Computational graph-based framework for integrating econometric models and machine learning algorithms in emerging data-driven analytical environments
Computational graph-based framework for integrating econometric models and machine learning algorithms in emerging data-driven analytical environments
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DOI：
10.1080/23249935.2021.1938744
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发表时间：
2021-06
期刊：
影响因子：
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通讯作者：
Taehooie Kim;Xuesong Zhou;R. Pendyala
中科院分区：
文献类型：
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作者：
Taehooie Kim;Xuesong Zhou;R. Pendyala
在大数据时代和颠覆性移动技术的出现，统计模型被用来揭示重要因素的影响，机器学习算法被用来探索大数据集中的复杂模式。专注于离散选择建模应用，本研究旨在引入基于计算图（CG）的框架，以整合计量经济模型和机器学习算法的优势。具体而言，选择多项logit （MNL）、嵌套logit （NL）和综合选择和潜在变量（ICLV）模型来展示面向图的函数表示的性能。在此基础上，利用自动微分（AD）实现对数似然函数的梯度计算。利用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Biogeme和Apollo获得的估计结果进行了比较。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，并且具有很高的计算效率。
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.