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.

翻译：离散选择模型是分析决策行为以向政策制定者和企业提供宝贵见解的强大框架。具有线性效用函数的多项式逻辑特模型（MNLs）因其易于使用和可解释性而在实践中得到应用。近年来，已开发出带有神经网络（如ASU-DNN）的MNLs，这些模型在行为选择中的预测准确性高于经典MNLs。然而，这些模型因结构复杂而缺乏可解释性。我们基于广义加性模型，开发了一种具有新型神经网络架构的效用函数，命名为广义加性效用网络（GAUNet），用于离散选择模型。我们利用在东京收集的出行调查数据评估了带有GAUNet的MNL的性能。我们的模型在准确性上与ASU-DNN相当，并且与以往模型相比展现出更高的可解释性。