Agent Based Modeling (ABM) and AI integration for smart tourism simulations

Agent Based Modeling (ABM) and AI integration for smart tourism simulations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Agent Based Modeling (ABM) and AI integration for smart tourism simulations Ivan Majic, Johannes Scholz, David Röbl, Rizwan Bulbul, Thomas Lampoltshammer, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6199028/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The ability to predict visitor demand at popular points of interest (POIs) and to understand tourists' visiting patterns in general is of vital importance for tourism management. We present an approach that integrates two complementary methods - agent based modeling (ABM) simulations and machine learning (ML) to enable accurate and realistic simulations of tourist movement and visiting of POIs. The ML model that predicts the next destination in the tourists' visiting sequence was trained on POI check-in data, that records tourist entrances into different attractions, using the XGBoost method.We compare different feature engineering set-ups and propose an approach for encoding the visiting history of each tourist so that it could be used in the prediction process. The model was trained and validated on 2017 data for Salzburg Card users and tested for the years 2018-2021. The results show that a large training set can yield short-term predictions with up to 75% accuracy. However, the later years are constantly predicted with lower accuracy (44%) regardless of the training set size.We also showcase the ability of our approach to produce realistic simulations of tourist visiting patterns by simulating 20 consecutive days of tourist visits in the city of Salzburg. Compared to the baseline method that makes tourists choose POIs based on popularity, and the random choice of the POIs, our ML prediction model was the only one that managed to learn different visiting patterns for different days of the week. It was also the only method that successfully learned the logical constraints of ride-type POIs where tourists usually have to take the upward ride first before coming down. Agent Based Modeling (ABM) Tourism POI data Machine Learning XGBoost Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Jul, 2025 Reviews received at journal 09 Jul, 2025 Reviewers agreed at journal 09 Jul, 2025 Reviews received at journal 07 Jun, 2025 Reviewers agreed at journal 16 May, 2025 Reviewers invited by journal 04 May, 2025 Editor assigned by journal 28 Apr, 2025 Submission checks completed at journal 13 Mar, 2025 First submitted to journal 10 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6199028","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428187436,"identity":"40c25bb1-0b74-4cca-b75f-3a09e5a39f84","order_by":0,"name":"Ivan 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