A Survey on Trajectory Data Management, Analytics, and Learning
By Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Gao Cong. ACM Computing Surveys, 2020
300 citations recorded by the source corpus.
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 time. Finally, we outline the essential qualities that a trajectory data management system should possess to maximize flexibility.
Subfields: Transportation, Signal Processing
View Paper: https://www.semanticscholar.org/paper/0df5a4f9cc8a244715fe9968732497d2ac2a7cd1
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Citations in the corpus
- Data-centric Artificial Intelligence: A Survey (2023, unknown): “Note that data-centric AI is related to data management, a very broad field entailing complex data practices for business intelligence and informed decision-making [206, 232].”
- A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, background): “[18] only survey the management and analytics of trajectories, which is one kind of spatial dynamic temporal dynamic data, so they lack the discussion on other kinds of spatiotemporal data.”