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Transportation Planning and Technology
Vol. 32, No. 2, April 2009, 167194

A suppressed demand analysis method of the transportation
disadvantaged in policy making
Yavuz Duvarcia* and Shoshi Mizokamiba5

Izmir Yuksek Teknoloji Enst., Sehir ve Bölge Pl., Gulbahce, Urla Izmir 35437, Turkey;
Department of Civil and Environmental Engineering, Kumamoto University, 2-39-1,
Kurokami, Kumamoto 860-8555, Japanb(Received 16 September 2007; final version received 2 March 2009)

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This paper proposes a method for estimating transportation supply
requirements when the suppressed demand of the transportation
disadvantaged (TD) can be calculated and added to existing demand
for travel. The underlying assumption is that the travel conditions ofthese TD groups must be equal to the ‘conventional’ demand, known as‘full release’. Utilising the modelling approach for TD, suppressed
demand analysis, diagnosis of difficulties and equity between conventional and disadvantaged groups were realised, while elaborating special
cases for the most vulnerable TD groups (such as elderly and disabled
persons) and simultaneously identifying areas of difficulty. From theearly virtual results, it is concluded that, for the full release of suppressed
trips (only a 5% increase), policy makers must be ready to face somefinancial burdens, requiring coordination of effort to both standardise
these TD groups and reduce the costs incurred by operators.
Keywords: suppressed travel demand; transportation disadvantage;
disabled and elderly persons

Introduction
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At present, there is continued growth in both the number of transportation
disadvantaged (TD) persons and the various forms and aspects ofdisadvantage faced by them. This is primarily due to demographic trends(an increasing number of elderly users, as well as disabled and young users indeveloping countries), suburbanisation and a de-emphasis on coordination
issues, still in their infancy. The essence of the problem is that elderly anddisabled persons will require more assistance (Howe 1992, Lucas 2006). Hineand Mitchell’s (2003) work summarises such efforts and points out policy
directives for future coordination and planning efforts, while also presenting
examples of prior applications that must be re-initiated. Many well-intended*Corresponding author. Email: yavuzduvarci@iyte.edu.tr
ISSN 0308-1060 print: ISSN 1029-0354 online# 2009 Taylor & Francis
DOI: 10.1080/03081060902861400
http://www.informaworld.com

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policies have been proposed, but in reality most have been hampered in
implementation by funding and coordination difficulties. Meanwhile,
continuing developments such as big retailers replacing smaller local shops,
increased distances to major local activities, centralisation of health services
and the removal of transit routes from low income districts are taking placeat an ever increasing rate (Pennycook et al. 2001, Lucas 2006). In order tofoster the coordination necessary for equity in accessibility, transportationand human services agencies need to collaborate during the planning process
and take into account all these social aspects (Grieco 2003, Hine and
Mitchell 2003). Successful transport policies must be sensitive to types ofpeople affected and differentiate accordingly (Heggie and Jones 1978). This
policy analysis can be fundamentally based upon identifying and separatingout TD and non-disadvantaged individuals (or households). Despite thegrowing literature and interest in this issue upon the recent call forgovernmental policy on matters of social exclusion in the UK, and as
identified in the 2004 Transport White Paper, there remain both methodological and conceptual struggles in tackling this issue in a comprehensive
manner even in developed countries. Multidimensionality in the definition of
TD prevails and complicates the frame of analysis for deriving travel
patterns in addition to the inherent measurement difficulties.
While locating the issue to an extent and enriching the discussion, moststudies have failed to define and address the issue of integrating suppressed
demand comprehensively into the modelling, which is necessary for aknowledge-based analysis of policies. In previous studies by Duvarci and
Yigitcanlar (2007), methodological issues of such integration were addressedto the extent that there is now at least a working model for TD travel
demand. The model of integration proposed therein was developed in orderto enable the formulation of policies sensitive to the needs of disadvantaged
persons. The measurability of suppressed demand, and that of disadvantaged groups in particular, remained unaddressed within the context of thatmodel and no clear method had been proposed to tackle this issue since
there had been no clear yardstick defined to measure it. In the new model
approach, the degree to which the disadvantages (the marks on the TD
yardstick) can be defined in both social and geographical terms has been
investigated; however, the levels of suppressed demand remain unexplored.
Defining disadvantage levels for each stage of the model and obtaining
assignment indicators is one thing, while defining the amount of suppressed
demand (and the release if all disadvantages are removed) is another.
Preceding the modelling steps, a clear review of travel demand types must be
undertaken. As Pendyala and Bhat (2006) stated, one of the key considerations in determining the efficacy of a model is to examine the ability of themodel to quantify induced or suppressed travel demand. Thus, the purpose ofthis paper is, first, to determine a method of calculation for quantifying
suppressed demand, or in other words, a measurement technique for theC:/3B2WIN/temp files/GTPT386312_S100.3d[x]6th April 2009 19:57:35

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inclusion of the TD population, or the socially excluded (SE) population, tointroduce a degree of quantification throughout the modelling steps, andthen, to find a measure of the impact from the hypothetical release of thissuppressed demand as the traffic assignment results of these TD groups,
especially those of the most vulnerable in this particular case. Full removalof all disadvantages for these TD groups means their being equated to theconventional population’s travel characteristics. This ‘completion’ rule
comprises the very backbone of the quantification method of the so-called‘suppressed demand difference’ in this study.
In the previous study, results from modelling showed that the TD
population produced an overall trip rate per person that was less than that of
the conventional population: 1.65 to that of 1.73. The present study
concludes that, if all the suppressed trips of TD were released, this number
will have risen to 1.78. Therefore this study takes as it an assumption thatshowing fewer trips is a sign of demand suppression. The TD population
was also overwhelmingly inclined to use public modes of travel, as was
expected, and this is also assumed to be a sign of suppression. The reasons
behind classifying these populations as TD, and why they produce fewer
trips, are extensively discussed in studies by Church et al. (2000), Pennycook
et al. (2001), Hine and Grieco (2003), Hine and Mitchell (2003) and Duvarci
and Yigitcanlar (2007).
To maintain equity, the hypothetical traffic impacts of the increased (orreleased) trips of the ‘once’ TD were observed and the additional costburdens on the local government providing transportation services as well as
the required infrastructure were determined, using a ‘what if ?’ approachbased upon previous modelling results. The realised results are only on thebasis of these increased productions, and remain hypothetical, but are still
informative and have the potential to be used as a decision support
mechanism by planners and traffic engineers. The simulation results tellus, first of all, that with the addition of the total release of the suppressed
trips (an increase of 5%), no serious stress on existing road capacity
appeared, with the qualification, however, of these results being very specificto the case city. Secondly, the results show that in the trip distribution stage,
the focus zone pairs of policy, and thus, the critical paths that the particulargroups under consideration (elderly and disabled persons) employ overwhelmingly could be identified with ease, along with the adjoining linkbased results concerning what existing problems and costs (or congestion)these groups may encounter with the release impact. Analysts will be able toobserve the special groups’ positions and define what special transportation
services and policies would be required in meeting this extra demand. Thus
policy makers can find means to alleviate the disadvantages, while still
following the Pareto optimality principle of not disturbing the present
situation of the remaining users.

