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World Wide Web

, Volume 20, Issue 1, pp 5–22 | Cite as

A feature based method for trajectory dataset segmentation and profiling

  • Wei Jiang
  • Jie Zhu
  • Jiajie Xu
  • Zhixu Li
  • Pengpeng Zhao
  • Lei ZhaoEmail author
Article

Abstract

The pervasiveness of location-acquisition and mobile computing techniques has generated massive spatial trajectory data, which has brought great challenges to the management and analysis of such a big data. In this paper, we focus on the sub-trajectory dataset profiling problem, and aim to extract the representative sub-trajectories from the raw trajectory as a subset, called profile, which can best describe the whole dataset. This problem is very challenging subject to finding the most representative sub-trajectories set by trading off the size and quality of the profile. To tackle this problem, we model the features of the trajectory dataset from the aspects of density, speed and the direction flow. Meanwhile we present our two-step method to select the representative trajectories based on the feature model. First, a novel trajectory segmentation algorithm is applied on a raw trajectory to identify the representative segments concerning their feature representativeness and automatically estimate the number of segments and the segment borders. Then, a sub-trajectory profiling method is performed to yield the most representative sub-trajectories in the dataset, based on a local heuristic evolution strategy. We evaluate our method based on extensive experiments by using two real-world trajectory datasets generated by over 12,000 taxicabs in Beijing and Shanghai. The results demonstrate the efficiency and effectiveness of our methods in different applications.

Keywords

Spatial-temporal database Trajectory Trajectory segmentation Data profiling 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61572335, 61402312, and 61402313, the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20151223, the Natural Science Foundation of Jiangsu Provincial Department of Education of China under Grant No. 12KJB520017, and Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China.

