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MPE: A Mobility Pattern Embedding Model for Predicting Next Locations
The wide spread use of positioning and photographing devices gives rise to a
deluge of traffic trajectory data (e.g., vehicle passage records and taxi
trajectory data), with each record having at least three attributes: object ID,
location ID, and time-stamp. In this paper, we propose a novel mobility pattern
embedding model called MPE to shed the light on people's mobility patterns in
traffic trajectory data from multiple aspects, including sequential, personal,
and temporal factors. MPE has two salient features: (1) it is capable of
casting various types of information (object, location and time) to an
integrated low-dimensional latent space; (2) it considers the effect of
``phantom transitions'' arising from road networks in traffic trajectory data.
This embedding model opens the door to a wide range of applications such as
next location prediction and visualization. Experimental results on two
real-world datasets show that MPE is effective and outperforms the
state-of-the-art methods significantly in a variety of tasks