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This methodology-driven study concerns only trip production and its
run-down implications up to the assignment stage, addressing operability
through a four-step model. The uncontaminated impact of trip production
suppressed demand release is also observed. Two assumptions to be stressed
here in the analytical framework are: (1) the Pareto optimality principle isapplied, not interfering with the demands of other populations; and (2) therequirement that the demand characteristics of the conventional population
be the ideal target for the TD population.
In the second section, the literature concerning the concepts of demandtypes in connection to the TD concept is briefly reviewed. In the third
section, the basic structure of the model for TD from the previous study
and its major findings are highlighted. In the fourth section, both the
suppressed demand calculation method and the method of integrating thiscalculation into the modelling for simulations of the net impacts are covered.
In the fifth section, the results from the simulation are analysed andevaluated as to their policy implications. Finally, the conclusions and areas
for further study are considered.
A review of the literature on transportation disadvantage and suppressed
demandWhile related to the TD concept, that of the SE population is a muchbroader term implying people or households that are not simply poor butthat have additionally lost the ability to both literally connect with many of
the jobs, services and facilities that they need to fully participate in society.
Accordingly, there are seven basic possible exclusion types: (a) physical;(b) geographical; (c) exclusion from facilities; (d) economic; (e) time-based
exclusion; (f) fear-based exclusion; and (g) space-based exclusion (Church
et al. 2000). The term SE is often used in place of TD in some literature but,in fact, their domains address related but separate issues (Figure 1). SE is a

Socially Excluded
Transport
Disadvantaged

Figure 1.

Cross impacting of social exclusion and transportation disadvantaged.

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term referring in general to socio-economic wellbeing, with this relationshipaffecting TD, and vice versa, while TD refers simply to the disadvantageousconditions of transport, or accessibility. Accessibility and transportation areof vital importance in maintaining contact with the rest of society, andtherefore, very closely linked to the concept of SE. Thus, the transportsystem itself has a role in creating barriers (Church et al. 2000, SEU 2003).
Hine and Grieco (2003) argue that the combination of poor accessibility
with low levels of mobility and low levels of sociability intensifies thisexclusion.
Because of the multidimensionality (accessibility, mobility, cost, convenience and access to information) of TD, difficulties arise in measurement
and levels of analysis. The present methods employed are far from being
mature. A few disjointed studies have attempted to measure levels of
exclusion. Research in the UK tended to adopt either a categorical approachor spatial approaches (Church et al. 2000) largely making use of GIS tools(PTAL or CAPITAL tools and ACCMAP, ABRA in those more advanced).
However, these were found to be inefficient (Grieco 2003). Particular groups
may not remain homogeneous throughout category analyses and other
underlying non-physical factors (such as mode reliability or the timevaluation of socio-demographic groups) may affect accessibility (Grieco
2003). Using the London Area Travel Survey done in 2001, the trip making
characteristics of elderly and disabled persons for four trip purposes using an
ordinal probit model technique were determined (Schmocker et al. 2005).
According to these results, retired people can make trips in a similar fashion
to those of the conventional population, but if disabilities intervene, overall
trip rates drop dramatically.
Recently, a consensus was achieved concerning these disjointed efforts
and requiring a comprehensive integration of these disadvantaged groups.
The issue should be of concern to many related institutions and thefollowing areas should be seen as priorities: the sustainability of accessible
transport; better demand forecasting methods; and the needs of TD persons(Suen and Mitchell 2000). In addition to the awareness in the UK
government, legislation in many other countries (including the USA,
Sweden, Canada, Australia, etc.) require that transportation services be
improved and made accessible to all members of the population, with routes
more accessible to health services and shopping centres as well as housing,
employing taxies with user-side subsidies, especially in assisting disabled
persons, and pedestrian infrastructure and travel information for people
with sensory, cognitive or linguistic impairments employing advanced
technologies (Suen and Mitchell 2000). For example, it was found that
low-floor buses increase the bus travel of TD groups (an inducement effect).
Special infrastructure may be required in order to aid disabled and elderlygroups, which may bring with it additional costs, and funding these will be
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heavy financial burdens. In order to relieve this funding obstacle, it is likelythat the money charged to those who benefit most from the system will be
specified for use in the improvement of public transport (PT), especially inemphasising the quality of service, and in improving service for those who
benefit less at present, or suffer from a lack of service (Newman and
Kenworthy 1999, Lucas 2006). There is currently a ‘local transport planning’
concept where equity between users is taken into consideration, and local
participation is involved in more areas than simple efficiency and congestion
concerns. The necessity for governmental assistance and intervention isstrongly emphasised, with policies of active intervention needed to repair theareas of exclusion once the measurement problems in modelling are solved(Grieco 2003, Mokhtarian et al. 2006). With the increasing numbers ofelderly and disabled populations, a gradual rise in the demand for demand
responsive, door-to-door, ITS (Intelligent Transportation Systems) transportation services, and community transport alternatives in-between private
and public modes are expected to replace private vehicles.
However, ascertaining the required amount of this additional infrastructure and its attendant costs depends, first of all, on determining the
suppressed demand of those TD groups. Although some research literatureexists on the measurability of induced demand using an elasticity approachas the ratio of the change in quantity of travel demanded over the change intravel supplied (Transtech Management Inc. and Bailly 2000, Cervero
2003a), less focus has been devoted to measuring the amount of suppressed
demand and its impact. In particular, to what extent overall conditions
should be improved remains an unknown issue. The elasticity technique canbe also used for calculating suppressed demand. Empirical impact studies
have been primarily confined to added lane miles on the amount and lengthof travel. Measuring the impact of induced or suppressed demand is alsodifficult due to the far reaching, complex and longer term impacts which are
hard to calculate and where the impacting factors and those impacted should
be clearly identified. The most easily forgone trips (suppressed trips) would
be trips for social or leisure purposes, or some for maintenance, which aretravel behaviours that are even harder to model. Most likely the suppressed
demand of TD groups would be of this form, and those that they can mosteasily forego rather than obligatory trips, such as those for work, although
their leisure activities and the number of trips are increasing (in the USA atleast) (Mokhtarian et al. 2006). If the public transportation service level TD
groups depend on is low, they are then more severely affected (Porter 2002,
Lucas 2006). It has also been observed that TD groups comprise a majorityof those ‘peak captives’ (Duvarci and Yigitcanlar 2007). As mentioned
previously, this is partly due to the fact that they either defer social andleisure trips which are less important to them, or strictly plan trips athousehold level, combining them with these obligatory trips into a one-end
vehicular trip. Family dependence in trip making is not only exhibited in theC:/3B2WIN/temp files/GTPT386312_S100.3d[x]6th April 2009 19:57:38

