# Clustering Human Mobility with Multiple Spaces

arXiv: https://arxiv.org/abs/2301.08524 · HTML: https://mlsanity.com/paper/2301.08524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics](https://mlsanity.com/paper/2009.10795/) (2020)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories](https://mlsanity.com/paper/2112.09339/) (2021)
- [Checkpoint Ensembles: Ensemble Methods from a Single Training Process](https://mlsanity.com/paper/1710.03282/) (2017)
- [DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis](https://mlsanity.com/paper/2003.01351/) (2020)

## Built on by (most cited first, 2 of 2)

- [Context-Aware Trajectory Anomaly Detection](https://mlsanity.com/paper/2410.19136/) (2024)
- [BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data](https://mlsanity.com/paper/2510.12076/) (2025)

## Abstract (arXiv)

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.