Search

Predictability in the aviation system affects costs to airlines and passengers. We propose a predictability metric based on a flight's gate-in fuel (GIF) which can be directly measured and monetized by aviation stakeholders. We estimate GIF for six major U.S. airlines. Since GIF data are not directly available, we develop an estimation methodology to obtain GIF from pushback weight and fuel burn, including a conversion from passenger to weight payload based on an econometric model. The methodology accounts for aircraft operating empty weight and payload. We find that GIF varies across airlines and time of year, and is highest during the summer period. We monetize GIF through a cost-to-carry analysis as extra fuel loading results in additional fuel burn. Our estimates reveal that, in 2012, airlines spent an additional $59 million to $667 million on carrying GIF, with a total across all six airlines of $1.46 billion.

Air traffic managers face challenging decisions due to uncertainity in weather and air traffic. One way to support their decisions is to identify similar historical days, the traffic management actions taken on those days, and the resulting outcomes. We develop similarity measures based on quarter-hourly capacity and demand data at four case study airports — EWR, SFO, ORD and JFK. We find that dimensionality reduction is feasible for capacity data, and base similarity on principal components. Dimensionality reduction cannot be efficiently performed on demand data, consequently similarity is based on original data. We find that both capacity and demand data lack natural clusters and propose a continuous similarity measure. Finally, we estimate overall capacity and demand similarities, which are visualized using Metric Multidimensional Scaling plots. We observe that most days with air traffic management activity are similar to certain other days, validating the potential of this approach for decision support.

In this paper, we perform a segment-level analysis of the structural differences in average gauge in the US and Europe and assess their impact on traffic and delay at major US airports. The results show that, while average gauge is consistently greater in Europe, the magnitude of the difference is larger for low density and longer segments. The 30% greater traffic at major US airports as compared to those in Europe is to the result of the structural difference in the average gauge. Flight delay at major US airports would decrease by an average of 5–6 min if the gauge relationship were governed by the European gauge relationship.

Multivariate statistical analysis is used to estimate the impacts of a major capacity expansion at Dallas-Fort Worth International Airport. The model relates the Daily Flight Time Index (DFTI) to demand, weather, origin airport congestion, and the expansion itself. The effect of the capacity expansion on DFTI is found to depend strongly on visibility. On average, the index in the post-expansion period is 1.3 min less as a consequence of investment. This change includes a larger reduction in departure delay that is offset by an increase in taxi time. Moreover, the reduction caused by the expansion has been more than offset by increases in the DFTI resulting from other factors, notably increased demand and worse weather.

We analyze runway delay externalities at Los Angeles International Airport (LAX) using a deterministic queuing model. The model allows us to estimate the delay impact of each specific arriving flight on each other specific arriving flight. We find that, despite being only moderately congested (average queuing delay only 4 min per arriving flight), individual flights can generate as much as 3 aircraft-hours of external delay impact on other flights, with an average impact of 26 aircraft-minutes and 3400 seat-minutes. About 90 percent of this impact is external to the airline as well as the flight, a consequence of the lack of a dominant airline at LAX. We then compare the delay impact of each individual flight to its contribution to schedule convenience by determining the amount of “schedule delay” that would result if the flight were eliminated and its passengers forced to use the previous flight flown by the same airline from the same origin. We find that a number of commuter flights serving high density, short-haul segments generate much more queuing delay than they save in schedule delay, with the ratio exceeding 10 in several cases. We argue that social welfare would increase if such flights were eliminated, upsizing others as necessary to accommodate the displaced loads.