Identification of latent variables in Mode choice.pdf

7/27/2019 Identification of latent variables in Mode choice.pdf

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Identification of latent modal attributes which affect

mode choice for work trips

Project report

Submitted in partial fulfillment of the requirements

for the award of M.Tech Degree in Civil Engineering

of University of Kerala

Submitted by

VARUN V.

M1 Traffic and Transportation Engineering

Roll No: 120271

DEPARTMENT OF CIVIL ENGINEERING

COLLEGE OF ENGINEERING

TRIVANDRUM

2013

DEPARTMENT OF CIVIL ENGINEERING


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COLLEGE OF ENGINEERING

TRIVANDRUM

2013

CERTIFICATE

This is to certify that this project report entitled IDENTIFICATION OF

LATENT MODAL ATTRIBUTES WHICH AFFECT MODE CHOICE FOR

WORK TRIPS is a bonafide record of the project done by Varun V. under my

guidance towards the partial fulfillment of the requirements for the award of M.Tech

Degree in Civil Engineering (Traffic and Transportation Engineering) under the

University of Kerala during the year 2013.

Guided by Professor (PG Studies)

Prof. Anu P. Alex Dr. M Satyakumar

Professor Professor

Department of Civil Engg. Department of Civil Engg.

College of Engineering College of Engineering

Trivandrum Trivandrum


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ACKNOWLEDGEMENT

I am sincerely indebted to my guide Prof Anu P. Alex, Professor, Department

of Civil Engineering, College of Engineering Trivandrum for her valuable guidance

and suggestions in doing this project.

I would also like to thank Dr. Syam Prakash, Professor and Head,

Department of Civil Engineering, Dr. M. Satyakumar, Professor (PG Studies), Prof.

Jayaprakash Jain, Staff Advisor and Prof. Leema Peter, Assistant Professor

(Project coordinator), Department of Civil Engineering, for their encouragement and

support.

I would also like to express my sincere gratitude to all my friends who helped

me in completing this seminar.

Above all, I thank the Lord Almighty for blessing me to complete this

project on time.

VARUN V.


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ABSTRACT

The choice of transport mode is probably one of the most important classicmodels in transport planning. In designing a socially desirable and environmentally

sustainable transportation system in line with peoples preferences, transportation

planners must increase their understanding of the hierarchy of preferences that drive

individuals choice of transportation. Understanding mode choice is important since it

affects how efficiently we can travel, how much urban space is devoted to

transportation functions as well as the range of alternatives available to the traveler. In

the empirical literature on travel mode choice, most choice models use modal

attributes to explain choice. Individual specific variables are included to control for

individual differences in preferences and unobservable modal attributes.

The present study made an attempt to identify the latent modal attributes

which affect mode choice which addresses the problem of unobservable factors in

mode choice for work trips that are able to provide insights into the individuals

decision making and to help to set priorities in governmental policy and decision

making. In their applications, the latent variables are measured through attitudes

towards the chosen travel mode. A survey was conducted on the respondents mode

choice and on the attitudinal and behavioral indicator variables that are used to

construct preferences for safety, flexibility, comfort and convenience. The

construction of safety is based on behavioral indicator variables and the construction

of comfort, convenience and flexibility variables is based on attitudinal indicator

variables. The data collected were analyzed by conducting a factor analysis by

principal component method.


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CONTENTS

Page No.

1. INTRODUCTION 1

1.1 General 1

1.2 Need for the study 2

1.3 Objectives and scope 2

2. LITERATURE REVIEW 3

2.1 General 3

2.2 Review of literature 3

2.3 Summary of literature 4

3. METHODOLOGY 5

3.1 General 5

3.2 Study area 6

4. IDENTIFICATION OF LATENT MODAL ATTRIBUTES FOR THE

PROJECT 7

4.1 General 7

4.2 Latent variables identified from previous literature 7

5. DATA COLLECTION 9

5.1 General 9

5.2 Questionnaire 10

5.3 Survey 9

5.3.1 Latent variables 9

6. DATA ANALYSIS 11


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6.1 General 11

6.2 Sample stratification 11

6.3. Factor Analysis 14

6.4. Steps involved in Factor Analysis 15

7. RESULTS AND INTERPRETATION 18

8. CONCLUSION 25

REFERENCES

LIST OF FIGURES

Figure No. Title Page No.

