Case Study

Variant Concept of Transportation-Disadvantaged:
Evidence from Aydin, Turkey,
and Yamaga, Japan
Yavuz Duvarci1; Tan Yigitcanlar2; Yalcin Alver3; and Shoshi Mizokami4

Abstract: Transportation-disadvantaged groups have been defined in previous studies as those who are low income earners, are family
dependent, have limited access to private motor vehicles and public transport services, and are obliged to spend relatively more timeand money on their trips. Additionally the disabled, young, and elderly are commonly considered to be among the transportationdisadvantaged. Although generally this definition seems correct, it is not specific enough to become a universal definition that could
apply to all urban contexts. This paper investigates whether perceptions of travel difficulty vary as does the definition of transportationdisadvantaged in socioculturally different urban contexts. For this investigation, the writers undertake a series of statistical analyses in acase study of Yamaga, Japan, and compare the findings with a previous case study, in which the same methodology, hypothesis, andassumptions were applied to a culturally and demographically different settlement in Aydin, Turkey. After comparing the findings observedin Aydin with the statistical analysis results in Yamaga, this paper reveals that there can be no detailed, universal definition of thetransportation-disadvantaged. The writers conclude that the characteristics of the transportation-disadvantaged are not globally identical,
and policies and solutions that work in one locality may not have the same results in another sociocultural context. DOI: 10.1061/(ASCE)UP.1943-5444.0000044. © 2011 American Society of Civil Engineers.
CE Database subject headings: Travel demand; Turkey; Japan; Social factors; Transportation management; Comparative studies.
Author keywords: Transportation-disadvantaged; Travel demand models; Travel behavior; Cluster analysis.

Introduction
In previous research undertaken by Duvarci and Yigitcanlar (2007)
focusing on a case study in Aydin, Turkey, transportation-disadvantaged (TDA) groups were found to be primarily composed of people who were disabled, young, and elderly; who were low income
earners; and who had no access or limited access to private motor
vehicles, urban activities, and public transport services. Duvarci
and Yigitcanlar (2007) indicated that people who were spending
relatively more time and money for their trips might be classified
as TDA. However, some people who appear to be transportation
nondisadvantaged (TND), those who are spending less time andmoney because they have less mobility or have discomfort using
public transport services, might also be experiencing some sortof disadvantage. These uncertainties create obscurity, especiallywhen undertaking a comprehensive statistical analysis to determine
TDA groups in a locality. This problem also causes difficulty inchoosing a statistical method for analysis (i.e., regression, factor,
1

Izmir Institute of Technology, Dept. of City and Regional Planning,
Izmir, Turkey. E-mail: yavuzduvarci@iyte.edu.tr
2
Queensland Univ. of Technology, School of Urban Development, Brisbane, Australia (corresponding author). E-mail: tan.yigitcanlar@qut.edu.au
3
Ege Univ., Dept. of Civil Engineering, Izmir, Turkey. E-mail:yalcinalver@yahoo.com
4
Kumamoto Univ., Dept. of Civil and Environmental Engineering,
Kumamoto, Japan. E-mail: smizo@gpo.kumamoto-u.ac.jp
Note. This manuscript was submitted on December 25, 2008; approvedon June 7, 2010; published online on June 10, 2010. Discussion period
open until August 1, 2011; separate discussions must be submitted for individual papers. This paper is part of the Journal of Urban Planning andDevelopment, Vol. 137, No. 1, March 1, 2011. ©ASCE, ISSN 0733-9488/
2011/1-82–90/$25.00.

or cluster analysis) and in deciding whether the indicator variablesof TDA (e.g., accessibility and mobility) or the reason-based anddisadvantage indicator variables of TDA (e.g., disability and carownership) should be employed in the analysis.
The research reported in this paper is a follow-up to a previous
case study on TDA for Aydin, Turkey (Duvarci and Yigitcanlar2007). Adding to the previous study’s findings, this research
hypothesizes that the definition of TDA may change from one
locality to another because of the different sociocultural settingsof these localities. This is to say that perceptions, which are influenced by different social settings, can be different, and this may
have an immense effect on the definition of TDA. The aim of thispaper, therefore, is to test this hypothesis in two socioculturally
distinctive case studies, which provides an opportunity for a comparative study to reveal the sociocultural differences in determining
the characteristics of TDA.
Aydin, Turkey, was selected as the first case study for the comparative study, and the findings of this case were published in aprevious issue of the Journal of Urban Planning and Development(Duvarci and Yigitcanlar 2007). For the second case study,
Yamaga, Japan, was chosen because the Japanese cultural context
is one of the more divergent of the world in both sociocultural (i.e.,a developed country with rigid and strict obedience to sociocultural
norms) and demographic (i.e., a superaged society) terms, and also
it is also significantly different from the Turkish context (i.e., a rapidly developing country with a resilient and superyoung society).
As the primary statistical method of this comparative study, a cluster analysis technique was used to determine both the TDA and the
TND populations in the two case studies. Similar to the Aydin
study, the Yamaga study’s primary data for the analysis was collected through a household travel survey.

