared w.r.t Constants 0.1785

Number of Cases 368

Number of iterations 12

Estimation status converged, with contants, with zeros, valid lic.

The goodness of fit measures for this model ,

, and

were improved to

0.4138, 0.2262, 0.3562, and 0.1785 compared with 0.405, 0.2145, 0.3526, and 0.1739

for model_4.

To study the hypothesis involving with the exclusion of OWTM variable from the

model, the likelihood ration test was applied between model_4 and model_5. The null

hypothesis to be tested stated that the OWTM variable can be excluded from the

model

(4.9)

98

The statistical test has a chi square value of - - - - thus

the null hypothesis can be rejected at significance level greater than 0.1 and one

degree of freedom (one variable OWTM was constrained to zero). According to this

result the OWTM variable has to be retaining in the model.

To study the effect of social characteristics on mode choice, gender variable

(GENDER) was added to both taxi and walking utilities and the age variable (AGE)

was added to motorcycle, bicycle and walking utility functions. The new model was

labeled as model_6 with the following utility functions

(4.10)

The results of model estimation were reported in Table 4.45. The results show that the

travel time and travel cost variables have a correct sign of estimators and they are

statistically significant at 90% confidence level where the t-statistics value is greater

than the critical one. The results also show that the OWTM S_taxi, DIST walking,,

FINCmotorcycle, FINC walking, and AGE bicycle are statistically significant at significance

level greater than 0.1 with a t-statistics values of 1.65, 4.4189, 2.7413, 2.8678, and

2.4589 respectively so the null hypothesis that these variables have no effect on mode

choice can be rejected at significance level greater than 0.1. Gender variable for both

taxi and walking modes and age variable for motorcycle and walking modes are

statistically insignificant at 90% confidence level as the t-statistics for these variables

have values of 0.2333, 1.1818, 0.9684, and 0.0109 which are less than the critical t-

value at 90% confidence level (1.645). So that the null hypothesis that these variables

have no effect on choice decision can’t be rejected at the specified significance level

thus these variables are suggested to be excluded from the model.

99

Table (4.45): Estimation results of model_6

Parameters Model_6 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.1129 -2.8731

TC -0.5919 -1.7198

-- Alternative Specific Parameters

CONSTANT S_Taxi 0.6138 0.7658

CONSTANT Taxi 0.6095 0.4515

CONSTANT Motorcycle 3.1076 1.2218

CONSTANT Bicycle -16.8546 -2.314

CONSTANT Walking 7.1476 3.3614

GENDER Taxi 0.1401 0.2333

GENDER Walking -0.9417 -1.1818

AGE Motorcycle 0.0714 0.9684

AGE Bicycle 0.4376 2.4589

AGE Walking -0.0004 -0.0109

OWTM S_Taxi -0.9442 -1.6562

DIST Walking -2.1831 -4.4189

FINC Motorcycle -0.0025 -2.7413

FINC Walking -0.0011 -2.8678

-- Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -106.7392

Rho Squared w.r.t. Zero 0.441

Rho Squared w.r.t Constants 0.262

Adjusted Rho Squared w.r.t. Zero 0.3572

Adjusted Rho Squared w.r.t Constants 0.1797

Number of Cases 368

Number of iterations 18

Estimation status converged, with contants, with zeros, valid lic.

The exclusion of gender variable from the utility functions of taxi and walking modes

in addition to the exclusion of age variable from the utility functions of motorcycle

and walking modes leads to new model labeled as model_7. The results of estimation

for the model which were reported in Table 4.46 show that all the variables have a

correct sign of estimators and they are statistically significance at a confidence level

of 90% as the t-statistics for them are greater than critical t-value at significance level

greater than 0.1 so the null hypothesis stated that these variables have no effect on

choice decision can be rejected at that confidence level.

111

Table (4.46): Estimation results of model_7

Parameters Model_7 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.1205 -3.0795

TC -0.574 -1.6783

Alternative Specific Parameters

CONSTANT S_Taxi 0.7542 0.9483

CONSTANT Taxi 0.6375 0.4745

CONSTANT Motorcycle 4.7429 2.5529

CONSTANT Bicycle -15.9109 -2.3185

CONSTANT Walking 6.9207 3.8584

AGE Bicycle 0.4105 2.4881

OWTM S_Taxi -0.9837 -1.7851

DIST Walking -2.0619 -4.3152

FINC Motorcycle -0.0021 -2.6665

FINC Walking -0.0011 -3.0245

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -107.9959

Rho Squared w.r.t. Zero 0.4344

Rho Squared w.r.t Constants 0.2533

Adjusted Rho Squared w.r.t. Zero 0.3716

Adjusted Rho Squared w.r.t Constants 0.1981

Number of Cases 368

Number of iterations 18

Estimation status converged, with contants, with zeros, valid lic.

In order to test the hypothesis involving with exclusion of gender variable (GENDER)

from the utility functions of taxi and walking modes in addition to exclusion of age

variable from the utility functions of motorcycle and walking modes, the likelihood

ratio test was applied between model_6 and model_7. The null hypothesis can be

written as the following

(4.11)

The statistical test has a chi square value of - - - -

which is less than critical chi square value at significance level greater than 0.1 with

four degrees of freedom ( four variables were constrained to zero) (7.78) . According

111

to this result the null hypothesis can’t be rejected even at 90% confidence level so

these variables seems to have no effect on choice decision.

For studying the possibility of excluding age variable from the utility function of

bicycle mode, the likelihood ratio test was applied between model_7 and model_5.

The statistical test has a chi square value of - - - - .

With this condition the null hypothesis that this variable can be excluded from the

model can be rejected even at significance level greater than 0.01 with one degree of

freedom as the chi square value (7.854) is greater than the critical value (6.63).

