This paper addresses the time slot allocation problem within the context of a liberalized passenger railway market as a multi-objective model. We propose two criteria for making …
In deregulated railway markets, efficient management of infrastructure charges is essential for sustaining railway systems. This study sets out a method for infrastructure managers …
The application of kernel-based Machine Learning (ML) techniques to discrete choice modelling using large datasets often faces challenges due to memory requirements and the …
This paper presents a software package developed in Python that allows the application of the technique known as Kernel Logistic Regression (KLR), a Machine Learning (ML) tool, to …
The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used …
Transport demand modelling plays a critical role in transportation planning, enabling the accurate prediction of future transport demand and the evaluation of transport policies …
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 …
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 …
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 …
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 …