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Transportation-Disadvantaged
Many cities around the world, particularly those in North Americaand Australia, are plagued by car-oriented suburbanization, which
is characterized by a low-density sprawl-like development, big
retailers replacing corner shops, doubled-up distances to major
local activities, and removal of public transport lines from the poor
districts (Lucas 2006; Yigitcanlar et al. 2008). A developmentsegregated view of the urban space can have an even larger responsibility for the resulting appearance of the disadvantaged (Church
et al. 2000). A poor local public transport system has a role in creating barriers for TDA or so-called socially excluded groups thathave become more and more inaccessible (Hine and Mitchell
2003; Yigitcanlar et al. 2007). Hine and Grieco (2003) argue thata combination of poor accessibility with low levels of mobility andsociability intensifies TDA. In such circumstances, it is more
important to provide assistance to the most vulnerable groups—
the poor, elderly, and disabled—particularly in rapidly aging societies (Lucas 2006).
Differences in the age structure of developed and developing
countries (i.e., the aging population of developed countries versus
the younger population of developing countries) have a serious
impact on the determination and perception of TDA. If TDA is
not accurately determined, for example, elderly people can become
more vulnerable and feel disadvantaged, insecure, and less supported because of the additional physical disabilities that they
possess. Another important trend in aging societies, which affects
the overall view of TDA, is senior citizens’ driving behaviors (e.g.,
reduced driving skills and acuity problems). An increased safety
risk exists when older people are driving, particularly in places withhigh concentrations of elderly people (Davidse 2006). Yet, for mostof these people, no alternative travel modes are available unlessspecial paratransit options are provided. On the contrary, becauseof the low income level and high concentration of young people,
most of a developing country’s TDA groups are heavily dependent
on public transport. The public transport dependency of TDA is a
widely discussed topic in the literature in both developed anddeveloping country contexts (Litman and Colman 2001; Hineand Grieco 2003).
In recent years, a number of studies have been conducted
to determine a TDA population more accurately. For example,
Duvarci and Yigitcanlar (2007) sought the integration of TDA
analysis with mainstream travel demand models by employing
perceptional data, in which TDA could be neatly determinedthrough a cluster analysis focusing on the community travel conditions in Aydin, Turkey. Some UK-based studies used accessibility measures to determine TDA (Church et al. 2000). For example,
Schmocker et al. (2005) determined the trip-making characteristicsof the elderly and disabled for four key-trip purposes by analyzing
the 2001 London Area Travel Survey. Wu and Hine (2003) provided a classification for TDA by deprivation domains of income,
employment, health, disability, education, geographical access toservices, social environment, and housing. Church et al. (2000)
defined seven basic TDA types: physical; geographical; exclusionfrom facilities; and economic-, time-, fear-, and space-based
exclusions. Despite the growing interest in and literature about
the recent call for governmental policy about social exclusion,
such as in the UK [Social Exclusion Unit (SEU) 2001], bothmethodological and conceptual problems still exist in tackling
the TDA issue comprehensively.
Almost all the widely accepted parameters in measuring TDA
do not determine TDA populations accurately in every sociocultural context. For example, TDA may spend less time on andmoney for their trips because of their low mobility. Likewise,

travel is assumed to be an impediment, which is true especiallyfor work trips, and a cost to be reduced whenever possible(Salomon and Mokhtarian 1998; Ory and Mokhtarian 2005).
Similarly, increasing leisure activities and the time allowed forsuch trips may not be considered costly, thus, accurately modeling leisure trips becomes more critical, especially for TDA
groups. It is useful if TDA groups are determined for trip purposes (e.g., journey to work, school, shopping, and leisure).
Inequity also arises between people who own a car and those
who do not own or have access to a car. Yet the cost of driving
in some developing countries (i.e., in Turkey) is quite highbecause of higher fuel prices and ever-increasing congestion.
Owning a car does not necessarily make people TND in developing countries, which is not the case in the United States or inAustralia, where driving is more affordable with lower fuel prices,cheaper cars, and poor and relatively costly public transport services. Additionally, peak-hour congestion is often perceived as
the biggest problem, but the perception is relative and toleranceof the congestion changes from one culture to another and frommetropolitan areas to remote settlements. Income levels may also
influence difficulty perceptions. Moreover, even disabled orelderly people may feel nondisadvantaged, if they are provided
with easy accessibility and mobility options.
TDA is a dynamic and multidimensional issue involving physical, temporal, economic, spatial, and psychological dimensions(Hine and Mitchell 2001; Schonfelder and Axhausen 2003).
Because of the multidimensionality of TDA (e.g., accessibility,
mobility, cost, comfort, and convenience), serious measurement
and level of analysis difficulties arise (Yigitcanlar et al. 2007).
Whereas most of the recent research managed to locate the problem
and enrich the discussion and convergence successfully, they havefailed to determine TDA accurately. As Cervero and Mason (1998)
identified, travel behaviors may show a variety in different culturesand even from one TDA group to another because of the cultural
significance of the issue. Thus, determination of TDA can be highly
place-, culture-, and context-dependent.

Cross-Cultural Comparative Transportation
Disadvantage Analysis
This research performed a comparative, cross-cultural study of two
urban settlements (Aydin and Yamaga) and used the methodology
described in Duvarci and Yigitcanlar (2007). As Duvarci and
Yigitcanlar (2007) did, this research used a cluster analysis technique to clearly separate the population into two clusters (i.e.,
TDA and TND), on the basis of not only a single variable, butmany. For both of the case studies, first, a generic TDA group
was defined. Second, the distance or membership degrees of surveyed individuals, considering their demographic, socioeconomic,
and travel characteristics, to this group’s delimitation boundary,were measured. Third, as a result, a metric gauge was obtained
to determine TDA accurately. The results of both case studies’ findings are compared and discussed.

Case of Aydin, Turkey
The Aydin study was undertaken in 2005, and the findings of it
were reported in Duvarci and Yigitcanlar (2007). The generic
TDA definition used in the case study of Aydin was developedon the basis of the following factors: income, car availability, accessibility, demographic characteristics (i.e., age, gender, disability,
family size, and structure), and the existing transportation service

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Table 1. Variables of the Aydin Study (Duvarci and Yigitcanlar 2007, ASCE)
Category

Category nameNotes

Acc. Lev.
Com. Pub.
Com. Veh.
Cum. Imp.

Accessibility level
Comfort level of public transport
Comfort level of private motor vehicle
Cumulative impedimentEdu. Lev.
Fam. Dep.
Inc. Lev.
Mop. Imp.

Education level
Economic dependency
Income level
Mode and peak impediment

Ptr. Imp.
Sch. Trp.
Veh. Ava.
Trp. Fre.

