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Detecting flight trajectory anomalies and predicting diversions in freight transportation
Di Ciccio, Claudio
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Aa, Han van der
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Cabanillas, Cristina
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Mendling, Jan
;
Prescher, Johannes
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DOI:
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https://doi.org/10.1016/j.dss.2016.05.004
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URL:
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https://www.sciencedirect.com/science/article/pii/...
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Weitere URL:
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https://www.researchgate.net/publication/323218924...
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Dokumenttyp:
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Zeitschriftenartikel
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Erscheinungsjahr:
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2016
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Titel einer Zeitschrift oder einer Reihe:
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Decision Support Systems : DSS
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Band/Volume:
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88
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Seitenbereich:
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1-17
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Ort der Veröffentlichung:
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Amsterdam [u.a.]
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Verlag:
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Elsevier
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ISSN:
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0167-9236 , 1873-5797
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Artificial Intelligence Methods (Juniorprofessur) (van der Aa 2020-)
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Fachgebiet:
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004 Informatik
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Abstract:
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Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an airplane diverts, logistics providers must promptly adapt their transportation plans in order to ensure proper delivery despite such an unexpected event. In practice, the different parties in a logistics chain do not exchange real-time information related to flights. This calls for a means to detect diversions that just requires publicly available data, thus being independent of the communication between different parties.
The dependence on public data results in a challenge to detect anomalous behavior without knowing the planned flight trajectory.
Our work addresses this challenge by introducing a prediction model that just requires information on an airplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behavior.
When an airplane displays anomalous behavior for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting airplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilizing the proposed prediction model, logistics companies gain a significant amount of response time for these cases.
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 | Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation. |
 Suche Autoren in
BASE:
Di Ciccio, Claudio
;
Aa, Han van der
;
Cabanillas, Cristina
;
Mendling, Jan
;
Prescher, Johannes
Google Scholar:
Di Ciccio, Claudio
;
Aa, Han van der
;
Cabanillas, Cristina
;
Mendling, Jan
;
Prescher, Johannes
ORCID:
Di Ciccio, Claudio, Aa, Han van der  ORCID: https://orcid.org/0000-0002-4200-4937, Cabanillas, Cristina, Mendling, Jan and Prescher, Johannes
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