提出时间增强解耦网络，提升交通流预测精度。
A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting
- 通过动态图与时间特征模块解耦复杂交通数据
- 在四个真实数据集上显著优于现有方法
- 适合研究交通预测与时空建模的学者
近年来，交通流预测已成为智能交通系统的研究热点。然而，由于交通数据具有时序变化和动态空间相关性，预测仍面临巨大挑战。传统时空网络依赖端到端训练，难以处理多种交通流模式下的复杂依赖关系。此外，交通流对时间信息变化高度敏感，但现有研究对此重视不足。为此，本文提出一种新型方法——时间增强型数据解耦网络（TEDDN），将原本复杂交织的交通数据分解为稳定模式与趋势。通过动态图结构与时间特征提取模块，灵活学习时间和节点信息，有效解耦并提取复杂交通特征。在四个真实数据集上的实验与消融研究验证了该方法的优越性。
原文摘要 · Abstract (English)
In recent years, traffic flow prediction has become a highlight in the field of intelligent transportation systems. However, due to the temporal variations and dynamic spatial correlations of traffic data, traffic prediction remains highly challenging.Traditional spatiotemporal networks, which rely on end-to-end training, often struggle to handle the diverse data dependencies of multiple traffic flow patterns. Additionally, traffic flow variations are highly sensitive to temporal information changes. Regrettably, other researchers have not sufficiently recognized the importance of temporal information.To address these challenges, we propose a novel approach called A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting (TEDDN). This network disentangles the originally complex and intertwined traffic data into stable patterns and trends. By flexibly learning temporal and node information through a dynamic graph enhanced by a temporal feature extraction module, TEDDN demonstrates significant efficacy in disentangling and extracting complex traffic information. Experimental evaluations and ablation studies on four real-world datasets validate the superiority of our method.
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