What If London Bridge Is Closed? Feature-Aware Subgraph Augmentation for Modeling Road Network Structure Changes

Structural disruptions in road networks, such as bridge closures or road outages, can severely impact traffic flow, leading to significant connectivity losses and unpredictable shifts in traffic patterns. Traditional traffic prediction models, designed for stable network conditions, often fail to adapt to these sudden changes in road capacity and connectivity. To address this challenge, we formalize flow redistribution caused by structural changes as a dynamic network prediction task. We then propose a novel feature-aware subgraph augmentation framework that enables Spatio-Temporal Graph Neural Networks (STGNNs) to learn robust redistribution patterns—even with limited historical data. Our framework simulates disruptions via subgraph perturbations to generate realistic training samples, effectively enriching the dataset and enhancing model generalizability to structural changes. Evaluated on the Hammersmith Bridge closure in London, the proposed augmentation strategy significantly improves model performance and outperforms data-hungry baselines, accurately capturing the disruption and its network-wide effects. This study demonstrates that targeted data augmentation can make STGNNs more effective in disruption scenarios with scarce historical data—offering a new, data-efficient paradigm for daily traffic prediction under both planned and unplanned network changes.