The Point Merge System (PMS) is one of the approaches that offer systematic sequencing in terminal maneuver area (TMA) which was developed at the EUROCONTROL Experimental Centre in 2006. The Parallel-Point Merge System (P-PMS) is used in TMAs with high air traffic density as a combination of two PMSs with opposite directions. However, it is difficult to optimize arrival sequences in such a system because of the additional separation requirements at common points as well as at merge points. Additionally, it is important to operate at maximum efficiency with parallel runway systems when using the runway reassignment process. Reassigning arriving/departing aircraft to a different runway, however, can significantly affect delays and fuel consumption depending on the difference in taxi-in/out times. This effect is overlooked in most aircraft sequencing and scheduling optimization studies with PMS. This study proposes single and multi-objective programming models for a TMA with P-PMS to minimize total fuel consumption, total flight time, and total delay including taxi-in/out times. The models were implemented on the current layout of Istanbul Airport having two PMS with two merge points, three common points, and five parallel runways. The considered traffic scenarios included both dependent and independent runway operations for mixed arrival–departure sequences. The results revealed that arrival–departure sequencing considering taxi-in/out times resulted in shorter delays up to 77.5% and low level of fuel consumption up to 8.7%.

İnsan Faktörleri Analiz ve Sınıflandırma Sistemi (HFACS) 2000 yılında Shappell ve Wiegman tarafından tanıtılmıştır. Temeli Reason’un İsviçre peyniri modeline dayanan HFACS insan faktörlerini 4 seviye hata ile tanımlar; emniyetsiz davranışlar (seviye-1), emniyetsiz davranışlara zemin hazırlayan koşullar (seviye-2), emniyetsiz yönetim (seviye-3), örgütsel etkiler (seviye-4). İlk olarak Amerikan deniz kuvvetleri için veri toplama ve analiz aracı olarak geliştirilmiştir. Günümüzde ise HFACS’in sivil ve askeri alanda birçok uygulaması mevcuttur. İnsan faktörleri veri toplama sürecinin kalitesini ve niceliğini artırırken veri odaklı araştırma stratejilerinin verimini de artıran HFACS literatürde en yaygın kullanılan kaza analiz aracıdır. Bu çalışmada HFACS içerisinde bulunan insan hatası seviyelerinin detaylı anlatımı yapılmıştır. Bununla birlikte HFACS’in havacılık literatüründe ve diğer sektörlerdeki yaygın kullanımı araştırılmış ve HFACS’in havacılık uygulamaları, insan hatası ve emniyet yaklaşımları açısından değerlendirilmiştir.

In some small airports, a parallel taxiway is not built due to space restrictions or financial issues; hence, the runway itself is often used as a taxiway in this type of airport. After touch down, aircraft move to the U-turn area at the end of the runway and turn 180 degrees, then move back to the desired point, such as a gate or the apron, using the runway. The runway is blocked to other arrivals and departures during this process. This process, called backtrack or back-taxi, can result in high delays for both arrivals and departures. Runway occupancy times (ROTs) vary depending on numerous conditions, including pilot performance, weather conditions, aircraft type, etc. Although there are speed restrictions and procedures announced in advance, the actual performance can be uncertain. In addition, most aircraft can make a U-turn as soon as they sufficiently reduce their speed before they reach the U-turn area especially if they are already delayed. These situations bring enormous uncertainties for traffic management at such an airport. Controllers may need help to sequence aircraft, particularly in busy traffic. In this study, a stochastic mathematical model is developed to sequence arrival/departure operations at such an airport considering the ROT uncertainties of arrivals. The objective function of the developed model is determined as the minimisation of the total delay. ROT data was obtained by observing radar tracks of 120 arriving flights. Reasonable ROT scenarios with various probabilities to represent ROT uncertainties were integrated into the mathematical modeling. In addition, two different sequencing approaches are presented as well as the first come first serve (FCFS) approach. As a result, the proposed stochastic approach provides robust sequences applicable for all ROT scenarios with significant delay savings up to an average of 18.4% and 39.5% compared to deterministic and FCFS approaches, respectively.