SIGHTSEEING TRAVEL DEMAND
AND ECONOMIC IMPACTS OF TOURISM

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

Ryuji KAKIMOTO
Associate Professor
Dep. of Civil and Environmental Eng.
Kumamoto University
2-39-1 Kurokami, Kumamoto,
860-8555 Japan
Fax: +81-96-342-3507
E-mail: kakimoto@gpo.kumamoto-u.ac.jp

Abstract: Up to now, only the qualitative analysis or measure by some macro econometrictechniques has been applied to the local economy with relation to the sightseeing-related
projects. Moreover, there are few studies that propose the evaluation techniques for user
benefit and economic impact of tourism adjusted to sightseeing demand forecasting approach.
The aims of this paper are as follows: 1) propose a forecasting system for sightseeing demand
which varies with the sightseeing-related trunk road projects. This system consists ofinter-regional sightseeing travel flow model and sightseeing excursion model; 2) to evaluate
the effect of the sightseeing-related trunk road projects, an Input-Output approach is proposed;
and finally, 3) to evaluate the economic impact by the Kei-Na-Wa trunk road project.
Key Words: Sightseeing travel demand, Excursion behavior, Economic impact, Tourism

1. INTRODUCTION
In Japan, as the establishment of public investments has progressed, the marginal utility of
newly established ones decrease. There may be some projects for which effects are not

Sightseeing-related
facility
Increase
Attraction Measures
Increase of
Attraction for
Allocation
Allocation of
Tourism-related
Industries

Figure 1

Congestionin sightseeing area and route
Increase
Sightseeing
DemandIncrease
Consumption expenditureTransportation
Facilities
Increase
Accessibility
Increase
Consumer Benefit

Increase
Tourism-related Output
Increase
Intermediate Demand

Increase
Expenditure

Induced Employment
Inside Area

Increase Income
Inside Area

Extended process of effects by sightseeing-related facilities

accounted for by the citizens. From the viewpoint of cost effectiveness, as well as national andlocal governmental financial crisis, prior assessment of projects using an economic techniquesuch as cost-benefit analysis is needed. Tourism has become one of the largest and most
rapidly growing activities in the local economy, so that the investment in tourism such assightseeing-related industries and transportation facilities has to be assessed sufficiently.
Especially in an area where tourism is a main industry, it is necessary to examine accurately
not only the cost-benefit ratio but also the local economic impacts of tourism.
In Figure 1, we show the extending process of effects in an area with the investment insightseeing-related activities and transportation facilities. These investments increasesightseeing demands. The increasing demand consumes additional goods and services
provided by sightseeing-related and transportation industries existing inside this area. They
require the goods and services of other sectors. Consequently, the employment and income
indirectly increase. Such increase in income will induce more consumption, which will
increase output and income of other sectors. The effects of increase in demand by investments
can be evaluated by technique such as Cost-Benefit analysis. However, this technique is basedon a partial equilibrium approach and a number of explicit and implicit assumptions must be
made during the formulation of models like a Travel Cost Method (TCM). As compared to the
Cost-Benefit analysis, the technique of Input-Output analysis has a number of advantages
when we evaluate an economic impact within the sightseeing area where sightseeing-related
investments has been made. One advantage is that it is a general equilibrium approach, so we
can focus our attention totally upon the industrial interdependencies that exist in economy.
The most important advantage is that it can study the impact of investments in its direct,
indirect and induced effects in the area.
Up to now, only the qualitative analysis or measures by some macro econometrics techniques
has been done on the local economy by sightseeing-related investments. Moreover, there arefew studies that propose the evaluation technique of user benefit and economic impact of
tourism adjusted to sightseeing demand forecasting approach. The aim of this paper is:1) to propose a forecasting system for sightseeing demand which varies by the establishmentof sightseeing-related article road. This system consists of inter-regional sightseeing travel
flow model and sightseeing excursion model.
2) to measure the local economic impact of tourism, an appropriate method using the
input-output analysis is proposed.
3) finally, we try to evaluate effects of the investments on Kei-Na-Wa arterial road by thistechnique.

