Performance evaluation of mode choice models under balanced and imbalanced data assumptions

One common limitation faced in mode choice modeling is data imbalance. Mode choice models, such as logit models, may output biased estimations for alternatives with smaller shares and consequently have high prediction errors. Since accurate prediction of the less commonly used modes is important in some applications, such as predicting transit mode share in many auto-oriented American cities, it is essential to improve the prediction capability of logit models for those modes. Hence, this study applies an imbalanced learning technique and evaluates the prediction capability and interpretability of logit models under both balanced and imbalanced datasets using a case study for the City of Nashville, Tennessee. The results show that the proposed method improves the accuracy of the less commonly used modes and the mean absolute percentage error by 18% and 2%, respectively, while keeping the models interpretable. Finally, we provide some high-level guidelines for mode choice modeling with imbalanced data.

Automated summary

This study addresses the issue of data imbalance in mode choice modeling by applying an imbalanced learning technique to improve the accuracy of predictions for less commonly used modes. The results demonstrate that the proposed method significantly enhances the prediction capability of logit models while maintaining interpretability.