TY - JOUR
T1 - A new approach to improve destination choice by ranking personal preferences
AU - Phan, Danh T.
AU - Vu, Hai L.
AU - Miller, Eric J.
N1 - Funding Information:
The authors would like to thank James Vaughan and the team at Data Management Group, the University of Toronto for providing great support and access to various data sets as well as computation resources for this study.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
KW - Bayesian personalised ranking
KW - Choice-set generation
KW - Location choice
KW - Matrix factorisation
UR - http://www.scopus.com/inward/record.url?scp=85135133526&partnerID=8YFLogxK
U2 - 10.1016/j.trc.2022.103817
DO - 10.1016/j.trc.2022.103817
M3 - Article
AN - SCOPUS:85135133526
SN - 0968-090X
VL - 143
JO - Transportation Research Part C: Emerging Technologies
JF - Transportation Research Part C: Emerging Technologies
M1 - 103817
ER -