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Hourly Capacity of a Two Crossing Runway Airport

Hourly Capacity of a Two Crossing Runway Airport infrastructures Article Paola Di Mascio and Laura Moretti * Department of Civil, Building and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18-00186 Rome, Italy; paola.dimascio@uniroma1.it * Correspondence: laura.moretti@uniroma1.it Received: 16 November 2020; Accepted: 3 December 2020; Published: 4 December 2020 Abstract: At the international level, the interest in airport capacity is growing in the last years because its maximization ensures the best performances of the infrastructure. However, infrastructure, procedure, human factor constraints should be considered to ensure a safe and regular flow to the flights. This paper analyzed the airport capacity of an airport with two crossing runways. The fast time simulation allowed modeling the baseline scenario (current trac volume and composition) and six operative scenarios; for each scenario, the trac was increased until double the current volume. The obtained results in terms of average delay and throughput were analyzed to identify the best performing and operative layout and the most suitable to manage increasing hourly movements within the threshold delay of 10 min. The obtained results refer to the specific examined layout, and all input data were provided by the airport management body: the results are reliable, and the pursued approach could be implemented to di erent airports. Keywords: airport capacity; fast time simulation; Pareto frontiers; saturation capacity; sustainable capacity 1. Introduction There are several definitions of “airside capacity” in the literature: it quantifies the aptitude of airport infrastructure to accommodate a number of movements in a unit of the reference time. According to the International Civil Aviation Organization (ICAO), the airport capacity is the maximum number of simultaneous movements of aircraft and vehicles that the system can safely support with an acceptable delay commensurate with the runway and taxiway capacity of the aerodrome [1]. On the other hand, ICAO [2] defines capacity as the number of movements per unit of time that can be accepted during di erent meteorological conditions. Therefore, the concept of capacity depends on visibility meteorological conditions, but many other variables exist: wind conditions, aircraft mix, systems capability, stang. Indeed, Airport Council International (ACI) defines capacity as maximum aircraft movements per hour, assuming an average delay of no more than four minutes, or such other number of delay minutes as the airport may set [3]. The Federal Aviation Administration (FAA)-sponsored Airport Cooperative Research Program (ACRP) assumes the capacity as the maximum number of sustained movements per unit of time that can be accepted during di erent local capacity factors [4–6]. ACRP introduces the concept of “sustainable capacity” and refers to local capacity factors: it would lead to specifying the definition to other parameters and obtaining di erent specific capacity values for each situation. It would provide more detailed information than the actual state of the infrastructure in its various configurations, but a single value of capacity is currently required and declared. According to Eurocontrol [7], capacity is the theoretical air trac movement capability of an airport. The introduced concept of possibility decouples capacity away from other factors or circumstances and describes the potential of the airport infrastructure. Infrastructures 2020, 5, 111; doi:10.3390/infrastructures5120111 www.mdpi.com/journal/infrastructures Infrastructures 2020, 5, 111 2 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 2 of 15 Therefore, the airport capacity is the attitude to dispose of air traffic at take-off and landing Therefore, the airport capacity is the attitude to dispose of air trac at take-o and landing movements: the maximum number of aircraft that can be disposed of is the saturation capacity [8]. movements: the maximum number of aircraft that can be disposed of is the saturation capacity [8]. The reference time unit depends on the reason of calculation: The reference time unit depends on the reason of calculation: • hourly capacity takes into account: demand peaks, fleet mix, runway dependencies, a mix of hourly capacity takes into account: demand peaks, fleet mix, runway dependencies, a mix of arrivals/departures, and variations in aircraft separations. arrivals/departures, and variations in aircraft separations. • daily capacity gives information on the maximum sustainable capacity for a relatively long daily capacity gives information on the maximum sustainable capacity for a relatively long period period of time: congestion or workload of the Air Traffic Control Operator (ATCO) can be of time: congestion or workload of the Air Trac Control Operator (ATCO) can be considered as considered as parameters that limit the analyses. parameters that limit the analyses. • annual capacity provides a high-level guide useful to plan the master plan and foresee the timing annual capacity provides a high-level guide useful to plan the master plan and foresee the timing of the airport saturation, taking into account the traffic forecast. of the airport saturation, taking into account the trac forecast. Moreover, different capacities can be determined as regards the weather conditions: optimal Moreover, di erent capacities can be determined as regards the weather conditions: capacity (i.e., number of movements that can be managed in 1 h in optimal qualified conditions when optimal capacity (i.e., number of movements that can be managed in 1 h in optimal qualified conditions a visual flight is possible) and reduced capacity (i.e., obtained for adverse weather conditions, when a visual flight is possible) and reduced capacity (i.e., obtained for adverse weather conditions, especially for low visibility conditions, when the flight should be instrumental) [8]. Other factors especially for low visibility conditions, when the flight should be instrumental) [8]. Other factors affect the overall airport capacity [9]: the geometrical layout of the runway and taxiway systems, a ect the overall airport capacity [9]: the geometrical layout of the runway and taxiway systems, geometrical and logistic setup of the apron areas, the size and speed of expected aircraft, the approach geometrical and logistic setup of the apron areas, the size and speed of expected aircraft, the approach and departure procedures [10], the procedures adopted by ATCO [11], the traffic routes and the air and departure procedures [10], the procedures adopted by ATCO [11], the trac routes and the air traffic management technologies, and the safety [12–14] and environmental procedures adopted to trac management technologies, and the safety [12–14] and environmental procedures adopted to manage the traffic. manage the trac. Having regard to the airside capacity (which takes into account runways, taxiways, aircraft Having regard to the airside capacity (which takes into account runways, taxiways, aircraft stand stand taxi lanes, apron taxiways, and aircraft stands), the technical capacity value is the maximum taxi lanes, apron taxiways, and aircraft stands), the technical capacity value is the maximum number number of aircraft movements that can be managed in a unit of time over a peak period, having of aircraft movements that can be managed in a unit of time over a peak period, having regard to the regard to the adopted procedures, traffic rules, and an acceptable average delay that reflects the adopted procedures, trac rules, and an acceptable average delay that reflects the quality of service quality of service (Figure 1 presents a technical capacity curve for a 10-min delay threshold). (Figure 1 presents a technical capacity curve for a 10-min delay threshold). Maximum technical capacity 10 20 30 40 50 Technical capacity with 10-min delay (movements) Figure 1. Example of the technical capacity curve for a 10-min delay threshold. Figure 1. Example of the technical capacity curve for a 10-min delay threshold. The declared capacity is the number of movements that can be processed in one hour. It is a fixed The declared capacity is the number of movements that can be processed in one hour. It is a fixed value provided by each airport manager. This value reveals a strategic choice made by the airport manager: value pro the vid declaration ed by eachof aia rp much ort mlower anager capacity . This va than lue re the veoptimal als a stra one tegiallows c choice reducing made by the th possible e airport manager: the declaration of a much lower capacity than the optimal one allows reducing the possible delays until the weather conditions are unfavorable, but the number of hourly movements that can be performed delays unis tilsmaller the wea . ther conditions are unfavorable, but the number of hourly movements that can be performed is smaller. The present study aims to calculate the technical capacity of an airport whose infrastructure layout The present study aims to calculate the technical capacity of an airport whose infrastructure has two crossing runways. Six scenarios have been considered: each one refers to the busiest volume layout has two crossing runways. Six scenarios have been considered: each one refers to the busiest in the reference year under di erent weather conditions. The current number of managed movements volume in the reference year under different weather conditions. The current number of managed movements has been increased, with 10% steps up to double it. Therefore, 10 clones have been Delay (minutes) Infrastructures 2020, 5, 111 3 of 15 has been increased, with 10% steps up to double it. Therefore, 10 clones have been examined for each starting scenario (one clone to each 10% increase). Once the various clones have been simulated, output data have been extrapolated in terms of occupancy, delay, and taxiing time in order to determine the airside capacity for each of them. The results from di erent scenarios have allowed the identification of the best performing configuration. 