Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction

• URL: http://arxiv.org/abs/2207.10830v1
• Date: Fri, 22 Jul 2022 00:50:39 GMT
• Title: Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
• Authors: Guangyin Jin, Fuxian Li, Jinlei Zhang, Mudan Wang, Jincai Huang
• Abstract summary: We propose an automated dilated-temporal synchronous graph network prediction named Auto-DSTS for traffic prediction. Specifically, we propose an automated dilated-temporal-temporal graph (Auto-DSTS) module to capture the short-term and long-term-temporal correlations. Our model can achieve about 10% improvements compared with the state-of-art methods.
• Score: 1.6449390849183363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN.

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