Regional Transferability of Graph Neural Networks for Traffic Forecasting
I. Kravcevs (TU Delft - Electrical Engineering, Mathematics and Computer Science)
E. Congeduti – Mentor (TU Delft - Computer Science & Engineering-Teaching Team)
E.A. Markatou – Graduation committee member (TU Delft - Cyber Security)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Efficient traffic forecasting is an important component of modern traffic management systems, enabling real-time route guidance and traffic control. Graph Neural Networks (GNN) have demonstrated state-of-the-art performance in this domain due to their ability to capture spatial and temporal dependencies in complex traffic data. However, GNNs typically require extensive historical data and are highly dependent on the specific road structure of the training region, posing challenges for their application in areas lacking such data. This study explores the transferability of GNN models in traffic forecasting, specifically how a GNN, trained in the region with long-horizon historical data, performs when applied to structurally different regional scenarios without historical data. The research investigates the impact of spatial differences between regions on the model's performance. The paper examines multiple metrics for regional similarity between training and transfer regions and shows their correlation with the transferred model's performance.