Railway track performance prediction considering track-drainage interdependencies

Effective prediction of infrastructure performance is essential for informed asset management. However, traditional approaches often treat different types of assets in isolation, overlooking critical interdependencies (such as those between track and drainage systems) that significantly influence asset degradation and risk. This paper proposes a hybrid model, BaGTA, that is temporally aware, spatially informed and probabilistically grounded to predict railway track performance while accounting for both uncertainty and inter-asset dependencies. The model was trained and validated on a dataset comprising 6072 track segments and 31,628 drainage assets across four UK railway routes. We demonstrate that incorporating track-drainage interdependencies improves prediction accuracy in both classification and regression tasks. Specifically, the inclusion of interdependencies reduced the prediction error for the Vertical Settlement Standard Deviation (VSD), which is a key indicator of track performance, by 24.65 %. The proposed method not only captures complex spatiotemporal relationships but also quantifies uncertainty in predictions, offering a robust decision-support tool for infrastructure operators. This approach has the potential to transform maintenance strategies by enabling proactive, risk-informed, and cost-effective asset management.