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%.