Project on Effect of Latent variables on Mode choice AV.pdf

8/13/2019 Project on Effect of Latent variables on Mode choice AV.pdf

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IDENTIFICATION OF LATENT VARIABLES IN MODE CHOICE

Varun V.

M.Tech Student (Traffic and Transportation Engineering) , College of Engineering, Trivandrum

ABSTRACT

The choice of transport mode is probably one of the most

important classic models 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 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 behavioral indicator variables

that are used to construct preferences for safety,

flexibility, comfort and convenience. The

construction of safety is based on behavioralindicator 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.

Keywords: Planning, mode choice, latent variables,

factor analysis

I. INTRODUCTION

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. 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. Where environmental preferences,

comfort and flexibility are significant for mode

choice, convenience and safety are insignificant.

Although modal time and cost still are important, it


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follows that there are other ways, apart from

economic incentives, to attract individuals to the

desirable public modes of transport. The results

should provide useful information to policy-makers

and transportation planners developing sustainable

transportation systems.

My project report deals with Identification

of latent modal attributes which affect mode choice

for work trips 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 among the

commuters and data are collected 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 the

safety is based on behavioural indicator variables

and the construction of the comfort, convenience

and flexibility variables is based on attitudinal

indicator variables. The data collected were

estimated containing individuals preferences in a

latent variable by conducting a factor analysis byprincipal component method.

II. LITERATURE REVIEW

Travel behaviour is complex and predicting it

difficult because there are many considerations and

hard and fast rules. Travel behaviour is modelled

as a function of measurable attributes such as socio-

demographic characteristics and physical

characteristics of the individual and of the system.

While researchers have long understood that

individuals personality, attitudes and perceptions

affect their travel behaviour and the literature to

support this has grown over the last 30 years.

Some of the previous studies conducted on

effect of latent factors on mode choice are given

below,

Michel et al. (2012) presented an integrated

choice and latent class model, where they identified

two segments of individuals having different

sensitivities to the attributes of the alternatives,

resulting from their individual characteristics.

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

a latent variables approach.

Maria et al. (2006) studied peoples attitudes

and personality traits to attribute the varying

importance of environmental consideration, safety,

comfort, convenience and flexibility. The study

was conducted between commuters of Stockholm

and Uppsala, 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.

Morikawa et al. (2002) included modal comfort

and convenience in the analyses of mode choice. In

their application, the latent variables were


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measured and modelled through attitudes towards

the chosen and an alternative travel mode.

Golob (2001) used a series of models to explain

how mode choice and attitudes regarding tolled

high occupancy vehicle lanes in San Diego differed

over the population.

Kitamura et al. (2000) introduced disaggregate

discrete alternative models such as multinomial

logit and nested logit model for vehicles type

choice.

Lothlorien Redmond (2000) Identified and

analysed travel related attitudinal, personality and

lifestyle cluster in San Francisco Bay Area

Ben et al. (1996) presented the incorporation of

the latent constructs of convenience and comfort in

a mode choice model. The model used data

collected in 1987 for the Netherlands Railways to

assess factors that influence the choice between

Rail and Car for intercity travel.

Dinesh Ambat Gopinath (1995) presented

latent class model for mode choice behaviour and

showed that different segments of population have

different decision protocols for the choice process

as well as different sensitivities for time and cost.

III. ATTITUDINAL AND BEHAVIOURAL

INDICATOR VARIABLES

Research in the area of attitudes and had

shown the attitudes are only distantly related to the

behaviour. For example, predicting a single

behaviour like paper recycling from a measure of

an individuals general environmental attitudes

may be very difficult but such a behaviour are often

correlated so that an individual with an

environmental personality trait performs more

environmental behaviours than an individual

without such a trait. Thereby exploring the

manifested behaviour in other areas of everyday

life can help to better understand the driving forces

behind mode choice. For example, someone who

uses safety gear when driving, boating and cycling

is more likely to choose a safer mode than a less

safety orientated individual or if someone who

recycles glass, paper, batteries and metal is more

likely to choose an environmentally friendly mode

than someone who does not.

An advantage with behavioural indicator

variables is that they are exogenous to the

individuals mode choice which means safety of

the vehicle doesnt depends on the individuals

choice. When latent variables are constructed from

attitudinal indicator variables the individuals

attitudes could be affected by the chosen mode, by

the individual rationalizes and reduces cognitive

dissonance of his/her choice, causing the latent

variable construction to be endogenously

determined.

