Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

WHAT IF THE SUPPRESSED TRAVEL DEMANDS OF THE TRANSPORTDISADVANTAGED WERE RELEASED: RESULTS OF A SIMULATION APPROACH
Yavuz DUVARCIAssistant Professor
Dept. of Civil and Env. Engineering
Kumamoto University
2-39-1, Kurokami, Kumamoto
860-8555 Japan
Fax: +81-96-342-3507
E-mail: yduvarci@gmail.com

Shoshi MIZOKAMI
Professor
Dept. of Civil and Env. Engineering
Kumamoto University
2-39-1, Kurokami, Kumamoto
860-8555 Japan
Fax: +81-96-342-3507
E-mail: smizo@gpo.kumamoto-u.ac.jp

Abstract: Modeling for the transport disadvantaged (TD) is relatively new subject since the2000’s. The study aimed to discuss the simulation results of what the required transportationneeds would be when also presumed suppressed demand of the TD are added. The underlying
assumption is that the travel conditions of those TD groups must be equated to the “normal”
demand, called full release. Based on the modeling approach for the TD, this task of equity
could be realized elaborating special case of the elderly and disabled groups with someinteresting results such as slightly increased costs, traffic and congestion, knowing also theirlocations. As of early virtual results, it is concluded that, for full release of suppressed trips(about 5%), local governments must be ready for extra financial burdens, which require acoordination effort both to standardize the TD and to reduce incurring costs on the operators.
Key Words: Suppressed Travel Demand, Transportation Disadvantaged, Disabled(Handicapped) and Elderly
1. INTRODUCTION
There is an increasing rate both in the number of those transportation disadvantaged (TD) andthe disadvantage levels in various forms and aspects, primarily due to the demographic trends(increasing rate of elderly and handicapped and young in the developing countries), theautomobilization (viz. especially in the developing countries such as China) and, less focus on
the coordination effort yet to be awakened. The reasons of being TD can be found in thestudies by Duvarci and Yigitcanlar (2007), Hine and Mitchell (2003), Hine and Grieco (2003),
Pennycook et al. (2001) and Church et al. (2000) extensively. What really matter is that theelderly and disabled (E&D) will require more assistance (Lucas, 2006). Hine and Mitchell(2003) extensively summarizes such efforts and points to policy direction for future
coordination and planning, giving examples from the past that must start again. For success,transport policies must be sensitive to person type affected, and differentiate accordingly(Heggie and Jones, 1978). The analysis can fundamentally be on the basis of transport
disadvantaged and advantaged person (or, household) type distinction. Despite the growing
literature and interest upon the recent call of governmental policy on social exclusion matterin UK, and identified by the 2004 Transport White Paper, there is still both methodologicaland conceptual struggling in tackling the issue in comprehensive manner.
As Pendyala and Bhat (2006) stated, “one of the key considerations in determining theefficacy of a model is to examine the ability of the model to quantify induced or suppressed
travel demand”. Most studies in the literature, while locating the issue to some level andenriching discussion, failed in defining and addressing to integrate the suppressed demands
comprehensively into the travel demand modeling, which is necessary for knowledge-base1433

