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

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. 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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Correspondence to Fernando Terroso-Saenz.

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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.

figuree

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

figuref

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

figureg

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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Keywords

  • Route prediction
  • Density-based clustering
  • Mobility mining