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.

KEYWORDS:

• time geography
• sampling error
• measurement error
• Minkowski distance metric
• reliability

Acknowledgements

We are grateful to the editor Dr Urska Demsar and the anonymous reviewers who provided insightful suggestions on improving this work. We also thank Mrs. Elaine Anson for her assistance on English editing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here.

Data and codes availability statement

The codes that support the findings of this study are available in https://doi.org/10.6084/m9.figshare.11297438.v5. The open datasets used in this study are available in their corresponding links as noted in Section 5.1.

Notes

1. http://research.microsoft.com/en-us/projects/geolife/

2. http://www.irc.atr.jp/crest2010_HRI/ATC_dataset/

3. https://gaia.didichuxing.com

4. https://crawdad.org/coppe-ufrj/RioBuses/20,180,319/

Additional information

Funding

This work was supported by the Ministry of Science and Technology of the People’s Republic of China [2019YFB2103102]; The Innovation and Technology Fund of the Hong Kong Government [ITP/053/16LP].

Notes on contributors

Wenzhong Shi

Wenzhong Shi is the Head and Chair Professor of Geographical Information Science and Remote Sensing in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. His research interests include GIScience and remote sensing, focusing on uncertainties and quality control of spatial data, satellite images and LiDAR data, 3D modelling, and human dynamics.

Pengfei Chen

Pengfei Chen works as an assistant professor in School of Geospatial Engineering and Science at Sun Yat-Sen University. His research interests include human mobility modeling, geospatial artificial intelligence and spatial uncertainty.

Xiaoqi Shen

Xiaoqi Shen is a PhD candidate in School of Environment Science and Spatial Informatics at China University of Mining and Technology. His research interest includes human mobility analysis, geographical data mining, and geospatial artificial intelligence.

Jianxiao Liu

Jianxiao Liu currently is a research assistant in the Department of Land Surveying and Geo-informatics at the Hong Kong Polytechnic University. His research interests include urban and rural planning, human mobility, and geographical data mining.