Tourism Demand Using Grey Theory Tourism Essay

Tourism prediction has become an of import constituent in touristry research and different attacks have been used to bring forth prognosiss of touristry demand. All industries are interested in hazard decrease, but this demand may be more ague in the touristry industry for the undermentioned grounds: the touristry merchandise is perishable, people are inseparable from the production-consumption procedure, client satisfaction depends on complementary services, leisure touristry demand is highly sensitive to natural and human-made catastrophes and touristry supply requires big, long lead-time investing in works, equipment and substructure ( Frechtling, 2001 ) .The survey presents one theoretical account that can be used to foretell touristry demand. This theoretical account is based on unreal intelligent ( AI ) : Grey Model GM ( 1,1 ) .

This AI theoretical account is estimated for tourer reachings to Romania during the period of 1996-2007.The aims of this paper are: to analyse the truth of prognosis consequence utilizing Grey Model GM ( 1,1 ) and to develop a short-run prognosis utilizing gray theoretical account GM ( 1,1 ) in order to enable all the stakeholders from touristry to do appropriate determinations.Tourism is a complex societal, cultural and economic phenomenon and one of the most important planetary phenomena, non to state the universe ‘s largest industry as many claim, bring forthing 11 % of planetary GDP, using 200 million people and transporting about 700 million international travellers per twelvemonth ( WTTC ) . Due to this dramatic growing in demand for touristry in the universe over the past decennaries, there are many surveies in touristry research meant to place any possible competitory advantage on the touristry market.In this context it is hard to conceive of the concern of touristry without prediction, therefore get bying with the turning investing hazards in the industry, due to the specificity of this industry – the demand is highly sensitive to natural and human-made catastrophes and touristry supply requires big, long lead-time investing in works, equipment and substructure and besides to the basic differences between merchandise and services – the touristry merchandise is perishable, clients are inseparable from the bringing procedure.Because of the perishable nature of the touristry industry, the demand to invent accurate prognosiss has become important ( Frechtling, 2001 ; Chandra & A ; Menezes, 2001 ; Law, 2000 ; Law & A ; Au, 1999 ) . Therefore, the big measure of academic literature that has been generated in this country is non surprising ( Morley, 2000 ) .

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Bing considered one of the most of import countries in touristry research, touristry demand patterning and prediction has attracted much attending of both faculty members and practicians. Harmonizing to Witt and Song ( 2000 ) and Li et Al. ( 2005 ) the huge bulk of surveies on this subject during the period 1960-2002 focused on the application of different techniques, both qualitative and quantitative, to pattern and calculate the demand for touristry in assorted finishs. These surveies besides were developed to set up prediction rules that could be used to steer the practicians in choosing prediction techniques ( Song and Li, 2008 ) . Compared with the published surveies prior to 2000, calculating methodological analysiss have been more diverse after twelvemonth 2000. In add-on to the most popular time-series and econometric theoretical accounts, a figure of new techniques have emerged in the literature, preponderantly from the class of unreal intelligence ( AI ) methods ( Song and Li, 2008 ) .The research tools of unreal intelligence ( AI ) include: fuzzed theory, Grey theory, nervous web theoretical account, Genetic Algorithms and adept systems. ( Wang, 2004, 368 )AI has grown quickly as a field of research across a assortment of subjects in recent old ages.

The chief advantage of AI techniques is that it does non necessitate any preliminary or extra information about informations, such as distribution and chance.The alone characteristics of AIs, such as the ability to accommodate to imperfect informations, nonlinearity, and supreme authority map function, do this method a utile option to the classical ( statistic ) arrested development prediction theoretical accounts ( Song and Li, 2008 ) .Their applications to tourism demand analysis was the object of many surveies, for illustration we can advert: Kon and Turner ( 2005 ) , Cho ( 2003 ) , Au and Law ( 2000, 2002 ) , Wang ( 2004 ) , Burger et al. , ( 2001 ) , Pai, Hong, Chang and Chen ( 2006 ) .



Since its origin ( Deng, 1982 ) , Grey System Theory has been developed quickly and caught the attending of many research workers. It has been widely and successfully applied to assorted systems such as societal, economic, fiscal, scientific and technological, agricultural, industrial, transit, mechanical, meteoric, ecological, hydrological, geological, medical, military, etc. ( Kayacan, 2009 )Grey theoretical accounts predict the hereafter values of a clip series based merely on a set of the most recent informations, depending on the window size of the forecaster. It is assumed that all informations values to be used in Grey theoretical accounts are positive, and the sampling frequence of the clip series is fixed ( Zhang et al. , 2003 ) .GM ( 1,1 ) type of Grey theoretical account is the most widely used in the literature, pronounced as ”Grey Model First Order One Variable ” . This theoretical account is a clip series calculating theoretical account.

The differential equations of the GM ( 1,1 ) theoretical account have time-varying coefficients. In other words, the theoretical account is renewed as the new informations become available to the anticipation theoretical account. ( Kayacan, 2009 )The Grey prediction theoretical account uses the operations of accrued coevals to construct differA­ential equations.

Intrinsically talking, it has the features of necessitating less informations. The GM ( 1,1 ) , can be denoted by the map as follows ( Wang after Hsu, 2001 ) :Measure 1: Assume an original series to be x ( 0 ) , expressed in the undermentioned signifier:ten ( 0 ) = ( x ( 0 ) ( 1 ) , x ( 0 ) ( 2 ) , x ( 0 ) ( 3 ) , aˆ¦ , x ( 0 ) ( n ) ) ( 1 )Measure 2: A new sequence x ( 1 ) is generated by the Accumulated Generating Operation ( AGO ) .ten ( 1 ) = ( x ( 1 ) ( 1 ) , x ( 1 ) ( 2 ) , x ( 1 ) ( 3 ) , aˆ¦ , x ( 1 ) ( n ) ) , ( 2 )where ten ( 1 ) ( K ) = x ( 0 ) ( I ) . ( 3 )Measure 3: Establishing a first-order differential equation.

( dx ( 1 ) /dt ) + az = U, ( 4 )where omega ( 1 ) ( K ) = I±x ( 1 ) ( K ) + ( 1-I± ) ten ( 1 ) ( k + 1 ) , ( 5 )K = 1,2, .. , n-1, I± denotes a horizontal accommodation coefficient, and 0 & lt ; I± & lt ; 1.

The choosing criterA­ion of I± value is to give the smallest forecasting mistake rate ( Wen, Huang & A ; Wen, 2000 ) .Measure 4: From Measure 3, we have:Xp ( 1 ) ( k+1 ) = ( x ( 0 ) ( 1 ) – ) e-ak + ) ( 6 ), ( 7 )where the matrices B and Y are defined as:( 8 )Y= ( x ( 0 ) ( 2 ) , x ( 0 ) ( 3 ) ten ( 0 ) ( 4 ) , … , x ( 0 ) ( n ) ) T ( 9 )Measure 5: Inverse Accumulated Generation Operation ( IAGO ) . Because the Grey prediction theoretical account is forA­mulated utilizing the information of AGO instead than original informations, IAGO can be used to change by reversal the prediction value.

Namely:Xp ( 0 ) ( K ) = xp ( 1 ) ( K ) – xp ( 1 ) ( k-1 ) , k=2,3, aˆ¦n. ( 10 )


The informations used were reachings to Romania from the major markets of Bulgaria, Hungary and Germany from 1996 to 2007 ( Table 1 ) in order to calculate touristry reachings to Romania utilizing Grey Model GM ( 1,1 ) . These three states are among the first five states that registered the highest figure of tourers ‘ reachings in Romania. Therefore, their influence is of import in the future public presentation of Rumanian touristry. The informations used in this survey ends in 2007 which is a constrained imposed by the beginning. Still, we consider that the research will hold a good impact and relevancy because of the clip horizon – 2013.

The Grey theoretical account GM ( 1,1 ) is a clip series anticipation theoretical account. It is non necessary to use all the information from the original series to build GM ( 1,1 ) , but the authority of the series must be more than 4 ( Wong, 2004, 371 ) .

