# A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting

arXiv: https://arxiv.org/abs/2506.02609 · HTML: https://mlsanity.com/paper/2506.02609/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 3 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2012.09641/) (2020)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction](https://mlsanity.com/paper/2301.07945/) (2023)
- [Graph Neural Controlled Differential Equations for Traffic Forecasting](https://mlsanity.com/paper/2112.03558/) (2021)
- [Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction](https://mlsanity.com/paper/2212.04475/) (2022)
- [Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs](https://mlsanity.com/paper/2202.08408/) (2022)
- [Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks](https://mlsanity.com/paper/1905.09646/) (2019)
- [AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting](https://mlsanity.com/paper/2011.11004/) (2020)
- [Pedestrian Trajectory Prediction with Convolutional Neural Networks](https://mlsanity.com/paper/2010.05796/) (2020)

## Abstract (arXiv)

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.