Hybrid Modeling for Forecasting of Domestic VFR Tourism Demand in Tehran

Document Type : Research Paper

Authors

1 Faculty Member of Allameh Tabatabaei University

2 Faculty Member of Tarbiat Modares University

3 Faculty Member of Management and Tourism Management Department of Islamic Azad University

Abstract

Extended Abstract
Most management decisions at all levels of the organization directly or indirectly depend on the state of future prediction, and through the same prediction that one can imagine a success or a status in the future, thereby minimizing risk or making any adjustments and adaptations in the program (Inskeep, 2004).
 
Introduction
One of the most important events in the tourism industry of each country is the demand for a product or destination of tourism. But it should be noted that predictions can never match 100% of what happens in practice (Claveria & Torra,2014). There will always be distances and deviations between actual and predicted values, but the use of scientific and modern methods of forecasting will cause the results to reach far more than an objective estimate to the truth (Shen & Song,2011). In recent years, with the changing pattern of holidays and the formation of short-term holidays, cities have found the opportunity for tourism development.
 
Materials and Methods
One of the most important types of domestic tourism in Tehran, based on the statistics of the National Center of Statistics and the views of the experts in this area, is VFR tourism. A definition was proposed by Backer (2003: 4); ‘VFR Tourism is a form of tourism involving a visit whereby either (or both) the purpose of the trip or the type of accommodation involves visiting friends and/ or relatives.’ This definition encompasses all VFR typologies and was put forward as a model (Backer, 2008b) to aid in explaining VFR typologies.
 
Source: Backer, 2008b
As represented in the VFR Travel Definitional Model (table 1), VFRs can fall into a number of distinct groups. The category in the top left hand box depicts what could be considered to be the “pure” VFR, whose purpose is to visit their friends and or relatives and also stays with them. The lower left hand box depicts a less pure form of VFR traveler, who is staying with friends and relatives, but whose purpose of visit may be unrelated to VFR. This issue that VFR travelers can have a non-VFR purpose of visit when staying with friends or relatives has been discussed in previous research (Backer, 2003; Seaton & Palmer, 1997).
 
Discussion and Results
For this purpose, the present study seeks to propose models for forecasting effective variables on forecasting domestic business tourism demand in Tehran. To do this, information was used between the years 2001 to 2015. Independent variable of this study is the number of domestic VFR tourists in Tehran, and dependent variables were selected based on Delphi and Fuzzy DEMATEL techniques. The model framework is a combination of regression, fuzzy neural network, and SVR algorithm, which combines these methods to measure forecast errors and compare the methods.
 
Conclusion
The results of this research show that the proposed hybrid approach of regression and Adaptive Neuro-Fuzzy Inference System (ANFIS) can have better prediction than other methods for forecasting domestic VFR tourism.
 
 
 
Table2: Test results of different methods on the demand of domestic VFR tourism in Tehran (evaluation data)





VFR Tourism


 


MSE


RMSE


NMSE


R2


MAE


MAPE




regression


0.001785


0.042249


0.025412


0.974588


0.000493


0.049325




fuzzy neural network


0.001395


0.037349


0.019856


0.980144


0.000436


0.043579




SVR algorithm


0.061316


0.247621


0.872842


0.127158


0.002746


0.274595




hybrid approach of regression and SVR algorithm


1.24E-05


0.003521


0.000177


0.999823


3.28E-05


0.00328




hybrid approach of regression and Adaptive Neuro Fuzzy Inference System (ANFIS)


8.74E-06


0.002956


0.000124


0.999876


2.78E-05


0.002779





 
Keywords: regression, adaptive neuro-fuzzy inference system (ANFIS), Support Vector Regression (SVR) Algorithm, VFR tourism, forecasting of domestic VFR tourism demand, Tehran.
 