Fig 1 Methodology of the study 5

Fig 2 Map of Thiruvananthapuram city 6

Fig 3 Gender wise classification of total work trips 12

Fig 4 Age classification of total work trips 12

Fig 5 Income stratification of total work trips 12

Fig 6 Vehicle ownership classification of total work trips 13

Fig 7 Distance travelled classification of total work trips 13

Fig 8 Procedure for Factor analysis 15

Fig 9 Factor analysis dialogue box 16

Fig 10 Factor Analysis Extraction dialogue box 17

Fig 11 Factor Analysis Scores dialogue box 17

Fig 12 Factor Analysis Option dialogue box 18


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Chapter 1

INTRODUCTION

1.1GeneralUrban transportation modelling system is a four-step process with trip generation, trip

distribution, mode choice and trip/route assignment. In trip generation, the region is subdivided into

a large number of smaller units of analysis called traffic analysis zones. Based on the number and

characteristics of the households in each zone, a certain number of trips are generated. In the second

step, trip distribution, trips are separated out into categories based on their origin and purpose.

Generally, these categories are home-based work, home-based other and non-home based. In each of

three categories, trips are matched to origin and destination zones using the data that has been

collected. In mode choice, trips are assigned to a mode based on whats available in a particular zone,

the characteristics of the household within that zone and the cost of the mode for each mode in terms

of money and time. Since most trips by bicycle or walking are generally shorter, they are assumed to

have stayed within one zone and are not included in the analysis. Finally, in route assignment, trips

are assigned to the network. As particular parts of the network are assigned trips, the vehicle speed

slows down, so some trips are assigned to alternate routes in such a way that all trip times are equal.

This is important because the ultimate goal is system-wide optimization, not optimization for any one

individual.

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. Public transport

modes make use of road space more efficiently than private transport. Also they have more social

benefits like if more people begin to use public transport, there will be less congestion on the roads

and the accidents will be less. Again in public transport, travel can be made to low cost. In addition,

the fuel is used more efficiently. Main characteristics of public transport are that they will have some

particular schedule, frequency etc.On the other hand, private transport is highly flexible. It provides more comfortable and

convenient travel. It has better accessibility also. The issue of mode choice, therefore, is probably the

single most important element in transport planning and policy making. It affects the general

efficiency of travel in urban areas. It is important then to develop and use models which are sensitive

to those travel attributes that influence individual choices of mode.


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In designing a socially desirable and environmentally sustainable transportation system in

line with peoples preferences, transportation planners must increase their understanding of the

hierarchy of preferences that drive individuals choice of transportation. Understanding mode

choice is important since it affects how efficiently we can travel, how much urban space is

devoted to transportation functions as well as the range of alternatives available to the traveller.

In the empirical literature on travel mode choice, most choice models use modal attributes to

explain choice. Individual specific variables are included to control for individual differences

in preferences and unobservable modal attributes.

The present study made an attempt to identify the latent modal attributes which affect mode

choice which addresses the problem of unobservable factors in mode choice for work trips that

are able to provide insights into the individuals decision making and to help to set priorities in

governmental policy and decision making. In their applications, the latent variables are

measured through attitudes towards the chosen travel mode. A survey was conducted on the

respondents mode choice and on the attitudinal and behavioural indicator variables that are

used to construct preferences for safety, flexibility, comfort and convenience. The construction

of safety is based on behavioural indicator variables and the construction of comfort,

convenience and flexibility variables is based on attitudinal indicator variables. The data

collected were analysed by conducting a factor analysis by principal component method.

1.2Need for the Study1. Travellers attitudinal behavioural factors and latent modal factors are important while choosing

a mode.

2. Traditional choice models do not consider latent modal attributes.

3. To improve service quality of Public transport, conventional mode choice modal has to be

enriched with latent modal attributes.

1.3Objectives and Scope of the studyThe objectives of the study are;

1. To recognize the latent variables which affect mode choice from the previous literature

2. To identify the latent modal attributes which affect mode choice for work trips in Trivandrum

city.

The study is limited to works trip in Thiruvananthapuram city.


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Chapter 2

LITERATURE REVIEW

2.1 General

In the literature review, most of the choice models use modal attributes to explain choice.

Individual specific variables are also often included to control for individual differences in

preferences and unobservable modal attributes. The papers reviewed below specifically addresses the

problem of unobservable, or latent, preferences in mode choice models.