The decision maker related variables such as average income, ownership of transport

means, family size and others can be included in the models by two approaches. The

first is to include them as specific variables to each or some of alternatives (except for

the reference alternative. All the models reported to this point used this approach for

inclusion of decision maker related variables in the models. The other approach is to

include such variables through interaction with mode attributes such as dividing cost

by income to reflect decreasing the importance of cost by increasing the annual

income.

To take this issue into account, the travel cost variables in the previous model

(model_7) was replaced by cost over person income variable (TC/PINC).The new

modes was labeled as model_8 with the following utility functions

(4.12)

The results of estimation which are presented in Table 4.47 show that travel time and

cost over personal income variables have a correct sign of estimators. The results also

show that except ownership of transport means (OWTM) which is statistically

significant at 90% confidence level, all the variables are statistically significant at

significance level greater than 0.05 as they have an absolute value of t-statistics larger

112

than critical t-value at 95% confidence level (1.96). This result leads to reject the null

hypothesis that these variables has no effect on choice decision at significance level

greater than 0.1 (90% confidence level)..

Table (4.47): Estimation results of model_8

Parameters Model_8 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.1299 -3.264

TC/PINC -227.5075 -2.7647

Alternative Specific Parameters

CONSTANT S_Taxi 1.0773 1.4379

CONSTANT Taxi -0.5826 -0.987

CONSTANT Motorcycle 4.9794 2.7175

CONSTANT Bicycle -15.3013 -2.2932

CONSTANT Walking 7.366 4.2017

AGE Bicycle 0.3979 2.4455

OWTM S_Taxi -1.0626 -1.9061

DIST Walking -2.16 -4.3961

FINC Motorcycle -0.0021 -2.6153

FINC Walking -0.001 -2.7697

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -105.8891

Rho Squared w.r.t. Zero 0.4454

Rho Squared w.r.t Constants 0.2679

Adjusted Rho Squared w.r.t. Zero 0.3826

Adjusted Rho Squared w.r.t Constants 0.2122

Number of Cases 368

Number of iterations 15

Estimation status converged, with contants, with zeros, valid lic.

The goodness of fit measures for this model ,

, and

were improved to

0.4454, 0.2679,0.3826, and 0.2122 compared with 0.4344, 0.2533,0.3716, and 0.1981

for model_7 respectively.

To compare model_7 with model_8 a non-nested hypothesis test was applied for this

purpose as the two models have the same number of variables. The null hypothesis to

be tested stated that the lower roh-square model is the true model. In non-nested

hypothesis test the adjusted roh-square is used to test the hypothesis. The null

113

hypothesis can be rejected at significance level SL determined by the following

equation

(4.13)

Where,

is the adjusted roh-square relative to the zero model with higher value.

is the adjusted roh-square relative to the zero model with lower value

is the standard normal cumulative distribution function

As model_8 has better goodness-of-fit than model_7 then the null hypothesis to be

tested is that the model_7 is the true model. The significance level to reject the null

hypothesis can be calculated as follows

As the significance level calculated from the equation above is less than 0.05 then the

null hypothesis that model_7 is the true model can be rejected at significance level

greater than 0.05. The result is consistence with the theory that the importance of

travel cost decreases as the income increases.

The above formulated models assume that the utility of in vehicle travel time (IVTT)

and out of vehicle travel time is (OVTT) is equal; however the workers may be more

sensitive to one of them than the other. In order to take this issue into account the total

travel time was disaggregated into two parts namely in vehicle travel time and out of

vehicle travel time and a new model was formulated with the following utilities

(4.14)

114

The new model was labeled as model_9 and the results of estimation for the model

can be seen in Table 4.48.

Table (4.48): Estimation results of model_9

Parameters Model_1 (MNL)

estimated value t-statistics

Generic Parameters

IVTT -0.2939 -3.873

OVTT -0.0647 -1.4002

TC/PINC -261.2765 -3.0919

Alternative Specific Parameters

CONSTANT S_Taxi 0.6629 0.8788

CONSTANT Taxi -0.7432 -1.2693

CONSTANT Motorcycle 5.7455 2.9782

CONSTANT Bicycle -13.8246 -1.867

CONSTANT Walking 7.5244 4.1174

AGE Bicycle 0.3913 2.2187

OWTM S_Taxi -0.9586 -1.6886

DIST Walking -0.9354 -1.3755

FINC Motorcycle -0.0024 -2.8245

FINC Walking -0.0011 -2.7988

Model Statistics

Log Likelihood at Zero -190.9438

Log Likelihood at Constants -144.6343

Log Likelihood at Convergence -102.2029

Rho Squared w.r.t. Zero 0.4647

Rho Squared w.r.t Constants 0.2934

Adjusted Rho Squared w.r.t. Zero 0.3967

Adjusted Rho Squared w.r.t Constants 0.2301

Number of Cases 368

Number of iterations 15

Estimation status converged, with contants, with zeros, valid lic.

The results shown in the table above show that in vehicle, out of vehicle travel time,

and total travel cost variables have a correct negative sign of estimators. The results

also show that all the variables except out of vehicle (OVTT) and distance variables

(DIST) are statistically significant at 90% confidence level where the absolute value

of t-statistics are greater than critical t-value (1.645) so that the null hypothesis that

these variables have no effect on choice decision can be rejected at level of

significance greater than 0.1. Although OVTT, and DIST are statistically insignificant

at 90% confidence level, caution should be taken before removing it from the model

as the dropping it may adversely affect the significance of other variables.

115

As can be seen from the results of estimation the in vehicle travel time has a larger

coefficient than out of vehicle travel time , this results contradict with the results of

some researches such as (Almasri 2011) which concluded that the travelers are more

sensitive to out of vehicle travel time than in vehicle travel time. The good

accessibility for the trips in Gaza city and the short access, egress and waiting time of

the trips may explain this result.

