Integrating Real-Time Environmental Data and User Proficiency for Intelligent Trail Recommendation: A Thematic Review

Adventure tourism has experienced significant growth in recent years, with outdoor recreational activities such as hiking and mountaineering attracting millions of participants worldwide. However, the increasing popularity of trail-based activities has raised concerns about participant safety, environmental sustainability, and the need for personalized experiences that match individual capabilities. Traditional trail recommendation systems often rely on static information and fail to account for dynamic environmental conditions or real-time assessment of user proficiency levels. This paper proposes a thematic review for intelligent trail recommendation that integrates real-time environmental data streams with continuous user proficiency assessment to deliver safe, personalized, and context-aware trail suggestions. The proposed of this study leverages machine learning algorithms, Internet of Things (IoT) sensors, and semantic data models to process multi-dimensional data including weather conditions, terrain characteristics, trail difficulty metrics, and physiological indicators of user capability. We present a thematic review of existing approaches in trail recommendation systems, environmental data integration, user proficiency modeling, and safety-aware computing. This study reveals that while existing systems address individual components, there is a critical gap in holistic frameworks that seamlessly integrate environmental dynamics with user-specific capabilities. The proposed approach contributes to the advancement of intelligent tourism systems by providing a foundation for safer, more personalized outdoor recreational experiences that adapt to changing conditions and individual user profiles.

Adventure Tourism, Trail Recommendation, Thematic review

1. Ogura, K., Watanabe, T., Yano, S., & Terada, N. (2004). U.S. Patent Application No. 10/467,167. [Google Scholar] [Crossref]

2. Golo, M. A. T., & Encarnacion, R. E. (2024). Revolutionizing the Rental Services in Siargao Island: Basis for Developing an Online Vehicle Rental Management System. International Journal of Advanced Research in Science. Tongxin Jishu, 438-439. [Google Scholar] [Crossref]

3. Hamza-Mayora, S., Labrador-Roca, V., & Fernández-Piqueras, R. (2025). Recommender systems for hiking and mountaineering: A conceptual framework and evaluation of leading outdoor apps. Journal of Outdoor Recreation and Tourism, 52, 100959. [Google Scholar] [Crossref]

4. Calbimonte, J. P., Martin, S., Calvaresi, D., Zappelaz, N., & Cotting, A. (2019, December). Semantic data models for hiking trail difficulty assessment. In Information and Communication Technologies in Tourism 2020: Proceedings of the International Conference in Surrey, United Kingdom, January 08–10, 2020 (pp. 295-306). Cham: Springer International Publishing. [Google Scholar] [Crossref]

5. Chen, Y., Li, J., Blasch, E., & Qu, Q. (2025). Future Outdoor Safety Monitoring: Integrating Human Activity Recognition with the Internet of Physical–Virtual Things. Applied Sciences, 15(7), 3434. [Google Scholar] [Crossref]

6. Calbimonte, J. P., Martin, S., Calvaresi, D., & Cotting, A. (2021, January). A platform for difficulty assessment and recommendation of hiking trails. In Information and Communication Technologies in Tourism 2021: Proceedings of the ENTER 2021 eTourism Conference, January 19–22, 2021 (pp. 109-122). Cham: Springer International Publishing. [Google Scholar] [Crossref]

7. Calbimonte, J. P., Zappellaz, N., Hébert, E., Simon, M., Délétroz, N., Hilfiker, R., & Cotting, A. (2018, June). SanTour: towards personalized recommendation of hiking trails to health profiles. In International Conference on Web Engineering (pp. 238-250). Cham: Springer International Publishing. [Google Scholar] [Crossref]

8. Vías, J., Rolland, J., Gómez, M. L., Ocaña, C., & Luque, A. (2018). Recommendation system to determine suitable and viable hiking routes: a prototype application in Sierra de las Nieves Nature Reserve (southern Spain). Journal of Geographical Systems, 20(3), 275-294. [Google Scholar] [Crossref]

9. Al Fathony, S. H., Rahajoe, A. D., & Zainal, R. F. (2022). KNN and Webgis Classification to Recommend Mountain Location According to Hiker Abilities. JEECS (Journal of Electrical Engineering and Computer Sciences), 7(1), 1239-1246. [Google Scholar] [Crossref]

