Detecting flight trajectory anomalies and predicting diversions in freight transportation


Di Ciccio, Claudio ; Aa, Han van der ; Cabanillas, Cristina ; Mendling, Jan ; Prescher, Johannes



DOI: https://doi.org/10.1016/j.dss.2016.05.004
URL: https://www.sciencedirect.com/science/article/pii/...
Additional URL: https://www.researchgate.net/publication/323218924...
Document Type: Article
Year of publication: 2016
The title of a journal, publication series: Decision Support Systems : DSS
Volume: 88
Page range: 1-17
Place of publication: Amsterdam [u.a.]
Publishing house: Elsevier
ISSN: 0167-9236 , 1873-5797
Publication language: English
Institution: School of Business Informatics and Mathematics > Methoden der künstlichen Intelligenz (Juniorprofessur) (van der Aa 2020-)
Subject: 004 Computer science, internet
Abstract: 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.

Dieser Datensatz wurde nicht während einer Tätigkeit an der Universität Mannheim veröffentlicht, dies ist eine Externe Publikation.




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