پیش‌بینی تعداد گردشگران بر اساس رکوردهای اطلاعاتی گوگل ترندز با روش یادگیری ماشینی (موردمطالعه: گردشگران شهر یزد)

نوع مقاله : مقاله پژوهشی

نویسنده

استادیار مدیریت دانشگاه علم و هنر

10.22034/jtd.2020.217294.1952

چکیده

با توسعة صنعت گردشگری و رشد کسب‌وکارهای مرتبط، کسب اطلاعات به‌روز در برنامه‌ریزی صحیح و برآورد دقیق تعداد گردشگران، به‌منظور به‌کارگیری کارآمد منابع با هدف توسعۀ زیرساخت‌ها و افزایش درآمد، ضروری است. وجود برنامه‌های دقیق درنهایت به ارتقای سطح رضایت گردشگران ورودی منجر می‌شود. با توسعۀ فرهنگ جست‌وجوگری اطلاعات، گردشگران معمولاً پیش از آغاز سفر، به جست‌وجوی اطلاعات مربوط به اقامتگاه‌ها و خدمات گردشگری موجود در مقصد، از طریق منابع اینترنتی، اقدام می‌کنند. در پژوهش حاضر، با استفاده از داده‌های منتخب مربوط به پرس‌وجوهای کاربران سراسر جهان در موتور جست‌وجوی گوگل درمورد امکانات و توانمندی‌های گردشگری شهر یزد، تعداد گردشگران آتی این شهر پیش‌بینی شده است. داده‌های پژوهش را آمارهای جست‌وجوی کاربران تشکیل می‌دهد که از سامانۀ گوگل ترندز پایین‌گذاری شده و با استفاده از روش یادگیری ماشینی مدل پیش‌بینی، طراحی و اعتبارسنجی شده است. پس از آماده‌سازی و تحلیل داده‌ها، مشخص شد که پرس‌وجوهای ثبت‌شده در گوگل ترندز، قدرت فراوانی (بیش از 95 درصد) در پیش‌بینی تعداد گردشگران شهر یزد در بازه زمانی سال 2014 تا 2019 در مقاطع ماهانه دارد

کلیدواژه‌ها


عنوان مقاله [English]

Forecasting the Tourists Demands Based on Google Trends Information Records by Machine Learning method (Case Study: Yazd tourists)

نویسنده [English]

  • Hamed Fallah Tafti
Faculty of Management and Accounting of Science and Arts university, Yazd
چکیده [English]

With the development of the tourism industry and the growth of related businesses, the need for up-to-date information in proper planning and accurate estimation of the number of tourists is essential for the efficient use of resources to develop infrastructure and increasing revenue. The existence of detailed plans will eventually lead to an increase in the satisfaction of incoming tourists. With the development of information search culture, tourists usually search for information about accommodation and tourism services in the destination through online resources before starting the trip. In the present study, using selected data related to user queries around the world in the Google search engine, about the tourism facilities and capabilities of Yazd, the number of future tourists in this city has been predicted. For this purpose, the research data consists of user search statistics, which were downloaded from the Google Trends system, and a prediction model was designed and validated using the machine learning method. After preparing and analyzing the data, it was found that the queries registered in Google Trends have a lot of power (more than 95%) in predicting the number of tourists in Yazd in the period from 2014 to 2019 in monthly periods.

کلیدواژه‌ها [English]

  • Tourists Quantity Forecasting
  • Tourism demands
  • Google Trends
  • Machine Learning
  • Yazd city
Athanasopoulos, G., Hyndman, R. J., Song, H., & Wu, D. C. (2011). “The tourism forecasting competition”. International Journal of Forecasting, 27(3), 822-844.
Bagheri, M., Shojaei, P., & Khorami, M. (2018). “A comparative survey of the condition of tourism infrastructure in Iranian provinces using VIKOR and TOPSIS”. Decision Science Letters, 7(1), 87-102.
Bangwayo-Skeete, P. F., & Skeete, R. W. (2015). “Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach”. Tourism Management, 46, 454-464.
Castle, J. L., Fawcett, N. W., & Hendry, D. F. (2009). “Nowcasting is not just contemporaneous forecasting”. National Institute Economic Review, 210(1), 71-89.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 1165-1188.
Choi, H., & Varian, H. (2012). “Predicting the present with Google Trends”. Economic record, 88(2012), 2-9.
Croes, R. R., & Vanegas Sr, M. (2005). “An econometric study of tourist arrivals in Aruba and its implications”. Tourism management, 26(6), 879-890.
Dergiades, T., Mavragani, E., & Pan, B. (2018). “Google Trends and tourists' arrivals: Emerging biases and proposed corrections”. Tourism Management, 66, 108-120.
Doornik, J. A. (2009). Improving the timeliness of data on influenza-like illnesses using Google search data. Working paper, 1-21.
Ettredge, M., Gerdes, J., & Karuga, G. (2005). “Using web-based search data to predict macroeconomic statistics”. Communications of the ACM, 48(11), 87-92.
Farashah, M. D. P., Aslani, E., & Khademzade, M. (2018). “Strategic planning of cultural tourism development in historic city of Yazd (case study: Fahadan neighbourhood)”. Almatourism: Journal of Tourism, Culture and Territorial Development, 9(18), 23-44.
Gawlik, E., Kabaria, H., & Kaur, S. (2011). “Predicting tourism trends with Google Insights”. Accessed December, 1, 2012.
Gunter, U., & Önder, I. (2015). “Forecasting international city tourism demand for Paris: Accuracy of uni-and multivariate models employing monthly data”. Tourism management, 46, 123-135.
Jansen, B. J., Spink, A., & Saracevic, T. (2000). “Real life, real users, and real needs: a study and analysis of user queries on the web”. Information processing & management, 36(2), 207-227.
Jones, R., Zhang, W. V., Rey, B., Jhala, P., & Stipp, E. (2008). “Geographic intention and modification in web search”. International Journal of Geographical Information Science, 22(3), 229-246.
Kumar, V., & Reinartz, W. (2018). Data mining. In Customer Relationship Management (pp. 135-155). Springer, Berlin, Heidelberg.
Mueller, J. P., & Massaron, L. (2016). Machine learning for dummies. John Wiley & Sons.
Önder, I. (2017). “Forecasting tourism demand with Google trends: Accuracy comparison of countries versus cities”. International Journal of Tourism Research, 19(6), 648-660.
Smeral, E. (2014). “Forecasting the city hotel market”. Tourism Analysis, 19(3), 339-349.
Song, H., & Li, G. (2008). “Tourism demand modelling and forecasting—A review of recent research”. Tourism management, 29(2), 203-220.
Vu, J. C., & Turner, L. W. (2006). “Regional data forecasting accuracy: The case of Thailand”. Journal of Travel Research, 45(2), 186-193.
Xiang, Z., & Pan, B. (2011). “Travel queries on cities in the United States: Implications for search engine marketing for tourist destinations”. Tourism Management, 32(1), 88-97.
Xu, F., Lin, Y., Huang, J., Wu, D., Shi, H., Song, J., & Li, Y. (2016). “Big data driven mobile traffic understanding and forecasting: A time series approach”. IEEE transactions on services computing, 9(5), 796-805.
Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). “Forecasting Chinese tourist volume with search engine data”. Tourism Management, 46, 386-397.
Yang, Y., Pan, B., & Song, H. (2014). “Predicting hotel demand using destination marketing organization’s web traffic data”. Journal of Travel Research, 53(4), 433-447.
Zhang, X., & Song, H. (2018). “An integrative framework for collaborative forecasting in tourism supply chains”. International Journal of Tourism Research, 20(2), 158-171.