2. SIGHTSEEING DEMAND FORECASTING SYSTEM
2.1 Framework of Sightseeing Demand Forecasting System
At first, we define an individual sightseeing facility or point as a sightseeing spot. A
sightseeing area or zone is a unit in which some sightseeing spots are aggregated. Moreover,some areas compose a region. Now, inter-regional demand between origin zones and someregions and excursion demand among sightseeing areas within a region are subjects of ourstudy. Sightseeing behavior can be divided into an inter-regional part and an excursion partwithin a region shown by Figure 2. Because there are some different characteristics between
them, both behaviors should be analyzed by appropriate methods respectively. Increase inOrigin 1

Origin o…

Origin O

Inter-regionalsightseeing travel
flow demand
Region 1

Region r

Area 2

Area 1Sightseeing
excursion demand

Region R

Area 3

Area I
Area iFigure 2

Framework of sightseeing demand forecasting system

the inter-regional sightseeing demand by improvement of sightseeing-related facilities iscaused by both the increase of generated demand and the diversion of demand from otherdestinations, so we try to formulate the inter-regional sightseeing demand flow as anaggregated type trip generation/distribution combined model. On the other hand, the excursion
behavior should be analyzed by individual because the excursion behavior of a traveler varies
not only by a little change of traffic services between sightseeing areas but also by individual
characteristics. We attempt to formulate individual sightseeing excursion behavior by the
disaggregated choice model.
2.2 Inter-regional Sightseeing Travel Demand Flow Model
We propose a system that can forecast the variation of sightseeing demand by the
improvement of an inter-regional trunk road. We focus on Kei-Na-Wa trunk road project.
The road project connection from Kyoto to the south of Nara is in progress. Nara was thefirst capital in Japanese history about thirteen hundred years ago. In Nara northern area,
there have been a lot of historic temples such as Tohdai-Ji and Hohryu-Ji, and imperial tombs
and historic sites such as ruins of Heijokyo and Takamatsu-zuka tomb and so on. UNESCO
has appointed this whole area as a world heritage. If this road will be completed and thiswill be increased accessibility to this area, a lot of guests from nearby prefectures can be
expected.
Our forecasting system consists of (1) an inter-regional sightseeing travel demand flow model;
and, (2) a sightseeing excursion demand model. In the inter-regional sightseeing travel
demand flow model, we can predict simultaneously the trip generation demand from origin
zone o and its share to sightseeing destination region r. This model is basically a trip
generation/distribution combined model, but we improved it by constructing an aggregatednested choice model as follows;
Qor = Po ⋅ p(o ) ⋅ p(r | o )(1)where Qor is the number of sightseeing flows between origin zone o to sightseeing region r.

is a population of origin zone o. In the lower decision level in the nested structure, themarginal share of destination r is described by the next multinomial logit model.
Pop(r | o ) = exp(γAr + δCor ) ∑ exp(γAr ′ + δCor ′ )

(2)

r′

Ar , Cor are the attraction measures of sightseeing region r and travel cost between o-r,

respectively. γ , δ are parameters which should be estimated. On the other hand, we
formulate a sightseeing trip generation frequency per capita of nighttime population in theupper decision level as follows;p(o ) = 1 [1 + exp(α + θWo + µVo + λΛ o )] ,(3)

where Λ o is the composite cost which explains the accessibility of origin o with respect to all
available sightseeing regions r ∈ Ro and defined as follows;Λ o = ln ∑ exp(γAr ′ + δC or ′ ) .

(4)

r ′∈Roare attributes native to generation zone o such as average income and car ownership
ratio per capita, respectively. α ,θ , µ , λ are unknown parameters.
Wo , Voi

2.3 Sightseeing Excursion Demand Model(1) Model Framework

First Area Choice Sub-modelHome
Return to home
Next visiting area
Excursion Choice Sub-Model

Figure 3

Staying timeFirst visiting area

Staying Time Choice Sub-Model

A traveler leaves home and visits some sightseeing areas sequentially and returns to his homein his one sightseeing travel activity. This excursion behavior is regarded as amulti-dimensional choice process. We assume his excursion behaviors to be sequential
choices of both a sightseeing area and staying time at this area. We connect these choicesub-models with sequentially in time. Model framework is shown in Figure 3. Concretely, atfirst, by the first area choice sub-model, the first sightseeing area choice is expressed. Next,
the staying time choice sub-model decides staying time in the first sightseeing area, forecasted
by the first area choice sub-model. Then, the traveler will decide whether to visit a next
sightseeing area or to return to his home. The Excursion choice sub-model describes his
behavior. If he decides to visit another sightseeing area, the excursion sub-model chooses thenext sightseeing area. Our model is able to describe this individual traveler’s sequential
choices until he returns home.

