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Artificial Intelligence Applications in Tourism
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Artificial Intelligence Applications in Tourism

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 December 2026 | Viewed by 1311

Editor

Prof. Dr. Emilio Soria-Olivas

E-Mail Website
Guest Editor
IDAL, Electronic Engineering Department, University of Valencia, Av. Universitat, SN, 46100 Burjassot, Valencia, Spain
Interests: deep learning applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The tourism industry is undergoing a profound transformation driven by technological advancements. While predictive AI has long optimized operations, the recent emergence of Generative AI and Large Language Models (LLMs) has marked a paradigm shift. Furthermore, the evolution towards Agentic AI—where autonomous agents not only process information but execute complex tasks and decision-making processes—is redefining the boundaries of travel automation.

The scientific context illustrates a rapidly evolving landscape where these technologies are reshaping the entirety of travelers' journeys. From LLMs providing human-like conversational planning to autonomous agents handling end-to-end booking and negotiation, the scope of AI in tourism has expanded exponentially. This research area is of paramount importance, moving beyond mere data analysis to content generation and autonomous action, necessitating a rigorous examination of its implications for personalized experiences, operational efficiency, and the future hospitality workforce.

Suggested themes and article types for submissions:

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  1. Generative AI and LLMs in Travel Planning and Experience:
    • Application of Large Language Models (LLMs) for creating hyper-personalized itineraries and content.
    • Enhancing customer interaction through Generative AI-powered chatbots and virtual travel assistants.
  2. Agentic AI and Autonomous Systems:
    • Development of autonomous AI agents capable of executing complex workflows (booking, reservations, payments) without human intervention.
    • Multi-agent systems for coordinating tourism supply chains and dynamic pricing negotiations.
    • From "Chat" to "Action": The transition from informational bots to transactional AI agents in hospitality.
  3. Operational Efficiency and Business Intelligence:
    • Optimization of logistics and inventory through machine learning.
    • Robotics and Computer Vision in hotel and airport operations.
  4. Tiny ML and Edge AI in Tourism:
    • Applications of lightweight machine learning models for real-time personalization on mobile devices.
    • Privacy-preserving on-device AI, and IoT sensors in smart tourism environments.
  5. Anomaly Detection in Tourism:
    • AI-driven systems for fraud detection in bookings.
    • Identification of unusual patterns in tourist behavior.
    • Early warning systems for safety and security management.

Prof. Dr. Emilio Soria-Olivas
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • tourism
  • autonomous systems

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Published Papers (2 papers)

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Research

12 pages, 3198 KB  
Article
Comparative Evaluation of Deep Transfer Learning Models for Ancient Coin Classification
by Omar Alhuniti, Sami Serahan and Imad Salah
Appl. Sci. 2026, 16(16), 8271; https://doi.org/10.3390/app16168271 - 19 Aug 2026
Viewed by 222
Abstract
In this paper, we present deep learning models for classifying ancient-coins using transfer learning applied to a custom dataset curated from a specialized collection. The dataset was meticulously developed through rigorous preparation and quality screening; the retained fine-grained classes are imbalanced. We initially [...] Read more.
In this paper, we present deep learning models for classifying ancient-coins using transfer learning applied to a custom dataset curated from a specialized collection. The dataset was meticulously developed through rigorous preparation and quality screening; the retained fine-grained classes are imbalanced. We initially performed binary classification before progressing to separate fine-grained assessments of DenseNet121, EfficientNetB0, EfficientNetV2S, and ResNet50. In confirmatory experiments using a physical-coin-aware 70/15/15 split and five training seeds, the coarse CITY-versus-NABATAEAN task remained approximately perfectly separable. Fine-grained performance was lower: ResNet50 achieved 68.28 ± 2.45% CITY accuracy, whereas DenseNet121 achieved 72.89 ± 2.79% NABATAEAN accuracy. These results show that near-perfect coarse classification does not by itself imply reliable fine-grained attribution. This analysis underscores the importance of meticulous dataset preparation and strategic model selection in optimizing performance. A matched DenseNet121 ablation further showed that ImageNet initialization substantially improved the difficult fine-grained tasks compared with random initialization. The proposed framework supports digital documentation and analysis of ancient coin collections, while the current single-collection evaluation still limits claims of cross-collection generalization. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Tourism)
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25 pages, 2385 KB  
Article
An Intelligent High-Freedom Travel Itinerary Planning System Based on Multi-Objective Optimization: A Case Study of Hainan Island Ring Road Self-Driving Tour
by Dan Huang, Gang Liu and Yingjun Xia
Appl. Sci. 2026, 16(14), 7077; https://doi.org/10.3390/app16147077 - 14 Jul 2026
Viewed by 552
Abstract
Existing self-driving itinerary planning systems rely on single-objective optimization and static cost estimation, limiting their ability to accommodate diverse user preferences and dynamic price fluctuations. Methods: This study presents an interactive itinerary planning system formulated as a Tourist Trip Design Problem (TTDP) with [...] Read more.
Existing self-driving itinerary planning systems rely on single-objective optimization and static cost estimation, limiting their ability to accommodate diverse user preferences and dynamic price fluctuations. Methods: This study presents an interactive itinerary planning system formulated as a Tourist Trip Design Problem (TTDP) with subset selection, integrating an improved NSGA-II algorithm for simultaneous optimization of travel time, cost, and experiential quality, a WPGA-based combined prediction model for dynamic cost forecasting, and a human-in-the-loop interface for real-time preference adjustment. Results: Evaluated on 30 Hainan Island attractions under a 7-day/6-night scenario with 30 independent runs, the improved NSGA-II selects 8.9 ± 1.0 stops with total time 26.2 ± 4.7 h, cost CNY 1526 ± 356, and experience index 36.69 ± 5.60, with backend optimization latency 0.5 ± 0.1 s (end-to-end user-perceived latency including network and frontend rendering: 1.8 s). Compared with standard NSGA-II, the improved version achieves 114.0% higher aggregate experience index while selecting more stops (8.9 vs. 4.2); this improvement is primarily driven by the larger number of selected stops, and normalized indicators (experience per stop: 4.12 vs. 4.08) provide a more balanced interpretation of the trade-offs. The cost model covers three categories (fuel, tickets, and hotel), with the optimization objective f2 capturing route-dependent costs (fuel + tickets) and the hotel treated as a fixed baseline. However, empirical validation is restricted to two geographic corridors. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Tourism)
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