Predictive task monitoring: Processing flight events to foresee diversions

Beschreibung

Timely identifying flight diversions is a crucial aspect of efficient multi-modal transportation. When an aeroplane 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 behaviour without knowing the planned flight trajectory. Our work addresses this challenge by introducing a prediction model that processes events bearing only information on an aeroplane's position, velocity, and intended destination. This information is used to distinguish between regular and anomalous behaviour. When an aeroplane displays anomalous behaviour for an extended period of time, the model predicts a diversion. A quantitative evaluation shows that this approach is able to detect diverting aeroplanes with excellent precision and recall even without knowing planned trajectories as required by related research. By utilising the proposed prediction model, logistics companies gain a significant amount of response time for these cases.

Zeitraum	23 Aug. 2016
Ereignistitel	Dagstuhl Seminar 16341
Veranstaltungstyp	Keine Angaben