  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.

Computer Vision and Pattern Recognition;Econometrics