References

  1. 1.
    Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: 2011 IEEE 27th international conference on data engineering (ICDE), pp. 900–911. IEEE (2011)Google Scholar
  2. 2.
    Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica: The International Journal for Geographic Information and Geovisualization 10(2), 112–122 (1973)CrossRefGoogle Scholar
  3. 3.
    Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 330–339. ACM (2007)Google Scholar
  4. 4.
    Hershberger, J.E., Snoeyink, J.: Speeding up the Douglas-Peucker line-simplification algorithm. University of British Columbia, Department of Computer Science (1992)Google Scholar
  5. 5.
    Jiang, W., Zhu, J., Xu, J., Li, Z., Zhao, P., Zhao, L.: Hv: a feature based method for trajectory dataset profiling, pp. 46–60. Springer (2015)Google Scholar
  6. 6.
    Kirkpatrick, S., Vecchi, M., et al.: Optimization by simmulated annealing. Science 220(4598), 671–680 (1983)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Lee, J.G., Han, J., Li, X.: Trajectory outlier detection: a partition-and-detect framework. In: ICDE 2008. IEEE 24th International Conference on Data Engineering, 2008, pp. 140–149. IEEE (2008)Google Scholar
  8. 8.
    Lee, J.G., Han, J., Whang, K.Y.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data, pp. 593–604. ACM (2007)Google Scholar
  9. 9.
    Li, X., Han, J., Lee, J.G., Gonzalez, H.: Traffic density-based discovery of hot routes in road networks. In: Advances in spatial and temporal databases, pp. 441–459. Springer (2007)Google Scholar
  10. 10.
    Long, C., Wong, R.C.W., Jagadish, H.V.: Direction-preserving trajectory simplification. Proc VLDB Endow 6(10), 949–960 (2013). doi: 10.14778/2536206.2536221 CrossRefGoogle Scholar
  11. 11.
    Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate gps trajectories. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 352–361. ACM (2009)Google Scholar
  12. 12.
    Monreale, A., Pinelli, F., Trasarti, R., Giannotti, F.: Wherenext: a location predictor on trajectory pattern mining. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 637–646. ACM (2009)Google Scholar
  13. 13.
    Panagiotakis, C., Pelekis, N., Kopanakis, I., Ramasso, E., Theodoridis, Y.: Segmentation and sampling of moving object trajectories based on representativeness. IEEE Trans Knowl Data Eng 24(7), 1328–1343 (2012)CrossRefGoogle Scholar
  14. 14.
    Pelekis, N., Kopanakis, I., Panagiotakis, C., Theodoridis, Y.: Unsupervised trajectory sampling. In: Machine learning and knowledge discovery in databases, pp. 17–33. Springer (2010)Google Scholar
  15. 15.
    Sacharidis, D., Patroumpas, K., Terrovitis, M., Kantere, V., Potamias, M., Mouratidis, K., Sellis, T.: On-line discovery of hot motion paths. In: Proceedings of the 11th international conference on Extending database technology: Advances in database technology, pp. 392–403. ACM (2008)Google Scholar
  16. 16.
    Taylor, K.M., Procopio, M.J., Young, C.J., Meyer, F.G.: Estimation of arrival times from seismic waves: a manifold-based approach. Geophys. J. Int. 185(1), 435–452 (2011)CrossRefGoogle Scholar
  17. 17.
    Trajcevski, G., Cao, H., Scheuermanny, P., Wolfsonz, O., Vaccaro, D.: On-line data reduction and the quality of history in moving objects databases. In: Proceedings of the 5th ACM international workshop on Data engineering for wireless and mobile access, pp. 19–26. ACM (2006)Google Scholar
  18. 18.
    Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: A geographical sparse additive generative model for spatial item recommendation. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1255–1264. ACM (2015)Google Scholar
  19. 19.
    Wang, Y., Zheng, Y., Xue, Y.: Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 25–34. ACM (2014)Google Scholar
  20. 20.
    Wei, L.Y., Zheng, Y., Peng, W.C.: Constructing popular routes from uncertain trajectories. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 195–203. ACM (2012)Google Scholar
  21. 21.
    Xue, A.Y., Zhang, R., Zheng, Y., Xie, X., Huang, J., Xu, Z.: Destination prediction by sub-trajectory synthesis and privacy protection against such prediction. In: 2013 IEEE 29th international conference on data engineering (ICDE), pp. 254–265. IEEE (2013)Google Scholar
  22. 22.
    Yin, H., Cui, B., Chen, L., Hu, Z., Zhang, C.: Modeling location-based user rating profiles for personalized recommendation. ACM Trans. Knowl. Discovery Data (TKDD) 9(3), 19 (2015)Google Scholar
  23. 23.
    Yin, H., Cui, B., Huang, Z., Wang, W., Wu, X., Zhou, X.: Joint modeling of users’ interests and mobility patterns for point-of-interest recommendation. In: Proceedings of the 23rd annual ACM conference on multimedia conference, pp. 819–822. ACM (2015)Google Scholar
  24. 24.
    Yin, H., Cui, B., Zhou, X., Wang, W., Huang, Z., Sadiq, S.: Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Trans. Inf. Syst. (2016)Google Scholar
  25. 25.
    Yin, H., Hu, Z., Zhou, X., Wang, H., Zheng, K., Nguyen, Q.V.H., Sadiq, S.: Discovering interpretable geo-social communities for user behavior predictionGoogle Scholar
  26. 26.
    Yuan, J., Zheng, Y., Xie, X., Sun, G.: T-drive: Enhancing driving directions with taxi drivers’ intelligence. IEEE Trans. Knowl. Data Eng. 25(1), 220–232 (2013)CrossRefGoogle Scholar
  27. 27.
    Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp. 99–108. ACM (2010)Google Scholar
  28. 28.
    Yuan, N.J., Zheng, Y., Zhang, L., Xie, X.: T-finder: A recommender system for finding passengers and vacant taxis. IEEE Trans. Knowl. Data Eng. 25(10), 2390–2403 (2013)CrossRefGoogle Scholar
  29. 29.
    Zheng, Y., Chen, Y., Li, Q., Xie, X., Ma, W.Y.: Understanding transportation modes based on gps data for web applications. ACM Trans. Web (TWEB) 4(1), 1 (2010)CrossRefGoogle Scholar
  30. 30.
    Zheng, Y., Zhang, L., Xie, X., Ma, W.Y.: Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th international conference on World wide web, pp. 791–800. ACM (2009)Google Scholar
  31. 31.
    Zheng, K., Zheng, Y., Yuan, N.J., Shang, S.: On discovery of gathering patterns from trajectories. In: 2013 IEEE 29th international conference on data engineering (ICDE), pp. 242–253. IEEE (2013)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Wei Jiang
    • 1
  • Jie Zhu
    • 1
  • Jiajie Xu
    • 1
  • Zhixu Li
    • 1
  • Pengpeng Zhao
    • 1
  • Lei Zhao
    • 1
    Email author
  1. 1.Soochow UniversitySuzhouPeople’s Republic of China

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