Visual analysis of uncertainty in trajectories

Lu Lu, Nan Cao, Siyuan Liu, Lionel Ni, Xiaoru Yuan, Huamin Qu

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations


Mining trajectory datasets has many important applications. Real trajectory data often involve uncertainty due to inadequate sampling rates and measurement errors. For some trajectories, their precise positions cannot be recovered and the exact routes that vehicles traveled cannot be accurately reconstructed. In this paper, we investigate the uncertainty problem in trajectory data and present a visual analytics system to reveal, analyze, and solve the uncertainties associated with trajectory samples. We first propose two novel visual encoding schemes called the road map analyzer and the uncertainty lens for discovering road map errors and visually analyzing the uncertainty in trajectory data respectively. Then, we conduct three case studies to discover the map errors, to address the ambiguity problem in map-matching, and to reconstruct the trajectories with historical data. These case studies demonstrate the capability and effectiveness of our system.

Original languageEnglish (US)
Pages (from-to)509-520
Number of pages12
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8443 LNAI
Issue numberPART 1
StatePublished - 2014
Event18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
Duration: May 13 2014May 16 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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