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Geographical Stacking Point Map of Staying Points

  • Jing He
  • , Haonan Chen
  • Tsinghua University
  • China University of Mining & Technology, Beijing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Regardless of the size of the study, the trajectory contains many behaviors of moving objects in dynamic and complex environments. The advent of modern computing and software environments for integrating and manipulating trajectory data has become an important driver of mobile object trajectory research. However, understanding the behavior of moving objects is still an indirect task, as it involves not only the decision process, but also space, time, attribute constraints, and context. Therefore, the development of characterization and computational frameworks that facilitate analysis and exploration of the trajectory state of moving objects is still considered a research challenge. In this paper, we introduce a semantic behavior that expresses the trajectory stop points by the visualization technique of stacked points, in order to find a better ability to predict how trajectories respond to environmental changes and how these responses are related in time and space.

Original languageEnglish
Title of host publication2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-79
Number of pages7
ISBN (Electronic)9781728141114
DOIs
StatePublished - May 2020
Externally publishedYes
Event5th IEEE International Conference on Big Data Analytics, ICBDA 2020 - Xiamen, China
Duration: 8 May 202011 May 2020

Publication series

Name2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020

Conference

Conference5th IEEE International Conference on Big Data Analytics, ICBDA 2020
Country/TerritoryChina
CityXiamen
Period8/05/2011/05/20

Keywords

  • moving object
  • semantic behavior
  • stacking point
  • stay point
  • trajectory data

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