OPENPATH: A Supervisor--Specialist Agent System for Personalized, Accessible, and Multi-stop Urban Trip Planning
For urban travelers and city planners, OPENPATH provides a single system that addresses heterogeneous trip planning and accessibility analysis, though it is an incremental application of LLM agents combined with classical algorithms.
OPENPATH is a supervisor-specialist multi-agent system for urban trip planning that handles personalized preferences, multi-stop itineraries, and wheelchair accessibility. Applied to NYC, it reveals ADA infrastructure gaps and quantifies their effect on job accessibility for wheelchair users.
Urban trip-planning systems are commonly optimized for travel time and cost, but they offer limited support for the heterogeneous needs that real travelers bring, such as personalized preferences, multi-stop itinerary construction, and end-to-end wheelchair accessibility. We present openpaths, a supervisor-specialist multi-agent system that handles all of these tasks within a single architecture. openpaths adopts a deliberate division of labor: LLM agents parse natural-language input, classify request intent, and orchestrate execution, while classical algorithms perform route optimization over curated mobility and accessibility data. This design ensures that the resulting trip honors heterogeneous user preferences and enforces strict accessibility requirements when requested. Beyond per-user planning, openpaths doubles as a measurement instrument for city-scale accessibility analysis: applied to NYC, the system reveals substantial ADA infrastructure gaps and quantifies their effect on job accessibility for wheelchair users. Overall, this study shows how a supervisor-specialist LLM agentic framework can support heterogeneous trip planning and transparent, equitable transportation analysis in real urban environments.