We revisit the practice of combining revealed (RP) and stated preference (SP) data (i.e., the data enrichment, DE, paradigm) in discrete choice models using secondary data obtained from emerging sources; these facilitate access to massive information about travel choices and can be used to improve transport models. Even though the benefits of the DE paradigm have been known for years, there is a large gap between the state of practice and the state of the art, particularly in Global South countries (but also in many industrialized nations). We use a SP dataset considering two new transport alternatives (train and metro) and a RP dataset based on a large mobility survey in Bogotá, Colombia, complemented with fairly precise level-of-service data obtained using GIS utilities and the Distance Matrix API by Google. Our results allow us to discuss good practice, identify barriers and challenges to the paradigm’s application, and draw recommendations for forecasting the demand for new alternatives using joint RP and SP data.

中文翻译：

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重新审视组合不同性质数据的好处：新模式替代方案的战略预测

我们使用从新兴来源获得的二手数据重新审视在离散选择模型中结合揭示 (RP) 和陈述偏好 (SP) 数据（即数据丰富、DE、范式）的做法；这些有助于获取有关旅行选择的大量信息，并可用于改进交通模式。尽管 DE 范式的好处已为人所知多年，但实践状态与最先进技术之间仍存在很大差距，尤其是在全球南方国家（以及许多工业化国家）。我们使用了一个 SP 数据集，考虑了两种新的交通替代方案（火车和地铁）和一个基于哥伦比亚波哥大的大型流动性调查的 RP 数据集，辅以使用 GIS 实用程序和距离矩阵 API 获得的相当精确的服务水平数据，谷歌。