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.

离散选择模型是一个强大的框架，用于分析决策行为，为决策者和企业提供有价值的洞察。GAUNet 是一种基于广义可加模型的神经网络结构的效用函数，用于离散选择模型。在东京的旅行调查数据上，我们的模型与 ASU-DNN 在准确性上相当，并且相对于以前的模型在解释性上有所改善。