2. Methods Di erent methods allow calculating airport capacity: they di er in terms of e ort, data requirements, and costs [10]. ACRP [6] distinguishes 5 types of airport capacity assessment tools: Level 1: the capacity performances depend on the runway layout and the fleet mix composition. An example of this level is provided by FAA AC 150/5060-5 [4]: it provides the maximum hourly capacity values and an annual capacity of the analyzed system, modeling it as a predefined layout that fits as well as possible the examined airport; Level 2: charts, nomograms, and spreadsheets consider the airport layout (i.e., taxiways and gates), the fleet mix composition, the percentage of arrivals to total operations, and the percentage of touch and go operations. Chapter 3 of FAA AC 150/5060-5 reports an example of this method [4]; Level 3: analytical models consider the final speed of approaching aircraft, separations, and ATC rules. Capacity values are relatively easy to calculate, but they overlook the airside layout and the airspace characteristics. Chapter 5 of [4] reports an example of this approach; Level 4: simulation models consider both input data of Level 3 and factors, such as the geometry of arrival and departure routes or fleet mix on each runway; Level 5: aircraft delay simulation models (e.g., fast time simulation or real-time models) are the most advanced tool to study the whole airport: they cover all aspects of the airport, not only the airside-related ones, reconstructing the overall gate-to-gate environment. The output data contain data on capacity and delays, curves of noise, engine-on waiting time, fuel consumption, environmental impacts, and workload of air trac controllers. These methods have been studied for several years, and, currently, there are some very sophisticated software products [15,16] that can give results very close to reality when the configuration of the infrastructure is complex or an advanced design level is required [8,10,17–22]. Level 5-models can incorporate a wide range of features (e.g., complete airport layouts, ATC procedures, airport operating procedures, runway entry and exit points and taxiways routes, fleet mix, wake turbulence classification, separation over time, turnaround times, and control tower activities) [11,18]. The output data contain data on capacity and delays, curves of noise, engine-on waiting time, fuel consumption, environmental impacts, and workload of air trac controllers [11]. In this study, a fast time simulation (FTS) model has been built to calculate the capacity of an international airport whose layout is composed of a two-runway system, with two dependent, not perpendicular runways (i.e., RWY 09/27 and RWY 05/23) (Figure 2). The used software is AirTOp (Air Trac Optimization), a gate-to-gate fast time simulator [16]. The simulation model is a simplified and virtual replica of a real system: given required levels of precision and detail, it reflects the set of relevant geometrical and procedural characteristics. The algorithms in the platform allow realistic simulations by creating airport models and air spaces in a 3D environment that changes over time and can be configured by setting a series of input variables. The analyses are faster than reality: depending on the number of input data, the software can take a few seconds or minutes to simulate one day of real-time. In the implemented model, all geometrical input data comply with the Aeronautical Information Publication of the airport: they consider runways, taxiways, rapid exit taxiways, aprons, and holding bays; moreover, this source has been considered to implement in the simulation all the adopted airside and ground side procedures (e.g., self-maneuvering or push-back procedures on the apron, standard instrument departure, and standard terminal arrival route for departures and arrivals, respectively). 09 Infrastructures 2020, 5, x FOR PEER REVIEW 4 of 15 Infrastructures 2020, 5, 111 4 of 15 Figure 2. Airport layout. Figure 2. Airport layout. In 2019, almost 45,000 movements (90% performed by A319 and A320) and more than 7,000,000 passengers were registered in this airport. The volume of movements strongly depends on In 2019, almost 45,000 movements (90% performed by A319 and A320) and more than 7,000,000 the month and the day of the week: the number of movements during the busiest month (i.e., August) passengers were registered in this airport. The volume of movements strongly depends on the month di ers by more than 30% from that of the less-tracked one (i.e., March); during the busiest month, and the day of the week: the number of movements during the busiest month (i.e., August) differs by the number of daily movements ranges between 145 and 186. Statistical anomalies of trac volume in more than 30% from that of the less-trafficked one (i.e., March); during the busiest month, the number the busiest month (e.g., days with unfavorable weather conditions, closure of airside infrastructure, of daily movements ranges between 145 and 186. Statistical anomalies of traffic volume in the busiest irregular occurrences, and errors in air trac management systems) have been ignored in order month (e.g., days with unfavorable weather conditions, closure of airside infrastructure, irregular to identify the statistically significant value of daily movement. Table 1 lists the input data about occurrences, and errors in air traffic management systems) have been ignored in order to identify the weather conditions. statistically significant value of daily movement. Table 1 lists the input data about weather conditions. Table 1. Input data of weather conditions. Humidity Visibility Pressure Rain Table 1. Input data of weather conditions. Air Temperature ( C) Wind Speed (km/h) Gusts (%) (km) (mb) (mm) Average Minimum Maximum Humidity Visibilit A yverage Maximum Pressure Rain Air Temperature (° C) Wind Speed (km/h) Gusts 19 13 23 80 20 11 21 1016 0 absent (%) (km) (mb) (mm) Average Minimum Maximum Average Maximum Under such conditions, the value of the occurred daily movements nearest to the statistical average 19 13 23 80 20 11 21 1016 0 absent value has been 178. Wake turbulence (WT) and size category of the airplanes composing the fleet mix have been analyzed in order to consider their negative e ects on throughput (e.g., on-air longitudinal Under such conditions, the value of the occurred daily movements nearest to the statistical separation and on-ground constraints). Indeed, a homogeneous trac fleet leads to higher throughput average value has been 178. Wake turbulence (WT) and size category of the airplanes composing the and a standard minimum separation equal to 3 NM. On the other hand, when the fleet mix is mixed (e.g., fleet mix have been analyzed in order to consider their negative effects on throughput (e.g., on-air variable VT category [4,23]), the airplane on-air longitudinal separation is up to 8 NM if a light aircraft longitudinal separation and on-ground constraints). Indeed, a homogeneous traffic fleet leads to follows a super heavy one. In this study, the mixed fleet mix has required the definition of a variable higher throughput and a standard minimum separation equal to 3 NM. On the other hand, when the airplane on-air longitudinal separation during simulations. Particularly, for each movement that fleet mix is mixed (e.g., variable VT category [4,23]), the airplane on-air longitudinal separation is up occurred during this day, the FTS simulations have considered origin/destination, route, aircraft type, to 8 NM if a light aircraft follows a super heavy one. In this study, the mixed fleet mix has required airline, flight number, and arrival/departure time. the definition of a variable airplane on-air longitudinal separation during simulations. Particularly, The baseline scenario (i.e., trac volume and composition in the busiest day) (BS) and six for each movement that occurred during this day, the FTS simulations have considered operative scenarios with increased trac volume have been analyzed: they di er for runway use origin/destination, route, aircraft type, airline, flight number, and arrival/departure time. under di erent wind conditions (in Figure 3, departures are represented by continuous arrows, arrivals The baseline scenario (i.e., traffic volume and composition in the busiest day) (BS) and six by dotted arrows): operative scenarios with increased traffic volume have been analyzed: they differ for runway use Scenario 05_27 (I_05_27): departures from threshold 05 and arrivals on threshold 27 (blue arrows under different wind conditions (in Figure 3, departures are represented by continuous arrows, in Figure 3); arrivals by dotted arrows): Scenario 09_23 (I_09_23): departures from threshold 09 and arrivals on threshold 23 (red arrows • Scenario 05_27 (I_05_27): departures from threshold 05 and arrivals on threshold 27 (blue arrows in Figure 3); in Figure 3); Scenario 27_27 (I_27_27): both departures and arrivals in threshold 27 (green arrows in Figure 3); • Scenario 09_23 (I_09_23): departures from threshold 09 and arrivals on threshold 23 (red arrows Scenario 09_09 (I_09_09): both departures and arrivals in threshold 09 (orange arrows in Figure 3); in Figure 3); Scenario 23_23 (I_23_23): both departures and arrivals in threshold 23 (purple arrows in Figure 3); 27 09 Infrastructures 2020, 5, x FOR PEER REVIEW 5 of 15 • Scenario 27_27 (I_27_27): both departures and arrivals in threshold 27 (green arrows in Figure 3); • Scenario 09_09 (I_09_09): both departures and arrivals in threshold 09 (orange arrows in Figure 3); Infrastructures 2020, 5, 111 5 of 15 • Scenario 23_23 (I_23_23): both departures and arrivals in threshold 23 (purple arrows in Figure 3); • Scenario 05_05 (I_05_05): both departures and arrivals in threshold 05 (pink arrows in Figure 3). Scenario 05_05 (I_05_05): both departures and arrivals in threshold 05 (pink arrows in Figure 3). Figure 3. Operative airport layout. Figure 3. Operative airport layout. The trac volume of BS has been increased with 10% steps up to double BS. Therefore, 10 clones The traffic volume of BS has been increased with 10% steps up to double BS. Therefore, 10 clones (CLs) have been examined for each increased scenario (one clone to each 10% increase). The FTS (CLs) have been examined for each increased scenario (one clone to each 10% increase). The FTS simulation has allowed to model and simulate separately all the examined cases in order to identify simulation has allowed to model and simulate separately all the examined cases in order to identify their performances and weaknesses. Particularly, for each CL, 10 runs have been calculated in order to their performances and weaknesses. Particularly, for each CL, 10 runs have been calculated in order minimize randomness in the obtained results. to minimize randomness in the obtained results. Data reports are automatically organized into 10-min buckets and rolling hours: for each period, Data reports are automatically organized into 10-min buckets and rolling hours: for each period, the total delay (i.e., both approaching delay and on-ground delay) and the hourly activity (i.e., the total delay (i.e., both approaching delay and on-ground delay) and the hourly activity (i.e., both both arrivals and departures) have been considered. For each increased scenario, two graphs are arrivals and departures) have been considered. For each increased scenario, two graphs are produced: produced: the former presents delays. Particularly, each delay value is the average of ten delay values • the former presents delays. Particularly, each delay value is the average of ten delay values obtained from 10 runs carried out for each clone. This representation allows verifying if the obtained from 10 runs carried out for each clone. This representation allows verifying if the average delay overcomes the average delay threshold (i.e., 10 min); average delay overcomes the average delay threshold (i.e., 10 min); the latter presents the daily throughput in a Cartesian plane. Given an average delay for a single • the latter presents the daily throughput in a Cartesian plane. Given an average delay for a single aircraft of less than 10 min, each point represents the number of operations that can be performed aircraft of less than 10 min, each point represents the number of operations that can be performed in one hour without violating air trac control rules, assuming continuous aircraft demand. in one hour without violating air traffic control rules, assuming continuous aircraft demand. The Pareto frontier (i.e., the set of pairs of arrivals and departures at which both the arrival and The Pareto frontier (i.e., the set of pairs of arrivals and departures at which both the arrival and the departure rate cannot be simultaneously increased) identifies the saturation capacity of the airport. the departure rate cannot be simultaneously increased) identifies the saturation capacity of the Firstly, the study has identified the maximum throughput at the balanced priority (i.e., the maximum airport. Firstly, the study has identified the maximum throughput at the balanced priority (i.e., the admitted movements on the 45-degree line) and then verified that other points on the frontier ensure maximum admitted movements on the 45-degree line) and then verified that other points on the the maximum throughput within the admitted delay. frontier ensure the maximum throughput within the admitted delay. 