| --- |
| license: mit |
| task_categories: |
| - time-series-forecasting |
| - tabular-regression |
| tags: |
| - traffic-prediction |
| - time-series |
| - graph-neural-networks |
| - transportation |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # METR-LA Traffic Dataset |
|
|
| ## Dataset Description |
|
|
| This dataset contains traffic flow data for time series forecasting tasks, commonly used with Graph Neural Networks and specifically the Diffusion Convolutional Recurrent Neural Network (DCRNN) model. |
|
|
| ## 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 (0-206 for METR-LA, 0-324 for PEMS-BAY) |
| - `t0_timestamp`: ISO 8601 timestamp of the reference time point (t+0) for each sequence |
| - `x_t*_d*`: Input features at different time offsets and dimensions |
| - `x_t-11_d0` to `x_t+0_d0`: Traffic flow values at 12 historical time steps |
| - `x_t-11_d1` to `x_t+0_d1`: Time-of-day features (normalized 0-1) |
| - `y_t*_d*`: Target values at future time steps and dimensions |
| - `y_t+1_d0` to `y_t+12_d0`: Traffic flow predictions for next 12 time steps |
| - `y_t+1_d1` to `y_t+12_d1`: Time-of-day features for prediction horizon |
|
|
| ### Dataset Statistics |
|
|
| - **Total time series samples**: ~34K (METR-LA) / ~52K (PEMS-BAY) |
| - **Total records**: ~7M (METR-LA) / ~17M (PEMS-BAY) |
| - **Records per sample**: 207 (METR-LA) / 325 (PEMS-BAY) sensors |
| - **Temporal resolution**: 5-minute intervals |
| - **Prediction horizon**: 1 hour (12 time steps) |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import Dataset, DatasetDict |
| import pandas as pd |
| |
| # Load from local parquet files |
| train_df = pd.read_parquet("METR-LA/train.parquet") |
| val_df = pd.read_parquet("METR-LA/val.parquet") |
| test_df = pd.read_parquet("METR-LA/test.parquet") |
| |
| ds = DatasetDict({ |
| "train": Dataset.from_pandas(train_df, preserve_index=False), |
| "val": Dataset.from_pandas(val_df, preserve_index=False), |
| "test": Dataset.from_pandas(test_df, preserve_index=False) |
| }) |
| |
| print(f"Train records: {len(ds['train']):,}") |
| print(f"Val records: {len(ds['val']):,}") |
| print(f"Test records: {len(ds['test']):,}") |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the original DCRNN paper: |
|
|
| ```bibtex |
| @inproceedings{li2018dcrnn_traffic, |
| title={{Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting}}, |
| author={{Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan}}, |
| booktitle={{International Conference on Learning Representations}}, |
| year={{2018}} |
| } |
| ``` |
|
|
| ## Dataset Generation |
|
|
| The code used to generate this Hugging Face-compatible dataset can be found at [witgaw/DCRNN](https://github.com/witgaw/DCRNN), a fork of the original DCRNN repository with enhanced data processing capabilities. |
|
|
| ## Original Data Source |
|
|
| This dataset is derived from the original METR-LA dataset used in the DCRNN paper. |
|
|
| ## License |
|
|
| MIT License - See the [original repository LICENSE](https://github.com/liyaguang/DCRNN/blob/master/LICENSE) for details. |
|
|
