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Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods

Harris Georgiou; Sophia Karagiorgou; Yannis Kontoulis; Nikos Pelekis; Petros Petrou; David Scarlatti; Yannis Theodoridis

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)
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