arXiv:2605.22355v1 Announce Type: cross Abstract: Public transit route planning traditionally depends on structured map infrastructure and complex routing engines, and no existing dataset supports training models to bypass this dependency. We present TransitLM, a large-scale dataset of over 13 million transit route planning records from four Chinese cities covering 120,845 stations and 13,666 lines, released as a continual pre-training corpus and benchmark data for three evaluation tasks with complementary metrics. Experiments show that an LLM trained on TransitLM produces structurally valid r
The proliferation of powerful large language models and the increasing availability of large-scale, real-world data are enabling new applications in complex domains like urban planning.
This development indicates a potential paradigm shift in how urban infrastructure, specifically transit, can be managed and optimized, moving away from rigid, pre-programmed systems to more adaptive, data-driven AI solutions.
Traditional route planning, reliant on static map infrastructure and complex routing engines, can now be augmented or potentially replaced by LLM-based systems trained on real-world usage patterns, offering more dynamic and efficient solutions.
- · AI developers and researchers
- · Public transit authorities
- · Urban commuters
- · Smart city initiatives
- · Traditional GIS software providers
- · Legacy mapping data companies
- · Transit planning consultants reliant on static models
AI-powered transit systems could lead to more efficient and adaptable public transportation networks, reducing congestion and improving urban mobility.
The successful deployment of such systems could spur similar AI applications in other complex infrastructure domains, accelerating the adoption of large language models in operational technology.
Dependence on AI for critical urban infrastructure could raise complex questions about algorithmic bias, resilience against adversarial attacks, and the future role of human planning expertise.
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