Human mobility clustering is an important problem for understanding human mobility behaviors (e.g., work and school commutes). Existing methods typically contain two steps: choosing or learning a mobility representation and applying a clustering algorithm to the representation. However, these methods rely on strict visiting orders in trajectories and cannot take advantage of multiple types of mobility representations. This paper proposes a novel mobility clustering method for mobility behavior detection. First, the proposed method contains a permutation-equivalent operation to handle sub-trajectories that might have different visiting orders but similar impacts on mobility behaviors. Second, the proposed method utilizes a variational autoencoder architecture to simultaneously perform clustering in both latent and original spaces. Also, in order to handle the bias of a single latent space, our clustering assignment prediction considers multiple learned latent spaces at different epochs. This way, the proposed method produces accurate results and can provide reliability estimates of each trajectory's cluster assignment. The experiment shows that the proposed method outperformed state-of-the-art methods in mobility behavior detection from trajectories with better accuracy and more interpretability.
翻译:人类流动群集是了解人类流动行为(例如工作和学校通勤)的一个重要问题。现有方法通常包含两个步骤:选择或学习流动代表制,并对代表制采用群集算法。然而,这些方法依赖于轨道上的严格的访问命令,不能利用多种类型的流动代表制。本文件提出了一种新的流动群集方法,用于检测流动性行为。首先,拟议方法包含一种对子轨道的调整等值操作,这些子轨道可能具有不同的访问命令,但对流动行为有类似影响。第二,拟议方法使用变异自动coder结构,同时在潜在空间和原始空间进行群集。此外,为了处理单一潜在空间的偏向,我们的群集任务预测考虑到不同地方的多学过的潜在空间。这样,拟议方法产生准确的结果,并能够提供每种轨迹群集的可靠估计。实验表明,拟议方法在从轨迹中检测流动性行为时,比从轨迹中测得更准确和更可解释性。