A systematic review of machine learning classification methodologies for modelling passenger mode choice

Hillel, Tim; Bierlaire, Michel; Elshafie, Mohammed; Jin, Ying

doi:10.1016/j.jocm.2020.100221

Cited by 75 publications

(28 citation statements)

References 51 publications

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“…Sin embargo, después del año 2007 se ha visto un incremento significativo en las publicaciones científicas relacionadas con la aplicación de técnicas de clasificación de aprendizaje de máquinas (Hillel, Bierlaire, Elshafie y Jin, 2020). Por ejemplo, Zenina y Borisov (2011) utilizaron análisis discriminantes escalonados y progresivos para la elección del modo del modelo y compararon los resultados con el modelo Multinomial Logit (MNL, por sus siglas en inglés) y los modelos de árbol de decisión (DT, por sus siglas en inglés).…”

Section: Conceptualizaciónunclassified

Comparación en la aplicación de métodos de clasificación para determinar el modo de transporte de los estudiantes para acceder a la sede Rodrigo Facio de la Universidad de Costa Rica en Montes de Oca, San José, Costa Rica

Hernández-Vega

Barboza

2022

Infraestructura Vial

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Este trabajo presenta los resultados de un proceso exploratorio donde se aplicaron diferentes métodos de clasificación para determinar el modo de transporte de los estudiantes para acceder a la sede Rodrigo Facio de la Universidad de Costa Rica. Dentro de los modelos analizados se encuentran la regresión logística binomial, análisis discriminante lineal, árboles de decisión, K-vecinos más cercanos, máquinas de soporte vectorial y redes neuronales. Se realizó una validación con el método de K-pliegues y se obtuvo una precisión superior al 83% para todos los modelos analizados. De manera similar, se aplicó el modelo de ensamble apilamiento para las técnicas de árboles de decisión, K-vecinos más cercanos, máquinas de soporte vectorial, bosques aleatorios, agregación de Bootstrap, regresión logística binomial y método de potenciación obteniendo valores de precisión superiores al 86% en todos los casos, siendo el modelo de bosques aleatorios el que presentó una mayor precisión.

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Section: Conceptualizaciónunclassified

Comparación en la aplicación de métodos de clasificación para determinar el modo de transporte de los estudiantes para acceder a la sede Rodrigo Facio de la Universidad de Costa Rica en Montes de Oca, San José, Costa Rica

Hernández-Vega

Barboza

2022

Infraestructura Vial

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“…Following the recommendations in a systematic review of classification methodologies (Hillel et al, 2020), the validation and test folds are sampled grouped by household to ensure that trips of the same household are not classified into different folds. At each level, the training, validation and test datesets are allocated the same number of households.…”

Section: Travel Mode Choice Model In Londonmentioning

confidence: 99%

An Early Stopping Bayesian Data Assimilation Approach for Mixed-Logit Estimation

Xie,

Hillel,

Jin

2021

Preprint

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The mixed-logit model is a flexible tool in transportation choice analysis, which provides valuable insights into inter and intra-individual behavioural heterogeneity. However, applications of mixed-logit models in practice are limited by the high computational and data requirements for model estimation. When estimating mixed-logit models on small samples, the Bayesian estimation approach becomes vulnerable to over and under-fitting. This is problematic for investigating the behaviour of specific population sub-groups or market segments, where a modeller may wish to estimate separate models for multiple similar contexts, each with low data availability. Similar challenges arise when adapting an existing model to a new location or time period, e.g., when estimating post-pandemic travel behaviour.In order to address the data and transferability issues of the mixed-logit model, in this paper we propose a new Early Stopping Bayesian Data Assimilation (ESBDA) simulator for estimation of mixed-logit which combines a Bayesian statistical approach with Machine Learning (ML) model estimation methodologies. The ESBDA simulator is intended to improve the transferability of mixed-logit models and to enable the estimation of robust choice models with low data availability. This approach can therefore provide new insights into people's choice behaviour where the traditional estimation of full mixed-logit models was not previously possible due to low data availability, and open up new opportunities for investment and planning decisions support.To assess the performance of the new approach, we benchmark the ESBDA estimator against the Direct Application approach and two reference simulators: (i) a basic hierarchical Bayes (HB) simulator with random starting parameter values; (ii) a Bayesian Data Assimilation (BDA) simulator without early stopping. Two case-studies are used to investigate the relative performance of the simulators in varied contexts.

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“…For ML, there are many uses, the most prominent of which is predictive data mining. Two major mechanisms can be broken into ML classification fulfillments; model development and model evaluation [6,7].…”

Section: Introductionmentioning

confidence: 99%

Classification Based on Decision Tree Algorithm for Machine Learning

Charbuty

Abdulazeez

2021

JASTT

1,022

211

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Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.

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A systematic review of machine learning classification methodologies for modelling passenger mode choice

Cited by 75 publications

References 51 publications

Comparación en la aplicación de métodos de clasificación para determinar el modo de transporte de los estudiantes para acceder a la sede Rodrigo Facio de la Universidad de Costa Rica en Montes de Oca, San José, Costa Rica

Comparación en la aplicación de métodos de clasificación para determinar el modo de transporte de los estudiantes para acceder a la sede Rodrigo Facio de la Universidad de Costa Rica en Montes de Oca, San José, Costa Rica

An Early Stopping Bayesian Data Assimilation Approach for Mixed-Logit Estimation

Classification Based on Decision Tree Algorithm for Machine Learning

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