Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2019

Abstract

We investigate the problem of predicting the future location of mobile objects, such as vehicles, ships, or people, in real time with a high degree of accuracy. Our premise is that an effective combination of recent and long-term historical data can significantly improve prediction performance by enabling a representation of the patterns-of-life of the objects. However, finding a feature representation that captures the long-term observed history of the objects in a compact yet informative manner is a key challenge in data mining and machine learning. To this end, we propose to combine “micro” features, which capture recent fine-grained trends, with “macro” features, designed to summarize the long-term history in a compact yet meaningful manner. Through extensive empirical studies on marine vessels and people movement data, we evaluate the impact of using both micro and macro features on the learned model and demonstrate that the proposed approach significantly enhances predictive accuracy.

Keywords

Movement prediction, Pattern of life, Spatiotemporal data

Discipline

Numerical Analysis and Scientific Computing

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019, Beijing, China, November 8-11

First Page

587

Last Page

594

ISBN

9781728146034

Identifier

10.1109/ICDMW.2019.00089

Publisher

IEEE Computer Society

City or Country

Washington, DC

Additional URL

https://doi.org/10.1109/ICDMW.2019.00089

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