Data augmentation for Sparse Graph Traversals
Exploring data augmentation options to enhance deep learning model performance
M. Lutgerink (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Ting Gao – Mentor (TU Delft - Traffic Systems Engineering)
Elvin Isufi – Mentor (TU Delft - Multimedia Computing)
Jing Sun – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
This research investigates the effectiveness of graph-based data augmentation techniques in improving the performance of DG4b, a deep learning model designed to estimate bicycle travel times in urban environments. Given the limitations of real-world cycling datasets, particularly data scarcity and trip-length imbalance, we propose two augmentation methods: Graph Stitching (GS), which combines segments of existing trips to form new trajectories, and Graphon-Inspired Trip Generation (GITG), which uses an empirically estimated transition kernel to simulate realistic trip patterns through probabilistic sampling.
Despite limited improvements, this study establishes a foundation for future research in graph-based trajectory augmentation. Integrating richer trip-level features, such as dynamic environmental conditions or behavioral data, with structural augmentation could lead to more effective training data and improved model generalization.