Human Mobility Identification by Deep Behavior Relevant Location Representation

Tao Sun, Fei Wang, Zhao Zhang, Lin Wu, Yongjun Xu

Abstract: This paper focuses on Trajectory User Link (TUL), which aims at identifying user identities through exploiting their mobility patterns. Existing TUL approaches are based on location representation, a way to learn location associations by embedding vectors that can indicate the level of semantic similarity between the locations. However, existing methods for location representation don’t consider the semantic diversity of locations, which will lead to a misunderstanding of the semantic information of trajectory when linking anonymous trajectories to candidate users. To solve this problem, in this paper, we propose Deep Behavior Relevant Location representation (DBRLr) to map the polysemous locations into distinct vectors, from the perspective of users’ behavior to reflect the semantic polysemy of locations. To learn this representation, we build a Location Prediction-based Movement Model (LP-based MM), which learns user behavior representation at each visited location from a large history trajectory corpora. LP-based MM considers both Continuity and Cyclicity characteristics of user’s movement. We employ the combination of the intermediate layer representation in LP-based MM as DBRLr. An effective recurrent neural network is used to link anonymous trajectories with candidate users. Experiments are conducted on two real-world datasets, and the result shows that our method performs beyond existing methods.