VARIABILITY OF THE PERCEPTION ABOUT SAFETY ... Tarigan and Susilo [17] found that negative experience and dissatisfaction affect users’ preferences. All these previous studies mentioned

http://www.iaeme.com/IJCIET/index.asp 188 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 2, February 2018, pp. 188–204, Article ID: IJCIET_09_02_019

Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=2

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

VARIABILITY OF THE PERCEPTION ABOUT

SAFETY, COMFORT, ACCESSIBILITY AND

RELIABILITY OF CITY BUS SERVICE ACROSS

DIFFERENT USERS’ GROUPS

Saikat Deb

Department of Civil Engineering, NIT Silchar, Assam, India

M. Ali Ahmed

Department of Civil Engineering, NIT Silchar, Assam, India

ABSTRACT

In this study, the service quality of the city bus service is estimated in terms of

users’ perceptions. The variations in the users’ perceptions are studied across

different users groups based on their income, sex, employment status, and vehicle

ownership. A questionnaire survey is conducted and the data collected through this

survey were analyzed by Exploratory Factor Analysis (EFA), Confirmatory Factor

Analysis (CFA) and Path analysis to meet with the study objectives. It is found that

four latent factors namely Safety, Comfort, Accessibility and Reliability affect the

users’ perceptions. The service levels of all these latent factors are estimated and the

worst performing factor is found to be the Reliability of the bus service. Only the

performance of the latent factor Accessibility is found to be merely above average.

Further analysis of the data reveals that these perception factors are affected by the

users’ socioeconomic and demographic characteristics. The female respondents are

found to be more concerned with Safety and Reliability of the service whereas for the

male respondents the most important factor is found to be Comfort. The study also

shows that with increasing income, respondents’ priorities Comfort and Reliability

over the other factors. For an employed user, Reliability and accessibility of the

service are found to be the most important. The study also shows that an improvement

of Reliability and Comfort is necessary to attract the non-transit users towards the bus

service.

Key words: Service quality, City bus service, Factor analysis, Path analysis, Users’

perception.

Cite this Article: Saikat Deb, M. Ali Ahmed, Variability of the Perception about

Safety, Comfort, Accessibility and Reliability of City Bus Service across Different

Users’ Groups. International Journal of Civil Engineering and Technology, 9(2),

2018, pp. 188-204.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=2

Variability of the Perception about Safety, Comfort, Accessibility and Reliability of City Bus Service across

Different Users’ Groups


1. INTRODUCTION

In developing countries like India, the travel demand in urban areas is becoming high due to

rapid urbanization. For this reason, the travel market is also expanding. The number of

motorized vehicles in India is rapidly increasing with a high growth rate for the last three

decades [1] and the maximum concentrations of the motorized vehicles is seen in the cities

alone [2]. The number of two wheelers in Indian cities is found to be highest with a

percentage of 73% of the total vehicle population followed by three wheelers with a

proportion of 15% and passenger vehicles with a proportion of 10% [3]. The market share of

commercial vehicle is found to be very low, near about 5% [3]. These enormous numbers of

two wheelers and three wheelers are mostly responsible for the oversaturation of the traffic

flow on the city roads which resulted in congested city traffic and polluted city environment

[3]. Moreover, the number of private vehicles is also increasing in India due to the increasing

purchase capacity of the people and the need for better mobility and comfort. According to

current data, the sales of private vehicles have increased by 9.23% where the sales of

commercial vehicles have increased only 4.16% in April-March 2017 over the same period

last year [4]. The poor service quality of the public transport has also accelerated the growth

of private vehicles and different paratransit modes in the cities [5]. Therefore, it is important

to improve the service quality of the public transport system so as to reduce the dependency

on the private cars and other modes of transport and helps to reduce the problems like traffic

congestion, air and noise pollution, parking problems and energy consumptions [6].

The service quality of the public transportation can be improved by improving the

different attributes of the public transportation as per the passengers’ perception and

expectations. The perception based measure of service quality is an extent of how well the

perception about the system matches with the desired quality [7], and it has turned out to be a

vital tool for assessing transit service quality. The perception based measures are qualitative

in nature and are evaluated from the satisfaction survey [8–10]. Eboli and Mazzulla [11]

found that reliability, information availability, frequency, overcrowding, cost, bus stop

availability and route characteristics affects the customer satisfaction. Filipovic et al. [12]

found that customer perception depends on the reliability and comfort of the vehicle.

dell’Olio, Ibeas and Cecin [13] found that cleanliness, waiting time and comfort were the

most important factors for the users. Lai and Chen [14] found that the involvement of public

transit services affect the passengers’ behavior intentions. Bordagaray et al. [15] found that

improvement of reliability, journey time, information availability and driver kindness improve

the users’ perception about the overall satisfaction. Jomnonkwao and Ratanavaraha [16]

found that characteristics of vehicles, drivers as well as the crews and management affect the

qualitative parameters of a sightseeing school bus of Thailand. Joewono, Tarigan and Susilo

[17] found that negative experience and dissatisfaction affect users’ preferences. All these

previous studies mentioned above described about the different factors which affect users’

perception of the transit service and the level of importance of these factors. But very few

studies have tried to estimate the service levels of these factors. Estimation of the service

levels of these factors is necessary to improve these factors so as to improve the transit

service. Therefore, in this study the service levels of different factors affecting the users’

perception are estimated. Moreover, user perception of the service is not same for all the

users. It differs between individuals [19–24] and different market segments based on

socioeconomic variables [19,21]. Customer perception of the service performance is affected

by their desired and acceptable levels of the servicer performance [24]. The service

performances may not be always similar for the same service; rather it can be heterogeneous

in nature. The acceptability of the heterogeneity in the service performance depends on the

tolerance levels of the customers which in turn depends on their characteristics and

Saikat Deb, M. Ali Ahmed


expectation levels [24]. These expectation levels of customers also depend on their education,

values and experience and personal needs [25]. Besides the heterogeneity in the performance

of the service, users’ perception of the service may also be heterogeneous across different

users [20]. The differences or the heterogeneity in the perception of the users depends on their

socioeconomic characteristics [20,21], their attitude towards the service [20], their

demographic characteristics and their trip habits [21,26]. Ahmed and Vaidya [27] studied the

effect of age, gender and travel purposes on the willing to pay for the travel time savings.

Phanikumar and Maitra [28] also reported some variation in the willing to pay across different

trip purposes. Maitra et al. [29] studied the differences in perception of choice and captive

riders in terms of willing to pay for the bus service in Kolkata, India. Hu et al. [26] reported

some differences in the users’ perception about the availability, reliability, safety and comfort

for the public transportation across individual characteristics and trip purposes. Gao et al. [30]

found that personality and mood of the users affect their satisfaction level towards the bus

service. It is very much important to study the effect of demographic and socioeconomic

characteristics of the passengers on their perception of the service to make the service more

appealing across different groups. Therefore, the objectives of this study are set to be: (1) to

find out the latent factors which affect user perception of the transit service, (2) determining

the service levels of each of the latent factors for a better understanding, (3) to find out the

effect of each of the latent factors on the overall satisfaction across different user groups. The

results of the study will be helpful to determine the various aspects of the transit system to

improve the system as per the users’ expectations.

2. STUDY AREA AND TRANSIT SYSTEM

The intra-city bus service of Agartala, capital city of Tripura, India has been selected for the

quality assessment. The population of Agartala is 400,004 as per the census of 2011. Agartala

is the second largest city in the North East India with an area of 62.02 km2. The demand for

transportation in the city is increasing rapidly due to urbanization and population expansion.

In the years 2005 - 2015 the average annual growth rate of the registered vehicles of Tripura

was found to be 14.5% which is highest among the different states of India [1]. The number

of two wheelers in Agartala is found to be highest with 69%, followed by light motor vehicles

(jeep/taxi/van/car) with 16.1%, three wheelers with 8.8% and buses with only 0.98% among

the total registered vehicles [31]. The congestion in the city is increased enormously due to

these growing numbers of two wheelers and light motor vehicles. The major mode of public

transport in Agartala city is city bus. But due to the poor service quality of the city buses, the

numbers of private vehicles and paratransit modes are increasing in the city. Therefore, it is

essential to improve the service quality of city buses to minimize the extensive numbers of

other modes. Therefore, the city bus service in Agartala is considered in this study.

3. METHODOLOGY

The study methodology comprises of the two steps. First, perception data needs to be

collected from the users, second these perception data needs to be analyzed with suitable

statistical tools to meet the study objectives. The data collection technique and the tools and

technique required for the analysis of data is presented below:

3.1. Data Collection Technique

To capture the users’ perception, it is needed to design a questionnaire considering the various

aspects of the bus service in Agartala. Therefore, the qualitative attributes of the questionnaire

was chosen on the basis some formal talk with some bus users of the city. Along with the

questions related to the qualitative attributes of the bus service, the questionnaire should also




contain some information of the respondents as the study objective requires to analyze the

variation in the users’ perception across different groups of people based on their

socioeconomic and demographic variables. The minimum sample size is determined on the

basis of following equation [32]:

[

( )(

)

]

(1)

Where,

n is the minimum sample size to be considered

N is the population of the city

P is the quality characteristics which are to be measured. As per Johnson, for neutral cases

or where no previous experience exists then the value of P is taken as 0.5

d is the margin of error which is taken as 5%

for 95% confidence interval

The questionnaire was divided into two parts. The first part of the questionnaire comprises

of some questions related to passengers’ socioeconomic and demographic characteristics like

age, sex, vehicle ownership, income, and employment status. The second part consists of

some qualitative attributes related to the bus service. Respondents were asked to rate the

qualitative attributes on a scale of 1 to 9, where 1 means they are absolutely satisfied with the

attribute and 9 means they are utterly dissatisfied with the attribute. A rating of 5 means a

neutral rating. Therefore, a rating value more than 5 indicates certain level of satisfaction and

a rating value less than 5 indicates certain level of dissatisfaction. In the last question of the

second part respondents were also asked to rate the overall performance of the current city bus

service on a similar scale.

The questionnaire data was collected by conducting face to face interview in various

locations of the Agartala city including market places, major bus stops, offices, parks, etc.

Persons with a previous intra-city bus experience are interviewed.

3.2. Tools and Techniques used

A comprehensive approach is presented here to analyze the users’ perception of the bus

service. A brief description of the overall modeling structure which includes EFA, CFA and

path analysis is described in this section.

3.2.1. EFA

Factor analysis is a multivariate data analysis technique to determine the underlying factors

affecting a set of correlated observed variables [33]. EFA is used to know the number of

unobservable summary variables or factors which are needed to explain the correlations

between variables [16] and the link between the latent factors and observed variables [16].

Therefore, the objective of the EFA is to reduce the number of variables (data reduction) and

to identify the relationship between observed variables and the latent factors. There are

different extraction methods available for the factor analysis. In this study, EFA is extracted

through Principle Component Analysis (PCA) for its relative effectiveness from the other

techniques [34]. PCA is mathematical tool to convert large number of correlated variables

into a reduced no of uncorrelated and more meaningful variables called principal components

[35]. The purpose of the PCA is to explain the variance of the variables with least number of



principal components [34,35]. The extraction of principal components is based on the values

of eigenvectors. From the covariance matrix the eigenvectors are calculated and ordered in a

descending order based on the eigenvalues [35]. This ordered eigenvectors are the

components in order of significance. The significance of this eigenvectors can also be viewed

graphically by drawing the scree plot [34,35]. The first component explains maximum

variation in the data. The second component explains maximum of the remaining variation in

the data and so on. In general, the components with eigenvalue greater than 1 are considered

as significant [35].

3.2.2. CFA

CFA is used for further analysis of the perception data. CFA verifies the structural

relationship between the latent variables and the observed variables through different

goodness of fit statistics values [16,26]. The researcher must have precise information of the

total number of latent factors and the relationship with the observed variables prior to the

CFA model [16,26]. Therefore, before conducting CFA, EFA has to be conducted. In this

context the latent factors extracted through CFA analysis can be termed as performance

factors. Two types of outcomes are estimated from the CFA analysis: factor loadings and

factor scores [33,35]. Standardized factor loadings represent the correlation between latent

factors and observed variables and the factor score values are the representation of the latent

factors in a standardized form which cannot be directly compared with the observed variables

[33,36]. Therefore, these factor score values need to be converted into same unit as of the

observed variable and this can be done with the help of regression analysis. Factor score

values can be treated as the linear combination of all observed variables representing the

particular factor [33,36]. In the regression analysis by using the standardized factor scores as

dependent variable and the observed variables under the same factor as independent variables,

the relative weights of all the variables can be found. From this relative weight, the factor

scores can be converted into the same unit as of the observed variables by weightage

averaging of the observed variables representing the factor.

3.2.3. Path Analysis

Path analysis is used to estimate the relationship between the extracted latent factors and the

effect of all the latent factors on overall satisfaction of the bus service. Path analysis is also

used to find out the variation of the perception of the respondents across different groups

based on their socioeconomic and demographic characteristics. Path analysis can be seen as a

more advanced version of the linear regression analysis [33]. In path analysis it is possible to

solve multiple related equations which is not possible in the regression analysis [37]. In

regression analysis, the variables can be either independent or dependent but in path analysis

the variables can be dependent or independent or both [37]. Therefore, path analysis is more

comprehensive and flexible than that of the regression analysis [37]. Moreover in path

analysis, the relationship between different variables is represented by path diagram which is

a convenient way to understand the effect and cause relationship.

4. RESULTS AND DISCUSSIONS

4.1. Descriptive Statistics of the Data

The minimum sample size for the survey was estimated using Equation 1 and it is found to be

384. Considering the minimum sample size a total number of 400 respondents was

interviewed for the survey. The survey respondents consist of male (42.8%) and female

respondents (52.2%) with different socioeconomic and demographic characteristics as shown

in Table 1. It has been found that 173 (43.3%) respondents are city bus users and the

remaining 227 (56.7%) respondents favor other available modes. The survey also reveals that




the respondents mostly prefer auto rickshaws (41.1%) for their daily commuting among the

other modes.

The survey also consists of twenty three city bus service related qualitative attributes

which are shown in Table 2. The respondents were asked to rate the qualitative attributes on a

scale of 1 to 9 as per their perception. The means and standard deviations of the ratings for

each of the attributes are also shown in the Table 2. By looking into the standard deviation

column, it can be said that the data are highly dispersed. An average rating value less than 5

indicates that the respondents are dissatisfied with the attribute and a rating above 5 indicates

that they are satisfied with the attribute. It can be noticed that passengers’ are mostly

dissatisfied with the availability of the seats. Among the different attributes they are only

happy with the regularity of the service. The reliability of the data set is checked by

Cronbach's alpha value which is found to be 0.941 indicating that the data set is highly

reliable [33].

Table 1 Sample characteristics

Demographic characteristics Percentage of the total surveyed

data

Gender Male 47.8

Female 52.2

Monthly

income

in Rupees

Less than 5000 37.2

5000 - 10000 34.2

10000 - 15000 12.8

15000 - 20000 6.1

More than 20000 9.4

Age (years)

15 – 20 20.6

20 – 30 55.5

30 – 50 22.2

Over 60 1.7

Choice of

mode

Auto rickshaw 41.1

Two wheelers 12.8

Private car 2.8

City bus 43.3

The inter-correlation between different variables of the data is also needed to be checked

to decide the further course of action for the data set. The correlations between the variables

are checked by Variance Inflation Factor (VIF). VIF represents the amount of variance of an

independent variable described by the remaining independent variables present in the

regression model [38]. The VIF values are checked with some cutoff values to check the

amount of correlation between the independent variables. For this study the cut-off value of

the VIF is considered as 2 [39]. The VIF values for all the variables are found to be more than

2 indicating high correlations among the observed variables. Factor analysis has been

conducted to analyze these highly correlated variables.



Table 2 Descriptive statistics of bus service attributes.

