The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.
A Survey on Trajectory Data Management, Analytics, and Learning
Open accessAbstract
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.
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Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
The R*-tree: an efficient and robust access method for points and rectangles
Beckmann, N., Kriegel, H.-P., Schneider, R., & Seeger, B. (1990) · Proceedings of the 1990 ACM SIGMOD international conference on Management of data · doi:10.1145/93597.98741 · Cited by 4,161
Traffic Flow Prediction With Big Data: A Deep Learning Approach
Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2014) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2014.2345663 · Cited by 3,000
Friendship and mobility: user movement in location-based social networks
Cho, E., Myers, S. A., & Leskovec, J. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020579 · Cited by 2,903
Mining interesting locations and travel sequences from GPS trajectories
Zheng, Y., Zhang, L., Xie, X., & Ma, W.-Y. (2009) · Proceedings of the 18th international conference on World wide web · doi:10.1145/1526709.1526816 · Cited by 1,928
R-trees: a dynamic index structure for spatial searching
Guttman, A. (1984) · ACM SIGMOD Record · doi:10.1145/971697.602266 · Cited by 1,612
Argoverse: 3D Tracking and Forecasting With Rich Maps
Chang, M.-F., Lambert, J., Sangkloy, P., Singh, J., Bak, S., Hartnett, A., Wang, D., Carr, P., Lucey, S., Ramanan, D., et al. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00895 · Cited by 1,474
Discovering similar multidimensional trajectories
Vlachos, M., Kollios, G., & Gunopulos, D. (2003) · Proceedings 18th International Conference on Data Engineering · doi:10.1109/icde.2002.994784 · Cited by 1,468
Trajectory Data Mining: An Overview
Zheng, Y. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2743025 · Cited by 1,460
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Metadata
- Authors
- Wang, S., Bao, Z., Culpepper, J. S., & Cong, G.
- Venue
- ACM Computing Surveys
- Published
- 2021-03-05
- Volume
- 54
- Issue
- 2
- Pages
- 1-36
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W3134624922
- Cited by
- 267
- References
- 111
- FWCI
- 22.07
- Type
- journal article
- Language
- en
- Open access
- green
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