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A multiattribute customer satisfaction evaluation approach for railtransit network: A real case study for Istanbul, Turkey

Erkan Celik, Nezir Aydin n, Alev Taskin Gumus

Department of Industrial Engineering, Yildiz Technical University, Besiktas, Istanbul 34349, Turkey

a r t i c l e i n f o

Article history:

Received 30 December 2013

Received in revised form1 July 2014

Accepted 25 September 2014

Keywords:

Customer satisfaction

Rail transit network

SERVQUAL

VIKOR

Interval type-2 fuzzy sets

Istanbul

a b s t r a c t

Rail transit is one of the most important public transportation types, especially in big and crowded cities.

Therefore, getting a high customer satisfaction level is an essential task for municipalities and govern

ments. For this purpose, a survey is conducted to question the attributes related to rail transit network(metros, trams, light rail and funicular) in Istanbul. In this study, we present a novel framework which

integrates statistical analysis, SERVQUAL, interval type-2 fuzzy sets and VIKOR to evaluate custome

satisfaction level for the rail transit network of Istanbul. Level of crowdedness and density in the trainair-conditioning system of trains' interior, noise level and vibration during the journey, and phone ser-vices are determined as the attributes need improvements. On the other hand, different improvement

strategies are suggested for the rail transit network. The proposed approach provides directions for thefuture investments and can be generalized and applied to complex decision making problems encounter

inexact, indenite and subjective data or uncertain information.& Elsevier Ltd. All rights reserved

1. Introduction and related literature

Assuring a high customer satisfaction (CS) level in publictransportation (PT) systems is an important task for the managers

and authorities. PT providers need to evaluate the performance of

their service quality (SQ) to determine how effective and adequate

their service is (Hassan et al., 2013). Also,Hassan et al. (2013)note

that the performance evaluation is needed to consider existing and

forecasted demand trends, top activities, concerns of stakeholders

and unmet service needs. Performance evaluation attributes can

be used for measuring economic performance, connecting the

service provider's output and encounters, and improving the SQ of

the organization (Transportation Research Board, 2003).SQ can be evaluated by considering customer perceptions and

expectations. Filipovi et al. (2009)present a comparative analysis

of the expected and perceived SQ of Belgrade public transportation

within the period 2005 and 2007. Also,De Oa et al. (2013)presents

a structural equation approach to evaluate the quality of service

perceived by users of a bus transit service. SQ consists both objec-

tive and subjective attributes as inEboli and Mazzulla (2011). CS is

determined based on the perception of the customers

(Tyrinopoulos and Antoniou, 2008;Eboli and Mazzulla, 2009,2011)

on the SQ considering multi-attributes. Eboli and Mazzulla (2008)

propose a multinomial logit model to measure SQ in PT. The

proposed model identies the importance of SQ attributes on globaCS and calculates an SQ index. Mouwen and Rietveld (2013)con-

sider multi-attributes to examine whether competitive tenderingimproves CS for public transport or not in Netherland. Service fre-quency, on-time performance, travel speed, and vehicle tidiness aredetermined as the most effective attributes on satisfaction in thetendered regions. Waiting time, cleanliness and comfort are speci-

ed as the most valuable PT variables in (DellOlio et al., 2011)Hassan et al. (2013) juxtapose the most common attributes otransit service as the reliability, frequency, capacity, price, cleanli-ness, comfort, security, staff, information, and the ticketing system

They also add loading/ridership, travel time, travel distance andservice duration as efciency indicators. Also, Redman et al(2013)investigate seven improvement attributes as reliability, fre-quency, price, speed, access, comfort, and convenience.Gerek et al

(2004)evaluate three alternative rail transit network (RTN) based

on four main attributes that are dened as nancial, economicsystem planning, and policy.

Multi-Attribute Decision Making (MADM) is preferred for per-

formance analyses and evaluation of the services when multi-at-tributes are considered to determine CS level. Many MADM ap-proaches are applied to evaluate service quality of PT and it

performance. Such as,Nathanail (2008)develops a framework tomeasure the quality of services provided to the passengers foHellenic railways. MADM procedures are exible to be combinedboth with other MADM and mathematical modeling approaches

As an example, Zak (2011) presents the ELECTRE III method andmulti-objective mathematical programming to evaluate mass

Contents lists available atScienceDirect

journal homepage: www.elsevier.com/locate/tranpol

Transport Policy

http://dx.doi.org/10.1016/j.tranpol.2014.09.005

0967-070X/& Elsevier Ltd. All rights reserved.

n Corresponding author. Phone: 90 212 383 3029.

E-mail address: [email protected](N. Aydin).

Transport Policy 36 (2014) 283293
http://www.sciencedirect.com/science/journal/0967070Xhttp://www.elsevier.com/locate/tranpolhttp://dx.doi.org/10.1016/j.tranpol.2014.09.005mailto:[email protected]://dx.doi.org/10.1016/j.tranpol.2014.09.005http://dx.doi.org/10.1016/j.tranpol.2014.09.005http://dx.doi.org/10.1016/j.tranpol.2014.09.005http://dx.doi.org/10.1016/j.tranpol.2014.09.005mailto:[email protected]://crossmark.crossref.org/dialog/?doi=10.1016/j.tranpol.2014.09.005&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.tranpol.2014.09.005&domain=pdfhttp://crossmark.crossref.org/dialog/?doi=10.1016/j.tranpol.2014.09.005&domain=pdfhttp://dx.doi.org/10.1016/j.tranpol.2014.09.005http://dx.doi.org/10.1016/j.tranpol.2014.09.005http://dx.doi.org/10.1016/j.tranpol.2014.09.005http://www.elsevier.com/locate/tranpolhttp://www.sciencedirect.com/science/journal/0967070X


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transit system and optimize the crew size in Czestochowa/Poland.Furthermore, Awasthi et al. (2011) integrate the SERVQUAL andTOPSIS to evaluate the SQ of Montreal metro services.Hassan et al.(2013)analyze 12 operating routes in Abu Dhabi city for the pro-

posed multi-level framework based on a multi-attribute evalua-tion procedure that involves weighted scoring techniques, TOPSISandk-means clustering methods. The proposed framework is usedto evaluate the public transit service performance at system and

route levels. Celik et al. (2013) propose an integrated GRA andTOPSIS approach based on type-2 fuzzy sets to improve CS in PTservices. They compare four different public transport rms; thebus rapid transit (BRT), IETT, private PT and Otobus Inc., in Istanbul.

In this study, we analyze the PT in Istanbul to determine theexisting CS level of the RTN and provide suggestions to increasethis level as studied in Beiro and Sarseld Cabral (2007). Theypresent a qualitative study involving PT users and car users. Hence,

a detailed interview is conducted in the region of Porto. To in-crease PT usage, the service should accommodate the levels ofservice required by customers and attracts potential users. In Is-tanbul, more than40% of the population of the Istanbulers prefers

PT. A large amount of people has private cars, but they may preferPT because of trafc congestion on roads. Therefore, the PT usage

rate is high. Also, the usage rate of PT in Istanbul is higher than thenational average. Even usage rate of PT is high; people are still

caught by the trafc congestion, especially during the rush hours.In our study, we distinguish crowdedness and trafc congestion bythe time and place they occur. Crowdedness refers to the pas-sengers while trafc congestion refers the jam on roads for cars.

With this point of view, we aimed to state that, conversely to othermodes of public transportations (bus, jitney, shuttle, etc.) no trafccongestion occurs on way when passengers use the RTN. On theother hand, crowdedness occurs in both rail and other modes of

public transportation during the rush hours, in Istanbul. In brief,both trafc congestion and crowdedness occur on public trans-portation modes while only crowdedness occurs in RTN. Thus,

passengers prefer rail transit. Since RTN (metros, trams, light railand funicular) is a vital urban transportation in Istanbul, CS be-comes a very important factor for managers and decision makers.

The objective of this study proposes an integrated approach,

which includes SERVQUAL, statistical analysis, type-2 fuzzy setsand Vlsekriterijumska Optimizacija I Kompromisno Resenje (VI-KOR) methods, to evaluate the CS for the RTN of Istanbul. By usingthese four methods together and in an integrated way, the data

implying CS levels and attributes evaluations can be collected andquantied in a healthy manner. The attributes are determinedfrom the survey and data using statistical analysis and SERVQUALmethods. The statistical analysis provides researchers to synthe-

size raw data and present valued information for additional ana-lysis. SERVQUAL is proposed byParasuraman et al. (1988)as one ofthe best evaluation methods for assessing the expectations andperceptions (Chou et al., 2011). Then, this data is transformed to

linguistic variables by using interval type-2 fuzzy sets principles tomake the evaluation process more realistic. Interval type-2 fuzzysets are more suitable, exible and intelligent than type-1 fuzzysets to represent uncertainties for handling fuzzy group decision

making problems (Mendel et al., 2006;Lee and Chen, 2008;Chenand Lee, 2010; Celik et al., 2013). We then combine VIKOR withinterval type-2 fuzzy sets to gain the rankings of the RTN. Themajor advantages of the VIKOR method are that it can trade off themaximum group utility of the majority and the minimum of

the individual regret of the opponent, and the calculations aresimple and straightforward. Combining VIKOR method and inter-val type-2 fuzzy sets is an interesting and important research to-pic. In brief, integrating all these four methods provides a valid and

reliable evaluation of CS level.

The rest of the paper is structured as follows. Section 2 de-

scribes the proposed methodology. In Section 3 collected data

used on the study is introduced and the application of the CS

evaluation is presented. The discussion and conclusion of the pa-

per are considered in the last section.

2. The proposed methodology

In this section, rstly the dimensions of SERVQUAL are dened

(Parasuraman et al., 1988;Awasthi et al., 2011). Then the proposed

integrated SERVQUAL and VIKOR approach based on interval type-

2 fuzzy numbers is presented.

2.1. SERVQUAL

SERVQUAL is a valuable tool for executing analysis where a gap

is measured as the difference between the customer expectations

and customer perceptions. The metrics of SERVQUAL are concisely

compiled as follows (Parasuraman et al., 1988; Awasthi et al.,

2011):

Tangibles comprise the physical appearance of the service fa-cility, tools, staff, and communication resources. For instance,

appearance of metro stations, public phones, etc. Reliability represents the ability of the service provider to

execute the promised service reliably and accurately. For in-

stance, on time departures and arrivals of metros (trams). Responsiveness shows the willingness of the service provider

(s) to be helpful and provide service immediately. For instance,availability of service staff when needed.

Assurance relates to the knowledge and politeness of the staffand their capability to reveal trust, faith and condence. Forinstance, communication of employees' during an emergencysituation.

Empathy denotes care and personalized attention of the em-ployees to customers. For instance, assisting elders or peoplewith children passing toll gates to get on station.

2.2. VIKOR based interval type-2 fuzzy sets

The Vlsekriterijumska Optimizacija I Kompromisno Resenje

(VIKOR) method is proposed as a MADM technique based on

compromise solution (Opricovic, 1998,Opricovic and Tzeng, 2004,

Tzeng et al., 2005). It provides a maximum group utility for the

majority and a minimum of an individual regret for the opponent.

Interval-valued fuzzy sets (Vahdani et al., 2010), interval-valuedfuzzy with gray relational analysis (Kuo, 2011), triangular in-

tuitionistic fuzzy numbers (Wan et al., 2013), and 2-tuple fuzzy

numbers (Ju and Wang, 2012) are integrated with VIKOR. Hence,

type-2 fuzzy sets reect more uncertainty than type-1 fuzzy sets

with additional degrees of freedom (Chen and Lee, 2010). In this

paper, the extended VIKOR method with interval type-2 fuzzy sets

is proposed to obtain the best CS level of RTN based on average

and the worst group scores among the set of alternatives. In the CS

evaluation process of RTN, it is assumed that there are m alter-

natives (rail lines), where R R R, , ... , }m1 2 , n attributes

A A A, , ... , }n1 2 and L customers C C C C, , ... , }L1 2 .The steps of the VIKOR, based on interval type-2 fuzzy sets, are

presented as follows:

E. Celik et al. / Transport Policy 36 (2014) 283293284


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Step 1. The importance weights of the attributes are calculatedusing Eq.(1).

= =

A a

A

A

A

a

a

a

...

(1)

n jk

nx

n

k

k

nk

1

1

2

1

2

where =

a ( )ja a a

L

j j jL1 2

, aj is an interval type-2 fuzzy set

i m j n k L1 , 1 , 1 and L denotes the number ofcustomers.

Step 2. The average fuzzy performance values of RTN are alsocalculated using Eq.(2).

= =

R R R

E e

A

A

A

e e e

e e e

e e e

( )

(2)

m

c ijk

n m

n

k km

k

k km

k

nk

nk

nmk

1 2

1

2

11 12 1

21 22 2

1 2

where = e e e e L( / )ij ij ij ijL1 2

, eij is an interval type-2 fuzzy set

i m j n k L1 , 1 , 1 and L denotes the number ofcustomers.

Step 3. The weighted type-2 fuzzy decision matrix is calculated

as follows:

= V v (3)ij mxn

where

=

=

v a e

f f f f H F H F f f f f H F H F, , , ; , , , , , ; ,

ij j ij

iU

iU

iU

iU

iU

iU

iL

iL

iL

iL

iL

iL

1 2 3 4 1 2 1 2 3 4 1 2

Step 4. The positive ideal solution * *P P( , )e v and negative ideal

solution ( Ne ) for upper and lower reference points of the in-terval type-2 fuzzy numbers are calculated (Kuo and Liang,2012).

*

*

*

*

= =

=

= =

=

= =

=

* * *

* * * * * * * *

* * *

* * * * * * * *

{ }

{ }(

{ }

(

(

P e e e e j Benefit

P e e e e H E H E e e e e

H E H E

P v v v v j Benefit

P f f f f H F H F f f f f

H F H F

N e e e e j Benefit

N e e e e H E H E e e e e

H E H E

, , , max

, , , ; max , max , , , , ;

max , max

, , ... , max

, , , ; max , max , , , , ;

max , max

, , ... , min

, , , ; min , min , , , , ;

min , min

eij ij ij

iij

eiU

iU

iU

iU i

UiU

iL

iL

iL

iL

iL

iL

v ij ij iji

ij

viU

iU

iU

iU

iU

iU

iL

iL

iL

iL

iL

iL

e ij ij iji

ij

eiU

iU

iU

iU i

UiU

iL

iL

iL

iL

iL

iL

1 2 3 4 1 2 1 2 3 4

1 2

1 2 3 4 1 2 1 2 3 4

1 2

1 2 3 4 1 2 1 2 3 4

1 2

Next, the average (Si) and the worst (Ri) group scores of the CSfor each RTN is calculated.

= + = =

( )S S S i m1

2 , 1, ,

(4i

j

n

ijU

ijL

1

= + = ( )R S S i mmax

1

2 , 1, ,

(5i

jijU

ijL

where,

= + + +

+ + +

=

= * * * *

= * * * *

( ) ( ) ( ) ( )

( ) ( ) ( ) ( )S

f f f f f f f f

e e e e e e e e

i m

,

1, ,

ijU

j

k iU

iU

iU

iU

iU

iU

iU

iU

k iU

iU

iU

iU

iU

iU

iU

iU

1

4 14

1 4

2

2 3

2

3 2

2

4 1

2

1

4 14

1 42

2 32

3 22

4 12

= + + +

+ + +

=

= * * * *

= * * * *

( ) ( ) ( ) ( )

( ) ( ) ( ) ( )S

f f f f f f f f

e e e e e e e e

i m

,

1, ,

ijL

j

k iL

iL

iL

iL

iL

iL

iL

iU

k iL

iL

iL

iL

iL

iL

iL

iL

1

4 14

1 4

2

2 3

2

3 2

2

4 1

2

1

4 14

1 42

2 32

3 22

4 12

Step 5. The Qi is calculated according to the Siand R i using Eq(6).

= *

*+

*

* *

( )( )

( )( )

Q vS S

S Sv

R R

R R(1 )

(6

i

i i

where * = = *= = S S S S R R R Rmin , max , min , maxi

ii

ii

ii

i, v [0, 1] is

the weight of the decision making strategy of the majority o

attributes (or maximum group utility). Then the smallest Qi isdetermined as a compromise solution if two conditions areacceptable.

Condition 1. The acceptable advantage: Q Q DQ R R2 1 , where= DQ m1/( 1)

Condition 2. Acceptable stability in decision-making

QR1alternative must also be the best ranked S Rand/or .

If Condition 1 and Condition 2 are not acceptable, then the

compromise solution stays same.

3. Customer satisfaction for rail transit network: a real case

study for Istanbul

In this section, we rst introduce the RTN infrastructure in Is-tanbul. Later, the related information about the CS survey is de-

scribed, and the proposed approach is applied by considering the

attributes provided by the CS survey. Lastly, the sensitivity ana-

lyses are presented.

