Enhancing discrete choice models with representation learning

被引:65
|
作者
Sifringer, Brian [1 ]
Lurkin, Virginie [2 ]
Alahi, Alexandre [1 ]
机构
[1] Ecole Polytech Fed Lausanne EPFL, Visual Intelligence Transportat Lab Vita, Lausanne, Switzerland
[2] Eindhoven Univ Technol, Dept Ind Engn & Innovat Sci, NL-5600 MB Eindhoven, Netherlands
关键词
Discrete choice models; Neural networks; Utility specification; Machine learning; Deep learning; ARTIFICIAL NEURAL-NETWORKS; MULTINOMIAL LOGIT-MODELS; TRAVEL MODE; UTILITY FUNCTION; MACHINE; SPECIFICATION; BEHAVIOR; LEGIT;
D O I
10.1016/j.trb.2020.08.006
中图分类号
F [经济];
学科分类号
02 ;
摘要
In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non-linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码:236 / 261
页数:26
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