Travel planning is a fragmented, multi-domain decision process: a traveller must separately research destinations, transport, accommodation, food, activities and weather before assembling a feasible schedule. This paper presents ATLAS (“Where Travel Meets AI”), an implemented AI-powered travel planning and decision-support platform that pairs a large language model (LLM) planner with deterministic grounding against live geospatial and environmental data. Given a destination, an inclusive date range, a boarding location, a budget and traveller preferences, ATLAS generates a day-by-day itinerary with the open-weight gpt-oss-120b model served through Groq, while route, weather, food and accommodation agents run concurrently and record the provenance of each data source. Model output is validated rather than trusted: a schema enforces exactly one itinerary day per requested calendar date, generated place names are resolved through OpenStreetMap’s Nominatim geocoder, and road distances and travel times are computed with OSRM from the traveller’s real boarding location, with unknown legs reported as unavailable instead of zero. The system is implemented as a React and TypeScript single-page application backed by FastAPI and PostgreSQL, and includes a live weather outlook, real restaurant and attraction listings, a simulated booking flow that issues a PDF e-ticket by e-mail, and a community Lost & Found module. We describe the architecture and grounding method, report a verification suite of 477 automated tests and preliminary functional observations, and state the system’s current limitations explicitly
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Kawariya et al. (2026) studied this question.
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