3.1. Rail transit network infrastructure of Istanbul

As the most crowded city of Turkey, Istanbul has been the most

important residential area culturally, economically, historically and

strategically. Istanbul acts as a bridge between Europe and Asia

continents. Based on the data obtained from Turkish StatisticaInstitute (TUIK, 2013), the population of Istanbul was 13,854,740 in

2012, and Istanbul has the highest population density as well

Above all according to TUIK, Istanbul's population has a high in-

cremental rate and the population is projected to be more

than15 M and16 M by the years of 2019 and 2023, respectivelyConsidering the rise in living standards, the people living in Is-

tanbul expect a better public transportation for buses, BRTs and

metros.

E. Celik et al. / Transport Policy 36 (2014) 283293 285


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The long distance passengers prefer private cars because tra-

veling by PT needs multi-interchanges, in Istanbul. In addition, el-

ders, handicapped, and passengers who transport with baby prefer

private cars because PT vehicles (bus, jitney, car etc.) are mostly not

well designed for them. For instance, lifting to get on buses or jit-

neys, room for wheelchair(s) and stroller (s) on jitneys are the main

problems tend passengers to use either private cars or RTN.So far metros and trams are seen as the most convenient way to

transport for Istanbulers. However, the length of lines in total wasabout100 km, in 2012. Since Istanbulers still are suffering from the

trafc congestion, it is clear that this length is not enough. Thus,

Istanbul Metropolitan Municipality (IMM) has projects for in-

creasing the number of lines and/or lengths. Besides, Istanbul

Ulasim A.S. (IUAS-Istanbul Public Transportation Co.) conducts

surveys to evaluate and increase CS.

IUAS is an afliate company, founded in 1988, of IMM which op-

erates metros, trams, light rails and funiculars in Istanbul. IUAS oper-

ates 7urban rail transit lines with, in total, about 100 km. Winning the

UITP's (International Association of Public Transport) best practice

award with its Zeytinburnu-Kabatas tram line in meeting high custo-

mer demand, IUAS serves more than 1,100,000 passengers every day.

3.2. Rail transit lines

In this study, we analyze the CS surveys, which are performed

for the year of 2012. The survey is conducted in only ve of the

seven rail transit lines, because two of these rail transit lines are

not used for public transportation but for touristic and outing

activities. These ve rail transit lines are shown in Fig. 1 as: F1

(Taksim-Kabatas Fenicular Line), M1 (Aksaray-Ataturk Airport LRT

Line), M2 (Sishane-Haciosman Metro Line), T1 (Bagcilar-Kabatas

Tram Line) and T4 (Topkapi-Habibler Tram Line). The detailed

information for these ve rail transit lines are presented inTable 1.F1, the Taksim-Kabatas funicular line acts as a bridge between

the Sishane-Haciosman metro line, Taksim-Tunnel Heritage Tram,

IETT buses, privately-owned public buses, dolmus (jitney) stations,Kabatas-Bagcilar tram line; IDO, cruise, ferry and seabus piers in

Kabatas. M1 was opened in 1989 and modied in 1994, 1995, 1999,

2002 and later in 2012. M1 serves with 6 tunnels,9 over ground,3

over ground viaduct and 4 under ground stations. M2 started

operating between Taksim and 4.Levent in 2000. Later in 2009

Sishane and Sanayi, and in 2011 Haciosman stations are added to

the line. It also has the branch line to Seyrantepe from Sanayi

Mahallesi station located on the main line. T1 line's rst phase

between Sirkeci and Aksaray started operating in 1992, and was

later extended to Topkapi and Zeytinburnu, and later to Eminonu.

Finally, in 2006, continuous rail transportation became available

from 4. Levent to Ataturk Airport thanks to T1 line's Kabatas ex-

tension and Taksim-Kabatas funicular line. In 2011, continuous

transportation from Kabatas to Bagcilar was materialized with the

merging of the T1 line with the T2 line. Starting its operations in

2007 and serving between Sehitlik (where it is integrated with

Metrobus-BRT) and Mescid-i Selam, the T4 tram is operational on

a line of15.3 km track.

Fig. 1. Istanbul rail transit network (RTN) map (http://www.istanbul-ulasim.com.tr/en ).

http://www.istanbul-ulasim.com.tr/enhttp://www.istanbul-ulasim.com.tr/en


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3.3. Survey and data collection

Surveys are conducted by the team of IUAS in all stations of the

ve rail transit lines between May 15, 2012 and June 3, 2012. For

instance, 1076 of the surveys those are conducted in line M1 are

carried out in all18 stations with different frequencies. These

frequencies are determined by using the real percentages of pas-sengers who use those stations. In total, 4966 surveys are carried

out: 1076in M1,1069in M2,1575in T1,1047in T4 and199 in F1.

Surveys are conducted on different days in a week and time of theday, i.e., Monday peak hours in the morning, Saturday afternoon.

The participants are selected as follows: once the pollster arrives

at the stations she/he counts the number of passengers who pass

the turnstiles and tries to carry out the survey with the 6th pas-

senger, if the passenger is not willing to then the pollster tries the

next passenger and so forth. Also statistical data according to the

order in which passengers are conducted is available upon request.

All surveys are carried out face to face with the passengers. The40% of the conducted surveys are controlled over the phone by

managers of the IUAS.The surveys include four main sections: (1) Stations and tick-

eting: Station, time and ticket type. Four questions are directed to

the passengers. (2) Train usage: where the passenger comes from

and goes to, usage of other types of transportation, trip time andthe frequency, days and the reason of usage. There are 8 questions

in this section. (3) Satisfaction: general satisfaction level fromtrains, stations and the service provided, satisfaction from the at-

tributes mentioned inTable 2. In total 42 questions are conducted

in this section. (4) Demographic: age, sex, education, job, income,

marriage status, disability, communication info and whether she/

he lets the information obtained are used by the IUAS. This section

includes 9 questions. The demographic and statistical summary of

the data is presented inTable 2.

3.4. Weights of the attributes

In this study, SERVQUAL is applied to classify the attributes.

Twenty-six attributes are classied under ve dimensions of theSERVQUAL, which are assurance, empathy, reliability, responsive-

ness and tangibles. CS attributes and related dimension classi-

cations are presented inTable 3.The weight of each attribute is determined based on ve ques-

tions (what is the rst, the second, the third, the fourth and the

fth important attribute for you, to improve CS level?), which are

asked to 4966 customers. The averages of the importance level of

the weights, which are in linguistic terms, are converted into type-2

fuzzy numbers using ve different scales in Table 4 via Eq. (1) in

Step 1. Consequently, based on type-2 fuzzy numbers, the weights

of 26 attributes are ranked. According to evaluated averages, the

weights of top ve attributes are determined as: waiting time for

metro before departure (Rl2), level of crowdedness and density in

the trains (As1), journey time (Rl1), access to metro stations (Rs4),

and security at metro stations (Rl3). The waiting time is determined

as the most important attribute as we were expected and, also, is

determined one of the three inDellOl et al. (2011)and one of the

four important attributes inCelik et al. (2013). Another attribute we

were expecting to be one of the most important attribute is the

crowdedness, because more than 40% of the Istanbulers prefe

public transportation and it is usually crowded. Foote (2004)note

that an improvement in crowdedness level increases CS by 7%

Therefore, we can conclude that crowdedness level is important for

the passenger and increases CS if PT vehicles (train, bus etc.) are lesscrowded. Also, journey time was one of the attribute that we were

expecting to be one of top ve among rail transit users. Because it is

one of the main reasons they prefer rail transit line instead of other

type of PT. Accessing to rail transit lines` stations is more difcul

than accessing to other types of PT stations, i.e., bus, jitney. Since rai

transit lines' stations are mostly underground and take long time to

access. Also we can inference the same result fromTable 2(time to

trainrows 2329, columns 68). Time to train takes more than

10 min for 43% of the customers.Furthermore, based on the results, the least important attribute

are up-to-datedness of the IUAS website (Rs5), service provided by

IUAS phone (Rs6), costliness of interchanges (Tn8), announcement

in stations during and after breakdowns (Tn6), and announcement

in trains during and after breakdowns (Tn7). The averages of theweights regarding all attributes are shown in Table 5.