2.2 Review of literature

Some of the previous studies conducted on effect of latent factors on mode choice are given

below,

Morikawa et al. (2002) presents the incorporation of the latent variables of convenience and

comfort in a mode choice model. The model uses data collected in 1987 for the Netherlands Railways

to assess factors that influence the choice between rail and car for intercity travel. The data contain

revealed choices between rail and auto for an intercity trip. In addition to revealed choices, the data

also include subjective evaluation of trip attributes for both the chosen and unchosen modes, which

were obtained by asking questions from questionnaire. The resulting subjective ratings are used as

indicators for latent attributes. It is presumed that relatively few latent variables may underlie the

resulting ratings data, and two latent variables, ride comfort and convenience, were identified through

exploratory factor analysis.

Camila et al. (2010) explored the role of psychological factors on mode choice models using a

latent variables approach. The aim of this work is to study the role of psychological factors on the

mode choice process. Measurement of these factors was made by mean of psychometric tools, fitting

them in the discrete choice models through a latent variables approach, using path analysis. The

Theory of the Interpersonal Behaviour, by Triandis, was used as the theoretical framework. This

theory states that observed behaviour corresponds to an intention which is mediated by habit and

facilitating conditions, intention being depending on three factors: attitude, affect, and social aspects.

Data come from a survey designed and collected in 2007 and 2008. Respondents were lectures,

researchers and clerical officers from a university, which were contacted and interviewed in their

working place with respect to their morning trip to work. Modes level of service and cost attributes,

and users socioeconomic and psychometric data were gathered as well. A total sample of 409 records


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was initially available for the estimation process. Inclusion of psychological factors through a latent

variables approach indeed helped to improve the fitness level of revealed preference models, and to

understand the role of level of service and cost attributes on the decision process.

Maria et al. (2006) studied peoples attitudes and personality traits to attribute the varying

importance of environmental consideration, safety, comfort, convenience and flexibility. The data for

this research comes from a 1998 mail-out/mail-back survey of 1,904 residents in three

neighbourhoods in the San Francisco Bay Area: Concord and Pleasant Hill represent two different

kinds of suburban neighbourhoods comprising about half the sample, and an area defined as North

San Francisco represents an urban neighbourhood comprising the remainder. The survey contained

questions about objective and perceived mobility, attitudes toward travel, lifestyle, personality,

relative desired mobility, travel liking, and demographic characteristics. The dependent variable,

make and model of the vehicle the respondent drives most often, is classified into nine vehicle type

categories: small, compact, mid-sized, large, luxury, sports, minivan/van, pickup, and sport utility

vehicle (SUV). The explanatory variables used in the vehicle type choice model are travel-related

attitudes, personality, lifestyle, mobility, travel liking, and demographic variables and found that both

attitude towards flexibility and comfort influence the individuals choice of mode.

Choo et al. (2004) used attitudes to explain vehicle type choice. They used several latent variables

distilled from a number of attitudinal indicator variables as explanatory in a discrete vehicle type

choice model. Vehicle types was related to latent variables factors like attitudes, personality, lifestyle,

mobility and demographic variables individually using ANOVA and chi-squared test. Then a

multinomial model for vehicle type choice was estimated.

2.3 Summary of literature

Based on the previous literatures latent variables enriched choice model outperforms a

traditional choice model and provides insights into the importance of unobservable individual specific

variables in mode choice such as environmental preferences, preferences for safety, comfort,

convenience and flexibility. Although modal time and cost still are important, to attract individuals

to the desirable public modes of transport, the latent modal attributes such as safety, comfort,

convenience and flexibility need to be considered. The results will provide useful information to

policy-makers and transportation planners developing sustainable transportation systems.


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Chapter 3

METHODOLOGY

3.1 General

A survey was conducted under the context of a commuter and data was collected based on

their response. Data were collected on respondents mode choice, on their attitudinal and

behavioural indicator and a series of Socio demographics. The raw data collected from the

respondents are coded based on their ordinal value. Those variables which are called the indicator

variables are transferred to SPSS software to conduct factor analysis. From the results of factor

analysis such as scores and factor loadings the exact Latent variables are identified.

