Discrete-Choice Model with Generalized Additive Utility Network
Tomoki Nishi Yusuke Hara
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.