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Developing a Web-Based Tourism Demand Forecasting System

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  • Haiyan Song
  • Stephen F. Witt
  • Xinyan Zhang

Abstract

Tourism demand is the foundation on which all tourism-related business decisions ultimately rest and so accurate forecasts of tourism demand are crucial for tourism industry practitioners. From the functional point of view, a tourism demand forecasting system (TDFS) is a forecasting support system capable of providing quantitative tourism demand forecasts and allowing users to make their own ‘what-if’ scenario forecasts. From the technical point of view, a TDFS is an information system consisting of a set of computer-based modules or components that support tourism demand forecasting and scenario analysis. This paper establishes a widely accessible Web-based TDFS which not only takes advantage of advanced econometric tourism demand forecasting techniques but also incorporates the real-time judgemental contribution of experts in the field. Furthermore, scenario forecasts are permitted within the system. Built on Web-based technology, the system provides advanced information sharing and communication and brings considerable convenience to various stakeholders engaged in tourism demand forecasting at different locations. In attempting to generate more accurate tourism demand forecasts, the system is designed to incorporate a two-stage forecasting methodology, which integrates judgemental adjustments with statistically based forecasts. The software architecture, detailed components and development environment of the Web-based TDFS are described in detail. A three-tiered client–server architecture is employed, which offers great flexibility, reusability and reliability. The prototype system has been developed and screen shots of interaction with the system are presented using Hong Kong tourism as an example.

Suggested Citation

  • Haiyan Song & Stephen F. Witt & Xinyan Zhang, 2008. "Developing a Web-Based Tourism Demand Forecasting System," Tourism Economics, , vol. 14(3), pages 445-468, September.
    • Handle: RePEc:sae:toueco:v:14:y:2008:i:3:p:445-468
    DOI: 10.5367/000000008785633578
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    References listed on IDEAS

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    1. Paul Goodwin, 2005. "How to Integrate Management Judgment with Statistical Forecasts," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 8-12, June.
    2. Haiyan Song & Peter Romilly & Xiaming Liu, 2000. "An empirical study of outbound tourism demand in the UK," Applied Economics, Taylor & Francis Journals, vol. 32(5), pages 611-624.
    3. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    4. Kuo, R. J., 2001. "A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm," European Journal of Operational Research, Elsevier, vol. 129(3), pages 496-517, March.
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    Cited by:

    1. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901.
    2. Liu, Anyu & Vici, Laura & Ramos, Vicente & Giannoni, Sauveur & Blake, Adam, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team," Annals of Tourism Research, Elsevier, vol. 88(C).
    3. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    4. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.

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