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

analysis of policies. In the previous study by Duvarci and Yigitcanlar (2007), themethodological issues of such integration were addressed to the extent that, at least, there is
now a working model, yet with limited data. The proposed model integration here isdeveloped to enable formulating disadvantage sensitive policies in the examples of the E&D.
Though the degree to which disadvantages is known both in social and geographical terms,
levels of suppressed demand were not yet investigated in the mentioned study. Thus, thespecial purpose of this study is, first, to determine a calculation method to quantify suppressed
demand, a measurement technique for inclusion of the TD, or socially excluded (SE), tointroduce a measurability through the modeling steps, and then, to find a measurability of
impacts of the hypothetical release of suppressed demand as the traffic assignment results of
those TD groups, with the special emphasis on the E&D. Full removal of all disadvantages of
the TD means being equal to the normal population’s travel characteristics. This assumption
comprises the very backbone of the method, so-called “suppressed demand difference”.
In the previous study, the model results showed that the TD population produced overall lesstrip rate per person than the normal population; 1.65 and 1.73 respectively. This study here
concludes that, if all suppressed trips of the TD were released, it could have been 1.78 in total.
Therefore, from now on, having fewer trips is assumed as a sign of suppression. The results ofthis study are virtual, yet very informative which can be used as a decision tool by planners
and traffic engineers. The simulation results, first, inform that, with the addition of total
release of the suppressed trips (5%), there actually appeared no serious stress on the existing
capacity of the roads, with the results being very specific to the case city. Second, succeeding
to the trip distribution stage, the focus zone-pairs of policy, and thus, the critical paths that theE&D overwhelmingly use could be identified, with the adjoining link-based results of what
existing problems and costs (or, congestion) these groups may encounter with the release
impact. The methodology-driven study concerns only the trip productions and its run-down
implications up to assignment stage, addressing the operability of the scenario approachthrough the modeling steps. Some assumptions to be stressed here for the analytical frameare; (1) adopting the Pareto optimality principle, which means not to intervene with demandsof those advantaged and of normal population. (2) requiring that the demand characteristics ofnormal population be the ideal target for the TD population.
In the second section, briefly the literature on the concepts of demand types in interrelation to
the TD concept will be introduced. In the third section, the basic structure of the modeling for
the TD from the previous paper study, and the major findings will be highlighted withemphasis on the peculiar results on the E&D. In the fourth section, both the suppressed
demand calculation method and the method of integrating this calculation into the modelingto see the impacts will be shown. In the fifth section, the simulation results were discussed
and evaluated for the probable policy implications. Finally, conclusions are drawn.

2. LITERATURE REVIEW ON THE TRANSPORTATION DISADVANTAGED AND
SUPPRESSED DEMAND
2.1 Social Exclusion and the Transportation Disadvantaged
Related to the TD concept, SE is a much broader term. “[S]ome people or households are notjust poor but are 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, seven basic
exclusion types were defined: physical, geographical, exclusion from facilities, economic,
time-based exclusion, fear-based exclusion and space-based exclusion (Church et al., 2000).

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Though the term SE is many times used replacing, TD refers rather to the disadvantageousconditions of traveling (or, accessibility), while SE referring rather to a socio-economic
wellness. Accessibility and transportation has vital importance in keeping contact with the restof the society, therefore, very closely linked to the concept of TD. Thus, the transport systemitself has a crucial role in creating barriers (SEU, 2003; Church et al. 2000). Hine and Grieco(2003) argues that combination of poor accessibility with low levels of mobility and low
levels of sociability intensifies the exclusion.
Because of the multidimensionality (basically accessibility, mobility, cost, convenience, andaccess to information) of the TD, there arise measurement and level of analysis difficulties.
Mostly UK based studies focused on measuring the accessibility and exclusion levels making
largely use of GIS tools but criticized to be inefficient (Grieco, 2003) and not matured(Church et al., 2000). Using the London Area Travel Survey (2001), trip making
characteristics of elderly and disabled for four trip purposes using ordinal probit modeltechnique were determined (Schmocker et al., 2005). According to this study, retired people
can make normal trips, but if disabilities intervene, trip rates reduce dramatically. Besides the
UK government’s awareness, after all, many other countries’ legislation (Swedish, Canadian,Australian, etc.) required that transportation services be improved and made accessible by all
members of population (Suen and Mitchell, 2000). It was found that low-floor buses increase
bus travels of the TD groups. Special infrastructure may be required aiding especially thosedisabled and elderly groups, yet might mean additional cost, and funding is the biggest
obstacle at which local authorities may be unwilling to if heavy financial burdens accrue.
Most plausibly money charged from those who most benefit from the system is to be used forthe improvement of public transport, especially on the quality of service delivery to improvethose who benefit less, or suffer (Lucas, 2006; Newman and Kenworthy, 1999). The necessity
for active policies and governmental intervention are emphasized under the recent “local
transport planning” concept for repairing the exclusion (Grieco, 2003; Mokhtarian et al.,2006). For the increasing rate of E&D populations, a gradual rise in the demand for
demand-responsive, ITS equipped transportation services, community transport alternatives
in-between private and public mode is expected to replace private car.
However, knowing required amount of such additional infrastructure and the costs depends,
first of all, on the determination of the amount of the suppressed demand of those TD groups.
Despite some research on the measurability of induced demand using elasticity approach(Cervero, 2003a), less focus is devoted to measuring the amount and the impacts ofsuppressed demand. Usually the empirical impact studies have been constrained to the addedlane miles on the travel amount and length. Measuring the impacts of induced or suppressed
demand is difficult due to their far-fetched, complex and longer term impacts hard to calculate,where the impacting factors and the impacted should be clearly identified. The most easily
forgone trips (suppressed trips) would be of social or leisure purpose trips, or somemaintenance trips, which are the travel behaviors even hard to model. Most probably the TD
groups’ suppressed demand would be from social and leisure activities that they most easily
avoid, although leisure activities and trips are increasing in the U.S. (Mokhtarian et al., 2006).
The TD is more prone to depend on public modes; if the service level of public transportation
is low, they are then more severely affected (Porter, 2002); the TD groups are either mostly
the “peak captives” (Duvarci and Yigitcanlar, 2007); they either defer or subvert social andleisure trips which are less important to them, but strictly plan trips at household levelcombining with the compulsory (as work) trips into one-stop trip. Family dependence intrip-making is not only in the economic sense but also in the mobility sense, which is verified
in the Srinavasan and Ferreira’s study (2002) in Boston. Even for the elderly people living in1435

Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

suburban environments, there are not alternative travel modes and they are forced to be “modecaptives” of car (Davidse, 2006). Especially, the E&D people seemed the most critical groups
among other disadvantaged groups as high as 48% of all disabled and 53% of all elderly
under the general category of TD (Duvarci and Gur, 2003). Thus, these groups of the TD
deserve more attention in the policy analyses among others.
2. 2 Travel Demand Types
Suppressed demand and induced demand are key subjects in the identification of the TD andmeasurement of the disadvantages, on which a heavy emphasis was placed in the last decade,by especially the studies of Cervero (2003b), Mokhtarian and Salomon (2001) from the U.S.
and Litman (2005) providing fairly vast empirical material to discuss. However, the debates
have remained on the definitions of demand types, and primarily the car traffic, which yethave measurability obscurities in demand models. Thus, there is an urgent need to understandtravel patterns and suppressed demands of those TD. Below are the basic demand types
presented in correspondence to suppressed demand and can be relevant in identifying thetravel disadvantages, and the nature of their travels:zzzGenerated and Induced demand
Latent (referred to as “real” demand)
Suppressed demand (with the “subverted” demands)

Wider definitions were provided in VTPI’s website by Litman (2005). Generated demand
definition is largely based on the assumption that the traffic is analogous to the behavior ofgas; it tends to expand if the network capacity is increased simultaneously (Litman, 2001).
When new roads are opened, the traffic quickly fills them and the expected marginal utility of
the new facility declines. On the other hand, induced demand is a sort of topping onto thegenerated one: By the changes made, more travel is attracted and the total volume of trips isalso increased usually derived from land use impacts (Litman, 2001; Cervero, 2003a). Thus,
total generated demand includes the induced demand as a substantial part of it. Cervero(2003a) criticizes the Hansen’s famous finding that each added 10% infrastructure spurs 9%
traffic as “overstated” because the mentioned 9% increase must not totally (but partially) be
due to the induce impact but to the release of the once suppressed demand that had not been
come true until the conditions of transport infrastructure are improved. However, pursuing theinducement equilibrium in reality would not be cost-effective. For example, huge suppressed
demand for private car in China does not seem satiable, and building enormous infrastructure
required to satiate this demand cannot be justified at all. Especially mode choice is affected by
the physical infrastructure available such as existence of sidewalk and topography (Rodriguez
and Joo, 2004). Thus, the demand of the TD is clearly the suppressed type since they are notprovided accessibility and transportation service due to various reasons. The possible releaseof this suppression could be interpreted as if the “induce” impact, as a way of analysis inequating them to normal population.

