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.
@inproceedings{shao2019onlineairtrajclus,
author = {Shao, Wei and Salim, Flora D and Chan, Jeffrey and Qin, Kai and Ma, Jiaman and Feest, Bradley},
booktitle = {2019 IEEE International Conference on Pervasive Computing and Communications (PerCom},
organization = {IEEE},
pages = {192--201},
title = {OnlineAirTrajClus: An Online Aircraft Trajectory Clustering for Tarmac Situation Awareness},
year = {2019}
}
© 2021 Flora Salim - CRUISE Research Group.