Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density‐based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub‐sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real‐world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems.

中文翻译：

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从行动数据到习惯和常见途径

我们生活的许多方面都与我们每天进行的场所和活动相关。他们中的大多数是经常性的，并且要求个人在上班，上学或其他重要的个人地点等常规地点之间流离失所。为了完成这些经常性的日常活动，人们倾向于遵循具有相似的时空特征的规则路径，特别是因为人类经常寻找统一的事物来支持他们的决策，并使他们的行动变得更轻松甚至自动。在这项工作中，我们提出了一种从人类移动性数据中发现跨用户习惯的常见途径的方法。通过使用基于密度的聚类算法，我们可以确定用户访问的最佳位置，并在这些位置上应用高斯混合模型，以自动在所有迹线之间进行分离，遵循模式以发现个人习惯表示的轨迹。通过使用最长的公共子序列算法，我们通过考虑成对的用户或习惯在同一条路径上共享的距离，来搜索在用户的习惯旅行集合上更相似的轨迹。该方法在两个真实世界的GPS数据集上进行了评估，结果表明该方法能够检测用户生活中最重要的地方，检测日常活动并确定具有相似习惯的用户之间的共同路线，为拼车，推荐和预测系统中的研究技术。我们通过考虑成对的用户或习惯在同一条路径上共享的距离，来搜索一组用户习惯旅行中更相似的轨迹。该方法在两个真实世界的GPS数据集上进行了评估，结果表明该方法能够检测用户生活中最重要的地方，检测日常活动并确定具有相似习惯的用户之间的共同路线，为拼车，推荐和预测系统中的研究技术。我们通过考虑成对的用户或习惯在同一条路径上共享的距离，来搜索一组用户习惯旅行中更相似的轨迹。该方法在两个真实世界的GPS数据集上进行了评估，结果表明该方法能够检测用户生活中最重要的地方，检测日常活动并确定具有相似习惯的用户之间的共同路线，为拼车，推荐和预测系统中的研究技术。