3. DESCRIPTION OF THE MODELING APPROACH ADOPTED FOR THE TD
3.1 The Data and Case City: Aydin
Together with the usual transport network data including the 2000 census data, a household
survey was conducted with 326 randomly selected households, with 932 persons interviewed
and the sampling ratio of 0.7 per cent. Questions related to individual household members aim
to determine individual travel patterns to reveal disadvantage-related information (see Table

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

1) (Duvarci and Yigitcanlar, 2007). The model was tested in the Turkish city of Aydin, which
is near Izmir with the then population of 135,365 (2000).
3.2 Brief findings of the Previous Model
The model structure contained two basic stages after processing data; (a) determining
disadvantaged population through cluster analysis; and (b) modeling and comparing themodel results of the disadvantaged and normal populations. 11 major disadvantages(variables) were identified which constitute the criteria in the clustering process to split the
sample population into two groups (for details see Duvarci and Yigitcanlar, 2007). The
persons with relatively low scores belonged to the disadvantaged, and the ones with highscores to the advantaged clusters. Consequently, the number of disadvantaged persons was
629 and advantaged was 303. The modeling approach required separate model runs for both
the disadvantaged and normal population. The data variables used in TD definition areprovided briefly in Table 1 (of which details can be examined in the respected study). These
are largely the attitudinal and perceptional data from the people surveyed as in combined
index values that can later be used easily as parameters, or policy intervention domains. But
also other usual parameters are solely used in the modeling steps such as travel cost. The
modeling study showed that the disadvantage is largely due to a lack of motor vehicle accessand poverty. This analysis also demonstrated that it was possible to determine zone clusters ofthe disadvantaged by the cluster analysis (Fig. 1). Apparently, the most disadvantaged zone is
the 8th zone, which has also very low figures of socio-economic status (Duvarci and
Yigitcanlar, 2007).
Table 1 Data variables used in the modeling for the TD

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0.5
0.45
0.4
0.35
0.3

% (Score) of
0.25
Disadvantage

Disad. Scores (%)

0.2

average
0.15
0.1
0.05
0
1

2

3

4

5

6
Zones

7

8

9

10

average
Disad. Scores (%)
11

12

Figure 1 Aggregate disadvantage levels by zones
TRANUSTM, an integrated land use and transport modeling software, is used for the transportmodeling of Aydin. As the public transport (PT), only one mode available at the pilot study
area, which is the contracted-out bus service that runs on 14 routes. For trip generations, using
multiple regression analysis, best fitting variables were educational level, income level, andhousehold economic dependency for normal population. For the disadvantaged, the following
variables were significant: vehicle comfort, comfort level of PT, and economic dependency(being common variable for both). R2 values were 0.78 for normal and 0.69 for the
disadvantaged. The overall average daily trip rate per person for the normal population was
1.73, compared to 1.65 for the disadvantaged. Trip production results for both disadvantaged
and non-disadvantaged are presented in Table 2, which will be used in the suppressed demandcalculation later. Still, the TD’s trip rates of few zones (as 2, 5 and 11) are greater than theNormal’s, but such deviations do not much affect the general situation. These higher rates
may be due to the different level of economic participation of the disadvantaged in somezones (also relating to accessibility, residential conditions or work types), etc., but the issue
will be examined in detail within the limited scope of this study. For trip distribution stage,
singly-constrained gravity model was used to determine the distributions. For mode split stage,
with binomial utility approach, the utility function for the disadvantaged could be explained
solely by the combined impediment variable. The mode choices in favor of PT (Public Mode)
were 0.43 for the disadvantaged (for private 0.57) and 0.37 for the normal population (forprivate 0.63). To see the final traffic results and performance indicator results for user
disutility levels, the assignments are run by TRANUS.
3.3 The findings peculiar to the disabled and elderly in the base model
The previous modeling also provided the proportions of both the elderly (people older than 65
years old) and the disabled per zone under the general category of TD (see Table 3). These
will be used in evaluating the special place of the E&D in the suppressed demand releaseanalysis later and in determining the priority areas and critical OD paths in policy making.
Accordingly, the most significant zones are 8, 4, 3, 2, 12, 10, 6, of which 8, 2 and 6 hadalready been defined to be seriously disadvantaged. However, mostly those E&D may overlap(old persons have higher probability to become disabled) but, for easy calculation andunknown information, we assume they are exclusively separate groups.

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Table 2 Trip rates and productions of the normal and the disadvantaged
Normalpopulation

Disadv’ed

population

zones

Model’s trip
Rate

Survey’s trip
rate

population

Trip
Product.

Model’s
trip rate

Survey’s
trip rate

population

Trip
product.

