Equitable and Efficient Coordination in Traffic Flow Management

Abstract

W hen air traffic demand is projected to exceed capacity, the Federal Aviation Administration implements traffic flow management (TFM) programs. Independently, these programs maintain a first-scheduled, firstserved invariant, which is the accepted standard of fairness within the industry. Coordinating conflicting programs requires a careful balance between equity and efficiency. In our work, we first develop a fairness metric to measure deviation from first-scheduled, first-served in the presence of conflicts. Next, we develop an integer programming formulation that attempts to directly minimize this metric. We further develop an exponential penalty approach and show that its computational performance is far superior and its tradeoff between delay and fairness compares favorably. In our results, we demonstrate the effectiveness of these models using historical and hypothetical scenarios. Additionally, we demonstrate that the exponential penalty approach exhibits exceptional computational performance, implying practical viability. Our results suggest that this approach could lead to system-wide savings on the order of $25 to $50 million per year.

Inequity averse optimization in operational research

“…The model is described as a macroscopic version of a previous model by Bertsimas and Stock Patterson (1998), with a different objective function, which is argued to "spread" the delay in an equitable way across affected flights. Similarly, Barnhart et al (2012) propose integer programming models that are based on the models discussed in Bertsimas and Stock Patterson (1998) and Giovanni, Lorenzo, and Guglielmo (2000). The models assign ground holding delays to flights in a multiresource traffic flow environment that also take equity in delay distribution into account.…”

“…This rule assigns the landing slots to unassigned flights on a First Scheduled First Served (FSFS) basis based on the arrival times submitted at the beginning of the daily operations. The studies that use the deviation from the FSFS solution as a measure of inequity (imbalance) in arrival slot allocations are Ball et al (2010), Balakrishnan and Chandran (2010), Barnhart et al (2012) and Glover and Ball (2013). In a queuing system Avi-Itzhak et al (2008) review two such seniority based fairness indicators, where FCFS rule is taken as the most equitable rule and inequity (imbalance) is quantified using measures of deviation from this schedule.…”

“…Lulli and Odoni (2007) use the same idea in an Air Traffic Flow Management model, where an equitable distribution of delay is achieved by using objective function cost coefficients that are a convex function of the tardiness of a flight. Similarly, Barnhart et al (2012) use an exponential delay penalty function. For each flight a worst-case FSFS delay is calculated and each interval delay beyond this worst-case FSFS delay is penalized by an exponentially increasing amount.…”

Evaluating Air Traffic Flow Management in a Collaborative Decision-Making Environment

“…The scenarios we develop in this chapter are similar to the ones developed in (10). The most significant divergence is that we limit the flight schedules to the 20 carriers represented in the Airline Service Quality Performance (ASQP) 2007 data set.…”

“…The percentage of unfair delay is based on the time-order deviation metric developed in (10 To compare the combined airline schedules, we consider flight cancelations, flight delays, and estimated passenger delays. From this stage on, we ignore fairness, because airlines are free to reschedule flight according to their own internal objectives.…”