On-ground aircraft trajectory information plays a key role in airport situations awareness prediction and management. Airport administration needs to arrange and schedule the time and order of aircraft landing and take-off events based on a precise and real-time information of on-ground aircraft. Recently, a large dataset of GPS-derived aircraft at airports, available from the Federal Aviation Administration (FAA), provides researchers with an opportunity to monitoring on-ground aircraft trajectory. In this paper, we present a framework to incrementally cluster airport aircraft trajectories based on the GPS data. The framework consists of two steps: 1) Classifying airport aircraft data according to spatial and temporal information. 2) Merging the similar aircraft trajectories incrementally. We evaluate our framework experimentally using a state-of-the-art test-bed technique, and find that it can effectively and efficiently construct and update on-ground aircraft trajectory map. Index Terms—On-ground aircraft, Spatio-temporal data, Trajectory clustering, situation awareness
@inproceedings{DBLP:conf/percom/0006SCQMF19,
author = {Wei Shao and
Flora D. Salim and
Jeffrey Chan and
Kyle Kai Qin and
Jiaman Ma and
Bradley Feest},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/conf/percom/0006SCQMF19.bib},
booktitle = {2019 IEEE International Conference on Pervasive Computing and Communications,
PerCom, Kyoto, Japan, March 11-15, 2019},
doi = {10.1109/PERCOM.2019.8767400},
pages = {192--201},
publisher = {IEEE},
timestamp = {Fri, 05 Jun 2020 01:00:00 +0200},
title = {OnlineAirTrajClus: An Online Aircraft Trajectory Clustering for Tarmac
Situation Awareness},
url = {https://doi.org/10.1109/PERCOM.2019.8767400},
year = {2019}
}
© 2021 Flora Salim - CRUISE Research Group.