Sequential Traffic Flow Optimization with Tactical Flight Control Heuristics

Abstract

A sequential optimization method is applied to manage air traffic flow under uncertainty in airspace capacity and demand. To support its testing, a decision support system is developed by integrating a deterministic integer programming model for assigning delays to aircraft under en route capacity constraints to reactively account for system uncertainties. To reduce computational complexity, the model assigns only departure controls, while a tactical control loop consisting of a shortest path routing algorithm and an airborne holding algorithm refines the strategic plan to keep flights from deviating into capacity constrained airspace. This integrated approach is used to conduct thirty-two, 6-hour fast-time simulation experiments to explore variations in the number and severity of departure controls, tactical reroutes, and airborne holding controls. Three feasible types of traffic flow controls emerged. The first type relied primarily on departure controls and strategic reroutes on the 300 to 400 nmi look-ahead horizon and worked best when rerouting occurred at a frequency of 10 to 15 minutes. The second type generated more tactical reroutes on the 200-300 nmi look-ahead horizon and required little airborne holding or pre-departure control when rerouting occurred at a frequency of 5 minutes. The last type relied heavily on airborne holding controls and infrequent updates to the weather avoidance reroutes. This last type was the least desirable solution due to the impact of its airborne holding on airspace complexity and airspace users.

Integrated Traffic Flow Management Decision Making

“…For the ration-byschedule (RBS) models (rows 2-4), F(t) is ordered by increasing scheduled departure times, and flights are scheduled subject to the airport and sector capacity constraints defined in Eqs. (1)- (3). Note that the greater than signs in Eqs.…”

“…This contour level is being used, because Ref. 4 showed that these are reasonable representations of the regions of airspace that flights typically avoid.…”

“…4 to keep fl ights from deviating into actual and forecasted regions with CWAM 60% deviation probability contours. Weather avoidance reroutes were developed using a grid-based Dijkstra algorithm 60 where link costs were proportional to the link distance and a penalty function was applied when the link intersected a weather hazard.…”

Applications of Formal Languages to Management of Manned and Unmanned Aircraft

“…These approaches don't usually fit the structure described in section 3.1. A flight path minimising the costs of a flight can be computed using dynamic programming [23], genetic algorithms [54], heuristic search algorithms [28], or a combination of both genetic algorithms and heuristic search [90]. Another way of minimising the costs of the flight consists on transforming the trajectory optimization problem into a non-linear programming problem that can be solved using various techniques.…”

“…Some of the most simple representations used in the literature include a two-dimensional (2D) position in the vertical plane [23,24] or in the horizontal plane [25][26][27][28]. The former are useful in the context of optimization in the vertical profile, while the latter can be used for optimizing traffic.…”