Bi-Directional Spatiotemporal Gated Graph Convolutional Network for Traffic Speed Prediction

Accurate traffic speed prediction plays a key role in transportation planning. Inspired by advanced natural language processing (NLP) techniques in speech recognition, this paper proposes a bidirectional spatio-temporal gated graph convolutional network (BiSTGG) based on the attention mechanism for urban traffic speed prediction. The model analogizes the average daily traffic speed with word frequencies in speech, thus establishing a novel semantic perspective for traffic modeling. In order to comprehensively assess the similarity between traffic speed trajectories, the model introduces a dynamic temporal regularization (DTW) algorithm, which enables the construction of dynamic semantic similarity subgraphs that go beyond the traditional spatial proximity modeling approach. Meanwhile, the model applies the multi-head graph attention mechanism to ensure that the complex and diverse interactions in the road network can be captured effectively. In addition, the model introduces spatial identification vectors to enhance robustness and direct attention to focus on key features. The model also incorporates a bi-directional gated recurrent unit (Bi-GRU) structure with an attention mechanism to capture the complex forward and backward time dependencies in traffic patterns. Experiments on two large-scale real datasets show that BiSTGG performs well in terms of prediction accuracy and robustness, and significantly improves real-time traffic speed prediction.