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economic sense but also in the mobility sense, which is verified in the
Srinavasan and Ferreira’s (2002) study using large household survey data
(1991) from Boston. Households without children tend to have fewer trips
overall. The presence of children, residential location and employment seemto affect modal choices within a household. For elderly people living insuburban environments, there are no other alternative travel modes and they
are forced to be ‘mode captives’ of the automobile after retiring (Davidse
2006). Elderly and disabled persons seem to be the most critical groups
among the disadvantaged population with as much as 48% of those withdisabilities and 53% of all elderly persons defined to be in the generalcategory of TD (Duvarci and Gur 2003).

Travel demand types
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Suppressed demand and induced demand are key subjects in the identification of the TD population and a heavy emphasis has been placed on
measurements of disadvantages in the last decade, especially in the studies ofCervero (2003a,b), Mokhtarian and Salomon (2001) from the USA, andLitman (2005) providing a vast amount of empirical data for discussion.
However, debates remain on specific definitions of demand types, primarily
those of automobile traffic, which still have measurability and integrationdifficulties in modelling demand. Thus, there is an urgent need to understandtravel patterns and the needs of the TD population and in parallel with this,the level of their suppressed demands in general.
The basic demand types are presented in correspondence with othertypes below and can be relevant in identifying inherent travel disadvantages,
and the nature of TD travel:. derived demand;. generated and Induced demand;. latent (referred to as ‘real’ demand); and. suppressed demand (along with ‘subverted’ demands).
Wider definitions are provided by Litman (2005). Of these, the concepts
related to suppressed demand will be discussed in depth in the next section.
Derived demand does not actually refer to a type of demand but to astate of analysis in demand modelling; from one extreme viewpoint, derived
demand is the ideal demand, free from the existing demand in reality. From
another viewpoint, according to that of descriptive modelling, the travel is‘derived’ if it is not made for the purpose of travel itself but pursuant toother activities (Mokhtarian and Salomon 2001). The definition ofgenerated demand is largely based on the assumption that the traffic
exhibits behaviour analogous to that of a gas rather than that of a fluid  ittends to expand if the network capacity is increased simultaneously (Litman

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2001). Thus for example, when new roads are opened, traffic fills themquickly, and the expected marginal utility of the new resource declines. On
the other hand, induced demand is a sort of layer placed on top of generated
demand. Through the changes made, more travel is attracted and the total
volume of trips is also increased, usually as a factor of land use impacts(Litman 2001, Cervero 2003a). Thus, the total generated demand includes
induced demand as a substantial part of its makeup.
New and longer trips quickly fill the increased capacity until thecongestion equilibrium is re-established; however, pursuing this equilibriumin the real world would not be cost-effective (Downs 1992). For example, thehuge suppressed demand for private cars in China does not seem satiable,
and building the enormous infrastructure required to satiate this demandcannot be justified by any means (Zhang and Hu 2003). Cervero (2003a)
criticises Hansen’s famous finding that each additional 10% infrastructure
spurs a 9% increase in traffic as ‘overstated’, because the increase must not
be totally due to the induced impact but rather to the release of the once
suppressed demand (or, the latent demand which was actually hidden and had
not come to light until transport infrastructure conditions were improved,
which he refers to as a ‘causality’ problem, and has yet to be well treated indemand estimation studies). He also relates the demand changes to the‘diverted’ trips as induced trips. However, some studies distinguish between
induced travel and diverted travel (Litman and Colman 2001, Noland andLem 2002). Mode choice especially (as a diverted demand) is affected by thephysical infrastructure available and the environmental characteristics suchas the existence of sidewalks and the topography (Rodriguez and Joo 2004).
Suppressed demand of transportation disadvantaged (TD) groups and its
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Suppressed demand can be linked to the induced demand concept. If theassumption that induced demand is the contrary side of suppressed demandis accepted, then this must be valid especially for the TD population,
whenever their travel conditions are improved. The suppressed demand of
TD groups is only released where conditions such as accessibility and easeof mobility, lowered travel costs, etc. are improved. Those with lower levelsof accessibility tend to plan and combine trips into one stop trips (Limanondand Niemeier 2004), which is also a form of demand suppression. But, somebelieve that induced demand is not simply a re-appearance of ‘deferred’
demand released at a later time, but that it is artificially created by trafficengineers and is beyond the impact of suppression (Newman and Kenworthy1999). But, even if increased capacity and improved infrastructure maketrips more attractive and cheaper, there must still be a ceiling point to thedemand, and this is what is actually meant by the derived or desired demand.
Suppressed demand can be alternatively defined as a form of oppression

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which is socio-spatially produced rather than naturally given. Thus, suchdisadvantaged individuals may tend to avoid travel if it becomes too
troublesome in regards to the compared net utility gain from the outcome(accomplishing the activity at the destination) versus the disutility of making
the trip, where quality of life depends to a large extent on ease of movement.
For example, if an individual wishes to go work by bicycle but is unable toand must go by motor vehicle simply because there are not enough facilities
for cycling, this means that their modal preference for the bicycle issuppressed. Thus, it is quite reasonable to think that suppression is primarily
based upon the existence of adequate physical facilities in order for the latentdemand to be fulfilled. Congestion may cause people to defer trips that are
not urgent, choose alternative destinations or modes or even forego
avoidable trips (Litman and Colman 2001). Many barriers were foundlimiting the social inclusion of excluded groups  limited travel choices (bothspatially and temporally); excessive access distances to PT services andvarious problems encountered on route; the time required to reachdestinations (as compared to going by car); poor service reliability (andthus, many trips cancelled or delayed); the limited availability of PT
information in suitable formats; and the cost of using PT. However, theimpacts and intensity of these barriers vary between population groups andthe time of day. Similarly, improved environments (perceived safety,
aesthetics of the road, etc.) and street design (traffic calming, rest facilities
and slope), the quality and existence of bicycle facilities along major routes
can all be cited as factors leading to an increase in bicycle trip rates. What
was once suppressed demand can turn out to be realised demand, improving
the level of equity, and thus, can help increase mobility with an increase in
the level of personal satisfaction.
One dimension of suppressed demand, and interestingly also that of
induced demand, is the number of ‘diverted’ trips that would be channelled
to usually undesired modes, times and/or paths (that is why this type could beclassified under suppressed demand). Another type is subverted demand,
meaning that the trips were actually made but the activity that was the objectof the trip was not accomplished because of late arrival or the absence of theactivity due to a lack of information (Grieco 2003). Subverted trips can also
likely be attributed to TD or SE groups. According to Grieco (2003),
1.4 million people missed appointments in one year in her UK study due toaccess difficulties. Subverted trips can be compensated for by more frequent,more direct and more reliable PT services.
From the literature review above, we can clearly see that the demand typeof the TD population is a type of suppressed demand, since they are notprovided with accessibility and transportation services to the extent that theconventional population is, and that this is due to personal or transportsystem based reasons, and the possible release of this suppressed demand
could be interpreted as ‘induced’ in one sense, and as finding their ‘derived