Qualitative attributes

Code assigned Mean Standard

deviation

VIF values

Boarding and alighting time q1 4.44 2.05 3.095

On board safety q2 3.55 2.36 3.217

Safety in terms of accidents q3 3.56 2.34 4.597

Safety in the bus stops q4 3.77 2.35 3.855

Condition of the vehicle q5 3.99 2.25 4.436

Cleanliness of the vehicle q6 3.79 2.26 2.924

Cleanliness of the seats q7 3.84 2.21 3.120

Condition of the doors and windows q8 4.18 2.29 3.268

Comfortability of the seats q9 3.75 2.52 2.902

Availability of the seats q10 2.57 2.07 3.461

Overcrowding nature q11 2.78 1.98 2.993

Behavior of staffs q12 3.60 2.29 3.015

Smoothness of the ride q13 4.38 2.17 2.024

Facilities provided for the disabled q14 2.93 2.29 3.192

Frequency of the breakdowns during journey q15 3.57 2.10 3.495

Availability of the service q16 4.67 2.03 2.634

Information availability q17 4.84 2.04 4.244

Travel cost q18 4.59 2.28 2.774

Travel speed q19 4.54 2.37 2.226

Arrival and departure time q20 3.59 2.07 2.209

Prior information about the journey time q21 3.62 1.98 2.517

Prior information about the waiting time q22 4.35 2.26 3.629

Regularity of the service q23 5.64 1.99 3.953

Overall satisfaction of the bus service Satisfaction 4.92 1.51

4.2. Identifying the total number of extracted latent factors by EFA

Intra-city bus service related qualitative attributes are analyzed by EFA to extract the

uncorrelated principal components or the latent factors. Total no of four latent factors are

extracted from the EFA analysis with the help of scree plot shown in Fig. 1. It can be seen

from the figure that the first component explains maximum variations in the data. Second,

third and fourth component also explain considerable portion of the variance of the data.

These latent factors explained 70% of the total variance. But after fourth component a

relatively flatter curve indicates that the percentage variance explained by them is minimal.

Their eigenvalues are also less than one. Therefore, the later components can be neglected.

Figure 1 Scree plot of the components




4.3. Estimating the relationship between perception factors and observed

variables by CFA

CFA is conducted to verify the structure of the latent factors. CFA is extracted using STATA

14 software. The fit statistics used for the model validation are chi-square to degree of

freedom ratio; comparative fit index (CFI); root mean squared error of approximation

(RMSEA); standardized root mean squared residual (SRMR); and coefficient of

determination (CD). Chi-square to degrees of freedom ratio of less than 5 [14], CFI value

greater than 0.9 [14,26], RMSEA value of less than 0.08 [14,26], SRMR value less than 0.08

[40] and CD value close to 1 [36] indicate a good fit. Based on this fit statistics some

modifications have been done to the model structures and the best fit models for the data set

with the standardized coefficients are presented in Fig. 2. The model in Fig. 2 is found to be

reasonably fit with a chi-square to degree of freedom ratio of 2.33, CFI value of 0.901.

RMSEA value of 0.078, SRMR value of 0.079, and CD value of 0.999.

The extracted latent factors for this model are Comfort, Reliability, Accessibility, and

Safety. In Fig. 2 the rectangle and oval boxes represent the observed and latent factors

respectively. The numbers written besides the single arrowed line are the standardized factor

loading values. The values written besides the double arrowed lines represent the correlation

coefficients between latent variables. The values written besides the small round shapes

represent the error variances of the observed variable which cannot be explained by the latent

variables.

Figure 2 CFA model structure

After the factor analysis the internal consistency of all the variables within the factors is

checked by convergent validity of the factors [33,41]. The convergent validity of the factors

can be checked through checking the standardized factor loadings of the observed variables,

composite reliability (CR) values of the factors and Average Variance Extracted (AVE) by the



factors [33]. For a rational convergent validity, the standardized factor loadings should be

more than 0.5, the CR values should be more than 0.7 and AVE should be more than 0.5 [33].

From Fig. 2 it can be observed that all the factor loadings are more than 0.5. The AVE and

CR values are indicated in Table 3 which is found to be within their acceptable limits.

Therefore, it can be said that the convergent validity of the models is acceptable. After the

CFA analysis the factor scores for all the latent variables are stored for further analysis.

Table 3 Values of the CR and AVE of the latent factors

CFA model for perception data set

Latent factors CR AVE

Safety 0.86 0.78

Comfort 0.92 0.73

Accessibility 0.81 0.67

Reliability 0.77 0.72

Table 4 Relative weights of the observed variables

Dependent

variable

Independent

variable

Standard regression

weight

R2 value Average value of the

dependent variable

Safety

q1 0.30

0.95

4.4

q2 0.07

q3 0.41

q4 0.37

Comfort

q5 0.16

0.98

4.5

q6 0.17

q7 0.01

q8 0.17

q9 0.10

q10 0.09

q11 0.09

q12 0.13

q13 0.10

q14 0.11

q15 0.13

Accessibility

q16 0.51

0.99

5.8

q17 0.32

q18 0.08

q19 0.13

q23 0.18

Reliability

q20 0.41

0.93

3.9 q21 0.06

q22 0.51

As discussed earlier, these factor score values are reported in standardized form which

cannot be compared with the observed variables. Therefore, it is needed to convert the factor

scores in the same unit as of the observed variables to know the performance of the latent

factors. This has been done with the help of regression analysis. In regression analysis, the

factor scores are used as dependent variables and the observed variables under same factor as

independent variables. From the regression coefficients, the relative weights of all the

observed variables are found out. The relative weights of all the variables along with the R2

values are shown in Table 4. From these relative weights, the factor score values for all the

latent factors is estimated in the same unit as of the observed variables by weightage




averaging method. The average values of all the converted factor score values are also shown

in Table 4. By observing the factor score it can be said that all the perception factors except

Accessibility perform very poorly. The worst performing perception factor is Reliability.

So to improve bus service quality, these factors needs to be improved according to their

priorities. The priorities of these factors are different for different stakeholders. Therefore, to

determine the priorities of these factors among different stakeholders, path analysis is

conducted. Path analysis is also conducted to know the relationship between the perception

factors and the effect of these perception factors on overall satisfaction of the respondents.

