期刊
2018 IEEE 34TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE)
卷 -, 期 -, 页码 617-628出版社
IEEE
DOI: 10.1109/ICDE.2018.00062
关键词
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资金
- Rapid-Rich Object Search (ROSE) Lab at Nanyang Technological University
- National Research Foundation, Prime Minister's Office, Singapore, under its IDM Futures Funding Initiative
- MOE [MOE2016-T2-1-137, RG31/17]
- NSFC [61602197, 61772537]
- Microsoft
- DiCyPS project
- Obel Family Foundation
- NSF [2016CFB192]
Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search.
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