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.

Detecting flight trajectory anomalies and predicting diversions in freight transportation / Di Ciccio, C.; van der Aa, H.; Cabanillas, C.; Mendling, J.; Prescher, J.. - In: DECISION SUPPORT SYSTEMS. - ISSN 0167-9236. - 88:(2016), pp. 1-17. [10.1016/j.dss.2016.05.004]

Detecting flight trajectory anomalies and predicting diversions in freight transportation

Di Ciccio C.
;
2016

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.
2016
Air transportation; Aircraft navigation; Airplane trajectory; Logistics; Machine learning; Prediction methods
01 Pubblicazione su rivista::01a Articolo in rivista
Detecting flight trajectory anomalies and predicting diversions in freight transportation / Di Ciccio, C.; van der Aa, H.; Cabanillas, C.; Mendling, J.; Prescher, J.. - In: DECISION SUPPORT SYSTEMS. - ISSN 0167-9236. - 88:(2016), pp. 1-17. [10.1016/j.dss.2016.05.004]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1352725
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