3.5. Customer satisfaction evaluation

The averages of type-2 fuzzy performance values for RTN ve-

hicles are calculated using Eq.(2)in Step 2. The linguistic terms are

converted into type-2 fuzzy numbers using six different scales as

shown inTable 6by using Eq.(2). For instance, type-2 fuzzy per-

formance value of M1 with respect to As1is evaluated considering

frequencies for each linguistic scale, i.e., poor (24), medium poor

(73), medium (75), medium good (98), good (640), and very good

(166), and then the type-2 fuzzy performance ( eA M1s1 ) is computed

as follows:

=

=

e

24 ((0; 1; 1; 3; 1; 1), (0. 5; 1; 1; 2; 0. 9; 0. 9))

73 ((1; 3; 3; 5; 1; 1), (2; 3; 3; 4; 0. 9; 0. 9))

75 ((3; 5; 5; 7; 1; 1), (4; 5; 5; 6; 0. 9; 0. 9))

98 ((5; 7; 7; 9; 1; 1), (6; 7; 7; 8; 0. 9; 0. 9))

640 ((7; 9; 9; 10; 1; 1), (8; 9; 9; 9. 5; 0. 9; 0. 9))

166 ((9; 10; 10; 10; 1; 1), (9. 5; 10; 10; 10; 0. 9; 0. 9))

/1076

((4.07; 5. 87; 5. 87; 7. 43; 1; 1),

(4. 97; 5. 87; 5. 87; 6. 65; 0. 9; 0. 9))

As M1 1

Similarly, the type-2 fuzzy performance values for each criterionwith respect to each rail transit line are obtained, and the results

are presented inTable 7.Then, the weighted type-2 fuzzy performance values of the

RTN are calculated by multiplying the importance weights o

Table 1

Characteristics ofve rail transit lines.

Li ne Operating hours Li ne length

(km)

Daily

ridership

Trip time

(mins)

Number of

stations

Number of

cars

Number of daily

tripsaFrequency (min)b

Name Color

F1 Carroty 06:1524:00 0.6 30.000 2.5 2 4 195 3

M1 Red 06:0024:00 19.6 220.000 32 18 85 180 5M2 Green 06:1524:00 16.5 230.000 27 13 124 225 4

T1 Navy 06:0024:00 18.5 320.000 65 31 92 295 2T4 Orange 06:0024:00 15.3 95.000 42 22 78 165 5

a Single direction.b Frequency is based on the peak hours.



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attributes (inTable 5) with type-2 fuzzy performance values (in

Table 7), as described in Step 3.The positive ( *Pe ), negative ( Ne ) and weighted ( *Pv ) type-2

fuzzy ideal solutions for upper and lower reference points are

determined using formulations in Step 4. Then, upper (SijU), lower

(SijL) and average (Sij) group scores are calculated using Eq. (4) in

Step 4, and the scores are shown in Table 8.In Step 5, nal rankings based on averages and the worst group

scores are calculated using Eq. (6). Maximum group utility (v) isconsidered as .5. Final rankings, and related regret and average

scores are presented in Table 9. The smaller Q values represent

higher CS level comparing other rail transit lines.Finally, acceptable advantage, the Condition (1) in Step 5, be-

tween line F1, ( =Q 0.00F1 ) and M1 ( =Q 0.56F1 ), is satised.Therefore, F1 has the best and M1 has the second best CS scores.

RTN can be juxtaposed as F1, M1, M2, T4 and T1 from the best to

the worst CS score, based on the survey.

3.6. Sensitivity analysis

In this subsection of the study, the concept of sensitivity ana-

lysis investigates the impact of attributes with the proposed in-

terval type-2 fuzzy VIKOR approach to validate the results of CS

level to be steadier. The maximum group utility (v) is used to

examine the ranking of RTN. This study assume that the v value is

= 1while theQvalues of each alternative M1, M2, T1, T4, and F1

are0, 32, 0.31, 1.00, 0.43and 0.00, respectively. The ranking or-

der of the ve RTN is F14M24M14T44T1. When v value

is = 0.5, then the Q values of each RTN, M1, M2, T1, T4, and F1,

are0, 56, 0.63, 0.88, 0.71and 0.00, respectively. The ranking or-

der of the ve RTN is F14M14M24T44T1. If v value is = 0,

then the ranking order is F14T14M14M24T4. The Qvalue ofeach RTN, M1, M2, T1, T4, and F1, is 0, 81, 0.94, 0.76, 1.00

and0.00, respectively.According to the aforementioned sample, this study uses each

maximum group utility value, v , from 0.00 to 1.00 increasing by

0.1 to examine the proposed approach, and then the obtained

results are found to be satisfactory, as shown in Table 10 and

graphically inFig. 2.The results show that, the variations of thev values for each rail

transit lines changes rankings of the rail transit lines as in Fig. 3.

The line F1 has the best CS rankings in all case-

s; =v 0.1, 0.2, .. , 1.0. T1 has the second best ranking when

=v 0.0, but gets the worst score when v increases to1.0. Fur-

thermore M1, M2, and T4's rankings improve as thev increases.

M2 has the second best ranking at almost all cases.

Table 2

Demographic and transportation information (n4966).

Question Option Frequency Percent Question Option Frequency Percent

Survey Date Monday 629 12.7 Access (by) Walk 2809 56.6Tuesday 780 15.7 Tram 563 11.3

Wednesday 710 14.3 Bus 805 16.2

Thursday 836 16.8 Cab 52 1.0

Friday 835 16.8 Shuttle 25 0.5

Saturday 676 13.6 Private car 146 2.9Sunday 500 10.1 Jitney 188 3.8

Survey Time Morning (peak hour) 807 16.9 Sea bus 125 2.5

Morning 667 14.0 . Other 253 5.1

Noon 1641 34.4 Disabled Yes 77 1.6

Evening (peak hour) 1175 24.6 No 4889 98.4Evening 477 10.0 Has private car Yes 1995 40.2

Age 1425 2317 46.7 No 2971 59.8

2635 1312 26.4 Aim for use Homework 2225 44.8

3645 675 13.6 Homeschool 1037 20.9

4655 397 8.0 Shopping 156 3.1

56 265 5.3 Business 661 13.3Gender Male 3844 77.4 Social 602 12.1

Female 1122 22.6 Medical services 76 1.5Education Non-educated 32 0.6 Visit 201 4.0

Primary school (5 years) 568 11.5 Other 8 0.2Middle school (8 years) 493 9.9 Time to train 010 2839 57.2

High School (11 years) 1711 34.5 (min) 11

20 1128 22.7Associate degree 123 2.5 2130 453 9.1Undergraduate(stud) 950 19.2 3140 153 3.1

Bachelor of Sc. 920 18.6 4150 152 3.1

Graduate (M.S., Ph.D.) 162 3.3 5160 130 2.6Has a job Yes 3192 64.3 60 111 2.2

No 1774 35.7 Travel time 010 399 8.0Marital status Married 1862 37.5 (min) 1120 1319 26.6

Single 3104 62.5 2130 1148 23.1Income NA 126 2.5 3140 661 13.3(TL/month) 01000 748 15.1 4150 509 10.2

10012000 2122 42.7 5160 352 7.1

20013000 1129 22.7 60 578 11.630014000 367 7.4 Day of Use Weekday 2908 58.6

4001-5000 248 5.0 Saturday 134 2.7

5000 226 4.6 Sunday 117 2.4

Ticket kind Token 475 9.6 Weekday and Saturday 638 12.8

Full Fare 2605 52.5 Weekday and Sunday 30 0.6

Student fare 1719 34.6 Saturday and Sunday 252 5.1

Discount fare 167 3.4 All week 887 17.9



7/11

This study conrms that the results of the ranking orders of all

ve rail transit lines, by using the proposed approach, are con-

sistent. Furthermore, the proposed approach nds the gap be-

tween the Q values of various rail transit lines. Q values get smaller

when the maximum group utility value is increased from 0.1 to

1.0. According to the analysis above, this paper nds that the

proposed approach produces satisfactory results and providesproper information to assist managers in decision making.

4. Discussion and conclusion

Railway transportation is one of the most important PT types

especially in big and crowded cities, i.e., Istanbul. In such cities,

RTN is used as the rst way to escape from trafc congestion,

specically during the rush hours. Therefore, getting a high CS

level is very important for municipalities and governments. In

order to assess the CS level, IUAS conducted a survey considering

ve different rail transit lines in Istanbul. The satisfaction level of

the Istanbulers related to RTN is questioned in this survey.

