提出更快更准的交通流预测模型，同步捕捉时空关联。
FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting
- 设计同步时空图卷积结构，统一建模时空相关性
- 在METR-LA和PEMS-Bay数据集上达到更高精度
- 模型轻量高效，适合实时交通系统部署
准确的交通流量预测高度依赖于交通数据的时空相关性。当前多数研究分别捕捉空间与时间维度的相关性，难以有效建模复杂的时空异质性，且常以增加模型复杂度为代价提升预测精度。尽管已有突破性尝试在时空同步建模领域取得进展，但在性能与复杂度控制方面仍存在显著局限。本研究提出一种更快速、更高效的时空同步交通流预测模型，以解决上述问题。
原文摘要 · Abstract (English)
Accurate traffic flow prediction heavily relies on the spatio-temporal correlation of traffic flow data. Most current studies separately capture correlations in spatial and temporal dimensions, making it difficult to capture complex spatio-temporal heterogeneity, and often at the expense of increasing model complexity to improve prediction accuracy. Although there have been groundbreaking attempts in the field of spatio-temporal synchronous modeling, significant limitations remain in terms of performance and complexity control.This study proposes a quicker and more effective spatio-temporal synchronous traffic flow forecast model to address these issues.
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