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demand’ in another, as method of analyses if the purpose is to equate themto the conventional population, thus conforming to the aforementioned
Pareto optimality rule. The basic relation set to induced demand is throughinducement impact as travel increases or the diverted traffic of added
infrastructure. Thus, considering a hypothetical scenario whereby means ofsome added capacity, technological support (such as ITS), special paratransit service and infrastructure, a sort of induced demand impact could be
artificially conceived as those groups could then take trips as far as
conventional people could and, thus, traffic impacts and other costs could
be observed through this analytical framework. These types of observations
are much more likely in a simulated environment based on the previous
TRANUS model prepared for TD groups than could possibly be observedin reality. The results of such a simulation would be informative and useful
as guidance in the policy-making stage. Also, measurability and ananalytical framework can be devised through such a scenario approach.
It is also intended herein to introduce a new concept called ‘suppressed
demand difference’ which is totally based on the unique cluster analysis
methodology of TD modelling. In the next section, addressing methodology,
we assume that the real (or latent) demand of the TD population is the ideal(or ‘derived’) demand, which is equal to the demand of the conventional
population, and a measure is devised showing to what extent the suppressed
demand of those TD groups could be released, a previous unknown. In
simple arithmetical terms, this idea can be stated symbolically as:
RTD DN DTD

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(1)

where, RTD (later denoted as Dij(rel) for the calculation of trip numbers in
Eq. (5)) is the demand difference to be released for the TD (Transport
Disadvantage) population, DN is the existing demand of the conventional
population but here assumed to be the actual latent demand of TD, and DTD
is the existing (suppressed) demand of the TD. If the transportation
conditions and accessibility of TD are restored, the released demand of
TD becomes a sort of ‘induced’ demand of the TD due to the improvement
impact.
Description of the modelling approach adopted for transportation
disadvantaged (TD)400

Case city and data: Aydin
Together with the usual transport network data included in the 2000 Census
for Turkey, a household survey was conducted of 326 randomly selectedhouseholds and interviews with a total of 932 persons. The sampling ratio of
this survey was 0.7 per cent. Questions related to individual household
members aimed to determine individual travel patterns in order to reveal
disadvantage-related information (Duvarci and Yigitcanlar 2007). The

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model was tested in the city of Aydin, which is near Izmir, and had apopulation of 135,365 in 2000 with a compact settlement form. Wealthiergroups reside in the areas surrounding the city centre, with the urban fringe
mainly home to low income groups in contrast to the situation in mostWestern cities.

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Brief findings from the previous model
The structure of the model that this study is based upon consisted of two
basic stages after processing the data: (a) determining the disadvantaged
population through cluster analysis and; (b) modelling the disadvantaged
and conventional populations and comparing the results. A brief summaryof the data variables used in the definition of TD are shown in Table 1 (thedetails of which can be examined in the respective study). These are basedlargely on attitudinal and perception data from the individuals surveyed in

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Table 1. Data variables used in the modelling for the TD.
Category

Category nameACCESS

Accessibility

Notes

Determine the number people with poor
accessibility level to the basic urban
amenities
DEPEND
Economic dependency Measure the economic dependency of thefamily members
EDU.FAM Education level
Indicates household level education statusthat reflects individual travel generation
INC.PER
Income level
Income per person
IMPED.MP Mode and peak
Represents combined effect of mode andimpedimentpeak captivity together with the emphasis on
the disabled
IMPED.PT Public transit
Indicates public transit conditions (i.e.
impediment
physical conditions of the bus stops,
service frequencies, number of transfers)
IMPED.CU Cumulative impediment Represents the cumulative effect of basic
impedance elements (i.e. travel time, cost anddistance to stop or car park)
COM.PUB Comfort level of
Measures passenger density and comfort
public transit
conditions of the public transit
SCH.TRIP Journey to school
Indicates travel conditions of students withvarious measures
VEH.AVA Motor vehicle
Determines the number people with noavailability
motor vehicle
COM.VEH Comfort level of private Private motor vehicle comfort level (i.e.
motor vehicle
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combined index values that can later be easily used as parameters, or aspolicy intervention domains. In addition, other more usual parameters areused solely in the modelling steps such as figuring travel costs. Eleven major
disadvantages (variables) were identified which constituted the criteria usedin the clustering process to split the sample population into the two groups.
The individuals with relatively low scores were placed in the disadvantaged,
and the ones with high scores to the advantaged clusters. Consequently, thenumber of the disadvantaged was 629 and the advantaged was 303. The
modelling approach required the separation of model runs of these two data
sets.
Cluster centre results point to the fact that disadvantages are largely due
to a lack of access to motor vehicles and to poverty. This analysis also
demonstrated that it was possible to determine zone clusters of disadvantage
through cluster analysis (Figure 2). Apparently, the most disadvantaged
zone is the eighth zone, which also has very low socio-economic status data
(Duvarci and Yigitcanlar 2007).
TRANUSTM, integrated land use and transport modelling software, wasemployed in the modelling of transport for Aydin. As for PT, only one mode
was available in the pilot study area, a contracted-out bus service that runs
on 14 routes. For trip generation, the best fitting variables were educational
level, income level and household economic dependency for the conventional population, using multiple regression analysis. For the disadvantaged,
the following variables were significant: vehicle comfort, comfort level of PT
and economic dependency (this being a common variable for both). The R2
value was as high as 0.78. The highest R2 value obtained for the
disadvantaged was 0.69. The overall average daily trip rate per person forthe conventional population was 1.73, compared to 1.65 for the disadvantaged. Trip production results by zone for both disadvantaged and nondisadvantaged groups are displayed in Table 2.
0.5
Disad. Scores (%)
average

0.45
0.4
0.35
% (Score) of
Disadvantage

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0.3
0.25
0.2
0.15
0.1
0.05
0
1

Figure 2.

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average
Disad. Scores (%)
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Aggregate disadvantage levels by zones (Duvarci and Gur 2003).

