A review of Machine Learning (ML) algorithms used for modeling travel mode choice

Abstract

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.

Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques

“…Machine learning (ML) is an implementation part of artificial intelligence (AI) that enables the machine to learn from data to complete the task efficiently. It is considered a backbone of artificial intelligence approaches that are used to develop the prediction to enhance performance [ 2 , 18 ].…”

“…Technologies could allow users to achieve the appropriate task at a low cost and save time. Artificial intelligence (AI) is a trending topic in current days, which allows machine learning to be implemented for efficiency and performance [ 2 ]. Education, health, industry, and finance use artificial intelligence to develop their fields.…”

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

“…When using machine learning, the choice of appropriate algorithms and the system features to be used in training models can both be critical factors on project outcomes because different algorithms fit or perform better depending on the use case, features' data quality and data architecture, and system architecture (O'Mahony et al, 2008;Pineda-Jaramillo, 2019). As noted by James et al (2013), "on a particular data set, one method may work best, but some other method may work better on a similar but different data set" [p. 29].…”

Predicting the traction power of metropolitan railway lines using different machine learning models

“…One alternative to deterministic models are machine learning (ML) approaches, which include several techniques that allow computers to mechanise data-driven model programming and build models by means of a methodical detection of non-linear connections between data [24][25][26][27]. Additionally, De Martinis and Corman [28] presented the potential of using data-driven approaches for improving energy efficiency in railways.…”

“…In order to do so, we will implement six ML models (linear regression, ridge regression, decision trees, random forests, gradient boosting and artificial neural network) and we will compare their performance in achieving this particular task. These models were chosen due its popularity for solving regression problems, and due the configuration of the dataset, where we have an intermediate number of observations (approximately 50,000) and a small number of predictors; moreover, these methods have either been successfully used in transport research or have shown promising results in many fields [27,36]. This paper is organised in four sections.…”