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پیشبینی ماهانۀ تقاضای گردشگر برای مجموعۀ تاریخی تخت جمشید | ||
| پژوهشهای جغرافیای انسانی | ||
| مقاله 5، دوره 46، شماره 1، اردیبهشت 1393، صفحه 69-84 اصل مقاله (442.04 K) | ||
| نوع مقاله: مقاله علمی پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jhgr.2014.50594 | ||
| نویسندگان | ||
| حسنعلی فرجی سبکبار* 1؛ داوود شاهسونی2؛ حسن بهنام مرشدی3؛ حسین روستا4 | ||
| 1دانشیار دانشکدة جغرافیا، دانشگاه تهران | ||
| 2استادیار، دانشکدۀ علوم ریاضی، دانشگاه شاهرود | ||
| 3کارشناس ارشد برنامهریزی گردشگری، دانشگاه تهران | ||
| 4دانشجوی کارشناسی ارشد اکوتوریسم، دانشگاه هرمزگان | ||
| چکیده | ||
| پیشبینی شمار ورود گردشگران، اهمیت ویژهای برای گردشگری و فعالیت‌های وابسته به گردشگری دارد؛ چرا که پیشبینی، شاخصی برای تقاضای آینده بوده و به موجب آن، در پی فراهمکردن اطلاعات پایه برای برنامهریزی و سیاستگذاریهای پیدرپی است. در برنامهریزی گردشگری، پیشبینی تعداد گردشگران بیشترین ارتباط و کاربرد را در مبحث مدیریت گردشگری دارد؛ زیرا یکی از ابعاد اصلی برای برنامهریزی گردشگری، برنامهریزی بازاریابی آیندهنگر است. تعداد گردشگران با عرضه و تقاضای بازار ارتباط مستقیم دارد. مدیران و برنامهریزان مرتبط با گردشگری، باید از یک سو در تلاش برای رفع نیاز گردشگران و ارائۀ تسهیلات بهتر به آنها باشند و از سوی دیگر، محصولات وابسته به گردشگری ماهیتی ذخیرهشدنی و انبارکردنی ندارند. چنانکه اتاق یک هتل که یک شب رزرو نشود، صندلی یک هواپیما که مسافری برای آن پیدا نشده و میز یک رستوران که خالی مانده است، منافعی است که از دست رفته و امکان ذخیرهکردن برای آینده وجود ندارد و این خود لزوم اطلاع از ورود گردشگران را برای مدیران مرتبط با این فعالیتها دوچندان میکند. بر همین اساس پیشبینی درست تقاضای گردشگران، میتواند به کاهش ریسک در تصمیمگیری و هزینه منجر شود و این مهم با اطلاع از تقاضای گردشگران به منطقه و نیازهایشان در آینده حاصل می‌شود. برای پیشبینی تقاضای گردشگر، از مدل‌های گوناگونی چون مدل‌های سری زمانی، آریما، سیستمهای عصبی ـ فازی، سیستمهای ماشین بردار و مانند آنها استفاده می‌شود که در این پژوهش، از مدل سری زمانی آریما استفاده شده است. نتایج نشان داده است که الگوی پیشبینی تقاضای گردشگر در مجموعۀ تاریخی ـ فرهنگی تخت جمشید، بر اساس دادههای رسمی سال‌های 1376 تا 1389مجموعۀ پارسه ـ پاسارگاد، فصلی بوده و لذا مدل‌های آمیخته فصلی برای گردشگران داخلی و خارجی، بهطور مجزا برآورد شده است. | ||
| کلیدواژه‌ها | ||
| : برنامهریزی گردشگری؛ پیشبینی تقاضا؛ پیشبینی تقاضای گردشگر؛ تخت جمشید؛ مدل آریما | ||
| عنوان مقاله [English] | ||
| Monthly Forecasting of Tourism Demand for Persepolis Site | ||
| نویسندگان [English] | ||
| Hasanali Faraji Saboksar1؛ Davood Shahsavani2؛ hasan behnam Morshedi3؛ Hossien Rousta4 | ||
| 1Associate Prof., Faculty of Geography, University of Tehran | ||
| 2Assistant Prof., Faculty of Mathematical Sciences, University of Shahrood | ||
| 3MA. in Tourism Planning, University of Tehran | ||
| 4MA. Student in Ecotourism, University of Hormozgan | ||
| چکیده [English] | ||
| Introduction For efficient organization and effective management of tourism and the pertinent activities, modeling and forecasting the tourist destination areas are vital issues for good performance. It helps make a better policy and plan for supplying tourist requirements. The number of tourists is related to the market supply and demand. Different services are cooperated in supplying tourism productions, such as reception, entertainment, residential, health and information services. On the other hand, regarding demand, there are many factors affecting the tourists’ destination. For example, economic-social conditions, language, culture and motivation that form the request process tourists. Undoubtedly, demand prediction is a drastic factor especially for activities related to tourism. In one hand, manager and planners relevant to tourism make attempt to fulfill tourisms' demands. On the other hand, many of tourisms products like hotel’s rooms, airplane seats, rent car, museum or cultural plans are not being reserved or stored naturally. A hotel room that is not reserved for a night, an airplane seat that has no passenger and a restaurant table that remains empty, are the benefits that have spoiled and they may not be reserved for the future. Therefore, the tourists demand shall be predicted. Alongside the prediction process and tourism entry demand model, the governments can organize their strategies better and prepare appropriate infrastructure for serving the tourists; the private sectors could make appropriate marketing strategies for obtaining the maximum benefits from tourist entry increase, as well. The forecasting of tourism demand is an essential tool for determining the required supply and the appropriate distribution method of tourism services. When services (like tourism) achieve desirable market, its current amount and the future potential volume shall be estimated precisely. Market underestimation or overestimation makes the supplier lose the main part of his/her interest. Hence, planning and development of tourism require identifying such these kinds of motivations and demands. Accordingly, what is vitally important for the tourism management is the amount of accuracy of prediction model that led to development and diversity of tools and new methods in prediction. Methodology In this article, the plan is to forecast the number of tourist arrival for the historical - cultural site of Perspolis in south Iran. The time series involves monthly data that were collected for both domestic and international tourists. In order to testify the performance of forecasting method, the collected data were divided into two sets, training (Farvardin 1376- Esfand 1387) and testing (Farvardin1388- Esfand 1389). We used seasonal ARIMA model to detect the hidden structure of data and finally forecast the arrivals for both data sets. Results and Discussion Based on the Box & Jenkins approach, both time series data were analyzed. In this approach, stationarity of time series is a preliminary condition. Therefore, before any attempts, the time series were made stationary by differencing. The result of data analysis of Persepolis- domestic tourism Since the number of visitors in Farvadin (April) of each year has considerable difference from the other menthes, therefore, it is likely that the forecasting model would be seasonal. The great amount of autocorrelation function in the lags 12, 24 & 36 confirms the existence of the seasonal model. Since the seasonal data are not stationary, differencing can help to make a steady time series. The results showed that, seasonal differencing in order 12, and then first differencing make the time series in an acceptable stationary form. Thus, we could determine the seasonal model of ARIMA (p,1,q) (P,1,Q)12 according to the ACF and PACF of the final series. Exponential decay of PACF in some of the first lags (figure 3, right frame) and the fact that autocorrelation amount in lag 1,r1,is significantly different from zero, shows no seasonal moving average model of order 1, MA(1), i.e. p=0, q=1. It is also observed in autocorrelation function (figure 3, left frame) that the amount of r24 is significant and this means a seasonal MA (2) (P=0, Q=2). Therefore, the final model of ARIMA (0,1,1) (01,2)12 may be written as the following: 1) 1 − 1 − 12 = 1 − 1 1 − 112 − 24 2) − 1 − 24 + 25 = − 11 − 112 + 1113 − 224 + 2125 The result of data analysis on Persepolis- international tourism The plot of this time series implies that it is non-stationary. However, seasonality is not obvious in the last example, but since the amount of r6 and r12 in autocorrelation diagram are located out of the 95% confidence interval, a seasonal differencing with a six-month course is suggested. The results show that the six-month seasonal differentiation series is not stationary, but if this series be re-differencing (first order) we may observe an approximately stationary series. In order to determine the order and the kind of series in non-seasonal part of ARIMA (p,1,q)(P,1Q)6, we could consider the amount of autocorrelation as an evidence of damping sine wave to zero and since the two first amount of partial autocorrelation are significant and different from zero, the unseasonal autoregressive model, p=2, q=0, is suggested. In the seasonal part, (P,1,Q), r6, r12,r18,…., are damping to zero and since the amount of partial autocorrelation in lag 6 is significant, the seasonal AR model with Q=0 & P=1 seems to be more appropriate. ARIMA (2,1,0)(1,1,0)6 is as the following. 3) 1 − 1 − 221 − 61 − 1 − 6 = Evaluation of the suggested model was made by comparing real test data versus the forecasted data. Figures 5 and 9 successfully showed that both real and forecasted values of tourist arrival have the same variation in different months. Conclusion In this research, we conclude that, the tourist arrival time series can be stationary by two differentiations (seasonal and first order differencing). In other words, the seasonal factor of this series is the inseparable part of them, with this difference that, the seasonal course for domestic and foreign visitors is 12 & 6 months, respectively. The results also show that the seasonal ARIMA model is an appropriate estimation for forecasting the number of tourists. | ||
| کلیدواژه‌ها [English] | ||
| ARIMA Model, Persepolis, Tourism demand, Tourism Demand Forecasting, tourism planning | ||
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