The methodology of the study is shown below in figure 1;

Figure 1. Methodology of the study

LITERATURE REVIEW

Recognition of LATENT variables

which affect mode choice from the

previous literature

SELECTION OF STUDY AREA

DATA COLLECTION

Design of Questionnaire form

Pilot survey

Modification of questionnaire

Final Survey

DATA ANALYSIS

Factor analysis

Principal component method

IDENTIFYING THE LATENT MODEL

ATTRIBUTES WHICH ARE AFFECTING THE

MODE CHOICE


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3.2Study area

Figure 2. Map of Thiruvananthapuram City

Thiruvananthapuram city is governed by Municipal Corporation which comes under

Thiruvananthapuram Metropolitan Region. As per provisional reports of Census India,

population of Thiruvananthapuram in 2011 is 752,490; of which male and female are 364,657

and 387,833 respectively. Although Thiruvananthapuram city has population of 752,490; its

urban / metropolitan population is 1,687,406 of which 815,200 are males and 872,206 are

females. The sex ratio of Thiruvananthapuram city is 1064 per 1000 males. Child sex ratio of

girls is 978 per 1000 boys.


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CHAPTER 4

IDENTIFICATION OF LATENT VARIABLES FROM PREVIOUS

LITERATURE

4.1 General

Unmeasured variables, factors, unobserved variables, constructs, or true scores are just a few

of the terms that researchers used to refer to variables in the model that are not present in the data

set. The latent variables has a vital role in determining the mode choice. Latentvariables which

are affecting the mode choice were identified from the previous literatures. The latent variables

identified from previous literatures are; Personality, Attitude, Lifestyle, Safety, Comfort,

Flexibility, Reliability, Convenience and Environmental factors. Latent variables can be

classified as Latent commuter attributes and Latent modal attributes.

4.2 Latent variables identified from previous literatures

The latent variables affecting the mode choice identified from previous literatures are listed

below;

4.2.1 Latent commuter attributes

1. Personality

2. Attitude

3. Lifestyle

4.2.2 Latent modal attributes

1. Safety

2. Comfort

3. Flexibility

4. Reliability

5. Protection

6. Convenience

7. Environmental factors

Personality

The complex of all the attributes - behavioural, temperamental, emotional and mental, that

characterize a unique individual. Personality is the totality of qualities and traits, as of characteror behaviour that are peculiar to a specific person.


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Attitude

A complex mental state involving beliefs and feelings and values and dispositions to act in

certain ways.

Lifestyle

When lifestyle became popular a generation ago, a number of critics objected to it as voguish

and superficial, perhaps because it appeared to elevate habits of consumption, dress, and

recreation to categories in a system of social classification. Nonetheless, the word has proved

durable and useful, if only because such categories do in fact figure importantly in the schemes

that Americans commonly invoke when explaining social values and behaviour

Safety

The condition of being safe; freedom from danger, risk, or injury.

Comfort

A state of being relaxed and feeling no pain. For example, "he is a man who enjoys his

comfort", "she longed for the comfortableness of her armchair".

Flexibility

The quality of being adaptable or variable. For example, "he enjoyed the flexibility of his

working arrangement".

Reliability

A form of trustworthiness. The trait of being answerable to someone for something or being

responsible for one's conduct. For example, "he holds a position of great responsibility".

Protection

The activity of protecting someone or something. For example, "the witnesses demanded for

police protection.

Convenience

The quality of being suitable to one's comfort, purposes, or needs.


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CHAPTER 5

DATA COLLECTION

5.1 General

The data collection was conducted by distributing the questionnaire to the commuters of

Thiruvananthapuram City. The question regarding the latent variables were developed from

the identified latent variables from previous literatures. The model attributes that are identified

from previous literatures are comfort, convenience, flexibility and safety. Firstly, a pilot survey

was conducted by distributing the questionnaire among 60 commuters and before the final

preparation of the questionnaire, modifications were done. Then, the final survey was

conducted.

5.2 Questionnaire See page 10

5.3 Survey

The data collected in this study comes from a 2-page self-descriptive questionnaire survey

containing questions about safety, convenience, comfort and flexibility of the chosen mode and

a series of demographic questions. The questions were set and given to 234 commuters in

Thiruvananthapuram city. Information obtained includes gender, age, educational background,

employment status, occupation, number of vehicles, and personal income, questions related tocommute time/distance, cost of commute and personal use of specific modes for work trips.

5.2.1 Latent variables

Safety

The questionnaire survey contains 5 statements expressing safety on various issues related to

travel and residential location. Respondents were asked to rate each statement using a five-point likert

type scale from Dont agree to strongly agree or No effect to Very strong effect. The loading

variables used are unsafe while switching from one mode to another, Walking to the bus stop

and Travelling on the bus.

Comfort

The Comfort section of the survey asks how well each of 8 phrases describes your mode,

on a five-point scale from very important to very unimportant. The loading variables used are

vehicle with foldable and cushioned seat, choose a mode with AC etc.