Hence, we present a novel approach which integrates statistica

analysis, SERVQUAL, type-2 fuzzy sets and VIKOR to evaluate CS

level for the RTN in Istanbul. The attributes which have the highest

and the lowest CS scores are determined for each rail transit lines

according to the results obtained by proposed approach.Opricovic (1998)and Opricovic and Tzeng (2004) suggest that

the weight of the strategy of maximum group utility (v) value

should generally be taken0.5. The rankings are occurred as

F14M14M24T44T1 when v is considered as0.5. This meansrail transit line F1 rated as the best line by passengers considering

all 26 attributes together. TheQ(minimum) values are determined

as 0.00, 0.56, 0.63, 0.71and 0.88 for F1, M1, M2, T4, and T1 re-

spectively as shown inTable 10row 7.Furthermore, the PT providers frequently states the comfort

attribute by favoring enhanced standards for cars or stations. In

their study, Wall and McDonald (2007), aimed to increase the

journey comfort by introducing of a new eet of buses. This im-

provement is highlighted by the customers as one of the most

important effects in transporting by PT services. Additionally

European Local Transport Information Service (2010) notes that

providing new covered shelters at the rail stations in Norwich/

England increased CS levels and the 98% of the respondents were

satised by the quality. In our study, most of the respondents are

Table 3

Dimensions and attributes of CS evaluation for rail transit services.

Dimension Attribute

Assurance Level of crowdedness and density in the trains (As1)Noise level and vibration during the journey (As2)

Lighting in the stations (As3)

Air-conditioning system of trains interior (temperature, hu-

midity) (As4)

Escalators, elevators and bent conveyors (As5)Comfort level in the stations (As6)

Empathy Attitude and behaviors of the security staff (Em1)

Reliability Journey time (Rl1)Waiting time before departure (Rl2)

Security at stations (Rl3)

Security inside trains (Rl4)

Arrival performance with respect to schedules (Rl5)

Responsiveness Ticketing service (Rs1)

Ticket vending machines/services (Rs2)

Smooth functioning of the turnstiles (Rs3)

Access to stations (Rs4)

Up-to-datedness of the IUAS website (Rs5)Service provided by IUAS phone (Rs6)

Tangibles Costliness of ticket (Tn1)Usage of modern equipment in stations (Screen, schedule,

routes) (Tn2)

Cleanliness of stations (Tn3)

Cleanliness of train interior (Tn4)

Usage of modern equipment inside the trains services

(Screens, route map, announcement) (Tn5)

Announcement in stations during and after breakdowns

(Tn6)

Announcement in trains during and after breakdowns (Tn7)

Costliness of interchanges (Tn8)

Table 4

Linguistic terms of the weights of attributes.

Linguistic terms Interval type-2 fuzzy sets

Medium low ((0.1;0.3;0.3;0.5;1;1), (0.2;0.3;0.3;0.4;0.9;0.9))

Medium ( (0.3;0.5;0.5;0.7; 1; 1) , (0.4;0.5;0.5; 0. 6;0. 9;0.9))

Medium high ((0.5;0.7;0.7;0.9;1;1), (0.6;0.7;0.7;0.8;0.9;0.9))High ((0.7;0.9;0.9;1;1;1), (0.8;0.9;0.9;0.95;0.9;0.9))

Very hig h ( (0.9;1;1;1;1;1), (0.95;1;1;1;0.9;0.9) )

Table 5

The importance weights of the attributes.

Attributes Weights

As1 ((0.618;0.784;0.784;0.896;1;1), (0.701;0.784;0.784;0.84;0.9;0.9))As2 ((0.406;0.599;0.599;0.768;1;1), (0.503;0.599;0.599;0.684;0.9;0.9))

As3 ((0.366;0.56;0.56;0.734;1;1), (0.463;0.56;0.56;0.647;0.9;0.9))As4 ((0.396;0.587;0.587;0.755;1;1), (0.492;0.587;0.587;0.671;0.9;0.9))As5 ((0.325;0. 517;0.517;0.693;1;1), (0.421;0.517;0.517;0.605;0.9;0.9))

As6 ((0.315;0.508;0.508;0.686;1; 1), (0.411;0.508;0.508;0.597;0.9;0.9))

Em1 ((0.519;0.7;0.7;0.839;1;1), (0.609;0.7; 0.7;0.769;0.9;0.9))

Rl1 ((0.57;0.747;0.747;0.875;1;1), (0.658;0.747;0.747;0.811;0.9;0.9))Rl2 ((0.689;0.835;0.835;0.911;1;1), (0.762;0.835;0.835;0.873;0.9;0.9))

Rl3 ((0.586;0. 767;0.767;0.89;1;1), (0.677;0.767;0. 767;0.828;0.9; 0.9))Rl4 ((0.53;0.716;0.716;0.856; 1;1), (0.623;0.716;0.716;0.786;0.9;0.9))Rl5 ((0.421;0.612;0.612;0.777;1;1), (0.517;0.612;0.612;0.695;0.9;0.9))

Rs1 ((0.43;0.619;0.619;0.781; 1;1), (0.525;0.619;0.619;0,7;0. 9;0.9))Rs2 ((0.343;0.538;0.538;0.716;1;1), (0.441;0.538;0.538;0.627;0.9;0.9))Rs3 ((0.295;0.491;0.491;0.674;1;1), (0.393;0.491;0.491;0.583;0.9;0.9))Rs4 ((0.565;0. 741;0.741;0.863;1;1), (0.653;0.741;0.741;0.802;0.9;0.9))Rs5 ((0.336;0. 518;0.518;0.673;1;1), (0.427;0.518;0.518;0.595;0.9;0.9))Rs6 ((0.1;0.3;0.3;0.5;1;1), (0.2;0.3;0.3;0.4;0.9;0.9))

Tn1 ((0.516;0.693;0.693;0.829;1;1), (0.605;0.693;0.693;0.761;0.9;0.9))

Tn2 ((0.388;0.581;0.581;0.752;1;1), (0.485;0.581;0.581;0.667;0.9;0.9))

Tn3 ((0.471;0.659;0.659;0.816;1;1), (0.565;0.659;0.659;0.737;0.9;0.9))Tn4 ((0.42;0.611;0.611;0.777;1;1), (0.516;0.611;0.611;0.694;0.9;0. 9))Tn5 ((0.299;0.494;0.494;0.676;1;1), (0.397;0.494;0.494;0.585;0.9;0.9))Tn6 ((0.352;0.543;0.543;0.715;1;1), (0.447;0.543;0.543;0.629;0.9;0.9))Tn7 ((0.294;0.487;0.487;0.666;1;1), (0.39;0.487;0.487;0.576;0.9;0.9))Tn8 ((0.378;0.565;0.565;0.727;1;1), (0.472;0.565;0.565;0.646;0.9;0.9))

Table 6

Linguistic terms for rail transit line rating.

Linguistic terms Interval type-2 fuzzy sets

Poor ((0;1;1;3;1;1), (0.5;1;1;2;0.9;0.9))

Medium poor ((1;3;3;5;1;1), (2;3;3;4;0.9;0.9))

Medium ((3;5;5;7;1;1), (4;5;5;6;0.9;0.9))Medium good ((5;7;7;9;1;1), (6;7;7;8;0.9;0.9))Good ((7;9;9;10;1;1), (8;9;9;9.5;0.9;0.9))

Very good ((9;10;10;10;1;1), (9.5;10;10;10;0.9;0.9))



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Table 7

The type-2 fuzzy performance values.

Attributes M1 M2 T1

As1 ((4.07;5.87;5.87;7.43;1;1),(4.97;5.87;5.87;6.65;0.9;0.9))

((4.96;6.8;6.8;8.24;1;1),(5.88;6.8;6.8;7.52;0.9;0.9))

((2.15;3.78;3.78;5.64;1;1),(2.97;3.78;3.78;4.71;0.9;0.9))

As2 ((5.69;7.52;7.52;8.77;1;1),

(6.6;7.52;7.52;8.15;0.9;0.9))

((6.42;8.3;8.3;9.46;1;1),

(7.36;8.3;8.3;8.88;0.9;0.9))

((5.05;6.95;6.95;8.43;1;1),

(6;6.95;6.95;7.69;0.9;0.9))As3 ((7;8.81;8.81;9.73;1;1),

(7.9;8.81;8.81;9.27;0.9;0.9))

((6.87;8.72;8.72;9.74;1;1),

(7.8;8.72;8.72;9.23;0.9;0.9))

((6.39;8.29;8.29;9.45;1;1),

(7.34;8.29;8.29;8.87;0.9;0.9))As4 ((5.79;7.62;7.62;8.84;1;1),

(6.71;7.62;7.62;8.23;0.9;0.9))

((5.94;7.8;7.8;9.04;1;1),

(6.87;7.8;7.8;8.42;0.9;0.9))

((4.55;6.37;6.37;7.9;1;1),

(5.46;6.37;6.37;7.14;0.9;0.9))As5 ((6.72;8.53;8.53;9.53;1;1),

(7.63;8.53;8.53;9.03;0.9;0.9))

