# Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation

arXiv: https://arxiv.org/abs/2111.03421 · HTML: https://mlsanity.com/paper/2111.03421/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation](https://mlsanity.com/paper/1801.05746/) (2018)
- [Utilizing UNet for the future traffic map prediction task Traffic4cast challenge 2020](https://mlsanity.com/paper/2012.00125/) (2020)
- [Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab](https://mlsanity.com/paper/1910.13824/) (2019)
- [Traffic map prediction using UNet based deep convolutional neural network](https://mlsanity.com/paper/1912.05288/) (2019)
- [Semi-Supervised Learning for Cancer Detection of Lymph Node Metastases](https://mlsanity.com/paper/1906.09587/) (2019)
- [Towards Good Practices of U-Net for Traffic Forecasting](https://mlsanity.com/paper/2012.02598/) (2020)
- [Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge Solution](https://mlsanity.com/paper/1911.05699/) (2019)

## Built on by (most cited first, 2 of 2)

- [Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes](https://mlsanity.com/paper/2203.17070/) (2022)
- [A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning](https://mlsanity.com/paper/2605.27884/) (2026)

## Abstract (arXiv)

In this technical report, we present our solution to the Traffic4Cast 2021 Core Challenge, in which participants were asked to develop algorithms for predicting a traffic state 60 minutes ahead, based on the information from the previous hour, in 4 different cities. In contrast to the previously held competitions, this year's challenge focuses on the temporal domain shift in traffic due to the COVID-19 pandemic. Following the past success of U-Net, we utilize it for predicting future traffic maps. Additionally, we explore the usage of pre-trained encoders such as DenseNet and EfficientNet and employ multiple domain adaptation techniques to fight the domain shift. Our solution has ranked third in the final competition. The code is available at https://github.com/jbr-ai-labs/traffic4cast-2021.