Handling uncertainty in trajectories of moving objects in unconstrained outdoor spaces

Eleazar Leal, Le Gruenwald, Jianting Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

A trajectory is a polygonal line consisting of the positions that a moving object occupies as time passes, and as such, it can be derived by periodically sampling the positions of the object. In this manner, and due to the proliferation of location-sensing devices, it has been possible to create large datasets of trajectories. Using these datasets it is possible to derive much information about the movement patterns of the objects. However, trajectory data is uncertain, and this can negatively impact the accuracy of data mining algorithms used to obtain the movement patterns of objects. One of the sources of trajectory uncertainty is the error inherent to GPS measurements, and another source relates to the fact that in practice many trajectories have sampling points that are on average too far in time, so it is difficult to determine its movement between points. In this paper, we propose a technique called TrajEstU that estimates the trajectory of a moving object in an unconstrained space. The algorithm is applied when there is uncertainty in trajectories due to measurement errors and/or low sampling rates. Experiments show that TrajEstU achieves up to 98% accuracy on real life and synthetic trajectory datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages492-501
Number of pages10
ISBN (Electronic)9781467390040
DOIs
StatePublished - 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: Dec 5 2016Dec 8 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States
CityWashington
Period12/5/1612/8/16

Keywords

  • Trajectory Mining
  • Trajectory Processing
  • Uncertain Trajectories

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