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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.

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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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