Novel deep learning approach for forecasting daily hotel demand with agglomeration effect |
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| Affiliation: | 1. School of Hospitality Business Management, Carson College of Business, Washington State University, 915 N. Broadway, Everett, WA, USA;2. School of Hospitality and Tourism Management, Purdue University, Marriott Hall, 900 W. State Street, West Lafayette, IN 47907, USA;1. Management and Marketing Department, College of Business, Louisiana State University Shreveport, One University Pl, Shreveport, LA 71115, United States;2. Hospitality and Tourism Management, College of Health and Human Sciences, Purdue University, 900 W. State Street, West Lafayette, IN 47907, United States;1. Montpellier Business School, France, and Lancaster University Management School, UK;2. Isenberg School of Management, University of Massachusetts-Amherst, 121 President Dr, Amherst, MA 01003, USA;1. Isenberg School of Management, University of Massachusetts-Amherst, 90 Campus Center Way, 209A Flint Lab, Amherst, MA 01003, United States;2. Lancaster University Management School, United Kingdom;1. Shenzhen Tourism College/JNU-UF International Joint Laboratory on Information Technology & Tourism, Jinan University, No.6, Qiaocheng East Avenue, Overseas Chinese Town, Nanshan District, Shenzhen, Guangdong, 518053, PR China;2. School of Hotel and Tourism Management, The Hong Kong Polytechnic University, 17 Science Museum Road, TST East, Kowloon, Hong Kong SAR, 999077, China;3. Faculty of Human Geography and Planning, Adam Mickiewicz University, Krygowskiego 10, 61-680 Poznan, Poland;4. School of Architecture and Urban Planning, Guangdong University of Technology, No.729, Dongfeng East Road, Yuexiu District, Guangzhou, Guangdong, 510090, PR China;1. Department of Management and Marketing, School of Business & Public Administration, California State University, Bakersfield, 9001 Stockdale Hwy, Bakersfield, CA 93311, USA;2. Department of Hospitality & Tourism Management, Sejong University, 209 Neungdong-ro, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea;3. William F. Harrah College of Hospitality, University of Nevada, Las Vegas, 4505 S. Maryland Parkway, Box 456021, Las Vegas, NV 89154-6021, USA |
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| Abstract: | ![]()
Increasing competition and adoption of revenue management practices in the hotel industry fuel the need for accurate forecasting to maximize profits and optimize operations. Considering the limitations of relevant research, this study focuses on the daily hotel demand with consideration of agglomeration effect, and proposes a novel deep learning-based model, namely, Deep Learning Model with Spatial and Temporal correlations. This model contributes to relevant research by introducing the agglomeration effect and integrating the attention mechanism and Bayesian optimization algorithm. Historical daily demand data of 210 hotels in Xiamen, China are used to verify the model performance. Results show that the proposed model is significantly better than the benchmarks. This study can help hotel managers improve revenue management through better matching potential demand to available capacity. |
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| Keywords: | Hotel demand forecasting Agglomeration effect Deep learning Long short-term-memory Bayesian optimization |
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