README.md

metadata

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

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:

@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, 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 for details.