1

1.81

1.88

12,261

23,075

1.72

1.93

7,802

15,042

2

1.75

1.51

11,37817,147

1.77

1.59

9,510

15,111

3

1.87

1.61

9,107

14,626

1.14

1.17

5,657

6,624

4

1.94

1.94

10,136

19,694

1.95

1.88

7,659

14,437

5

1.88

1.94

13,477

26,132

1.99

2.3

7,067

16,268

6

1.74

1.89

15,35929,0901.71

1.49

11,384

16,985

7

2.02

2.08

11,93824,807

2.02

1.9

8,661

16,499

8

1.23

1.33

13,046

17,312

1.24

1.31

12,042

15,799

9

1.54

1.4

9,251

12,924

1.48

1.3

5,829

7,601

10

2.12

2.15

9,683

20,828

1.73

1.94

3,289

6,394

11

1.4

1.28

9,899

12,710

1.64

1.39

5,377

7,452

12

1.51

1.61

9,830

15,816

1.4

1.51

5,990

9,063

total

1.72

1.73

135,365

234,162

1.64

1.65

90,267

147,275

4. THE METHOD OF ACCOUNTING THE SUPPRESSED DEMAND OF THE TD
Observing pure impacts of the “once-suppressed” but now released demand (trip rate increaseonly) can be handled in two basic steps: first, calculating the demand surplus (difference) tobe released by the transportation disadvantaged and, second, integrating this released demand
to the mainstream model structure and observing the impacts as assignment results.
4.1 The method of calculating “suppressed demand difference”
It is not easy task to measure a hidden fact because of simply being not apparent at sight, butalso of the uncertainties in definition and delimitation of what is to be measured. Handling
suppressed demand in four-step modeling is difficult due to the difference between theconditions drawing general trip making behavior due to socio-economic conditions and theconditions at actual trip making. The measurement process requires an assumption to start
with, and a “yardstick” to which measurements are done as well. The calculation arithmetic
will be different than the mentioned elasticity measurements. As the sum of disadvantaged
and advantaged together make the normality, the suppressed demand of the TD can be foundon the very reason of this “completion” assumption; suppressed demand is the “deviance”(difference) of the disadvantaged from the normality by all means. In the following, themethod of how to derive the released “suppressed demand difference” will be shown for thecase city model results.
Table 3 Proportions of elderly and disabled among the total disadvantaged populationelderly %*disab’d %*

1

3

11

14

2

28

3

31

3

21

18

39

4

9

34

43

5

2

8

10

6

20

5

25

7

19

3

22

8

31

19

50

Zones

total (%)

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9

5

15

20

10

9

17

26

11

6

4

10

12

12

16

28

*percentages above the city averages of %14 and %12 are highlighted for elderly and disabled respectively

4.2 Method of Integrating Suppressed Demand into Modeling
The study is constrained only to the impacts of trip rate increases to observe, viz. theadditional released suppressed demand of the disadvantaged. Since the previous modelingstudy used the singly-constrained (Oi) trip distributions approach, only the productions will be
introduced to the trip distribution modeling, and their sole run down impacts to theassignment stage, specifying no any other value throughout the model stages. The integrationis reflected at four steps finally ending up at the trip distribution stage as;
1. Calculating “Released” Productions of the Disadvantaged (Oidis(rel)): Multiply the
population (Pidis for each zone) of the disadvantaged by the normal populations’ trip
production rates;
Oidis(rel)= Pidis . tinorm(1)
2. Calculating Released Trip Distributions of the TD (Tijdis(rel)): Previous attraction factorsof the normal population’s trip distribution calibration is directly multiplied by the above
found (step 1) released productions (Oidis(rel) ) (as if future productions);
3.
(2)
Tijdis(rel) = Oidis(rel) . aijnorm
where, aijnorm = dj . f(cij) / Σj dj . f(cij), viz, calibrated for the normal population. The
reason for taking normal population’s attraction factors, instead of the TD’s is that
normal’s values are the ideal for the TD. dj here means attractions by jth column zone.
4. Run of all model steps on TRANUS (or, any other software available) as the two
separate models, and finally getting the no-release Assignment Results (T(base)ijkl andthe released demand Assignment Results T(rel)ijkl) for comparison: This requires first
the run of the base year model, and then, the run of the suppressed demand’s released trips,
without changing any other parameter in the models.
5. Running of the Reporting Programs of TRANUS for the Analysis of the Assignment
Results, the Performance Indicators, such as Disutility, Cost and Service Levels:
General indicator outputs are derived from the Reporting Programs (IMPTRA and
MATESP) to compare the basic results of the two models.
The results are to be discussed in the next section. There, what these results mean for thosedisadvantaged E&D is analyzed. In the analysis, those issues can be addressed; (a) the costs
or additional burden by having the TD released suppressed demand, then, (b) what costs anddifficulties will the E&D encounter by the increased traffic, and, (c) what infrastructure
improvement and facilities are required according to the needs of these special groups. The
process is briefed as the steps of the chart in Fig. 2. In this chart, also the joint released
demand integration of the E&D (JDED) (explained later) is shown. In simulations, different
from traditional modeling, the relevant parameters to focus can be PT comfort and service
quality parameters (as proxi to car ownership and income).

