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Open AccessArticle

Multi-Aspect Embedding for Attribute-Aware Trajectories

by 1,2,*, 1,2 and 1,2,*
1
University of Chinese Academy of Sciences, Beijing 100049, China
2
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
*
Authors to whom correspondence should be addressed.
Symmetry 2019, 11(9), 1149; https://doi.org/10.3390/sym11091149
Received: 15 August 2019 / Revised: 31 August 2019 / Accepted: 3 September 2019 / Published: 10 September 2019
Motivated by the proliferation of trajectory data produced by advanced GPS-enabled devices, trajectory is gaining in complexity and beginning to embroil additional attributes beyond simply the coordinates. As a consequence, this creates the potential to define the similarity between two attribute-aware trajectories. However, most existing trajectory similarity approaches focus only on location based proximities and fail to capture the semantic similarities encompassed by these additional asymmetric attributes (aspects) of trajectories. In this paper, we propose multi-aspect embedding for attribute-aware trajectories (MAEAT), a representation learning approach for trajectories that simultaneously models the similarities according to their multiple aspects. MAEAT is built upon a sentence embedding algorithm and directly learns whole trajectory embedding via predicting the context aspect tokens when given a trajectory. Two kinds of token generation methods are proposed to extract multiple aspects from the raw trajectories, and a regularization is devised to control the importance among aspects. Extensive experiments on the benchmark and real-world datasets show the effectiveness and efficiency of the proposed MAEAT compared to the state-of-the-art and baseline methods. The results of MAEAT can well support representative downstream trajectory mining and management tasks, and the algorithm outperforms other compared methods in execution time by at least two orders of magnitude.
Keywords: trajectory similarity computation; multi-aspect embedding; representation learning trajectory similarity computation; multi-aspect embedding; representation learning
MDPI and ACS Style

Boonchoo, T.; Ao, X.; He, Q. Multi-Aspect Embedding for Attribute-Aware Trajectories. Symmetry 2019, 11, 1149.

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