Public transport impediment
Journey to school
Motor vehicle availability
Trip frequency

Determines accessibility of basic urban services and amenities
Determines passenger density and comfort conditions of public transport services
Determines private motor vehicle comfort levels
Determines the cumulative effect of basic travel impedance elements—This variable has two
subvariables of travel time impediment (Imp. Tim) and travel distance impediment (Imp. Dis)
Determines household education levels that reflect individual trip generation
Determines economic dependency levels of the family members that affect trip generation
Determines individual income levels that affect trip generation
Determines combined effects of mode and peak captivity together with an emphasison the disabled population
Determines public transport service, both quality and quantity, conditions
Determines travel quality and conditions to and from school for students
Determines the number of people with no access to motor vehicles
Determines the frequency of all trip types, i.e., commuting, education, leisure, health, and socialand facilities (i.e., frequency and quality). The variables employed
to determine TDA in the Aydin study are summarized in Table 1.
The model developed for the Aydin study was capable of precisely determining the trips of the TDA by multivariate modelingusing the knowledge derived from the differences between the
TDA and TND. The pilot study revealed that travel patterns could
be accurately determined through the steps of this model, theTDA concentrations could be geographically determined, andthe degrees and the types of disadvantages could be definedstraightforwardly (Duvarci and Yigitcanlar 2007).

Case of Yamaga, Japan
Yamaga is a town located within the Kumamoto prefecture, about
25 km north of Kumamoto on Kyushu Island, Japan, with a population of almost 60,000 people, a total area of about 300 km2 , anda population density of approximately 200 persons=km2. Currently, females make up 53% of the population, and the average
household size is 2.8 persons with a slow annual gradual decrease.
Yamaga is one of the regional cultural and natural attraction locations in the Kumamoto prefecture and is known for its famous traditional theaters and various hot spring baths. Yamaga was chosen
for this case study for several reasons. First, it is a content town
very close to Kumamoto University and has quite a large numberof elderly and typical TDA populations. Second, it was chosen for
its suitability for examining TDA groups in a locality with sociocultural characteristics different from those of Aydin. Last, Yamaga
was chosen for this case study because of the opportunity to use
the data from a recently conducted 2008 household travel
survey.
Generally in Japan, and particularly in Yamaga, people do not
suffer adversely from the negative impacts of transportation systems (e.g., poor public transport services, low accessibility levels,or inefficient transportation infrastructure). The primary problem isthe inadequate space allocation for pedestrians, cyclists, and evencars (e.g., narrowness or absence of streets, footpaths, bikeways,
and parking lots). Interviews with the residents of Yamaga revealed
that most of the population is happy with what is provided, andseems to be not bothered much by the aforementioned inefficiencies. This is probably attributable to cultural reasons (i.e., a noncontentious, respectful, and patient people), which is an indicationthat the TDA concept is perceived significantly differently in
Yamaga than in Aydin.

Japan is a superaging society, with those aged 65 and older composing 21% of the current population and projected to become 25%of the population in 2013; about one-third of the society will beolder than 75 in 2050 [National Institute of Population and Social
Security Research (NIPSSR) 2007]. In addition to having a superaging population, Yamaga has a strong economy and technologydriven way of life and does not have the transportation infrastructureand service problems observed in Aydin. Many public transportfacilities are provided for the elderly and disabled. In most places,
traffic is guided through intelligent transport systems, and public
transport always has priority over private motor vehicles Yamaga’s
primary transportation-related problem originates from the lack ofspace, which results in narrow roads without separate bike lanes.
However, cultural norms in Yamaga do not allow intolerance or
the showing of explicit anxiety, and because it is a patient, understanding, and respectful society, perceptions of transportation difficulty in Yamaga are significantly different from in Aydin. Suchstrong cultural qualities and peculiarities make Yamaga a very
interesting case study to compare with the previous study of Aydin.

Data
Secondary data was collected from the Transport Department,
Census Bureau, and Kumamoto University. The primary data for
the Yamaga case study was collected by using semistructured interviews and a household travel survey. Interviews were conductedwith experts and residents to ensure a broad understanding of
how TDA was generally perceived in Yamaga. A household travel
survey was also undertaken, as in the Aydin case, to collect data tocalculate a spatial analysis model to determine the TDA populations. Of 655 household surveys in Yamaga, 335 of them had
reliable responses that met the sampling ratio target of 1% for adisaggregate data analysis. About 45% of the surveyed population
were male and 55% were female. Nearly 6% of the population wasunder the age of 18, 65% was between 18 and 65, 19% was between 65 and 75, and 10% was older than 75. Survey data described
the typical profile of the Japanese demographic structure, indicating a superaging society (i.e., 29% of Yamaga residents are older
than 65) in which 75% of them had a valid driving license. Salient
travel characteristics for Yamaga in nine categories of trip purposes
and their differences by mode choice, which have a significant
impact on the definition of having a disadvantage, are includedin Table 2. In modal choice, car use, together with the mode of

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Table 2. Salient Travel Characteristics of Yamaga by Trip Types
CommutingBusinessDaily
shopping

