Conference paper Open Access

On-the-fly mobility event detection over aircraft trajectories

Kostas Patroumpas; Nikos Pelekis; Yannis Theodoridis

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    <subfield code="a">eng</subfield>
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    <subfield code="a">air traffic management, geostreaming, mobility events, trajectories</subfield>
  <controlfield tag="005">20190107084137.0</controlfield>
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    <subfield code="a">K. Patroumpas, N. Pelekis, and Y. Theodoridis: "On-the-fly Mobility Event Detection over Aircraft Trajectories".  In proceeding of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2018), November 6 -  9, 2018 Seattle, Washington, USA</subfield>
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    <subfield code="u">University of Piraeus, Hellas</subfield>
    <subfield code="a">Nikos Pelekis</subfield>
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    <subfield code="u">University of Piraeus, Hellas</subfield>
    <subfield code="a">Yannis Theodoridis</subfield>
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    <subfield code="s">1768243</subfield>
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    <subfield code="l">open</subfield>
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    <subfield code="c">2018-11-06</subfield>
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    <subfield code="u">IMSI, Athena Research Center, Hellas</subfield>
    <subfield code="a">Kostas Patroumpas</subfield>
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    <subfield code="a">On-the-fly mobility event detection over aircraft trajectories</subfield>
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  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">687591</subfield>
    <subfield code="a">Big Data Analytics for Time Critical Mobility Forecasting</subfield>
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    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
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    <subfield code="a">&lt;p&gt;We present an application framework that consumes streaming positions from a large fleet of flying aircrafts monitored in real time over a wide geographical area. Tailored for aviation surveillance, this online processing scheme only retains locations conveying salient mobility events along each flight, and annotates them as stop, change of speed, heading or&amp;nbsp; altitude, etc. Such evolving trajectory synopses must keep in pace with the incoming raw streams so as to get incrementally annotated with minimal loss in accuracy. We also develop one-pass heuristics to eliminate inherent noise and provide reliable trajectory representations. Our prototype implementation on top of Apache Flink and Kafka has been tested against various real and synthetic datasets offering concrete evidence of its timeliness, scalability, and compression efficiency, with tolerable concessions to the quality of resulting trajectory approximations.&lt;/p&gt;</subfield>
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    <subfield code="a">10.1145/3274895.3274970</subfield>
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