Gaussian process latent class choice models
•Integration of machine learning and discrete choice models.•New choice model referred to as Gaussian process latent class choice model.•Derivation and implementation of an expectation-maximization algorithm.•More complex and flexible representation of unobserved heterogeneity.•The model improves pr...
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| Vydáno v: | Transportation research. Part C, Emerging technologies Ročník 136; s. 103552 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.03.2022
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| ISSN: | 0968-090X, 1879-2359 |
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| Abstract | •Integration of machine learning and discrete choice models.•New choice model referred to as Gaussian process latent class choice model.•Derivation and implementation of an expectation-maximization algorithm.•More complex and flexible representation of unobserved heterogeneity.•The model improves prediction accuracy without weakening economic interpretability.
We present a Gaussian Process – Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based algorithms that incorporate expert knowledge by assuming priors over latent functions rather than priors over parameters, which makes them more flexible in addressing nonlinear problems. By integrating a Gaussian Process within a LCCM structure, we aim at improving discrete representations of unobserved heterogeneity. The proposed model would assign individuals probabilistically to behaviorally homogeneous clusters (latent classes) using GPs and simultaneously estimate class-specific choice models by relying on random utility models. Furthermore, we derive and implement an Expectation-Maximization (EM) algorithm to jointly estimate/infer the hyperparameters of the GP kernel function and the class-specific choice parameters by relying on a Laplace approximation and gradient-based numerical optimization methods, respectively. The model is tested on two different mode choice applications and compared against different LCCM benchmarks. Results show that GP-LCCM allows for a more complex and flexible representation of heterogeneity and improves both in-sample fit and out-of-sample predictive power. Moreover, behavioral and economic interpretability is maintained at the class-specific choice model level while local interpretation of the latent classes can still be achieved, although the non-parametric characteristic of GPs lessens the transparency of the model. |
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| AbstractList | •Integration of machine learning and discrete choice models.•New choice model referred to as Gaussian process latent class choice model.•Derivation and implementation of an expectation-maximization algorithm.•More complex and flexible representation of unobserved heterogeneity.•The model improves prediction accuracy without weakening economic interpretability.
We present a Gaussian Process – Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based algorithms that incorporate expert knowledge by assuming priors over latent functions rather than priors over parameters, which makes them more flexible in addressing nonlinear problems. By integrating a Gaussian Process within a LCCM structure, we aim at improving discrete representations of unobserved heterogeneity. The proposed model would assign individuals probabilistically to behaviorally homogeneous clusters (latent classes) using GPs and simultaneously estimate class-specific choice models by relying on random utility models. Furthermore, we derive and implement an Expectation-Maximization (EM) algorithm to jointly estimate/infer the hyperparameters of the GP kernel function and the class-specific choice parameters by relying on a Laplace approximation and gradient-based numerical optimization methods, respectively. The model is tested on two different mode choice applications and compared against different LCCM benchmarks. Results show that GP-LCCM allows for a more complex and flexible representation of heterogeneity and improves both in-sample fit and out-of-sample predictive power. Moreover, behavioral and economic interpretability is maintained at the class-specific choice model level while local interpretation of the latent classes can still be achieved, although the non-parametric characteristic of GPs lessens the transparency of the model. |
| ArticleNumber | 103552 |
| Author | Abou-Zeid, Maya Rodrigues, Filipe Sfeir, Georges |
| Author_xml | – sequence: 1 givenname: Georges surname: Sfeir fullname: Sfeir, Georges email: geosaf@dtu.dk organization: DTU Management Engineering, Transport DTU, Technical University of Denmark, Kgs. Lyngby 2800, Denmark – sequence: 2 givenname: Filipe surname: Rodrigues fullname: Rodrigues, Filipe email: rodr@dtu.dk organization: DTU Management Engineering, Transport DTU, Technical University of Denmark, Kgs. Lyngby 2800, Denmark – sequence: 3 givenname: Maya surname: Abou-Zeid fullname: Abou-Zeid, Maya email: ma202@aub.edu.lb organization: American University of Beirut, Beirut, Riad el Solh 1107 2020, Lebanon |
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| Keywords | Latent class choice models Gaussian process Discrete choice models EM algorithm Machine learning |
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