Keumjin Lee

Conflict Management in Air Traffic Control Using Complexity Map

This study proposes a new method of conflict management in air traffic control that ensures a safe distance between aircraft while minimizing the workload for sector controllers. When an aircraft approaches a sector, the proper entry point is determined based on the concept of a complexity map. Using detailed information on how the traffic responds to an entering aircraft provided by the complexity map, its entry point can be modified to minimize the conflicts inside the sector. To compute the complexity map efficiently, the reachable entry points on the sector boundary are defined considering the maneuverability of the entering aircraft. Next, the variance quadtree algorithm is utilized to construct a grid of the complexity map in a coarse-to-fine manner. The performance of the proposed algorithm is verified by numerical simulations.

Conflict Management Considering a Smooth Transition of Aircraft Into Adjacent Airspace

A new framework for conflict resolution in air traffic control is studied using air traffic complexity, which ensures conflict avoidance among aircraft and smooth transition of aircraft into adjacent airspace. The air traffic complexity is modeled as deviations in heading angle or speed of the aircraft inside a sector to resolve any conflict induced by the entering aircraft. For conflict management in multisector planning, a two-level hierarchical architecture is proposed. In the higher level, the maneuver constraints of the aircraft are constructed to minimize complexity for the neighboring sectors. In the lower level, the conflict avoidance problem is formulated as a mixed integer linear programming considering the maneuver constraints, which are defined in the higher level. With the two-level hierarchical architecture, the aircraft can find the optimal solution to resolve conflicts among the aircraft and reduce the air traffic complexity of the neighboring sectors. The performance of the proposed conflict management is demonstrated using numerical simulations.

Nonlinear Conflict Resolution and Flow Management Using Particle Swarm Optimization

A new optimization problem to resolve conflicts by allowing aircraft to change both their heading angle and speed is considered. The performance index in the optimization problem is formulated to reduce the variation of the aircraft arrival time that is caused by conflict resolution maneuvers. To achieve both conflict resolution and flow management, metering constraints are introduced together with separation constraints. Even though the performance index and constraints are nonlinear, the optimal solution can be easily obtained by utilizing particle swarm optimization. Numerical simulations are performed to evaluate the performance of the proposed algorithm, and the numerical experimental results showed a significant reduction in the variation of the aircraft arrival time as well as the magnitude of heading angle and speed changes.

Application of Complexity Map to Reduce Air Traffic Complexity in a Sector

A decision making procedure for an aircraft entering a sector is proposed to reduce the degree of air traffic complexity. The entering aircraft determines the proper entry point based on a complexity map to minimize the influence of the entering aircraft on other aircraft inside the sector. The variance quadtree (VQT) algorithm is adopted to make a grid in a coarse-to-fine manner, and therefore the complexity map can be constituted efficiently. In this procedure, considering the maneuver limitation of the entering aircraft, Dubins path is utilized to generate a feasible flight path. The effect of the shortest lookahead distance allowed for the entering aircraft is analyzed using Monte Carlo simulation, and the performance of the proposed algorithm is demonstrated by numerical simulation.

Optimal Scheduling Algorithm for Air Traffic Point Merge System Using MILP

This study proposes an optimal scheduling algorithm for air traffic Point Merge System (PMS) which is a new arrival flow integration technique. Performance index and constraint equations are derived with consideration for the characteristics of PMS. Mixed integer linear programming formulation is used to design arrival procedures conducted in PMS. Through numerical simulations, the effect of the number of points constituting PMS on performance index is investigated, and an example is introduced where the entering or final merging time of a particular flight in PMS is predetermined.

Dynamic Robust Sequencing and Scheduling Under Uncertainty for the Point Merge System in Terminal Airspace

This paper proposes a new sequencing and scheduling algorithm for the point merge system based on mixed integer linear programming considering the uncertain flight time of aircraft. In the first stage of the proposed algorithm, for a static environment, deterministic robust solutions are determined. To consider the uncertainty, an extra buffer is introduced in the sequencing and scheduling algorithm, and the buffer size is analytically derived by generating a deterministic robust counterpart problem. In the second stage, to compensate for unforeseen situations under a dynamic environment, the static solution determined in the first stage is adjusted by using the proposed heuristic algorithm, with a sliding time window to reduce the computational load. The performances of the proposed algorithm are verified via numerical simulations based on historical data analysis.

Probabilistic prediction model of air traffic controllers' sequencing strategy based on pairwise comparisons

Sequencing arrival flights is a major task of air traffic management, and there exist various optimization tools to support the air traffic controllers. It is, however, difficult to employ these tools in the actual operational environments since they lack consideration on the human cognitive process. This paper proposes a new framework to predict the arrival sequences based on a preference learning approach, where we learn the sequence data operated by human controllers. The proposed algorithm works in two-stages: it first learns the pairwise preference functions between arrivals using binomial logistic regression, and then it induces the total sequence for a new set of arrivals by comparing the scores of each aircraft, which are the sums of pairwise preference probabilities. The proposed model is demonstrated with real traffic data at Incheon International Airport and its performance is assessed using the Spearmans rank correlation.

Modeling the Air Traffic Controller’s Direct-to Operation Using Logistic Regression

With the purpose to analyze decisions of air traffic controllers and to make accurate predictions on departure transit time, this paper defines the factors which influence controllers’ direct-to (DCT) operations and constructs related model. Various traffic complexity factors were examined as independent variables using univariate analysis, and the air traffic controllers’ DCT operation was modeled using logistic regression. Results showed that the variation of departure traffic density, the departure and arrival traffic density of nearby airports and the controller’s previous decision on DCT were statistically significant variables. Test flights demonstrated that the formulated model can predict the DCT with 79 percent accuracy. The proposed method is expected to be used in Departure Management (DMAN) and simulation models in the future.

Optimal Airspace Design for Continuous Climb Operation

This paper proposes a new airspace design method for continuous climb operation in the presence of a complex interaction between traffic flows. The mathematical model is formulated as Mixed Integer Linear Programming (MILP) to maximize the upper altitude limit on each waypoint of the departure procedure. In the proposed method, the separations with other departure and arrival traffic flows as well as the climb performance of aircraft are considered. The proposed method is applied to the departure procedure of Incheon International Airport to demonstrate the performance.