In an era of big data and emergence of disrupting mobility technologies, statistical models have been utilized to uncover the influence of significant factors, and machine learning algorithms have been used to explore complex patterns in large datasets. Focusing on discrete choice modeling applications, this research aims to introduce computational graph (CG)-based frameworks for integrating the strengths of econometric models and machine learning algorithms. Specifically, multinomial logit (MNL), nested logit (NL), and integrated choice and latent variable (ICLV) models are selected to demonstrate the performance of the graph-oriented functional representation. Furthermore, the calculation of gradients in the log-likelihood function is accomplished using automatic differentiation (AD). Using the 2017 National Household Travel Survey data and synthetic datasets, we compare estimation results from the proposed methods with those obtained from Biogeme and Apollo. The results indicate that the CG-based choice modeling approach can produce consistent estimates of parameters with substantial computational efficiency.

在大数据和颠覆性移动技术出现的时代，统计模型已被用于揭示重要因素的影响，机器学习算法已被用于探索大型数据集中的复杂模式。本研究专注于离散选择建模应用，旨在引入基于计算图 (CG) 的框架，以整合计量经济学模型和机器学习算法的优势。具体来说，选择多项式 logit (MNL)、嵌套 logit (NL) 和集成选择和潜在变量 (ICLV) 模型来展示面向图形的功能表示的性能。此外，对数似然函数中梯度的计算是使用自动微分 (AD) 完成的。使用 2017 年全国家庭旅行调查数据和合成数据集，我们将所提出方法的估计结果与从 Biogeme 和 Apollo 获得的估计结果进行比较。结果表明，基于 CG 的选择建模方法可以产生一致的参数估计，具有很高的计算效率。