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.

翻译：视觉图像在众多多属性决策情境中不可或缺。在出行行为研究中，此类决策情境包括住宅选址、车辆选择、旅游目的地选择及各类与安全相关的决策。然而，现有离散选择模型无法处理图像数据，因而难以将图像中的信息纳入选择行为的表征。这种离散选择模型能力与其试图建模的真实行为之间的鸿沟，可能导致不完整甚至具有误导性的结论。为弥合这一差距，本研究提出"计算机视觉增强的离散选择模型"（CV-DCMs）。该模型通过整合计算机视觉与传统离散选择模型，能够处理同时包含数值属性与图像的选择任务。此外，由于CV-DCMs基于随机效用最大化原理，其保持了传统离散选择模型坚实的行为理论基础。我们通过一项涉及住宅选址的新型陈述偏好实验数据对所提出的CV-DCM进行验证。在该实验中，受访者需在通勤时间、月住房成本及街景条件（以图像形式呈现）之间进行权衡选择。本研究为出行行为领域内整合离散选择建模与机器学习的日益增长的文献体系做出了贡献。