Combining Event Processing and Support Vector Machines for Automated Flight Diversion Predictions

In this work, the research is focused on transportation processes involving aircrafts. In particular, the objective is to design and realise a service-oriented software architecture allowing for the run-time automated detection of aircrafts’ diversions. A diversion consists in the landing of the aircraft in an airport that differs from the planned one. Though rare, diversions can seriously prejudice the successful completion of the transportation process. In the example, adverse weather conditions in the area of Schiphol impose the pilot to make the aircraft land in Brussels. Therefore, the LSP must reroute the truck from Schiphol to the Belgian airport to let goods be delivered to the final destination. In order for these corrective actions to be effective, it is crucial that the LSP is aware of the aircraft diversion as soon as possible. Unfortunately, experience reveals that the communication between LSPs and cargo airlines are not as prompt as required. Specifically, LSPs do not have access to real-time information and are only notified of the diversion once the aircraft has landed at another airport. This delayed notification threatens the ability of LSPs to meet their objectives. For this reason, the approach presented here sets out to reduce the impact of diversions by detecting them in a timely manner, i.e., as soon as an anomalous behaviour is recognised, while the aicraft is still flying. This approach utilises data that are publicly available, i.e., event streams reporting subsequent flight positions, altitude and speed. Thus, it is independent of the communication with airlines.