English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In many choice modeling applications, people demand is frequentlycharacterized as multiple discrete, which means that people choose multipleitems simultaneously. The analysis and prediction of people behavior inmultiple discrete choice situations pose several challenges. In this paper, toaddress this, we propose a random utility maximization (RUM) based model thatconsiders each subset of choice alternatives as a composite alternative, whereindividuals choose a subset according to the RUM framework. While this approachoffers a natural and intuitive modeling approach for multiple-choice analysis,the large number of subsets of choices in the formulation makes its estimationand application intractable. To overcome this challenge, we introduce directedacyclic graph (DAG) based representations of choices where each node of the DAGis associated with an elemental alternative and additional information suchthat the number of selected elemental alternatives. Our innovation is to showthat the multi-choice model is equivalent to a recursive route choice model onthe DAG, leading to the development of new efficient estimation algorithmsbased on dynamic programming. In addition, the DAG representations enable us tobring some advanced route choice models to capture the correlation betweensubset choice alternatives. Numerical experiments based on synthetic and realdatasets show many advantages of our modeling approach and the proposedestimation algorithms.

Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis