{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T12:41:21Z","timestamp":1776775281891,"version":"3.51.2"},"reference-count":69,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,26]],"date-time":"2023-02-26T00:00:00Z","timestamp":1677369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Forecasting tourism volume can provide helpful information support for decision-making in managing tourist attractions. However, existing studies have focused on the long-term and large-scale prediction and scarcely considered high-frequency and micro-scale ones. In addition, the current approaches are limited regarding forecasting the visitor volume of a designated sub-area in a tourist attraction. This sub-area forecast can assist local-scaled managing decisions of tourist attractions, particularly for large-scale tourist attractions. Therefore, to achieve high-frequency forecasts of tourist volume for finer scale areas such as parks and their sub-areas and generate more controllable and flexible forecasts, this study developed a novel method that incorporates a forecasting model composed of multiple deep learning components and a designed control mechanism. The control mechanism produces high-temporal-resolution sequences of tourist volume for designated sub-areas, and the forecasting model is built on an attention-based deep-bidirectional neural network to better capture the long-range dependencies of the sequence and enhance the forecasting accuracy and robustness. The experimental research was performed at Taiyangdao Park and its two designated sub-areas to validate the effectiveness and superiority of the proposed method compared to other widely used deep-learning methods; three types of performance evaluations were adopted including fitting methods, error measures, and Diebold\u2013Mariano tests. The results demonstrated that the proposed method provided outstanding performance in high-frequency forecasts and yielded more desired forecasting outcomes than other widely used forecasting methods. Furthermore, the comparison with the performances of various other deep learning models provide insights concerning their forecasting capacity; for instance, bidirectional RNN models tend to achieve better forecasts than general RNN models in the high-frequency forecasts. The proposed method has significant practical applicability in aiding short-term micro-scale management decisions and can also serve as an alternative approach in the field of tourist volume forecasting.<\/jats:p>","DOI":"10.3390\/ijgi12030098","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T04:13:16Z","timestamp":1677471196000},"page":"98","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Novel Approach for Spatially Controllable High-Frequency Forecasts of Park Visitation Integrating Attention-Based Deep Learning Methods and Location-Based Services"],"prefix":"10.3390","volume":"12","author":[{"given":"Ming","family":"Lu","sequence":"first","affiliation":[{"name":"School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7112-4057","authenticated-orcid":false,"given":"Qian","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Architecture, Harbin Institute of Technology; Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, Harbin 150006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1016\/j.tourman.2005.05.006","article-title":"Designing an artificial neural network for forecasting tourism time series","volume":"27","author":"Palmer","year":"2006","journal-title":"Tour. 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