Analysis and causality of airport surface delays

Abstract

A prototype departure advisor is under development at the Sensis Corporation, with support from the New York State Energy Research and Development Authority (NYSERDA). The prototype is being developed based on operations data at John F. Kennedy International (JFK) Airport, and depends on surface surveillance data (ramp area, movement area), flight plans, and traffic flow constraints. The prototype will provide recommended departure pushback times for airline ramp managers, and will deliver departure sequences to the movement area in an improved order. Use of the prototype will reduce taxi-out times, fuel burned, and emissions, and improve efficiency of departure runway use. Measurement of the location, duration, and cause of holding on the airport surface by taxiing aircraft is an important part of demonstrating potential benefits from a departure advisor (e.g., reduction in excess fuel burn) and also helps with the development of the prototype itself. As mentioned above, one outcome of the prototype is improved departure sequences, with optimized groups of departures bounded by successive arrivals. Hold categories and algorithms for detection and classification have been developed for arrivals and departures taxiing on the airport surface. Hold categories and algorithms depend on having high-quality surveillance data. Some hold classifications require gate-area surveillance data, such as is available at JFK. The arrival hold categories are: arrival held short of crossing runway and arrival held waiting for departure to clear ramp alleyway. Departure hold categories are: departure queue hold, departure runway pre-roll hold, hold at gate, hold at pushback, and hold at movement area/ramp area ”spot.” Hold categories not specific to operation type (e.g., arrival) are: operation held behind arrival holding short of crossing runway, operation held to merge with or follow taxiing traffic, and operation held to yield to crossing traffic. This paper presents results from the application of the holding algorithms. For example, the material presents statistics on the holding of arrivals short of an active departure runway. Also, examples of departure queue statistics (maximum departure queue depth vs. service time, fraction of time spent in a held position while in departure queue) will be presented. This work extends the state-of-the-art for detection and quantification of aircraft taxi delays on the airport surface by providing some causal attribution (e.g., blocked ramp alleyway). Causality is important to business cases made to support adoption of airport automation technology by an air navigation service provider (ANSP), airline, or airport authority.