Destination resilience is often compromised by the dual pressures of seasonality and homogenization, where distinct local identities are obscured during peak tourist flows by generic, globalized service offerings. This paper introduces a novel analytical framework within the INDIANA (Intelligent Destination Management) project, proposing a Temporal Destination Uniqueness Index (T-DUI) that measures when a destination retains its authentic character and when it succumbs to commodification.

While traditional tourism indicators focus on visitor volume, INDIANA utilizes Artificial Intelligence and Big Data to analyze the nature of the destination's "Activity Supply". The system ingests vast repositories of Points of Interest (POIs) and events, classifying them according to established tourism taxonomies (e.g., Cultural Heritage, Creative Gastronomy, Nature-Based, vs. Generic Leisure/Retail).

The core innovation lies in the T-DUI algorithm, which evaluates two critical variables for every available activity:
Originality: The extent to which an activity is endemic to the location (derived from expert rules and cultural data) versus being a standardized, replicable experience found globally. Take for example the local easter festivities introduced in Corfu, Greece, that form a unique temporal cluster of religious and cultural activities.
Frequency: The temporal availability of the activity (e.g., a unique annual festival vs. a daily bus tour). The abovely mentioned example of Corfu easter celebration is clearly defined in a temporal manner every year, which changes according to the religious calendar.

By correlating these variables with real-time visitor behavior (Digital Twins), the T-DUI generates a temporal heatmap of authenticity. This allows stakeholders to visualize the destination's identity fluctuations throughout the year. For instance, the index may reveal high "Uniqueness Scores" during specific cultural windows (e.g., Easter in Corfu) where high-originality/low-frequency activities dominate, contrasted with "standardization troughs" during peak summer months, where high-frequency/low-originality activities dilute the local character.

The paper discusses how this temporal intelligence empowers DMOs to enhance resilience. By identifying periods where the experience becomes "generic," the system can automatically promote alternative, high-originality niche activities to specific user typologies, effectively flattening the curve of cultural commodification. We conclude that measuring the timing of uniqueness is as crucial as measuring its presence, offering a roadmap for sustaining a resilient creative economy that aligns visitor flows with the true pulse of local culture.

Keywords: Temporal Destination Uniqueness Index, Activity Taxonomies, AI in Tourism, Cultural Resilience, Seasonality, Intelligent Destination Management.