Learning Behavioral Representations of Human Mobility
This work addresses the challenge of representing human mobility behavior for applications like urban planning or social analysis, but it appears incremental as it builds on existing representation learning methods.
The paper tackled the problem of analyzing behavioral similarity in human mobility using CDR trajectories by developing mob2vec, a framework that generates low-dimensional vector representations preserving mobility behavior similarity, as shown through empirical experimentation on real data.
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.