Visual analysis of uncertainty in trajectories

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

Research output: Contribution to journalConference article

5 Citations (Scopus)

Abstract

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
DOIs
StatePublished - Jan 1 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

Fingerprint

Trajectories
Trajectory
Uncertainty
Map Matching
Visual Analytics
Historical Data
Vision
Measurement errors
Measurement Error
Lens
Mining
Lenses
Encoding
Sampling
Demonstrate

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

@article{96c1891c2b5e44699ea79a7e90513e4e,
title = "Visual analysis of uncertainty in trajectories",
abstract = "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.",
author = "Lu Lu and Nan Cao and Siyuan Liu and Lionel Ni and Xiaoru Yuan and Huamin Qu",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-06608-0_42",
language = "English (US)",
volume = "8443 LNAI",
pages = "509--520",
journal = "Lecture Notes in Computer Science",
issn = "0302-9743",
publisher = "Springer Verlag",
number = "PART 1",

}

Visual analysis of uncertainty in trajectories. / Lu, Lu; Cao, Nan; Liu, Siyuan; Ni, Lionel; Yuan, Xiaoru; Qu, Huamin.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8443 LNAI, No. PART 1, 01.01.2014, p. 509-520.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Visual analysis of uncertainty in trajectories

AU - Lu, Lu

AU - Cao, Nan

AU - Liu, Siyuan

AU - Ni, Lionel

AU - Yuan, Xiaoru

AU - Qu, Huamin

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84901248504&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901248504&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-06608-0_42

DO - 10.1007/978-3-319-06608-0_42

M3 - Conference article

AN - SCOPUS:84901248504

VL - 8443 LNAI

SP - 509

EP - 520

JO - Lecture Notes in Computer Science

JF - Lecture Notes in Computer Science

SN - 0302-9743

IS - PART 1

ER -