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README.md

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----
-license: mit
-task_categories:
-- time-series-forecasting
-- tabular-regression
-tags:
-- traffic-prediction
-- time-series
-- graph-neural-networks
-- transportation
-- spatiotemporal
-size_categories:
-- 10M<n<100M
----
-
-# LargeST-GLA Traffic Dataset
-
-## Dataset Description
-
-This dataset contains traffic flow data from the Greater Los Angeles (GLA) area, preprocessed from the LargeST benchmark. It covers 3,834 traffic sensors across 5 counties (Los Angeles, Orange, San Bernardino, Riverside, Ventura) for the year 2019.
-
-## Dataset Structure
-
-### Data Format
-
-- **Format**: Parquet files for efficient loading and analysis
-- **Splits**: train (70%), validation (10%), test (20%) - **temporal splits** preserving chronological order
-- **Features**: Time series traffic flow data with temporal and spatial dimensions
-
-### Split Strategy
-
-- **Temporal splitting**: Data is split chronologically to prevent data leakage
-- **All sensors included**: Each split contains data for all sensors at each time step
-- **Training period**: Earliest 70% of time samples across all sensors
-- **Validation period**: Next 10% of time samples across all sensors
-- **Test period**: Latest 20% of time samples across all sensors
-- **Graph structure preserved**: Spatial relationships maintained in all splits
-
-### Data Schema
-
-- `node_id`: Sensor/node identifier (0-3833 for LargeST-GLA)
-- `t0_timestamp`: ISO timestamp for the prediction target time
-- `x_t*_d0`: Input features at different time offsets
-  - Traffic flow values at 12 historical time steps (t-11 to t+0)
-- `y_t*_d0`: Target values at future time steps
-  - Traffic flow predictions for next 12 time steps (t+1 to t+12)
-
-### Dataset Statistics
-
-- **Region**: Greater Los Angeles Area (GLA)
-- **Time period**: 2019 (full year)
-- **Total sensors**: 3,834 sensors across 5 counties
-- **Counties**: Los Angeles, Orange, San Bernardino, Riverside, Ventura
-- **Temporal resolution**: 5-minute intervals
-- **Prediction horizon**: 1 hour (12 time steps)
-- **Total samples**: ~403 million samples
-
-## Usage
-
-```python
-import pandas as pd
-from datasets import load_dataset
-
-# Load from HuggingFace Hub
-dataset = load_dataset("emelle/LargeST-GLA")
-
-# Or load directly from parquet
-train_df = pd.read_parquet("hf://datasets/emelle/LargeST-GLA/train.parquet")
-val_df = pd.read_parquet("hf://datasets/emelle/LargeST-GLA/val.parquet")
-test_df = pd.read_parquet("hf://datasets/emelle/LargeST-GLA/test.parquet")
-
-print(f"Train records: {len(train_df):,}")
-print(f"Val records: {len(val_df):,}")
-print(f"Test records: {len(test_df):,}")
-```
-
-## Citation
-
-If you use this dataset, please cite the original LargeST paper:
-
-```bibtex
-@inproceedings{liu2023largest,
-  title={LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting},
-  author={Liu, Xu and Xia, Yutong and Liang, Yuxuan and Hu, Junfeng and Wang, Yiwei and Bai, Lei and Huang, Chao and Liu, Zhenguang and Hooi, Bryan and Zimmermann, Roger},
-  booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
-  year={2023}
-}
-```
-
-## Original Data Source
-
-This dataset is derived from the LargeST benchmark, preprocessed to be compatible with METR-LA and PEMS-BAY formats for spatiotemporal graph neural network research.
-
-- **Original dataset**: [LargeST on Kaggle](https://www.kaggle.com/datasets/liuxu77/largest)
-- **Compatible with**: METR-LA, PEMS-BAY dataset formats
-
-## License
-
-MIT License
+# LargeST-GLA Dataset
+Preprocessed from LargeST benchmark (California traffic data).
+Region: GLA
+Year: 2019
+Sensors: 3834
+Compatible with METR-LA and PEMS-BAY format.

test.parquet

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train.parquet

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val.parquet

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