4.4. Casual Relationship between Perception Factors

From Fig. 2 it is found that all the perception factors are linearly related with each other and

the strength of their relationship is presented by the correlation coefficients values. The

relationship between Reliability and Comfort is found to be the strongest one with a

correlation coefficient of 0.78. This is because of the fact that an improved Reliability will

make the users to plan their trip more precisely which in turns reduce their waiting time and

improve their satisfaction towards comfort. Again a reduced waiting time will make the users

less exposed in the open places which make them less likely to fall prey to some mischievous

activities and hence will improve their perception of safety. For this reason, the perception

factor Safety and Reliability are found to be reasonably correlated. From Fig. 2 it also can be

said that the latent factors Comfort and Safety are also reasonably correlated. Because an

improvement of the safety parameters of the bus service will definitely make the users more

comfortable in using the service.

4.5. Effect of latent Perception Factors on Overall Satisfaction

The effect of latent factors on overall satisfaction is represented by a path diagram in Fig. 3.

The fit statistics estimated for this path analysis are found to be acceptable. From Fig. 3 it can

be said that accessibility is most important factor for the respondents to assess overall

satisfaction. Due to relatively better performance of the accessibility, some of the respondents

prefer intra-city bus service for commuting within the city. The relationship between the

various perception factors and overall satisfaction can be attributed by the following equation:

(2)

Figure 3 Relationship between latent factors and overall satisfaction



4.6. Priorities of the perception factors among different groups

Path analysis is used to estimate the priorities of different groups of respondents on the latent

perception factors based on their age, income, sex, employment status and vehicle ownership.

It will help to find out the priorities of different segment of respondents on the perception

factors to assess overall satisfaction of the bus service.

The coefficients of the demographic variable age are found to be insignificant for all the

perception factors. It indicates that the attitudes towards different perception factors are

similar across different age group people.

4.6.1. Differences in the priorities of the respondents based on their income

Table 5 represents the priorities of different users group based on their income. The users are

divided into five income groups. All the perception factors except accessibility are found to be

insignificant for the users with income less than 5000 INR. The perception factor

Accessibility includes the attribute travel cost as shown in Table 2. The users with lower

income mainly concentrate on cost of the travel. Because of this, the perception factor

Accessibility is found to be significant for this group of users.

The perception factor safety is found to be insignificant for the income groups 15000 –

20000 INR and more than 20000 INR. These groups of users want to have a bus service with

good comfort, accessibility and Reliability. From Table 5 it also can be seen that, as the

income increases, the importance of comfort and Reliability also increases. Because of the

importance of Comfort and Reliability, the users belonging to the higher income groups may

opt for other transport services rather than bus service.

Table 5 Market segmentation analysis between different groups based on their income

Dependent variable Independent variable Standard

coefficients

P value

Overall satisfaction

Safety :

Less than 5000

5000 - 10000

10000 – 15000

15000 – 20000

More than 20000

0.55

0.25

0.18

0.08

0.09

0.21

0.03

0.04

0.22

0.45

Comfort

Less than 5000

5000 - 10000

10000 – 15000

15000 – 20000

More than 20000

0.04

0.24

0.26

0.28

0.32

0.80

0.01

0.00

0.02

0.00

Accessibility

Less than 5000

5000 - 10000

10000 – 15000

15000 – 20000

More than 20000

0.23

0.26

0.34

0.35

0.31

0.00

0.00

0.01

0.00

0.05

Reliability

Less than 5000

5000 - 10000

10000 – 15000

15000 – 20000

More than 20000

0.20

0.22

0.23

0.28

0.30

0.26

0.16

0.04

0.09

0.04




4.6.2. Differences in the priorities of the respondents based on their sex

The priorities and perception of the service attributes are different for male and female

passengers and their difference in the perceptions of the bus service is presented in Table 6.

From Table 6 it can be said that the female passengers are more concerned with safety than

the male passengers. For male passengers the coefficient of safety is found to be insignificant

which indicates that they have a very little concern for the safety. For the male passengers the

most important factor is comfort. For the female passengers, as long as the safety,

accessibility and Reliability of the service are good, comfort is not a significant factor for

them.

Table 6 Market segmentation analysis between different groups based on their sex

Dependent variable Independent variable Standard coefficients P value


Safety

Male

Female

0.09

0.44

0.08

0.01

Comfort

Male

Female

0.46

0.11

0.00

0.17

Accessibility

Male

Female

0.28

0.25

0.00

0.00

Reliability

Male

Female

0.14

0.32

0.03

0.02

4.6.3. Differences in the priorities of the respondents based on their employment status

Users perceptions of the service quality also vary with their employment status and the

variation of perceptions between employed and unemployed users are shown in Table 7. From

Table 7 some important outcomes can be drawn. For the employed users, the most important

factor is Reliability of the service followed by accessibility and comfort. As long as the

service is good in terms of comfort, accessibility and Reliability, safety is not a significant

factor for them. These outcomes of the results are also similar with the practical scenario.

Because for an employed person, during office hours, the most important thing is to reach the

office in time and a better accessibility of a transport service help them to minimize their loss

of the time. Similarly, for unemployed users, Reliability is not significant for them, as they do

not have any rush to go in time. For them, the most important factor is comfort followed by

safety and accessibility.

Table 7 Market segmentation analysis between different groups based on their employment status

Dependent variable Independent variable Standard coefficients P value


Safety

Unemployed

Employed

0.32

0.07

0.04

0.27

Comfort

Unemployed

Employed

0.36

0.21

0.04

0.01

Accessibility

Unemployed

Employed

0.14

0.28

0.00

0.00

Reliability

Unemployed

Employed

0.19

0.39

0.14

0.03



4.6.4. Differences in the priorities of the respondents based on their vehicle ownership

In this category the respondents are divided into two categories based on their vehicle

ownership. The preferences of the respondents who owned any types of motorized vehicle

(either four wheelers or two wheelers) are found to be different from the respondents who do

not owned any vehicles and their priorities are shown in Table 8. From Table 8 it can be said

that the most important factor for the respondents who owned any vehicle is Comfort

followed by Reliability, Accessibility, and Safety. They perceive that their own vehicles serve

better in terms of these perception factors than from the bus service. But comfort and

Reliability are found to be insignificant for the respondents who do not have any vehicle. This

means that the users without any vehicle have to rely on transit and paratransit modes despite

of their poor Reliability and Comfort.

Table 8 Market segmentation analysis between different groups based on vehicle ownership

Dependent variable Independent variable Standard

coefficients

P value


Safety

Who do not have vehicles

Who have any types of vehicles

0.24

0.10

0.04

0.59

Comfort



0.20

0.40

0.00

0.00

Accessibility



0.37

0.18

0.00

0.00

Reliability



0.16

0.30

0.08

0.00

An interesting outcome of the result is that the perception factor Safety is found to be

insignificant across different users groups. This outcome of the result may draw some

contradiction among the readers. But in the context of the study area the results are justifiable.

Agartala is a medium sized city and it makes it quite easy for the administration to maintain

the law and order situation in the city. And the crime rate of the city is also decreasing

rapidly. As per the police report, the crime rate of Agartala has declined 18% during the 2014-

15 compared to the previous year. This proves the effectiveness of the administration in the

city. Rather than this, maximum people residing in the city are quite literate with an average

literacy rate of 94.45%. Therefore, maximum portion of the residents of this city are

concerned about the social values. Therefore, they have a very little experience of the crime

during the busy hours in an open place like bus stops and in the buses. Moreover, the traffic

polices are also very active in the city limits during the busy hours. Most of the conflict points

in the city are operated by the traffic polices and heavy fined are imposed on the traffic rule

breakers. Therefore, there are very little chances of fatal accident within the city limits. For

these reasons, the city dwellers do not observe safety as an important parameter for them to

decide upon their trip. They possibly think that the city is quite safe and they do not have to

bother about this. This may be the possible reason for safety being an insignificant facto for

most of the users groups.

5. CONCLUSIONS

The objective of the study is to estimate the service levels of different factors which affect

users’ perception and to show the variations in the users perceptions across different users’

groups based on their income, sex, employment status, and vehicle ownership. A




questionnaire survey is conducted to collect the data for this study. The questionnaire consists

of some attitudinal questions related to bus service attribute and some questions related to

respondents’ personal information like age, sex, vehicle ownership etc. The data collected

through this survey was analyzed by EFA and CFA to find out the unobservable variables

which effect users’ perception. The four latent factors extracted from the analysis are Safety,

Accessibility, Comfort and Reliability. The performance of all the latent factors is found to be

below average except the latent factor Accessibility. Reliability of the bus service is found to

be the worst one among the four latent factors. The study also reveals that all the perception

factors are linearly correlated. It is found that, improvement of the Reliability of the bus

service will improve the perception of Comfort and Safety.

The study also reveals that the priorities of the users towards the bus service changes with

their characteristics. It is found that, the people belonging to lower income groups are not

concerned about the Safety, Comfort and Reliability of the service. As long as the cost of the

service is kept minimal, this user groups will not switch to other services. But the priority of

Comfort and Reliability increases with the increase of income of the users. Reliability denotes

the strictness of the service to follow their schedule. As the income increases people valued

their time and therefore they need a service with minimum unproductive time. As the people

with higher income have the ability to afford any other services like paratransit modes or

personal vehicles, therefore, they will not compromise their level of comfort for the bus

service.

The perceptions of the Safety, Accessibility, Comfort and Reliability are found to be

different for male and female users. For the male users Comfort is found to be the most

important factor. But for the female passengers Safety and Reliability are found to be the most

important factors. Therefore, an improvement in the safety and Reliability of the service will

attract more female passengers to use the bus service. Again Reliability and Safety are

correlated. Therefore, if the Reliability of the bus service is improved than perception of

Safety will also be improved for some extent.

Analyses of the data also show that, for an employed user the most important factor is

Reliability followed by accessibility. Comfort and Safety are not important for them as long

as the service is accessible and reliable. But for an unemployed user Comfort is found to be

most important factor. Reliability of the service is not important for them as they do not have

any rush to go to workplaces. Therefore, the operators of the bus service should instruct the

drivers not to waste any time unnecessarily during the busy hours and they should be

instructed to follow the schedule strictly so that the employed users can reach their office in

time. The improvement of the comfort level of the service will attract the unemployed users to

the service and this can be done by maintaining and cleaning the buses at regular interval and

increasing the number of buses to reduce the overcrowding.

Vehicle ownership is another factor which is found to effect the users’ perception. Users

owning any types of vehicles are found to be most concerned with the comfort of the service.

Reliability is also an important parameter for them. They perceive that their personal vehicles

are more efficient in terms of Comfort and Reliability than the bus service. On the other hand,

Comfort and Reliability are found to be insignificant for the respondents who do not have any

vehicles. This portion of the users has to rely on paratransit or bus service despite of their

poor performances.

As the study addresses the priorities and perception of different users’ groups based on

their socioeconomic and demographic characteristics, therefore, this study will be helpful in



addressing the community needs towards the city bus service. Moreover, this study provides a

methodology to estimate the service levels of the perception factors in an easily

understandable way which will be helpful in comparing the bus service of the study area with

the bus service of any other cities.

