mlsanityData to 7 Oct 2026

Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph Attention Networks

arXiv 2501.09117 ↗First posted 15 Jan 2025cs.LGcited by 4

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Abstract (arXiv)

Solving traffic assignment problem for large networks is computationally challenging when conventional optimization-based methods are used. In our research, we develop an innovative surrogate model for a traffic assignment when multi-class vehicles are involved. We do so by employing heterogeneous graph neural networks which use a multiple-view graph attention mechanism tailored to different vehicle classes, along with additional links connecting origin-destination pairs. We also integrate the node-based flow conservation law into the loss function. As a result, our model adheres to flow conservation while delivering highly accurate predictions for link flows and utilization ratios. Through numerical experiments conducted on urban transportation networks, we demonstrate that our model surpasses traditional neural network approaches in convergence speed and predictive accuracy in both user equilibrium and system optimal versions of traffic assignment.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 15 Jan 2025.

Builds on

11 of 38 references.

References · 11

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  5. 2019Heterogeneous Graph Attention Networkcited by 732
  6. 2020MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embeddingcited by 304
  7. 2018Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Networkcited by 26
  8. 2023End-to-End Heterogeneous Graph Neural Networks for Traffic Assignmentcited by 13
  9. 2022Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Networkcited by 12
  10. 2022Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systemscited by 4
  11. 2024Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignmentcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Scalable and Reliable Multi-agent Reinforcement Learning for Traffic Assignmentcited by 2
  2. 2025From Optimization to Prediction: Transformer-Based Path-Flow Estimation to the Traffic Assignment Problemcited by 1
  3. 2026Metalearning traffic assignment for network disruptions with graph convolutional neural networkscited by 1
  4. 2026GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Modelscited by 0