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4.3 The Simulation Results and Comparison with the Base “No-release” Case
To obtain the released suppressed demand impacts, simulation results of the released trips as
of assignment results were evaluated on the TRANUS software. Both display results andgeneral (summary) performance indicators for both the “release” and “no-release” (base)cases were contrasted. The total suppressed trip releases of the disadvantaged from theprevious section is 103,930 while this was once 101,317, excluding the intra-zonal trips,
which meant almost only 3% (5% with intra-zonal) increase in the release (Table 4). In Table
4, the negative values are out of concern, which are capsulated in parentheses meaning there
occurs literally no released trip. The most significant releases (bold and underlined) are to be
the policy concern zone-pairs. Those with no high joint demands of the E&D, as shown in
Table 6, are excluded since no relevance to policy analysis. The net release can be denoted as:
Dij(rel) = Tijdis(rel) - Tijdis(base)(3)

Calculating “Released” Productions of the disadvantaged

The JDED integration
Summed up proportion of joint E&D
populations (for each zone)(S)

Calculating Released Trip Distributions of the Disadvantaged
multiply

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

Determining the TRANUS’ Exogeneous Trip Distribution Factors
Calculated JDED trips releasesuppr. dema.differences

Run of 4-step model as two separate models

Running of the Reporting Programs and Evaluation of the
Performance Indicators

contrast
Only select OD pairs with JDED
above 1 (or close to 1) for policy focus

Policy analysis

Figure 2 Steps of released demand calculation together with the JDED integration
The basic differences were evaluated for three basic display indicators (public and privatemode equivalent vehicles, LOS and waiting time all with the same scaling) that showed
literally no significant difference. Slight differences in LOS were observed on previously
congested links (because of the available capacity to accommodate more traffic) (Fig. 3).
Similarly, utilizing the IMPTRA reporting program of TRANUS, the numerical results ofbasic cost and mobility related indicators could be obtained (Table 5). Details of comparison
between the release case and non-release (base) case are provided in the Discussions section.
4.4 Results Peculiar to the E&D and the Policy Zone-pairs
Based on the information provided from the previous modeling study for the TD, and the joint
proportions of the disadvantaged E&D given in the previous section, the “Joint trip Demandof the Elderly and Disabled” (JDED) within the overall released demand can approximately
be found and special travel needs of the E&D be captured. The JDED were calculated in three

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steps as follows (See also the chart in Fig 2);
1. first, sum up previously found (Table 3) E&D population proportions for each zone, andfind a joint total percentage value for each zone (pij).
2. then, multiplying these joint proportions (pij) by the trip distributions with releases (i.e.,
Tijdis(rel)). Proportions are assumed the same all throughout the raw taken and evenly
distributed to each cell because of the data insufficiency
3. finally, as a metric of significance, the trip distribution shares of JDED can be observed
along with the trip distributions found before. The E&D releases are found in comparison
to release differences. The Sijj values are expected to be positive and assumed critical (Sc)if exceeds 1, showing the significance of elderly and disabled trips among general releasedifference if exceeds 1.
Sijj = (Tijdis(rel) . pij )/ Dij(rel)(4)

Table 4 Differences Between the Base and Released Trip Distributions of the TD
1

1

2

4

5

6

8

9

3159-2653

820(1207)

489

(214)

(506)

556-4212223-2104

1112-1262

1554-1026

(528)

11

12

(135)

879-774

(120)

