The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Discovery of convoys in trajectory databases
Abstract
As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for real-life applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the well-known filter-refinement framework. In the filter step, we apply line-simplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper's proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.
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Cites
(1996) · Cited by 20,179
DOUGLAS, D. H., & PEUCKER, T. K. (1973) · Cartographica · doi:10.3138/fm57-6770-u75u-7727 · Cited by 4,229
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 clustering: a partition-and-group framework
Lee, J.-G., Han, J., & Whang, K.-Y. (2007) · Proceedings of the 2007 ACM SIGMOD international conference on Management of data · doi:10.1145/1247480.1247546 · Cited by 1,363
Robust and fast similarity search for moving object trajectories
Chen, L., Özsu, M. T., & Oria, V. (2005) · Proceedings of the 2005 ACM SIGMOD international conference on Management of data · doi:10.1145/1066157.1066213 · Cited by 1,336
On The Marriage of Lp-norms and Edit Distance
Chen, L., & Ng, R. (2004) · Proceedings 2004 VLDB Conference · doi:10.1016/b978-012088469-8.50070-x · Cited by 774
On Discovering Moving Clusters in Spatio-temporal Data
Kalnis, P., Mamoulis, N., & Bakiras, S. (2005) · Lecture Notes in Computer Science · doi:10.1007/11535331_21 · Cited by 420
Spatiotemporal Compression Techniques for Moving Point Objects
Meratnia, N., & de By, R. A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24741-8_44 · Cited by 335
Computing longest duration flocks in trajectory data
Gudmundsson, J., & van Kreveld, M. (2006) · Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems · doi:10.1145/1183471.1183479 · Cited by 277
Li, Y., Han, J., & Yang, J. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014129 · Cited by 231
Cited by
Urban Computing: Concepts, Methodologies, and Applications
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2629592 · Cited by 1,507
A review of moving object trajectory clustering algorithms
Yuan, G., Sun, P., Zhao, J., Li, D., & Wang, C. (2017) · Artificial Intelligence Review · doi:10.1007/s10462-016-9477-7 · Cited by 323
A Survey on Trajectory Data Management, Analytics, and Learning
Wang, S., Bao, Z., Culpepper, J. S., & Cong, G. (2022) · ACM Computing Surveys · doi:10.1145/3440207 · Cited by 267
Hierarchical and Networked Vehicle Surveillance in ITS: A Survey
Tian, B., Morris, B. T., Tang, M., Liu, Y., Yao, Y., Gou, C., Shen, D., & Tang, S. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2552778 · Cited by 160
Metadata
- Authors
- Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., & Shen, H. T.
- Venue
- Proceedings of the VLDB Endowment
- Published
- 2008-08
- Volume
- 1
- Issue
- 1
- Pages
- 1068-1080
- ISSN
- 2150-8097 (ISSN-L)
- OpenAlex
- W2141136363
- Cited by
- 431
- References
- 14
- FWCI
- 34.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., & Shen, H. T. (2008). Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment. https://doi.org/10.14778/1453856.1453971
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