Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)
This addresses a specific challenge in transportation planning by improving classification for minority classes, though it appears incremental as it builds on existing augmentation techniques.
The paper tackles the problem of accurately classifying minority classes in trip mode choice datasets by introducing Ensemble Synthesizer (ENSY), a data augmentation method that nearly quadruples the F1 score for minority classes and improves overall accuracy by nearly 3%.
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