GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data.

中文翻译：

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从用户行为中学习：纵向移动数据收集的调查辅助算法

基于 GPS 的出行调查广泛应用于出行研究，以收集重要的定性数据，例如目的、交通方式和替代方式。然而，调查响应仍然给用户带来负担，特别是在长期流动性研究中，导致响应疲劳。我们探索了一种调查辅助策略，通过一种新颖的用户级建模方法来减轻这一负担，该方法利用每个用户过去的响应来预测新行程的响应，而不依赖于 GIS 数据等外部数据源。