The test of hypothesis of equal value of in and out of vehicle travel time, the

likelihood ratio test was applied between model_8 and model_9. The statistical test

has a chi square value of - - - - . This result leads

to reject the null hypothesis and accordingly reject the constraints imposed by

model_8 at a significance level greater than 0.05 with one degree of freedom as the

chi square value calculated above is larger than the critical chi square value at 95%

and one degree of freedom (3.84).

By comparing the above formulated models it is clear that model_8 and model_9 have

the best goodness of fit measures among the estimated models. Although model_9 has

goodness of fit measures ,

, and

of value 0.4647, 0.2934, 0.3967, and

0.2301 respectively which is better than the goodness of fit measures for model_8

which has a goodness of fit measures ,

, and

of 0.4454, 0.2679, 0.3826,

and 0.2122 respectively , but this model suffer from shortage represents on that some

of variables in this model ( out of vehicle (OVTT), and distance (DIST) are

statistically insignificant even at 90% confidence level. So based on the criteria which

were mentioned in the methodology in chapter 3 for comparing the models and

choosing the most satisfactory one, model_8 seems to be the most satisfactory one for

representing the behavior of employed people in choosing the mode of transport in

Gaza city as this model has a correct sign of estimators and all the variables are

statistically significant at 90% confidence level in addition it has a good goodness of

fit measures while some of variables in model_9 which is better than it in goodness of

fit measures are statistically insignificant at 90% confidence level. The utility

functions for the model can be written as following

116

4.8 Validation for revealed model

Model validation is considered very important process to evaluate the performance of

the calibrated model and its ability to predict modal split for data other than that used

for calibration process. The validation process is tested on three different phases. The

first phase is the test of reasonableness validation process which was tested during the

calibration process depending on the expected sign of estimators. All models with a

wrong sign of estimators would not consider as a valid model. Based on this criterion,

it is clear that model_8 which was chosen as the most satisfactory model for work

trips in Gaza city is considered as a valid model because the travel time and travel

cost variables have correct sign of estimators (negative signs).

The second phase of validation process is the statistical validation test which is

conducted by the likelihood ratio test (LRTS). This test was conducted for model_8

using about 1/3rd

of the data sets (184 observations). The details for this test were

discussed in chapter three. The results of this test show that the calculated chi square

was - - - - . With twelve degrees of freedom (number

of restricted coefficients) as indicted in equation 4.12, the calculated chi square value

can’t lead to reject the null hypothesis stated that there is no difference between the

predicted and observed behavior because the calculated chi square value is less than

critical chi square value at 95% confidence level and twelve degrees of freedom

(21.026).

The last phase for validation process is calculated the prediction capability of the

calibrated model (model_8). To calculate the prediction ratio, the utility for each trip

maker was calculated then the probability of each alternative (mode) was estimated.

The alternative with the highest probability is predicted to be the chosen mode for that

particular. The number of travelers correctly predicted was summed up to each

alternative and compared to yield the prediction value. The calculated prediction

value was 0.69 which means that the model is capable to predict about 69% of the

choices of the trip makers’ correctly.

117

4.9 Calibration of stated preference model

The stated preference model was calibrated using the data collected from the answers

of respondents about the hypothetical questions in part V of questionnaire. On the

basis of data analysis there are three modes to be considered for modeling which are

shared taxi, minibus and bus modes. The main objective for this model is to

investigate the market share when introducing bus service to transport system in Gaza

city. The list of variables that have been used in model and their abbreviations can be

seen in Table 4.49

Table (4.49): Abbreviation and description of explanatory variable used in stated

preference model

Variable Description

TT In vehicle travel time in minutes (generic variable)

FARE Total travel cost in ILS (generic)

FREQ Service frequency in minutes (generic variable)

GENDER Gender of respondent ( I if female and 0 otherwise)

AGE Age of respondent in years

DIST Length of the trip in kilometers

FINC Average family monthly income in ILS

PINC Average monthly income per person in ILS

FARE/PINC Total cost over person income in ILS

Constant Mode specific constant

The criteria that have been used for calibrating the stated preference model are the

same that have been used in revealed model and mentioned above.

The first model has been estimated (model_S1) includes travel time (TT), fare of trip

(FARE), and service frequency (FREQ). Constants have been added to bus and

minibus modes. The utility functions for different modes can be written as the

following

118

(4.15)

The results of estimation for this model have been reported in Table 4.50

Table (4.50): Estimation results of model_S1

Parameters model_S1 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.4093 -3.4477

FARE -3.0676 -4.316

FREQ -0.06 -3.3729

Alternative Specific Parameters

CONSTANT Minibus -0.6597 -3.3386

CONSTANT Bus -1.549 -3.9467

Model Statistics

Log Likelihood at Zero -542.7145

Log Likelihood at Constants -380.0125

Log Likelihood at Convergence -359.5908

Rho Squared w.r.t. Zero 0.3374

Rho Squared w.r.t Constants 0.0537

Adjusted Rho Squared w.r.t. Zero 0.3282

Adjusted Rho Squared w.r.t Constants 0.0456

Number of Cases 494

Number of iterations 13

Estimation status converged, with contants, with zeros, valid lic.

The results of estimation shown in the table above indicated that all the variables have

a correct sign of coefficients and they are statistically significance at 95% confidence

level as the t-statistics for these variables are greater than critical t-value at 95%

confidence interval (1.96), hence the null hypothesis that these variables have no

effect on choice decision cab be rejected at significance level greater than 0.05. The

goodness of fit measures for this model is considered low where the , and

for

this model are 0.0537 and 0.0456 respectively.

For improvement the statistics of the previous model a new explanatory variables

were added to the utility functions for different modes. The age variable (AGE),

average family monthly income variable (FINC), and gender variable (ENDER) were

added to minibus and bus modes. The new model was labeled as model_S2. The

utilities for different modes can be written as the following

119

(4.16)

The results of estimation for model_S2 presented in Table 4.51 show that the

goodness of fit measures was improved dramatically compared with model_S1 where

the values of ,

, and

for this model are 0.5323, 0.3321, 0.512, and 0.3068

respectively while these values for model_S1 are 0.3374, 0.0537, 0.3282, and 0.0456

respectively.

