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A review of Machine Learning (ML) algorithms used for modeling travel mode choice
Una revisión de los algoritmos de Machine Learning (ML) utilizados para la modelación de la elección de modo de viaje
DOI:
https://doi.org/10.15446/dyna.v86n211.79743Palabras clave:
modeling travel mode choice, Artificial Neural Networks (ANN), Decision Trees (DT), Support-Vector Machines (SVM), Cluster Analysis (CA), Multinomial Logit Model (MNL), Machine Learning (ML) algorithms (en)modelación de la elección de modo de viaje, Redes Neuronales Artificiales (ANN), Árboles de Decisión (DT), Máquinas de Vector de Soporte (SVM), Análisis de Grupos (CA), Modelo Logit Multinomial (MNL), algoritmos de Machine Learning (ML) (es)
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In recent decades, transportation planning researchers have used diverse types of machine learning (ML) algorithms to research a wide range of topics. This review paper starts with a brief explanation of some ML algorithms commonly used for transportation research, specifically Artificial Neural Networks (ANN), Decision Trees (DT), Support Vector Machines (SVM) and Cluster Analysis (CA). Then, these different methodologies used by researchers for modeling travel mode choice are collected and compared with the Multinomial Logit Model (MNL) which is the most commonly-used discrete choice model. Finally, the characterization of ML algorithms is discussed and Random Forest (RF), a variant of Decision Tree algorithms, is presented as the best methodology for modeling travel mode choice.
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