3. Results 3. Results The simultaneous study of the delay report and the Pareto frontier has permitted to evaluate The simultaneous study of the delay report and the Pareto frontier has permitted to evaluate the the saturation of the examined airport having regard to its throughput. For each examined scenario, saturation of the examined airport having regard to its throughput. For each examined scenario, the the average hourly delay and the number of movements have been considered: the average delay is average hourly delay and the number of movements have been considered: the average delay is calculated considering the sum runs of each clone; when its value reaches or exceeds 10 min, the run is calculated considering the sum runs of each clone; when its value reaches or exceeds 10 min, the run considered. The throughput representation refers to all 10 runs for each clone: it allows studying the is considered. The throughput representation refers to all 10 runs for each clone: it allows studying daily trend of movements. Figures 4–6 represent the results for I_05_27: average delay, throughput, and Pareto frontier, respectively. 27 Infrastructures 2020, 5, x FOR PEER REVIEW 6 of 15 Infrastructur the daily estre 2020 nd , 5 , o 111 f movements. Figures 4–6 represent the results for I_05_27: average d 6el of a15 y, throughput, and Pareto frontier, respectively. CL00 I_05_27 3,5 2,5 1,5 0,5 (a) CL10 I_05_27 3,5 2,5 1,5 0,5 (b) CL50 I_05_27 (c) Figure 4. Average delay for I_05_27 (a) CL00; (b) CL10; (c) CL50. Figure 4. Average delay for I_05_27 (a) CL00; (b) CL10; (c) CL50. Average delay (min) Average delay (min) Average delay (min) 01:00:00 01:00:00 01:00:00 01:30:00 01:30:00 01:30:00 02:00:00 02:00:00 02:00:00 02:30:00 02:30:00 02:30:00 03:00:00 03:00:00 03:00:00 03:30:00 03:30:00 03:30:00 04:00:00 04:00:00 04:00:00 04:30:00 04:30:00 04:30:00 05:00:00 05:00:00 05:00:00 05:30:00 05:30:00 05:30:00 06:00:00 06:00:00 06:00:00 06:30:00 06:30:00 06:30:00 07:00:00 07:00:00 07:00:00 07:30:00 07:30:00 07:30:00 08:00:00 08:00:00 08:00:00 08:30:00 08:30:00 08:30:00 09:00:00 09:00:00 09:00:00 09:30:00 09:30:00 09:30:00 10:00:00 10:00:00 10:00:00 10:30:00 10:30:00 10:30:00 11:00:00 11:00:00 11:00:00 11:30:00 11:30:00 11:30:00 12:00:00 12:00:00 12:00:00 12:30:00 12:30:00 12:30:00 13:00:00 13:00:00 13:00:00 13:30:00 13:30:00 13:30:00 14:00:00 14:00:00 14:00:00 14:30:00 14:30:00 14:30:00 15:00:00 15:00:00 15:00:00 15:30:00 15:30:00 15:30:00 16:00:00 16:00:00 16:00:00 16:30:00 16:30:00 16:30:00 17:00:00 17:00:00 17:00:00 17:30:00 17:30:00 17:30:00 18:00:00 18:00:00 18:00:00 18:30:00 18:30:00 18:30:00 19:00:00 19:00:00 19:00:00 19:30:00 19:30:00 19:30:00 20:00:00 20:00:00 20:00:00 20:30:00 20:30:00 20:30:00 21:00:00 21:00:00 21:00:00 21:30:00 21:30:00 21:30:00 22:00:00 22:00:00 22:00:00 22:30:00 22:30:00 22:30:00 23:00:00 23:00:00 23:00:00 23:30:00 23:30:00 23:30:00 Infrastructures 2020, 5, 111 7 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 7 of 15 In Figure 4, the average delay for I_05_27 CL00 (current trac volume) is below the limit (i.e., In Figure 4, the average delay for I_05_27 CL00 (current traffic volume) is below the limit (i.e., 10 min) (Figure 4a); with CL10, the average delay has increased, but it is below the limit (Figure 4b); 10 min) (Figure 4a); with CL10, the average delay has increased, but it is below the limit (Figure 4b); CL50 is the first examined clone where average delays coincide with the limit (Figure 4a): it is the CL50 is the first examined clone where average delays coincide with the limit (Figure 4a): it is the last last step of increased trac not overcoming the average delay threshold. Therefore, in Figure 4, the step of increased traffic not overcoming the average delay threshold. Therefore, in Figure 4, the authors have not presented the results from other clones. authors have not presented the results from other clones. The results in Figure 5 highlight that peaks of hourly throughput correspond to peaks of average The results in Figure 5 highlight that peaks of hourly throughput correspond to peaks of average delay (Figure 4); for each clone, throughput in di erent runs is almost constant: during simulations, delay (Figure 4); for each clone, throughput in different runs is almost constant: during simulations, trac jams or diculty in the management of the demand have been absent. Moreover, throughput traffic jams or difficulty in the management of the demand have been absent. Moreover, throughput peaks are increasing with the clones (Figure 5a,b) until CL50 saturation conditions do not occur. peaks are increasing with the clones (Figure 5a,b) until CL50 saturation conditions do not occur. Over Over CL50 (Figure 5c,d), the already achieved throughput peaks are almost constant: by increasing the CL50 (Figure 5c,d), the already achieved throughput peaks are almost constant: by increasing the airport trac by over 50%, the current volume causes saturation of the infrastructure. airport traffic by over 50%, the current volume causes saturation of the infrastructure. CL00 I_05_27 run1 run2 run3 run4 run5 run6 run7 10 run8 run9 run10 (a) CL10 I_05_27 run1 run2 run3 run4 run5 run6 run7 run8 run9 run10 (b) Figure 5. Cont. Throughput Throughput 01:20:00 01:20:00 01:50:00 01:50:00 02:20:00 02:20:00 02:50:00 02:50:00 03:20:00 03:20:00 03:50:00 03:50:00 04:20:00 04:20:00 04:50:00 04:50:00 05:20:00 05:20:00 05:50:00 05:50:00 06:20:00 06:20:00 06:50:00 06:50:00 07:20:00 07:20:00 07:50:00 07:50:00 08:20:00 08:20:00 08:50:00 08:50:00 09:20:00 09:20:00 09:50:00 09:50:00 10:20:00 10:20:00 10:50:00 10:50:00 11:20:00 11:20:00 11:50:00 11:50:00 12:20:00 12:20:00 12:50:00 12:50:00 13:20:00 13:20:00 13:50:00 13:50:00 14:20:00 14:20:00 14:50:00 14:50:00 15:20:00 15:20:00 15:50:00 15:50:00 16:20:00 16:20:00 16:50:00 16:50:00 17:20:00 17:20:00 17:50:00 17:50:00 18:20:00 18:20:00 18:50:00 18:50:00 19:20:00 19:20:00 19:50:00 19:50:00 20:20:00 20:20:00 20:50:00 20:50:00 21:20:00 21:20:00 21:50:00 21:50:00 22:20:00 22:20:00 22:50:00 22:50:00 23:20:00 23:20:00 23:50:00 23:50:00 Infrastructures 2020, 5, 111 8 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 8 of 15 CL50 I_05_27 run1 run2 run3 run4 run5 20 run6 run7 run8 run9 run10 (c) CL100 I_05_27 run1 run2 run3 run4 run5 run6 run7 run8 run9 run10 (d) Figure 5. Throughput for I_05_27 (a) CL00; (b) CL10; (c) CL50; (d) CL100. Figure 5. Throughput for I_05_27 (a) CL00; (b) CL10; (c) CL50; (d) CL100. Figure 6 represents the envelope of the Pareto frontier (green line) obtained for I_05_27 clones Figure 6 represents the envelope of the Pareto frontier (green line) obtained for I_05_27 clones from CL00 to CL50: the FTS software has increased by 50% the current arrival/departure mix to obtain from CL00 to CL50: the FTS software has increased by 50% the current arrival/departure mix to obtain that of CL50. In the balanced mode (i.e., on the main diagonal), the overall number of movements is that of CL50. In the balanced mode (i.e., on the main diagonal), the overall number of movements is 22 (11 departures and 11 arrivals), and it is the maximum throughput value that ensures a not more 22 (11 departures and 11 arrivals), and it is the maximum throughput value that ensures a not more than 10 min delay. Once identified the maximum balanced throughput, it is taken as a reference for than 10 min delay. Once identified the maximum balanced throughput, it is taken as a reference for identifying other points on the frontier, varying the number of movements, and having delay values identifying other points on the frontier, varying the number of movements, and having delay values under the established threshold. Therefore, points obtained for clones with more than 50% traffic under the established threshold. Therefore, points obtained for clones with more than 50% trac increase are not represented because they refer to average delay values over 10 min. increase are not represented because they refer to average delay values over 10 min. Throughput Throughput 01:20:00 01:20:00 01:50:00 01:50:00 02:20:00 02:20:00 02:50:00 02:50:00 03:20:00 03:20:00 03:50:00 03:50:00 04:20:00 04:20:00 04:50:00 04:50:00 05:20:00 05:20:00 05:50:00 05:50:00 06:20:00 06:20:00 06:50:00 06:50:00 07:20:00 07:20:00 07:50:00 07:50:00 08:20:00 08:20:00 08:50:00 08:50:00 09:20:00 09:20:00 09:50:00 09:50:00 10:20:00 10:20:00 10:50:00 10:50:00 11:20:00 11:20:00 11:50:00 11:50:00 12:20:00 12:20:00 12:50:00 12:50:00 13:20:00 13:20:00 13:50:00 13:50:00 14:20:00 14:20:00 14:50:00 14:50:00 15:20:00 15:20:00 15:50:00 15:50:00 16:20:00 16:20:00 16:50:00 16:50:00 17:20:00 17:20:00 17:50:00 17:50:00 18:20:00 18:20:00 18:50:00 18:50:00 19:20:00 19:20:00 19:50:00 19:50:00 20:20:00 20:20:00 20:50:00 20:50:00 21:20:00 21:20:00 21:50:00 21:50:00 22:20:00 22:20:00 22:50:00 22:50:00 23:20:00 23:20:00 23:50:00 23:50:00 Infrastructures 2020, 5, 111 9 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 15 0 5 10 15 20 0 5 10 15 20 Departures Departures Figure 6. The envelope of Pareto frontier for I_05_27 clones from CL00 to CL50. Figure 6. The envelope of Pareto frontier for I_05_27 clones from CL00 to CL50. Figure 6. The envelope of Pareto frontier for I_05_27 clones from CL00 to CL50. The presented output have been considered for all the examined scenarios. Figure 7a,b represent The presented output have been considered for all the examined scenarios. Figure 7a,b represent The presented output have been considered for all the examined scenarios. Figure 7a,b represent the comparison of average delay for CL00 and CL30, respectively. the comparison of average delay for CL00 and CL30, respectively. the comparison of average delay for CL00 and CL30, respectively. 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 CL00 I_05_27 CL00 I_23_23 CL00 I_09_23 CL00 I_05_27 CL00 I_23_23 CL00 I_09_23 CL00 I_09_09 CL00 I_27_27 CL00 I_05_05 CL00 I_09_09 CL00 I_27_27 CL00 I_05_05 (a) (a) Figure 7. Cont. Average delay (min) Arrivals Average delay (min) Arrivals Infrastructures 2020, 5, 111 10 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 10 of 15 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 CL30 I_05_27 CL30 I_23_23 CL30 I_09_23 CL30 I_09_09 CL30 I_27_27 CL30 I_05_05 (b) Figure 7. Comparison of the average delay for all the examined scenarios (a) CL00; (b) CL30. Figure 7. Comparison of the average delay for all the examined scenarios (a) CL00; (b) CL30. In Figure 7a, all delay values are below the limit threshold: on average, the lowest average one is in In Figure 7a, all delay values are below the limit threshold: on average, the lowest average one I_05_27. This result is related to the runway layout: RWY 27, where arrivals are scheduled, crosses in its is in I_05_27. This result is related to the runway layout: RWY 27, where arrivals are scheduled, initial part RWY 05, where departures are scheduled. This allows departures to leave immediately after crosses in its initial part RWY 05, where departures are scheduled. This allows departures to leave the landing of the arriving aircraft. Scenarios I_09_09 and I_27_27 have the average delay curve similar immediately after the landing of the arriving aircraft. Scenarios I_09_09 and I_27_27 have the average to that of I_05_27: it is consistent because they are the same physical runway, but di erent thresholds delay curve similar to that of I_05_27: it is consistent because they are the same physical runway, but are considered. Delays of I_09_09 and I_27_27 are greater than I_05_27 because of the runway’s layout: different thresholds are considered. Delays of I_09_09 and I_27_27 are greater than I_05_27 because it is necessary to wait for the arriving aircraft to cover the entire runway before being able to free it. of the runway’s layout: it is necessary to wait for the arriving aircraft to cover the entire runway I_ 09_23 crosses RWY 23, where the arrivals are expected: this means that a departing aircraft on RWY before being able to free it. I_ 09_23 crosses RWY 23, where the arrivals are expected: this means that 09 should wait for the arriving aircraft on RWY 23 to clear at least half runway: such conditions justify a departing aircraft on RWY 09 should wait for the arriving aircraft on RWY 23 to clear at least half the high average delay values. Finally, I_05_05 and I_23_23 give the worst performance because of runway: such conditions justify the high average delay values. Finally, I_05_05 and I_23_23 give the double crossings. With the same trac increase (30%), the I_05_05 configuration has the highest delay worst performance because of double crossings. With the same traffic increase (30%), the I_05_05 (i.e., more than 18 min) and does not satisfy the 10-min limit threshold at di erent times of the day. configuration has the highest delay (i.e., more than 18 min) and does not satisfy the 10-min limit Therefore, a trac increase of 30% is not sustainable for I_05_05; the same is for I_23_23 (maximum threshold at different times of the day. Therefore, a traffic increase of 30% is not sustainable for average delay 15 min). The scenario that best supports this increase in terms of delays is I_05_27 I_05_05; the same is for I_23_23 (maximum average delay 15 min). The scenario that best supports (maximum average delay almost equal to that of CL_00). I_27_27 and I_09_09 tend to remain below the this increase in terms of delays is I_05_27 (maximum average delay almost equal to that of CL_00). limit threshold (maximum average delay of 11 min and 10.5 min, respectively). However, I_09_09 has I_27_27 and I_09_09 tend to remain below the limit threshold (maximum average delay of 11 min a slightly lower average delay (i.e., 10.5 min) than I_27_27 as the arrivals can take the quick exits on the and 10.5 min, respectively). However, I_09_09 has a slightly lower average delay (i.e., 10.5 min) than runway, leaving the runway free faster than I_27_27. Finally, I_09_23 has a maximum average delay I_27_27 as the arrivals can take the quick exits on the runway, leaving the runway free faster than (13 I_27 min), _27. Fi higher nally, than I_09the _23other has a critical maxim scenarios um averI_05_05, age dela I_27_27, y (13 mand in), I_09_09. higher than the other critical In all the examined scenarios, the highest delays are during morning hours, but important scenarios I_05_05, I_27_27, and I_09_09. di erIn ences all ar the e in exterms amined of scena throughput rios, th of e CL00 highest and del CL30 ays (Figur are duri e 8n a,b, g m respectively). orning hours, but important In Figure 8a, all scenarios have the same throughput trend: CL_00 does not report increases, differences are in terms of throughput of CL00 and CL30 (Figure 8a,b, respectively). and, consequently, it is expected that the behavior of each scenario is almost similar. Only scenarios I_05_05 and I_23_23 deviate from other throughput curves because they cannot support the same number of movements of others. It requires moving some movements to the following time slots with a consequent increase in delays and deviation of the movement curves compared to the standard trend. The other four scenarios could eciently manage the movements even after the 30% increase. Average delay (min) Infrastructures 2020, 5, 111 11 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 11 of 15 00:00:00 05:45:36 11:31:12 17:16:48 23:02:24 04:48:00 CL00 I_05_27 CL00 I_23_23 CL00 I_09_23 CL00 I_09_09 CL00 I_27_27 CL00 I_05_05 (a) 00:00:00 05:45:36 11:31:12 17:16:48 23:02:24 04:48:00 CL30 I_05_27 CL30 I_23_23 CL30 I_09_23 CL30 I_09_09 CL30 I_27_27 CL30 I_05_05 (b) Figure 8. Comparison of throughput for all scenarios (a) CL00; (b) CL30. Figure 8. Comparison of throughput for all scenarios (a) CL00; (b) CL30. Finally In Figure , the 8a,authors all scena have rios h compar ave the ed sam values e through of delays put trend: and CL movements _00 does nobtained ot report ifor ncrea saturation ses, and, clones conseqof ueeach ntly, scenario it is expe (Figur cted e th 9a ). t the behavior of each scenario is almost similar. Only scenarios I_05_0 The 5 a clones nd I_2that 3_23 cause devia saturation te from oon ther each thro scenario ughput ar curv e die s er b ent: ecau CL50 se thfor ey ca I_05_27, nnot suppo CL10rt for th I_09_23, e same CL40 number for oI_27_27, f movemen CL30 ts offor othI_09_09, ers. It reqCL20 uires m for ov I_23_23, ing some and mov CL00 emenfor ts to I_05_05. the folloTher wing efor time, e sl the otsmost with performing a consequen scenarios t increase arie nI_05_27, delays a I_27_27, nd deviand ation I_09_09 of the because movement they cur have ves higher compa per red centages to the st of an tra da  rd c incr trenease d. The within otherthe four thr scena eshold riodelay s could limit. efficAmong iently ma these, nage I_05_27 the mov has ement thes e lowest ven adelay fter thand e 30% the in highest crease. increase, Finalbut ly, th it e is a aut mixed hors h configuration ave compared and valhas ues the of d disadvantage elays and moof ver m unway ents ob cr ta ossing. ined foParticularly r saturation, for clon I_27_27, es of each sce it is possi narioble (Fito gure reach 9). a higher increase compared to I_09_09, but it involves a greater delay. The choice of the most advantageous scenario between them depends on a strategic decision: the airport manager will opt for I_27_27 if he prefers having more trac; on the other hand, he will opt for I_09_09 if he prefers having lower delay values. I_09_23 has rather low delays, but the maximum trac increase value is equal to 10%: a limited number of movements can be accepted in order not to Throughput Throughput Infrastructures 2020, 5, x FOR PEER REVIEW 12 of 15 CL50 I_05_27 CL10 I_09_23 CL40 I_27_27 CL30 I_09_09 CL20 I_23_23 2 CL0 I_05_05 Infrastructures 2020, 5, 111 12 of 15 exceed the delay. Due to runway crossing, both I_05_05 and I_23_23 have quite high delays and rather HOURS Infrastructures 2020, 5, x FOR PEER REVIEW 12 of 15 limited trac increases. 12 Figure 9. Comparison of saturation capacity average delay for each scenario. The clones that cause saturation on each scenario are different: CL50 for I_05_27, CL10 for I_09_23, CL40 for I_27_27, CL30 for I_09_09, CL20 for I_23_23, and CL00 for I_05_05. Therefore, the most performing scenarios are I_05_27, I_27_27, and I_09_09 because they have higher percentages CL50 I_05_27 CL10 I_09_23 of traffic increase within the threshold delay limit. Among these, I_05_27 has the lowest delay and CL40 I_27_27 the highest increase, but it is a mixed configuration and has the disadvantage of runway crossing. CL30 I_09_09 CL20 I_23_23 Particularly, for I_27_27, it is possible to reach a higher increase compared to I_09_09, but it involves 2 CL0 I_05_05 a greater delay. The choice of the most advantageous scenario between them depends on a str ategic decision: the airport manager will opt for I_27_27 if he prefers having more traffic; on the other hand, he will opt for I_09_09 if he prefers having lower delay values. I_09_23 has rather low delays, but the maximum traffic increase value is equal to 10%: a limited number of movements can be accepted in HOURS order not to exceed the delay. Due to runway crossing, both I_05_05 and I_23_23 have quite high Figure 9. Comparison of saturation capacity average delay for each scenario. delays and rather limited traffic increases. Figure 9. Comparison of saturation capacity average delay for each scenario. Having regard to the average results obtained for the saturation clone of each scenario, Figure Having regard to the average results obtained for the saturation clone of each scenario, Figure 10 1 compar 0 compa esre the s th thr e th oughput roughpu output. t output. The clones that cause saturation on each scenario are different: CL50 for I_05_27, CL10 for I_09_23, CL40 for I_27_27, CL30 for I_09_09, CL20 for I_23_23, and CL00 for I_05_05. Therefore, the most performing scenarios are I_05_27, I_27_27, and I_09_09 because they have higher percentages of traffic increase within the threshold delay limit. Among these, I_05_27 has the lowest delay and the highest increase, but it is a mixed configuration and has the disadvantage of runway crossing. Particularly, for I_27_27, it is possible to reach a higher increase compared to I_09_09, but it involves CL50 I_05_27 a greater delay. The choice of the most advantageous scenario between them depends on a str ategic CL10 I_09_23 CL40 I_27_27 decision: the airport manager will opt for I_27_27 if he prefers having more traffic; on the other hand, CL30 I_09_09 he will opt for I_09_09 if he prefers having lower delay values. I_09_23 has rather low delays, but the CL20 I_23_23 maximum traffic increase value is equal to 10%: a limited number of movements can be accepted in CL0 I_05_05 order not to exceed the delay. Due to runway crossing, both I_05_05 and I_23_23 have quite high delays and rather limited traffic increases. Having regard to the average results obtained for the saturation clone of each scenario, Figure 10 compares the throughput output. HOURS Figure 10. Comparison of saturation capacity throughput for each scenario. Figure 10. Comparison of saturation capacity throughput for each scenario. The throughput analysis highlights that I_05_27 has the highest number of hourly movements: it complies with the discussed