Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction

• URL: http://arxiv.org/abs/2310.08138v1
• Date: Thu, 12 Oct 2023 08:52:36 GMT
• Title: Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction
• Authors: Haiyang Liu, Chunjiang Zhu, Detian Zhang, Qing Li
• Abstract summary: We propose a Multi-Scale Spatial-Temporal Recurrent Network for traffic flow prediction, namely MSSTRN. We propose a spatial-temporal synchronous attention mechanism that integrates adaptive position graph convolutions into the self-attention mechanism to achieve synchronous capture of spatial-temporal dependencies. Our model achieves the best prediction accuracy with non-trivial margins compared to all the twenty baseline methods.
• Score: 13.426775574655135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic flow prediction is one of the most fundamental tasks of intelligent transportation systems. The complex and dynamic spatial-temporal dependencies make the traffic flow prediction quite challenging. Although existing spatial-temporal graph neural networks hold prominent, they often encounter challenges such as (1) ignoring the fixed graph that limits the predictive performance of the model, (2) insufficiently capturing complex spatial-temporal dependencies simultaneously, and (3) lacking attention to spatial-temporal information at different time lengths. In this paper, we propose a Multi-Scale Spatial-Temporal Recurrent Network for traffic flow prediction, namely MSSTRN, which consists of two different recurrent neural networks: the single-step gate recurrent unit and the multi-step gate recurrent unit to fully capture the complex spatial-temporal information in the traffic data under different time windows. Moreover, we propose a spatial-temporal synchronous attention mechanism that integrates adaptive position graph convolutions into the self-attention mechanism to achieve synchronous capture of spatial-temporal dependencies. We conducted extensive experiments on four real traffic datasets and demonstrated that our model achieves the best prediction accuracy with non-trivial margins compared to all the twenty baseline methods.

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