Discrete-Choice Model with Generalized Additive Utility Network
This work addresses the need for interpretable decision-making models for policymakers and businesses, though it is incremental as it builds on existing neural network approaches.
The paper tackled the trade-off between accuracy and interpretability in discrete-choice models by developing a generalized additive utility network (GAUNet) for multinomial logit models, achieving comparable accuracy to neural network-based models while improving interpretability on trip survey data from Tokyo.
Discrete-choice models are a powerful framework for analyzing decision-making behavior to provide valuable insights for policymakers and businesses. Multinomial logit models (MNLs) with linear utility functions have been used in practice because they are ease to use and interpretable. Recently, MNLs with neural networks (e.g., ASU-DNN) have been developed, and they have achieved higher prediction accuracy in behavior choice than classical MNLs. However, these models lack interpretability owing to complex structures. We developed utility functions with a novel neural-network architecture based on generalized additive models, named generalized additive utility network ( GAUNet), for discrete-choice models. We evaluated the performance of the MNL with GAUNet using the trip survey data collected in Tokyo. Our models were comparable to ASU-DNN in accuracy and exhibited improved interpretability compared to previous models.