Derived from the Karush–Kuhn–Tucker (KKT) optimality conditions of utility maximization, the multiple discrete–continuous extreme value (MDCEV) models are extensively used in the field of time use. This is because they are grounded in a framework that maxi ...
Increasing environmental concerns drive interest in sustainable solutions across various fields. Vehicle sharing systems such as one-way station-based bike sharing systems (BSSs) offer one such solution in transportation, although they pose operational cha ...
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 considerable number of parameters involved in these models. This complexity hampers ...
We propose a cross-nested logit (CNL) approach to investigate how individuals adjust their migration decisions in response to changes in the global landscape. In contrast to the widely used logit model, the CNL enables more intricate substitution patterns ...
This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...
In the context of discrete choice modeling, the extraction of potential behavioral insights from large datasets is often limited by the poor scalability of maximum likelihood estimation. This paper proposes a simple and fast dataset-reduction method that i ...
Social Engagement is a novel business model whose goal is transforming final users of a service from passive components into active ones. In this framework, people are contacted by the decision-maker (generally a company) and they are asked to perform task ...
Springer Science and Business Media Deutschland GmbH2024
Choice models have been applied to explain and predict the transportation choices of individuals for half a century. The advent of big data brings about new opportunities and poses new challenges for forecasting. This chapter discusses the major methodolog ...
The generation of synthetic households is challenging due to the necessity of maintaining consistency between the two layers of interest: the household itself, and the individuals composing it. Hence, the problem is typically tackled in two steps, first fo ...
Transportation and mobility service providers face challenges when designing their services to ensure that resources align with demand effectively. To address this problem, one approach is to integrate individual preferences directly into operational decis ...