7

8

9

10

11

12

sum

97-47

527-312790-312263-31297-312

(50)

(50)

(214)

(478)

49

215

556-1262

556-841

102-641112-1262

1112-841

1946-1683

1946-1683

11776-11836

54-38754-581586-581

54-194293-387

54-19454-34854-736

333

527

(6)

140

94

140

294

682

3691-5236

150

706

97-47

286

(39)

150

(270)

(263)

(263)

14507-13360

(105)

97-51

1053-706

1211-1518

527-1059

1580-1059

790-1059

97-53

685-1518

158-106

527-4592265-2224

(46)

(348)

(306)

532

(521)

269

(44)

833

(52)

(68)

(41)

1937-1581

2421-1318

89-40

968-1054

89-2641211-922

2179-1713

13342-10284

11055-12705

8991-9811

1453-756484-264

89-2642421-2108

494-433

91-629988-839

3460-3776

988-1259

988-83991-63

494-1259

494-4201236-1175

1730-2014

(149)

316

270

(149)

(28)

764

(75)

(61)

284

303-367

3640-3423

910-1467

1759-1711

56-37

303-244

303-489

1972-2078

2427-2567

63

(217)

557

(48)

(19)

(59)

186

108

141

754-680

656-591984-886656-591

6586-6561

63-34

3124-3826

(697)
(61)
196

(221)
538

56-489

433

174

(313)

(356)

(1103)

(49)

86

174

(288)

(466)

328-4660-45

328-591656-1182

656-591525-473984-886

63-37

343-228686-1141343-1141

63-34

63-34

686-456

63-34

686-456

63-228

455

798

(29)

(29)

(230)

(29)

(230)

165

(29)

535-33

(282)
(26)

99-32
10

6

527-312(25)

56-2527

52633-3121

(144)

54-29
3

4

5265-4058

556-4122

31053-1873

(16)

(115)

526

(65)

(52)

(97)

535-1090

535-763535-436

(66)

119

554

227

(100)

66-25

359-172

66-1031

1796-1598

1078-1031

66-172420-227420-241

77-4811261-1203(58)

420-48161

1261-722
(539)

(42)

(193)

99-218

263

(187)
(180)

965
404

(198)

(47)

(65)

(97)

(65)

99-33

99-436803-654803-981

(66)

337

(149)

177

66-172359-172359-172359-172

77-481
404

77-36
(41)

840-722(119)210-120(90)

99-436

(502)
106

(75)

337
106

(187)

(187)

898-1083

(187)

185

1681-1563(117)

11785-13125

4241-5110

5474-5800

6745-6277

Note: the external trips are not added to these figures

Having such a metric makes it possible to know how far the released trip is comprised of the
E&D trips, if their travel conditions are to be improved. Those with high (Sijj) ratios shoulddraw the policy-maker’s attention to those zone-pairs, and the paths between these zone pairsthat must have a high priority for probably denser disabled and elderly travels. The results
showing the most significant shares (close to 1 or over) are observed in those zone-pairs withassociated share values; 1-4(0.9), 2-5 and 2-9(2.6), 4-3(2.1), 4-7(1.7), 6-4(2.9), 6-5(1.2),
6-12(1.8), 7-11(4.3), 7-12(4.0), 8-3 and 8-4(1.1) and 10-12(1.4). It is checked whether the
E&D releases at those paths collide with any impediment caused by the additions of thegeneral suppressed demand releases of the TD, such as increased volumes, thus, generalized
costs, LOS and waiting time durations for public transportation services.

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

15

15

8

8

7

7
9
14

11 12

10

13

9

1

17

13

10
3

13

16

8
7

7

1

6

17

15

8

9

16

2

1

15

14

6
5

4

3

2

1

11 12

10

14

5

4

3

6

11 12

6

9

1

5

4

10

14

3

2
17

13

11 12
5

4

2

1

17

Figure 3 Comparison of release (left) and no-release (right) LOS and wait time (down)

5. DISCUSSION AND THE POLICY IMPLICATIONSAs the general outcome of the method approach, the total trip demand of the disadvantaged,
147,123 changed to 154,009 trips, with the extra 6,886 trips which is supposed to be the latentdemand, increasing the total new trip rate to 1.78. A slight increase in trip numbers actually
has not brought a serious burden on the existing roadway capacity, with only 3%. Even thisadditional demand might not mean additional costs and burden onto the existing infrastructure
but returns in case the existing network and transport system capacity is underutilized.
Table 5 Comparison of the base case and the simulated released demand
Criteria

no-release

released suppr.