Table (4.51): Estimation results of model_S2

Parameters model_S2(MNL)

Estimated value t-statistics

Generic Parameters

TT -0.4118 -2.708

FARE -2.7787 -3.0092

FREQ -0.0762 -3.1972

Alternative Specific Parameters

CONSTANT Minibus 1.2998 1.9289

CONSTANT Bus 3.1458 2.595

GENDER Minibus 0.4946 1.8102

GENDER Bus -0.4395 -0.8498

AGE Minibus 0.0603 3.5379

AGE Bus 0.1036 3.78

FINC Minibus -0.0019 -8.021

FINC Bus -0.0044 -8.1287

Model Statistics

Log Likelihood at Zero -542.7145

Log Likelihood at Constants -380.0125

Log Likelihood at Convergence -253.8182

Rho Squared w.r.t. Zero 0.5323

Rho Squared w.r.t Constants 0.3321

Adjusted Rho Squared w.r.t. Zero 0.512

Adjusted Rho Squared w.r.t Constants 0.3068

Number of Cases 494

Number of iterations 15

Estimation status converged, with contants, with zeros, valid lic.

111

As can be seen from the table above TT, FARE, FREQ, AGE, and FINC variables

have expected sign of coefficients and they are statistically significant at 95%

confidence level so the null hypothesis that these variables have no effect on choice

decision can be rejected at this confidence level ,hence these variables should be

retain in the model. In contrast gender variable (GENDER) has unexpected sign of

coefficient for minibus and it is statistically insignificant for bus mode even at 90%

confidence level so the GENDER variable is suggested to remove from the model.

The dropping of gender variable from the model leads to new model labeled as

model_S3. The results of estimation for this model can be seen in Table 4.52.

Table (4.52): Estimation results of model_S3

Parameters model_S3 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.3862 -2.6172

FARE -2.6531 -2.89

FREQ -0.0761 -3.25

Alternative Specific Parameters

CONSTANT Minibus 1.4636 2.2021

CONSTANT Bus 2.935 2.4903

AGE Minibus 0.0588 3.4747

AGE Bus 0.1082 3.9707

FINC Minibus -0.0018 -7.8992

FINC Bus -0.0045 -8.1862

Model Statistics

Log Likelihood at Zero -542.7145

Log Likelihood at Constants -380.0125

Log Likelihood at Convergence -256.7447

Rho Squared w.r.t. Zero 0.5269

Rho Squared w.r.t Constants 0.3244

Adjusted Rho Squared w.r.t. Zero 0.5103

Adjusted Rho Squared w.r.t Constants 0.3044

Number of Cases 494

Number of iterations 13

Estimation status converged, with contants, with zeros, valid lic.

For testing the null hypothesis stated that gender variable can be excluded from the

utility functions of minibus and bus modes the likelihood ratio test was applied

between model_S2 and model_S3. The statistical test has a chi square value of

- - - - . With this condition the null hypothesis can’t

111

be rejected at significance level greater than 0.05 with two degrees of freedom as the

chi square value (5.85) is less than the critical value (5.99) hence the gender variable

can be dropped from the model.

The theory and the practices in mode choice modeling indicated that the importance

of cost decreases as the monthly income increases. To take this issue into account the

fare variables in the previous model was replaced by fare over person income variable

(FARE/PINC). The new model was labeled as model_S4. The results of estimation

for the model were reported in Table 4.53.

Table (4.53): Estimation results of model_S4

Parameters model_S4(MNL)

Estimated value t-statistics

Generic Parameters

TT -0.2106 -1.7393

FREQ -0.0689 -2.9793

FARE/PINC -404.2799 -2.4162

Alternative Specific Parameters

CONSTANT Minibus 1.7941 2.6785

CONSTANT Bus 4.1263 3.631

AGE Minibus 0.0477 2.8001

AGE Bus 0.0743 2.5139

FINC Minibus -0.0018 -7.6681

FINC Bus -0.0042 -7.5742

Model Statistics

Log Likelihood at Zero -542.7145

Log Likelihood at Constants -380.0125

Log Likelihood at Convergence -258.0411

Rho Squared w.r.t. Zero 0.5245

Rho Squared w.r.t Constants 0.321

Adjusted Rho Squared w.r.t. Zero 0.508

Adjusted Rho Squared w.r.t Constants 0.301

Number of Cases 494

Number of iterations 20

Estimation status converged, with contants, with zeros, valid lic.

The results shown in the table above show that all the variables have expected sign of

coefficients and they are statistically significant at significance level greater than 0.05

except the travel time variable which is statistically significant at significance level

greater than 0.1.in order to compare this model with model_S3 the non-nested

hypothesis test was applied as the two models have the same number of variables

112

where model_S4 is the lower model and model_S3 is the higher model. The null

hypothesis to be tested stated that model_S4 is the true model. The significance level

for the test of model_S4 being true is - - - -

= [-1.58]=0.057 which is >0.05. This result can’t lead to reject the null hypothesis at

significance level greater than 0.05 and implies that Model_S4 with fare over income

variables is the true model. This result is consistent with the theory and practices

indicated that the value of money decreases as the income increases.

For further improvement of the statistics of the previous model, a distance (DIST)

variable was added to the utility function of bus mode. The reason for adding this

variable to bus mode is the hunch generated from the descriptive analysis of data that

this variable will be significant for bus mode as it is noted that the large percent of

travelers who choose the bus mode as their preferred mode have a long trips. The

results of estimation (model_S5) were reported in Table 4.54.

