A new approach to improve destination choice by ranking personal preferences. (October 2022)

• Record Type: Journal Article
• Authors: Phan, Danh T.; Vu, Hai L.; Miller, Eric J.
• Is Part Of: Transportation research. Volume 143(2022)
• Publication Date: 2022-10
• Subjects: Choice-set generation -- Matrix factorisation -- Bayesian personalised ranking -- Location choice; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103817
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: It is vital to have the right choice-sets when dealing with many alternatives in discrete choice models, which play a critical role in transport modelling. Various approaches have been proposed to address the issue when forming individual choice-sets. While these methods have been continuously improved, they seem not effectively explain how individuals form their choice-sets when facing a large number of alternatives. To know individual choice-sets, one possible way is to ask all of them about their preferred alternatives directly. However, this is costly and impractical for a large population. This paper proposes a novel behavioural choice-set generation approach by ranking personal preferences of destinations using a matrix factorisation model with Bayesian personalised ranking. From a large travel survey, we form a user-zone-visited frequency matrix for shopping locations. We then use the model to factorise the user-zone-visited frequency matrix into two lower-rank latent matrices. The matrix factorisation model is optimised by using Bayesian personalised ranking. After estimation, the model's outputs, which are user-factor and zone-factor latent matrices, can produce top preferred destinations for individuals. Our experiment from a large travel survey with thousands of alternatives shows that the proposed choice-set generation framework can significantly improve the predictive capability of discrete choice model evaluation with even small choice-set sizes. Highlights: A novel behavioural approach to implicitly learn personal preferences of destinations from a large travel survey. A choice-set generation framework based on ranking the top preferred locations for individuals. A significant improvement in the predictive capability for model evaluation with small and practical choice-set sizes.