References:

Backer, E. (2003). VFR tourism – the forgotten tourism phenomenon. Issues and Developments in Tourism Futures. In H. Richins (ed.) Proceedings from the Second National Conference on Tourism Futures Sustainable Growth, Strategic Alliances and Positive Futures in Challenging Times 2003,pp 1-12.
Backer, E. (2008b). Opportunities for Commercial Accommodation in VFR. International Journal of Tourism Research (under review).
Claveria, O. and Torra, A. (2014), Forecasting Tourism Demand to Catalonia: Neural Networks vs. Time Series Models, Economic Modeling, 36, pp. 220-228.
Inskeep.E,(2004).Tourism planning is an integrated and sustainable approach to tourism planning and development.translated by: Mahmood Hasanpoor and Saeid Daghestani. Tehran: Mahkameh.
Shen, S., Li, G., & Song, H. (2011). Combination forecasts of international tourism demand. Annals of Tourism Research, No.38:72–89 
Seaton, A., Palmer, C. (1997). Understanding VFR Tourism behaviour: the first five years of the United Kingdom tourism survey. Journal of Tourism Management, 18 (6)pp345-355.

 

Keywords

References

  1. Arbel, A. and Revid, A. (2001).On recreation demand: A time series approach. 22(3/4), pp7-20.
  2. Athanasopoulos, G, Deng, M, Li, G, Song, H,(2014), Modeling substitution between domestic and outbound tourism in Australia: A system-of-equations approach, journal of Tourism Management 45 .pp 159-170
  3. Athanasopoulos, G. Hyndman. R.(2008)” Modeling and Forecasting Australian domestic, journal of Tourism Management, 33(3) .pp 415-423.
  4. Backer,E.R(2005). The Relationship of VFR Travellers with Tourism Industries. University Drive, Mount Helen.
  5. Becker L. A. (2003). Effect size calculators. Retrieved from tiny.cc/beckerescalc Google Scholar.
  6. Braunlich, C. & Nadkarni, N. (1995). The Importance of the VFR Market to the Hotel Industry. The Journal of Tourism Studies. Vol6 (1)pp 38-47.
  7. Burkart, A, J. and Medelik, S.(1981), Tourism: Past, Present and Future, 2nd edn.Oxford.
  8. Can, V.(2013). Modeling tourism demand, travel mode choice and destination loyalty, a dissertation for the degree of Philosophies Doctor, Faculty of Biosciences, Fisheries and Economics, Troms University Business School.
  9. Chang, P-T.  (1998). the fuzzy Delphi method via fuzzy statistics andmembership function fitting and application to the human resources.  Fuzzy Sets and Systems, pp112-123.
  10. Chen, K.-Y., & Wang, C.-H. (2007). Support vector regression with genetic algorithms in forecasting tourism demand. journal of Tourism Management, 28, pp215–216.
  11. Cho, V. (2003).A comparison of three different approaches to tourist arrival forecasting. journal of Tourism Management, Vol. 24, pp323–330.
  12. Claveria, O. and Torra, A. (2014), Forecasting Tourism Demand to Catalonia: Neural Networks vs. Time Series Models, Economic Modeling, 36, pp. 220-228.
  13. Crouch, G. I., & Ritchie, J. R. B. (1999). Tourism, competitiveness, and societal prosperity. Journal of Business Research, 44,pp 137-152.
  14. Diamond, J. (2000). Tourism role in economics development, the case Re-examines economic development. Vol 25 (3): 539-53
  15. Forbes, K, Berthur, M, Sebastian, V, (2014), Pricing and domestic tourism performance in Zimbabwe, African Journal of Hospitality, Tourism and Leisure Vol. 3 (2):1-12.
  16. Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within theframework of DEMATEL. Switzerland, Geneva: Battelle Geneva Research Centre.
  17. Hamal, K. (2007), Modeling domestic holiday tourism demand in Australia: problems and solutions, Asia Pacific Journal of Tourism Research, 1(2): 35-46.
  18. Jaume, R, Aon, W(2015), The Use of Tourism Demand Models in the Estimation of the Impact of Climate Change on Tourism, Special Issue, 26(1):  4-20.
