A Hybrid Traffic Speed Forecasting Approach Integrating Wavelet Transform and Motif-based Graph Convolutional Recurrent Neural Network

Understand

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.