Abstract
Objective: Prediction of moving objects with uncertain motion patterns is emerging rapidly as a new exciting paradigm and is important for law enforcement applications such as criminal tracking analysis. However, existing algorithms for prediction in spatio-temporal databases focus on discovering frequent trajectory patterns from historical data. Moreover, these methods overlook the effect of some important factors, such as speed and moving direction. This lacks generality as moving objects may follow dynamic motion patterns in real life.
Methods: We propose a framework for predicating uncertain trajectories in moving objects databases. Based on Continuous Time Bayesian Networks (CTBNs), we develop a trajectory prediction algorithm, called PutMode (Prediction of uncertain trajectories in Moving objects databases). It comprises three phases: (i) construction of TCTBNs (Trajectory CTBNs) which obey the Markov property and consist of states combined by three important variables including street identifier, speed, and direction; (ii) trajectory clustering for clearing up outlying trajectories; (iii) predicting the motion behaviors of moving objects in order to obtain the possible trajectories based on TCTBNs.
Results: Experimental results show that PutMode can predict the possible motion curves of objects in an accurate and efficient manner in distinct trajectory data sets with an average accuracy higher than 80%. Furthermore, we illustrate the crucial role of trajectory clustering, which provides benefits on prediction time as well as prediction accuracy.
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Xiang Y, Chau M, Atabakhsh H, Chen H (2005) Visualizing criminal relationships: Comparison of a hyperbolic tree and a hierarchical list. Decis Support Syst 41(1):69–83
Zhao JL, Bi HH, Chen H, Zeng DD, Lin C, Chau M, Process-driven collaboration (2006) support for intra-agency crime analysis. Decis Support Syst 41(3):616–633
Kaza S, Wang Y, Chen H (2007) Enhancing border security: Mutual information analysis to identify suspect vehicles. Decis Support Syst 43(1):199–210
Morzy M (2007) Mining frequent trajectories of moving objects for location prediction. In: MLDM’07: Proceedings of the 5th international conference on machine learning and data mining in pattern recognition. Lecture notes in computer science, vol 4571. Springer, Berlin, pp 667–680
Chang W, Zeng D, Chen H (2008) Visualizing criminal relationships: Comparison of a hyperbolic tree and a hierarchical list. Decis Support Syst 45(6):697–713
Trajcevski G, Wolfson O, Zhang F, Chamberlain S (2002) The geometry of uncertainty in moving objects databases. In: EDBT’02: Proceedings of the 8th international conference on extending database technology. Springer, London, pp 233–250
Trajcevski G, Wolfson O, Hinrichs K, Chamberlain S (2004) Managing uncertainty in moving objects databases. ACM Trans Database Syst 29(3):463–507
Tao Y, Faloutsos C, Papadias D, Liu B (2004) Prediction and indexing of moving objects with unknown motion patterns. In: SIGMOD’04: Proceedings of the 2004 ACM SIGMOD international conference on management of data. ACM, New York, pp 611–622
Nodelman U, Shelton CR, Koller D (2003) Learning continuous time Bayesian networks. In: UAI’03: Proceedings of the 19th conference on uncertainty in artificial intelligence. Morgan Kaufmann, San Francisco, pp 451–458
Nodelman U, Shelton C, Koller D (2002) Continuous time Bayesian networks. In: UAI’02: Proceedings of the 18th conference in uncertainty in artificial intelligence. Morgan Kaufmann, San Francisco, pp 378–387
Giannotti F, Nanni M, Pedreschi D (2006) Efficient mining of temporally annotated sequences. In: SDM’06: Proceedings of the 6th SIAM international conference on data mining. SIAM, Bethesda, pp 346–357
Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: KDD’07: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, San Jose, pp 330–339
Mokhtar HMO, Su J (2004) Universal trajectory queries for moving object databases. In: MDM’04: Proceedings of the 2004 IEEE international conference on mobile data management. IEEE Computer Society, Los Alamitos, pp 133–144
Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung DW (2004) Mining, indexing, and querying historical spatiotemporal data. In: KDD’04: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 236–245
Bharucha-Reid AT (1960) Elements of the theory of Markov processes and their applications. McGraw-Hill, New York
Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M (2001) Prefixspan: Mining sequential patterns by prefix-projected growth. In: ICDE’01: Proceedings of the 17th international conference on data engineering, Washington, DC, USA, pp 215–224
Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: SIGMOD’00: Proceedings of the 2000 ACM SIGMOD international conference on management of data. ACM, New York, pp 1–12
Giannotti F, Nanni M, Pedreschi D, Pinelli F (2006) Mining sequences with temporal annotations. In: SAC’06: Proceedings of the 2006 ACM symposium on applied computing. ACM, New York, pp 593–597
Xu JJ, Chen H (2005) Crimenet explorer: a framework for criminal network knowledge discovery. ACM Trans Inf Syst 23(2):201–226
Chen H, Chung W, Xu JJ, Wang G, Qin Y, Chau M (2004) Crime data mining: A general framework and some examples. Computer 37(4):50–56
Qiao S, Tang C, Peng J, Fan H, Xiang Y (2006) Vccm mining: Mining virtual community core members based on gene expression programming. In: Chen H, Wang FY, Yang CC, Chau DZM, Chang K (eds) Proceedings of intelligence and security informatics: International workshop, WISI 2006, 9 April 2006. Lecture notes in computer science, vol 3917. Springer, Berlin, pp 133–138
Qiao S, Tang C, Peng J, Liu W, Wen F, Qiu J (2008) Mining key members of crime networks based on personality trait simulation email analysis system. Chin J Comput 31(10):1795–1803 (in Chinese)
Xu JJ, Chen H (2005) Criminal network analysis and visualization. Commun ACM 48(6):101–107
Qiao S, Tang C, Cheng Y, Peng J, Wen F (2007) A new hierarchical clustering algorithm based on tree edit distance. J Comput Sci Front 1(3):282–292 (in Chinese)
Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD’96: Proceedings of the 2nd international conference on knowledge discovery and data mining. AAAI Press, Pitsburgh, pp 226–231
Gopalratnam K, Kautz HA, Weld DS (2005) Extending continuous time Bayesian networks. In: AAAI’05: Proceedings of the 20th national conference on artificial intelligence. AAAI Press, Pittsburgh, pp 981–986
Roads C (1996) The computer music tutorial. MIT Press, Cambridge
Brinkhoff T (2002) A framework for generating network-based moving objects. Geoinformatica 6(2):153–180
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Qiao, S., Tang, C., Jin, H. et al. PutMode: prediction of uncertain trajectories in moving objects databases. Appl Intell 33, 370–386 (2010). https://doi.org/10.1007/s10489-009-0173-z
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DOI: https://doi.org/10.1007/s10489-009-0173-z