用1300万条数据训练大模型，实现无需地图的公交路线自动生成。
TransitLM: A Large-Scale Dataset and Benchmark for Map-Free Transit Route Generation
- 基于真实数据训练大模型，直接从起点终点生成路线。
- 模型在无地图情况下准确将坐标匹配到站点，正确率达92%以上。
- 适合做智能出行、交通规划或零样本路线生成的研究者使用。
公共交通路线规划传统上依赖结构化地图基础设施和复杂路由引擎，现有数据集无法支持绕过这一依赖的模型训练。我们提出TransitLM，一个涵盖中国四个城市超过1300万条公交路线规划记录的大规模数据集，覆盖120,845个站点和13,666条线路，作为持续预训练语料库与三个评估任务的基准数据。实验表明，基于TransitLM训练的大语言模型可高精度生成结构合理路线，并在无显式地图的情况下隐式将任意GPS坐标对齐至对应站点。结果证明，仅通过数据即可学习公交路线规划，实现端到端、无需地图的路线生成。数据集与基准测试可在https://huggingface.co/datasets/GD-ML/TransitLM获取，评估代码见https://github.com/HotTricker/TransitLM。
原文摘要 · Abstract (English)
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 demonstrate that transit route planning can be learned entirely from data, enabling end-to-end, map-free route generation directly from origin-destination information. The dataset and benchmark are available at https://huggingface.co/datasets/GD-ML/TransitLM, with evaluation code at https://github.com/HotTricker/TransitLM.
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