Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)

TL;DR

This paper introduces Ensemble Synthesizer (ENSY), a novel data augmentation method using probability distributions that significantly improves mode choice classification accuracy, especially for minority classes, in transportation datasets.

Contribution

ENSY is a new data augmentation approach that enhances classification accuracy in mode choice datasets by effectively capturing minority class patterns.

Findings

Nearly quadruples the F1 score of minority classes

Improves overall classification accuracy by nearly 3%

Outperforms existing augmentation techniques like SMOTE-NC and CTGAN

Abstract

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