• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.207-215
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• 2021

The number of tourists visiting Nepal has shown rapid growth in recent years, and Nepal is expecting more tourist arrivals in the future. This paper, thus, attempts to analyze the tourist arrivals in Nepal and predict the number of visitors until 2025. This paper has examined the international tourist arrival trend in Nepal using the Gompertz and Logistic growth model. The international tourist arrival data from 1991 to 2018 is used to investigate international tourist arrival trends. The result of the analysis found that the Gompertz model performs a better fit than the Logistic model. The study further forecast the expected tourist arrival below one million (844,319) by 2025. Nevertheless, the government of Nepal has the goal of two million tourists in a year. The present study also discusses system dynamics scenarios for the two million potential visitors within a year. Scenario analysis shows that proper advertisement and positive word-of-mouth will be key factors in achieving a higher number of tourists. The current study could fill the gap of theoretical and empirical forecasting of tourist arrivals in the Nepalese tourism industry. Also, the study findings would be beneficial for government officers, planners and investors, and policy-makers in the Nepalese tourism industry.

• 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.

• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.117-122
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• 2022

One of the promising sectors of the economy today is tourism in all forms and types. The multiplier effect of tourism is huge: the income received from one tourist exceeds the amount of money spent by him at the location on the purchase of services and goods in the range from 1.5 to 4 times. Countries known as world centers of tourism have made it a state policy, taking on the functions of forecasting, coordinating and controlling. The architectural monuments of the city historical structure are a pretty resource for tourism. Cultural tourism as a type of sociocultural human activity is one of the popular and mass types of tourism. The number of people wishing to get acquainted with historical and cultural sights is growing every year. In the cultural aspect, tourism has an impact on the spiritual and material spheres of human life, his way of life, value system, social behavior.Thus, the main task of the study is to analyze the features of the architecture of tourism and tourist complexes. As a result of the study, current trends and prerequisites for the architecture of tourism and tourist complexes were investigated.

• 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.

• Journal of Korean Society of Rural Planning
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• v.17 no.1
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• pp.1-11
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• 2011

본 연구에서는 ARIMA(Autoregressive Integrated Moving Average) 모델을 이용하여 농촌관광마을의 월별 관광객을 추정하였다. 단일 마을에 대한 시계열 자료를 경상북도 안동시에 위치한 하회마을을 대상으로 구축하였다. 월별 시계열 자료는 2000년부터 2010년까지 구성되었는데(2008년도 누락), 2000년에서 2007년까지 자료는 최적 모델의 도출에 나머지는 예측치의 검정에 사용되었다. 연구 결과 최적모델에 필요한 시계열 자료의 길이는 6년으로 나타났으며, 최적모델은 계절성을 고려한 SARIMA(2,1,1)(1,1,2)12로 나타났다. 최적 시계열 년수로 나타난 6년을 사용하여 2000-2005, 2001-2006, 그리고 2002-2007의 자료로부터 각각 SARIMA(2,1,1)(1,1,2)12를 도출하여, 차기년도들에 대한 예측결과를 비교한 결과, 높은 $R^2$값을 보였다.

• 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 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.