Weeklyshopping

Doctor’ssurgery

Hospital

Socialvisits

LeisureSightseeing

21.2
9.67
74
5
1.2
3.7
15.7

25.5
15.6
87
5.3
0
0
7.5

11.8
4.87
78.9
9
0
0
12

35.25
18.5
81.6
13.5
0
3.3
1

13.47
6.07
74.6
10
1.2
0
13.7

59
30.4
81.2
15
0
2.5
0

23.1
11.78
79.2
9.1
1
2.4
8.3

52.2
29.52
80.1
12.5
0
5.9
0

78.38
63.47
74.8
16.9
2.9
4.7
0

Time (min)Distance (km)Mode (%) car driver
Mode (%) car passenger
Mode (%) taxi
Mode (%) public transport
Mode (%) walking andcyclingcar passengers, were dominant in Yamaga; public transport and taxi
modes were negligible for such a small town, and not surprisingly,
walking and cycling modes were quite popular. The use of the public transport and walking and cycling modes seemed to indicate
variations in different trip purposes. Interestingly, the public transport mode was the preferred mode for both commuting and socialrecreational activities, whereas it was not the case for business,
shopping, or health-related trips. Similarly, there was a large difference observed between daily and weekly shopping trips for thewalking and cycling modes, although walking and cycling were
not the preferred modes for leisure and sightseeing trips. Travel
time also fluctuated by the purpose of the trip.
Surveyed household size was around 2.2 persons, of which only
seven-year-olds and older were surveyed. Trip rates for each day of
the week were observed as: Monday 1.25, Tuesday 1.20, Wednesday 1.25, Thursday 1.23, Friday 1.25, Saturday 1.32, and Sunday
1.22. The overall distance traveled and the travel time averages of
all trip purposes were 21.1 km and 35.2 min, respectively, yet they
were not weighted with the trip frequencies for each trip purpose.
The following number of individual observations were evaluatedfor each trip purpose: 81 business trips (81=335 ¼ 24% of all trips),
106 commuting trips (31%), 162 doctor’s surgery trips (48%), 87
hospital trips (26%), 182 daily shopping trips (54%), 155 weekly
shopping trips (46%), 102 leisure trips (30%), 152 social visit trips(45%), and 98 sightseeing trips (29%). Shopping and social andrecreational trips constituted for more than half (61%) of all trips(Fig. 1).

of a different group (Ripley 1999; Hauser et al. 2000). Cluster
analyses are used to divide the population on the basis of the nearest
neighbor rule. All variables and value scales are assumed to haveequal weights in the clustering process and all variables are
assumed to be scaled so that the downward values represent
TDA and the upward values represent TND. Cluster center values
indicate the total central value of all values measured, in Euclidian
terms, per each cluster for all variables and concerning all values
assumed in the clustering process. Thus, the distance between thetwo cluster values shows the severity of disparity between theclusters and the severity of having a disadvantage. This method
uses K-means type clustering.
The Aydin study enabled a clear definition of two distinct
clusters by K-means cluster analysis with positive sign direction
assumptions of variables. The Yamaga study, by using the same
method, determined whether the same technique and assumptions
were applicable in a culturally different setting. Thus, the same typeof data used in the Aydin study was collected and clustered for the
Yamaga study, and the results were analyzed to determine whether
the same type of clustering, K-means, yielded a clearly defined
TDA. Additionally, different types of trip purposes were also
compared.
The clustering method was analyzed with SPSS software (SPSS
Statistics 19.0.0 2010) with no threshold values introduced. Prior
to the cluster analysis, all variables were converted to rate values
between 0 and 1 in a value standardization process by using thesigmoid function for noncategorical data. Calculations to determine
the usual conversion function for variables werez ¼ ðxi  xmin Þ=ðxmin  xmax Þ

Methodology
Because the Yamaga study aims to examine whether the findings of
the Aydin study are applicable elsewhere and whether the structureof the TDA definition is variant from one culture to another, theYamaga study adopts the same methodology as the Aydin study,
which is a sophisticated clustering technique. Cluster analysis isa statistical technique that is used for grouping similar cases(i.e., TDA and TND). Clustering algorithms are methods to divide
a set of observations into groups so that the members of the samegroup are more similar to one another than they are to the members

z ¼ 0:01

where z = the standardized value between 0 and 1; xi = any observation value to be standardized; and, xmin  xmax = the difference
between minimum and maximum values in the data set of thevariable.
Some categorical or Likert scale value standardizations (e.g., carownership, mode captivity, trip frequency, satisfaction, and comfort-safety variables) do not require a sigmoid function but a directconversion from a categorical value assignment. A conversion
example of a weekly trip frequency is as follows:ðfor 0 can not be usedÞ if t ðtrip rateÞ ¼ 0ðcategory 4Þ

z ¼ 0:33

if t ¼ 1 trip in a weekðcategory 3Þ

z ¼ 0:67

if t ¼ 2 or 3 trips in a weekðcategory 2Þ

z¼1

if t ¼ at least 1 trip everydayðcategory 1Þ

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Fig. 1. Percentages for major trip purposes in Yamaga

Different from the others, the mode captivity index value
determination is prepared by considering all trip purposes rather
than calculating each trip purpose separately. An example is asfollows:z ¼ up =uk
where up = the choice of public modes (e.g., public transport, andwalking and cycling options combined) for all purposes of a person’s weekly trips; and uk = the total of all modes used for all trip
purposes.
The positive utility sign assumption setting is a key step before
the clustering process commences. Therefore, in a sound, multivariate clustering procedure, it is important to decide which variables
represent a positive meaning when their observation values increase, and which others represent the negative. For the positive
direction, negative variables should be converted to positive simply
by subtracting the values from 1. In the Yamaga study, the directions for each variable were accepted as defined in the Aydin study.
Before the clustering process, as an important step, 335 observations were analyzed for each trip purposes. Only those having
trips clearly for the purpose concerned (e.g., 106 observations were
for commuting trips) were considered for one cluster analysis, thenother cluster analyses were conducted consecutively for the othertrip purposes (e.g., sightseeing trips).

Cluster analysis accommodates a number of variables to identify
the disadvantaged population, and in a comparative study, using thesame variables is important. The Yamaga study, therefore, usedeight of the 12 variables of the Aydin study in determining
TDA, which is necessary to determine whether different local
and cultural settings made a difference in identifying TDA populations (Table 3). However, as the literature suggests, criteria orvariables used in one place may not necessarily be applicableelsewhere because of the specific sociocultural and economic conditions of each community and location. Therefore, the sociodemographic indicators of the Aydin and Yamaga studies were notassumed to be variables in measuring disadvantages; they wereconsidered as the dimensions of TDA rather than the core factors.
Only eight variables were used to compare both case studies. TDA
was defined as a multifaceted term in the Aydin study; that is, TDA
can be determined by entering simultaneous input of various measurable criteria in a cluster analysis model. The subsequent section
details the Yamaga case study and the study methodology. For
more information about the Aydin case study, see Duvarci and
Yigitcanlar (2007).
In the cluster center results, those downward values (i.e., those
approaching 0) indicated disadvantage. For example, calculations
for the values of family dependence included the total number ofolder (i.e., those older than 65) and younger (i.e., those younger
than 7) people. As the number of family members from these
two age groups increased, indicating a clear disadvantage, it would
be considered an upward value for these indexes. Therefore, thevalue needed to be converted to upward utility values (i.e., those
approaching 1) by subtracting the value from 1. The standardizationof values conversion was necessary for a sound clustering process.
The calculations were for six variables; those approaching a valueof 1 indicated a nondisadvantage, and those approaching 0 indicated a disadvantage. The remaining variables of comfort- andsatisfaction-related variable results were already upward utility
values; therefore, there was no need for a conversion.
As shown in the cluster center results of the Aydin study inTable 4, the variable cluster center values were in accordance with
their utility sign assumption settings: low values appear in the left
column, and high values appear in the right column in a scale bar of
0–1. Thus, in the Yamaga study, similar assumptions for eachvariable are made as follows:

Table 3. Variables of the Aydin and Yamaga studies
Variables of the Aydin study

Variables of the Yamaga study

Notes

Fam. Dep.
Veh. Ava.
Mop. Imp.
Ptr. Imp.
Trp. Fre.
Cum. Imp. (Imp. Tim.)

Family dependence
Car availability
Mode captivity
Satisfaction
Trip rate
Time

Cum. Imp. (Imp. Dis.)

Distance

Com. Pub. and Com. Veh.

ComfortAydin and Yamaga studies employ the same family-dependence variables
Aydin and Yamaga studies employ the same car-availability variables
Aydin and Yamaga studies employ the same mode-captivity variables
Aydin and Yamaga studies employ the same trip-satisfaction variables
Aydin and Yamaga studies employ the same trip-rate variables
Travel-time impediment subvariable of Aydin’s cumulative-impediment variable andYamaga’s travel-time variable are equivalent variables
Travel-distance impediment subvariable of Aydin’s cumulative-impediment variable
and Yamaga’s travel-distance variable are equivalent variables
As the Comfort variable of the Yamaga study concerns both public transport Com.
Pub. and private motor vehicle Com. Veh. comfort levels, both Aydin and Yamaga
studies employ the same travel comfort variables
A variable indicating accessibility levels was not employed in the Yamaga study
A variable indicating education levels was not accommodated in the Yamaga study
A variable indicating income levels was not accommodated in the Yamaga study
A variable indicating school trip levels was not accommodated in the Yamaga study

Acc. Lev.
Edu. Lev.
Inc. Lev.
Sch. Trp.

————

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• Family dependence: Family dependence data showed a downward value for the Yamaga residents by approaching 0, indicating disadvantage or TDA concentration. As Fujii et al. (1999)
stated, elderly and young people potentially create a mobility
dependence on the other members of the family, and a lower
level of mobility. Therefore, the variable values for family
dependence must be subtracted from 1 to convert them into
upward values indicating nondisadvantage.
zi ¼ 1  ½ðno þ ny Þ=nh 
zi ¼ 1  xsi

xsi ¼ 0:16;

if xi ¼ 1

ðsurvey data value for having a car eachÞ

xsi ¼ 0:5;

if xi ¼ 2

ðsurvey data value for having a car in the familyÞ

xsi ¼ 0:83;

if xi ¼ 3

ðsurvey data value for not having a carÞwhere xsi = the scaled data value; and xi = the raw survey data
(i.e., the categorical value).
• Time: Travel time is generally accepted as an impediment factor,
although not necessarily for leisure trips. Therefore, the value
obtained from the raw survey data was subtracted from 1 to be converted into an upward value. Open-ended values were converted aszi ¼ 1  xsi

xsi ¼ ðxi  1Þ=ð90  1Þðxmax ¼ 90 minÞ• Distance: Similar to travel time, distance traveled is also considered to be an impediment, but in some cases, it is also anindicator of being nondisadvantaged. This causes a problem
in defining the direction sign for trip distances. Hence, obtained
data results were subtracted from 1. Open-end values were converted aszi ¼ 1  xsi

where zi = the scaled and sign-converted value for the ith person; no = the number of elderly (i.e., older than 65 yr old);ny = the number of young (i.e., younger than 7 yr old); andnh = the household size.
• Car availability: Those who do not have access to a private
motor vehicle are most likely disadvantaged. Therefore, it isexpected that the first cluster’s center values would be greaterthan the second cluster’s center results. Categorical data values
are converted asxsi ¼ ðxi  1Þ=ð50  1Þ

• Trip rate: The number of trips or average trip frequency
throughout the week did not need a positive sign conversion,
because it already had higher values. Categorical data values
were converted asxsi ¼ 0:01

ðinstead of 0Þ if xi ¼ 0

xsi ¼ 0:33

if xi ¼ 2

xsi ¼ 0:67

if xi ¼ 3

xsi ¼ 1

if xi > 3

ðno tripÞ

• Satisfaction: General transportation system satisfaction perceived before commencing travel, such as convenience, reliability, and system quality, required conversion. Categorical data
values were converted asðxmax ¼ 50 kmÞ

xsi ¼ 0:0834

if xi ¼ 1

ðhighly dissatisfiedÞ

• Mode captivity: Mode captivity values did not need conversion
because they already had values close to 1.

xsi ¼ 0:251

if xi ¼ 2

xsi ¼ 0:417

if xi ¼ 3

xsi ¼ 0:583

if xi ¼ 4

xsi ¼ 0:75

if xi ¼ 5

xsi ¼ 0:917

if xi ¼ 6

ðhighly satisfiedÞ

zi ¼ ðnp =na Þ
where np = the total of all public modes chosen by trip purposes;
and na = the number of all modes chosen. If private modes werechosen for all trip purposes, a value of 0.01 was assigned insteadof 0 for the ease of calculation.
Table 4. Variable Cluster Center Values of the Aydin Study (Duvarci and
Yigitcanlar 2007, ASCE)
Cluster’s center values

Acc. Lev.
Com. Pub.
Com. Veh.
Cum. Imp.
Fam. Dep.
Edu. Lev.
Inc. Lev.
Mop. Imp.
Ptr. Imp.
Sch. Trp.
Veh. Ava.
Trp. Fre.