Modeltrip rate

1.81
1.75
1.87
1.94
1.88
1.74
2.02
1.23
1.54
2.12
1.4
1.51
1.72

Zones

1
2
3
4
5
6
7
8
9
10
11
12
Total

1.88
1.51
1.61
1.94
1.94
1.89
2.08
1.33
1.4
2.15
1.28
1.61
1.73

Surveytrip rate
12,261
11,3789107
10,136
13,477
15,35911,93813,046
9251
9683
9899
9830
135,365

Population
23,075
17,147
14,626
19,694
26,13229,09024,807
17,312
12,924
20,82812,71015,816234,162

Trip
production

Conventional population

1.72
1.77
1.14
1.95
1.99
1.71
2.02
1.24
1.48
1.73
1.64
1.4
1.64

Model
trip rate

Table 2. Trip rates and productions of conventional and disadvantaged groups.

1.93
1.59
1.17
1.88
2.3
1.49
1.9
1.31
1.3
1.94
1.39
1.51
1.65

Surveytrip rate
7802
9510
5657
7659
7067
11,3848661
12,042
5829
3289
5377
5990
90,267

Population

15,042
15,1116624
14,437
16,26816,985
16,49915,799
7601
6394
7452
9063
147,275

Trip
production

Disadvantaged population

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180

450

455

6th April 2009 19:57:41

Y. Duvarci and S. Mizokami

For the trip distribution stage, a singly constrained (production) gravity
model was used to determine the distribution (for a more detailed
explanation, see Duvarci and Yigitcanlar 2007). For the mode split stage,
employing a binomial utility approach, the utility function for the
disadvantaged could be explained solely by the combined impedimentvariable. The mode split preferences in favour of PT were 0.43 for the
disadvantaged and 0.37 for the conventional population. To ascertain the
final traffic results and performance determining indicator results for user
disutility levels, the assignments were run using TRANUS.
Method of accounting for the suppressed demand of transportation
disadvantaged (TD)

460

465

470

475

480

Observing the pure impacts of the ‘once-suppressed’ but now released
demand (trip rate increase only) can be handled in two basic steps: first,
calculating the demand surplus (difference) released by the TD and; second,
integrating this released demand to the mainstream model structure andobserving the impacts as the result of this assignment.

Method of calculating ‘suppressed demand difference’
It is no easy task to measure a hidden fact, not simply because of its not
being apparent, but also due to the uncertainties in definition anddelimitation of what is to be measured. This difficulty in handling
suppressed demand in four-step modelling has been stated because of thedifferences between the conditions for drawing up general trip making
behaviour based on socio-economic conditions (the derived, or ideal
demand) and the actual demand in actual trip making situations (theapparent demand) (Kitamura 1996). As mentioned earlier, the measurement
process requires a ‘yardstick’ with which these measurements are to betaken. Here, the calculation method will be different from the aforementioned elasticity measurements as simply put in Eq. (1). Since the sum of thedisadvantaged and the advantaged together make up normality, thesuppressed demand of the TD can be found on the basis of this ‘completion’assumption; suppressed demand is the ‘deviance’ of the disadvantaged fromthe normality in the existing situation by all means. Simply put, the subjectof this paper is to explore what impact this extra generated demand would
create on the existing transportation network and infrastructure. In thefollowing, the released ‘suppressed demand difference’ for Aydin city issimilar to the simple method as in the example shown above.

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181

Method of integrating suppressed demand into modelling to find model outputs
485

490

495

The study is limited to the impacts of observable trip rate increases, that is,to the additional released suppressed demand of the disadvantaged. Since
the previous modelling study used the singly constrained (Oi) trip distribution approach, only trip production will be introduced in the trip
distribution modelling, and their impacts to the assignment stage, specifying
no other value throughout the model stages, in order to carry out a pure
impact analysis of the increased trips. The integration process can be
explained in four basic steps based upon the previous modelling steps to
obtain the disadvantage population, its trip production and trip rates.
Calculating ‘Released’ production of the disadvantaged (Odisi (rel))disMultiply the population (/Pi for each zone) of the disadvantaged by theconventional populations’ trip production rates:
dis normOdis
i (rel) Pi :ti(2)

Calculating released trip distributions of the transportation disadvantaged(TD) (Tdis
ij (rel))
500

Previous attraction factors of the conventional population’s trip distribution
calibration is directly multiplied by the above result (Step 1) for released
production (/Odisi (rel)) (as if future production):
dis
norm
T dis
ij (rel) O i (rel):aij

505

510

(3)

where, anorm
d j :f (cij )=aj d j :f (cij ); which is calibrated for the conventional
ij
population. dj here means the total attractions by jth zones.
The reason for taking the conventional population’s calibrated attraction
factors, instead of the disadvantaged, is that the conventional values arealways the ideal target for TD. The previously defined conventionalparameter values are assumed constant for the new modelling with thereleased suppressed demands.
Running of all model steps on TRANUS (or other available software) as thetwo separate models, and finally getting the no-release assignment results(T(base)ijkl and the released demand assignment results T(rel)ijkl) forcomparison

515This requires, first, a run of the base year model, and then, a run of the
suppressed demand’s released trips, without changing any other parameterin the models.

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182

520

525

530

535

6th April 2009 19:57:42

Y. Duvarci and S. Mizokami

Running the reporting programmes of TRANUS for the analysis of theassignment results, the performance indicators, such as disutility, cost andservice levels
General indicator outputs are derived from the Reporting Programmes(IMPTRA and MATESP) to compare the basic results of the two models,
checking the appearance of additional volumes, costs, vehicle demand,
worsened level of service (LOS) conditions and disutilities. The results can becontrolled either from the display results from the TRANUS Assignments
Tabs, or from the numeric results from the reporting programmes.
Costs incurring to the operator (or local government) are of special
concern here. The results are discussed in the next section covering policy
implications. Finally, the meaning of these results for special TD groups,
such as elderly and disabled persons, falling under the disadvantaged
category can be analysed at one’s convenience. Through this analysis, thefollowing issues can be addressed: (a) the costs or additional burden (orfacilities) of TD released suppressed demand; (b) what costs and difficulties
TD groups may encounter as a result of the increased traffic; and (c) what
infrastructure improvement and facilities are required according to the needsof the TD population. The process can be shown briefly as steps in chart(Figure 3). In this chart, the released Demand Integration of Special TD
Groups (DSG) (to be explained later) is also shown.

Simulation results and comparison with the base ‘no-release’ case540

To obtain the impact of released suppressed demand, simulation results of
the released trips as of assignment results derived from the base model of the
Calculating “Released” Productions of the disadvantaged

The DSG integration (if required)
Summed up proportion of SG
populations (for each zone)(S)

Calculating Released Trip Distributions of the Disadvantaged
multiply

Selecting most signific. proportions(e.g. above 20%)

Determining the TRANUS’ Exogeneous Trip Distribution Factors
suppr. dema.differences(Sc)

Calculated DSG trips release
contrast
Only select OD pairs with SG above

Run of 4-step model as two separate models

Running of the Reporting Programs and Evaluation of the
Performance Indicators

Figure 3.

1 (or close to 1) for policy focus

Policy analysis

Steps of released demand calculation together with the JDED integration.

C:/3B2WIN/temp files/GTPT386312_S100.3d[x]6th April 2009 19:57:42

Transportation Planning and Technology

545

550

555

560

‘no-release’ case were evaluated on the TRANUS software. Both display
results and general (summary) performance indicators for the ‘release’ and‘no-release’ (base) cases were contrasted. The total suppressed trip release of
the disadvantaged from the previous section was 154,009 including intrazonal trips, this number was previously 147,123, which meant only anapproximately 5% increase from the suppressed situation before releaseoccurs. However, since the intra-zonal trips were not accounted for in theprevious modelling, these numbers became 103,930 and 101,317, respectively, which is a trivial increase of about 3% (Table 4). There are also theconstant external trips added to the base-year trips that pass through fiveexternal zones, which stay constant in all calculations in both the base andthe ‘release’ case. Finally, the net released trips were obtained by subtracting
the number of released trips with from the number of base year (or, ‘norelease’ case) trips (see Table 4). As shown in Table 3, we were not interested
in the negative sign results and, thus, they stayed out of our concerns, which
are marked by parentheses, meaning that literally no released trips occurred.
The most significant releases are the bold and underlined ones. These are tofunction as the policy concern zone pairs. Only the bold ones are determined
to be significant. The net release (difference) can be denoted as:dis
Dij(rel) T dis
ij (rel)T ij (base)

565

570

575

580

183

(4)

Finally, the results from the simulation and base-year results were compared,
and the basic differences between the two were evaluated across three basic
display indicators (from top to bottom: public and private mode equivalent
vehicles, the LOS and wait times, all with the same scaling) that showed
literally no significant difference between the base ‘release’ and ‘no-release’
cases (because of the present available capacity to accommodate an increase
traffic in the case of Aydin). There were only a few slight differences in the
LOS as released trips heightened congestion levels a bit, especially on
previously congested links (Figure 4).
Similarly, utilising the IMPTRA reporting programme of TRANUS,
numerical results for basic costs, disutility and mobility related indicators
could be obtained. The general results achieved, according to the simulations, are summarised in Table 4 and compared to the base-year results.
From these results, it can be generalised that the new case (release of
demand) imposes slightly higher cost burdens on both private and publicmodes but, at the same time brings higher revenues, too. A detailed
explanation of these findings is provided in the discussion section.
As mentioned earlier, results peculiar to special demand groups (SDG)
such as the elderly and disabled, and their required transport needs, policies
and the necessary investments could also be captured and approximated bysimply adjoining the population ratios into the trip distribution stage (seeFigure 3), which have been already addressed elsewhere (Duvarci and879774(105)1053706(348)
484264(221)
91629538
56489
433
6045
(16)
343228(115)
99218119359172(187)
420241(180)8234

12111518(306)
89264174
988839(149)
30336763
328591263
6861141455
5351090
554
661031965
77481404
13,03852654058(1207)a556421(135)

324212108(313)
34603776
316
36403423(217)
6561182526
3431141798
535763
227
17961598(198)
12611203(58)
25,94726333121
489
22232104(120)
15541026(528)

4

9881259270
9101467557
656591
(65)
6334
(29)
535436(100)10781031(47)
42048161
9775

527312(214)11121262
150
543873335271059
532

517591711(48)
525473
(52)
6334
(29)
53533(502)
661721061261722(539)
17,42631592653(506)
5561262706
54581
527
15801059(521)
19371581(356)

6

984886(97)
686456(230)
99436337
66172106
77481404
12,0539747(50)
556841286
586581(6)
7901059
269
24211318(1103)
988839
(149)

7

6334
(29)
9933
(66)
359172
(187)
7736
(41)
10,3679747(50)
10264(39)
54194
140
9753
(44)
8940
(49)
9163
(28)
5637
(19)

8

99436337
359172(187)
840722(119)
10,895527312(214)11121262
150
293387946851518833
968105486
4941259764
303244(59)754680(75)

9359172(187)
210120(90)7053

790312(478)
1112841(270)
54194140
158106(52)
89264174
494420(75)
303489186
656591(65)
686456(230)

1016811563(117)
12,714

26331249
19461683(263)
54348294
527459(68)
1211922(288)
12361175(61)
19722078108984886(97)
63228165803654(149)

11

15,42897312
215
19461683(263)
54736682
22652224(41)
21791713(466)
17302014
284
24272567141
656591
(65)
6334
(29)
803981177
8981083
185

12

10,131767456277

54745800

42415110

31243826

65866561

11,78513,125

11,05512,705

13,34210,28489919811

36915236

11,77611,836

14,50713,360

Sum

The values in parentheses are negative sign values meaning that the actual demand is already greater than the released (supposed to be more) demand. Thus,
they are not concerned due to the non-erratic behaviour in this study.
Note: External trips are not added to these figures.

a

dj

12

11

10

9

8

7

6

5

556412
(144)
5429
(25)
9751
(46)
1453756(697)
494433(61)
56252
196
32846
(282)
6337
(26)
9932
(66)
6625
(42)
420227(193)
4213

10531873
820

2

184

4

3

2

1

1

Table 3. Differences between the base and released trip distributions of the TD.

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Y. Duvarci and S. Mizokami

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Transportation Planning and Technology
585

590

595

185

Mizokami 2007). A summary of results showing the most significant shares(close to one or over; here those over 0.8 were highlighted) is presented inTable 5, where those in bold and underlined are the most significant ones,
while the grey-toned ones are under the proportionate value 1, but still
showing potential significance, and the others not having the critical numberof trips necessary for evaluation. Here, those that are most significant are
selected. Simply put, these ‘special demand’ traffic releases of significant
ratios (those having larger shares) to other zones can be observed on the
network paths, and also whether these groups encounter any impedimentcaused by the additions of the general suppressed demand releases of thoseTD groups, such as the increased volumes, generalised costs, LOS and lengthof wait time for PT services. Evaluations of these factors for significant zone
pairs, as well as related paths, are covered in the following section.
Discussion and policy implications

600

605

As a general outcome of this methodological approach, the total trip
demand of the TD, which was 147,123, increased to 154,009 trips, and these
extra 6886 trips can be seen as the latent or derived demand, which cameabout as a result of the aforementioned hypothetical release of the
suppressed demand. If this gain is added to the total number of trips
undertaken by the conventional population (6886234,244 241,130), anddivided by the total population (134,265), a new trip rate of 1.78 is obtained,
which is 0.05 higher than the previous value. A slight increase in trip
numbers has not actually brought about much more of burden on existing

Figure 4. Comparison of simulation (left side: release) and the base year (right side:
no-release) assignments display results.

