论文标题
学习人类流动性的行为表现
Learning Behavioral Representations of Human Mobility
论文作者
论文摘要
在本文中，我们研究了基于CDR轨迹的行为相似性分析最先进的表示学习方法的适用性。该贡献的核心是一种新颖的方法学框架，即MOB2VEC，集中在最近的符号轨迹分割方法中的综合使用中，用于消除噪声，一种结合行为信息的新型轨迹泛化方法，以及一种无用的技术，用于从顺序数据中学习矢量表示。 MOB2VEC是通过广泛的实验对实际CDR数据进行的经验研究的结果。结果，结果表明，MOB2VEC在低维空间中生成了CDR轨迹的矢量表示，以保留个人的移动性行为的相似性。
In this paper, we investigate the suitability of state-of-the-art representation learning methods to the analysis of behavioral similarity of moving individuals, based on CDR trajectories. The core of the contribution is a novel methodological framework, mob2vec, centered on the combined use of a recent symbolic trajectory segmentation method for the removal of noise, a novel trajectory generalization method incorporating behavioral information, and an unsupervised technique for the learning of vector representations from sequential data. Mob2vec is the result of an empirical study conducted on real CDR data through an extensive experimentation. As a result, it is shown that mob2vec generates vector representations of CDR trajectories in low dimensional spaces which preserve the similarity of the mobility behavior of individuals.