10. Al Fararni, K., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J., & Sabri, A. (2021). Hybrid recommender system for tourism based on big data and AI: A conceptual framework. Big Data Mining and Analytics, 4(1), 47-55. [Google Scholar] [Crossref]

11. Daiber, F., Kosmalla, F., Wiehr, F., & Krüger, A. (2017, September). Follow the pioneers: towards personalized crowd-sourced route generation for mountaineers. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (pp. 1051-1055). [Google Scholar] [Crossref]

12. Hoque, T. T., Ferreira, L., Faria, L., Martins, C., Mesquita, R., Conceição, L., ... & Ramos, C. (2018, December). Tourism and Heritage Routes Including Visitants Profile Adaptation and Context Awareness. In 2018 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 947-953). IEEE. [Google Scholar] [Crossref]

13. Sekhar, S. M., Ahmed, D. M., & Siddesh, G. M. (2024). Analysis and recommendation of outdoor activities for smart city users based on real-time contextual data. Computing Open, 2, 2450004. [Google Scholar] [Crossref]

14. Siriaraya, P., Wang, Y., Zhang, Y., Wakamiya, S., Jeszenszky, P., Kawai, Y., & Jatowt, A. (2020). Beyond the shortest route: A survey on quality-aware route navigation for pedestrians. IEEE Access, 8, 135569-135590. [Google Scholar] [Crossref]

15. Sun, A. Y., Zhong, Z., Jeong, H., & Yang, Q. (2019). Building complex event processing capability for intelligent environmental monitoring. Environmental modelling & software, 116, 1-6. [Google Scholar] [Crossref]

16. Huang, X. (2022). Personalized travel route recommendation model of intelligent service robot using deep learning in big data environment. Journal of Robotics, 2022(1), 7778592. [Google Scholar] [Crossref]

17. Cepeda-Pacheco, J. C., & Domingo, M. C. (2022). Deep learning and Internet of Things for tourist attraction recommendations in smart cities. Neural Computing and Applications, 34(10), 7691-7709. [Google Scholar] [Crossref]

18. Kolkos, G., Kantartzis, A., Stergiadou, A., & Arabatzis, G. (2024). Development of Semi-Mountainous and Mountainous Areas: Design of Trail Paths, Optimal Spatial Distribution of Trail Facilities, and Trail Ranking via MCDM-VIKOR Method. Sustainability, 16(22), 9966. [Google Scholar] [Crossref]

19. Tenemaza, M., Luján-Mora, S., De Antonio, A., & Ramirez, J. (2020). Improving itinerary recommendations for tourists through metaheuristic algorithms: an optimization proposal. Ieee Access, 8, 79003-79023. [Google Scholar] [Crossref]

20. Minakata, M., Maruyama, T., Tada, M., Ramasamy, P., Das, S., & Kurita, Y. (2022). Safe Walking Route Recommender Based on Fall Risk Calculation Using a Digital Human Model on a 3D Map. IEEE Access, 10, 8424-8433. [Google Scholar] [Crossref]

21. Islam, F. T., Hashem, T., & Shahriyar, R. (2023). A crowd-enabled approach for privacy-enhanced and personalized safe route planning for fixed or flexible destinations. IEEE Transactions on Knowledge and Data Engineering, 35(11), 10922-10936. [Google Scholar] [Crossref]

22. Cao, Y., Hassan, T., Fan, J., Saaty, M., Wang, W. L., Stelter, T. L., & McCrickard, S. (2025, April). Listening to the Wild: Risk and Preparedness in Hiker-Wildlife Conflicts during Outdoor Recreation. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (pp. 1-8). [Google Scholar] [Crossref]

23. Wu, T. (2021). Design for Hiking Safety and Fun. Rochester Institute of Technology. [Google Scholar] [Crossref]

• The Impact of Ownership Structure on Dividend Payout Policy of Listed Plantation Companies in Sri Lanka
• Urban Sustainability in North-East India: A Study through the lens of NER-SDG index
• Performance Assessment of Predictive Forecasting Techniques for Enhancing Hospital Supply Chain Efficiency in Healthcare Logistics
• The Fractured Self in Julian Barnes' Postmodern Fiction: Identity Crisis and Deflation in Metroland and the Sense of an Ending
• Impact of Flood on the Employment, Labour Productivity and Migration of Agricultural Labour in North Bihar