---
tags:
- traffic-forecasting
- time-series
- graph-neural-network
- dcrnn
datasets:
- metr-la
---

# DCRNN Model - METR-LA

Diffusion Convolutional Recurrent Neural Network (DCRNN) trained on METR-LA dataset for traffic speed forecasting.

## Model Description

This model uses a graph neural network architecture that combines:
- Diffusion convolution to capture spatial dependencies on road networks
- Recurrent neural networks (GRU) for temporal modeling
- Sequence-to-sequence learning for multi-step ahead forecasting

## Evaluation Metrics

- **MAE (val split)**: 2.7835


## Dataset

**METR-LA**: Traffic speed data from highway sensors.

## Usage

```python
from dcrnn_pytorch.model.pytorch.dcrnn_supervisor import DCRNNSupervisor
from utils.dcrnn import load_from_hub

# Download checkpoint
checkpoint_path = load_from_hub("your-username/dcrnn-metr-la")

# Load model (requires config and adj_mx from checkpoint)
# See documentation for full loading example
```

## Training

Model was trained using the DCRNN PyTorch implementation with default hyperparameters.

## Citation

If you use this model, please cite the original DCRNN paper:

```
@inproceedings{li2018dcrnn,
  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 (ICLR)},
  year={2018}
}
```

## License

This model checkpoint is released under the same license as the training code.