  Accurate classification of mode choice datasets is crucial for transportation
planning and decision-making processes. However, conventional classification
models often struggle to adequately capture the nuanced patterns of minority
classes within these datasets, leading to sub-optimal accuracy. In response to
this challenge, we present Ensemble Synthesizer (ENSY) which leverages
probability distribution for data augmentation, a novel data model tailored
specifically for enhancing classification accuracy in mode choice datasets. In
our study, ENSY demonstrates remarkable efficacy by nearly quadrupling the F1
score of minority classes and improving overall classification accuracy by
nearly 3%. To assess its performance comprehensively, we compare ENSY against
various augmentation techniques including Random Oversampling, SMOTE-NC, and
CTGAN. Through experimentation, ENSY consistently outperforms these methods
across various scenarios, underscoring its robustness and effectiveness
