Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Spatiotemporal Data: Trajectories

  • Xiaofang ZhouEmail author
  • Lei Li
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_221



Let p(l, t) be a spatiotemporal point with location l at time t. A trajectory is defined as τ =< p1, p2pn > where pi.tpj.t if i < j. That is, a trajectory is a sequence of spatiotemporal points ordered by time.

Location l can be represented as a longitude and latitude pair in geographical space or a road segment ID and distance offset in a road network. A trajectory without temporal information is often called route or path, and a collection of trajectories of an object is called its trace. The trajectory with a specific origin and destination pair (OD pair) is also called a trip.


A trajectory records how an object moved in a space. Such information is easier than ever to acquire with the prevalence of location-capturing devices such as GPS nowadays. Therefore, large volumes of trajectory data are being accumulated from various sources every day, for animals, human, vehicles, and natural...

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  1. Cheng R, Kalashnikov, DV, Prabhakar, S (2004) Querying imprecise data in moving object environments. IEEE Trans Knowl Data Eng 16(9):1112–1127CrossRefGoogle Scholar
  2. Deng K, Xie K, Zheng K, Zhou X (2011) Trajectory indexing and retrieval. Computing with spatial trajectories. Springer, New York, pp 35–60Google Scholar
  3. Draxler RR, Rolph, GD (2003) Hysplit (hybrid single-particle lagrangian integrated trajectory). NOAA air resources laboratory, silver spring, MD. model access via NOAA ARL ready websiteGoogle Scholar
  4. Koide S, Tadokoro Y, Xiao C, Ishikawa Y (2017) CiNCT: compression and retrieval for massive vehicular trajectories via relative movement labeling. arXiv preprint arXiv:1706.02885Google Scholar
  5. Lee J-G, Han J, Whang K-Y (2007) Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on Management of data. ACM, pp 593–604Google Scholar
  6. Li Z, Ding B, Han J, Kays R (2010a) Swarm: mining relaxed temporal moving object clusters. Proc VLDB Endow 3(1–2):723–734CrossRefGoogle Scholar
  7. Li Z, Ding B, Han J, Kays R, Nye P (2010b) Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1099–1108Google Scholar
  8. Song R, Sun W, Zheng B, Zheng Y (2014) Press: a novel framework of trajectory compression in road networks. Proc VLDB Endow 7(9):661–672CrossRefGoogle Scholar
  9. Su H, Zheng K, Wang H, Huang J, Zhou X (2013) Calibrating trajectory data for similarity-based analysis. In: Proceedings of the 2013 ACM SIGMOD international conference on management of data. ACM, pp 833–844Google Scholar
  10. Tao Y, Papadias D (2001) The mv3r-tree: a spatio-temporal access method for timestamp and interval queries. In: Proceedings of very large data bases conference (VLDB), 11–14 Sept, RomeGoogle Scholar
  11. Wang H, Su H, Zheng K, Sadiq S, Zhou X (2013) An effectiveness study on trajectory similarity measures. In: Proceedings of the twenty-fourth Australasian database conference, vol 137. Australian Computer Society, Inc., pp 13–22Google Scholar
  12. Yang B, Guo C, Jensen CS (2013) Travel cost inference from sparse, spatio temporally correlated time series using Markov models. Proc VLDB Endow 6(9): 769–780CrossRefGoogle Scholar
  13. Yuan J, Zheng Y, Xie X, Sun G (2013) T-drive: enhancing driving directions with taxi drivers’ intelligence. IEEE Trans Knowl Data Eng 25(1):220–232CrossRefGoogle Scholar
  14. Zheng Y (2015) Trajectory data mining: an overview. ACM Trans Intell Syst Technol (TIST) 6(3):29Google Scholar
  15. Zheng Y, Xie X (2011) Learning travel recommendations from user-generated GPS traces. ACM Trans Intell Syst Technol (TIST) 2(1):2Google Scholar
  16. Zheng Y, Zhou X (2011) Computing with spatial trajectories. Springer Science & Business Media, New YorkCrossRefGoogle Scholar
  17. Zheng K, Zheng Y, Xie X, Zhou X (2012) Reducing uncertainty of low-sampling-rate trajectories. In: 2012 IEEE 28th international conference on data engineering (ICDE). IEEE, pp 1144–1155Google Scholar
  18. Zheng K, Zheng Y, Yuan NJ, Shang S, Zhou X (2014) Online discovery of gathering patterns over trajectories. IEEE Trans Knowl Data Eng 26(8):1974–1988CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.School of Information Technology and Electrical EngineeringUniversity of QueenslandBrisbaneAustralia