Journal article
A Survey on Trajectory Data Management, Analytics, and Learning
S Wang, Z Bao, JS Culpepper, G Cong
ACM Computing Surveys | Published : 2022
DOI: 10.1145/3440207
Abstract
Recent advances in sensor and mobile devices have enabled an unprecedented increase in the availability and collection of urban trajectory data, thus increasing the demand for more efficient ways to manage and analyze the data being produced. In this survey, we comprehensively review recent research trends in trajectory data management, ranging from trajectory pre-processing, storage, common trajectory analytic tools, such as querying spatial-only and spatial-Textual trajectory data, and trajectory clustering. We also explore four closely related analytical tasks commonly used with trajectory data in interactive or real-Time processing. Deep trajectory learning is also reviewed for the first..
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Grants
Awarded by Australian Research Council
Funding Acknowledgements
Zhifeng Bao is supported in part by ARC DP200102611, DP180102050, and a Google Faculty Award. J. Shane Culpepper is supported in part by ARC DP190101113. Gao Cong is supported in part by a MOE Tier-2 grant MOE2019-T2-2-181, and a MOE Tier-1 grant RG114/19.