Traffic-GGNN: Predicting Traffic Flow via Attentional Spatial-Temporal Gated Graph Neural Networks

Abstract

Recent spatial-temporal graph-based deep learning methods for Traffic Flow Prediction (TFP) problems have shown superior performance in modeling higher-level spatial interactions and temporal correlations. However, most of these methods suffer from post-fusion efficiency difficulty caused by separate explorations of the spatial communications and the temporal dependencies, which could result in delayed and biased predictions. To address that, we propose a Traffic Gated Graph Neural Networks (Traffic-GGNN) for real-time-fused spatial-temporal representation modeling. Firstly, we adopt bidirectional message passing to capture the location-wise spatial interactions. Secondly, we apply a GRU-based module to explore and aggregate the spatial interactions with the temporal correlations in a real-time fusion way. Lastly, we introduce a self-attention mechanism to reweight the location-based importance and produce the final prediction. Moreover, our proposed model allows end-to-end training thus it is easy to scale to diverse types of traffic datasets and yield better efficiency and effectiveness on three real-world datasets (SZ-taxi, Los-loop, and PEMS-BAY).