data in Figure 9. Finally, the Pareto frontiers of the examined saturation The throughput analysis highlights that I_05_27 has the highest number of hourly movements: scenarios are in Figure 11. it complies with the discussed data in Figure 9. Finally, the Pareto frontiers of the examined saturation I_05_27 is the scenario that ensures the higher number of hourly movements (i.e., 22 with balanced CL50 I_05_27 scenarios are in Figure 11. CL10 I_09_23 conditions); both I_27_27 and I_09_09 allow 20 hourly movements with balanced conditions: they have CL40 I_27_27 a similar frontier. Both runway layout and aeronautical procedures a ect the maximum hourly capacity CL30 I_09_09 of other scenarios: in balanced conditions, I_23_23, I_05_05, and I_09_23 have the same maximum CL20 I_23_23 number of allowable movements per hour (i.e., 16), but with decreasing performances in unbalanced CL0 I_05_05 conditions (when departures are given priority 12, 11, and 10, respectively; when arrivals are given priority 12, 10, and 7, respectively). HOURS Figure 10. Comparison of saturation capacity throughput for each scenario. The throughput analysis highlights that I_05_27 has the highest number of hourly movements: it complies with the discussed data in Figure 9. Finally, the Pareto frontiers of the examined saturation scenarios are in Figure 11. Average delay (min) Average delay (min) Average throughput Average throughput 2 01:00:00 2 01:00:00 2 01:00:00 2 01:00:00 2 01:30:00 2 01:30:00 2 01:40:00 2 01:40:00 2 02:00:00 2 02:00:00 2 02:30:00 2 02:30:00 2 02:20:00 2 02:20:00 2 03:00:00 2 03:00:00 2 03:00:00 2 03:00:00 2 03:30:00 2 03:30:00 2 03:40:00 2 03:40:00 2 04:00:00 2 04:00:00 2 04:30:00 2 04:30:00 2 04:20:00 2 04:20:00 2 05:00:00 2 05:00:00 2 05:00:00 2 05:00:00 2 05:30:00 2 05:30:00 2 05:40:00 2 05:40:00 2 06:00:00 2 06:00:00 2 06:20:00 2 06:20:00 2 06:30:00 2 06:30:00 2 07:00:00 2 07:00:00 2 07:00:00 2 07:00:00 2 07:30:00 2 07:30:00 2 07:40:00 2 07:40:00 2 08:00:00 2 08:00:00 2 08:20:00 2 08:20:00 2 08:30:00 2 08:30:00 2 09:00:00 2 09:00:00 2 09:00:00 2 09:00:00 2 09:30:00 2 09:30:00 2 09:40:00 2 09:40:00 2 10:00:00 2 10:00:00 2 10:20:00 2 10:20:00 2 10:30:00 2 10:30:00 2 11:00:00 2 11:00:00 2 11:00:00 2 11:00:00 2 11:30:00 2 11:30:00 2 11:40:00 2 11:40:00 2 12:00:00 2 12:00:00 2 12:20:00 2 12:20:00 2 12:30:00 2 12:30:00 2 13:00:00 2 13:00:00 2 13:00:00 2 13:00:00 2 13:40:00 2 13:40:00 2 13:30:00 2 13:30:00 2 14:20:00 2 14:20:00 2 14:00:00 2 14:00:00 2 14:30:00 2 14:30:00 2 15:00:00 2 15:00:00 2 15:00:00 2 15:00:00 2 15:40:00 2 15:40:00 2 15:30:00 2 15:30:00 2 16:20:00 2 16:20:00 2 16:00:00 2 16:00:00 2 16:30:00 2 16:30:00 2 17:00:00 2 17:00:00 2 17:00:00 2 17:00:00 2 17:40:00 2 17:40:00 2 17:30:00 2 17:30:00 2 18:20:00 2 18:20:00 2 18:00:00 2 18:00:00 2 19:00:00 2 19:00:00 2 18:30:00 2 18:30:00 2 19:00:00 2 19:00:00 2 19:40:00 2 19:40:00 2 19:30:00 2 19:30:00 2 20:20:00 2 20:20:00 2 20:00:00 2 20:00:00 2 21:00:00 2 21:00:00 2 20:30:00 2 20:30:00 2 21:40:00 2 21:40:00 2 21:00:00 2 21:00:00 2 21:30:00 2 21:30:00 2 22:20:00 2 22:20:00 2 22:00:00 2 22:00:00 2 23:00:00 2 23:00:00 2 22:30:00 2 22:30:00 2 23:40:00 2 23:40:00 2 23:00:00 2 23:00:00 2 23:30:00 2 23:30:00 3 00:00:00 3 00:00:00 Infrastructures 2020, 5, 111 13 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 13 of 15 I_05_05 I_09_23 I_23_23 I_09_09 I_27_27 I_05_27 0 5 10 15 20 Departures Figure 11. The Pareto frontier of the examined scenarios. Figure 11. The Pareto frontier of the examined scenarios. 4. Conclusions I_05_27 is the scenario that ensures the higher number of hourly movements (i.e., 22 with The airside capacity is the attitude to manage air trac at take-o and landing movements balanced conditions); both I_27_27 and I_09_09 allow 20 hourly movements with balanced with safety and within acceptable delays. It can be assessed with di erent five levels of analysis: conditions: they have a similar frontier. Both runway layout and aeronautical procedures affect the simplistic and no longer updates methods attempt to describe real situations by approximating them maximum hourly capacity of other scenarios: in balanced conditions, I_23_23, I_05_05, and I_09_23 to predefined configurations (levels 1 and 2); analytical capacity models (level 3) permit to analyze have the same maximum number of allowable movements per hour (i.e., 16), but with decreasing small size airports and some more information in inputs; most recent simulation techniques (levels 4 performances in unbalanced conditions (when departures are given priority 12, 11, and 10, and 5) allow extremely detailed analyses. respectively; when arrivals are given priority 12, 10, and 7, respectively). In the present study, a fast time simulation model has been built to assess the airside capacity of an international airport whose layout is composed of two crossing runways. The obtained results, 4. Conclusions which depend on the input data (e.g., geometry, aeronautical procedures, ground handling processes, The airside capacity is the attitude to manage air traffic at take-off and landing movements with anemometric conditions, fleet mix composition), allow the airport manager to identify the most critical safety and within acceptable delays. It can be assessed with different five levels of analysis: simplistic configurations in terms of delay and number of movements managed in 1 h having regard to the and no longer updates methods attempt to describe real situations by approximating them to current trac volume. Six scenarios have been considered: each one refers to the busiest volume predefined configurations (levels 1 and 2); analytical capacity models (level 3) permit to analyze small in the reference year under di erent weather and usage conditions. On the other hand, the current size airports and some more information in inputs; most recent simulation techniques (levels 4 and number of managed movements has been increased with 10% steps up to double it in order to explore 5) allow extremely detailed analyses. the technical or logistic feasibility of grown trac. The most performing configurations are I_05_27 In the present study, a fast time simulation model has been built to assess the airside capacity of (22 movements/hour), I_09_09 (20 movements/hour), and I_27_27 (20 movements/hour). However, an international airport whose layout is composed of two crossing runways. The obtained results, I_05_27 is less preferable than the others because it has an intersection between runways that slows which depend on the input data (e.g., geometry, aeronautical procedures, ground handling down the airplane path fluidity. Therefore, it allows a possible increase in trac and a lower delay processes, anemometric conditions, fleet mix composition), allow the airport manager to identify the value, but it cannot be considered the most performing because movements are less fluid than I_09_09 most critical configurations in terms of delay and number of movements managed in 1 h having and I_27_27. regard to the current traffic volume. Six scenarios have been considered: each one refers to the busiest volume in the reference year under different weather and usage conditions. On the other hand, the Author Contributions: Conceptualization, P.D.M. and L.M.; methodology, P.D.M.; software, L.M.; validation, L.M. and P.D.M.; formal analysis, L.M.; investigation, L.M.; data curation, P.D.M. and L.M.; writing—original draft current number of managed movements has been increased with 10% steps up to double it in order preparation, L.M.; review and editing, P.DM. and L.M.; supervision, P.D.M. All authors have read and agreed to to explore the technical or logistic feasibility of grown traffic. The most performing configurations the published version of the manuscript. are I_05_27 (22 movements/hour), I_09_09 (20 movements/hour), and I_27_27 (20 movements/hour). Funding: This research received no external funding. However, I_05_27 is less preferable than the others because it has an intersection between runways Acknowledgments: The authors sincerely thank Eng. Martina Possanzini for her support in building the FTS that slows down the airplane path fluidity. Therefore, it allows a possible increase in traffic and a model and Italian air navigation service provider (ENAV) for having granted the use of the software AirTOp. lower delay value, but it cannot be considered the most performing because movements are less fluid Conflicts of Interest: The authors declare no conflict of interest. than I_09_09 and I_27_27. Author Contributions: Conceptualization, P.D.M. and L.M.; methodology, P.D.M.; software, L.M.; validation, L.M. and P.D.M.; formal analysis, L.M.; investigation, L.M.; data curation, P.D.M. and L.M.; writing—original Arrivals Infrastructures 2020, 5, 111 14 of 15 References 1. International Civil Aviation Organization (ICAO). Advanced Surface Movement Guidance and Control Systems (A-SMGCS) Manual. Doc 9830 AN/452, First Edition. Available online: https://www.icao.int/ Meetings/anconf12/Document%20Archive/9830_cons_en[1].pdf (accessed on 9 October 2020). 2. International Civil Aviation Organization (ICAO). Manual on Global Performance of the Air Navigation System. Doc 9883, First Edition. Available online: http://www.aviationchief.com/uploads/9/2/0/9/92098238/ icao_doc_9883_-_manual_on_global_performance_of_air_navigation_system_-_1st_edition_-_2010.pdf (accessed on 9 October 2020). 3. Airports Council International (ACI). 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Hourly Capacity of a Two Crossing Runway Airport

Mascio, Paola Di; Moretti, Laura
Infrastructures , Volume 5 (12) – Dec 4, 2020
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Multidisciplinary Digital Publishing Institute
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2412-3811
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10.3390/infrastructures5120111
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Abstract