Table 1. Arrivals of foreign visitants in Romania, by 3 beginning states





199647500082500026400019976040007960002720001998464000829000259000199948900010310002490002000363000120300025500020013920001131000328000200236300011530003590002003340000153700038000020043750002603000296000200538900015220003540002006399000136700034300020078180001743000473000Source: Rumanian National Institute of StatisticssThe work takes the touristry flows from Bulgaria to be the calculative illustration.The original entire touristry series are:Based on the initial sequence X ( 0 ) , a new sequence is generated by the AGO.In this survey we used I±= 0,999 and obtained the undermentioned consequence:Taking into equation:Xp ( 0 ) ( K ) = ,k=2,3, aˆ¦n, aˆ¦ , n+1For k=11 ; the prognosis value generated utilizing GM ( 1,1 ) is 817546 tourers.Using the old computation stairss, the prognosis value of touristry reachings in Romania from Hungary is 1846567 tourers and from Germany 473000 tourers ( twelvemonth 2008 ) .

Based on Figures 1 and 2 we can besides calculate the figure of foreign visitants for future periods ( 2010-2013 ) .Table 3 lists the mistake of the prognosis theoretical accounts for three states mentioned above utilizing GM ( 1,1 ) theoretical account.

Table 2. The analysis of mistakes

ModelBulgaria ( % )Hungary ( % )Germany ( % )GM ( 1,1 )1,7654,4532,546Writers ‘ computation


Having discussed the traditional prediction techniques for touristry demand, the part of this work is to show an alternate method to the more traditional 1s – e.

g. AI methods, that does non do many premises when organizing the prediction theoretical account ( Wang, 2004 ) , viz. that of Grey theoretical account to foretell touristry demand reaching to Romania.The theoretical account truth scrutiny consequences show that GM ( 1,1 ) theoretical account achieves an accurate prognosis when the sample informations show a stable addition tendency.

Therefore, we can appreciate that the method is suited for the Rumanian touristry industry and it will offer good chances for realistic future dimensions of the concern.However, the theoretical account GM ( 1,1 ) does non give us the same public presentation when the crude informations sequence additions like as in a S-curve or it has a impregnation part. In this instance we must utilize other techniques of prediction ( Kayacan, 2009 ) .

From the anticipation, touristry demand will continuously turn in the hereafter ( Fig.1 and Fig. 2 ) . This determines a more active engagement from the Rumanian stakeholders in order to program and to do proper determinations in touristry industry. They will hold to develop different policies, related to different possible foreign visitants, harmonizing with the specific anticipations.

As we can see from Figures 1 and 2 the consequences do non bespeak the same anticipations for the three analyzed states. Therefore, the schemes will be adjusted in order to obtain the maximal benefits.Tourism demand prognosiss can be helpful to sellers and other directors in cut downing the hazard of determinations sing the hereafter.

For illustration, touristry sellers use demand prognosiss to: put selling ends, either strategic or for the one-year selling program ; explore possible markets as to the feasibleness of carrying them to purchase their merchandise and the awaited volume of these purchases ; imitate the impact of future events on demand, including alternate selling policies every bit good as unmanageable developments such as the class of the economic system and the actions of rivals ( Frechtling, 2001 ) .

Fig. 1.

Tendency of existent value and prognosis values based on GM ( 1,1 )

Writers ‘ computation

Fig. 2. Tendency of existent value and prognosis values based on GM ( 1,1 )

Writers ‘ computationDirectors use touristry demand forecasts to:determine operational demands, such as staffing, supplies, and capacity ;survey undertaking feasibleness, such as the fiscal viability of constructing a new hotel tower, spread outing a eating house, building a new subject park or offering air hose service to a new finish.Planners and others in public bureaus use touristry demand forecasts to:predict the economic, social/cultural, environmental effects of visitants ;assess the possible impact of regulative policies, such as monetary value ordinance and environmental quality controls ;undertaking public grosss from touristry for the budgeting procedure ;guarantee equal capacity and substructure, including airdromes and air passages, Bridgess and main roads, and energy and H2O intervention public-service corporations ( Frechtling, 2001 ) .In short, tourism demand accurate prognosiss can cut down the hazards of determinations and the costs of pulling and functioning the going populace.


For future research, we have to take into consideration seasonality which has ever been a possible restraint of touristry demand analysis, more specific the ways in which the seasonality in touristry demand patterning and prediction could be better handled. Beside seasonality, we can pay attending to regionalism and we may develop the hereafter anticipations utilizing the Grey theoretical account taking into consideration the fact that we can associate different foreign visitants to certain parts within the state. We assume that we will be able to acquire more accurate information for the practicians being involved in touristry concern.


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