REFERENCES

[1] MORTH, New Delhi, India, 2004. http://morth.nic.in/mts.htm.

[2] S. Singh, Review of Urban Transportation in India, Journal of Public Transportation. 8

(2005) 79–97. doi:10.5038/2375-0901.8.1.5.

[3] R.D. Sharma, S. Jain, K. Singh, Growth rate of Motor Vehicles in India - Impact of

Demographic and Economic Development, Journal of Economic and Social Studies. 1

(2011) 137–154. http://eprints.ibu.edu.ba/1073/1/vol1-no2-

Journal.Of.Economic.And.Social.Studies-1-2_p137-p153.pdf.

[4] SIAM, Performance of Auto Industry during 2016-17, (2017).

http://www.siamindia.com/statistics.aspx?mpgid=8&pgidtrail=9 (accessed August 9,

2017).

[5] M.G. Badami, M. Haider, An analysis of public bus transit performance in Indian cities,

Transportation Research Part A: Policy and Practice. 41 (2007) 961–981.

doi:10.1016/j.tra.2007.06.002.

[6] S. Nocera, The Key Role of Quality Assessment in Public Transport Policy, Traffic

Engineering & Control. 52 (2011) 394–398. https://trid.trb.org/view.aspx?id=1122322.

[7] TRB, Transit Capacity And Quality Of Service Manual: TCRP Report 100, 2nd ed.,

Transportation Research Board, Washington, D.C, 2003.

http://www.trb.org/Main/Blurbs/153590.aspx.

[8] M. Diana, Measuring the satisfaction of multimodal travelers for local transit services in

different urban contexts, Transportation Research Part A: Policy and Practice. 46 (2012)

1–11. doi:10.1016/j.tra.2011.09.018.

[9] H. Iseki, M.J. Smart, How Do People Perceive Service Attributes at Transit Facilities?

Examination of perceptions of transit service by transit user demographics and trip

characteristics, Transportation Research Record: Journal of the Transportation Research

Board. 2274 (2012) 164–174. doi:10.3141/2274-18.

[10] J. de Oña, R. de Oña, Quality of service in public transport based on customer satisfaction

surveys : A review and assessment of methodological approaches, Transportation Science.

49 (2014) 605–622. doi:10.1016/j.tranpol.2013.07.001.

[11] L. Eboli, G. Mazzulla, Service Quality Attributes Affecting Customer Satisfaction for Bus

Transit, Journal of Public Transportation. 10 (2007) 21–34. doi:10.5038/2375-

0901.10.3.2.

[12] S. Filipović, S. Tica, P. Živanović, B. Milovanović, Comparative analysis of the basic

features of the expected and perceived quality of mass passenger public transport service

in Belgrade, Transport. 24 (2009) 265–273. doi:10.3846/1648-4142.2009.24.265-273.

[13] L. dell’Olio, A. Ibeas, P. Cecin, The quality of service desired by public transport users,

Transport Policy. 18 (2011) 217–227. doi:10.1016/j.tranpol.2010.08.005.

[14] W.T. Lai, C.F. Chen, Behavioral intentions of public transit passengers-The roles of

service quality, perceived value, satisfaction and involvement, Transport Policy. 18 (2011)

318–325. doi:10.1016/j.tranpol.2010.09.003.

[15] M. Bordagaray, L. Olio, A. Ibeas, P. Cecín, Transportmetrica A : Transport Science

Modelling user perception of bus transit quality considering user and service




heterogeneity, Transportmetrica A: Transport Science. 10(8) (2014) 37–41.

doi:10.1080/23249935.2013.823579.

[16] S. Jomnonkwao, V. Ratanavaraha, Measurement modelling of the perceived service

quality of a sightseeing bus service: An application of hierarchical confirmatory factor

analysis, Transport Policy. 45 (2015) 240–252. doi:10.1016/j.tranpol.2015.04.001.

[17] T.B. Joewono, A.K.M. Tarigan, Y.O. Susilo, Road-based public transportation in urban

areas of Indonesia: What policies do users expect to improve the service quality?,


[18] J.L. Machado-León, R. de Oña, J. de Oña, The role of involvement in regards to public

transit riders’ perceptions of the service, Transport Policy. 48 (2016) 34–44.

doi:10.1016/j.tranpol.2016.02.014.

[19] C.R. Bhat, J.Y. Guo, S. Sen, L. Weston, Measuring Access to Public Transportation

Services: Review of Customer-Oriented Transit Performance Measures and Methods of

Transit Submarket Identification, CTR Research Report: 0-5178-1. 7 (2005).

http://www.ce.utexas.edu/prof/bhat/REPORTS/5178_R1_rev_Sept05.pdf.

[20] C. Cirillo, L. Eboli, G. Mazzulla, On the Asymmetric User Perception of Transit Service

Quality, International Journal of Sustainable Transportation. 5 (2011) 37–41.

doi:10.1080/15568318.2010.494231.

[21] A. Dantas, R. Antunes, Y. Yamashita, M. Lamar, Marketing in Public Transportation:

Neural Networks approach, in: 9th World Conference in Transportation Research: D1-

4137, University of Canterbur, Seol, South Korea, 2001.

https://ir.canterbury.ac.nz/handle/10092/604.

[22] S. Das, D. Pandit, Determination of level-of-service scale values for quantitative bus

transit service attributes based on user perception, Transportmetrica A: Transport Science.

9935 (2015) 1–21. doi:10.1080/23249935.2014.910563.

