Risk-Hedged Traffic Flow Management Under Airspace Capacity Uncertainties

Abstract

This paper presents a novel solution to the problem of air traffic flow management under airspace capacity uncertainty arising from weather or environmental effects. The traffic flow management problem is formulated as a stochastic linear program with multiple available routes between origin–destination pairs, with the weather/environmental factors constraining the probable capacities along these routes. The performance index consists of the delays introduced by deterministic and stochastic capacity constraints. Primary impact of the weather/environmental factors is to require the rerouting of aircraft, causing additional delays. These additional delays require the aircraft to carry additional fuel or to incur costs associated with the failure to meet the schedules in hub-spoke operations. Thus, the variance of delays can be used to define the degree of risk in stochastic traffic flow management, and flow management algorithms that assure the variations in the delay below a specified value can be considered as providing a hedge against uncertain weather or environmental factors. The algorithm developed in this paper produces a risk-hedged decision that results in the least delay at a specified level of acceptable variance. This algorithm represents a dramatic departure from the more traditional stochastic traffic flow management algorithms, which minimize expected value of delays, without attempting to control their variances. The performance of the present stochastic traffic flow management algorithm is demonstrated on a use case representing National Airspace System operations on a regional scale.