• Asia-Pacific Journal of Business
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• v.15 no.1
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• pp.319-334
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• 2024

Purpose - The purpose of this study is to examine the gravity model commonly used for demand forecasting upon the implementation of new tourist facilities and analyze the main causation of forecasting errors to provide a suggestion on how to improve. Design/methodology/approach - This study first measured the errors in predicted values derived from past feasibility study reports by examining the cases of five national science museums. Next, to improve the predictive accuracy of the gravity model, the study identified the five most likely issues contributing to errors, applied modified values, and recalculated. The potential for improvement was then evaluated through a comparison of forecasting errors. Findings - First, among the five science museums with very similar characteristics, there was no clear indication of a decrease in the number of visitors to existing facilities due to the introduction of new facilities. Second, representing the attractiveness of tourist facilities using the facility size ratio can lead to significant prediction errors. Third, the impact of distance on demand can vary depending on the characteristics of the facility and the conditions of the area where the facility is located. Fourth, if the distance value is below 1, it is necessary to limit the range of that value to avoid having an excessively small value. Fifth, depending on the type of population data used, prediction results may vary, so it is necessary to use population data suitable for each latent market instead of simply using overall population data. Finally, if a clear trend is anticipated in a certain type of tourist behavior, incorporating this trend into the predicted values could help reduce prediction errors. Research implications or Originality - This study identified the key factors causing prediction errors by using national science museums as cases and proposed directions for improvement. Additionally, suggestions were made to apply the model more flexibly to enhance predictive accuracy. Since reducing prediction errors contributes to increased reliability of analytical results, the findings of this study are expected to contribute to policy decisions handled with more accurate information when running feasibility analyses.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.35-37
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• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.

• Land and Housing Review
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• v.9 no.4
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• pp.10-18
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• 2018

As the volatility increasement of the number of tourist, there was been controversy over supply-demand imbalance in hotel market. The purpose of this study is to analysis on determinants of hotel occupancy rate in Jeju Island. The quantitative method is based on cointegrating regression, using an empirical dataset with hotel from 2000 to 2017. The primary results of research is briefly summarized as follows; First, there are high relationship between total hotel occupancy rate and hotel occupancy of foreign tourist. The volatility of hotel occupancy is caused by foreigner user than local tourists though local tourist high propotion of hotel occupancy in Jeju Island. Second, hotel occupancy of local tourist has not relationship with demand and supply variables. Because some hotel users are not local tourists but local resident, and effects to other variables of hotel consumer trend, accommodation such as Guest house, Airbnb. Third, there are high relationship between foreign hotel occupancy rate and demand-supply variables. These research imply that total management of supply-demand is very important to seek stability of hotel occupancy rate in Jeju Island. Also it can provide a useful solution regarding mismatch problem between supply-demand as well as development the systematic forecasting model for hotel market participants.

• The Journal of Information Systems
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• v.26 no.3
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• pp.171-185
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• 2017

Purpose Recently, there has been an increase in attempts to analyze social phenomena, consumption trends, and consumption behavior through a vast amount of customer data such as web search traffic information and social buzz information in various fields such as flu prediction and real estate price prediction. Internet portal service providers such as google and naver are disclosing web search traffic information of online users as services such as google trends and naver trends. Academic and industry are paying attention to research on information search behavior and utilization of online users based on the web search traffic information. Although there are many studies predicting social phenomena, consumption trends, political polls, etc. based on web search traffic information, it is hard to find the research to explain and predict tourism demand and establish tourism policy using it. In this study, we try to use web search traffic information to explain the tourism demand for major cities in Gangwon-do, the representative tourist area in Korea, and to develop a nowcasting model for the demand. Design/methodology/approach In the first step, the literature review on travel demand and web search traffic was conducted in parallel in two directions. In the second stage, we conducted a qualitative research to confirm the information retrieval behavior of the traveler. In the next step, we extracted the representative tourist cities of Gangwon-do and confirmed which keywords were used for the search. In the fourth step, we collected tourist demand data to be used as a dependent variable and collected web search traffic information of each keyword to be used as an independent variable. In the fifth step, we set up a time series benchmark model, and added the web search traffic information to this model to confirm whether the prediction model improved. In the last stage, we analyze the prediction models that are finally selected as optimal and confirm whether the influence of the keywords on the prediction of travel demand. Findings This study has developed a tourism demand forecasting model of Gangwon-do, a representative tourist destination in Korea, by expanding and applying web search traffic information to tourism demand forecasting. We compared the existing time series model with the benchmarking model and confirmed the superiority of the proposed model. In addition, this study also confirms that web search traffic information has a positive correlation with travel demand and precedes it by one or two months, thereby asserting its suitability as a prediction model. Furthermore, by deriving search keywords that have a significant effect on tourism demand forecast for each city, representative characteristics of each region can be selected.

