Air traffic management in parallel-point merge systems under wind uncertainties

The point merge system (PMS) was developed by EUROCONTROL in 2006 to enable controllers to implement systematic sequences and replace the traditional vector technique in Terminal Maneuver Area (TMA) even during peak hours. A conventional PMS includes two arc-shaped route segments referred to as sequencing legs and a single merge point to merge arrivals. When necessary, arrivals can be delayed along these legs using path stretching before they are directed to the merge point. After merging, aircraft join the final approach. The parallel-point merge system (P-PMS), however, has a more complex route structure which consists of two oppositely located PMS and a set of common points located between the merge points and final paths of runways. This system increases the capacity of the airspace and provides the advantages of the single PMS. However, especially in simulation studies where P-PMS has been tested, the emphasis on wind sensitivity came to the fore as one of the main issues affecting the use of the system. The use of P-PMS was stated as feasible but more difficult due to the loss of symmetry, especially when the wind is perpendicular to the sequencing legs. Controllers may need help in providing safe and efficient sequences in this type of structure. In this study, a multi-objective two-stage stochastic programming model is developed for P-PMS to obtain robust aircraft sequences, schedules, and runway assignments considering the uncertainties of both wind direction and speed. The model was implemented on the existing layout of Istanbul Airport having a P-PMS serving five parallel runways using the real traffic and wind data. A scenario-based approach was adopted to represent the uncertainties of the model. Also, to meet the demands of the various stakeholders in the air traffic system, the minimization of total fuel consumption, total flight time, and total delays were considered as single and multi-objectives. As a result, it was found that the stochastic approach provides better solutions in terms of objective values and emission outputs compared to deterministic and FCFS approaches under wind uncertainties. The differences between these solutions are quantified through numerical experiments.