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Online route prediction based on clustering of meaningful velocity-change areas

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Abstract

Personal route prediction has emerged as an important topic within the mobility mining domain. In this context, many proposals apply an off-line learning process before being able to run the on-line prediction algorithm. The present work introduces a novel framework that integrates the route learning and the prediction algorithm in an on-line manner. By means of a thin-client and server architecture, it also puts forward a new concept for route abstraction based on the detection of spatial regions where certain velocity features of routes frequently change. The proposal is evaluated by real-world and synthetic datasets and compared with a well-established mechanism by exhibiting quite promising results.

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Notes

  1. In the present work, we equally use the terms route or trajectory to refer to this continuous movement of a person.

References

  • Alvarez-Garcia J, Ortega J, Gonzalez-Abril L, Velasco F (2010) Trip destination prediction based on past GPS log using a Hidden Markov Model. Expert Syst. Appl. 37(12):8166–8171. doi:10.1016/j.eswa.2010.05.070

    Article  Google Scholar 

  • Barouni F, Moulin B (2012) An extended complex event processing engine to qualitatively determine spatiotemporal patterns. In: Proceedings of Global Geospatial Conference 2012, Quebec City, pp 201–2133

  • Carroll A, Heiser G (2010) An analysis of power consumption in a smartphone. In: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference, USENIX Association, Boston, MA, USENIXATC’10, pp 21–21, http://dl.acm.org/citation.cfm?id=1855840.1855861

  • Chen L, Lv M, Ye Q, Chen G, Woodward J (2011) A personal route prediction system based on trajectory data mining. Inf. Sci. 181(7):1264–1284, doi:10.1016/j.ins.2010.11.035

  • Civilis A, Jensen CS, Pakalnis S (2005) Techniques for efficient road-network-based tracking of moving objects. IEEE Trans Knowl Data Eng 17(5):698–712

    Article  Google Scholar 

  • Deguchi Y, Kuroda K, Shouji M, Kawabe T (2004) HEV charge/discharge control system based on navigation information. Technical report, SAE Technical Paper

  • de Vries G (2012) Kernel methods for vessel trajectories. PhD thesis, University of Amsterdam

  • Dunkel J, Bruns R, Stipkovic S (2013) Event-based smartphone sensor processing for ambient assisted living. In: 2013 IEEE Eleventh international symposium on autonomous decentralized systems (ISADS), pp 1–6. doi:10.1109/ISADS.2013.6513422

  • Etzion O, Niblett P (2010) Event processing in action, 1st edn. Manning Publications Co., Greenwich

    Google Scholar 

  • Giannotti F, Nanni M, Pinelli F, Pedreschi D (2007) Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, KDD 07. ACM, New York, pp 330–339. doi:10.1145/1281192.1281230

  • Guttman A (1984) R-trees: A dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM SIGMOD international conference on management of data, SIGMOD ’84. ACM, New York, pp 47–57. doi:10.1145/602259.602266

  • He W, Li D, Zhang T, An L, Guo M, Chen G (2012) Mining regular routes from gps data for ridesharing recommendations. In: Proceedings of the ACM SIGKDD international workshop on urban computing, UrbComp ’12. ACM, New York, pp 79–86. doi:10.1145/2346496.2346510

  • Hendawi AM, Mokbel MF (2012) Predictive spatio-temporal queries: a comprehensive survey and future directions. In: Proceedings of the First ACM SIGSPATIAL international workshop on mobile geographic information systems, MobiGIS ’12. ACM, New York, pp 97–104. doi:10.1145/2442810.2442828

  • Hong K, Lillethun D, Ramachandran U, Ottenwälder B, Koldehofe B (2013) Opportunistic spatio-temporal event processing for mobile situation awareness. In: Proceedings of the 7th ACM international conference on distributed event-based systems, DEBS ’13. ACM, New York, pp 195–206. doi:10.1145/2488222.2488266

  • Jeung H, Shen H, Zhou X (2007) Mining trajectory patterns using Hidden Markov Models. In: Song I, Eder J, Nguyen T (eds) Data warehousing and knowledge discovery. Lecture Notes in Computer Science, vol 4654. Springer, Berlin, pp 470–480

  • Körner C, May M, Wrobel S (2012) Spatiotemporal modeling and analysis-introduction and overview. KI - Künstliche Intell 26(3):215–221. doi:10.1007/s13218-012-0215-2

    Article  Google Scholar 

  • Krumm J (2006) Real time destination prediction based on efficient routes. Technival Report, SAE Technical Paper

  • Krumm J (2010) Where will they turn: predicting turn proportions at intersections. Pers. Ubiquitous Comput. 14(7):591–599. doi:10.1007/s00779-009-0248-1

    Article  Google Scholar 

  • Krumm J, Gruen R, Delling D (2013) From destination prediction to route prediction. J Locat Based Serv 7(2):98–120. doi:10.1080/17489725.2013.788228

    Article  Google Scholar 

  • Lin M, Hsu WJ (2014) Mining GPS data for mobility patterns: a survey. Pervasive Mobile Comput 12(0):1–16. doi:10.1016/j.pmcj.2013.06.005

  • Lin M, Hsu WJ, Lee ZQ (2012) Predictability of individuals’ mobility with high-resolution positioning data. In: Proceedings of the 2012 ACM conference on ubiquitous computing, UbiComp ’12. ACM, New York, pp 381–390. doi:10.1145/2370216.2370274

  • Liou SC, Huang YM (2005) Trajectory predictions in mobile networks. Int J Inf Technol 11(11):109–122

    Google Scholar 

  • Pappalardo L, Simini F, Rinzivillo S, Pedreschi D, Giannotti F, Barabási AL (2015) Returners and explorers dichotomy in human mobility. Nat Commun 6

  • Pham N, Ganti R, Uddin Y, Nath S, Abdelzaher T (2010) Privacy-preserving reconstruction of multidimensional data maps in vehicular participatory sensing. In: Silva J, Krishnamachari B, Boavida F (eds) Wireless sensor networks. Lecture Notes in Computer Science, vol 5970. Springer, Berlin, pp 114–130

  • Qiu D, Papotti P, Blanco L (2013) Future locations prediction with uncertain data. In: Blockeel H, Kersting K, Nijssen S, Železnỳ F (eds) Machine learning and knowledge discovery in databases. Lecture Notes in Computer Science, vol 8188. Springer, Berlin, pp 417–432

  • Song C, Qu Z, Blumm N, Barabási AL (2010) Limits of predictability in human mobility. Science 327(5968):1018–1021

    Article  MathSciNet  MATH  Google Scholar 

  • Steinfeld A, Manes D, Green P, Hunter D (1996) Destination entry and retrieval with the ali-scout navigation system. Technical Report UMTRI-96-30. University of Michigan, Transportation Research Institute (UMTRI)

  • Stipkovic S, Bruns R, Dunkel J (2013) Pervasive computing by mobile complex event processing. In: 2013 IEEE 10th international conference on e-business engineering (ICEBE), pp 318–323. doi:10.1109/ICEBE.2013.49

  • Terroso-Saenz F, Valdes-Vela M, Campuzano F, Botia JA, Skarmeta-Gómez AF (2015) A complex event processing approach to perceive the vehicular context. Inf Fusion 21(0):187–209. doi:10.1016/j.inffus.2012.08.008

  • Wang L, Hu K, Ku T, Yan X (2013) Mining frequent trajectory pattern based on vague space partition. Knowl Based Syst 50(0):100–111, doi:10.1016/j.knosys.2013.06.002

  • Xue A, Zhang R, Zheng Y, Xie X, Huang J, Xu Z (2013) 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. doi:10.1109/ICDE.2013.6544830

  • Zhang J, Goodchild MF (2002) Uncertainty in geographical information. Taylor & Francis, London

    Book  Google Scholar 

  • Zheng Y, Xie X, Ma WY (2010) Geolife: a collaborative social networking service among user, location and trajectory. IEEE Data Eng Bull 33(2):32–39

    Google Scholar 

  • Zhou C, Frankowski D, Ludford P, Shekhar S, Terveen L (2004) Discovering personal gazetteers: an interactive clustering approach. In: Proceedings of the 12th annual ACM international workshop on Geographic information systems. ACM, pp 266–273

  • Zhou J, Tung AK, Wu W, Ng WS (2013) A “semi-lazy” approach to probabilistic path prediction in dynamic environments. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’13. ACM, New York, pp 748–756. doi:10.1145/2487575.2487609

  • Ziebart BD, Maas AL, Dey AK, Bagnell JA (2008) Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior. In: Proceedings of the 10th international conference on ubiquitous computing, UbiComp ’08. ACM, New York, pp 322–331. doi:10.1145/1409635.1409678

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Acknowledgments

This research is partially funded by the Spanish Ministry of Economy and Competitiveness’ project “Dynamic and Emergent intelligent for Smart Cities based on Internet of Things” TIN2014-52099-R and the European Commission through the ENTROPY-649849 EU Project.

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Computer Science, University of Murcia, Campus de Espinardo S/N, 30100, Murcia, Spain

    Fernando Terroso-Saenz, Mercedes Valdes-Vela & Antonio F. Skarmeta-Gomez

Authors
  1. Fernando Terroso-Saenz
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  2. Mercedes Valdes-Vela
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  3. Antonio F. Skarmeta-Gomez
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Corresponding author

Correspondence to Fernando Terroso-Saenz.

Additional information

Responsible editor: Pierre Baldi.

Appendices

Appendix 1: Event-based rules

Broadly speaking, event-processing rules usually comprises two different parts, (1) a condition part where the requirements for the rule to fire are listed and (2) an action part that indicates the actions to be done if the condition part is fulfilled. Hereafter, the rules pseudocode included in PRoPTurn are listed.

figure e

where the -> stands for the followed-by operator.

figure f

where [1:n] stands for a range between 1 and n events.

figure g

where .within defines the time window with no filtered GPS events for the rule to fire.

Appendix 2: Geolife Users’ profiles

# user

Total

Per route

Locations

Routes

Time period

Locations

Time length

1

867,170

2111

2007-07-21 \(\rightarrow \) 2012-06-17

408

22\('\)

2

205,168

982

2008-10-23 \(\rightarrow \) 2009-07-29

208

19\('\)

3

280,256

838

2007-04-12 \(\rightarrow \) 2012-07-27

334

26\('\)

4

180,324

691

2008-10-23 \(\rightarrow \) 2009-07-05

260

26\('\)

5

343,401

559

2008-09-14 \(\rightarrow \) 2009-09-13

614

26\('\)

6

240,135

523

2008-03-01 \(\rightarrow \) 2009-02-17

459

25\('\)

7

175,850

496

2009-01-13 \( \rightarrow \) 2009-07-29

354

22\('\)

8

261,627

450

2008-12-15 \( \rightarrow \) 2009-07-11

581

33\('\)

9

280,076

443

2008-10-30 \( \rightarrow \) 2009-07-04

632

32\('\)

10

116,404

392

2008-04-28 \( \rightarrow \) 2009-09-24

296

23\('\)

11

123,604

390

2007-04-18 \( \rightarrow \) 2011-03-10

316

30\('\)

12

180,034

387

2007-12-07 \( \rightarrow \) 2008-12-15

465

34\('\)

13

74,978

357

2008-10-23 \( \rightarrow \) 2009-07-05

210

21\('\)

14

168,990

324

2008-02-13 \( \rightarrow \) 2009-09-28

521

35\('\)

15

147,514

321

2008-10-20 \( \rightarrow \) 2009-04-17

459

20\('\)

16

157,084

317

2008-04-02 \( \rightarrow \) 2009-02-22

495

28\('\)

17

125,441

312

2007-04-28 \( \rightarrow \) 2009-09-28

402

20\('\)

18

138,703

254

2008-07-21 \( \rightarrow \) 2009-09-11

546

40\('\)

19

120,110

247

2008-10-23 \( \rightarrow \) 2009-03-22

486

36\('\)

20

72,677

227

2009-02-11 \( \rightarrow \) 2009-07-12

320

31’\('\)

Total

4259,546

10,606

2007-04-12 \(\rightarrow \) 2012-07-27

418

27\('\)

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Terroso-Saenz, F., Valdes-Vela, M. & Skarmeta-Gomez, A.F. Online route prediction based on clustering of meaningful velocity-change areas. Data Min Knowl Disc 30, 1480–1519 (2016). https://doi.org/10.1007/s10618-016-0452-3

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