[HTML][HTML] Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions
In recent years, planners have started considering Machine Learning (ML) techniques as an
alternative to discrete choice models (CM). ML techniques are primarily data-driven and …
alternative to discrete choice models (CM). ML techniques are primarily data-driven and …
[HTML][HTML] Towards machine learning for moral choice analysis in health economics: A literature review and research agenda
Abstract Background Discrete choice models (DCMs) for moral choice analysis will likely
lead to erroneous model outcomes and misguided policy recommendations, as only some …
lead to erroneous model outcomes and misguided policy recommendations, as only some …
Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?
Classical demand modeling analyzes travel behavior using only low-dimensional numeric
data (ie sociodemographics and travel attributes) but not high-dimensional urban imagery …
data (ie sociodemographics and travel attributes) but not high-dimensional urban imagery …
A new flexible and partially monotonic discrete choice model
The poor predictability and the misspecification arising from hand-crafted utility functions are
common issues in theory-driven discrete choice models (DCMs). Data-driven DCMs improve …
common issues in theory-driven discrete choice models (DCMs). Data-driven DCMs improve …
[HTML][HTML] Attitudes and Latent Class Choice Models using Machine Learning
Abstract Latent Class Choice Models (LCCM) are extensions of discrete choice models
(DCMs) that capture unobserved heterogeneity in the choice process by segmenting the …
(DCMs) that capture unobserved heterogeneity in the choice process by segmenting the …
A neural network based choice model for assortment optimization
Discrete-choice models are used in economics, marketing and revenue management to
predict customer purchase probabilities, say as a function of prices and other features of the …
predict customer purchase probabilities, say as a function of prices and other features of the …
Computer vision-enriched discrete choice models, with an application to residential location choice
S van Cranenburgh, F Garrido-Valenzuela - arXiv preprint arXiv …, 2023 - arxiv.org
Visual imagery is indispensable to many multi-attribute decision situations. Examples of
such decision situations in travel behaviour research include residential location choices …
such decision situations in travel behaviour research include residential location choices …
Explaining deep learning-based activity schedule models using SHapley Additive exPlanations
Artificial neural networks are often criticized for their black box nature in travel behavior
literature. The lack of understanding of variable influence induces little confidence in model …
literature. The lack of understanding of variable influence induces little confidence in model …
Images in Discrete Choice Modeling: Addressing Data Isomorphism in Multi-Modality Inputs
B Sifringer, A Alahi - arXiv preprint arXiv:2312.14724, 2023 - arxiv.org
This paper explores the intersection of Discrete Choice Modeling (DCM) and machine
learning, focusing on the integration of image data into DCM's utility functions and its impact …
learning, focusing on the integration of image data into DCM's utility functions and its impact …
Modeling travel mode choice behavior for planned special events using CPT-based HI-MADM approach
H Cui, W Sun, M Zhu, X Ma, J Xie - Transportation Letters, 2024 - Taylor & Francis
Planned special events (PSEs) can significantly increase traffic demand, posing greater
challenges than daily operations. In response, host cities often invest heavily in …
challenges than daily operations. In response, host cities often invest heavily in …