1

2

45.88
68.17
37.59
86:51
58.53
36.68
9.43
73.40
95.89
63.78
6:37
29:69

50:48
68:47
38:41
85.05
64:81
43:41
18:63
73:94
96:07
69:70
54:73
41:11

where xsi values = the mean values for each category between
0 and 1.
• Comfort: The travel comfort perception usually refers to the
comfort and quality levels of in-vehicle and travel conditions.
Similar to the transportation system satisfaction, travel comfort
requires conversion. The standardization process of the values
for comfort is identical to the transportation system satisfaction’s process.
In the Yamaga study, four basic trip purposes, each having amajor (e.g., hospital trips) and a minor (e.g., doctor’s surgery trips)
activity, were analyzed: work-related trips consisted of commuting
and business trips; health-related trips consisted of doctor’s surgery
and hospital trips; shopping-related trips consisted of daily andweekly shopping trips; and recreational trips consisted of leisure,social visit, and sightseeing trips.
The Aydin study accommodated 12 variables (see Table 1)
whereas the Yamaga study employed only eight variables to determine TDA populations. All the variables used in the Yamaga study
were identical to the equivalent variables in the Aydin study, andonly these eight variables were used for the comparison. For example, the Family Dependence variable in the Yamaga study wassame as the Fam. Dep. variable in the Aydin study. Similarly
the Car Availability variable in Yamaga was same as the Veh.
Ava. in Aydin, and so on. The travel time impediment subvariable

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in the Aydin study’s cumulative impediment variable and the
Yamaga study’s travel time variable were equivalent variables.
In the same way, the travel distance impediment subvariable of
the Aydin study’s cumulative impediment variable and the Yamaga
study’s travel distance variable were equivalent variables. Only the
Comfort variable of the Yamaga study, because it includes bothpublic transport and private motor vehicle comfort dimensions,
was equivalent to the two variables of the Aydin study, Com.
Pub. and Com. Veh. Four of the variables of the Aydin study were
not adopted by the Yamaga study, Acc. Lev., Edu. Lev., Inc. Lev.,
and Sch. Trp. for the reasons explained previously (see Table 3).
Variables of the analysis worked efficiently to determine the disadvantaged cluster. For example, the interdependence of household
members (i.e., Fam. Dep. in the Yamaga study and Family Dependence in the Aydin study) to each other attributable to their demographic status (i.e., those who have children, the elderly, and thedisabled) was very critical in organizing daily trips. The daily travel
pattern of members of a family became severely dependent on eachother, especially children depending on their parents not only froman economic but also from a mobility angle. This was clearly verified in the Srinavasan and Ferreira study (2002) which scrutinized atransport survey analysis of almost 4,000 households in Boston.
According to Srinavasan and Ferreira, children and the numberof workers in a household did affect the mode choice.
The generalized cost was the most important determinant for
TDA and was usually considered as a key factor with the distance(i.e., accessibility) to a car park for car users or to a bus stop for
public transport users. According to a 2007 National Center forTransit Research report, huge differences are evident in distanceaccording to the differences in urban size, ethnicity, income, andcar ownership, which are key to determine TDA groups. For example, fewer than 40% of Americans have access to public transport
services within a quarter-mile walk from home, making a significant impact on their modal choice. The time cost is generally thesummation of the access to a stop or a car park whereas walking,the waiting time at a bus stop, and travel duration or access time to a
destination. In the Aydin study, the generalized cost Cum. Imp. was
not an effective divider between the two clusters, nor was mode andpeak captivity with an emphasis on the disabled or the elderly.
Public transport and peak captivity may be added to disadvantage
variables because one’s dependency on a single mode of transportation and travel time affects travel quality. Peak-hour congestion
adversely affects both public transport users and car drivers but notequally (Downs 1992). Personal disabilities and weaknesses are themagnifiers of the level of disadvantages of those peoples’ travel
experiences rather than separate factors, per se. The demographic
dimension also adds to this as the number of disabled and elderly
people is increasing throughout almost all nations (Blaser 1996;
Deakin 2003).
When the results of the clustering analysis of the Aydin study
were examined (see Table 4), it was evident that the disadvantaged
cluster center values were less than the nondisadvantaged cluster’s,with an exception for the cumulative impediment variable Cum.
Imp. showing the generalized travel costs for individuals. Therefore, contrary to what was hypothesized, a TDA group is notnecessarily disadvantaged in all categories. It was for only threevariables that significant differences were observed: vehicle availability Veh. Ava., income Inc. Lev, and education level Edu. Lev.
For the remaining variables, the difference between the two cluster
center values was negligible, and as hypothesized, the disadvantaged cluster had lower center values, which shows the robustnessof the methodology.

Comparison of the Aydin and Yamaga Case Study
Findings
The cluster center results of the Aydin study indicated that thesecond cluster center values compared to the first cluster becamealmost always greater, with the one exception of the cumulative
impediment variable (see Table 4). Car availability, income, accessibility, and education levels played an especially crucial role in theformation of two distinct clusters (Cluster 1 and Cluster 2). That is
to say, there was a clear distinction between these two clusters,
except what was obtained from the obligatory trips (i.e., journey
to work and school) in the Aydin case.
Contrary to the case of Aydin, the Yamaga case findings provided very different cluster center results, which is an unclear distinction between the two clusters. However, in Yamaga, if the utility
directions for each variable were differently hypothesized, then the
results may have provided two distinct clusters. But, under either
circumstance, profiles of the TDA would be different for Aydin and
Table 5. Yamaga Work-Related Trips Cluster Center Results
Business trip clusters

Family dependence
Car availability
Time
Distance
Mode captivity
Trip rate
Satisfaction
ComfortCommuting trip clusters

1

2

1

2

0:76
0:77
0.83
0.85
0:96
0:65
0:47
0:60

0:43
0:60
0:92
0:95
0:70
0:82
0:29
0:79

0.71
0:77
0:60
0:67
0:88
0.92
0:50
0:65

0:72
0:63
0:91
0:94
0:73
0:94
0:17
0:24

Table 6. Yamaga Shopping-Related Trips Cluster Center Results
Local shopping trip
clusters