Framework of sightseeing excursion demand forecasting system

(2) Model Formulation
The First Area Choice Sub-Model: The first area choice sub-model expresses the choiceprobability Pin that an individual n chooses i’th sightseeing area among his available
sightseeing areas choice set An as following the disaggregated multinomial logit formula;Pin =exp[λVin ]∑ exp[λV jn ]

(5)

j∈An

Where Vin is the deterministic term of his random utility, we can use his departure time fromhis home and travel time to each area and its attraction measures as its explanatory variables.
Staying Time Choice Sub-Model: We apply the concept of Hazard function to the staying
time choice sub-model. This model can show a distribution of time interval until a different
event happens after the existing event. In our model, a different event is the departure from his
existing area. When we introduce a Staying Time Choice Sub-model, that is Hazard function,
we can assume some kinds of functions as the probability function F(t) that a different event
happens by period t. We use the Weibull distribution as its density function f(t), because the
distribution function of staying time should be an extreme value distribution. We assume theWeibull distribution function as the f(t), the probability that staying time is equal to t is 1
 − βX in 
S (t ) = exp − t σ exp
 σ 


(6)

where σ is a scale parameter. X in is the attribute vector of sightseeing area i for traveler n,with which we can introduce his arrival time and the attraction measures of its area. The
arrival time is given by sum of departure time from home and travel time to his existing area.
Excursion Choice Sub-model: At first, in this model, the choice whether the traveler returns
to his home or visits another sightseeing area should be described, or if he has already decided
to visit another, his next destination should be chosen. We apply the nested logit model toestimate such multi-dimensional choice probability. The joint, unconditional probability of a
combined choice of excursion e and next destination area j is written as follows;
Pn ( j , e) = Pn ( j e) ⋅ Pn (e) =exp[λ1V( j e ) n ]exp[λ 2 (Ven + Ven* )]∑ exp[λ1V( j′ e) n ] exp[λ2 (Ven + Ven* )] + exp[λ2Vhn ]⋅(7)j ′∈An′where Ven* is the composite cost that is expected to give in the case he visits another area asfollows
Ven* =

1

λ1ln ∑ exp[λ1V( j e ) n ]

(8)

j∈An'

is the conditional utility of selecting the next area j, given that excursion has alreadybeen chosen and include travel time needed to visit next destination area j and so on as the
explanatory variables. Vhn is the marginal utility of homecoming. Ven is the marginal
excursion utility which consists of the departure time from his existing area. We are able touse the departure time, which is sum of the staying time and arrival time at his existing area.
V ( j |e ) n

3. ECONOMIC IMPACT OF TOURISM
3.1 Method of Measuring Economic Impact of Sightseeing-related Projects
Economic impact of sightseeing-related projects is complex because it does not occur within
the framework of a single industrial sector. There are a variety of methods that can be
employed to study economic impact. The final choice of methodology should be determinedby the quantity and quality of effects within an area where it is going to be analyzed. To begin
with, a simple, but crude approach is to compare the available data on tourism activity withthe key economic indicators such as GDP and domestic employment of some projects which
are similar to this project. However, such an approach will give only a partial effect of the
impact of sightseeing-related projects. Second, we can adopt a more sophisticated approach byusing a technique like a cost-benefit analysis. However, this technique is based on a partial
equilibrium theory, so we can not evaluate the impact of travelers nor can we measure theeconomic impact for other sectors within an area. Finally, the technique of input-output
analysis can be employed to determine economic impact of sightseeing-related projects.
The technique of input-output analysis has a number of advantages when compared withalternative methodologies. First, it is based on a general equilibrium theory and provides acomprehensive view of the regional economy to decision-makers who evaluate the project.
Second, we can focus our attention totally upon the industrial interdependencies that exist in
economy. Third, the flexibility of the input-output structure enables us to construct a model tosuit the purpose at hand. Finally, we can study the impact of sightseeing-related projects at its
three levels: direct, indirect and induced effects by using input-output analysis. Then, weapply this input-output analysis technique to measuring the economic impact of asightseeing-related trunk road project. When we adopt the input-output approach to measure
the economic impact, there are some issues which should be considered.
1) The sightseeing-related consumption according to increase of sightseeing demands shouldbe estimated;
2) Goods consumed by travelers should be distributed to the relevant industrial sectors of theavailable input-output table;3) We have to calculate the regional economic impact of a part of the whole Nara prefecture
DirectSightseeing excursion demand D
Consumption of sightseeing-related goods ∆FE

