# Traffic map prediction using UNet based deep convolutional neural network

arXiv: https://arxiv.org/abs/1912.05288 · HTML: https://mlsanity.com/paper/1912.05288/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 7 of 7)

- [Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes](https://mlsanity.com/paper/2203.17070/) (2022)
- [A Graph-based U-Net Model for Predicting Traffic in unseen Cities](https://mlsanity.com/paper/2202.06725/) (2022)
- [Traffic Forecasting on Traffic Moving Snippets](https://mlsanity.com/paper/2110.14383/) (2021)
- [SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers](https://mlsanity.com/paper/2201.06390/) (2022)
- [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)
- [Axial-UNet: A Neural Weather Model for Precipitation Nowcasting](https://mlsanity.com/paper/2504.19408/) (2025)

## Abstract (arXiv)

This paper describes our UNet based deep convolutional neural network approach on the Traffic4cast challenge 2019. Challenges task is to predict future traffic flow volume, heading and speed on high resolution whole city map. We used UNet based deep convolutional neural network to train predictive model for the short term traffic forecast. On each convolution block, layers are densely connected with subsequent layers like a DenseNet. Trained and evaluated on the real world data set collected from three distinct cities in the world, our method achieved best performance in this challenge.