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Airport surface congestion results in significant increases in taxi times, fuel burn and emissions at major airports. This paper describes the field tests of a congestion control strategy at Boston Logan International Airport. The approach determines a suggested rate to meter pushbacks from the gate, in order to prevent the airport surface from entering congested states and to reduce the time that flights spend with engines on while taxiing to the runway. The field trials demonstrated that significant benefits were achievable through such a strategy: during eight four-hour tests conducted during August and September 2010, fuel use was reduced by an estimated 12,250-14,500 kg (4,000-4,700 US gallons), while aircraft gate pushback times were increased by an average of only 4.4 minutes for the 247 flights that were held at the gate.

The airline industry is moving toward proactive risk management, which aims to identify and mitigate risks before accidents occur. However, existing methods for such efforts are limited. They rely on predefined criteria to identify risks, leaving emergent issues undetected. This paper presents a new method-ClusterAD-Flightwhich can support domain experts in detecting anomalies and associated risks from routine airline operations. The new method, enabled by data from the flight data recorder, applies clustering techniques to detect abnormal flights of unique data patterns. Compared with existing methods, the new method no longer requires predefined criteria or domain knowledge. Tests were conducted using two sets of operational data consisted of 365 B777 flights and 25519 A320 flights. The performance of ClusterAD-Flight was compared with those of Multiple Kernel Anomaly Detection (MKAD), another data-driven anomaly detection algorithm in recent years, and with Exceedance Detection (ED), the current method employed by the airline industry. Results showed that both ClusterAD-Flight and MKAD were able to identify operationally significant anomalies, surpassing the capability of ED. ClusterAD-Flight performed better with continuous parameters, while MKAD was more sensitive towards discrete parameters. Nomenclature v = high-dimensional vector to represent a flight x i j = value of the i th flight parameter at sample time j