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

模态选择数据集的准确分类对交通规划和决策过程至关重要。本研究提出了一种概率分布数据增强模型——Ensemble Synthesizer（ENSY），用于提高模态选择数据集的分类准确性，通过实验证明了ENSY在增强少数类模式的F1分数几乎增加了四倍，并大幅提高了整体分类准确率近3%。与其他增强技术相比，ENSY在各种情景下始终表现出色，凸显其稳健性和高效性。

通过集成合成器（ENSY）改进行程模式选择建模