The time slots allocation problem in liberalised passenger railway markets: a multi-objective approach
nikola-besinovic
Ricardo Garcia Rodenas
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1 min de lectura
ROBIN: Rail mOBIlity simulatioN
ester-del-castillo-herrera
A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
Traditionally, Random Utility Maximization (RUM) models have been widely applied to travel mode choice modelling. Currently, Machine Learning (ML) models are being applied as an …
jose-carlos-garcia-garcia
IoT based monitoring of air quality and traffic using regression analysis
Dynamic traffic management (DTM) systems are used to reduce the negative externalities of traffic congestion, such as air pollution in urban areas. They require traffic and …
jose-angel-martin-baos
A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
In the last few years, the success of Machine Learning (ML) algorithms has led to the extension of their applications to areas such as transport planning. One of the main tasks …
jose-angel-martin-baos
A comparative study of machine learning, deep neural networks and random utility maximization models for travel mode choice modelling
jose-carlos-garcia-garcia
A Python package for performing penalized maximum likelihood estimation of conditional logit models using Kernel Logistic Regression
Revisiting kernel logistic regression under the random utility models perspective. An interpretable machine-learning approach
The success of machine-learning methods is spreading their use to many different fields. This paper analyses one of these methods, the Kernel Logistic Regression (KLR), from the …
jose-angel-martin-baos
A python package for performing penalized maximum likelihood estimation of conditional logit models using kernel logistic regression
A comparison of general-purpose optimization algorithms for finding optimal approximate experimental designs
Several common general purpose optimization algorithms are compared for finding A- and D-optimal designs for different types of statistical models of varying complexity, including …
ricardo-garcia-rodenas