Computer vision-enriched discrete choice models, with an application to residential location choice
This addresses a gap in travel behavior research for more accurate modeling of multi-attribute decisions, though it is incremental as it builds on existing discrete choice and computer vision methods.
The study tackled the inability of discrete choice models to incorporate image data, which limits their accuracy in modeling real-world decisions like residential location choice, by proposing Computer Vision-enriched Discrete Choice Models (CV-DCMs) that integrate computer vision with traditional models, and demonstrated it on a stated choice experiment involving trade-offs between commute time, housing cost, and street-level images.
Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination choices, and various safety-related choices. However, current discrete choice models cannot handle image data and thus cannot incorporate information embedded in images into their representations of choice behaviour. This gap between discrete choice models' capabilities and the real-world behaviour it seeks to model leads to incomplete and, possibly, misleading outcomes. To solve this gap, this study proposes "Computer Vision-enriched Discrete Choice Models" (CV-DCMs). CV-DCMs can handle choice tasks involving numeric attributes and images by integrating computer vision and traditional discrete choice models. Moreover, because CV-DCMs are grounded in random utility maximisation principles, they maintain the solid behavioural foundation of traditional discrete choice models. We demonstrate the proposed CV-DCM by applying it to data obtained through a novel stated choice experiment involving residential location choices. In this experiment, respondents faced choice tasks with trade-offs between commute time, monthly housing cost and street-level conditions, presented using images. As such, this research contributes to the growing body of literature in the travel behaviour field that seeks to integrate discrete choice modelling and machine learning.