# Traffic4cast-Traffic Map Movie Forecasting -- Team MIE-Lab

arXiv: https://arxiv.org/abs/1910.13824 · HTML: https://mlsanity.com/paper/1910.13824/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 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)
- [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)
- [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)
- [A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning](https://mlsanity.com/paper/2605.27884/) (2026)

## Abstract (arXiv)

The goal of the IARAI competition traffic4cast was to predict the city-wide traffic status within a 15-minute time window, based on information from the previous hour. The traffic status was given as multi-channel images (one pixel roughly corresponds to 100x100 meters), where one channel indicated the traffic volume, another one the average speed of vehicles, and a third one their rough heading. As part of our work on the competition, we evaluated many different network architectures, analyzed the statistical properties of the given data in detail, and thought about how to transform the problem to be able to take additional spatio-temporal context-information into account, such as the street network, the positions of traffic lights, or the weather. This document summarizes our efforts that led to our best submission, and gives some insights about which other approaches we evaluated, and why they did not work as well as imagined.