Digital Twin Implementation in Cultural Tourism: A Systematic Review

By Noor Abdullah

Abstract

Digital twin (DT) technology—virtual replicas of physical systems—has gained traction in tourism, especially in cultural heritage contexts. This article offers a systematic literature review and bibliometric synthesis of digital twin applications in tourism, classifying use cases and identifying future research avenues. Thirty‑four peer‑reviewed studies from major databases (e.g., Scopus, Web of Science) were analyzed using a robust methodology. The review highlights a growing trend in virtual cultural heritage preservation and destination planning. However, current DT models remain largely unilateral in data flow, and few systems achieve true two‑way synchronization. Future research should target comprehensive data integration, real‑time twin synchronization, and practitioner‑oriented frameworks. This study contributes a taxonomy of DT applications and outlines research gaps ahead of further empirical validation.

1. Introduction

The concept of digital twins—originally from manufacturing and engineering—has crossed disciplines into tourism. A digital twin is a dynamic, virtual model that mirrors the state of a real‑world counterpart. Where digital twins once simulated physical equipment, they now map real environments like museums, historical sites, and even entire tourist destinations. The aim: to support cultural preservation, destination management, visitor experience, and sustainability (Almeida et al., 2025).

This article reviews the current state of DT research in tourism, particularly cultural tourism, using a systematic approach that emphasizes bibliometric and thematic analysis. It draws on studies published between 2021 and early 2025.

2. Methodology

Following established SLR protocols, the review included concrete steps:

1. Data Collection – Keywords like “digital twin” and “tourism” were applied to Scopus, Web of Science, and major conference proceedings.

2. Study Selection – Thirty‑four studies were selected based on inclusion criteria and peer‑review status.

3. Data Extraction and Classification – Each study was coded along dimensions such as domain (e.g., cultural heritage), spatial scale (site, destination), data type (sensor-based or manual), visualization methods, and data‑link dynamics.

4. Bibliometric Mapping – Thematic clusters, keyword co‑occurrence, and publication trends were mapped to understand domain growth patterns.

This approach ensures a structured overview of DT research in tourism, identifying both practical and theoretical contributions.

3. Key Findings

3.1 Evolution and Focus

Digital twin research in tourism began surfacing around 2021, coinciding with rising interest in smart destination management and cultural site digitization. 2025 findings suggest a modest acceleration in applied research.

3.2 Application Domains

• Cultural heritage tourism is the primary focus—over 70 % of the surveyed studies.

• Destination and urban tourism account for roughly 30 %, often featuring smart‑city integrations.

3.3 Spatial Scales

• Site‑level DTs dominate (e.g., museums, monuments).

• Few studies explore destination‑level twins incorporating multiple sites or entire city planning processes.

3.4 Data Flow Dynamics

• Most systems are unilateral, where real‑world data updates the twin passively.

• Only a minority implement bilateral synchronization, enabling real‑time updates in both directions.

3.5 Visualization and Interfaces

• Common digital twin outputs include 3D models, GIS overlays, VR tours, and interactive dashboards for planners.

• Few systems offer immersive or multi‑modal experiences, indicating a gap between output and end‑user interaction.

4. Discussion

4.1 Benefits and Promise

• DT systems improve heritage preservation by enabling virtual reconstructions and risk modeling.

• They aid destination management via predictive analytics and crowd monitoring.

• They enhance visitor engagement by offering virtual previews, accessibility options, and personalization.

4.2 Technical Challenges

• Building twin fidelity is resource‑intensive, requiring high‑resolution scanning, sensor deployment, and data pipelines.

• Data integration remains fragmented—sensor feeds, GIS data, and user input rarely converge seamlessly.

• Real‑time bidirectional updating is largely absent; this limits modeling accuracy and system adaptability.

4.3 Research Gaps

• Pursuit of hybrid frameworks (integrating GIS, smart‑city data, and IoT) to elevate DT grounding.

• Focus on bi‑directional and real‑time digital twin architectures to foster dynamic interaction.

• User-centric studies assessing how digital twins affect visitor satisfaction, interpretive value, and accessibility.

5. Conceptual Taxonomy

This review suggests a structured taxonomy of DT in tourism:

Dimension	Categories
Application domain	Cultural heritage; urban destinations
Spatial scale	Site‑level; destination‑level
Data flow	Unilateral; bilateral
Visualization	Static 3D/VR; interactive dashboards; immersive AR/VR
Purpose	Preservation; engagement; management

This schema helps researchers and practitioners position their work and understand where innovation is still needed—particularly in moving toward comprehensive, integrated, and dynamic twin ecosystems.

6. Future Directions

1. Integrated real‑time DT ecosystems—linking IoT, GIS, and social media feeds to drive adaptive twin behaviors.

2. User‑oriented design—studying how digital twins impact educational outcomes, learning, and inclusiveness for diverse audiences.

3. Governance and ethical frameworks—considering privacy, sustainability, and data stewardship in DT implementations.

4. Scalable deployment models—developing templates and open‑source toolkits for destinations with limited technical capacity.

7. Conclusion

Digital twins in tourism represent a fast‑emerging frontier, especially in cultural heritage and site management. Despite promising case studies, most remain unidirectional data replicas, lacking full system integration or real‑time responsiveness. Substantial research and technical work is still needed to transition DTs into adaptive, user‑centric ecosystems that support sustainable tourism development. This review highlights both current achievements and important gaps, providing a foundation for future exploration.

Hashtags

#DigitalTwin #CulturalTourism #SmartDestinations #HeritagePreservation #TourismTech

References

Almeida, D. S. de, Brito e Abreu, F., & Boavida‑Portugal, I. (2025). Digital twins in tourism: a systematic literature review. ArXiv preprint.

Choi, Y., & Kim, D. (2024). Artificial Intelligence in The Tourism Industry: Current Trends and Future Outlook. Tourism & Hospitality Research, 14(6).

Diao, T., Wu, X., Yang, L., Xiao, L., & Dong, Y. (2025). A novel forecasting framework combining virtual samples and enhanced Transformer models for tourism demand forecasting. ArXiv preprint.

World Travel & Tourism Council. (2025). Global tourism trends report.

Fazio, G., Fricano, S., & Pirrone, C. (2024). Evolutionary Game Dynamics Applied to Strategic Adoption of Immersive Technologies in Cultural Heritage and Tourism. ArXiv preprint.