Spatio-temporal GRU for trajectory classification

Liu, Hong-Bin, Wu, Hao, Sun, Weiwei, and Lee, Ickjai (2019) Spatio-temporal GRU for trajectory classification. In: Proceedings of the 19th IEEE International Conference on Data Mining. pp. 1228-1233. From: ICDM 2019: 19th IEEE International Conference on Data Mining, 8-11 November 2019, Beijng, China.

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Spatio-temporal trajectory classification is a fundamental problem for location-based services with many real-world applications such as travel mode classification, animal mobility detection, and location recommendation. In the literature, many approaches have been proposed to solve this classification task including deep learning models like LSTM recently for sequence classification. However, these approaches fail to consider both spatial and temporal interval information simultaneously, but share some common drawbacks: omitting either the spatial information or the temporal interval information out. Some models like Time-LSTM, have been proposed to handle the temporal interval information for spatio-temporal trajectories, but they do not take into account the spatial information. Note that, considering both spatial and temporal interval information is crucial for spatio-temporal data mining in order not to miss any spatio-temporal pattern. In this study, we propose a trajectory classifier called ST-GRU to better model the spatio-temporal correlations and irregular temporal intervals prevalently present in spatio-temporal trajectories. We introduce a novel segmented convolutional weight mechanism to capture short-term local spatial correlations in trajectories and propose an additional temporal gate to control the information flow related to the temporal interval information. Performance evaluation demonstrates that our proposed model outperforms popular deep learning approaches for the travel model classification problem.

Item ID: 61161
Item Type: Conference Item (Research - E1)
ISBN: 978-1-7281-4604-1
ISSN: 2374-8486
Keywords: trajectory classification, spatio-temporal trajectory,GRU, travel model classification, deep learning
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Copyright Information: Copyright © 2019 by The Institute of Electrical and Electronics Engineers, Inc.
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A version of this publication was included as Chapter 4 of the following PhD thesis: Liu, Hong-Bin (2020) Predictive spatio-temporal modelling with neural networks. PhD thesis, James Cook University. which is available Open Access in ResearchOnline@JCU. Please see the Related URLs for access.

Date Deposited: 26 Feb 2020 02:33
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%
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