TY - GEN
T1 - Spatio-temporal GRU for trajectory classification
AU - Liu, Hongbin
AU - Wu, Hao
AU - Sun, Weiwei
AU - Lee, Ickjai
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - 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 Spatio-Temporal 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.
AB - 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 Spatio-Temporal 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.
KW - Deep learning
KW - GRU
KW - Spatio-temporal trajectory
KW - Trajectory classification
KW - Travel model classification
UR - http://www.scopus.com/inward/record.url?scp=85078957134&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2019.00152
DO - 10.1109/ICDM.2019.00152
M3 - Conference Proceeding
AN - SCOPUS:85078957134
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 1228
EP - 1233
BT - Proceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
A2 - Wang, Jianyong
A2 - Shim, Kyuseok
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Conference on Data Mining, ICDM 2019
Y2 - 8 November 2019 through 11 November 2019
ER -