Indirect

Output supply X 0 = A ⋅ ∆FE

Value added B0 = V ⋅ X 0 ,SurplusIntermediate demand

[ (

)]

−1
∆X 1 = I − I − Mˆ A ΓX 0

Income ∆C0

Value added∆B1 = V ⋅ ∆X 1

Surplus

Income ∆C1

InducedHousehold Expenditure∆C = ∆C 0 + ∆C1 = H Γ V ( ∆FE + ∆X 1 )Induced production

[ (

)]

−1
∆X 2 = I − I − Mˆ A ∆C

Figure 4

Measuring economic impact

for which the input-output table is prepared.
3.2 Measuring Local Economic Impact of Tourism by Input-Output Analysis Technique
We explain the process to evaluate economic impact by using Figure 4.
1) Increase of sightseeing demand from outside the region where we want to measure local
economic impact, D , increase its consumption, ∆FE , for sightseeing-related goods andservices such as souvenirs, transportation services and hotel staying. This expenditure canbe regarded as the export factor of final demand because money flows from outside theregion to inside.
2) Demands for these goods and services require the supply of intermediate output,
X 0 = A∆FE and value added, B0 = V ⋅ ∆X 0 , directly. A and V are the input-output table andthe value added ratio, respectively, those are prepared normally.
3) To produce these outputs that should be supplied, more intermediate demand goods,−1∆X 1 = I − (I − Mˆ )A ΓA∆FE , as well as value added, B1 = V ⋅ ∆X 1 , are required as input

[

]

[

]

resources, indirectly. I − (I − Mˆ )A and Γ are the open-economic type input-output table
and the degree of self sufficiency vector.
4) Both direct and indirect values added are divided into surplus and householdincomes, ∆C0 = HΓB0 and ∆C1 = HΓB1 , where H is the average propensity to consume.
5) The sum of direct and indirect income, ∆C = ∆C 0 + ∆C1 = HΓV (∆X 1 + ∆FE ) , becomes thehousehold expenditure consumed inside this region.
−16) Induced output is produced by this adding household expenditure, ∆X 2 = I − (I − Mˆ )A ∆C .
7) The production multiplier m by the sightseeing-related projects is defined as
m = (∆FE + ∆X 1 + ∆X 2 ) / ∆FE .
−1[

Kyoto-Fu

4. CASE STUDY

Kyoto City

4.1 Alternatives
Nara was the first capital in Japanese history
about thirteen hundred years ago and isolder than the well-known Kyoto. There aresome historically famous temples, imperial
tombs and historic sites. UNESCO has
appointed this area as one of the world’s
heritage. Nevertheless, recently, the total
visitors per year decreased. The Kei-Na-Wa
trunk road project connecting Kyoto to thesouth of Nara is in progress. The economic
impacts for three alternatives on this project
are compared. First alternative is the present
network and is the benchmark. Second is
the future network without Kei-Na-Wa
trunk road and third alternative is the future
network with Kei-Na-Wa trunk road. We
will call these three alternatives as Case-1,]

Hyogo-Ken

Kobe City

Osaka-Fu
Osaka City

Nara City

Osaka Bay
Kei-Na-Wa Road
KansaiInternational
AirportNara-Ken

Wakayama City

Figure 5

Wakayama-Ken

Study area and Kei-Na-Wa roadCase-2 (without Kei-Na-Wa Road case) and Case-3 (with case), respectively. We will show
the stud area and alignment of Kei-Na-Wa road in Figure 5.
4.2 Estimation of Sightseeing Demand Model(1) Inter-regional Sightseeing Travel Demand Flow Model
An origin region is correspondent with a zone of residence, which consists of cities and towns.
The destination region is Nara northern area. Models were estimated using the data of roadtraffic census and the national census carried on in 1995. The estimation results of bothmodels are shown in Tables 1 and 2. The sign of parameters are as expected and t-values arestochastically significant at 95% except for car ownership ratio. The correlation coefficientvalues of both models are about 0.9, so these models are able to predict inter-regionalsightseeing travel flows.
Table 1 Estimation results of trip generation ratio model