Family dependence
Car availability
Time
Distance
Mode captivity
Trip rate
Satisfaction
ComfortWeekly shopping trip
clusters

1

2

1

2

0:39
0:42
0:90
0:96
0:16
0:75
0.34
0:72

0:64
0:75
0.89
0.89
0:93
0.74
0:39
0.70

0.60
0:47
0.67
0.76
0:12
0:45
0.57
0:64

0.60
0:74
0:72
0:78
0:93
0:54
0.57
0:55

Table 7. Yamaga Health-Related Trips Cluster Center Results
Doctor surgery trip clusters

Family dependence
Car availability
Time
Distance
Mode captivity
Trip rate
Satisfaction
Comfort

Hospital trip clusters

1

2

1

2

0:38
0:41
0:90
0:93
0:13
0:48
0:29
0:71

0:62
0:75
0.86
0.85
0:92
0.42
0:38
0.62

0:34
0:59
0.51
0.48
0:86
0:33
0:49
0:49

0:81
0:78
0:56
0:54
0.79
0:14
0:38
0.39

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Table 8. Yamaga Leisure-Related Trips Cluster Center Results
Leisure trip
clusters

Family dependence
Car availability
Time
Distance
Mode captivity
Trip rate
Satisfaction
ComfortSocial-visit trip
clusters

Sightseeing trip
clusters

1

2

1

2

1

2

0:64
0:74
0.65
0:69
0:91
0.39
0:52
0:57

0:50
0:43
0:74
0:81
0:18
0:40
0:68
0:73

0:47
0:42
0.83
0:84
0:15
0.40
0:71
0:68

0:65
0:75
0:85
0.83
0:93
0:45
0.65
0.61

0.63
0:73
0:59
0.91
0:93
0:45
0.57
0:55

0:65
0:54
0.56
0:94
0:14
0:31
0:67
0:68

Yamaga because of their contradictory sociocultural contexts.
When the findings listed in Tables 5–8 were analyzed, sometimes
Cluster 1 and sometimes Cluster 2 behaved like TDA depending on
the particular variable’s cluster center results. In these tables, bold
figures indicated higher values than the other cluster’s values, andunderlined figures indicated distinctly separated values. The behavioral shift between the two clusters was a polar shift, which creates
ambiguity and does not clearly explicate distinctly separated clusters (i.e., TDA and TND). Even when the obligatory trips (i.e.,journey to work and school) of the Yamaga study were comparedwith those from the Aydin study, no resemblance between the two
could be observed.
The following observations and findings from the Aydin andYamaga case studies are worth mentioning:• In daily shopping and work-related trips, especially in thecluster’s center results of mode captivity and car availability
variables, vast differences were observed. A polar shift occurred
between the two clusters (i.e., higher values appeared in thesecond cluster).
• Those who were dependent on public modes were found to be
more disadvantaged in work, shopping, and school trips. However, disadvantages could vary by trip purposes. For example,
disadvantage from mode captivity appeared in the first cluster
for work-related trips, whereas the same kind of disadvantage
appeared in the second cluster for shopping-related trips.
• The mode captivity variable was dominant in distinguishing two
clusters, but many times, polar shifting of the clusters wasnecessary.
• The satisfaction variable was the indicator in which vast differences were measured in work-related trips.
• For health-related trips, equivocally family dependence and caravailability variables were the primary reasons for the large
differences.
• Family dependence requires scrutinized trip planning at thehousehold level, and played a crucial role in timely access tohealth facilities. Access to a car was considered a better option
for urgent health-related trips.
• In social and leisure trips, mode captivity played a key role.
Polar shifting of the clusters was necessary for social visits
and leisure trips when using satisfaction, comfort, and mode
activity variables.
• Variables, such as trip rate, in hospital, and sightseeing trips, andtime and distance in commuting trips, were not significant
dividers but only showed discernible differences between thetwo clusters.
Aydin’s clustering was only calculated for work trips and hence,
only compared to the work-related trips of the Yamaga study. The
cluster center values for the eight variables for both Aydin andYamaga show large variations (see Tables 4 and 5). The mode

captivity for the Yamaga study seemed to be an especially important divider between TDA and TND, whereas this was not the casefor the Aydin study. In the Aydin study, the comfort variables ofCom. Pub. and Com. Veh. and their TDA cluster center values
did not differ clearly from TND, whereas this observation was
opposite for the Yamaga study. There was not much difference
in all impediment variables (i.e., Cum. Imp., Mop. Imp., andPtr. Imp.) between the two clusters for the Aydin study, whereas
there were considerable differences for time and distance, mode
captivity, and satisfaction in the Yamaga study. The clusteringresults of car availability showed similar significant differences
between the two clusters in both Aydin and Yamaga.
Contrary to Aydin, in Yamaga, people feel more disadvantagedby driving a car than by using a form of public transport, primarily
because public modes are more convenient for work-related trips in
Yamaga. Generally passengers in Yamaga find public modes muchsafer, less costly, and more comfortable. However, not having
access to a car in health-related and shopping trips was a disadvantage. In weekly shopping trips, there was a clear distinction between
the two clusters.
The results of the research reported in this paper reveal that the
hypothesis was verified, as the clustering findings in the Yamaga
study were quite different than the findings in the Aydin study. In
contrast to Yamaga, in Aydin, two clusters (TDA and TND) were
determined discretely. The findings for the Yamaga study did notindicate two significantly separated clusters, and thus, a clear determination of a TDA group was not possible despite the use of thesame variables in both studies. TDA seemed so variable, hence, it
could not be said that TDA could clearly be determined in every
sociocultural context by using the same variables and methodology
because the observed TDA definitions needed to be local and casespecific as proved in this cross-cultural comparative study.

