# Learning from user's behaviour of some well-known congested traffic networks

arXiv: https://arxiv.org/abs/2508.14804 · HTML: https://mlsanity.com/paper/2508.14804/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 20 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [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)
- [WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning](https://mlsanity.com/paper/2410.06656/) (2024)
- [Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment](https://mlsanity.com/paper/2109.06713/) (2021)

## Abstract (arXiv)

The traffic assignment problem (TAP) aims to predict how traffic flows distribute themselves across a road network, traditionally requiring computationally expensive iterative simulations to reach a user equilibrium (UE) where no driver can unilaterally reduce their travel time. Recent developments in machine learning (ML), particularly Graph Neural Networks (GNNs) and hybrid approaches, aim to solve this faster while maintaining accuracy