# Towards Good Practices of U-Net for Traffic Forecasting

arXiv: https://arxiv.org/abs/2012.02598 · HTML: https://mlsanity.com/paper/2012.02598/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers](https://mlsanity.com/paper/2201.06390/) (2022)
- [Dual Encoding U-Net for Spatio-Temporal Domain Shift Frame Prediction](https://mlsanity.com/paper/2110.11140/) (2021)
- [Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation](https://mlsanity.com/paper/2111.03421/) (2021)
- [Attention Gate in Traffic Forecasting](https://mlsanity.com/paper/2109.13021/) (2021)

## Abstract (arXiv)

This technical report presents a solution for the 2020 Traffic4Cast Challenge. We consider the traffic forecasting problem as a future frame prediction task with relatively weak temporal dependencies (might be due to stochastic urban traffic dynamics) and strong prior knowledge, textiti.e., the roadmaps of the cities. For these reasons, we use the U-Net as the backbone model, and we propose a roadmap generation method to make the predicted traffic flows more rational. Meanwhile, we use a fine-tuning strategy based on the validation set to prevent overfitting, which effectively improves the prediction results. At the end of this report, we further discuss several approaches that we have considered or could be explored in future work: (1) harnessing inherent data patterns, such as seasonality; (2) distilling and transferring common knowledge between different cities. We also analyze the validity of the evaluation metric.