((6.37;8.23;8.23;9.38;1;1),

(7.3;8.23;8.23;8.81;0.9;0.9))

((5.86;7.75;7.75;9.03;1;1),

(6.81;7.75;7.75;8.39;0.9;0.9))As6 ((6.46;8.31;8.31;9.4;1;1),

(7.39;8.31;8.31;8.85;0.9;0.9))((6.61;8.49;8.49;9.59;1;1),(7.55;8.49;8.49;9.04;0.9;0.9))

((5.74;7.66;7.66;8.99;1;1),(6.7;7.66;7.66;8.32;0.9;0.9))

Em1 ((6.67;8.5;8.5;9.54;1;1),

(7.59;8.5;8.5;9.02;0.9;0.9))

((6.52;8.34;8.34;9.43;1;1),

(7.43;8.34;8.34;8.88;0.9;0.9))

((6.61;8.41;8.41;9.45;1;1),

(7.51;8.41;8.41;8.93;0.9;0.9))

Rl1 ((6.91;8.69;8.69;9.62;1;1),

(7.8;8.69;8.69;9.16;0.9;0.9))

((6.92;8.72;8.72;9.7;1;1),

(7.82;8.72;8.72;9.21;0.9;0.9))

((5.77;7.68;7.68;9.02;1;1),

(6.73;7.68;7.68;8.35;0.9;0.9))Rl2 ((6.28;8.11;8.11;9.2;1;1),

(7.2;8.11;8.11;8.66;0.9;0.9))

((6.66;8.47;8.47;9.52;1;1),

(7.56;8.47;8.47;9;0.9;0.9))

((5.64;7.55;7.55;8.9;1;1),

(6.6;7.55;7.55;8.22;0.9;0.9))Rl3 ((6.53;8.37;8.37;9.42;1;1),

(7.45;8.37;8.37;8.89;0.9;0.9))

((6.42;8.26;8.26;9.38;1;1),

(7.34;8.26;8.26;8.82;0.9;0.9))

((6.19;8.07;8.07;9.25;1;1),

(7.13;8.07;8.07;8.66;0.9;0.9))Rl4 ((6.38;8.21;8.21;9.3;1;1)

(7.29;8.21;8.21;8.76;0.9;0.9))

((6.53;8.37;8.37;9.47;1;1),

(7.45;8.37;8.37;8.92;0.9;0.9))

((6.05;7.92;7.92;9.13;1;1),

(6.99;7.92;7.92;8.53;0.9;0.9))Rl5 ((6.7;8.54;8.54;9.56;1;1),

(7.62;8.54;8.54;9.05;0.9;0.9))

((6.76;8.62;8.62;9.66;1;1),

(7.69;8.62;8.62;9.14;0.9;0.9))

((6.09;8;8;9.26;1;1), (7.05;8;8;8.63;0.9;0.9))

Rs1 ((6.67;8.48;8.48;9.47;1;1),

(7.57;8.48;8.48;8.97;0.9;0.9))

((6.47;8.32;8.32;9.42;1;1),

(7.39;8.32;8.32;8.87;0.9;0.9))

((6.22;8.08;8.08;9.24;1;1),

(7.15;8.08;8.08;8.66;0.9;0.9))Rs2 ((6.79;8.59;8.59;9.56;1;1),

(7.69;8.59;8.59;9.08;0.9;0.9))

((6.62;8.48;8.48;9.57;1;1),

(7.55;8.48;8.48;9.02;0.9;0.9))

((6.36;8.24;8.24;9.4;1;1)

(7.3;8.24;8.24;8.82;0.9;0.9))Rs3 ((7.1;8.91;8.91;9.81;1;1),

(8;8.91;8.91;9.36;0.9;0.9))

((6.85;8.7;8.7;9.72;1;1),

(7.77;8.7;8.7;9.21;0.9;0.9))

((6.68;8.55;8.55;9.63;1;1),

(7.61;8.55;8.55;9.09;0.9;0.9))Rs4 ((6.66;8.5;8.5;9.53;1;1),

(7.58;8.5;8.5;9.02;0.9;0.9))

((6.6;8.45;8.45;9.54;1;1),

(7.53;8.45;8.45;8.99;0.9;0.9))

((6.24;8.15;8.15;9.35;1;1),

(7.19;8.15;8.15;8.75;0.9;0.9))Rs5 ((6.05;7.81;7.81;8.95;1;1),

(6.93;7.81;7.81;8.38;0.9;0.9))

((6.57;8.03;8.03;8.96;1;1),

(7.3;8.03;8.03;8.5;0.9;0.9))

((5.92;7.81;7.81;8.95;1;1),

(6.93;7.81;7.81;8.38;0.9;0.9))Rs6 ((5.14;6.82;6.82;8.05;1;1),

(5,98;6,82;6,82;7,44;0,9;0,9))

((6.46;7.88;7.88;8.79;1;1),

(7.17;7.88;7.88;8.34;0.9;0.9))

((5.52;6.82;6.82;8.05;1;1),

(5.98;6.82;6.82;7.44;0.9;0.9))

Tn1 ((4.51;6.24;6.24;7.65;1;1),

(5.38;6.24;6.24;6.94;0.9;0.9))

((5.32;7.13;7.13;8.47;1;1),

(6.22;7.13;7.13;7.8;0.9;0.9))

((4.2;6.02;6.02;7.61;1;1),

(5.11;6.02;6.02;6.82;0.9;0.9))Tn2 ((6.85;8.68;8.68;9.65;1;1),(7.77;8.68;8.68;9.16;0.9;0.9))

((6.73;8.59;8.59;9.64;1;1),(7.66;8.59;8.59;9.11;0.9;0.9))

((6.41;8.29;8.29;9.44;1;1),(7.35;8.29;8.29;8.86;0.9;0.9))

Tn3 ((6.66;8.47;8.47;9.49;1;1),

(7.57;8.47;8.47;8.98;0.9;0.9))

((6.64;8.5;8.5;9.58;1;1),

(7.57;8.5;8.5;9.04;0.9;0.9))

((5.98;7.88;7.88;9.14;1;1),

(6.93;7.88;7.88;8.51;0.9;0.9))

Tn4 ((6.56;8.35;8.35;9.37;1;1),

(7.45;8.35;8.35;8.86;0.9;0.9))

((6.63;8.48;8.48;9.57;1;1),

(7.55;8.48;8.48;9.03;0.9;0.9))

((6.17;8.06;8.06;9.28;1;1),

(7.11;8.06;8.06;8.67;0.9;0.9))Tn5 ((6.94;8.75;8.75;9.7;1;1),

(7.84;8.75;8.75;9.22;0.9;0.9))

((6.73;8.58;8.58;9.65;1;1),

(7.66;8.58;8.58;9.12;0.9;0.9))

((6.46;8.34;8.34;9.47;1;1),

(7.4;8.34;8.34;8.9;0.9;0.9))Tn6 ((6.92;8.73;8.73;9.68;1;1),

(7.82;8.73;8.73;9.2;0.9;0.9))

((6.62;8.49;8.49;9.59;1;1),

(7.55;8.49;8.49;9.04;0.9;0.9))

((6.1;7.97;7.97;9.18;1;1),

(7.03;7.97;7.97;8.58;0.9;0.9))Tn7 ((6.99;8.79;8.79;9.72;1;1),

(7.89;8.79;8.79;9.26;0.9;0.9))

((6.58;8.44;8.44;9.55;1;1),

(7.51;8.44;8.44;8.99;0.9;0.9))

((6.3;8.17;8.17;9.34;1;1),

(7.24;8.17;8.17;8.75;0.9;0.9))Tn8 ((4.95;6.7;6.7;8.02;1;1),

(5.82;6.7;6.7;7.36;0.9;0.9))

((6.52;7.93;7.93;8.84;1;1),

(7.23;7.93;7.93;8.39;0.9;0.9))

((5.19;6.7;6.7;8.02;1;1),

(5.82;6.7;6.7;7.36;0.9;0.9))Attributes T4 F1

As1 ((5.08;6.92;6.92;8.32;1;1), (6;6.92;6.92;7.62;0.9;0.9)) ((4.27;6.06;6.06;7.64;1;1), (5.16;6.06;6.06;6.85;0.9;0.9))