Flexibility


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The Flexibility section of the survey asks how you utilize the mode other than travelling on

a five point likert scale from strongly disagree to strongly agree. The loading variables used are

to shop, to pick or drop children or wife etc.

Convenience

The convenience section of the survey asks the accessibility of the particular mode on a five

point likert scale from strongly disagree to strongly agree. The loading variables used are to

reach the destination on time and to avoid queues and congestion.


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CHAPTER 6

DATA ANALYSIS

6.1 General

The questions distributed among the commuters were fed into the computer for analysis. The

responses to each questions were coded based on their ordinal values. The questions were

reduced into indicator variables, and their values were imported into SPSS software. The

indicator variables were initially highlighted and given to the software. The output from the

SPSS software were interpreted to give the final results.

6.2 Sample stratification

The sample was classified based on;

1. Gender

2. Age

3. Vehicle ownership

4. Income

5. Mode

6. Distance travelled

6.2.1 Classification based on Gender

Figure 3 Gender-wise classification of total work trips

male

60%

female

40%

Total number of work trips (Gender wise)

male female


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6.2.2 Classification based on Age

Figure 4. Age stratification of total number of work trips

6.2.3 Classification based on Income

Figure 5. Income stratification of total number of work trips

0%

< 20

1%

20 35

48%35 50

42%

50 65

7%

65 80

2%

Total number of female

commuters

< 20

2%

20 35

45%35 50

38%

50 65

14%

65 80

1%

Total number of male

commuters

< 5000

5%

5000 -

15000

8%

15000

30000

38%

30000

45000

21%

45000

60000

11%

> 60000

17%

Females

< 5000

6%

5000 -

15000

9%

15000

30000

32%

30000

45000

22%

45000

60000

13%

> 60000

18%

Males


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6.2.4 Classification based on Vehicle ownership

Figure 6. Vehicle ownership stratification of total number of work trips

6.2.5 Classification based on Distance travelled

Figure 7. Distance travelled stratification of total number of work trips

Car

48%

Two wheeler

45%

Auto rickshaw

3%

Bus / Lorry

2%

Cycle

2%

percentage of vehicles

< 5

10%

5

1052%

10 15

9%

15 20

19%

20 25

9%

25 30

0%

30 35

1%

% of Commuter


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6.3 Factor Analysis

Analysis of data was done by Factor analysis with Principal Component method. The procedure

for Factor analysis is shown in the Figure 8.

Figure 8. Procedure for Factor analysis

Factor Analysis and Principal Components Analysis are both used to reduce a large set of items

to a smaller number of dimensions and components. These techniques are commonly used

when developing a questionnaire to see the relationship between the items in the questionnaire

and underlying dimensions. It is also used in general to reduce a larger set of variables to a

smaller set of variables that explain the important dimensions of variability. Specifically,

Factor analysis aims to find underlying latent factors, whereas principal components analysis

aims to summarise observed variability by a smaller number of components.

There are three stages in factor analysis:

1. First, a correlation matrix was generated for all the variables. A correlation matrix was

a rectangular array of the correlation coefficients of the variables with each other.

FACTOR

ANALYSIS

PRINCIPAL COMPONENT

ANALYSIS: UNITIES IN DIAGONAL

OF CORRELATION MATRIX

HOW MANY FACTORS

TO BE RETAINED?FACTOR ROTATION

RESULTS FROM FACTOR

ANALYSIS

FACTOR LOADINGS

FACTOR SCORES

INTERPRETATION OF

THE RESULTS


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2. Second, factors are extracted from the correlation matrix based on the correlation

coefficients of the variables.

3. Third, the factors are rotated in order to maximize the relationship between the variables

and some of the factors.

6.4 Steps involved in Factor Analysis

1. From the menu bar select ANALYSE and choose DATA REDUCTION and then click

on FACTOR. Highlight related variables and send them to variables lists. The Figure 9

shown below shows the factor analysis dialogue box.

Figure 9. Factor analysis: Dialogue box

2. Click on the DESCRIPTIVES button and its dialogue box will be loaded on the screen.

Within this dialogue box select the following check boxes Initial solution, Coefficients,

and Significance level. Click on Continue to return to the Factor Analysis dialogue box.

The Factor Analysis: Descriptives dialogue box is completed.

