Computer vision-enriched discrete choice models, with an application to residential location choice

TL;DR

This paper introduces computer vision-enriched discrete choice models (CV-DCMs) that incorporate image data into decision-making analysis, demonstrated through residential location choice experiments, bridging the gap between visual data and behavioral modeling.

Contribution

It proposes a novel modeling framework that integrates computer vision with discrete choice models, maintaining behavioral foundations while handling image data.

Findings

CV-DCMs successfully incorporate image data into choice modeling

Application to residential location choice demonstrates practical utility

Enhances understanding of visual influences on decision-making

Abstract

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.