  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.
