Graph Conal Gated Recurrent Neural Network for Multi-step Traffic Volume Prediction

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Machine Learning

Graph Conal Gated Recurrent Neural Network for Multi-step Traffic Volume Prediction

Network-wide Multi-step Traffic Volume Prediction using Graph Convolutional Gated Recurrent Neural Network

We propose a novel deep learning model, graph convolutional gatedrecurrent neural network (gcgrnn), to predict network-wide multi-step traffic volume.

Our model can automatically capture spatial correlations between traffic sensors and temporal dependencies in historical traffic data.

We have evaluated our model using two traffic datasets extracted from 150 sensors in los angeles,california, at the time resolutions one hour and 15 minutes, respectively.

The results show that our model outperforms the other five benchmark models in terms of prediction accuracy.

For instance, our model reduces mae by 25.3, rmse by 29.2, and mape by 20.2% compared to the state-of-the-art diffusionconvolutional recurrent neural network (dcrnn) model using the hourly dataset.

Our model also achieves faster training than dcrnn by up to 52%.

Authors

Lei Lin, Weizi Li, Lei Zhu

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