Zone Dummy

α1
α2
α3

Car ownership ratio µ
Composite cost λCorrelation coefficientParameter
3.320
3.713
3.776
-0.0246-0.8140.899

t-value
13.5
16.4
20.9
0.21
4.15

Table 2 Estimation results of the marginal shear of destination
Attraction measures γ
Travel time δ
Correlation coefficientParameter
0.00011-0.02614
0.889

t-value
79.7
564.4(2) Sightseeing Excursion Demand Model
We used the data collected in the Nara sightseeing survey to estimate sightseeing excursion
demand sub-models. The Nara prefecture government conducted this survey three times inTable 3 Nara sightseeing survey
Date
Weather
Survey time
No. of points
No. of deliverNo. of respondents (rate)
Samples
MethodSpringSummerAutumn25 May (Sunday)
2 Aug. (Saturday)
24 Oct. (Holiday)
fine
fine
fine
business hour of each facility (mainly 7:00～19:00)
2258
27
13,33010,27239,5501,977 (14.8%)
1,064 (10.3%)
3,121 (7.9%)
All visitors
Distribute questionnaire at entrance of facility

Response by post

1997. It aimed to know the existing conditions of sightseeing for the northern area of Nara andto evaluate the economic impact of tourism by the Kei-Na-Wa road project. The details of thissurvey are shown in Table 3. The survey was partitioned into three sections as follows:a) Attributes of this travel like mode, aim, total expenditure, departure time from home et al.,b) Visiting sightseeing points and arrival time, departure time, excursion route on road map,c) and Characteristics of individual and household like sex, age, companion and so on.
First area choice sub-model: Table 4 presents the estimation results of the first area choicesub-model by season. We can obtain the logical results that the higher the Attraction measuresand the shorter the Travel time to first sightseeing area from home is, the higher the utility ofthe sightseeing area. There is a difference in relative weights of the coefficient of the area
dummy by season. In spring, the parameter of Historic heritage is bigger than Nature, but it isin different in summer and autumn. ρ values of every season’s model are not high andgoodness-of-fits are less than 40% , because the choice set seems to include seven and a lot ofsightseeing area alternatives. The reliability of these models is not statistically high.
2

Table 4 Parameter estimates of the first area choice sub-model
Explanatory variables
Historic heritage
NatureTravel time to the first sightseeing areafrom home（10-2）
Attraction measures（10-3）
Sample size
Goodness-of-fit（％）
Area dummy

ρ2Parameter (t-Statistic)
SpringSummerAutumn0.2072 (1.59)
0.4434 (2.21)-0.2464 (2.69)-0.1328 (0.95)
0.5453 (3.19)
0.0116 (0.13)-0.4303 (3.30)-0.0254 (0.23)-0.7237 (6.46)

0.7064 (7.06)
831
35.8
0.05990.3937 (3.58)
231
29.0
0.04170.4637 (9.97)
1339
28.6
0.0299

Staying time choice sub-model: The parameter estimates of staying time choice sub-model isshown in Table 5. We used LIFELEG Procedure of SAS to estimate them. We could get
logically reasonable results that the earlier the Arrival time and the higher the Attraction
measures are, the higher the long-staying probability is. Almost all estimates are statisticallysignificant because χ 2 values are sufficiently large.
Excursion choice sub-model: Table 6(a) shows the results of Level-1 in second visiting area
choice model which is one of the excursion choice sub-model. All estimates have expected
sign and high t-value. There is a difference in relative weights of the area dummy by season.
Travel time to next sightseeing area from existing area is statistically significant, so traveler
selects the area that is near in time length as next visiting area. The coefficient estimates ofLevel-2 in second visiting area choice model are shown in Table 6(b).
Departure time from existing area has expected sign and statistically significant coefficientestimate in all seasons, then the earlier the departure time is, the higher the utility of excursion
is. The coefficient estimate for the Composite value term is significantly different statisticallyfrom both 0.0 and 1.0 in every season’s model. Goodness-of-fit of all season’s models is rather

high compared to one of Level-1.