Table (4.54): Estimation results of model_S5

Parameters model_S5 (MNL)

Estimated value t-statistics

Generic Parameters

TT -0.3213 -2.5138

FREQ -0.0518 -2.127

FARE/PINC -372.5231 -2.003

Alternative Specific Parameters

CONSTANT Minibus 1.8179 2.7047

CONSTANT Bus 1.1165 0.7961

AGE Minibus 0.0493 2.8485

AGE Bus 0.069 2.2217

DIST Bus 0.6234 3.6936

FINC Minibus -0.0018 -7.6099

FINC Bus -0.0039 -6.6471

Model Statistics

Log Likelihood at Zero -542.7145

Log Likelihood at Constants -380.0125

Log Likelihood at Convergence -250.54

Rho Squared w.r.t. Zero 0.5384

Rho Squared w.r.t Constants 0.3407

Adjusted Rho Squared w.r.t. Zero 0.5199

Adjusted Rho Squared w.r.t Constants 0.318

Number of Cases 494

Number of iterations 18

Estimation status converged, with contants, with zeros, valid lic.

113

The results in the table above show that all the variables are statistically significant at

significance level greater than 0.05 which means that these variables have significant

effect on choice decision. In addition the TT, FARE/PINC, and FREQ have a negative

sign of estimators which is consistent with the expected sign. The distance variable

has a positive sign which matches with the expectation that the desire of using bus

mode increases as the trip length increases and also consistent with the results of

descriptive analysis of data. The goodness of fit measures for this model was

improved compared with model_S4 as the value of ,

, and

for this model

are 0.5384, 0.3407, 0.5199, and 0.318 respectively while these values are 0.5245,

0.321, 0.508, and 0.301 respectively for model_S4. In order to test the hypothesis that

the distance variable can be excluded from the model, this model was compared with

model_S4. The likelihood ratio test was applied between the two models where the

distance variable in model_S4 was constrained to zero. The statistical test has a chi

square value of - - - - which is greater than the critical

chi square value at 95% confidence level so the null hypothesis can be rejected at

significance level greater than 0.05 hence distance variable can’t be excluded from the

model.

By comparing the above estimated models, it is clear that model_S5 has the best

goodness of fit measures with a value of 0.5384, 0.3407, 0.5199, and 0.318 for ,

,

and

respectively. in addition all variables in this model have a correct sign of

coefficients and they are statistically significant at confidence level of 95%, hence this

model can be chosen as the most satisfactory model to describe the behavior of

travelers in choosing the mode of transport for work trips in Gaza city based on the

criteria which were mentioned in the methodology for comparing the models and

choosing the most satisfactory one. The utility functions for different modes can be

written as the following

114

4.10 Validation of stated preference model

To test the validation of the stated preference model, the same approach was used as

in revealed model. The test of reasonableness indicated that the model which was

chosen as the most satisfactory one (model_S5) is considered as a valid model

because all the variables included in this model have correct signs of coefficients.

The likelihood ratio test (LRTS) which is the second phase of validation process was

conducted on the chosen model (model_S5) using about 1/3rd

of the data sets. The

result of this test show that the calculated chi square value was

- - - - which is less than the critical chi square

value at 95% confidence level and ten degrees of freedom ( the number of variables in

the chosen model), therefore the null hypothesis that there is no difference between

the predicted and observed behavior can’t be rejected at this significance level.

The prediction capability for the model which is the last phase of the validation

process indicated that the calculated prediction ratio of the model is 0.80 which means

that the model is capable to predict about 80% of the choices of the trip makers’

correctly.

115

Chapter 5: Conclusions & Recommendations

5.1 Summary

The main objective of this research was to investigate the factors that affect the choice

behavior of the employed people and developing a mode choice model for work trips

in Gaza city. For achieving the above mentioned aim the work in this research was

divided into six phases. The first phase involving reviewing the literature in the field

of urban transportation planning and mode choice modeling. The subjects which were

discussed in this phase, included background about transportation planning process

and travel demand forecasting, historical development of mode choice models, types

of mode choice models and comparison among them, the estimation techniques used

in mode choice models, and sampling and data collection methods. In addition

previous case studies for mode choice models were presented in this phase. According

to the literature review the logit model was chosen to be used for calibrating the mode

choice for work trips in Gaza city because it is simple in terms of formulation and

estimation in addition to its good accuracy compared with the other types of the

models. The factors that affect the mode choice models and which will be included in

questionnaire were determined according to the literature review.

The second phase involving the design of initial survey questionnaire and conducting

of pilot study and analysis of results for pilot study. In the third phase of this study the

final questionnaire was designed by adding, removing or editing questions in the

initial questionnaire based on the results of the pilot study. This phase also includes

determination of sample size, distributing the questionnaire to the target group and

analysis of the results.

The fourth phase included calibration of mode choice model for work trips in Gaza

city using both revealed and stated preference data. The ELM software was used.

Nine models were calibrated for revealed data and five models for stated preference

data and the most satisfactory one was chosen based on the goodness of fit measures

and t-statistics of the attributes. The model which yielded significance coefficients

estimators for all variables and high goodness of fit values was selected as the best

one which was Model_8 for revealed data and Model_S5 for stated preference data.

116

The fifth phase involved validation of the selected model based on three different

levels. The first level in validation process is the test of reasonableness of the selected

model which is testing during the calibration process where all the models with wrong

signs of coefficients will not be considered as a valid model. The next level of

validation process is the statistical test of the null hypothesis that there is no

difference between observed and predicted behavior using likelihood ratio test. The

last level of validation process is the calculation of prediction ratio for the selected

model to test the prediction capability of the model. The results of validation test for

the selected revealed and stated preference models in this research show that they are

reasonable because the two models have correct signs of coefficients. The results also

indicated that there is no difference between the predicted and observed behavior

where the calculated chi square values for both revealed and stated preference models

are 16.25 and 9.226 respectively which are less than the critical chi square values at

95% confidence level and 13 and 10 degrees of freedom respectively. The prediction

ratio for revealed and stated preference models are 0.69, and 0.80 respectively.

