# Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction Source: [Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:54.500Z Authors: Guangyin Jin, Fuxian Li, Jinlei Zhang, Mudan Wang, Jincai Huang Published: 2022-07-22T00:50:39.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Spatio-temporal forecasting on METR-LA (20% test)](): Spatio-temporal forecasting; METR-LA (20% test); MAE: 2.77. - [Spatio-temporal forecasting on SDWPF (20% test)](): Spatio-temporal forecasting; SDWPF (20% test); MAE: 56.62. - [Spatio-temporal forecasting on PEMS-BAY (20% test)](): Spatio-temporal forecasting; PEMS-BAY (20% test); MAE: 1.36. ## Original sources and implementation [Original paper]() [PDF]()