A Bidirectional Context-Aware and Multi-Scale Fusion Hybrid Network for Short-Term Traffic Flow Prediction

Keywords: intelligent transportation system, gated recurrent unit, short-term traffic flow prediction, Bidirectional Context-aware, interpolation back propagation


Short-term traffic flow prediction is to automatically predict the traffic flow changes in a period of future time based on the extraction of the spatiotemporal features in the road network. For governments, timely and accurate traffic flow prediction is crucial to plan road manage-ment and improve traffic efficiency. Recent advances in deep learning have shown their dominance on short-term traffic flow prediction. However, previous methods based on deep learning are mainly limited to temporal features and have so far failed to predict the bidirectional con-textual spatiotemporal relationship correctly. Besides, the precision and the practicality are limited by the road network scale and the single time scale. To remedy these issues, a Bidirectional Context-aware and Multi-scale fusion hybrid Network (BCM-Net) is proposed, which is a novel short-term traffic flow prediction framework to predict timely and accurate traffic flow changes. In BCM-Net, the Bidirectional Context-aware (BCM) block is added to the feature extraction structure to effective-ly integrate spatiotemporal features. The Interpolation Back Propagation sub-network is used to merge multi-scale information, which further improves the robustness of the model. Experiment results on diverse datasets demonstrated that the proposed method outperformed the state-of-the-art methods.


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How to Cite
CHEN Z, ZHENG G. A Bidirectional Context-Aware and Multi-Scale Fusion Hybrid Network for Short-Term Traffic Flow Prediction. Promet [Internet]. 2022May31 [cited 2022Jul.3];34(3):407-20. Available from: https://traffic.fpz.hr/index.php/PROMTT/article/view/3957