Users costs (per capita
and total values)

Distances

General costs (private)
General costs (public)
Revenue (private)
Revenue (public)veh/km (mobility, private)veh/km(mobility, public)
Monetary cost (generation)
Passenger travel time
Passenger wait time
Passenger distance (private)
Passenger distance (public)
Passenger distance (pedestrian)Distance (average)(total values)
Cost (average)(total values)
Travel time (average)(total values)

14,593,131
11,506,755
4,865,501
19,590,252
112,991
59,96851,293,628
59,804
4,62 (E18)
225,983
820,284
147,589
1.61
162,06339.68
4,000,035
0.22
21,931

Base case(no-release)
14,829,517
11,759,606
5,017,459
20,014,686
115,951
61,592
52,343,596
71,1584,37 (E18)
231,903837,365
144,455
1.68
177,05442.91
4,515,849
0.20
20,623

Simulation (released
suppressed)

1.61
2.19
3.12
2.16
2.62
2.71
2.05
18.98
5.41
2.62
2.08
2.12
4.35
9.25
8.14
12.89
9.09
5.96

Percentage (%)
change186

Costs

Modal cost and benefit

Criteria

Table 4. Comparison of the base case and the simulation of released demand.

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Y. Duvarci and S. Mizokami

Waiting time (average)(total values)
Disutility (average)(total values)
Private trips (disadvantage)(total)Public trips (disadvantage)(total)Total trips (disadvantage)(total)Criteria
8.22
8,292 (13E)
356,2173, 59 (10E)
1171
50,23599,641
297,888
100,812348,123

Base case(no-release)
7.5
7,87 (13E)
294,127
3,1 (10E)
1196
51,318104,046
301,234
105,242
352,553

Simulation (released
suppressed)

Note: bold values show the important areas of interest directly related to the situation of those disabled and elderly.

Modal trip changes

Table 4 (Continued)

8.76
5.09
17.43
13.65
2.13
2.16
4.42
1.12
4.39
1.27

Percentage (%)
change

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Transportation Planning and Technology187

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Y. Duvarci and S. Mizokami

Table 5. The proportion of released demand of elderly and disabled (DED) to thereleased suppressed demand difference of the TD (for significant groups only).
1
1
2
3
4
5
6
7
8
9
10
11
12

610

615

620

625

630

2

3

0.32

4

5

6

7

8

9

2.60

0.55
0.43

0.91

2.60

1.69

0.78

10

11

12

0.89

2.13

0.86
2.98

1.12
0.50
0.51

1.16
0.58

0.42

0.41
0.58

4.29

1.77
4.02

1.12
0.29
1.44

roadway capacities, and so is not deemed a serious burden on local
government, as this is only 3% with the exclusion of the intra-zonal trips(most of which are expected to be non-vehicular). That is to say, almost 40%of the suppressed trips remain close to home. Even much of the inter-zonal
released demand still remains in the close vicinity of the home. These
conclusions bring back to mind the importance of walkways and related
street infrastructure in the removal of suppressed demand. Ironically, these
additional trips might not always mean additional costs and burdens to theexisting infrastructure but, instead, returns to the system in these cases where
the existing network and transport system capacity is underutilised, which isanother facet of cost. For example, revenues slightly higher than costs werealso observed (if no discount for the TD population was assumed) in thosecases where some increases in operational costs appeared. Thus, theevaluation of questions concerning additional costs or benefits brought
about by the release of suppressed demand requires careful interpretationand is case sensitive.
Once the critical traffic paths to be emphasised are clearly determined,
the next step is to entail these policy variables for possible scenarios in policy
making.
According to the special results for the elderly and disabled that arose in
the simulation, in examining the impacts by paths (one direction) on the
Paths property of TRANUS, summary observations of critical OD (OriginDestination) pairs, only those with a demand of elderly and disabled (DED)
ratio above two, are listed here: (25), (29), (43), (64), (711) and (712).
The zone pair (65) with a DED (Demand of Elderly and Disabled) ratio of
1.16 is also examined for its unique quality as shown in Figure 5. TRANUS
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Transportation Planning and Technology

189

shortest path rule from that of the least generalised cost to that of the most.
The general impedance characteristics of these zone pairs were, primarily,
that they usually have walk modes proposed by TRANUS as the best choicepaths (the first two or three alternatives). This was primarily due to thesepaths having extremely long and indirect transit lines which seemed to be an
impediment that was unable to be overcome. Even while the proposal ofthese walk modes can be interpreted as a positive development, the distance
between the zones may not allow for this option, especially for the elderly
and disabled. Secondly, of the various PT choices available at the beginningof trip, the majority of these are reduced to a choice of only one or two
options, and there may not be any direct routes to and from the destination
zone. Moreover, even in those cases where many PT alternatives exist, thedistance to either access points or egress points is often rather far. Thirdly,
many trips are interrupted by a lower LOS on some of their links (this may
have deteriorated slightly due to the impact of these released trips but had
been prevalent on some of the major through-traffic links previously, andtherefore, it may be better to re-design these transit service routes, or develop
other solutions to deal with the overflow), or delayed by long wait times,
which may be due to infrequent or unreliable bus services. In those cases
where such impediments occur, policy makers should employ case sensitive,
relevant policies to help improve the quality of service available to elderly
and disabled persons, adopting methods such as on-call paratransit services
equipped with the necessary technology and other infrastructure directed
towards their particular needs. In Figure 5, which shows a common attributein the simulation results, one of the most important factors causing a
disadvantaged situation was seen to be the extreme indirectness of theexisting lines of PT, and in these cases the path choice of a walk mode (evenFigure 5. Path examples (65 and 712) of extremely indirect PT routes as major
impedance.

