Semantics-Aware Hidden Markov Model for Human Mobility

Abstract

Understanding human mobility bene ts numerous applications such as urban planning, tra c control and city management. Previous work mainly focuses on modeling spatial and temporal patterns of human mobility. However, the semantics of trajectory are ignored, thus failing to model people's motivation behind mobility. In this paper, we propose a novel semantics-aware mobility model that captures human mobility motivation using large-scale semantics-rich spatialtemporal data from location-based social networks. In our system, we rst develop a multimodal embedding method to project user, location, time, and activity on the same embedding space in an unsupervised way while preserving original tra..