# Utilizing UNet for the future traffic map prediction task Traffic4cast challenge 2020

arXiv: https://arxiv.org/abs/2012.00125 · HTML: https://mlsanity.com/paper/2012.00125/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task](https://mlsanity.com/paper/2104.10066/) (2021)
- [Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes](https://mlsanity.com/paper/2203.17070/) (2022)
- [A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecasting](https://mlsanity.com/paper/2208.01252/) (2022)
- [Traffic Forecasting on Traffic Moving Snippets](https://mlsanity.com/paper/2110.14383/) (2021)
- [A Variational U-Net for Weather Forecasting](https://mlsanity.com/paper/2111.03476/) (2021)
- [Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and Sparse-UNet](https://mlsanity.com/paper/2111.05990/) (2021)
- [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)
- [Efficient spatio-temporal weather forecasting using U-Net](https://mlsanity.com/paper/2112.06543/) (2021)
- [Attention Gate in Traffic Forecasting](https://mlsanity.com/paper/2109.13021/) (2021)
- [A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning](https://mlsanity.com/paper/2605.27884/) (2026)

## Abstract (arXiv)

This paper describes our UNet based experiments on the Traffic4cast challenge 2020. Similar to the Traffic4cast challenge 2019, the task is to predict traffic flow volume, direction and speed on a high resolution map of three large cities worldwide. We mainly experimented with UNet based deep convolutional networks with various compositions of densely connected convolution layers, average pooling layers and max pooling layers. Three base UNet model types are tried and predictions are combined by averaging prediction scores or taking median value. Our method achieved best performance in this years newly built challenge dataset.