  19. Köber, J, Prettenthaler, F, Neil Bird, D.(2016), Modeling climate change impacts on tourism demand: A comparative study from Sardinia (Italy) and Cap Bon (Tunisia), journal of Science of The Total Environment, Volume 543, Part B: 1039–1053.
  20. Law, R and Au, N. (1999).A Neural network model to forecast Japanese demand for travel to Hong Kong. Journal of Tourism Management. No. 20: 89-97.
  21. Lehto, X., Morrison, A. O’leary, J. (2001). Does the Visiting Friends and Relatives’ Typology Make a Difference? A study of the International VFR  market to the United States. Journal of Tourism Research. 40 (November)pp201-212.
  22. Leiper, N. (2004). Tourism Management. Third edn. Australia: Pearson.
  23. Lohmann, M.(2004), New Demand Factors in Tourism. presented to the European Tourism Forum, Budapest.
  24. Makridakis, S., Hibon, M. (٢٠٠٠).The M٣-competition: Results, conclusions and implications. International Journal of Forecasting, No. ١٦:٤٥١–٤٧٦.
  25. Massidda, C, Etzo, I.(2012), The Determinants of Italian Domestic Tourism: A Panel Data Analysis, Journal of Tourism Management 33(3) : 415-423.
  26. Mustafa M. H. (2012), Improving the contribution of domestic tourism to the economy of Jordan, Asian Social Science , 8(2): 49-61.
  27. Pai, P. F., Hong, W. C., Chang, P. T., & Chen, C. T. (2006). The application of support vector machines to forecast tourist arrivals in Barbados: an empirical study. International Journal of Management, No.23: 375–385.
  28. Palmer, A; Jose, Montanoو J and Sese, A.(2006).Designing an artificial neural network for forecasting tourism time series. journal of Tourism Management. Vol. 27:781-790.
  29. Patuelli, R,  Mussoni, M,  Candela, G(2013), The Effects of World Heritage Sites on Domestic Tourism: A Spatial Interaction Model for Italy, The Rimini Centre for Economic Analysis Journal, Volume 11: 189–213.
  30. Pearce, D. G., (2001). Towards a Regional Analysis of Tourism inSoutheast Asia. In: Teo, P., Chang, T. C., Ho, K. C. (Eds.) Interconnected Worlds: Tourism in Southeast Asia. Oxford, Pergamum.
  31. Rostamzadeh, R, Sofian, S(2011), Prioritizing effective 7 Ms to improve production systems performance using fuzzy AHP and fuzzy Topsis(case study), Expert system with Applications, Vol.38:5166-5177.
  32. Romilly P. Liu, X. and Song. H.(2009) .Economic and social determinant of international Tourism spending: A panel data analysis. Tourism analysis. 3(1):3-16.
  33. Seaton, A., Palmer, C. (1997). Understanding VFR Tourism behaviour: the first five years of the United Kingdom tourism survey. Journal of Tourism Management, 18 (6) :345-355.
  34. Song, H., and Turner, L. (2006).Tourism demand forecasting. In L. Dwyer, & P. Forsyth (Eds.), International handbook on the economics of tourism. Cheltenham: Edward Elgar.pp89-114.
  35. Shen, S., Li, G., & Song, H. (2011). Combination forecasts of international tourism demand. Annals of Tourism Research, No.38:72–89.
  36. Tzeng, G.-H., Chiang, C.-H., & Li, C.-W. (2007). Evaluating intertwined effects in e-learningprograms: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, No.32:1028–1044.
  37. Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag, New York.
  38. Vetitnev, A, Kopyirin, A, Kiseleva, A,(2015). System dynamics modeling and forecasting health tourism demand: the case of Russian resorts, Journalof Current Issues in Tourism , 18( 7): 618-623
  39. Yang,y, Liu, Z, Qi, Q.(2014) Domestic tourism demand of urban and rural residents in China: Does relative income matter?, Journal of Tourism Management, Vol 40:  193–202.
  40. Yepremian..G, (2005), Forecasting Tourism Demand in Japon, International Journal of Forecasting, Vol 12.pp447-75.
  41. Yuan, T., Fridgem, J., Hsieh, S., O’Leary, J. (1995). Visiting Friends and Relatives Travel Market: The Dutch Case. The Journal of Tourism Studies6(1) :19-26.
Journal of Tourism Planning and Development
Volume 7, Issue 26
December 2018
Pages 122-145

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