matrix-multiply/alphatransit-checkpoints
Reinforcement Learning • Updated
Datasets:
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Bloomington TRNDP Benchmark
Bloomington, Indiana network, demand, and existing-route files used by AlphaTransit: Learning to Design City-scale Transit Routes.
Files
| File | Rows | Description |
|---|---|---|
standard/bloomington_nodes_standard.csv |
143 | Node IDs and projected coordinates. |
standard/bloomington_links_standard.csv |
243 | Directed road links with length and free-flow speed. |
standard/bloomington_demand_standard.csv |
5,737 | Origin-destination demand table. |
standard/bloomington_existing_routes.json |
16 routes | Bloomington Transit routes used as the real-world baseline. |
Loading
from datasets import load_dataset
nodes = load_dataset("matrix-multiply/bloomington-tndp", "nodes", split="benchmark")
links = load_dataset("matrix-multiply/bloomington-tndp", "links", split="benchmark")
demand = load_dataset("matrix-multiply/bloomington-tndp", "demand", split="benchmark")
routes = load_dataset("matrix-multiply/bloomington-tndp", "existing_routes", split="benchmark")
Links
- Paper: https://huggingface.co/papers/2605.28730
- Code: https://github.com/poudel-bibek/AlphaTransit
- Checkpoints: https://huggingface.co/matrix-multiply/alphatransit-checkpoints
- Research preview: https://alphatransit.app/
- WandB report: https://api.wandb.ai/links/bibek-poudel/wa4zd1il
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