KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지
DOI QR코드

DOI QR Code

Semantic Trajectory Based Behavior Generation for Groups Identification

  • Cao, Yang (Faculty of Information Technology, Beijing University of Technology) ;
  • Cai, Zhi (Faculty of Information Technology, Beijing University of Technology) ;
  • Xue, Fei (College of Information, Beijing Wuzi University) ;
  • Li, Tong (Faculty of Information Technology, Beijing University of Technology) ;
  • Ding, Zhiming (Faculty of Information Technology, Beijing University of Technology)
  • Received : 2018.04.02
  • Accepted : 2018.07.28
  • Published : 2018.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.

Keywords

References

  1. S. Dhar, U. Varshney, "Challenges and business models for mobile location-based services and advertising," Communications of the ACM, vol. 54, no. 5, pp. 121-128, 2011. https://doi.org/10.1145/1941487.1941515
  2. E. Cho, S. A. Myers and J. Leskovec, "Friendship and mobility: user movement in location-based social networks," in Proc. of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
  3. F. Wang, "Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications," IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 630-638, 2010. https://doi.org/10.1109/TITS.2010.2060218
  4. N. Buch, S. A. Velastin and J. Orwell, "A review of computer vision techniques for the analysis of urban traffic," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 920-939, 2011. https://doi.org/10.1109/TITS.2011.2119372
  5. L. Boudjeloud-Assala, T. M. Thuy, "A clustering algorithm based on elitist evolutionary approach," International Journal of Bio-Inspired Computation, vol. 10, no. 4, pp. 258-266, 2017. https://doi.org/10.1504/IJBIC.2017.087922
  6. W. Pan, Y. Zhou and Z. Li, "An exponential function inflation size of multi-verse optimisation algorithm for global optimization," International Journal of Computing Science and Mathematics, vol. 8, no. 2, pp. 115-128, 2017. https://doi.org/10.1504/IJCSM.2017.083758
  7. X. Cai, H. Wang, Z. Cui, J. Cai, Y. Xue and L. Wang, "Bat algorithm with triangle-flipping strategy for numerical optimization," International Journal of Machine Learning and Cybernetics, vol. 9, no. 2, pp. 199-215, 2018. https://doi.org/10.1007/s13042-017-0739-8
  8. S. Zhan, Y. Zhong, Z. Zhang, D. Zhong and H. Zhang, "Comparative analysis of selection schemes used in artificial bee colony algorithm," International Journal of Computing Science and Mathematics, vol. 8, no. 3, pp. 218-227, 2017. https://doi.org/10.1504/IJCSM.2017.085739
  9. M. Zhang, H. Wang, Z. Cui and J. Chen, "Hybrid multi-objective cuckoo search with dynamical local search," Memetic Computing, vol. 10, no. 2, pp. 199-208, 2018. https://doi.org/10.1007/s12293-017-0237-2
  10. U. Rajput, M. Kumari, "Mobile robot path planning with modified ant colony optimization," International Journal of Bio-Inspired Computation, vol. 9, no. 2, pp. 106-113, 2017. https://doi.org/10.1504/IJBIC.2017.083133
  11. Z. Cui, Y. Cao, X. Cai, J. Cai and J. Chen, "Optimal LEACH protocol with modified bat algorithm for big data sensing systems in Internet of Things," Journal of Parallel and Distributed Computing, 2018.
  12. X. Chen, J. Pang and R. Xue, "Constructing and comparing user mobility profiles for location-based services," in Proc. of the 28th Annual ACM Symposium on Applied Computing, 2013.
  13. F. Giannotti, M. Nanni, F. Pinelli and D. Pedreschi, "Trajectory pattern mining," in Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
  14. J. H. Kang, W. Welbourne, B. Stewart and G. Borriello, "Extracting places from traces of locations," in Proc. of the 2nd ACM International Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, 2004.
  15. J. Niedermayer, A. Z U Fle, T. Emrich, M. Renz, N. Mamoulis, L. Chen and H. Kriegel, "Probabilistic nearest neighbor queries on uncertain moving object trajectories," Proceedings of the VLDB Endowment, vol. 7, no. 3, pp. 205-216, 2013. https://doi.org/10.14778/2732232.2732239
  16. L. Wei, Y. Zheng and W. Peng, "Constructing popular routes from uncertain trajectories," in Proc. of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012.
  17. C. Zhou, N. Bhatnagar, S. Shekhar and L. Terveen, "Mining personally important places from GPS tracks," in Proc. of the 23rd International Conference on Data Engineering Workshop, 2007.
  18. C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. L. Damiani, A. Gkoulalas-Divanis, J. Macedo, N. Pelekis and Others, "Semantic trajectories modeling and analysis," ACM Computing Surveys (CSUR), vol. 45, no. 4, pp. 42, 2013.
  19. Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra and K. Aberer, "Semantic trajectories: Mobility data computation and annotation," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 4, no. 3, pp. 49, 2013.
  20. Y. Zheng, "Trajectory data mining: an overview," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 6, no. 3, pp. 29, 2015.
  21. C. Guan, X. Lu, X. Li, E. Chen, W. Zhou and H. Xiong, "Discovery of college students in financial hardship," in Proc. of the 2015 IEEE International Conference on Data Mining (ICDM), 2015.
  22. M. Lv, L. Chen, Z. Xu, Y. Li and G. Chen, "The discovery of personally semantic places based on trajectory data mining," Neurocomputing, vol. 173, pp. 1142-1153, 2016. https://doi.org/10.1016/j.neucom.2015.08.071
  23. L. Tang, Y. Zheng, J. Yuan, J. Han, A. Leung, W. Peng and T. L. Porta, "A framework of traveling companion discovery on trajectory data streams," ACM Transactions on Intelligent Systems and Technology (TIST), vol. 5, no. 1, pp. 3, 2013.
  24. D. Ashbrook, T. Starner, "Using GPS to learn significant locations and predict movement across multiple users," Personal and Ubiquitous computing, vol. 7, no. 5, pp. 275-286, 2003. https://doi.org/10.1007/s00779-003-0240-0
  25. X. Cao, G. Cong and C. S. Jensen, "Mining significant semantic locations from GPS data," Proceedings of the VLDB Endowment, vol. 3, no. 1-2, pp. 1009-1020, 2010. https://doi.org/10.14778/1920841.1920968
  26. H. Su, K. Zheng, K. Zeng, J. Huang and X. Zhou, "STMaker: a system to make sense of trajectory data," in Proc. of Proceedings of the VLDB Endowment, vol. 7, no. 13, pp. 1701-1704, 2014. https://doi.org/10.14778/2733004.2733065
  27. Y. Zheng, L. Zhang, X. Xie and W. Ma, "Mining interesting locations and travel sequences from GPS trajectories," in Proc. of the 18th International Conference on World Wide Web, 2009.
  28. K. Zheng, Y. Zheng, N. J. Yuan and S. Shang, "On discovery of gathering patterns from trajectories," in Proc. of the 29th International Conference on Data Engineering (ICDE), 2013.
  29. M. Lv, L. Chen and G. Chen, "Mining user similarity based on routine activities," Information Sciences, vol. 236, pp. 17-32, 2013. https://doi.org/10.1016/j.ins.2013.02.050
  30. L. Barkhuus, B. Brown, M. Bell, S. Sherwood, M. Hall and M. Chalmers, "From awareness to repartee: sharing location within social groups," in Proc. of the SIGCHI Conference on Human Factors in Computing Systems, 2008.
  31. V. Panthi, D. P. Mohapatra, "A framework for generating prioritised test scenarios using firefly optimisation technique," International Journal of Computing Science and Mathematics, vol. 8, no. 3, pp. 228-237, 2017. https://doi.org/10.1504/IJCSM.2017.085724
  32. Z. Cui, B. Sun, G. Wang, Y. Xue and J. Chen, "A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber--physical systems," Journal of Parallel and Distributed Computing, vol. 103, pp. 42-52, 2017. https://doi.org/10.1016/j.jpdc.2016.10.011
  33. X. You, Y. Ma and Z. Liu, "An improved artificial bee colony algorithm for solving parameter identification problems," International Journal of Computing Science and Mathematics, vol. 8, no. 6, pp. 570-579, 2017. https://doi.org/10.1504/IJCSM.2017.088971
  34. R. Sivaraj, R. D. Priya, "Bayesian-based parallel ant system for missing value estimation in large databases," International Journal of Bio-Inspired Computation, vol. 9, no. 2, pp. 114-120, 2017. https://doi.org/10.1504/IJBIC.2017.083142
  35. Z. Cui, F. Xue, X. Cai, Y. Cao, G. Wang and J. Chen, "Detection of Malicious Code Variants Based on Deep Learning," IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3187-3196, 2018. https://doi.org/10.1109/TII.2018.2822680
  36. Z. Li, G. Li, Y. Sun, G. Jiang, J. Kong and H. Liu, "Development of articulated robot trajectory planning," International Journal of Computing Science and Mathematics, vol. 8, no. 1, pp. 52-60, 2017. https://doi.org/10.1504/IJCSM.2017.083152
  37. L. M. Torres-Trevi N O, "Let the swarm be: an implicit elitism in swarm intelligence," International Journal of Bio-Inspired Computation, vol. 9, no. 2, pp. 65-76, 2017. https://doi.org/10.1504/IJBIC.2017.083145
  38. S. S. Reddy, B. K. Panigrahi, "Optimal power flow using clustered adaptive teaching learning-based optimization," International Journal of Bio-Inspired Computation, vol. 9, no. 4, pp. 226-234, 2017. https://doi.org/10.1504/IJBIC.2017.084316
  39. Y. Zheng, L. Zhang, Z. Ma, X. Xie and W. Ma, "Recommending friends and locations based on individual location history," ACM Transactions on the Web (TWEB), vol. 5, no. 1, pp. 5, 2011.
  40. J. J. Ying, W. Lee, T. Weng and V. S. Tseng, "Semantic trajectory mining for location prediction," in Proc. of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2011.
  41. J. J. Ying, E. H. Lu, W. Lee, T. Weng and V. S. Tseng, "Mining user similarity from semantic trajectories," in Proc. of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks, 2010.
  42. Y. Zheng, L. Wang, R. Zhang, X. Xie and W. Ma, "GeoLife: Managing and understanding your past life over maps," in Proc. of the 9th International Conference on Mobile Data Management, 2008.