As2 ((6.03;7.88;7.88;9.12;1;1), (6.96;7.88;7.88;8.5;0.9;0.9)) ((6.25;8.05;8.05;9.2;1;1), (7.15;8.05;8.05;8.62;0.9;0.9))As3 ((6.7;8.55;8.55;9.64;1;1), (7.63;8.55;8.55;9.1;0.9;0.9)) ((7.32;8.98;8.98;9.75;1;1), (8.15;8.98;8.98;9.37;0.9;0.9))

As4 ((6.05;7.89;7.89;9.09;1;1), (6.97;7.89;7.89;8.49;0.9;0.9)) ((5.9;7.66;7.66;8.82;1;1), (6.78;7.66;7.66;8.24;0.9;0.9))As5 ((6.58;8.43;8.43;9.54;1;1), (7.5;8.43;8.43;8.98;0.9;0.9)) ((6.93;8.64;8.64;9.55;1;1), (7.79;8.64;8.64;9.1;0.9;0.9))As6 ((6.46;8.32;8.32;9.48;1;1), (7.39;8.32;8.32;8.9;0.9;0.9)) ((6.78;8.54;8.54;9.54;1;1), (7.66;8.54;8.54;9.04;0.9;0.9))

Em1 ((6.51;8.35;8.35;9.46;1;1), (7.43;8.35;8.35;8.91;0.9;0.9)) ((7.23;8.81;8.81;9.59;1;1), (8.02;8.81;8.81;9.2;0.9;0.9))

Rl1 ((6.36;8.2;8.2;9.34;1;1), (7.28;8.2;8.2;8.77;0.9;0.9)) ((7.29;8.9;8.9;9.69;1;1), (8.1;8.9;8.9;9.3;0.9;0.9))Rl2 ((6.19;8.05;8.05;9.28;1;1), (7.12;8.05;8.05;8.67;0.9;0.9)) ((7.24;8.89;8.89;9.72;1;1), (8.07;8.89;8.89;9.31;0.9;0.9))Rl3 ((6.49;8.33;8.33;9.45;1;1), (7.41;8.33;8.33;8.89;0.9;0.9)) ((6.84;8.52;8.52;9.44;1;1), (7.68;8.52;8.52;8.98;0.9;0.9))

Rl4 ((6.47;8.32;8.32;9.44;1;1), (7.4;8.32;8.32;8.88;0.9;0.9)) ((6.94;8.59;8.59;9.47;1;1), (7.77;8.59;8.59;9.03;0.9;0.9))Rl5 ((6.62;8.48;8.48;9.58;1;1), (7.55;8.48;8.48;9.03;0.9;0.9)) ((7.32;8.91;8.91;9.67;1;1), (8.12;8.91;8.91;9.29;0.9;0.9))Rs1 ((6.47;8.31;8.31;9.42;1;1), (7.39;8.31;8.31;8.86;0.9;0.9)) ((6.73;8.4;8.4;9.32;1;1), (7.57;8.4;8.4;8.86;0.9;0.9))

Rs2 ((6.61;8.46;8.46;9.56;1;1), (7.54;8.46;8.46;9.01;0.9;0.9)) ((6.89;8.61;8.61;9.55;1;1), (7.75;8.61;8.61;9.08;0.9;0.9))Rs3 ((6.69;8.55;8.55;9.64;1;1), (7.62;8.55;8.55;9.1;0.9;0.9)) ((5.9;7.58;7.58;8.96;1;1), (6.74;7.58;7.58;8.27;0.9;0.9))Rs4 ((6.63;8.47;8.47;9.55;1;1), (7.55;8.47;8.47;9.01;0.9;0.9)) ((7.26;8.89;8.89;9.68;1;1), (8.08;8.89;8.89;9.29;0.9;0.9))

Rs5

((5.81;7.66;7.66;8.96;1;1).(6.74;7.66;7.66;8.31;0.9;0.9)) ((6.38;8.23;8.23;9.38;1;1).(7.3;8.23;8.23;8.8;0.9;0.9))



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happy with the quality of the lighting system in the trains (in the

lines F1, M1, M2, and T4) and the provided comfort level both in

the train and stations. The comfort level provided by the PT service

supplier may also increases the number of ridership as noted in

Foote (2004). Foote (2004) analyzed the improvements that arerelated to comfort level issues, i.e., safety, cleanliness, in the Chi-

cago Transit Authority's PT services and they showed that the

number of trips is increased by 5% (per annum) after the comfort

level is improved. Besides lighting, passengers are happy and very

satised with the smooth functioning of turnstiles, usage of

modern equipment in stations and trains and arrival performances

with respect to schedules, and journey time.Speed affects journey time and the arrival performance of the

trains. Therefore, speed is also critical variable in affecting CS of PT

Table 8

Upper, lower and average group scores.

M1 M2 T1 T4 F1

Attributes

S S,ij

UijL Sij

S S,ij

UijL Sij

S S,ij

UijL Sij

S S,ij

UijL Sij

S S,ij

UijL Sij

As1 [0.81;0.52] 0.67 [0.8;0.48] 0.64 [0.96;0.85] 0.90 [0.8;0.47] 0.64 [0.81;0.51] 0.66As2 [1.24;0.98] 1.11 [1.26;0.96] 1.11 [1.26;1.04] 1.15 [1.25;0.96] 1.11 [1.25;0.95] 1.10

As3 [1.56;1.39] 1.47 [1.57;1.4] 1.48 [1.57;1.42] 1.50 [1.57;1.4] 1.49 [1.55;1.37] 1.46As4 [1.17;0.86] 1.02 [1.18;0.87] 1.02 [1.18;0.95] 1.07 [1.18;0.87] 1.02 [1.16;0.85] 1.01As5 [1.41;1.18] 1.29 [1.41;1.19] 1.30 [1.41;1.22] 1.31 [1.41;1.19] 1.30 [1.4;1.17] 1.29

As6 [1.36;1.17] 1.26 [1.37;1.17] 1.27 [1.36;1.2] 1.28 [1.37;1.17] 1.27 [1.36;1.16] 1.26

Em1 [1.63;1.54] 1.59 [1.63;1.56] 1.60 [1.62;1.54] 1.58 [1.64;1.56] 1.60 [1.58;1.49] 1.54

Rl1 [1.35;1.03] 1.19 [1.36;1.03] 1.20 [1.41;1.18] 1.30 [1.37;1.08] 1.23 [1.32;1] 1.16Rl2 [1.2;0.95] 1.07 [1.18;0.89] 1.04 [1.27;1.1] 1.18 [1.22;0.97] 1.09 [1.14;0.84] 0.99Rl3 [1.54;1.48] 1.51 [1.55;1.49] 1.52 [1.56;1.51] 1.54 [1.55;1.49] 1.52 [1.51;1.45] 1.48Rl4 [1.54;1.39] 1.46 [1.54;1.38] 1.46 [1.55;1.43] 1.49 [1.54;1.39] 1.47 [1.5;1.35] 1.43

Rl5 [1.5;1.25] 1.37 [1.51;1.25] 1.38 [1.52;1.3] 1.41 [1.51;1.26] 1.38 [1.47;1.21] 1.34

Rs1 [1.54;1.49] 1.52 [1.55;1.5] 1.52 [1.55;1.5] 1.53 [1.55;1.5] 1.52 [1.52;1.47] 1.49Rs2 [1.55;1.51] 1.53 [1.56;1.52] 1.54 [1.56;1.53] 1.54 [1.56;1.52] 1.54 [1.54;1.5] 1.52Rs3 [1.36;1.01] 1.18 [1.36;1.01] 1.19 [1.36;1.01] 1.19 [1.36;1.02] 1.19 [1.34;1.05] 1.20Rs4 [1.48;1.31] 1.40 [1.49;1.32] 1.41 [1.51;1.37] 1.44 [1.49;1.32] 1.40 [1.44;1.26] 1.35Rs5 [1.31;1.22] 1.27 [1.29;1.18] 1.23 [1.32;1.22] 1.27 [1.33;1.24] 1.29 [1.34;1.23] 1.29Rs6 [0.96;0.64] 0.80 [0.99;0.64] 0.81 [0.96;0.64] 0.80 [0.98;0.64] 0.81 [0.95;0.66] 0.80

Tn1 [1.17;0.99] 1.08 [1.18;0.93] 1.05 [1.2;1.03] 1.12 [1.18;0.93] 1.05 [1.17;1] 1.09Tn2 [1.55;1.51] 1.53 [1.56;1.52] 1.54 [1.56;1.53] 1.54 [1.56;1.52] 1.54 [1.54;1.5] 1.52

Tn3 [1.49;1.29] 1.39 [1.5;1.3] 1.40 [1.51;1.35] 1.43 [1.5;1.3] 1.40 [1.47;1.28] 1.37Tn4 [1.52;1.43] 1.47 [1.54;1.44] 1.49 [1.54;1.46] 1.50 [1.53;1.44] 1.49 [1.51;1.42] 1.47Tn5 [1.53;1.48] 1.50 [1.53;1.48] 1.51 [1.53;1.48] 1.51 [1.53;1.48] 1.51 [1.51;1.46] 1.49Tn6 [1.45;1.28] 1.36 [1.46;1.28] 1.37 [1.45;1.3] 1.38 [1.46;1.28] 1.37 [1.44;1.27] 1.35Tn7 [1.47;1.35] 1.41 [1.48;1.35] 1.42 [1.47;1.36] 1.42 [1.48;1.35] 1.41 [1.46;1.34] 1.40Tn8 [1.27;1.01] 1.14 [1.24;0.91] 1.08 [1.26;1.01] 1.13 [1.26;0.94] 1.10 [1.27;1.01] 1.14

Table 9

The nal rankings for ve rail transit lines.

