Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

Liu, Aoyu; Zhang, Yaying

doi:10.48550/arxiv.2205.08689

Cited by 2 publications

(2 citation statements)

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“…This is because we did not use the strategy of stacking repeated modules to capture features. Results from experiments indicate that the effectiveness of the ADDGCN model is significantly affected by the number of feature channels as illustrated in Figure 8 [46]. However, adding more feature channels does not always result in an improvement in the performance of AD-DGCN.…”

Section: Impact Of Different Hyperparameter Configurationsmentioning

confidence: 98%

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ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting

Li,

Wei,

Wang

et al. 2024

Applied Sciences

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An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby neglecting the temporally and spatially heterogeneous features among nodes. Simultaneously, many existing methods overlook the long-term relationships included in traffic data, subsequently impacting prediction accuracy. We introduce a novel method to traffic flow forecasting based on the combination of the feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism. Our method presents a feature augmentation mechanism to integrate traffic data features at different scales. The subsampled convolutional network enhances information interaction in spatio-temporal data, and the dynamic graph convolutional network utilizes the generated graph structure to better simulate the dynamic relationships between nodes, enhancing the model’s capacity for capturing spatial heterogeneity. Through the feature-enhanced subsampled dynamic graph convolutional network, the model can simultaneously capture spatio-temporal dependencies, and coupled with the process of multi-head temporal attention, it achieves long-term traffic flow forecasting. The findings demonstrate that the ADDGCN model demonstrates superior prediction capabilities on two real datasets (PEMS04 and PEMS08). Notably, for the PEMS04 dataset, compared to the best baseline, the performance of ADDGCN is improved by 2.46% in MAE and 2.90% in RMSE; for the PEMS08 dataset, compared to the best baseline, the ADDGCN performance is improved by 1.50% in RMSE, 3.46% in MAE, and 0.21% in MAPE, indicating our method’s superior performance.

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Section: Impact Of Different Hyperparameter Configurationsmentioning

confidence: 98%

“…The collected data were then aggregated into 5 min time windows, producing 12 observations each hour. Table 1 contains comprehensive details about the datasets [46]. The distances between the sensors in these real traffic road networks were used to construct the adjacency matrices utilized in the experiments conducted in this study.…”

Section: Datasetmentioning

confidence: 99%