% change

general costs (priv)

14593131

14829517

1.61

general costs (publ)

11506755

11759606

2.19

revenue (publ)

19590252

20014686

2.16

monetary cost (gen)51293628

52343596

wait time (all aver.)

8.22

7.5

2.05
-8.76

Once the critical zone-pair paths are clarified according to the special results of the E&D outof the simulation, examining the impacts by paths (one direction) on the Paths property of
TRANUS, policy observations can be made. The specific analysis results are not provided
here due to the limited space, but most common conclusions can be drawn as; the absence of,or extremely long and indirect PT lines can be the major cause of disadvantages, as far as
observed for the concerned zone-pairs. Second, most of the various PT choices at the
beginning of travel diminish to one or two choices. Many times either the access or the egress
distances to the nearest stop are far. Third, travels are interrupted with degraded quality ofLevel of Service on some links and waiting times which may be due to the infrequent or
unreliable bus services. It may be better to re-design the transit service routes, or many othersolutions can be thought. In the Fig. 4, two examples indicate one of the most importantfactors causing the disadvantage; excessively indirect PT lines (i.e., PT service quality) in theexisting situation, even the path choice of walk (if straight, around 1km long) becomes more
beneficial in terms of generalized cost on the critical paths selected (walkway facilities
related). Basic differences between the no-release case and release case are; around 2%
increase in costs on operators though a general 5% increase in trip rate was observed, wherecosts and revenues even out each other (assuming ticket price is not discounted for the E&D).
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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

It is interesting to have a general 5.4% less waiting time, which is a good indicator. Looking
at the per user (average) results, there are more dramatic decreases in waiting time (8.8%)(probably due to increased frequency), disutility (17.4%) and travel time (9.1%).
Table 6 Joint Released Demands of the E&D (JDED)
zones

1

2

3

4

5

6

7

8

9

10

11

12

sum

1

0

262

568

437

44

371

7

7

44

44

44

44

1870

2

128

0

130

652

391

391

261

20

391

261

522

522

3669

3

11

302

0

400

151

226

226

75

151

75

136

287

2042

4

22

304

653

0

455

455

455

23

653

46

197

956

4219

5

76

26

26

211

0

158

132

4

105

26

92

171

1028

6

108

157

210

944

315

0

210

16

315

105

294

503

3176

7

55

108

81

753

323

377

0

8

54

108

457

565

2888

8

23

22

295

591

295

236

443

0

340

295

443

295

3281

9

7

46

228

228

7

7

91

7

0

91

46

7

765

10

8

57

283

198

113

9

113

9

113

0

170

255

1329

11

2

17

103

160

103

17

17

17

17

17

0

108

580

12

64

67

135

337

135

202

135

10

202

34

438

0

1757

What can be done in practice with these results is also of the policy making process andplanning taking into account the coordination issue. First of all, complying with the newly
arisen amendments that already launched in many countries, along with the street design, theuse of advanced technology is very possible to aid those vulnerable groups such as elderly
and disabled right after all their demands and the paths they use are known in the very firstplace. Funding issue is another point to be resolved urgently for relatively the ITS-based
costly applications but which are, in a sense, the compensation to provide the TD’s right backand to restore its deteriorated accessibility conditions.
8

7

10

3

7

11 12

4

6

9

10

5
3

2

11 12

4

6
5

2

Figure 4 Path examples (6-5 and 7-12) of extremely indirect PT routes as major impedance
The issue of measuring the impacts of the hypothetical releases of the suppressed demand,
specifically of the TD through simulation-based approach was the subject of this study. Under
the assumption that the clustering-based modeling approach for the TD groups can have ahigh potential to be also used as a yardstick in measuring suppressed travel demands of thesegroups, the release impact could be observed. Here, a metric was developed based on “whatif” approach in the measurement of both the suppressed demand and the would-be impacts ifthey were to be released. The main purpose was to answer whether the impacts and costs ofsuch additional trips, which are assumed the human rights of those TD, are heavier than thelocal government’s and the existing infrastructure’s capability.

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Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007

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