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Dynamic Spatial-Temporal Memory Augmentation Network for Traffic Prediction
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DOI：10.3390/s24206659.png)
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摘要
En 中文
Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogeneity. To better manage the spatio-temporal correlations inherent in traffic flow, we present a novel model called Dynamic Spatio-Temporal Memory-Augmented Network (DSTMAN). Firstly, we design three spatial-temporal embeddings to capture dynamic spatial-temporal contexts and encode the unique characteristics of time units and spatial states. Secondly, these three spatial-temporal components are integrated to form a multi-scale spatial-temporal block, which effectively extracts hierarchical spatial-temporal dependencies. Finally, we introduce a meta-memory node bank to construct an adaptive neighborhood graph, implicitly representing spatial relationships and enhancing the learning of spatial heterogeneity through a secondary memory mechanism. Evaluation on four public datasets, including METR-LA and PEMS-BAY, demonstrates that the proposed model outperforms benchmark models such as MTGNN, DCRNN, and AGCRN. On the METR-LA dataset, our model reduces the MAE by 4% compared to MTGNN, 6.9% compared to DCRNN, and 5.8% compared to AGCRN, confirming its efficacy in traffic flow prediction.
Keyword:
smart city
traffic flow prediction
multiple self-attention mechanism
graph convolutional network
meta-knowledge learning
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IF:
3.5
论文数:
7.2W
被引数:
20.9W
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