infrastructures Article Paola Di Mascio and Laura Moretti * Department of Civil, Building and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18-00186 Rome, Italy; paola.dimascio@uniroma1.it * Correspondence: laura.moretti@uniroma1.it Received: 16 November 2020; Accepted: 3 December 2020; Published: 4 December 2020 Abstract: At the international level, the interest in airport capacity is growing in the last years because its maximization ensures the best performances of the infrastructure. However, infrastructure, procedure, human factor constraints should be considered to ensure a safe and regular flow to the flights. This paper analyzed the airport capacity of an airport with two crossing runways. The fast time simulation allowed modeling the baseline scenario (current trac volume and composition) and six operative scenarios; for each scenario, the trac was increased until double the current volume. The obtained results in terms of average delay and throughput were analyzed to identify the best performing and operative layout and the most suitable to manage increasing hourly movements within the threshold delay of 10 min. The obtained results refer to the specific examined layout, and all input data were provided by the airport management body: the results are reliable, and the pursued approach could be implemented to di erent airports. Keywords: airport capacity; fast time simulation; Pareto frontiers; saturation capacity; sustainable capacity 1. Introduction There are several definitions of “airside capacity” in the literature: it quantifies the aptitude of airport infrastructure to accommodate a number of movements in a unit of the reference time. According to the International Civil Aviation Organization (ICAO), the airport capacity is the maximum number of simultaneous movements of aircraft and vehicles that the system can safely support with an acceptable delay commensurate with the runway and taxiway capacity of the aerodrome [1]. On the other hand, ICAO [2] defines capacity as the number of movements per unit of time that can be accepted during di erent meteorological conditions. Therefore, the concept of capacity depends on visibility meteorological conditions, but many other variables exist: wind conditions, aircraft mix, systems capability, stang. Indeed, Airport Council International (ACI) defines capacity as maximum aircraft movements per hour, assuming an average delay of no more than four minutes, or such other number of delay minutes as the airport may set [3]. The Federal Aviation Administration (FAA)-sponsored Airport Cooperative Research Program (ACRP) assumes the capacity as the maximum number of sustained movements per unit of time that can be accepted during di erent local capacity factors [4–6]. ACRP introduces the concept of “sustainable capacity” and refers to local capacity factors: it would lead to specifying the definition to other parameters and obtaining di erent specific capacity values for each situation. It would provide more detailed information than the actual state of the infrastructure in its various configurations, but a single value of capacity is currently required and declared. According to Eurocontrol [7], capacity is the theoretical air trac movement capability of an airport. The introduced concept of possibility decouples capacity away from other factors or circumstances and describes the potential of the airport infrastructure. Infrastructures 2020, 5, 111; doi:10.3390/infrastructures5120111 www.mdpi.com/journal/infrastructures Infrastructures 2020, 5, 111 2 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 2 of 15 Therefore, the airport capacity is the attitude to dispose of air traffic at take-off and landing Therefore, the airport capacity is the attitude to dispose of air trac at take-o and landing movements: the maximum number of aircraft that can be disposed of is the saturation capacity [8]. movements: the maximum number of aircraft that can be disposed of is the saturation capacity [8]. The reference time unit depends on the reason of calculation: The reference time unit depends on the reason of calculation: • hourly capacity takes into account: demand peaks, fleet mix, runway dependencies, a mix of hourly capacity takes into account: demand peaks, fleet mix, runway dependencies, a mix of arrivals/departures, and variations in aircraft separations. arrivals/departures, and variations in aircraft separations. • daily capacity gives information on the maximum sustainable capacity for a relatively long daily capacity gives information on the maximum sustainable capacity for a relatively long period period of time: congestion or workload of the Air Traffic Control Operator (ATCO) can be of time: congestion or workload of the Air Trac Control Operator (ATCO) can be considered as considered as parameters that limit the analyses. parameters that limit the analyses. • annual capacity provides a high-level guide useful to plan the master plan and foresee the timing annual capacity provides a high-level guide useful to plan the master plan and foresee the timing of the airport saturation, taking into account the traffic forecast. of the airport saturation, taking into account the trac forecast. Moreover, different capacities can be determined as regards the weather conditions: optimal Moreover, di erent capacities can be determined as regards the weather conditions: capacity (i.e., number of movements that can be managed in 1 h in optimal qualified conditions when optimal capacity (i.e., number of movements that can be managed in 1 h in optimal qualified conditions a visual flight is possible) and reduced capacity (i.e., obtained for adverse weather conditions, when a visual flight is possible) and reduced capacity (i.e., obtained for adverse weather conditions, especially for low visibility conditions, when the flight should be instrumental) [8]. Other factors especially for low visibility conditions, when the flight should be instrumental) [8]. Other factors affect the overall airport capacity [9]: the geometrical layout of the runway and taxiway systems, a ect the overall airport capacity [9]: the geometrical layout of the runway and taxiway systems, geometrical and logistic setup of the apron areas, the size and speed of expected aircraft, the approach geometrical and logistic setup of the apron areas, the size and speed of expected aircraft, the approach and departure procedures [10], the procedures adopted by ATCO [11], the traffic routes and the air and departure procedures [10], the procedures adopted by ATCO [11], the trac routes and the air traffic management technologies, and the safety [12–14] and environmental procedures adopted to trac management technologies, and the safety [12–14] and environmental procedures adopted to manage the traffic. manage the trac. Having regard to the airside capacity (which takes into account runways, taxiways, aircraft Having regard to the airside capacity (which takes into account runways, taxiways, aircraft stand stand taxi lanes, apron taxiways, and aircraft stands), the technical capacity value is the maximum taxi lanes, apron taxiways, and aircraft stands), the technical capacity value is the maximum number number of aircraft movements that can be managed in a unit of time over a peak period, having of aircraft movements that can be managed in a unit of time over a peak period, having regard to the regard to the adopted procedures, traffic rules, and an acceptable average delay that reflects the adopted procedures, trac rules, and an acceptable average delay that reflects the quality of service quality of service (Figure 1 presents a technical capacity curve for a 10-min delay threshold). (Figure 1 presents a technical capacity curve for a 10-min delay threshold). Maximum technical capacity 10 20 30 40 50 Technical capacity with 10-min delay (movements) Figure 1. Example of the technical capacity curve for a 10-min delay threshold. Figure 1. Example of the technical capacity curve for a 10-min delay threshold. The declared capacity is the number of movements that can be processed in one hour. It is a fixed The declared capacity is the number of movements that can be processed in one hour. It is a fixed value provided by each airport manager. This value reveals a strategic choice made by the airport manager: value pro the vid declaration ed by eachof aia rp much ort mlower anager capacity . This va than lue re the veoptimal als a stra one tegiallows c choice reducing made by the th possible e airport manager: the declaration of a much lower capacity than the optimal one allows reducing the possible delays until the weather conditions are unfavorable, but the number of hourly movements that can be performed delays unis tilsmaller the wea . ther conditions are unfavorable, but the number of hourly movements that can be performed is smaller. The present study aims to calculate the technical capacity of an airport whose infrastructure layout The present study aims to calculate the technical capacity of an airport whose infrastructure has two crossing runways. Six scenarios have been considered: each one refers to the busiest volume layout has two crossing runways. Six scenarios have been considered: each one refers to the busiest in the reference year under di erent weather conditions. The current number of managed movements volume in the reference year under different weather conditions. The current number of managed movements has been increased, with 10% steps up to double it. Therefore, 10 clones have been Delay (minutes) Infrastructures 2020, 5, 111 3 of 15 has been increased, with 10% steps up to double it. Therefore, 10 clones have been examined for each starting scenario (one clone to each 10% increase). Once the various clones have been simulated, output data have been extrapolated in terms of occupancy, delay, and taxiing time in order to determine the airside capacity for each of them. The results from di erent scenarios have allowed the identification of the best performing configuration. 2. Methods Di erent methods allow calculating airport capacity: they di er in terms of e ort, data requirements, and costs [10]. ACRP [6] distinguishes 5 types of airport capacity assessment tools: Level 1: the capacity performances depend on the runway layout and the fleet mix composition. An example of this level is provided by FAA AC 150/5060-5 [4]: it provides the maximum hourly capacity values and an annual capacity of the analyzed system, modeling it as a predefined layout that fits as well as possible the examined airport; Level 2: charts, nomograms, and spreadsheets consider the airport layout (i.e., taxiways and gates), the fleet mix composition, the percentage of arrivals to total operations, and the percentage of touch and go operations. Chapter 3 of FAA AC 150/5060-5 reports an example of this method [4]; Level 3: analytical models consider the final speed of approaching aircraft, separations, and ATC rules. Capacity values are relatively easy to calculate, but they overlook the airside layout and the airspace characteristics. Chapter 5 of [4] reports an example of this approach; Level 4: simulation models consider both input data of Level 3 and factors, such as the geometry of arrival and departure routes or fleet mix on each runway; Level 5: aircraft delay simulation models (e.g., fast time simulation or real-time models) are the most advanced tool to study the whole airport: they cover all aspects of the airport, not only the airside-related ones, reconstructing the overall gate-to-gate environment. The output data contain data on capacity and delays, curves of noise, engine-on waiting time, fuel consumption, environmental impacts, and workload of air trac controllers. These methods have been studied for several years, and, currently, there are some very sophisticated software products [15,16] that can give results very close to reality when the configuration of the infrastructure is complex or an advanced design level is required [8,10,17–22]. Level 5-models can incorporate a wide range of features (e.g., complete airport layouts, ATC procedures, airport operating procedures, runway entry and exit points and taxiways routes, fleet mix, wake turbulence classification, separation over time, turnaround times, and control tower activities) [11,18]. The output data contain data on capacity and delays, curves of noise, engine-on waiting time, fuel consumption, environmental impacts, and workload of air trac controllers [11]. In this study, a fast time simulation (FTS) model has been built to calculate the capacity of an international airport whose layout is composed of a two-runway system, with two dependent, not perpendicular runways (i.e., RWY 09/27 and RWY 05/23) (Figure 2). The used software is AirTOp (Air Trac Optimization), a gate-to-gate fast time simulator [16]. The simulation model is a simplified and virtual replica of a real system: given required levels of precision and detail, it reflects the set of relevant geometrical and procedural characteristics. The algorithms in the platform allow realistic simulations by creating airport models and air spaces in a 3D environment that changes over time and can be configured by setting a series of input variables. The analyses are faster than reality: depending on the number of input data, the software can take a few seconds or minutes to simulate one day of real-time. In the implemented model, all geometrical input data comply with the Aeronautical Information Publication of the airport: they consider runways, taxiways, rapid exit taxiways, aprons, and holding bays; moreover, this source has been considered to implement in the simulation all the adopted airside and ground side procedures (e.g., self-maneuvering or push-back procedures on the apron, standard instrument departure, and standard terminal arrival route for departures and arrivals, respectively). 