 Computational Intelligence in Smart Tourism Destinations: A Systematic Review of AI-Driven Optimization Methods for Visitor Experience and Resource ManagementDimitrios Papadopoulos,
Konstantinos Giotopoulos and
Constantinos Halkiopoulos ()
A chapter in Synergizing Management, Culture, and Arts for Tourism Development - Vol. 1, 2026, pp 445-504 from Springer Abstract: Abstract Smart Tourism Destinations (STDs) leverage computational intelligence, artificial intelligence (AI), and big data analytics to enhance visitor experiences while optimizing resource management and service delivery. This systematic review, conducted following PRISMA guidelines, comprehensively analyzes 74 studies examining AI-driven optimization methods in smart tourism destinations. The review addresses four key research questions: (1) How can advanced AI algorithms predict visitor behavior patterns and tourist demand to enable proactive resource allocation? (2) What combination of data sources and computational methods provides the most robust framework for real-time optimization? (3) How do AI-driven personalization systems correlate with visitor satisfaction and experience quality? (4) To what extent can computational intelligence predict and optimize long-term tourism sustainability outcomes? The findings reveal that artificial intelligence and machine learning methods dominate current research (51 studies), with neural networks and hybrid approaches demonstrating superior performance in prediction accuracy and multi-objective optimization. Sustainability emerges as a central theme (48 studies), reflecting a paradigm shift from purely economic optimization to multi-criteria approaches balancing environmental, social, and economic objectives. Integration of multiple data sources—including IoT sensors, mobile applications, social media, and transportation systems—proves critical for real-time optimization, though data fragmentation remains a significant challenge. AI-driven personalization shows strong correlations with visitor satisfaction, achieving up to 94% satisfaction rates when combining AI with human expertise. The review identifies significant gaps between research demonstrations and real-world deployment, highlighting challenges in scalability, stakeholder coordination, and ethical implementation. Future research directions emphasize the need for integrated frameworks, human-centered design, and practical implementation strategies that balance technological capabilities with the preservation of authentic tourism experiences. This work contributes to understanding how computational intelligence can transform tourism destinations into adaptive, sustainable, and visitor-centric ecosystems while addressing the complex challenges of modern tourism management. Keywords: Smart tourism destinations; Artificial intelligence; Machine learning algorithms; Computational intelligence; Visitor behavior prediction; Data integration frameworks; IoT sensors; Real-time optimization; AI-driven personalization; Sustainable tourism; Hybrid optimization models; Tourism ecosystem management (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-17545-8_19 Ordering information: This item can be ordered from DOI: 10.1007/978-3-032-17545-8_19 Access Statistics for this chapter More chapters in Springer Proceedings in Business and Economics from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.