In response to the absence of demographics in increasingly emerging big data sets, we propose a novel method for inferring the missing demographic information based on similarity in people’s daily multi-dimensional activity-travel patterns as well as the characteristics of the area they move about. Instead of using isolated activity-travel attributes to infer social demographic features, our proposed method first calculates the similarity of people’s multidimensional daily activities and travels as well as characteristics of their visiting locations, between those for whom the social demographics are to be imputed (target) and those with known demographics (base) using a polynomial function. The weights of the function are determined using the permutation feature importance method, and then dynamic time warping is used to align the multidimensional activity sequences of the base and target sample and measure their similarities. For each person in the target database, a matched list is created consisting of those with the most similar activity-travel sequences in the base sample. A support vector machine is then trained using the base sample as input to impute the demographics of the target sample. The proposed model is trained using a national travel survey and validated by applying it to a GPS dataset. The results show that the proposed method outperforms existing methods in predicting four selected demographics: gender, age, education level, and work status, with an accuracy range between 91% and 94% for the national dataset and 88% to 91% for the GPS data. This study highlights the importance of considering the multidimensional and sequential nature of peoples’ daily activity-travel patterns in the imputation of demographic features.

中文翻译：

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基于多维活动-旅行模式相似性的社会人口统计插补：两步法


针对日益出现的大数据集中人口统计信息的缺失，我们提出了一种新方法，根据人们日常多维活动-旅行模式的相似性以及他们活动区域的特征来推断缺失的人口统计信息。我们提出的方法不是使用孤立的活动-旅行属性来推断社会人口统计特征，而是首先计算人们多维日常活动和旅行的相似性以及他们访问地点的特征，在那些要估算社会人口统计数据的人之间（目标）以及使用多项式函数已知人口统计数据（基数）的人。使用排列特征重要性方法确定函数的权重，然后使用动态时间规整来对齐基础样本和目标样本的多维活动序列并测量它们的相似度。对于目标数据库中的每个人，都会创建一个匹配列表，其中包含基础样本中具有最相似活动旅行序列的人。然后使用基本样本作为输入来训练支持向量机，以估算目标样本的人口统计数据。所提出的模型使用全国旅行调查进行训练，并通过将其应用于 GPS 数据集进行验证。结果表明，所提出的方法在预测性别、年龄、教育水平和工作状态这四种选定的人口统计数据方面优于现有方法，国家数据集的准确度范围在 91% 到 94% 之间，GPS 的准确度范围在 88% 到 91% 之间。数据。这项研究强调了在人口统计特征估算中考虑人们日常活动-旅行模式的多维性和连续性的重要性。