Computer vision-enriched discrete choice models, with an application to residential location choice
计算机视觉增强的离散选择模型：居住区位选择应用AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确以随机效用最大化为基础，将图像与数值属性纳入居住区位选择，直接对应离散选择建模。
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计算机视觉增强的离散选择模型：居住区位选择应用
- 研究问题
- 当选择同时取决于数值属性和图像呈现的环境特征时，如何将视觉信息纳入离散选择模型并保留随机效用行为基础？
- 方法与思路
- 作者整合计算机视觉与传统离散选择模型，利用居住区位陈述选择实验，建模通勤时间、住房成本和街景之间的权衡。
- 材料陈述的贡献
- 摘要称提出可同时处理数值属性与图像的 CV-DCM，并通过新设计的居住区位选择实验展示视觉信息进入选择模型的方式。
- 阅读关注点
- 应核对视觉表征怎样进入效用函数、模型识别与解释方式，并检查实验设计、预测验证及陈述选择结果的外推范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未报告样本规模和性能数值，也未证明图像加入必然改善所有选择分析。
原文摘要
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.