OnlineAirTrajClus: An Online Aircraft Trajectory Clustering for Tarmac Situation Awareness

Publication Year: 2019 Publication Type : ConferenceProceeding


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


    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,},
    biburl = {},
    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 = {},
    year = {2019}


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