The last phase of this research introduce summarize for the main finding and

conclusions and recommendations for the study.

5.2 Conclusions

Based on the findings of the research the following conclusions can be drawn

1. Based on the descriptive analysis of the data, the ownership of transport means,

trip length, age, monthly income and gender can be considered as the most

important factors that affect the mode choice as they have the strongest Cramar’s

V value while marital status, family size, and job seem to have low effects on

choice decision of travelers as they have low values of Cramer’s V statistics.

2. There are six factors that affect the captive ridership which are gender, job, private

car ownership, motorcycle ownership, bicycle ownership, and distance.

3. Among the variables that affect the captive ridership, distance and gender

variables seem to have the highest effect on captivity as they have the highest

Cramer’s V statistics.

4. For revealed model, the total travel time, total travel cost divided by personal

income, ownership of transport means, age, distance and average family monthly

income are the factors that affect the mode choice of employed people in Gaza

117

city. While the gender and out of vehicle time are statistically insignificant at 90%

confidence level so they are excluded from the model.

5. The utility functions for the selected revealed model for different modes have the

following formulas

6. For stated preference model, the travel time, ratio of fare over personal income,

frequency of service, age, average monthly family income, and distance have an

effect on mode choice decision of employed people as they are statistically

significant at 95% confidence level while the gender variable has no effect on

mode choice decision as it is statistically insignificant even at 90% confidence

level.

7. The utility functions for the selected stated preference model for different modes

have the following formulas

8. The developed revealed at stated preference models are able to predict the choice

behavior of employed people in Gaza city as the two models are valid at 95%

confidence level.

9. The prediction ratio for revealed model is 0.69 while the prediction ratio for stated

preference model is 0.80.

118

5.3 Recommendations

Several recommendations have emerged from this research

1. Using the developed revealed model in travel demand analysis and in developing

transport policies for Gaza city.

2. Using the developed stated preference model in studying the possibility and the

feasibility of introducing the bus services for transport system in Gaza city.

3. Using the developed stated preference model in establishing the time table and in

determining the appropriate fare for bus services in Gaza city.

4. Awareness campaigns should be implemented to encourage young people for

using a bicycle mode.

5. In case on introducing a bus service to transport system in Gaza city awareness

campaigns may be needed to encourage the young people for using bus modes.

6. Further study for developing mode choice models for trips other than work trips

such as social, recreational and study trips.

7. Studying the effect of captive travelers on mode choice models.

8. Calibrating the mode choice using probit and generalized extreme model and

comparing them with logit model.

119

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123

ANNEX1: QUESTIONNAIRE IN ARABIC

غزة- اإلسالميةالجامعة كلية الهندسة

قسم الهندسة المدنية برنامج ماجستير البنية التحتية

Development of mode choice model for Gaza city

بناء نموذج الختيار وسائل النقل في مدينة غزة

العاملينيهدف هذا االستبيان إلى دراسة واقع المواصالت في مدينة غزة و العوامل التي تؤثر في اختيار نموذج رياضي وصواًل إلى بناء ....(مشي , دراجة , مواصالت عامة, سيارة خاصة) لوسائل النقل

.في مدينة غزة الختيار وسائل النقل لرحالت العمل

يرجى التكرم بمأل االستبيان بالحقائق المناسبة و الدقيقة قدر اإلمكان حيث أن هذه المعلومات سوف تستخدم لغرض البحث العلمي فقط و سوف يتم المحافظة على سريتها و إحاطة المشاركين بنتائج

. الدراسة فور االنتهاء من إعدادها

إعداد الباحث

الراعي إبراهيمسعدي

إشراف

عصام المصري. د

1122 فبراير

124

اديةالمعلومات االجتماعية و االقتص: األولالجزء

هذه األسئلة هي لغرض البحث العلمي فقط و سوف يتم المحافظة على سريتها و لن يتم ذكر اسم صاحبها في .البحث

أنثى [ ]ذكر [ ]: الجنس -2

.سنة................................... : العمر -1

متزوج [ ]أعزب [ ]: الحالة االجتماعية -3

: المهنة -4

موظف وكالة الغوث [ ] موظف قطاع خاص [ ] موظف حكومي [ ] ..............( ......حدد) أخرى [ ] عامل بأجر يومي [ ] رجل أعمال تاجر أو [ ]

................................. :(المسئول عنهم) التي تعيلها عدد أفراد األسرة -5

ال [ ]نعم [ ] : مركبة خاصة ؟ لديكهل يوجد -6

ديزل [ ]بنزين [ ]ما هو نوع محركها؟ , إذا كانت اإلجابة بنعم

ال [ ]نعم [ ] : ؟ دراجة نارية لديكهل يوجد -7 ال [ ]نعم [ ] : ؟ دراجة هوائية لديكهل يوجد -8

......................بجوار ...............رع الشا...................الحي :عنوان السكن كاماًل -9

......................بجوار ................الشارع ...................الحي :عنوان العمل كاماًل -21

:لعائلتك متوسط الدخل الشهري -22

شيكل 0511-0110من [ ] شيكل 0111اقل من [ ] شيكل 0111-5110من [ ] شيكل 5111-0510من [ ] شيكل 5110أكثر من [ ] شيكل 5111-0110من [ ] شيكل 0111-0110من [ ]

125

العوامل المؤثرة في اختيار وسيلة النقل: الثانيالجزء , مركبة خاصة ) التي يستخدمونها النقل لوسائل العاملينفيما يلي عدد من العوامل التي قد تؤثر في اختيار

الرجاء تحديد مدي أهمية كٍل من هذه العوامل من وجهة نظرك , ( الخ.... ,دراجة نارية , سيارة أجرة

