Discrete choice models (DCMs) require a priori knowledge of the utility
functions, especially how tastes vary across individuals. Utility
misspecification may lead to biased estimates, inaccurate interpretations and
limited predictability. In this paper, we utilize a neural network to learn
taste representation. Our formulation consists of two modules: a neural network
(TasteNet) that learns taste parameters (e.g., time coefficient) as flexible
functions of individual characteristics; and a multinomial logit (MNL) model
with utility functions defined with expert knowledge. Taste parameters learned
by the neural network are fed into the choice model and link the two modules.
Our approach extends the L-MNL model (Sifringer et al., 2020) by allowing the
neural network to learn the interactions between individual characteristics and
alternative attributes. Moreover, we formalize and strengthen the
interpretability condition - requiring realistic estimates of behavior
indicators (e.g., value-of-time, elasticity) at the disaggregated level, which
is crucial for a model to be suitable for scenario analysis and policy
decisions. Through a unique network architecture and parameter transformation,
we incorporate prior knowledge and guide the neural network to output realistic
behavior indicators at the disaggregated level. We show that TasteNet-MNL
reaches the ground-truth model's predictability and recovers the nonlinear
taste functions on synthetic data. Its estimated value-of-time and choice
elasticities at the individual level are close to the ground truth. On a
publicly available Swissmetro dataset, TasteNet-MNL outperforms benchmarking
MNLs and Mixed Logit model's predictability. It learns a broader spectrum of
taste variations within the population and suggests a higher average
value-of-time.

本文介绍一种使用神经网络学习个体特征下的估价函数参数的离散选择模型。该模型可以生成可解释的行为指标参数，并且在瑞士公共交通数据集上表现良好。