09 Infrastructures 2020, 5, x FOR PEER REVIEW 4 of 15 Infrastructures 2020, 5, 111 4 of 15 Figure 2. Airport layout. Figure 2. Airport layout. In 2019, almost 45,000 movements (90% performed by A319 and A320) and more than 7,000,000 passengers were registered in this airport. The volume of movements strongly depends on In 2019, almost 45,000 movements (90% performed by A319 and A320) and more than 7,000,000 the month and the day of the week: the number of movements during the busiest month (i.e., August) passengers were registered in this airport. The volume of movements strongly depends on the month di ers by more than 30% from that of the less-tracked one (i.e., March); during the busiest month, and the day of the week: the number of movements during the busiest month (i.e., August) differs by the number of daily movements ranges between 145 and 186. Statistical anomalies of trac volume in more than 30% from that of the less-trafficked one (i.e., March); during the busiest month, the number the busiest month (e.g., days with unfavorable weather conditions, closure of airside infrastructure, of daily movements ranges between 145 and 186. Statistical anomalies of traffic volume in the busiest irregular occurrences, and errors in air trac management systems) have been ignored in order month (e.g., days with unfavorable weather conditions, closure of airside infrastructure, irregular to identify the statistically significant value of daily movement. Table 1 lists the input data about occurrences, and errors in air traffic management systems) have been ignored in order to identify the weather conditions. statistically significant value of daily movement. Table 1 lists the input data about weather conditions. Table 1. Input data of weather conditions. Humidity Visibility Pressure Rain Table 1. Input data of weather conditions. Air Temperature ( C) Wind Speed (km/h) Gusts (%) (km) (mb) (mm) Average Minimum Maximum Humidity Visibilit A yverage Maximum Pressure Rain Air Temperature (° C) Wind Speed (km/h) Gusts 19 13 23 80 20 11 21 1016 0 absent (%) (km) (mb) (mm) Average Minimum Maximum Average Maximum Under such conditions, the value of the occurred daily movements nearest to the statistical average 19 13 23 80 20 11 21 1016 0 absent value has been 178. Wake turbulence (WT) and size category of the airplanes composing the fleet mix have been analyzed in order to consider their negative e ects on throughput (e.g., on-air longitudinal Under such conditions, the value of the occurred daily movements nearest to the statistical separation and on-ground constraints). Indeed, a homogeneous trac fleet leads to higher throughput average value has been 178. Wake turbulence (WT) and size category of the airplanes composing the and a standard minimum separation equal to 3 NM. On the other hand, when the fleet mix is mixed (e.g., fleet mix have been analyzed in order to consider their negative effects on throughput (e.g., on-air variable VT category [4,23]), the airplane on-air longitudinal separation is up to 8 NM if a light aircraft longitudinal separation and on-ground constraints). Indeed, a homogeneous traffic fleet leads to follows a super heavy one. In this study, the mixed fleet mix has required the definition of a variable higher throughput and a standard minimum separation equal to 3 NM. On the other hand, when the airplane on-air longitudinal separation during simulations. Particularly, for each movement that fleet mix is mixed (e.g., variable VT category [4,23]), the airplane on-air longitudinal separation is up occurred during this day, the FTS simulations have considered origin/destination, route, aircraft type, to 8 NM if a light aircraft follows a super heavy one. In this study, the mixed fleet mix has required airline, flight number, and arrival/departure time. the definition of a variable airplane on-air longitudinal separation during simulations. Particularly, The baseline scenario (i.e., trac volume and composition in the busiest day) (BS) and six for each movement that occurred during this day, the FTS simulations have considered operative scenarios with increased trac volume have been analyzed: they di er for runway use origin/destination, route, aircraft type, airline, flight number, and arrival/departure time. under di erent wind conditions (in Figure 3, departures are represented by continuous arrows, arrivals The baseline scenario (i.e., traffic volume and composition in the busiest day) (BS) and six by dotted arrows): operative scenarios with increased traffic volume have been analyzed: they differ for runway use Scenario 05_27 (I_05_27): departures from threshold 05 and arrivals on threshold 27 (blue arrows under different wind conditions (in Figure 3, departures are represented by continuous arrows, in Figure 3); arrivals by dotted arrows): Scenario 09_23 (I_09_23): departures from threshold 09 and arrivals on threshold 23 (red arrows • Scenario 05_27 (I_05_27): departures from threshold 05 and arrivals on threshold 27 (blue arrows in Figure 3); in Figure 3); Scenario 27_27 (I_27_27): both departures and arrivals in threshold 27 (green arrows in Figure 3); • Scenario 09_23 (I_09_23): departures from threshold 09 and arrivals on threshold 23 (red arrows Scenario 09_09 (I_09_09): both departures and arrivals in threshold 09 (orange arrows in Figure 3); in Figure 3); Scenario 23_23 (I_23_23): both departures and arrivals in threshold 23 (purple arrows in Figure 3); 27 09 Infrastructures 2020, 5, x FOR PEER REVIEW 5 of 15 • Scenario 27_27 (I_27_27): both departures and arrivals in threshold 27 (green arrows in Figure 3); • Scenario 09_09 (I_09_09): both departures and arrivals in threshold 09 (orange arrows in Figure 3); Infrastructures 2020, 5, 111 5 of 15 • Scenario 23_23 (I_23_23): both departures and arrivals in threshold 23 (purple arrows in Figure 3); • Scenario 05_05 (I_05_05): both departures and arrivals in threshold 05 (pink arrows in Figure 3). Scenario 05_05 (I_05_05): both departures and arrivals in threshold 05 (pink arrows in Figure 3). Figure 3. Operative airport layout. Figure 3. Operative airport layout. The trac volume of BS has been increased with 10% steps up to double BS. Therefore, 10 clones The traffic volume of BS has been increased with 10% steps up to double BS. Therefore, 10 clones (CLs) have been examined for each increased scenario (one clone to each 10% increase). The FTS (CLs) have been examined for each increased scenario (one clone to each 10% increase). The FTS simulation has allowed to model and simulate separately all the examined cases in order to identify simulation has allowed to model and simulate separately all the examined cases in order to identify their performances and weaknesses. Particularly, for each CL, 10 runs have been calculated in order to their performances and weaknesses. Particularly, for each CL, 10 runs have been calculated in order minimize randomness in the obtained results. to minimize randomness in the obtained results. Data reports are automatically organized into 10-min buckets and rolling hours: for each period, Data reports are automatically organized into 10-min buckets and rolling hours: for each period, the total delay (i.e., both approaching delay and on-ground delay) and the hourly activity (i.e., the total delay (i.e., both approaching delay and on-ground delay) and the hourly activity (i.e., both both arrivals and departures) have been considered. For each increased scenario, two graphs are arrivals and departures) have been considered. For each increased scenario, two graphs are produced: produced: the former presents delays. Particularly, each delay value is the average of ten delay values • the former presents delays. Particularly, each delay value is the average of ten delay values obtained from 10 runs carried out for each clone. This representation allows verifying if the obtained from 10 runs carried out for each clone. This representation allows verifying if the average delay overcomes the average delay threshold (i.e., 10 min); average delay overcomes the average delay threshold (i.e., 10 min); the latter presents the daily throughput in a Cartesian plane. Given an average delay for a single • the latter presents the daily throughput in a Cartesian plane. Given an average delay for a single aircraft of less than 10 min, each point represents the number of operations that can be performed aircraft of less than 10 min, each point represents the number of operations that can be performed in one hour without violating air trac control rules, assuming continuous aircraft demand. in one hour without violating air traffic control rules, assuming continuous aircraft demand. The Pareto frontier (i.e., the set of pairs of arrivals and departures at which both the arrival and The Pareto frontier (i.e., the set of pairs of arrivals and departures at which both the arrival and the departure rate cannot be simultaneously increased) identifies the saturation capacity of the airport. the departure rate cannot be simultaneously increased) identifies the saturation capacity of the Firstly, the study has identified the maximum throughput at the balanced priority (i.e., the maximum airport. Firstly, the study has identified the maximum throughput at the balanced priority (i.e., the admitted movements on the 45-degree line) and then verified that other points on the frontier ensure maximum admitted movements on the 45-degree line) and then verified that other points on the the maximum throughput within the admitted delay. frontier ensure the maximum throughput within the admitted delay. 