• Journal of the Korean Society for Railway
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• v.17 no.4
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• pp.307-312
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• 2014

This study suggested the ARIMA model taking into consideration the seasonal characteristic factor as a method for efficiently forecasting passenger transport demand of the Joongang Line. The forecasting model was built including the demand for the central inland region tourist train (O-train, V-train), which was opened to traffic in April-, 2013 and run in order to reflect the recent demand for the tourism industry. By using the monthly time series data (103) from January-, 2005 to July-, 2013, the optimum model was selected. The forecasting results of passenger transport demand of the Joongang Line showed continuous increase. The developed model forecasts the short-term demand of the Joongang Line.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.638-644
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• 2008

Mungyeng line(Jupyung${\sim}$Mungyeng) was closed due to a rapid decrease in demand in 1995. However, as the rail transportation demand is expected to increase with the plan to develop a tourist resort and a traffic network in Mungyeng area, it is required to forecast future demand to meet the change of transportation environment in this region. This study predicts the rail transportation demand and analyzes financial benefit in operator's side in case of reopening this line, based on nation-wide traffic volume data from Korean Transportation Database(KTDB). The results of this research can be applied to not only establishing a train operation plan also improving customer service. Moreover, Korail will have an opportunity to develop new business by linking train service to tourist attractions around the Mungyeng area.

• Journal of Agricultural Extension & Community Development
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• v.19 no.2
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• pp.301-320
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• 2012

The study uses a seasonal ARIMA model to forecast the number of tourists of Yeongdeok in an uni-variable time series. The monthly data for time series were collected ranging from 2006 to 2011 with some variation between on-season and off-season tourists in Yeongdeok county. A total of 72 observations were used for data analysis. The forecast multiplicative seasonal ARIMA(1,0,0)$(0,1,1)_{12}$ model was found the most appropriate one. Results showed that the number of tourists was 10,974 thousands in 2012 and 13,465 thousands in 2013, It was suggested that the grasping forecast model is very important in respect of how experts in tourism development in Yeongdeok county, policy makers or planners would establish strategies to allocate service in Yeongdeok tourist destination and provide tourism facilities efficiently.

• Journal of Korea Port Economic Association
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• v.27 no.4
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• pp.219-235
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• 2011

Today FTAs(Free Trade Agreements) are revving up among countries in the course of glocalization. Dubai, Pudong of Shanghai, Binhai shinku of Tianjin are actively pursuing Free Zones, and Saemangeum District in Korea is under development as growth base in North East Asia. This study aims to present the proper development scale and other development schemes for Samangeum Newport. In conclusion, following several schemes are required; firstly more sophisticated forecasting of demand and supplementation for Saemangeum Newport, secondly development of dedicated container terminals and dedicated food terminals, and finally cruise terminal for the tourist activation.

• Management & Information Systems Review
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• v.34 no.1
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• pp.179-190
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• 2015

Local governments are commercializing natural environment, one of tourist commodities, to ensure that the proceeds from sale of tourist commodities are returned to local residents(Han Yeong-joo, Lee Moo-yong, 2001). In Songdo beach, Busan, cable car dismantled in 1980s due to the run-down state of the facility is poised for restoration in 26 years and can be said to be of great value as tourist commodity of the region and necessitates the demand forecast. To overcome limitations of demand forecast in existing studies, the analysis was made based on feasibility rate(Gruber index, self-confidence index), the realizable predictive value, for the willingness-to-visit rate when forecasting the demand of visitors. The results of demand forecast suggested that number of visitors would range from approximately 550,684 persons to 1,514,416 persons when the target region for demand forecast was confined to Busan Metropolitan City, and was in the range between 1,013,740 persons and 2,854,340 persons when the target region was expanded to cover Busan, Ulsan, and Gyeongnam. Based on the results of this study, estimation of visitors and demand forecast for Songdo offshore cable car restoration which reflect characteristics of Songdo beach of Busan would provide important basis for proceeding with tourism industry development project.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.343-356
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• 2023

Purpose - This study attempted to predict short-term transportation demand using trains and getting off at Gangchon Station. Through this, we present numerical data necessary for future tourist inflow policies in the Gangchon area of Chuncheon and present related implications. Design/methodology/approach - This study collected and analyzed transportation demand data from Gangchon Station using the Gyeongchun Line and ITX-Cheongchun Train from January 2014 to August 2023. Winters exponential smoothing model and ARIMA model were used to reflect the trend and seasonality of the raw data. Findings - First, transportation demand using trains to get off at Gangchon Station in Chuncheon City is expected to show a continuous increase from 2020 until the forecast period is 2024. Second, the number of passengers getting off at Gangchon Station was found to be highest in May and October. Research implications or Originality - As transportation networks are improving nationwide and people's leisure culture is changing, the number of tourists visiting the Gangchon area in Chuncheon City is continuously decreasing. Therefore, in this study, a time series model was used to predict short-term transportation demand alighting at Gangchon Station. In order to calculate more accurate forecasts, we compared models to find an appropriate model and presented forecasts.