TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation
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 routes at high accuracy and implicitly grounds arbitrary GPS coordinates to appropriate stations without any explicit mapping. These results demonst...
| Authors | Hanyu Guo, Jiedong Yang, Chao Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
Models and datasets referencing this paper
- GD-ML/TransitLMDataset
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