Development of a graph neural network surrogate for travel demand modelling

TL;DR

This paper introduces GATv3, a new Graph Attention Network variant, and a fine-grained classification framework to improve travel demand modelling, demonstrating enhanced performance and interpretability in transportation applications.

Contribution

The paper presents GATv3 with residual connections for deeper GNNs and a fine-grained classification approach, advancing travel demand modelling with improved accuracy and interpretability.

Findings

GATv3 significantly improves classification performance.

GNN models with synthetic data outperform traditional methods.

Fine-grained classification offers better differentiation for transportation tasks.

Abstract

As urban environments grow, the modelling of transportation systems becomes increasingly complex. This paper advances the field of travel demand modelling by introducing advanced Graph Neural Network (GNN) architectures as surrogate models, addressing key limitations of previous approaches. Building on prior work with Graph Convolutional Networks (GCNs), we introduce GATv3, a new Graph Attention Network (GAT) variant that mitigates over-smoothing through residual connections, enabling deeper and more expressive architectures. Additionally, we propose a fine-grained classification framework that improves predictive stability while achieving numerical precision comparable to regression, offering a more interpretable and efficient alternative. To enhance model performance, we develop a synthetic data generation strategy, which expands the augmented training dataset without overfitting. Our experiments demonstrate that GATv3 significantly improves classification performance, while the GCN model shows unexpected dominance in fine-grained classification when supplemented with additional training data. The results highlight the advantages of fine-grained classification over regression for travel demand modelling tasks and reveal new challenges in extending GAT-based architectures to complex transport scenarios. Notably, GATv3 appears well-suited for classification-based transportation applications, such as section control and congestion warning systems, which require a higher degree of differentiation among neighboring links. These findings contribute to refining GNN-based surrogates, offering new possibilities for applying GATv3 and fine-grained classification in broader transportation challenges.