IV. METHODOLOGY

This chapter provides the steps involved in

this work. A self-answerable questionnaire were

distributed among the work class of the population.

The questionnaire comprises of Details regarding

household structure and 18 self-answerable

question that contains the latent variable. The work

was carried out in several steps as explained below.


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The methodology of the study is shown in Fig 3.1

Fig. 3.1 Methodology of the study

LATENT ATTRIBUTES identified from the previous literature

Personality Attitude Lifestyle Safety Comfort Flexibility Reliability Protection Convenience Environmental factors

IDENTIFYING THE LATENT MODEL ATTRIBUTES WHICH ARE AFFECTING THE

MODE CHOICE

DATA ANALYSIS

Factor analysis Principal component method

DATA COLLECTION

Design of

Questionnaire form Pilot survey

Modification of

questionnaire Final Survey

SELECTION OF STUDY AREA

LITERATURE REVIEW

Recognition of LATENT variables which affect mode choice from the previous literature

Latent commuter attributes

Latent Modal attributes


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V. Identification of LATENT Modal Attributes

for the project

This section describes the explanatory

variables used in the mode choice model: Safety,

Comfort, Flexibility and Convenience.

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

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.

VI. DATA COLLECTION

A survey of commuters among theThiruvananthapuram city was conducted. There are

five different modes available for the commuter;

car, two wheelers, auto rickshaw, walk and train.

The survey was conducted on 500 commuters and

the analytical sample size was 300. The sample

consists to 60% of men. The average sample age is

37 years and the average sample household

monthly income is Rs 36,244. In the sample of 233

respondents 49% uses car for commuting, 39% uses

two wheelers, 10% uses public transport for work

trips

and 49% uses car, 37% uses two wheelers, 7.72%

uses public transport and 4.3% uses auto rickshaw

for shopping trips .


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Sample data stratification

Total number of commuters for work trips (Gender

wise)

Gender-wise classification of total work trips

Male Female

139 94

Gender-wise classification of total work trips

Age stratifiction

Age stratification of total number of

work trips

Age

group

Number of

commutersFemale Male

< 20 1 2

2035 48 60

3550 42 51

5065 7 19

6580 2 1

Age stratification of total number of

work trips

Income stratification

Income stratification of total number of work

trips

Income group Male Female

< 5000 8 5

500015000 12 7

1500030000 44 34

3000045000 30 19

4500060000 18 10

> 60000 24 15

Total number of work

trips (Gender wise)

male female

Total number of

female commuters

< 20 20 35 35 50

50 65 65 80

Total number of

male commuters

< 20 20 35 35 50

50 65 65 80


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Income stratification of total number of work

trips

VII. DATA ESTIMATION

Estimation of data using Factor analysis by

Principal Component method.

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 observedvariability by a smaller number of components.

There are three stages in factor analysis:

1. First, a correlation matrix is generated forall the variables. A correlation matrix is a

rectangular array of the correlation

coefficients of the variables with each

other.

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

coefficients of the variables.

3. Third, the factors are rotated in order tomaximize the relationship between the

variables and some of the factors.

VIII. RESULTS

Final Latent variables

The Figure shows the final latent variables obtained

from Factor loading and indicator variables

Final latent variables obtained

IX. CONCLUSION

1. The latent variables identified fromprevious literatures

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

Convenience, and Flexibility

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

travelling with public in stage carriers.

4.

Commuters are less reluctant to SWITCHmode and prefers to reach the

DESTINATION DIRECTLY by a single

mode

5. Commuter give more importance tounexpected congestion that causes DELAY

6. The importance of SPACIOUSness invehicle are also expressed by the

commuters

7. Private mode are more flexible than publicmode, helps the commuters to shopwhile

Females

< 5000 5000 - 15000

15000 30000 30000 45000

45000 60000 > 60000


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travel,pick or drop children duringwork

trips and prefers less variation to travel

time.

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

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 greaterimportance to facilities provided at

bus stops, spaciousness and calm

environment

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, PerJohansson (2006) The effects of attitude

and personality traits on mode choice.

Transportation Research Part A 40 (2006)

507525.

3. Bilge Atasoy, Aurelie Glerum, andMichel Bierlaire (2012) Attitudes towards

mode choice in Switzerland. Report

TRANSP-OR 110502, Transport and

Mobility Laboratory Ecole Polytechnique

Federale de Lausanne transp-or.epfl.ch

4. Camila Galdames, Alejandro Tudela, andJuan 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.

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Project on Effect of Latent variables on Mode choice AV.pdf