Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)

TL;DR

Ensemble Synthesizer (ENSY), a novel data model tailored specifically for enhancing classification accuracy in mode choice datasets, demonstrates remarkable efficacy by nearly quadrupling the F1 score of minority classes and improving overall classification accuracy by nearly 3%.

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

Figures (4)

• Figure 1: ENSY Flowchart
• Figure 2: Actual Data, Fitted GMM, and Component Distributions
• Figure 3: Generated Datapoints With Different Methods
• Figure 4: Generator and Discriminator Loss Over 500 Epochs in CTGAN