A Dirichlet process mixture model of discrete choice: Comparisons and a case study on preferences for shared automated vehicles

R Krueger, TH Rashidi, A Vij - Journal of choice modelling, 2020 - Elsevier
This paper i) compares parametric and semi-parametric representations of unobserved
heterogeneity in hierarchical Bayesian logit models and ii) applies these methods to infer …

An online updating method for time-varying preference learning

X Zhu, J Feng, S Huang, C Chen - Transportation Research Part C …, 2020 - Elsevier
The rapid proliferation of smart, personal technologies has given birth to smart
Transportation Demand Management (TDM) systems that can give personalized incentives …

Scaling Bayesian inference of mixed multinomial logit models to very large datasets

F Rodrigues - arXiv preprint arXiv:2004.05426, 2020 - arxiv.org
Variational inference methods have been shown to lead to significant improvements in the
computational efficiency of approximate Bayesian inference in mixed multinomial logit …

[PDF][PDF] Reminder nudge, attribute nonattendance, and willingness to pay in a discrete choice experiment

GT Kassie, F Zeleke, MY Birhanu, R Scarpa - 2020 - researchgate.net
Attribute non-attendance (ANA) is one of the choice simplification strategies respondents
employ in choosing among alternatives in stated preference elicitation methods. Studies …

[图书][B] Individual Preference Learning with Collaborative Learning Framework

X Zhu - 2020 - search.proquest.com
Smart, personal devices that interact with individuals make it possible to trigger desired
behavioral changes with personalized incentives. Personalized incentives are the incentives …

[PDF][PDF] Fast Bayesian Estimation of Spatial Count Data Models

R KRUEGER, DJ GRAHAM - 2020 - researchgate.net
Spatial count data models are used to explain and predict the frequency of phenomena such
as traffic accidents in geographically distinct entities such as census tracts or road segments …