Aircraft sequencing under the uncertainty of the runway occupancy times of arrivals during the backtrack procedure

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.