Conclusion
The literature indicates that it is not possible to develop and implement policies to solve acute problems of disadvantage, unless TDA
groups are clearly determined. This paper, therefore, examined astatistical model used to determine TDA in Aydin in trying to determine TDA in the different sociocultural environment of Yamaga.
Clustering results of the Yamaga study did not yield a distinctly
separated TDA and TND cluster structure, as was the case in Aydin.
Additionally, the variations of different trip purposes for Yamaga
were investigated, but no clear conclusions could be drawn fromthis investigation. The research found that the definition of TDA
was quite different in Yamaga than it was in Aydin. The resultsof the comparative study confirmed that perceptions under the
influence of dissimilar sociocultural settings could be diverse,
and may affect the methods and variables accommodated to determine TDA in different localities. It was very difficult to define TDA
clearly by using the exact methodology in every sociocultural context. This research also proved Cervero and Mason’s (1998) claimthat characteristics of transportation disadvantage are not globally
identical, and policies and solutions that work in one locality
may not show the same results in another sociocultural context.
Characteristics of TDA are not globally identical, and tailoredpolicies and solutions are necessary for different socioculturalcontexts. However, the methodology developed in this paper on
the basis of cluster analysis was found to be a suitable method
to distinctively divide the population into TDA and TND, of course,
if appropriate variables were carefully selected for each socioculturally different case.

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The research findings indicated that technicians and policymakers must be aware of the sociocultural differences when determining TDA and developing policies to overcome disadvantages.
Therefore, it is essential to define a generic, universal set of variables and criteria for determining TDA globally, albeit in its sociocultural nature. This may help the standardization of a crosscultural generic TDA definition. Developing a broad, generaldefinition without culture-sensitive parameters of TDA would be
beneficial to local authorities as a customizable template for local
needs in which to integrate local sociocultural parameters. Suchcustomized TDA models would be useful in supporting urban
and transport planning and development, in which TDA-integrated
policy-making is critical to provide equity in the provision of public
transport infrastructure and services. In other words, local governments and transport authorities would benefit greatly from TDAsensitive models with embedded local sociocultural aspects andby deploying the most relevant policy measures for the community.
Therefore, further research is currently being conducted by thewriters to develop generic variables to determine broad TDA communities in different sociocultural contexts. At the same time, thewriters acknowledge that to specifically pinpoint TDA clusters,case-specific variables need to be defined for each locality under
scrutiny. Another case study in Brisbane, Australia, is being investigated to test the effectiveness of generic as well as locally sensitive variables. The writers are also aware of the importance ofimproving the statistical methodology for determining TDA
accurately by considering particularly the inclusion of a factor
analysis technique to the methodology.

References
Blaser, A. (1996). “A brilliant future with disabilities.” Futurist, 30(5),40–42.
Cervero, R., and Mason, J., eds. (1998). Conf. on transportation in developing countries, Univ. of California Transportation Center and the
Office of the President, Univ. of California, Berkeley, CA.
Church, A., Frost, M., and Sullivan, K. (2000). “Transport and social
exclusion in London.” Transp. Policy, 7(3), 195–205.
Davidse, R. (2006). “Older drivers and ADAS.” IATSS Res., 30(1),
6–20.
Deakin, E. (2003). “Trends and policy choices.” ACCESS, 23(1),
12–15.
Downs, A. (1992). Still stuck in traffic coping with peak hour traffic
congestion, Brookings Institution, Washington, DC.

Duvarci, Y., and Yigitcanlar, T. (2007). “Integrated modeling approach forthe transportation disadvantaged.” J. Urban Plann. Dev., 133(3),188–200.
Fujii, S., Kitamura, R., and Kishizawa, K. (1999). “Analysis of individuals’
joint-activity engagement using a model system of activity-travel
behavior and time use.” Transp. Res. Rec., 1676, 99–0755.
Hauser, T., Scherer, W., and Smith, B. (2000). Signal system data mining,
Univ. of Virginia Press, Charlottesville, VA.
Hine, J., and Grieco, M. (2003).“Scatters and clusters in time and space.”
Transp. J., 10, 299–306.
Hine, J., and Mitchell, F. (2001). “Better for everyone? Travel experiences
and transport exclusion.” Urban Stud., 38(2), 319–332.
Hine, J., and Mitchell, F. (2003). Transport disadvantage and social
exclusion, Ashgate, London.
Litman, T., and Colman, S. (2001). “Generated traffic.” ITE J., 71(4),38–47.
Lucas, K. (2006). “Providing transport for social inclusion within framework for environmental justice in UK.” Transp. Res. Part A, 40,
801–809.
National Institute of Population and Social Security Research (NIPSSR).
(2007). Population projections for Japan 2001–2050. NIPSSR. Tokyo.
Ory, D., and Mokhtarian, P. (2005). “When is getting there half the fun?”Transp. Res. Part A, 39, 97–123.
Ripley, B. (1999). Pattern recognition and neural networks. Cambridge
Univ. Press, London.
Salomon, I., and Mokhtarian, P. (1998). “What happens when mobilityinclined market segments face accessibility enhancing policies?”
Transp. Res. Part D, 3(3), 129–140.
Schmocker, J., Quddus, M., Noland, R., and Bell, M. (2005). “Estimating
trip generation of elderly and disabled people.” Transp. Res. Rec., 1924,
9–18.
Schonfelder, S., and Axhausen, K. (2003). “Activity spaces.” Transp.
Policy, 10, 273–286.
Social Exclusion Unit (SEU). (2001). Preventing social exclusion, Cabinet
Office, London.
SPSS Statistics 19.0.0 [Computer software]. Chicago, IBM.
Srinavasan, S., and Ferreira, J. (2002). “Travel behavior at the household
level.” Transp. Res. Part D, 7, 225–242.
Wu, B., and Hine, J. (2003). “A PTAL approach to measuring changes inbus service accessibility.” Transp. Policy, 10, 307–320.
Yigitcanlar, T., Fabian, L., and Coiacetto, E. (2008). “Challenges to urban
transport sustainability and smart transport in a tourist city.” Open
Transp. J., 2, 19–36.
Yigitcanlar, T., Sipe, N., Evans, R., and Pitot, M. (2007). “A GIS-based land
use and public transport accessibility indexing model,” Aust. Planner,
44(3), 30–37.

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