A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif based Graph Convolutional Recurrent Neural Network
Traffic forecasting is crucial for urban traffic management and guidance. However, existing methods rarely exploit the time frequency properties of traffic speed observations, and often neglect the propagation of traffic flows from upstream to downstream road segments. In this paper, we propose a hybrid approach that learns the spatio temporal dependency in traffic flows and predicts short term traffic speeds on a road network. Specifically, we employ wavelet transform to decompose raw traffic data into several components with different frequency sub bands. A Motif based Graph Convolutional Recurrent Neural Network (Motif GCRNN) and Auto Regressive Moving Average (ARMA) are used to train and predict low frequency components and high frequency components, respectively. In the Motif GCRNN framework, we integrate Graph Convolutional Networks (GCNs) with local sub graph structures Motifs to capture the spatial correlations among road segments, and apply Long Short Term Memory (LSTM) to extract the short term and periodic patterns in traffic speeds. Experiments on a traffic dataset collected in Chengdu, China, demonstrate that the proposed hybrid method outperforms six state of art prediction methods.
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