3. From the Factor Analysis dialogue box click on the EXTRACTION button and its

dialogue box will be loaded on the screen. Select the check box for Scree Plot. Click

on Continue to return to the Factor Analysis dialogue box. The Factor Analysis:

Extraction dialogue box is completed as sown below in Figure 10


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Figure 10. Factor analysis: Extraction Dialogue box

4. From the Factor Analysis dialogue box click on the ROTATION button and its dialogue

box will be loaded on the screen. Click on the radio button next to VARIMAX to select

it. Click on Continue to return to the Factor Analysis dialogue box. The Factor Analysis:

Rotation dialogue box is completed.

5. From the Factor Analysis dialogue box click on the SCORES button and its dialogue

box will be loaded on the screen. Click on the radio button next to REGRESSION

method to select it. Click on display factor score coefficient matrix to select it. The

Factor Analysis: scores dialogue box is completed as shown below in Figure 11

Figure 11. Factor analysis: Scores Dialogue box


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6. From the Factor Analysis dialogue box click on the OPTIONS button and its dialogue

box will be loaded on the screen. Click on the check box of Suppress absolute values

less than to select it. Type 0.50 in the text box. Click on Continue to return to the Factor

Analysis dialogue box. Click on OK to run the procedure. The Factor Analysis: Options

dialogue box should be completed as shown below in Figure 12

Figure 12. Factor analysis: Options Dialogue box


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Chapter 7

Results and Discussions

7.1 Results

7.1.1 Descriptive Statistics

The first output from the analysis is a table of descriptive statistics for all the variables under

investigation. Typically, the mean, standard deviation and number of respondents (N) who had

participated in the survey are given in Table 1. From the mean, it can be concluded that Time

to reach the destination in stipulated time is the most important variable that influence the

commuters mode choice. It has the highest mean of 4.45

Table 1. Descriptive Statistics

Mean Std. Deviation Analysis N

DESTINATIONTIME 4.4522 1.07568 230

DISLIKELATE 4.3000 1.18266 230

SWITCHMODE 3.9391 1.44044 230

WALKTOBUSSTOP 3.2217 1.52088 230

WAITINGFORBUS 3.1739 1.52568 230

TRAVELLINGONBUS 3.5043 1.43185 230

FOLDABLESEAT 3.5391 1.21696 230

ACMODE 3.7217 1.22598 230

ADJUSTABLELEWINDOW 3.8087 1.19968 230

MORESPACE 3.7217 1.16760 230

HEARINGMUSIC 3.8783 .90267 230

CALM 3.9565 .95663 230SPACIOUSVEHICLE 3.9870 1.10371 230

TRAVELLINGWITHLUGGAGES 3.9087 1.13130 230

DIRECTTODESTINATION 3.8130 1.10371 230

SHOPWHILETRAVEL 4.0348 .97058 230

VARIATIONTIME 4.2696 .90916 230

PICKORDROP 4.3043 .82190 230


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7.1.2 Communalities

The next item from the output is a Table of communalities which was shown in Table 2 how

much of the variance in the variables has been accounted for by the extracted factors. For

instance over 86% of the variance in safety while switching the mode is accounted for while

57% of the variance in Time to reach the destination in stipulated time is accounted for.

Table 2. Communalities

Initial Extraction

DESTINATIONTIME 1.000 .570

DISLIKELATE 1.000 .715SWITCHMODE 1.000 .856

WALKTOBUSSTOP 1.000 .836

WAITINGFORBUS 1.000 .830

TRAVELLINGONBUS 1.000 .827

FOLDABLESEAT 1.000 .767

ACMODE 1.000 .806

ADJUSTABLELEWINDOW 1.000 .823

MORESPACE 1.000 .695

HEARINGMUSIC 1.000 .628

CALM 1.000 .732

SPACIOUSVEHICLE 1.000 .666

TRAVELLINGWITHLUGGAGES 1.000 .728

DIRECTTODESTINATION 1.000 .704

SHOPWHILETRAVEL 1.000 .691

VARIATIONTIME 1.000 .647

PICKORDROP 1.000 .723

Extraction Method: Principal Component Analysis.


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7.1.3 Total Variance Explained

The Table 3 shows all the factors extractable from the analysis along with their eigenvalues, the percent of variance attribute to each factor, and the

cumulative variance of the factor and the previous factors.

Table 3. Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %

1 8.107 45.041 45.041 8.107 45.041 45.041 4.747 26.371 26.371

2 2.747 15.262 60.303 2.747 15.262 60.303 4.361 24.226 50.597

3 2.388 13.268 73.571 2.388 13.268 73.571 4.135 22.974 73.571

4 .946 5.256 78.827

5 .646 3.586 82.414

6 .495 2.749 85.163

7 .464 2.578 87.741

8 .374 2.077 89.818

9 .323 1.794 91.613

10 .292 1.621 93.234

11 .232 1.289 94.522

12 .213 1.184 95.706

13 .204 1.132 96.838

14 .176 .978 97.81615 .122 .679 98.495

16 .110 .612 99.107

17 .096 .532 99.639

18 .065 .361 100.000


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7.1.4 Scree Plot

The scree plot is a graph of the eigenvalues against all the factors. The graph shown in Figure 13 is useful for determining how many factors to retain.

The point of interest is where the curve starts to flatten. It can be seen that the curve begins to flatten between factors 3 and 4. Note also that factor 4

has an eigenvalue of less than 1, so only three factors have been retained.

Figure 13. Scree plot


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7.1.5 Component (Factor) Matrix

The Table 4 below shows the loadings of the eight variables on the three factors extracted. The

higher the absolute value of the loading, the more the factor contributes to the variable. The

gap on the Table represent loadings that are less than 0.5, this makes reading the Table easier.

We suppressed all loadings less than 0.5.

Table 4. Component Matrix

Component

1 2 3

DESTINATIONTIME .634

DISLIKELATE .636SWITCHMODE .522 .717

WALKTOBUSSTOP .656 -.511

WAITINGFORBUS .668

TRAVELLINGONBUS .726 -.547

FOLDABLESEAT .777

ACMODE .747

ADJUSTABLELEWINDOW .770

MORESPACE .731

HEARINGMUSIC .545 -.513

CALM .540 -.564

SPACIOUSVEHICLE .765

TRAVELLINGWITHLUGGAGE

S.681

DIRECTTODESTINATION .631

SHOPWHILETRAVEL .582

VARIATIONTIME .729

PICKORDROP .656


a. 3 components extracted.

7.1.6 Component score coefficient Matrix

The idea of rotation is to reduce the number factors on which the variables under investigation

have high loadings. Rotation does not actually change anything but makes the interpretation of


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the analysis easier. The Table 5 below, we can see that availability of product, and cost of

product are substantially loaded on Factor (Component) 3 while experience with product,

popularity of product, and quantity of product are substantially loaded on Factor 2. All the

remaining variables are substantially loaded on Factor 1. These factors can be used as variables

for further analysis.

Table 5. Component Score Coefficient Matrix

Component

1 2 3

DESTINATIONTIME -.049 .172 .025

DISLIKELATE -.056 .221 -.018

SWITCHMODE -.041 .265 -.112WALKTOBUSSTOP .217 .036 -.152

WAITINGFORBUS .206 .050 -.149

TRAVELLINGONBUS .233 -.063 -.047

FOLDABLESEAT .183 -.057 .020

ACMODE .163 -.106 .090

ADJUSTABLELEWINDOW .161 -.099 .090

MORESPACE .185 -.042 -.010

HEARINGMUSIC -.042 -.036 .220

CALM -.071 -.031 .249

SPACIOUSVEHICLE .005 .143 .021

TRAVELLINGWITHLUGGA

GES-.062 .209 .012

DIRECTTODESTINATION -.074 .218 .005

SHOPWHILETRAVEL -.085 .014 .227

VARIATIONTIME -.033 .060 .149

PICKORDROP -.035 -.017 .216


Rotation Method: Varimax with Kaiser Normalization.

Component Scores.


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7.2Latent variables obtained from factor analysis

The Figure 141 shows the final latent variables obtained from Factor loading and indicator

variables

Figure 14. Final latent variables obtained

The latent identified from factor analysis are;

1. Safety

2. Convenience

3. Flexibility


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Chapter 8

CONCLUSION

1. The latent variables identified from previous literatures

2. The latent modal attributes identified for work trips in Trivandrum city are Safety,

Convenience, and Flexibility

3. Commuters expresses the lack of SAFETY at waiting stops, walking to mode and

travelling with public in stage carriers.

4. Commuters are less reluctant to SWITCH mode and prefers to reach the

DESTINATION DIRECTLY by a single mode

5. Commuter give more importance to unexpected congestion that causes DELAY6. The importance of SPACIOUSness in vehicle are also expressed by the commuters

7. Private mode are more flexible than public mode, helps the commuters to shop while

travel,pick or drop children during work trips and prefers less variation to travel

time.

