A systematic review of machine learning classification methodologies for modelling passenger mode choice
Abstract Machine Learning (ML) approaches are increasingly being investigated as an
alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These …
alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These …
[HTML][HTML] A review of Machine Learning (ML) algorithms used for modeling travel mode choice
JD Pineda-Jaramillo - Dyna, 2019 - scielo.org.co
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 …
learning (ML) algorithms to research a wide range of topics. This review paper starts with a …
Predicting the travel mode choice with interpretable machine learning techniques: A comparative study
MT Kashifi, A Jamal, MS Kashefi… - Travel Behaviour and …, 2022 - Elsevier
Prediction of mode choice for travelers has been the subject of keen interest among
transportation planners. Traditionally, mode choice analysis is conducted by statistical …
transportation planners. Traditionally, mode choice analysis is conducted by statistical …
Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients
COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus
is highly desired. Convolutional neural networks (CNNs) have shown outstanding …
is highly desired. Convolutional neural networks (CNNs) have shown outstanding …
RF-CNN-F: random forest with convolutional neural network features for coronary artery disease diagnosis based on cardiac magnetic resonance
Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality
rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is …
rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is …
A systematic comparative evaluation of machine learning classifiers and discrete choice models for travel mode choice in the presence of response heterogeneity
Discrete choice models has been for decades the most used technique to model travel
mode choice, being the multinomial logit (MNL) the most popular model among them …
mode choice, being the multinomial logit (MNL) the most popular model among them …
Machine learning travel mode choices: Comparing the performance of an extreme gradient boosting model with a multinomial logit model
The multinomial logit (MNL) model and its variations have been dominating the travel mode
choice modeling field for decades. Advantages of the MNL model include its elegant closed …
choice modeling field for decades. Advantages of the MNL model include its elegant closed …
RFCNN: Traffic accident severity prediction based on decision level fusion of machine and deep learning model
Traffic accidents on highways are a leading cause of death despite the development of traffic
safety measures. The burden of casualties and damage caused by road accidents is very …
safety measures. The burden of casualties and damage caused by road accidents is very …
Modeling road accident severity with comparisons of logistic regression, decision tree and random forest
MM Chen, MC Chen - Information, 2020 - mdpi.com
To reduce the damage caused by road accidents, researchers have applied different
techniques to explore correlated factors and develop efficient prediction models. The main …
techniques to explore correlated factors and develop efficient prediction models. The main …
Deep neural networks for choice analysis: Architecture design with alternative-specific utility functions
Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is
challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN …
challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN …