Paper Info
Title
An Adaptive Approach For Modelling The Movement Uncertainty In Trajectory Data Based On The Concept Of Error Ellipses
Abstract
Modelling movement uncertainty is of profound significance in promoting effective trajectory analysis and mining. However, classic uncertainty models are limited by rigid assumptions on moving speed and distance, which ignores the stochastic nature of individual's travel behaviour. This study introduces a novel method using adaptive ellipses to represent the movement uncertainty in a planar space under the framework of time geography. Two models are established by considering different error sources in trajectory data. The first model captures the uncertainty caused by sampling error, and the second one further, takes the measurement error into account. The Minkowski distance metric is adopted to determine the size of uncertainty ellipses, in which the Minkowski parameter is optimized for each segment in the raw trajectory on the basis of the geometric characteristics extracted. Compared with state-of-the-art methods on five real-life trajectory datasets, the proposed method is proved to produce more effective uncertain regions, which significantly reduce the redundant uncertain area, while retaining at a comparative level of actual movement coverage. Given the heterogeneity of human mobility patterns, this study provides a robust and applicable solution for adaptively modelling individual's movement uncertainty, which is expected to benefit trajectory-related applications in various scenarios.
Year
DOI
Venue
2021
10.1080/13658816.2020.1828591
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
DocType
Volume
time geography, sampling error, measurement error, Minkowski distance metric, reliability
Journal
35
Issue
ISSN
Citations 
6
1365-8816
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Wenzhong Shi177886.23
Pengfei Chen26213.05
Xiaoqi Shen301.01
Jianxiao Liu400.68