[23] L. dell’Olio, A. Ibeas, P. Cecín, Modelling user perception of bus transit quality,


[24] V.A. Zeithaml, L.L. Berry, A. Parasuraman, The Nature and Determinants of Customer

Expectations of Service, Journal of the Academy of Marketing Science. 21 (1993) 1–12.

doi:10.1177/0092070393211001.

[25] W.H. Davidow, B. Uttal, Service Companies: Focus or Falter, Harvard Business Review.

67 (1989). https://hbr.org/1989/07/service-companies-focus-or-falter.

[26] X. Hu, L. Zhao, W. Wang, Impact of perceptions of bus service performance on mode

choice preference, Advances in Mechanical Engineering. 7 (2015) 1–11.

doi:10.1177/1687814015573826.

[27] F. Ahmad, V.G. Kirit, The Valuation of Travel Time Savings in Least Developed

Countries: Theoretical and Empirical Challenges and Results from Field Study, in: World

Conference of Transportation Research, Istanbul, Turkey, 2004: pp. 1–13.

http://www.ittransport.co.uk/documents/VoT in Least Developed Countries paper WCTR

Paper.pdf.

[28] C. V Phanikumar, B. Maitra, Modeling Willingness-to-Pay Values for Rural Bus

Attributes Under Different Trip Purposes, Journal of the Transportation Research Forum.

45 (2006) 31–44.

http://journals.library.oregonstate.edu/index.php/trforum/article/viewFile/911/806.

[29] B. Maitra, S. Dandapat, P. Chintakayala, Differences between the perceptions of captive

and choice riders toward Bus service attributes and the need for segmentation of bus

services in urban India, Journal of Urban Planning and Development. 141 (2014) 1–8.

doi:10.1061/(ASCE)UP.1943-5444.0000205.



[30] Y. Gao, S. Rasouli, H. Timmermans, Y. Wang, Effects of traveller ’ s mood and

personality on ratings of satisfaction with daily trip stages, Travel Behaviour and Society.

7 (2017) 1–11. doi:10.1016/j.tbs.2016.11.002.

[31] G. of T. Transport Department, No Ti, (n.d.). http://transport.tripura.gov.in/ (accessed

August 9, 2017).

[32] R.A. Johnson, D.W. Wichern, Applied Multivariate Statistical Analysis, 5th ed., Prentice

Hall, Upper Saddle River, NJ, 2002.

[33] J.F. Hair, W.C. Black, B.J. Babin, R.E. Anderson, MULTIVARIATE DATA ANALYSIS

A Global Perspective, 7th ed., Pearson, New York, 2010.

https://pdfs.semanticscholar.org/6885/bb9a29e8a5804a71bf5b6e813f2f966269bc.pdf.

[34] A.B. Costello, J.W. Osborne, Best Practices in Exploratory Factor Analysis : Four

Recommendations for Getting the Most From Your Analysis, Practical Assessment,

Research & Evaluation. 10 (2005) 216–232. http://pareonline.net/getvn.asp?v=10&n=7.

[35] A. Field, Discovering Statistics Using SPSS, 3rd ed., SAGE Publications India Pvt Ltd.,

New Delhi, India, 2009. doi:10.1111/insr.12011_21.

[36] Stata Corp., STATA STRUCTURAL EQUATION MODELING REFERENCE

MANUAL, 13th ed., Stata Press, 2013. http://www.stata.com/manuals13/sem.pdf.

[37] O.D. Duncan, Path Analysis : Sociological Examples ", American Journal of Sociology.

72 (1966). doi:10.1086/224256.

[38] T.A. Craney, J.G. Surles, Model-Dependent Variance Inflation Factor Cutoff Values,

Quality Engineering. 14 (2002) 391–403. doi:10.1081/QEN-120001878.

[39] S. Banerjee, B. Guha, G. Bandyopadhyay, A Post Factor Analysis of Financial Ratios of

Selected IPOs and its Impact on Grading: An Empirical Inquest, Journal of Business

Studies Quarterly. 8 (2016) 23–34. jbsq.org/wp-

content/uploads/2016/09/September_2016_3.pdf.

[40] J.B. Schreiber, A. Nora, F.K. Stage, E.A. Barlow, J. King, Reporting Structural Equation

Modeling and Confirmatory Factor Analysis Results: A Review, The Journal of

Educational Research. 99 (2006) 323–338. doi:10.3200/JOER.99.6.323-338.

[41] D.T. Campbell, Convergent and Discriminant Validity by the MultitraitMultimethod

Metrix, Psychological Bulletin. 56 (1959). doi:10.1037/h0046016.

[42] Vetri Selvi. M and Dr. V.M. Ponniah, Service Quality (SERVQUAL) on Business and

Management Student Perceived and Desired Satisfaction: The Antecedents with Private

Higher Education Institutions in Sikkim State, India, International Journal of Mechanical

E ngineering and Technology 9(1), 2018. pp. 670–679.

[43] R. Khader Mohideen and S. Abdul Rajak. A Study on The Determinants of Service

Quality In Airline Sector At Tiruchirappalli Airport, International Journal of Management

(IJM), 7 (2), 2016, pp. 510 - 515

[44] Nur Hayati and Desi Novitasari, An Analysis of Tourism Service Quality Toward

Customer Satisfaction (Study on Tourists in Indonesia Travel Destinations to Bali).

International Journal of Marketing and Human Resource Management, 8(2), 2017, pp. 09–

20.

Documents

VARIABILITY OF THE PERCEPTION ABOUT SAFETY ... Tarigan and Susilo [17] found that negative experience and dissatisfaction affect users’ preferences. All these previous studies mentioned