Spatio-Temporal Graph Mixformer for Traffic Forecasting
Traffic forecasting is of great importance for intelligent transportation systems (ITS). Because of the intricacy implied in traffic behavior and the non-Euclidean nature of traffic data, it is challenging to give an accurate traffic prediction. Despite that previous studies considered the relationship between different nodes, the majority have relied on a static representation and failed to capture the dynamic node interactions over time. Additionally, prior studies employed RNN-based models to capture the temporal dependency. While RNNs are a popular choice for forecasting problems, they tend to be memory hungry and slow to train. Furthermore, recent studies start utilizing similarity algorithms to better express the implication of a node over the other. However, to our knowledge, none have explored the contribution of node $𝑖$’s past, over the future state of node $𝑗$. In this paper, we propose a Spatio-Temporal Graph Mixformer (STGM) network, a highly optimized model with low memory footprint. We address the aforementioned limits by utilizing a novel attention mechanism to capture the correlation between temporal and spatial dependencies. Specifically, we use convolution layers with a variable field of view for each head to capture long–short term temporal dependency. Additionally, we train an estimator model that express the contribution of a node over the desired prediction. The estimation is fed alongside a distance matrix to the attention mechanism. Meanwhile, we use a gated mechanism and a mixer layer to further select and incorporate the different perspectives. Extensive experiments show that the proposed model enjoys a performance gain compared to the baselines while maintaining the lowest parameter counts.
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Tasks
Task | Dataset | Model | Metric Name | Metric Value | Global Rank | Benchmark |
---|---|---|---|---|---|---|
Traffic Prediction | METR-LA | STGM | MAE @ 12 step | 3.229 | # 1 | |
12 steps MAE | 3.229 | # 1 | ||||
12 steps RMSE | 7.099 | # 3 | ||||
12 steps MAPE | 9.39 | # 1 | ||||
Traffic Prediction | PEMS-BAY | STGM | MAE @ 12 step | 1.857 | # 4 | |
RMSE | 4.369 | # 4 | ||||
Traffic Prediction | PeMSD7(M) | STGM | 12 steps MAE | 3.002 | # 4 | |
12 steps RMSE | 6.331 | # 1 | ||||
12 steps MAPE | 8.01 | # 1 |