This paper presents an approach to estimate mode-choice models from spatially anonymized revealed preference travel survey data. We propose an algorithm to find a feasible sequence of activity locations for each individual that minimizes the maximum error of each trip’s Euclidean distance within the activity chain. The synthetic activity locations are then used to create unchosen alternatives within the choice set for each individual. This is followed by the mode-choice model estimation. We test our approach on three large-scale travel surveys conducted in Switzerland, Île-de-France, and São Paulo. We find that our methodological approach can reconstruct activity locations that accurately match trip Euclidean distances but with location errors that still provide location protection. The discrete mode-choice models estimated on the synthetic locations perform similarly, in terms of goodness of fit and prediction, to the ones obtained from the observed activity locations.

本文提出了一种从空间匿名的显示偏好旅行调查数据中估计模式选择模型的方法。我们提出了一种算法来为每个人找到一个可行的活动位置序列，以最小化活动链中每次旅行的欧几里得距离的最大误差。然后使用合成活动位置在每个人的选择集中创建未选择的替代方案。接下来是模式选择模型估计。我们在瑞士、法兰西岛和圣保罗进行的三项大规模旅行调查中测试了我们的方法。我们发现我们的方法可以重建准确匹配行程欧几里得距离但仍提供位置保护的位置错误的活动位置。