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190
635

640

645

650

655

660

6th April 2009 19:57:45

Y. Duvarci and S. Mizokami

where the zone pairs were not so close to each other) becomes more
beneficial in terms of the generalised cost on the critical paths selected.
An evaluation of the data in Table 4 is required even though the results
do not tell us much about the differences between the base (no-release) caseand that of the simulated release. However, the results indicate the general
results of the entire city, not only those of the TD population. Looking at thecosts and benefits for operators, there are around 23% increases with theobserved increase of 5% in the general trip rate, so one can say that costs andrevenues (assuming ticket prices are not discounted for the TD groups)
almost even out. It is interesting to note, however, that there is an almost
5.4% decline in overall wait times, which is a positive indicator. Looking at
per user (average) results, although there is a fair increase in the amount of
distance travelled (4.3%) and the costs (8.1%) per person, there are evenmore dramatic decreases in the wait time at bus stops (8.8%) (due mostprobably to the increased frequency of service), disutility (17.4%) and travel
time (9.1%), and these results are beyond what was to be expected. In thecriteria for modal trip changes, there is a slight increase in the number ofprivate trips (2.15) by the disadvantaged, which can be interpreted as an
improvement in their mobility. Of even more benefit is the greater inclinationof the disadvantaged to use PT modes, most likely because of the improved
conditions in PT, and this allows for transportation policies which focus on
improvements of PT conditions, since it promises greater benefits fordisabled persons.
Accordingly, issues of what can be carried out in practice and how theseresults can be utilised are those of the policy making and planning process,taking into account coordination issues. Although these issues are beyond
the scope of this paper, there is a necessity to connect the outcomes of thisresearch to these policy making and coordination efforts. First of all, incompliance with new legislation that has been proposed in many countries,
the use of advanced technology show great promise in aiding thosevulnerable groups, such as the elderly and the disabled, after the level of
their demand and the paths they will use are known. Planners must becomeaware of the fact that only if appropriate policies are applied andimprovements, primarily technological aids and facility improvements, areundertaken for the purpose of providing the TD with service, will
disadvantaged groups release their real travel demands. This is therefore amethod of returning the deferred transportation service utility back to them.
Many on-going UK-based projects as PEDFLOW (Design of Pedestrian
Networks and Facilities especially modelling vehicle/pedestrian interaction),
PERMEATE (Pedestrian Activity Measurement in the Transport Environment) and studies exploring the role of transportation in social exclusion,and projects such as TELSCAN (TELematic Standards and Coordinationof Advanced Telematics systems in relation to elderly and disabledtravellers), a detailed study aimed at developing a design guidebook forC:/3B2WIN/temp files/GTPT386312_S100.3d[x]6th April 2009 19:57:45

Transportation Planning and Technology

665

670

675

680

685

690

695

700

191

ITS designers, all show some promise in helping to improve present travel
conditions, especially those of the elderly and the handicapped. In the
improvement of trips for the disadvantaged and the release of suppressed
demand, the possible role of ICTs has been shown to be of great worth(Church et al. 2000).
Information is the second essential parameter on the way to improving
travel conditions, especially for those who have hearing, visual or othersensual impairments as well as for conventional travellers, which is an issue
that can also be solved with ITS, and while it was not a major issue among
our sample of PT users, it would seem  as Grieco (2003) has put it  thatthose who are dependent on a service tend to know their way around it insome detail. But the use of some forms of ITS technology, while offeringmeans of ‘virtual mobility’ to assist TD groups in accomplishing activities
without actually travelling, does not appear to be a satisfactory solution, as
this could lead to more isolated individuals, and more suppression of real(latent) travel demand under the guise of this ‘virtual mobility’, even in thosecases where the activities are accomplished, this conflicts with the very ideaof helping the disadvantaged to release their suppressed demand. Above all,coordination efforts and the role of active participation for effective
feedback mechanisms concerning applications is a necessity that local
authorities must encourage. As Grieco (2003) puts it, unless the direct
participation of the SE is built into the design of transport research andtransport operations, the consequences of poor system design remain borneby the excluded and will continue to go largely unvoiced.
Funding issues are another point that needs to be resolved urgently forthese costly ITS-based applications, but these are, in a sense, a form ofcompensation which helps return to the TD groups their deferred rights bothin terms of the effective utilisation of transport facilities and the restorationof full accessibility. Local governments should be prepared to have available
funds and new organisational structures ready to accomplish this task ofrestoration, while not requiring as large a share of budget as previously
hypothesised (Grieco 2003).
In practice, efforts aimed at reducing travel by private vehicle, employing
non-monetary measures such restrictive car parking (thereby penalising theuse of automobiles), priorities in enabling PT (greater frequency oralternative modes and more coverage in urban areas) and pedestrianism
have been proposed as a means of overcoming the present inequities. Pricing
and other monetary measures focused on motorists and used as a means ofcompensation favouring disadvantaged groups are suggested as strong tools.
These funds can then be directed to the improvement of the TD population’s
travel conditions. There can be many policy measures of both supply anddemand types, and among these of special interest are: (1) additional transit
services with particular characteristics and improvements in services; and/or(2) on-call paratransit service operators with flexible routes and discounted

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705

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ticket prices for elderly, disabled and low income riders, which are heavily
utilised by the elderly and the disabled. These propositions may be rather
general but are presented as examples for possible future action. In previous
modelling studies carried out by Duvarci and Gur (2003), similar scenarios
showed good results in simulations, especially for those areas densely
populated by the elderly, disabled and poor. In these areas paratransit
services attracted most of the existing transit rideshare (as much as 90%).
Conclusion

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725

730

735

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745

Measuring suppressed demand has, in general, been a neglected subject.
Over and above the small number of elasticity approaches, other techniques
are required to take into consideration matters of transportation equity andthe particular circumstances of TD groups. Measuring the impacts brought
about by the hypothetical release of suppressed demand, and specifically ofthat of the TD population, through a simulation-based approach was the
subject of this study.
Under this assumption, a cluster-based modelling approach for TD
groups shows great promise in use as a yardstick for measuring the
suppressed travel demands of these groups. Using the previous results ofmodelling done by Duvarci and Yigitcanlar (2007), a metric was developed
based on a ‘what if ?’ approach in the measurement of both the suppressed
demand and the would-be impacts if that demand was released. The main
purpose of this was to answer the question of whether the impacts and costsof such additional trips are heavier than the local government’s and theexisting infrastructure’s capacity to handle them. In actuality, as wasobserved in the ‘release’ case scenario simulation run on TRANUS software,
there was none of the expected heavy burden on the system. Secondly, theaim was also to identify, using the example of elderly and disabled groups,
problems that can be diagnosed on the basis of a demand analysis of these
special TD groups  the paths that will see heavy use (with the released trips),
the attendant infrastructure needs on these paths and whether or not the
impediments they encounter can be overcome. Thus, transportation
authorities can make use of these results and deploy the appropriate
policies, infrastructure designs and technologies in the right places in orderto help to alleviate both general disadvantages and the particular disadvantages of the TD population, without worsening the present situation of
conventional groups through added costs and congestion. The methodology
proposed has worked successfully, and is in compliance with the previous
modelling approach, producing results that can give guidance for evaluationin the policy-making stage. This study is limited to trip production and theirimpacts through the modelling steps. Further studies should also focus on
more detailed interpretation of simulation results for the purpose of policy
analysis, or for future estimation of suppressed demand.

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