Parameter Estimates of staying time choice sub-model

Table 5

SpringParameter ( χ 2 -Statistics)
SummerAutumn-0.635 (78.9)
4.827 (481.3)
6.393 (467.6)
4.572 (426.2)
4.403 (420.0)
2.725 (325.9)-0.00164 (131.2)
0.00531 (482.5)-0.277# (1.1)-0.463 (3.1)-0.710 (6.8)
0.514
1018

-1.588 (23.1)
0.277 (3.9)
4.923 (52.7)
2.735 (55.4)-0.227 (2.6)
3.847 (62.0)-0.00102 (10.8)
0.00269 (63.4)
0.318# (0.3)
0.170# (0.1)-0.036# (0.01)
0.588
252

-0.144 (8.2)
3.036 (994.1)
4.908 (1033.3)
6.506 (1039.3)
2.474 (818.8)
4.712 (1016.4)-0.00186 (231.1)
0.00316 (1118.3)-0.164# (1.0)-0.420 (6.7)-0.446 (6.8)
0.522
1676

Explanatory variables
1
2
3
4
5
6

Area dummy

Arrival time (min)Attraction measures
First
Visiting order Second
Third
Scale parameter
Sample size#

= not significant at 10%

Parameter estimate of Excursion choice sub-model (Leve-1)

Table 6(a)Explanatory variables
Historic heritage
NatureTravel time to the next sightseeing areafrom existing area
(10-1)
Travel time to the next sightseeing areafrom existing area
(10-2）
Attraction measures(10-4)
Sample size
Goodness-of-fit （%）
Area dummy

ρ2

Spring0.8519 (5.12)
0.2892 (1.59)

Parameter (t-Statistic)
SummerAutumn-0.2242 (0.47)
0.2751 (1.62)-0.5018 (1.21)
0.3775 (2.35)-0.1881 (6.37)-0.2251 (3.33)-0.1686 (8.06)-0.3012 (1.50)-0.0985 (0.38)-0.5212 (3.19)

0.9885 (0.58)
321
38.9
0.077

1.147 (0.65)
71
36.6
0.037

4.129 (4.97)
405
39.7
0.078

Table 6(b) Parameter estimate of Excursion choice sub-model (Level-2)
Explanatory variables
Constant
Departure time from existing area
Composite value
Sample size
Goodness-of-fit (%)
ρ2

(101)
(10-2)

Parameter (t-Statistic)
SpringSummerAutumn0.4992 (7.46)
0.3804 (3.83)
0.8067 (12.3)-0.5890 (8.04)-0.4344 (3.83)-1.0370 (13.7)
0.1574 (0.72)
0.6924 (1.81)
0.4615 (2.07)
633
148
984
64.7
63.5
74.1
0.086
0.109
0.223

4.3 Prediction Procedure of Sightseeing Demand
In Figure 6, forecasting procedure of future excursion demands is shown. The individual n is
identified in every departure region and his excursion behavior is predicted by this Monte
Carlo micro simulation. The inter-regional flow, which is predicted by the inter-regionalsightseeing travel demand flow model, is the population set of this sample.
Traveler nTravel time
Attraction

Standard Normal
Distribution

Departure Time

Uniformed Distribution
First Area
Uniformed Distribution
Staying Time

Travel time
Attraction

Log-sum Value
Uniformed Distribution
Return homeor Excursion

Return home

Excursion

Home

Uniformed Distribution

Next Area

Figure 6

Micro simulation procedure to predict sightseeing travel demand

The inter-regional sightseeing demand flows model predict that the total sightseeing demandflows from all origin regions to Nara northern area will become 32,742 trips in case of Case-2
and 34,570 trips in case of Case-3, respectively, there is an increase 5.6%.

Table 7 The forecasted number of visiting sightseeing areas
Number of visiting areas

Case-1Case-2 (without case)Case-3 (with case)Difference between
Case-3 and Case-1
Difference between
Case-3 and Case-2

one

two

three

14,779(53.1%)
16,789(51.3%)
17,408(50.4%)

8,960(32.2%)
10,624(32.4%)
11,035(31.9%)

4,090(14.7%)
5,329(16.3%)
6,127(17.7) %

▼ 2.7

▼ 0.3

△ 3.0

▼ 0.9

▼ 0.5

△ 1.4

total visiting
areas

total
demandflows

average

44,96927,8291.615

54,02432,742

1.650

57,85934,5701.673△ 12,890(△ 28.7%)△ 3,835(△ 7.1%)△ 4,913(△ 17.6%)△ 1,828(△ 5.6%)△ 0.058(△ 3.6%)△ 0.024(△ 1.4%)

