This work contributes to the development of microscopic traffic performance models in the airport. It enhances the existing models and develops new ones. An important contribution of this research is the empirical work, i.e. estimating models using statistically rigorous methods and microscopic data collected from real traffic. With the ever increasing congestion at airports around the world, studies into ways of maximizing infrastructures capacity and minimizing delay costs while meeting the goals of the airlines are necessary. The methodology applied for the calculation of runway capacity start from the traffic data elaboration of the Naples International Airport: we have been determined this aim from the airline pattern in the above airport. The hourly capacity is calculated as the inverse of the headway of consecutive aircraft operation; this is drawn with an average of the time headway in the “critical periods”. The determination of the “capacity periods" it happens in three phases: in the first one we are drawn by the sample the stationary periods; in the second we are considered, of these, only those with the lowest averages time headway, these are called "critical" periods; subsequently we are examined only that (critical periods) had time length less than the 60 minutes and that have an average of time headway that it is almost attested around to a constant value. The stationary periods, as it says the same definition, are characterized by the relatives constant time headway, in other words there aren’t meaningful phenomenon of increase or diminution of the traffic flow. The critical periods are static periods that are found on the traffic flow curve defined "critic", this curve we have obtained to envelop some values that mark the lower limit of the diagram defined time length vs. averages of the static periods. Defined the critical periods we have been determined the experimental headway curves and we compared with those theoretical. Of such experimental curves we have been considered the characteristics values: the average and the standard deviation. In this way we have determined a matrix of the average time headway both for operations flight that for type of airplane. The following step has been that to define and to implement the analytical model. We suggest a mathematic algorithm that is able to optimize, a posteriori, the flight operation under the delay restraint. The algorithm determines the best sequence of aircrafts that minimizes the delays and it maximizes the runway capacity. The proposed methodology, even if determine an evident improvement of the runway capacity, in the respect of thresholds of acceptable average delays (constrain), represents an initial methodological phase that will desirably conclude in the determination of a "dynamic" model, that is able to assist the inspectors' job in way real-time, assigning, opportunely, an excellent sequence of the successions of flight operations.

### A static algorithm to solve the air traffic sequencing problem

#### Abstract

This work contributes to the development of microscopic traffic performance models in the airport. It enhances the existing models and develops new ones. An important contribution of this research is the empirical work, i.e. estimating models using statistically rigorous methods and microscopic data collected from real traffic. With the ever increasing congestion at airports around the world, studies into ways of maximizing infrastructures capacity and minimizing delay costs while meeting the goals of the airlines are necessary. The methodology applied for the calculation of runway capacity start from the traffic data elaboration of the Naples International Airport: we have been determined this aim from the airline pattern in the above airport. The hourly capacity is calculated as the inverse of the headway of consecutive aircraft operation; this is drawn with an average of the time headway in the “critical periods”. The determination of the “capacity periods" it happens in three phases: in the first one we are drawn by the sample the stationary periods; in the second we are considered, of these, only those with the lowest averages time headway, these are called "critical" periods; subsequently we are examined only that (critical periods) had time length less than the 60 minutes and that have an average of time headway that it is almost attested around to a constant value. The stationary periods, as it says the same definition, are characterized by the relatives constant time headway, in other words there aren’t meaningful phenomenon of increase or diminution of the traffic flow. The critical periods are static periods that are found on the traffic flow curve defined "critic", this curve we have obtained to envelop some values that mark the lower limit of the diagram defined time length vs. averages of the static periods. Defined the critical periods we have been determined the experimental headway curves and we compared with those theoretical. Of such experimental curves we have been considered the characteristics values: the average and the standard deviation. In this way we have determined a matrix of the average time headway both for operations flight that for type of airplane. The following step has been that to define and to implement the analytical model. We suggest a mathematic algorithm that is able to optimize, a posteriori, the flight operation under the delay restraint. The algorithm determines the best sequence of aircrafts that minimizes the delays and it maximizes the runway capacity. The proposed methodology, even if determine an evident improvement of the runway capacity, in the respect of thresholds of acceptable average delays (constrain), represents an initial methodological phase that will desirably conclude in the determination of a "dynamic" model, that is able to assist the inspectors' job in way real-time, assigning, opportunely, an excellent sequence of the successions of flight operations.
##### Scheda breve Scheda completa Scheda completa (DC)
2008
scheduling
simulation
airport capacity
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Utilizza questo identificativo per citare o creare un link a questo documento: `https://hdl.handle.net/20.500.14086/199`