Graph Wavelet Long Short-Term Memory Neural Network: A Novel Spatial-Temporal Network for Traffic Prediction

Abstract

Timely accurate traffic prediction is important in the Intelligent Traffic System (ITS). It has time-varying traffic patterns and the complicated spatial dependencies on traffic network topology which makes the prediction challenging. In this paper, we propose a novel deep learning framework—Graph Wavelet Long Short-Term Memory Neural Network (GWNN-LSTM) to capture the spatial and temporal dependence simultaneously. Moreover, Graph Wavelet Neural Network (GWNN) is utilized for spatial correlations and Long Short-Term Memory Neural Network (LSTM) is used to capture the dynamic temporal correlations in traffic time series data. Experiments on real-world datasets from loop detectors in the highway of Los Angeles County (METR-LA) demonstrate that the proposed GWNN-LSTM model outperforms the state-of-the-art baselines.