Accurate estimation of bicycle trip travel times remains a challenge due to the limited availability of structured cycling data. This paper investigates how graph-based data augmentation can be used to address this limitation, specifically within the context of the DG4B model, a Graph Convolutional Neural Network for travel time estimation. We explore and evaluate three augmentation techniques: Random Walk (with and without node revisiting), Dijkstra Walk and Subgraph Stitching. These methods generate new trips by traversing or recombining paths within an existing road network graph, aiming to expand the training dataset while preserving realistic routing behavior. The augmented data is evaluated both statistically, using metrics like mean, variance and Frobenius norm, and in terms of model performance using RMSE, MAE and MAPE. Experimental results show that Subgraph Stitching and Dijkstra Walk yield the most effective improvements in model accuracy, with each method exhibiting strengths across different trip duration ranges. This work demonstrates that carefully designed graph-based data augmentation can improve GCNN-based travel time predictions in settings with limited cycling trip data.

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.

Estimating bike trip times is becoming more and more important in many different areas such as urban mobility and route planning. However, especially in real-world, the GPS data used to generate these estimations is frequently noisy, irregularly sampled, or incomplete. With an emphasis on how these strategies interact with trip length and speed variance, this study intends to examine the effects of various data resampling techniques on the precision of bicycle travel time estimations. To analyze the impact of different preprocessing methods, we apply and assess a graph neural network model using various resampling techniques. Instinctively, the assumption that we expect to be concluded from this research is that there is no single resampling technique works well for every kind of trip. Rather, trip parameters like duration and speed fluctuation have a significant impact on accuracy.

Accurate prediction of bicycle travel time is critical for efficient urban mobility and sustainable transport planning. However, real-world datasets are noisy, imbalanced and lack rich contextual features. This limits the effectiveness of current graph-based neural network models. This research aims to explore how feature engineering and model enhancements can improve the performance of a Graph Convolutional Neural Network (GCNN) in the context of travel time prediction. Building on a currently existing DG4b architecture, the input data is enriched with temporal, spatial and traffic-related features. Architectural enhancements are integrated by employing techniques such as graph data augmentation, and multi-scale graph learning. Using a dataset from Berlin, the improvements are evaluated primarily in terms of prediction accuracy across varying trip lengths, which implicitly reflect speed variability and route diversity. The goal is to explore how targeted feature engineering and graph-based modeling techniques influence the accuracy of bicycle travel time estimation, especially across different trip durations that reflect real-world cycling variability. The results show that optimal feature engineering improved the model up to 6% and a combination of the model enhancement techniques improved the model up to 2%.