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. The work summarized in this extended abstract has been published in [Di16].
Detecting flight trajectory anomalies and predicting diversions in freight transportation (extended abstract) / Di Ciccio, Claudio; van der Aa, Han; Cabanillas, Cristina; Mendling, Jan; Prescher, Johannes. - 1701:(2016), pp. 56-59. (Intervento presentato al convegno 7th International Workshop on Enterprise Modeling and Information Systems Architectures: Fachgruppentreffen der GI-Fachgruppe Entwicklungsmethoden fur Informationssysteme und deren Anwendung, EMISA 2016 - 7th International Workshop on Enterprise Modeling and Information Systems Architectures: Professional Group Meeting of the GI Special Interest Group on Development Methods for Information Systems and their Application, EMISA 2016 tenutosi a Vienna, Austria).
Detecting flight trajectory anomalies and predicting diversions in freight transportation (extended abstract)
Di Ciccio, Claudio
;
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. The work summarized in this extended abstract has been published in [Di16].I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
