DCRNN_PEMS-BAY – AI Model by witgaw | AlphaNeural AI

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witgaw/
DCRNN_PEMS-BAY

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traffic-forecasting

graph-neural-network

10.57967/hf/6888

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DCRNN Model - PEMS-BAY

Diffusion Convolutional Recurrent Neural Network (DCRNN) trained on PEMS-BAY 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): 1.6123

Dataset

PEMS-BAY: 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-pems-bay")

# 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.