Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
TravelMiner: On the benefit of path-based mobility prediction
KTH, School of Electrical Engineering (EES), Automatic Control.
2016 (English)In: 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, article id 7733023Conference paper, Published paper (Refereed)
Abstract [en]

Mobility predictions are becoming more valuable in various applications with the rise of mobile devices. Given that existing prediction techniques are composed of two key procedures: 1) profiling past mobility trajectories as sequences of discrete atomic states (e.g., grid locations, semantic locations) and capturing them with an appropriate statistical model, 2) making a prediction on the next state using the statistical model, TravelMiner tackles the former with paths utilized as the atomic states for the first time, where the paths are defined as sub-trajectories with no branches. Comparing to available location-based predictors, TravelMiner makes a fundamental difference in that it is able to predict the sequence of paths rather than locations, which is far more detailed in the perspective of knowing the exact route to follow. TravelMiner enables this benefit by extracting disjoint paths from GPS trajectories via a similarity metric for curves, called Frechet distance and keeping the sequences of such paths in a statistical model, called probabilistic radix tree. Our extensive simulations over the GPS trajectories of 124 users reveal that TravelMiner outperforms other predictors in diverse popular performance metrics including predictability, prediction accuracy and prediction resolution.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. article id 7733023
Keywords [en]
Clustering algorithms, Location, Semantics, Trajectories, Extensive simulations, Mobility predictions, Performance metrics, Prediction accuracy, Prediction techniques, Semantic locations, Similarity metrics, Statistical modeling
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-202036DOI: 10.1109/SAHCN.2016.7733023ISI: 000392494000033Scopus ID: 2-s2.0-85000925696ISBN: 9781509017324 (print)OAI: oai:DiVA.org:kth-202036DiVA, id: diva2:1076568
Conference
13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016, 27 June 2016 through 30 June 2016
Note

QC 20170223

Available from: 2017-02-23 Created: 2017-02-23 Last updated: 2017-02-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Jeong, Jaeseong
By organisation
Automatic Control
Other Engineering and Technologies

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 14 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf