# Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment

arXiv: https://arxiv.org/abs/2505.11230 · HTML: https://mlsanity.com/paper/2505.11230/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment](https://mlsanity.com/paper/2310.13193/) (2023)
- [Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network](https://mlsanity.com/paper/2202.10508/) (2022)
- [Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks](https://mlsanity.com/paper/2310.17729/) (2023)
- [WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning](https://mlsanity.com/paper/2410.06656/) (2024)

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

- [Metalearning traffic assignment for network disruptions with graph convolutional neural networks](https://mlsanity.com/paper/2603.06763/) (2026)
- [Generalising Travel Time Prediction To Varying Route Choices In Urban Networks](https://mlsanity.com/paper/2605.06918/) (2026)
- [GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models](https://mlsanity.com/paper/2607.19270/) (2026)

## Abstract (arXiv)

The Traffic Assignment Problem is a fundamental, yet computationally expensive, task in transportation modeling, especially for large-scale networks. Traditional methods require iterative simulations to reach equilibrium, making real-time or large-scale scenario analysis challenging. In this paper, we propose a learning-based approach using Message-Passing Neural Networks as a metamodel to approximate the equilibrium flow of the Stochastic User Equilibrium assignment. Our model is designed to mimic the algorithmic structure used in conventional traffic simulators allowing it to better capture the underlying process rather than just the data. We benchmark it against other conventional deep learning techniques and evaluate the model's robustness by testing its ability to predict traffic flows on input data outside the domain on which it was trained. This approach offers a promising solution for accelerating out-of-distribution scenario assessments, reducing computational costs in large-scale transportation planning, and enabling real-time decision-making.