8. Commuters give less importance to A/C, FOLDABLE SEAT, ADJUSTABLELE

WINDOWS, and HEARING MUSIC etc.

9. Comfort was less significant due to :

Due to individual heterogeneity

Higher travel cost due to high fuel cost,

Commuter giving greater importance to facilities provided at bus stops,

spaciousness and calm environment


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REFERENCES

1. Sangho Choo, P. L. Mokhtarian (2004) What type of vehicle do people drive? The

role of attitude and lifestyle in influencing vehicle type choice. Transportation

Research Part A 38 (2004) 201222.

2. Maria Vredin Johansson, Tobias Heldt, Per Johansson (2006) The effects of attitude

and personality traits on mode choice. Transportation Research Part A 40 (2006) 507

525.

3. Bilge Atasoy, Aurelie Glerum, and Michel Bierlaire (2012) Attitudes towards mode

choice in Switzerland. Report TRANSP-OR 110502, Transport and MobilityLaboratory Ecole Polytechnique Federale de Lausanne transp-or.epfl.ch

4. Camila Galdames, Alejandro Tudela, and Juan Antonio Carrasco (2010) Exploring the

role of psychological factors on mode choice models using a latent variables approach

Department of Civil Engineering, Universidad de Concepcin.

5. Choo et al. (2002) The relationship of vehicle type choice to personality, lifestyle,

attitudinal, and demographic variables University of California, Davis, California.


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COLLEGE OF ENGINEERING, TRIVANDRUM

Department of Civil Engineering

STATED PREFERENCE SURVEY

1. Gender: M / F 2. Age: 3. Marital status: Married / Unmarried / Widowed4. Employment Status: Govt sector / Private Sector / Self-employed or Business / Student / Unemployed or Retired5. Monthly Income (Rs.): < 5000 / 5000 15000 / 15000 30000 / 30000 45000 / 45000 60000 / >600006. Driver Licence Status: 4-Wheeler / 2-Wheeler / Auto Rickshaw / Heavy vehicle7.

Education: SSLC / Plus two / Degree / PG8.

Vehicle

Ownership

Car/Jeep/Van Two Wheeler Auto rickshaw Cycle Bus / Lorry

No:

9. Which is the usual mode you are choosing for your work trips :Car / Two Wheelers / Public Transport / Auto Rickshaw / Walk / Train

10.Distance to Work place:11.Travelling Time to Work place:12.What is the cost you usually pay for the trip:13.Which is the usual mode you are choosing for your Shopping trips : Car / Two Wheeler / Public Transport / Auto

Rickshaw / Walk / Train

Si.

no

1Is it important for you to arrive at your destination before

the stipulated time of work

Not at all

important

Quite

importantNeutral Important

Very

important

2I dislike services that cause me to be late to my

destinationDont agree Slightly agree Neutral Agree

Strongly

agree

3

I felt unsafe while switching from one mode to another

during work tripsDont agree Slightly agree Neutral Agree

Strongly

agree

4 Please indicate the one stage in which you feel the least safe :

a. Walking to the bus stop or from the bus stopNo effect Some effect neutral

Strong

effect

Very

strong

effect

b. Waiting at the bus stopsNo effect Some effect neutral

Strong

effect

Very

strong

effect

c. Travelling on the busNo effect Some effect neutral

Strong

effect

Very

strong

effect

Very

unimportant

Somewhat

unimportantNeutral

Somewhat

important

Very

important

5Is the vehicle with foldable and cushioned seat important

for you

6 Is it important for you to choose a mode with AC

7Mode having proper adjustable windows that make me

sightseeing without any hurdle is important for me

8 Vehicle with more space is important for me

9Hearing music and watching videos is important for me

while travelling


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Very

unimportant

Somewhat

unimportantNeutral

Somewhat

important

Very

important

10It is important for me to travel in a calm , non noisy

environment

11 Vehicles spaciousness is important for me

12I must be comfortable while travelling with bags and

luggages

Strongly

agree Disagree Neutral Agree

Strongly

agree

13It important for me to reach the destination directly

rather than switching several modes

14 It is important for me to shop on the way to / from work

15It is important for me to drop / pick children / wife in my

way to / from work

16It is important for me to avoid queues and congestion

while travelling

17It is important for me having little or no variation in my

daily travel time

18 It is important for me to travel in safer mode

Documents

Identification of latent variables in Mode choice.pdf