AlterAtlas: Shifting Travel Planning from AI Generation to Validation via Persona-Driven Simulations
For travelers and AI travel tool designers, AlterAtlas introduces a validation-first approach that improves plan-persona alignment and user trust, but the evaluation is small-scale and the concept is incremental.
AlterAtlas shifts travel planning from AI generation to validation by using persona-driven simulations to expose mismatches between plans and user preferences. Expert evaluation of 51 paired itineraries showed simulation-guided revisions significantly improve plan-persona alignment, and a within-subjects study (N=11) found it helps users uncover hidden constraints and build trust.
Travel planning requires balancing interacting goals and constraints across time and space. Current AI travel tools provide limited support for encoding these constraints and understanding how generated travel plans may fail users. We present AlterAtlas, an interactive travel planning system that supports high-fidelity itinerary validation and revision through persona-based simulations grounded in geospatial information. AlterAtlas models travelers as editable personas, generates candidate itineraries from prioritized places of interest, and simulates how different personas would experience each plan. Simulations expose route-level tradeoffs, temporal user states (e.g., fatigue, hunger), and mismatches between plans and user preferences to allow users to iteratively refine both itineraries and user personas. An expert evaluation of 51 paired itineraries demonstrates that simulation-guided revisions significantly improve plan-persona alignment. Furthermore, a within-subjects study (N=11) reveals that AlterAtlas empowers users to uncover hidden constraints, fluidly compare alternatives, and build trust in their final plans. Our results suggest that simulation-based validation is a powerful, transparent interaction layer for AI-assisted travel planning.