M1 M2 T1 T4 F1

Si 33.60 33.58 34.48 33.74 33.18

Ri 1.59 1.60 1.58 1.60 1.54

=Q v( 0.5)i 0.56 0.63 0.88 0.71 0.00

Table 10

TheQi values for different maximum group utilities.

M1 M2 T1 T4 F1

v0.0 0.81 0.94 0.76 1.00 0.00

v0.1 0.76 0.88 0.78 0.94 0.00

v0.2 0.71 0.81 0.81 0.89 0.00

v0.3 0.66 0.75 0.83 0.83 0.00

v0.4 0.61 0.69 0.86 0.77 0.00

v0.5 0.56 0.63 0.88 0.71 0.00

v0.6 0.52 0.56 0.90 0.66 0.00

v0.7 0.47 0.50 0.93 0.60 0.00

v0.8 0.42 0.44 0.95 0.54 0.00

v0.9 0.37 0.37 0.98 0.48 0.00

v1.0 0.32 0.31 1.00 0.43 0.00

Table 7 (continued )

Attributes T4 F1

Rs6 ((5.25;7.08;7.08;8,33;1;1), (6,17;7,08;7,08;7,71;0,9;0,9)) ((4,25;6;6;7,5;1;1), (5,13;6;6;6,75;0,9;0,9))

Tn1 ((5,44;7,25;7,25;8,56;1;1), (6,35;7,25;7,25;7,9;0,9;0,9)) ((4,44;6,12;6,12;7,56;1;1), (5,28;6,12;6,12;6,84;0,9;0,9))Tn2 ((6,69;8,54;8,54;9,62;1;1), (7,62;8,54;8,54;9,08;0,9;0,9)) ((6.8;8.53;8.53;9.49;1;1).(7.67;8.53;8.53;9.01;0.9;0.9))Tn3 ((6.61;8.44;8.44;9.54;1;1).(7.52;8.44;8.44;8.99;0.9;0.9)) ((6.98;8.67;8.67;9.56;1;1).(7.83;8.67;8.67;9.12;0.9;0.9))Tn4 ((6.58;8.43;8.43;9.53;1;1).(7.5;8.43;8.43;8.98;0.9;0.9)) ((6.87;8.59;8.59;9.53;1;1), (7.73;8.59;8.59;9.06;0.9;0.9))

Tn5 ((6.66;8.5;8.5;9.61;1;1), (7.58;8.5;8.5;9.06;0.9;0.9)) ((6.89;8.63;8.63;9.56;1;1), (7.76;8.63;8.63;9.1;0.9;0.9))Tn6 ((6.65;8.48;8.48;9.58;1;1), (7.56;8.48;8.48;9.03;0.9;0.9)) ((6.75;8.49;8.49;9.47;1;1), (7.62;8.49;8.49;8.98;0.9;0.9))

Tn7 ((6.64;8.48;8.48;9.58;1;1), (7.56;8.48;8.48;9.03;0.9;0.9)) ((6.7;8.43;8.43;9.4;1;1), (7.57;8.43;8.43;8.91;0.9;0.9))Tn8 ((5.86;7.53;7.53;8.63;1;1), (6.7;7.53;7.53;8.08;0.9;0.9)) ((5.02;6.77;6.77;8.11;1;1), (5.9;6.77;6.77;7.44;0.9;0.9))



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services (Redman et al., 2013). US Federal Transit Administration

(2010)notes that the ridership is increased by 24.5% over one year

after the commuting time is reduced by 15 min each way betweencentral New York stations and outlying areas.

On the other hand, there are several attributes need to be im-

proved in order to increase the CS level for RTN in Istanbul.

Crowdedness and density of passengers is determined as one of

the attribute that needs to be improved. Adding new rail transit

line, increasing number of cars per trip are the two suggestions to

be made to reduce the crowdedness. Also, re-optimization of the

schedules should be considered, to reduce the crowdedness,

considering the changes in the crowdedness level. Besides, opti-

mization of the speed arrangement between stations is an im-portant factor that affects the crowdedness. Pucher et al. (2005)

suggest adding new bus lanes in Seoul, Korea aimed at improving

the speed of PT. Thus, putting new rail transit line(s) into services

will affect the speed and consequently the crowdedness level atstations. There are several new rail transit lines under construction

for Istanbulers (IUAS, 2013), i.e., M5Uskudar/Sancaktepe, M6Levent/Rumelihisarustu, M7Mecidiyekoy/Mahmutbey, inFig. 1. A

subsequent survey should be conducted, once the new rail transit

lines are putted into service, to analyze CS level. Moreover, Air-

conditioning is determined as another attribute that need to be

enhanced because air conditioning systems on vehicles are one of

the motivations to use PT (Beiro and Sarseld Cabral, 2007). Ac-

cording to IUAS, air-conditioning system fails during the days andit is not possible to x it while the trains are on the move.

Therefore, preventive maintenance of the air-conditioning system,

during the off times, is important to prevent fails. Another attri-

bute that causes low CS level is noise and vibration during the

journey. As in air-conditioning, preventive maintenance of the

trains and rail transit lanes are the key subjects in reducing noiseand vibration. One of the applied preventive maintenance activity

is the periodic and continuous inspection of the rail lanes. Re-duction in noise and vibration will provide a comfortable journey

to the passengers. Lastly, the phone service is another attribute

that resulted in low CS level. Training of the phone service per-

sonnel and increasing the number of personnel should improve CSlevel.

In summary, the attributes need to be improved are de-termined, and, for all lines different improvement suggestions are

proposed. The contributions of the paper to the literature are asfollows: (1) it proposes a novel CS evaluation approach for RTN of

Istanbul, by using survey study, statistical analysis and MADM. By

the integration of these three methods together, the CS levels can

be analyzed and evaluated in a healthy manner; (2) an integratednovel interval type-2 fuzzy MADM method is proposed based on

SERVQUAL and VIKOR to evaluate and improve CS in Istanbul RTN.Hence, the proposed MADM benets from the advantages of all

three methods. Also, the interval type-2 fuzzy sets and VIKOR in-

tegration reveals and solves ambiguity and uncertainty in a more

realistic way; (3) the proposed method provides directions for the

future investments that can be made; (4) the proposed method

can be generalized and applied to complex decision making pro-blems encountering inexact, indenite and subjective data or un-certain information and (5) it is aimed that, the proposed method

will be used in big and crowded cities' rail transit activities like

Istanbul, to evaluate and improve CS levels by policy and decision

makers.As aforementioned, IMM and IUAS have plans to quadruple the

length of the total RTN of Istanbul by the end of 2019. As a futuredirection, for new lines in Istanbul or for different countries` RTN,

this study can be taken as a reference point in terms of CS eva-luation and the determination of the most important and vital

attributes to improve.

Acknowledgment

The authors would like to express their gratitude to IUAS (Is-

tanbul Public Transportation Co.) for their understanding, support,and the data provided. Finally, the authors would like to thank the

two anonymous referees for their helpful comments and sug-

gested improvements.

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0,00

0,20

0,40

0,60

0,80

1,00V=0

V=0,1

V=0,2

V=0,3

V=0,4

V=0,5V=0,6

V=0,7

V=0,8

V=0,9

V=1

M1

M2

T1

T4

F1

Fig. 2. Sensitivity analyses ofQi values for each rail transit lines.

0

1

2

3

4

5

6

Rank

Majority of Attributes

M1M2

T1

T4

F1

Fig. 3. The rankings of rail transit lines.

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a multiattribute customer satisfaction evaluation approach for rail transit network a real case...

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