Pilot 5:
Compliance-ready Data Spaces
for Sustainable Tourism
The creation of a ‘Tourism Data Hub’ where mobility data and tourism service data can be shared, analysed and utilised securely, in full compliance with the GDPR, the AI Act and the Data Act. To this end, data flows will be implemented that take regulatory compliance into account, integrating the mobility datasets of AMT Genova, Genoa’s main public transport operator, as well as the tourism datasets of MaikeTour, a digital platform for tourism services and experiences. AI-based services will provide personalised and sustainable travel recommendations for tourists. To support innovation whilst protecting privacy, synthetic data and federated learning will be used to simulate scenarios such as tourist flows during peak season or accessibility improvements without exposing sensitive personal data.
Improvement in recommendations
≥20%
Massive generation of Compliance Cards
100% Datasets used in the pilot test
Reduction of manual work
≥50%
Objectives
Use Compliance Cards to certify datasets from diverse domains, ensuring interoperability, transparency, reusability, and regulatory conformity (e.g., GDPR for passenger-linked data).
Integrate Explainability (XAI) modules to provide tourists with evidence for why a certain recommendation was generated.
Demonstrate how datasets feed into cross-system planning tools (Arrival/Departure Managers, Transport Optimizers), ensuring interoperability and compliance-aware decision-making.
Provide a blueprint for trustworthy AI for sustainable tourism operations, balancing performance gains with transparency and compliance.
Technologies used/developed
AI-powered Compliance Certifier
to validate datasets used jointly in the domains of mobility and tourism
Compliance Card Generator
for digital passports documenting the trustworthiness and compliance of the shared datasets
Explainable AI (SHAP, LIME, Captum)
for transparent recommendations, improving passenger trust
Semantic Knowledge Graph
linking mobility-related data (passenger flows, transport accessibility) with tourism data (bookings, itineraries, events) and regulatory and operational requirements
Privacypreserving technologies
to handle sensitive datasets (e.g., passenger flow data) without exposing personal information.