Network-based representations and dynamic discrete choice models for multiple discrete choice analysis

• Main Authors: TRAN, Huy Hung, MAI, Tien
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Multiple discrete choice; Network-based representation; Recursive route choice model; Operations Research, Systems Engineering and Industrial Engineering; OS and Networks; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/8890; https://ink.library.smu.edu.sg/context/sis_research/article/9893/viewcontent/Network_basedRep_DDC_sv.pdf
• Institution: Singapore Management University

In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such as the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.