{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T01:33:34Z","timestamp":1775612014224,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61563012"],"award-info":[{"award-number":["61563012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021GXNSFAA220074"],"award-info":[{"award-number":["2021GXNSFAA220074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012547","name":"General Project of Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["61563012"],"award-info":[{"award-number":["61563012"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012547","name":"General Project of Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2021GXNSFAA220074"],"award-info":[{"award-number":["2021GXNSFAA220074"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Tourist flow prediction plays a crucial role in enhancing the efficiency of scenic area management, optimizing resource allocation, and promoting the sustainable development of the tourism industry. To improve the accuracy and real-time performance of tourist flow prediction, we propose a BP model based on a hybrid genetic algorithm (GA) and ant colony optimization algorithm (ACO), called the GA-ACO-BP model. First, we comprehensively considered multiple key factors related to tourist flow, including historical tourist flow data (such as tourist flow from yesterday, the previous day, and the same period last year), holiday types, climate comfort, and search popularity index on online map platforms. Second, to address the tendency of the BP model to get easily stuck in local optima, we introduce the GA, which has excellent global search capabilities. Finally, to further improve local convergence speed, we further introduce the ACO algorithm. The experimental results based on tourist flow data from the Elephant Trunk Hill Scenic Area in Guilin indicate that the GA-AC*O-BP model achieves optimal values for key tourist flow prediction metrics such as MAPE, RMSE, MAE, and R2, compared to commonly used prediction models. These values are 4.09%, 426.34, 258.80, and 0.98795, respectively. Compared to the initial BP neural network, the improved GA-ACO-BP model reduced error metrics such as MAPE, RMSE, and MAE by 1.12%, 244.04, and 122.91, respectively, and increased the R2 metric by 1.85%.<\/jats:p>","DOI":"10.3390\/informatics12030089","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T09:18:57Z","timestamp":1756977537000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Tourist Flow Prediction Based on GA-ACO-BP Neural Network Model"],"prefix":"10.3390","volume":"12","author":[{"given":"Xiang","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3670-3952","authenticated-orcid":false,"given":"Yongliang","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China"}]},{"given":"Minggang","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin 541006, China"}]},{"given":"Xiaolan","family":"Xie","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Guilin University of Technology, Guilin 541006, China"},{"name":"Guangxi Key Laboratory of Embedded Technology and Intelligent System, Guilin University of Technology, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1080\/19388160.2022.2049943","article-title":"Big data in China tourism research: A systematic review of publications from English journals","volume":"18","author":"Li","year":"2022","journal-title":"J. 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