# Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors

arXiv: https://arxiv.org/abs/2303.07758 · HTML: https://mlsanity.com/paper/2303.07758/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 44 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 18 of 18)

- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods](https://mlsanity.com/paper/2110.14446/) (2021)
- [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021)
- [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016)
- [Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks](https://mlsanity.com/paper/2007.03113/) (2020)
- [Deep Learning for Road Traffic Forecasting: Does it Make a Difference?](https://mlsanity.com/paper/2012.02260/) (2020)
- [Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes](https://mlsanity.com/paper/2203.17070/) (2022)
- [Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks](https://mlsanity.com/paper/2008.12767/) (2020)
- [Transposed Variational Auto-encoder with Intrinsic Feature Learning for Traffic Forecasting](https://mlsanity.com/paper/2211.00641/) (2022)
- [Hierarchical Graph Structures for Congestion and ETA Prediction](https://mlsanity.com/paper/2211.11762/) (2022)
- [Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities](https://mlsanity.com/paper/2302.08761/) (2023)
- [A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation](https://mlsanity.com/paper/2202.01888/) (2022)
- [An Explainable Stacked Ensemble Model for Static Route-Free Estimation of Time of Arrival](https://mlsanity.com/paper/2203.09438/) (2022)
- [Large scale traffic forecasting with gradient boosting, Traffic4cast 2022 challenge](https://mlsanity.com/paper/2211.00157/) (2022)
- [Similarity-based Feature Extraction for Large-scale Sparse Traffic Forecasting](https://mlsanity.com/paper/2211.07031/) (2022)
- [Multi-task Learning for Sparse Traffic Forecasting](https://mlsanity.com/paper/2211.09984/) (2022)
- [An Efficient Two-stage Gradient Boosting Framework for Short-term Traffic State Estimation](https://mlsanity.com/paper/2302.10400/) (2023)

## Built on by (most cited first, 3 of 3)

- [Flood and Echo Net: Algorithmically Aligned GNNs that Generalize](https://mlsanity.com/paper/2310.06970/) (2023)
- [Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing](https://mlsanity.com/paper/2502.13188/) (2025)
- [A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning](https://mlsanity.com/paper/2605.27884/) (2026)

## Abstract (arXiv)

The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our dynamic road graph data combine information from road maps, 1012 probe data points, and stationary vehicle detectors in three cities over the span of two years. While stationary vehicle detectors are the most accurate way to capture traffic volume, they are only available in few locations. Traffic4cast 2022 explores models that have the ability to generalize loosely related temporal vertex data on just a few nodes to predict dynamic future traffic states on the edges of the entire road graph. In the core challenge, participants are invited to predict the likelihoods of three congestion classes derived from the speed levels in the GPS data for the entire road graph in three cities 15 min into the future. We only provide vehicle count data from spatially sparse stationary vehicle detectors in these three cities as model input for this task. The data are aggregated in 15 min time bins for one hour prior to the prediction time. For the extended challenge, participants are tasked to predict the average travel times on super-segments 15 min into the future - super-segments are longer sequences of road segments in the graph. The competition results provide an important advance in the prediction of complex city-wide traffic states just from publicly available sparse vehicle data and without the need for large amounts of real-time floating vehicle data.