A system for destination and future route prediction based on trajectory mining

Pervasive and Mobile Computing

Volumn 6, Issue 6, 2010, Pages 657-676

  Chen, Ling ^a   Lv, Mingqi ^a   Chen, Gencai ^a  

^a ZHEJIANG UNIVERSITY   (China)

Author keywords

Data mining; Destination; Location aware computing; Prediction; Route

Indexed keywords

ABSTRACTING; BEHAVIORAL RESEARCH; CLUSTERING ALGORITHMS; FORECASTING; LOCATION; TRAJECTORIES;

CLUSTERING-BASED ALGORITHMS; DESTINATION; LOCATION-AWARE COMPUTING; ORIGIN DESTINATION; PERVASIVE APPLICATIONS; ROUTE; SPACE PARTITIONING; TRAJECTORY MININGS;

DATA MINING;

EID: 77958490542     PISSN: 15741192     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pmcj.2010.08.004     Document Type: Conference Paper

References (31)

1. M.C. Gonzlez, C.A. Hidalgo, and A.L. Barabsi Understanding individual human mobility patterns Nature 453 7196 2008 779 782
2. J. Hightower, and G. Borriello Location systems for ubiquitous computing Computer 34 8 2001 57 66
3. M. Beigl MemoClip: a location-based remembrance appliance Personal and Ubiquitous Computing 4 4 2000 230 233
4. P.J. Ludford, D. Frankowski, K. Reily, K. Wilms, L. Terveen, Because I carry my cell phone anyway: functional location-based reminder applications, in: Proc. of SIGCHI Conference on Human Factors in Computing Systems, 2006, pp. 889898.
5. L. Chen, S. Benford, Designing planned route based interactions for context-aware applications, in: Proc. of International Conference on Mobile Technology, Applications, and Systems, 2007, pp. 628631.
6. J. Chung, C. Schmandt, Going my way: a user-aware route planner, in: Proc. of International Conference on Human Factors in Computing Systems, 2009, pp. 18991902.
7. J. Froehlich, J. Krumm, Route prediction from trip observations, in: Proc. of Society of Automotive Engineers World Congress, 2008.
8. R. Simmons, B. Browning, Y. Zhang, V. Sadekar, Learning to predict driver route and destination intent, in: Proc. of Intelligent Transportation Systems Conference, 2006, pp. 127132.
9. J. Krumm, E. Horvitz, Predestination: inferring destinations from partial trajectories, in: Proc. of International Conference on Ubiquitous Computing, 2006.
10. T. Nakata, J. Takeuchi, Mining traffic data from probe-car system for travel time prediction, in: Proc. of International Conference on Knowledge Discovery and Data Mining, 2004, pp. 817822.
11. A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti, WhereNext: a location predictor on trajectory pattern mining, in: Proc. of International Conference on Knowledge Discovery and Data Mining, 2009, pp. 637646.
12. D. Tiesyte, C.S. Jensen, Similarity-based prediction of travel times for vehicles traveling on known routes, in: Proc. of International Conference on Advances in Geographic Information Systems, 2008.
13. H.A. Karimi, X. Liu, A predictive location model for location-based services, in: Proc. of ACM Internation Symposium on Advances in Geographic Information Systems, 2003, pp. 126133.
14. D. Ashbrook, and T. Starner Using GPS to learn significant locations and predict movement across multiple users Personal and Ubiquitous Computing 7 5 2003 275 286
15. M. Ester, H.P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proc. of International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226231.
16. C. Zhou, D. Frankowski, P. Ludford, S. Shekhar, and L. Terveen Discovering personally meaningful places: a interactive clustering approach ACM Transaction on Information Systems 25 3 2007
17. A.T. Palma, V. Bogorny, B. Kuijpers, L.O. Alvares, A clustering-based approach for discovering interesting places in trajectories, in: Proc. of ACM Symposium on Applied Computing, 2008, pp. 863868.
18. R. Hariharan, K. Toyama, Project lachesis: parsing and modeling location histories, in: Proc. of GIScience, 2004, pp. 106124.
19. J.H. Kang, W. Welbourne, B. Stewart, G. Borriello, Extracting places from traces of locations, in: Proc. of ACM International Workshop on Wireless Mobile Applications and Services on WLAN Hotspots, 2004, pp. 110118.
20. N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, D.W. Cheung, Mining, indexing, and querying historical spatiotemporal data, in: Proc. of International Conference on Knowledge Discovery and Data Mining, 2004, pp. 236245.
21. H. Cao, N. Mamoulis, D.W. Cheung, Mining frequent spatio-temporal sequential patterns, in: Proc. of IEEE International Conference on Data Mining, 2005, pp. 8289.
22. M.A. Bayir, M. Demirbas, and N. Eagle Mobility profiler: a framework for discovering mobility profiles of cell phone users Pervasive and Mobile Computing 6 1 2010
23. F. Giannotti, M. Nanni, F. Pinelli, D. Pedreschi, Trajectory pattern mining, in: Proc. of International Conference on Knowledge Discovery and Data Mining, 2007, pp. 330339.
24. G. Gidfalvi, and T.B. Pedersen Mining long, sharable patterns in trajectories of moving objects Geoinformatica 13 1 2009 27 55
25. C.S. Jensen, D. Lin, B.C. Ooi, R. Zhang, Effective density queries on continuously moving objects, in: Proc. of International Conference on Data Engineering, 2006.
26. H. Jeung, H.T. Shen, X. Zhou, Mining trajectory patterns using hidden Markov models, in: Proc. of International Conference on Warehousing and Knowledge Discovery, 2007, pp. 470480.
27. Q. Ye, L. Chen, and G.C. Chen Predict personal continuous route Journal of Zhejiang University 10 2009 221 231
28. G. Castelli, M. Mamei, A. Rosi, and F. Zambonelli Extracting high-level information from location data: the W4 diary example Mobile Networks and Applications 14 1 2009 107 119
29. L. Liao, D. Fox, and H. Kautz Extracting places and activities from GPS traces using hierarchical conditional random fields International Journal of Robotics Research 26 1 2007 119 134
30. J. Pei, J. Han, B. Mortazavi-Asl, and H. Pinto Mining sequential patterns by pattern-growth: the PrefixSpan approach IEEE Transactions on Knowledge and Data Engineering 16 11 2004 1424 1440 (Pubitemid 40242330)
31. D.P. Huttenlocher, G.A. Klanderman, and W.A. Rucklidge Comparing images using the Hausdorff distance IEEE Transactions on Pattern Analysis and Machine Intelligence 15 9 1993 850 863