  Data augmentation has emerged as a powerful technique in machine learning,
strengthening model robustness while mitigating overfitting and under-fitting
issues by generating diverse synthetic data. Nevertheless, despite its success
in other domains, data augmentation's potential remains largely untapped in
mobility data analysis, primarily due to the intricate nature and unique format
of trajectory data. Additionally, there is a lack of frameworks capable of
point-wise data augmentation, which can reliably generate synthetic
trajectories while preserving the inherent characteristics of the original
data. To address these challenges, this research introduces AugmenTRAJ, an
open-source Python3 framework designed explicitly for trajectory data
augmentation. AugmenTRAJ offers a reliable and well-controlled approach for
generating synthetic trajectories, thereby enabling the harnessing of data
augmentation benefits in mobility analysis. This thesis presents a
comprehensive overview of the methodologies employed in developing AugmenTRAJ
and showcases the various data augmentation techniques available within the
framework. AugmenTRAJ opens new possibilities for enhancing mobility data
analysis models' performance and generalization capabilities by providing
researchers with a practical and versatile tool for augmenting trajectory data,
Its user-friendly implementation in Python3 facilitates easy integration into
existing workflows, offering the community an accessible resource to leverage
the full potential of data augmentation in trajectory-based applications.
