Journal article
Similar Trajectory Search with Spatiooral Deep Representation Learning
DA Tedjopurnomo, X Li, Z Bao, G Cong, F Choudhury, AK Qin
ACM Transactions on Intelligent Systems and Technology | Published : 2021
DOI: 10.1145/3466687
Abstract
Similar trajectory search is a crucial task that facilitates many downstream spatial data analytic applications. Despite its importance, many of the current literature focus solely on the trajectory's spatial similarity while neglecting the temporal information. Additionally, the few papers that use both the spatial and temporal features based their approach on a traditional point-to-point comparison. These methods model the importance of the spatial and temporal aspect of the data with only a single, pre-defined balancing factor for all trajectories, even though the relative spatial and temporal balance can change from trajectory to trajectory. In this article, we propose the first spatioor..
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Awarded by Australian Research Council
Funding Acknowledgements
This research is supported in part by ARC DP200102611, DP180102050, and LP180100114. Gao Comp; acknowledges the support by Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALEPNTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund -Industry Collaboration Projects Grant, and a Tier-1 project RG111/19.