العوامل المؤثرةمهم بدرجة

قليلة جدا

مهم بدرجة

قليليه

مهم بدرجة

متوسطة

مهم بدرجة

كبيرة

مهم بدرجة

كبيرة جدا

العمر

الجنس

الدخل الشهري

(األجرة) تكلفة الرحلة

زمن الرحلة

زمن االنتظار لوسيلة النقل

األحوال الجوية

الخصوصية في وسيلة النقل

الراحة داخل وسيلة النقل

امتالك سيارة خاصة

الحالة الصحية

العملالمسافة بين البيت و

خصائص الرحلة: الثالثالجزء

(واحدة فقط إجابةالرجاء اختيار ) للذهاب إلى العمل؟وسيلة المواصالت التي تستخدمها معظم األحيان .2 (طلب) تاكسي [ ] سيارة أجرة [ ] سيارة خاصة [ ]

األقدام مشيًا على [ ] دراجة هوائية [ ]دراجة نارية [ ]

؟( غير التي حددتها في السؤال السابق) للذهاب إلى العملهل تستخدم وسائل نقل أخرى أحيانًا .1 ال [ ]نعم [ ]

يمكنك اختيار أكثر من ) الرجاء تحديد هذه الوسائل ؟ , "نعم "عن السؤال السابق إذا كانت اإلجابة .3

(إجابة (طلب) تاكسي [ ] سيارة أجرة [ ] سيارة خاصة [ ] مشيًا على األقدام [ ] دراجة هوائية [ ]دراجة نارية [ ]

126

الرجاء القيام بتعبئة البيانات المطلوبة و المتعلقة بزمن و تكلفة الرحلة لجميع وسائل النقل التي .4 (التي قمت بتحديدها سابقًا في السؤال األول و الثالث ) تستخدمها للعودة من الجامعة

ممشيًا على األقدا - أ .ةدقيق.................. مكان العملإلى البيت الزمن المستغرق من

دراجة هوائية - ب .دقيقة.................. مكان العملإلى البيتالزمن المستغرق من

.شهر /شيكل .................. تكلفة الصيانة للدراجة

سيارة خاصة -ج .دقيقة.................. مكان العملإلى البيتالزمن المستغرق من

.كيلو متر................ المسافة بين البيت و مكان العمل شهر/لتر................ متوسط االستهالك الشهري من الوقود

كيلو متر............. المسافة التي تقطعها المركبة باستخدام ا لتر من الوقود .شهر /شيكل................. تكلفة الصيانة للمركبة .شهر /شيكل ..................تكلفة ترخيص المركبة .شهر /شيكل.................. تكلفة التامين للمركبة

سيارة أجرة -د .دقيقة .................. المكان الذي تستقل منه المركبةإلى البيتالزمن المستغرق من

.دقيقة.................. الزمن المستغرق في انتظار المركبة .دقيقة.................. المركبة الزمن المستغرق أثناء الرحلة داخل

دقيقة............... مكان العملالزمن المستغرق من لحظة نزولك من المركبة حتى وصولك إلى .شيكل ................. األجرة المدفوعة

(طلب) تاكسي -ه .دقيقة.................. الزمن المستغرق في انتظار التاكسي

.دقيقة.................. خل التاكسي الزمن المستغرق دا .شيكل ................. األجرة المدفوعة

دراجة نارية -و .دقيقة.................. مكان العمل إلى البيتالزمن المستغرق من

كيلو متر................ المسافة بين البيت و مكان العمل شهر/لتر.......... ......متوسط االستهالك الشهري من الوقود

كيلو متر............. باستخدام ا لتر من الوقود الدراجةالمسافة التي تقطعها شهر /شيكل................. تكلفة الصيانة للمركبة .شهر /شيكل ..................تكلفة ترخيص الدراجة شهر /شيكل.................. تكلفة التامين للدراجة

127

االختيارات االفتراضية : الرابعالجزء الرجاء ترتيب , افترض انه سوف يتم إدخال خدمة النقل بالباصات إلى وسائل النقل المستخدمة داخل مدينة غزة

حسب ( سيارة أجرة, باص صغير, باص كبير) وسائل النقل المفضلة لديك في حالة توفر الخيارات التالية فقط :المعطيات التالية

المستوى األول :اوالً

سيارة أجرة باص كبير باص صغير وسيلة النقل \العامل

عاااان % 30زيااااادة بنساااابة زمن الرحلة

زمااااان الرحلاااااة بواسااااا ة

سيارة األجرة

عن زمان % 41زيادة بنسبة

الرحلااااااة بواساااااا ة ساااااايارة

األجرة

-

عن أجرة % 25اقل بنسبة تكلفة الرحلة

السااااافر بواسااااا ة سااااايارة

أجرة

عاان أجاارة %51اقاال بنساابة

- السفر بواس ة سيارة أجرة

الزمن بين قدوم وسيلتي نقل

(التكرار) متتاليتين دقائق 5كل دقيقة 41كل دقيقة 21كل

............................ الخيار الثالث........................ الخيار الثاني....................... الخيار األول

الثاني المستوى: ثانياً

يارة أجرة س باص كبير باص صغير وسيلة النقل \العامل

عاااان % 21زيااااادة بنساااابة زمن الرحلة

زمااااان الرحلاااااة بواسااااا ة

سيارة األجرة

عااااان % 30زيااااادة بنسااااابة

زمن الرحلة بواسا ة سايارة

األجرة

-

عن أجرة % 15اقل بنسبة تكلفة الرحلة

السااااافر بواسااااا ة سااااايارة

أجرة

عاان أجاارة %41اقاال بنساابة

- السفر بواس ة سيارة أجرة

الزمن بين قدوم وسيلتي نقل

(التكرار) متتاليتين دقائق 5كل دقيقة 31كل دقيقة 15كل

......................... الخيار الثالث......................... الخيار الثاني........................ الخيار األول

