End-to-End Heterogeneous Graph Neural Networks for Traffic Assignment

TL;DR

This paper introduces a novel end-to-end heterogeneous graph neural network model for traffic assignment that captures spatial traffic patterns and ensures flow conservation, outperforming traditional models in accuracy and convergence.

Contribution

The paper proposes a new heterogeneous graph neural network with adaptive attention and virtual links for improved traffic assignment prediction.

Findings

Outperforms conventional neural networks in accuracy and convergence.

Effectively captures spatial traffic patterns across links.

Generalizes well to different network topologies.

Abstract

The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed. However, deploying these approaches for large-scale networks poses significant challenges. In this paper, we leverage the power of heterogeneous graph neural networks to propose a novel end-to-end surrogate model for traffic assignment, specifically user equilibrium traffic assignment problems. Our model integrates an adaptive graph attention mechanism with auxiliary "virtual" links connecting origin-destination node pairs, This integration enables the model to capture spatial traffic patterns across different links, By incorporating the node-based flow conservation law into the overall loss function, the model ensures the prediction results in compliance with flow conservation principles, resulting in highly accurate predictions for both link flow and flow-capacity ratios. We present numerical experiments on urban transportation networks and show that the proposed heterogeneous graph neural network model outperforms other conventional neural network models in terms of convergence rate and prediction accuracy. Notably, by introducing two different training strategies, the proposed heterogeneous graph neural network model can also be generalized to different network topologies. This approach offers a promising solution for complex traffic flow analysis and prediction, enhancing our understanding and management of a wide range of transportation systems.