Learning traffic flows: Graph Neural Networks for Metamodelling Traffic Assignment

2025-05-16

AI Summary

Traffic assignment is computationally expensive and impractical for real-time applications, especially on large-scale road networks. To address this, we propose an interpretable meta-model based on Message Passing Neural Networks (MPNNs), the first to align Graph Neural Network (GNN) architecture with the logic of Stochastic User Equilibrium (SUE) solving—directly mapping origin-destination (OD) demands to equilibrium flows without iterative simulation. The model takes a traffic graph as input and explicitly encodes path-choice behavior and flow allocation mechanisms, substantially improving out-of-distribution generalization. Experiments demonstrate that our approach reduces computational time by over 90% while preserving prediction accuracy, enabling real-time analysis on large-scale networks. Moreover, it exhibits strong robustness across distributionally shifted scenarios, overcoming the limited extrapolation capability typical of purely data-driven models.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Accelerating traffic flow equilibrium computation for large networks
• Reducing computational costs in transportation planning scenarios
• Enabling real-time decision-making with robust flow predictions

Innovation

• Message-Passing Neural Networks approximate traffic equilibrium
• Mimics algorithmic structure of traditional traffic simulators
• Enables robust out-of-distribution scenario predictions