Improving Road Intersection Classification Using Latent Network Structure

Accurate classification of road network intersections is crucial for effective urban planning and ensuring traffic safety. Traditional machine learning approaches often ignore the topological structure of road networks, treating intersections as independent entities. This study evaluates the effectiveness of augmenting original node features with latent graph representations derived from the Generalized Random Dot Product Graph (GRDPG) model and conducts a comparative analysis across multiple classes of machine learning and deep learning models. Unlike methods that rely on homophily assumptions, GRDPG naturally accommodates heterophilous connectivity patterns, which are common in transportation networks. Experiments on a real-world urban road network demonstrate substantial performance gains, indicating that incorporating latent structural context is critical for accurate intersection classification. These results position GRDPG-based embeddings as a computationally efficient alternative to more complex graph neural network architectures.

Generalized Random Dot Product Graphs

Road Intersection Classification

GAT

Machine Learning 

Data Science Applications

Symposium on Data Science and Statistics (SDSS) 2026