These flows are the control totals of excursion demand forecasting model and we carried out

the Monte Carlo simulation per individual as shown in Figure 6. We showed the trips by the
number of visiting areas in Table 7. Compared with Case-1, the ratios of trips, which will visit
only one and two sightseeing areas decrease in case of Cases-2 and 3. In Case-3, the ratio oftrips, which will visit over three areas, will increase by 3.0%, so that its average gets bigger by
3.6%. The total number of visiting areas will be 12,890 and will increase by 28.7%. The
Kei-Na-Wa trunk road seems to be a very effective project for sightseeing demands in Nara
northern area.

5. MEASURING LOCAL ECONOMIC IMPACT OF KEI-NA-WA PROJECT
We could obtain much accurate excursion demands by our sightseeing demand forecasting
system. The traveler’s expenditure seems to be different with his excursion pattern. The
statistical difference in expenditure by excursion pattern is analyzed by ANOVA, which is theanalysis of variance. Because the excursion patterns, for which is the number of levels is many,
we replace an excursion pattern by both the number of visiting sightseeing areas and first
visiting area. The results are shown in Table 8. There is a statistically significant difference inthe amount of travelers’ expenditure by the number of visiting areas. Then, we set the averagevalues by the number of visiting areas as a unit amount of expenditure.

Table 8 ANOVA results on consumption of tourists

Main effects(1) No. of visiting areas(2) First visiting area
2-way interactions
Error

Sum of squares

DF

Mean square

F-valuep-value465.38175.60289.78504.816164.97

6
1
5
11
168

233.55
175.60
57.95
45.89
36.69

4.78
1.57
1.25

0.03
0.16
0.25

Table 9 shows the results of economic impact of all cases. Compared with Case-1, output
becomes 1.18 times in Case-2 and 1.24 times in Case-3. The production multiplier with theincrease of sightseeing demand is 1.71. This value is bigger than the average of all sectors,
which is 1.29. We found out that the Kei-Na-Wa trunk road project significantly affects theregional economy in Nara northern region.
Table 9 Economic impact of Kei-Na-Wa road project
Case

Travelers’ expenditure

Total output

Case-1

0.863

1.472

Case-2 (without)

1.019

1.739

Case-3 (with)

1.071

1.826

*Billion dollars

6. CONCLUSIONSIn this paper, we proposed a method that could measure the local economic impact of tourism.
This method consists of the following two sub-systems:
1) A forecasting system of sightseeing demand that varies with the sightseeing-related trunk
road projects. This system consists of the inter-regional sightseeing travel flow model andthe sightseeing excursion model.
2) An effect evaluation system that can measure the local economic impact due to the increasein sightseeing demand. This system is based on the input-output approach with which we
can evaluate the local economic impact of tourism by using a standard open type
input-output table.
These two sub-systems are applied to the economic impact evaluation of the Kei-Na-Wa trunk
road project. As a result, it was found out that our economic impact evaluation method isapplicable and useful. Usually, the economic benefit in the Cost-Benefit analysis can evaluate
only the effect on the amelioration in accessibility by the provision of transportation facility.
In the case of the sightseeing-related trunk road projects, however, we sometimes cannot
expect the good evaluation because of the shortage of the demand. This kind of road projects
affects the regional economy greatly through tourism activity, so we have to evaluate theregional economic impact as well as the economic benefit. In our case study, the economic
benefit by the provision of the Kei-Na-Wa trunk road is bigger than its construction cost in
only the case that the time value is fairly big and the social discount rate is rather small. The
regional economic impact, that is the total output value, by using I/O method proposed,
corresponds to about 25% of the total economic benefit, so we cannot neglect this amount.
We will have to grasp the economic impact of tourism much accurately. In order to do so, weneed to improve not only the sightseeing travel demand-forecasting model but also theeconomic impact measurement model.

REFERENCES
Adrian Bull (1991) The Economics of Travel and Tourism, Pitman.
John, E. Fletcher (1989) Input-Output Analysis and Tourism Impact Studies, Annals of
Tourism Research, Vo.16, pp.514-529.
Mizokami, S. et al. (2000) A forecasting system of sightseeing travel demand considering area
attraction and excursion behavior, Proceedings of JSCE (in Japanese), No.639/ Ⅳ -46,
pp.65-75.