10.5281/zenodo.1309181
https://zenodo.org/records/1309181
oai:zenodo.org:1309181
Harris Georgiou
Harris Georgiou
0000-0003-3462-0745
University of Piraeus, Greece
Sophia Karagiorgou
Sophia Karagiorgou
University of Piraeus, Greece
Yannis Kontoulis
Yannis Kontoulis
University of Piraeus, Greece
Nikos Pelekis
Nikos Pelekis
University of Piraeus, Greece
Petros Petrou
Petros Petrou
University of Piraeus, Greece
David Scarlatti
David Scarlatti
Boeing Research & Technology Europe, Spain
Yannis Theodoridis
Yannis Theodoridis
University of Piraeus, Greece
Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods
Zenodo
2018
mobility data
moving object trajectories
trajectory prediction
future location prediction
moving object analytics
2018-07-11
eng
Technical note
10.5281/zenodo.1309180
https://zenodo.org/communities/eu
1.0
Creative Commons Attribution 4.0 International
The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecasting of forthcoming positions). This paper focuses on predictive analytics for moving objects (could be pedestrians, cars, vessels, planes, animals, etc.) and surveys the state-of-the-art in the context of future location and trajectory prediction. We provide an extensive review of over 50 works, also proposing a novel taxonomy of predictive algorithms over moving objects. We also list the properties of several real datasets used in the past for validation purposes of those works and, motivated by this, we discuss challenges that arise in the transition from conventional to Big Data applications.
This work was partially supported by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreements No 687591 (datACRON), No 699299 (DART), No 777695 (MASTER), and No 780754 (Track & Know). Yannis Kontoulis acknowledges a grant received by the State Scholarships Foundation of Greece.
This work is licensed under Creative Commons Attribution Non-Commercial Non-Derivatives, v4.0/intl. (CC-BY-NC-ND) http://creativecommons.org/licenses/by-nc-nd/4.0/
European Commission
10.13039/501100000780
687591
Big Data Analytics for Time Critical Mobility Forecasting
European Commission
10.13039/501100000780
699299
Data-driven AiRcraft Trajectory prediction research