arXiv:2606.15315v1 Announce Type: new Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into routing algorithms. This paper presents ChatPlanner, a novel framework that leverages Large Language Models (LLMs) to enable preference aware public transit routing. Our approach employs fine-tuned LLMs with Retrieval-Augmented Generation (RAG) to extract routing parameters and interpret nuanced user preferences from natural language queries, subsequently integrating these preferences i
The proliferation of mature LLM technology and the increasing demand for personalized services in urban environments are converging to enable solutions like ChatPlanner.
This development signals a practical application of AI in daily life, enhancing efficiency and user experience in public transit, which has broader implications for smart cities and logistics.
Public transit routing can become significantly more personalized and flexible, moving beyond static algorithms to dynamic, preference-aware systems driven by natural language.
- · Public transit authorities
- · Urban commuters
- · AI software developers
- · Smart city initiatives
- · Traditional navigation app providers slow to adapt
- · Inflexible public transit systems
Increased adoption and satisfaction with public transit due to personalized experiences.
Demand for more sophisticated AI integration in urban planning and infrastructure management across other sectors.
Reduced reliance on private vehicles as public transit becomes more convenient and tailored, impacting automotive sales and urban congestion.
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Read at arXiv cs.AI