الثالث المستوى: ثالثاً

سيارة أجرة باص كبير باص صغير وسيلة النقل \العامل

عاااان % 10زيااااادة بنساااابة زمن الرحلة

زمااااان الرحلاااااة بواسااااا ة

سيارة األجرة

عااااان % 20زيااااادة بنسااااابة

زمن الرحلة بواسا ة سايارة

األجرة

-

عن أجرة % 11اقل بنسبة تكلفة الرحلة

السااااافر بواسااااا ة سااااايارة

أجرة

عاان أجاارة %31اقاال بنساابة

- السفر بواس ة سيارة أجرة

الزمن بين قدوم وسيلتي نقل

(التكرار) متتاليتين دقائق 5كل دقيقة 15كل دقائق 8كل

........................... الخيار الثالث........................ الخيار الثاني........................ الخيار األول

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ANNEX2: QUESTIONNAIRE IN ENGLISH

The Islamic University-Gaza Higher Education Deanship

Faculty of Engineering Civil Engineering Department

Development of mode choice model for Gaza city

This questionnaire aims to study the situation of transport system and the factors that

affect the employed people’s choice for transportation modes (Private car, shared taxi,

walking,….etc) in Gaza city reached to build a mathematical mode choice model for

work trips in Gaza city.

Please fill the attached questionnaire with accurate facts, knowing that this

information will be used for the purpose of scientific study only and will be treated

confidently.

Prepared by researcher

Eng. Sadi AL-raee

Supervised by

Dr. Essam Almasri

February 2011

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Part I: socioeconomic information

The questions bellow are for statistical purpose only , they will be confidential and no

individual will be identified in the research

1. What is your gender?

[ ] Male [ ] Female

2. What is your age? ------------------- years.

3. What is your status?

[ ] Single [ ] Married

4. What is your occupation?

[ ] Governmental employee. [ ] Private sector employee

[ ] UN employee. [ ] business man.

[ ] others (specify) ----------------------------------.

5. What is your family size? ----------------------- Persons.

6. Do you have a private car?

[ ] yes [ ] No

If yes, what is your car type?

[ ] Diesel [ ] Gasoline

7. Do you have motorcycle?

[ ] yes [ ] No

8. Do you have bicycle?

[ ] yes [ ] No

9. What is your home a dress?

--------------------------------------------------------------------------------------

10. What is your work address?

-----------------------------------------------------------------------------------------------

11. What is average family monthly income?

[ ] Less than 1000 ILS. [ ] 1001-1500 ILS. [ ] 1501-2000 ILS.

[ ] 2001-3000 ILS. [ ] 3001-4000 ILS [ ] 4001-5000 ILS.

[ ] More than 5000 ILS.

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Part II: factors affecting mode choice

To what extent do you think that the following Factors are important in choosing the

transport mode for work trips

Attributes Very low

important

Low

important

Medium

important

High

important

Very high

important

Age

Gender

Income

Cost of travel (fare)

Travel time

Waiting time

Weather conditions

Having some

privacy

Comfort

Availability of

private car

Health status

Part III: Trip Characteristics

1. What is the mode of transport you usually use to go to your work?

[ ] Private car. [ ] Shared taxi [ ] Private taxi

[ ] Motorcycle [ ] bicycle. [ ] walking

2. For your work trips, did you ever use modes other than the mode you usually use to

go to your work?

[ ] yes [ ] No

If yes, please specify the other modes (you can choose more than one choice)

[ ] Private car. [ ] Shared taxi [ ] Private taxi

[ ] Motorcycle [ ] bicycle. [ ] walking

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3. Please give your estimation for travel time and cost for the modes you can use in

your trip? ( fill only for the modes you can use)

a) walking mode

Travel time ……………………..minutes.

b) bicycle mode

Travel time ……………………..minutes.

Maintenance cost ……………… ILS/month

c) private car mode

Travel time …………………...…....minutes.

Average monthly fuel consumption …………… liters

No of kilometers cut using 1 liter ……………... km

Maintenance cost ………………………..… ILS/month

License fees ……………………………….. ILS/month

Insurance fees ……………………………....ILS/month

d) Shared taxi mode

Time from home to station …………………...…....minutes.

Time waiting a taxi ……………………………..… minutes.

In-vehicle travel time ……………………………....minutes.

Time from station to work ………………………….minutes.

fare ……………………….… ILS

e) taxi mode

Time waiting a taxi ……………………………..… minutes.

In-vehicle travel time ……………………………....minutes.

Fare ……………………….… ILS

f) motorcycle mode

Travel time …………………...…....minutes.

Average monthly fuel consumption …………… liters

No of kilometers cut using 1 liter ……………... km

Maintenance cost ………………………..… ILS/month

License fees ……………………………….. ILS/month

Insurance fees ……………………………....ILS/month

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Part V: stated preference survey

Assume that a new bus service will be introduce to transport system in Gaza city,

please rate your preferred modes according to the following conditions:

1. Level 1

Attribute/mode Mini bus Bus Shared Taxi

Journey time 30% more than

shared Taxi

40% more than

shared taxi -

Journey cost 25% less than

shared taxi

50% less than

shared taxi -

Service frequency Every 20 minutes Every 40 minutes Every 5 minutes

Please Rank your choice:

First choice …………… Second choice ……………… Third choice…………..

2. level 2

Attribute/mode Mini bus Bus Shared Taxi

Journey time 20% more than

shared Taxi

30% more than

shared taxi -

Journey cost 15% less than

shared taxi

40% less than

shared taxi -

Service frequency Every 15 minutes Every 30 minutes Every 5 minutes

Please Rank your choice:

First choice ……………Second choice ……………… Third choice ………………..

3. level 3

Attribute/mode Mini bus Bus Shared Taxi

Journey time 10% more than

shared Taxi

20% more than

shared taxi -

Journey cost 10% less than

shared taxi

30% less than

shared taxi -

Service frequency Every 8 minutes Every 15 minutes Every 5 minutes

Please Rank your choice:

First choice …………… Second choice ……………… Third choice …………...