Journal article
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models
Transport, Department of Technology, Management and Economics, Technical University of Denmark1
Machine Learning, Transport, Department of Technology, Management and Economics, Technical University of Denmark2
Department of Technology, Management and Economics, Technical University of Denmark3
Swiss Federal Institute of Technology Lausanne4
Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task.
This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner.
Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed DCM-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to very accurately recover the true utility function specifications that govern the observed choices.
Moreover, when applied to real choice data, DCM-ARD is shown to be able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability.
Language: | English |
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Publisher: | IEEE |
Year: | 2022 |
Pages: | 3126-3136 |
ISSN: | 15580016 and 15249050 |
Types: | Journal article |
DOI: | 10.1109/TITS.2020.3031965 |
ORCIDs: | Rodrigues, Filipe , 0000-0003-3767-0430 and Pereira, Francisco |