Datasets › LargeST
LargeST (LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting)
In this work, we propose LargeST as a new benchmark dataset (see Figure 1), with the goal of facilitating the development of accurate and efficient methods in the context of large-scale traffic forecasting. The distinguishing characteristic of LargeST lies not only in its extensive graph size, encompassing a total of 8,600 sensors in California, but also in its substantial temporal coverage and rich node information – each sensor contains 5 years of data and comprehensive metadata.
LargeST comprises four sub-datasets, each characterized by a different number of sensors. The biggest one is California (CA), including a total number of 8,600 sensors. We also construct three subsets of CA by selecting three representative areas within CA and forming the sub-datasets of Greater Los Angeles (GLA), Greater Bay Area (GBA), and San Diego (SD).
Benchmarks archive 2025-07-28
All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.
| First row (archive order) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Traffic Prediction | LargeST | PatchSTG SD MAE 16.90 | Efficient Large-Scale Traffic Forecasting with... | lmissher/stgnn +2 | 6 | Compare |
Papers archive 2025-07-28
6 shown of 6 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 25. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective | 3 | 1 | 13 Dec 2024 | ran 3 of 6 samples (3 unverified) |
| RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data | 0 | 1 | 16 Feb 2024 | not harvested |
| When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks | 1 | 1 | 26 Jul 2023 | not harvested |
| Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting | 2 | 1 | 10 Aug 2022 | not harvested |
| Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting | 1 | 1 | 24 Jun 2021 | ran 0 of 3 samples (3 unverified) |
| Graph WaveNet for Deep Spatial-Temporal Graph Modeling | 9 | 1 | 31 May 2019 | ran 5 of 7 samples (2 unverified) |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
CC BY-NC 4.0 International License
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- LargeST
1 variant name, as the archive lists them.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections