Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas

  • 1 Citations

Abstract

User trajectories contain a wealth of implicit information. The places that people visit, provide us with information about their preferences and needs. Furthermore, it provides us with information about the popularity of places, for example at which time of the year or day these places are frequently visited. The potential for behavioral analysis of trajectories is widely discussed in literature, but all of these methods need a pre-processing step: the geometric trajectory data needs to be transformed into a semantic collection or sequence of visited points-of-interest that is more suitable for data mining. Especially indoor activities in urban areas are challenging to detect from raw trajectory data. In this paper, we propose a new algorithm for the automated detection of visited points-of-interest. This algorithm extracts the actual visited points-of-interest well, both in terms of precision and recall, even for the challenging urban indoor activity detection. We demonstrate the strength of the algorithm by comparing it to three existing and widely used algorithms, using annotated trajectory data, collected through an experiment with students in the city of Hengelo, The Netherlands. Our algorithm, which combines multiple trajectory pre-processing techniques from existing work with several novel ones, shows significant improvements.
Original languageEnglish
Title of host publicationSAC'16, Proceedings of the 31st ACM Symposium on Applied Computing, ACM SAC 2016
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages552-559
Number of pages8
ISBN (Print)978-1-4503-3739-7
DOIs
StatePublished - 4 Apr 2016
Event31st Annual ACM Symposium on Applied Computing, SAC 2016 - Pisa, Italy

Conference

Conference31st Annual ACM Symposium on Applied Computing, SAC 2016
Abbreviated titleSAC
CountryItaly
CityPisa
Period4/04/168/04/16
Internet address

Fingerprint

trajectory
urban area
data mining
student
experiment

Keywords

  • EWI-26521
  • IR-98159
  • METIS-315068

Cite this

de Graaff, V., de By, R. A., & van Keulen, M. (2016). Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas. In SAC'16, Proceedings of the 31st ACM Symposium on Applied Computing, ACM SAC 2016 (pp. 552-559). New York: Association for Computing Machinery (ACM). DOI: 10.1145/2851613.2851709

de Graaff, V.; de By, R.A.; van Keulen, Maurice / Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas.

SAC'16, Proceedings of the 31st ACM Symposium on Applied Computing, ACM SAC 2016. New York : Association for Computing Machinery (ACM), 2016. p. 552-559.

Research output: Scientific - peer-reviewConference contribution

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de Graaff, V, de By, RA & van Keulen, M 2016, Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas. in SAC'16, Proceedings of the 31st ACM Symposium on Applied Computing, ACM SAC 2016. Association for Computing Machinery (ACM), New York, pp. 552-559, 31st Annual ACM Symposium on Applied Computing, SAC 2016, Pisa, Italy, 4-8 April. DOI: 10.1145/2851613.2851709

Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas. / de Graaff, V.; de By, R.A.; van Keulen, Maurice.

SAC'16, Proceedings of the 31st ACM Symposium on Applied Computing, ACM SAC 2016. New York : Association for Computing Machinery (ACM), 2016. p. 552-559.

Research output: Scientific - peer-reviewConference contribution

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de Graaff V, de By RA, van Keulen M. Automated semantic trajectory annotation with indoor point-of-interest visits in urban areas. In SAC'16, Proceedings of the 31st ACM Symposium on Applied Computing, ACM SAC 2016. New York: Association for Computing Machinery (ACM). 2016. p. 552-559. Available from, DOI: 10.1145/2851613.2851709