머신러닝기법을 활용한 지역별 호텔 수요에 관한 연구
A Study on Regional Hotel Demand using Machine Learning Techniques
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The purpose of this study is to find regional hotel demand in the midst of the recovery in tourist numbers following COVID-19 and to provide suggestions. In addition, it employs machine learning techniques, which has recently become popular, to draw regional hotel demand in the country and thereby make suggestions. To predict regional hotel room demand, it used machine learning models in which exchange rates, real GDP, economically active population, trade volume, and the number of foreign arrivals were set as input variables. As a result, the national supply of hotel rooms was 28,499 thousand, and the support vector machine model predicated an oversupply until 2030, whereas XGBoost model forecasted a shortage of 761 thousand rooms in 2029 and 2,027 thousand rooms in 2030. According to the support vector machine based supply analysis by city and province, the machine learning model predicted a shortage in Ulsan and Chungcheongnam-do after 2025, in Gangwon and Jeju-do after 2026, and in Seoul and Daegu after 2029. The XGBoost based supply analysis revealed that it predicted a shortage in Ulsan and Chungcheongnam-do after 2025, in Seoul after 2027, in Daejeon after 2028, in Busan, Incheon and Jeju-do after 2029.
Abstract
The purpose of this study is to find regional hotel demand in the midst of the recovery in tourist numbers following COVID-19 and to provide suggestions. In addition, it employs machine learning techniques, which has recently become popular, to draw regional hotel demand in the country and thereby make suggestions. To predict regional hotel room demand, it used machine learning models in which exchange rates, real GDP, economically active population, trade volume, and the number of foreign arrivals were set as input variables. As a result, the national supply of hotel rooms was 28,499 thousand, and the support vector machine model predicated an oversupply until 2030, whereas XGBoost model forecasted a shortage of 761 thousand rooms in 2029 and 2,027 thousand rooms in 2030. According to the support vector machine based supply analysis by city and province, the machine learning model predicted a shortage in Ulsan and Chungcheongnam-do after 2025, in Gangwon and Jeju-do after 2026, and in Seoul and Daegu after 2029. The XGBoost based supply analysis revealed that it predicted a shortage in Ulsan and Chungcheongnam-do after 2025, in Seoul after 2027, in Daejeon after 2028, in Busan, Incheon and Jeju-do after 2029.
참고문헌 (20)
- 머신러닝 기반의 경기도 광역버스 이용변화 예측 연구, "김병관", 경기연구원, ~ (2021)
- 김석출, "Comparison of Forecasting Accuracy With Time Series Models : With a Focus on Demand for Luxury Hotels in Gyongju", 호텔경영학연구, 13(3), pp.23~36 (2004)
- 김재석, "계절 ARIMA 모형을 이용한 호텔객실매출액의 예측 : 경주지역 특급 호텔을 중심으로", 관광학연구, 30(2), pp.381~398 (2006)
- 김태구, "ARIMA모형을 적용한 외국인 이용객 호텔객실 수요예측모형 선정 : 서울특1급 호텔을 중심으로", 호텔경영학연구, 15(5), pp.97~118 (2006)
- 박수호, "엔데믹 후 진가 드러났다 『호텔』의 재발견", 매경ECONOMY), (2255), pp.40~45 (2024)
- "법제처 국가법령정보센터"[URL]
- 배준호, "호텔 식음료 사업의 최적 수요예측 방법에 관한 연구", 관광레저연구, 23(6), pp.119~135 (2011)
- 유지현, "머신러닝을 이용한 관중 수요 예측에 관한 연구", 전기전자학회논문지, 23(4), pp.128~134 (2019)
- 윤설민, "호텔산업의 합리적 수요예측 방법에 관한 연구 : 정성적 방법과정량적 방법 간 예측치의 정확도 비교", 호텔경영학연구, 18(1), pp.1~16 (2009)
- 이충기, "계량기법과 질적 기법을 이용한 복합리조트 카지노 수요예측", 호텔관광연구, 17(4), pp.302~320 (2015)
- "한국관광협회중앙회 호텔등급관리국 홈페이지"[URL]
- "전국 관광숙박업 등록 현황(2022년 기준)", 한국호텔업협회[URL]
- 한국문화관광연구원, "코로나19의 관광분야 영향 및 전망", 문화·관광 인사이트, 143, pp.1~5 (2020)
- "코로나19로 흔들리는 호텔·관광업계, 장기적 지원책 절실-①" (2020)[URL]
- Hill, T., "Artificial neural network models for times forecasts", Management Science, 42, pp.1082~1092 (1996)
- Hua, J., "A hybrid support vector machines and logistics regression appproach for forecasting intermittent demand for spare parts", Applied Mathematics and Computation, 181, pp.1035~1048 (2006)
- Rajopadhye, M., "Forecasting uncertain hotel room demand", Information Sciences, 132(1), pp.1~11 (2001)
- "Global Hotel Inventory Has Grown 18% in 10 Years" (2018)[URL]
- Yin, P., "A DEA-based two-stage network approach for hotel performance analysis : An internal cooperation perspective", Omega, 93, pp.102035~ (2020)
- Yüksel, S., "An integrated forecasting approach to hotel demand", Mathematical and Computer Modelling, 46(7-8), pp.1063~1070 (2007)