3. Results 3. Results The simultaneous study of the delay report and the Pareto frontier has permitted to evaluate The simultaneous study of the delay report and the Pareto frontier has permitted to evaluate the the saturation of the examined airport having regard to its throughput. For each examined scenario, saturation of the examined airport having regard to its throughput. For each examined scenario, the the average hourly delay and the number of movements have been considered: the average delay is average hourly delay and the number of movements have been considered: the average delay is calculated considering the sum runs of each clone; when its value reaches or exceeds 10 min, the run is calculated considering the sum runs of each clone; when its value reaches or exceeds 10 min, the run considered. The throughput representation refers to all 10 runs for each clone: it allows studying the is considered. The throughput representation refers to all 10 runs for each clone: it allows studying daily trend of movements. Figures 4–6 represent the results for I_05_27: average delay, throughput, and Pareto frontier, respectively. 27 Infrastructures 2020, 5, x FOR PEER REVIEW 6 of 15 Infrastructur the daily estre 2020 nd , 5 , o 111 f movements. Figures 4–6 represent the results for I_05_27: average d 6el of a15 y, throughput, and Pareto frontier, respectively. CL00 I_05_27 3,5 2,5 1,5 0,5 (a) CL10 I_05_27 3,5 2,5 1,5 0,5 (b) CL50 I_05_27 (c) Figure 4. Average delay for I_05_27 (a) CL00; (b) CL10; (c) CL50. Figure 4. Average delay for I_05_27 (a) CL00; (b) CL10; (c) CL50. Average delay (min) Average delay (min) Average delay (min) 01:00:00 01:00:00 01:00:00 01:30:00 01:30:00 01:30:00 02:00:00 02:00:00 02:00:00 02:30:00 02:30:00 02:30:00 03:00:00 03:00:00 03:00:00 03:30:00 03:30:00 03:30:00 04:00:00 04:00:00 04:00:00 04:30:00 04:30:00 04:30:00 05:00:00 05:00:00 05:00:00 05:30:00 05:30:00 05:30:00 06:00:00 06:00:00 06:00:00 06:30:00 06:30:00 06:30:00 07:00:00 07:00:00 07:00:00 07:30:00 07:30:00 07:30:00 08:00:00 08:00:00 08:00:00 08:30:00 08:30:00 08:30:00 09:00:00 09:00:00 09:00:00 09:30:00 09:30:00 09:30:00 10:00:00 10:00:00 10:00:00 10:30:00 10:30:00 10:30:00 11:00:00 11:00:00 11:00:00 11:30:00 11:30:00 11:30:00 12:00:00 12:00:00 12:00:00 12:30:00 12:30:00 12:30:00 13:00:00 13:00:00 13:00:00 13:30:00 13:30:00 13:30:00 14:00:00 14:00:00 14:00:00 14:30:00 14:30:00 14:30:00 15:00:00 15:00:00 15:00:00 15:30:00 15:30:00 15:30:00 16:00:00 16:00:00 16:00:00 16:30:00 16:30:00 16:30:00 17:00:00 17:00:00 17:00:00 17:30:00 17:30:00 17:30:00 18:00:00 18:00:00 18:00:00 18:30:00 18:30:00 18:30:00 19:00:00 19:00:00 19:00:00 19:30:00 19:30:00 19:30:00 20:00:00 20:00:00 20:00:00 20:30:00 20:30:00 20:30:00 21:00:00 21:00:00 21:00:00 21:30:00 21:30:00 21:30:00 22:00:00 22:00:00 22:00:00 22:30:00 22:30:00 22:30:00 23:00:00 23:00:00 23:00:00 23:30:00 23:30:00 23:30:00 Infrastructures 2020, 5, 111 7 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 7 of 15 In Figure 4, the average delay for I_05_27 CL00 (current trac volume) is below the limit (i.e., In Figure 4, the average delay for I_05_27 CL00 (current traffic volume) is below the limit (i.e., 10 min) (Figure 4a); with CL10, the average delay has increased, but it is below the limit (Figure 4b); 10 min) (Figure 4a); with CL10, the average delay has increased, but it is below the limit (Figure 4b); CL50 is the first examined clone where average delays coincide with the limit (Figure 4a): it is the CL50 is the first examined clone where average delays coincide with the limit (Figure 4a): it is the last last step of increased trac not overcoming the average delay threshold. Therefore, in Figure 4, the step of increased traffic not overcoming the average delay threshold. Therefore, in Figure 4, the authors have not presented the results from other clones. authors have not presented the results from other clones. The results in Figure 5 highlight that peaks of hourly throughput correspond to peaks of average The results in Figure 5 highlight that peaks of hourly throughput correspond to peaks of average delay (Figure 4); for each clone, throughput in di erent runs is almost constant: during simulations, delay (Figure 4); for each clone, throughput in different runs is almost constant: during simulations, trac jams or diculty in the management of the demand have been absent. Moreover, throughput traffic jams or difficulty in the management of the demand have been absent. Moreover, throughput peaks are increasing with the clones (Figure 5a,b) until CL50 saturation conditions do not occur. peaks are increasing with the clones (Figure 5a,b) until CL50 saturation conditions do not occur. Over Over CL50 (Figure 5c,d), the already achieved throughput peaks are almost constant: by increasing the CL50 (Figure 5c,d), the already achieved throughput peaks are almost constant: by increasing the airport trac by over 50%, the current volume causes saturation of the infrastructure. airport traffic by over 50%, the current volume causes saturation of the infrastructure. CL00 I_05_27 run1 run2 run3 run4 run5 run6 run7 10 run8 run9 run10 (a) CL10 I_05_27 run1 run2 run3 run4 run5 run6 run7 run8 run9 run10 (b) Figure 5. Cont. Throughput Throughput 01:20:00 01:20:00 01:50:00 01:50:00 02:20:00 02:20:00 02:50:00 02:50:00 03:20:00 03:20:00 03:50:00 03:50:00 04:20:00 04:20:00 04:50:00 04:50:00 05:20:00 05:20:00 05:50:00 05:50:00 06:20:00 06:20:00 06:50:00 06:50:00 07:20:00 07:20:00 07:50:00 07:50:00 08:20:00 08:20:00 08:50:00 08:50:00 09:20:00 09:20:00 09:50:00 09:50:00 10:20:00 10:20:00 10:50:00 10:50:00 11:20:00 11:20:00 11:50:00 11:50:00 12:20:00 12:20:00 12:50:00 12:50:00 13:20:00 13:20:00 13:50:00 13:50:00 14:20:00 14:20:00 14:50:00 14:50:00 15:20:00 15:20:00 15:50:00 15:50:00 16:20:00 16:20:00 16:50:00 16:50:00 17:20:00 17:20:00 17:50:00 17:50:00 18:20:00 18:20:00 18:50:00 18:50:00 19:20:00 19:20:00 19:50:00 19:50:00 20:20:00 20:20:00 20:50:00 20:50:00 21:20:00 21:20:00 21:50:00 21:50:00 22:20:00 22:20:00 22:50:00 22:50:00 23:20:00 23:20:00 23:50:00 23:50:00 Infrastructures 2020, 5, 111 8 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 8 of 15 CL50 I_05_27 run1 run2 run3 run4 run5 20 run6 run7 run8 run9 run10 (c) CL100 I_05_27 run1 run2 run3 run4 run5 run6 run7 run8 run9 run10 (d) Figure 5. Throughput for I_05_27 (a) CL00; (b) CL10; (c) CL50; (d) CL100. Figure 5. Throughput for I_05_27 (a) CL00; (b) CL10; (c) CL50; (d) CL100. Figure 6 represents the envelope of the Pareto frontier (green line) obtained for I_05_27 clones Figure 6 represents the envelope of the Pareto frontier (green line) obtained for I_05_27 clones from CL00 to CL50: the FTS software has increased by 50% the current arrival/departure mix to obtain from CL00 to CL50: the FTS software has increased by 50% the current arrival/departure mix to obtain that of CL50. In the balanced mode (i.e., on the main diagonal), the overall number of movements is that of CL50. In the balanced mode (i.e., on the main diagonal), the overall number of movements is 22 (11 departures and 11 arrivals), and it is the maximum throughput value that ensures a not more 22 (11 departures and 11 arrivals), and it is the maximum throughput value that ensures a not more than 10 min delay. Once identified the maximum balanced throughput, it is taken as a reference for than 10 min delay. Once identified the maximum balanced throughput, it is taken as a reference for identifying other points on the frontier, varying the number of movements, and having delay values identifying other points on the frontier, varying the number of movements, and having delay values under the established threshold. Therefore, points obtained for clones with more than 50% traffic under the established threshold. Therefore, points obtained for clones with more than 50% trac increase are not represented because they refer to average delay values over 10 min. increase are not represented because they refer to average delay values over 10 min. Throughput Throughput 01:20:00 01:20:00 01:50:00 01:50:00 02:20:00 02:20:00 02:50:00 02:50:00 03:20:00 03:20:00 03:50:00 03:50:00 04:20:00 04:20:00 04:50:00 04:50:00 05:20:00 05:20:00 05:50:00 05:50:00 06:20:00 06:20:00 06:50:00 06:50:00 07:20:00 07:20:00 07:50:00 07:50:00 08:20:00 08:20:00 08:50:00 08:50:00 09:20:00 09:20:00 09:50:00 09:50:00 10:20:00 10:20:00 10:50:00 10:50:00 11:20:00 11:20:00 11:50:00 11:50:00 12:20:00 12:20:00 12:50:00 12:50:00 13:20:00 13:20:00 13:50:00 13:50:00 14:20:00 14:20:00 14:50:00 14:50:00 15:20:00 15:20:00 15:50:00 15:50:00 16:20:00 16:20:00 16:50:00 16:50:00 17:20:00 17:20:00 17:50:00 17:50:00 18:20:00 18:20:00 18:50:00 18:50:00 19:20:00 19:20:00 19:50:00 19:50:00 20:20:00 20:20:00 20:50:00 20:50:00 21:20:00 21:20:00 21:50:00 21:50:00 22:20:00 22:20:00 22:50:00 22:50:00 23:20:00 23:20:00 23:50:00 23:50:00 Infrastructures 2020, 5, 111 9 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 9 of 15 0 5 10 15 20 0 5 10 15 20 Departures Departures Figure 6. The envelope of Pareto frontier for I_05_27 clones from CL00 to CL50. Figure 6. The envelope of Pareto frontier for I_05_27 clones from CL00 to CL50. Figure 6. The envelope of Pareto frontier for I_05_27 clones from CL00 to CL50. The presented output have been considered for all the examined scenarios. Figure 7a,b represent The presented output have been considered for all the examined scenarios. Figure 7a,b represent The presented output have been considered for all the examined scenarios. Figure 7a,b represent the comparison of average delay for CL00 and CL30, respectively. the comparison of average delay for CL00 and CL30, respectively. the comparison of average delay for CL00 and CL30, respectively. 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 CL00 I_05_27 CL00 I_23_23 CL00 I_09_23 CL00 I_05_27 CL00 I_23_23 CL00 I_09_23 CL00 I_09_09 CL00 I_27_27 CL00 I_05_05 CL00 I_09_09 CL00 I_27_27 CL00 I_05_05 (a) (a) Figure 7. Cont. Average delay (min) Arrivals Average delay (min) Arrivals Infrastructures 2020, 5, 111 10 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 10 of 15 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 04:48:00 CL30 I_05_27 CL30 I_23_23 CL30 I_09_23 CL30 I_09_09 CL30 I_27_27 CL30 I_05_05 (b) Figure 7. Comparison of the average delay for all the examined scenarios (a) CL00; (b) CL30. Figure 7. Comparison of the average delay for all the examined scenarios (a) CL00; (b) CL30. In Figure 7a, all delay values are below the limit threshold: on average, the lowest average one is in In Figure 7a, all delay values are below the limit threshold: on average, the lowest average one I_05_27. This result is related to the runway layout: RWY 27, where arrivals are scheduled, crosses in its is in I_05_27. This result is related to the runway layout: RWY 27, where arrivals are scheduled, initial part RWY 05, where departures are scheduled. This allows departures to leave immediately after crosses in its initial part RWY 05, where departures are scheduled. This allows departures to leave the landing of the arriving aircraft. Scenarios I_09_09 and I_27_27 have the average delay curve similar immediately after the landing of the arriving aircraft. Scenarios I_09_09 and I_27_27 have the average to that of I_05_27: it is consistent because they are the same physical runway, but di erent thresholds delay curve similar to that of I_05_27: it is consistent because they are the same physical runway, but are considered. Delays of I_09_09 and I_27_27 are greater than I_05_27 because of the runway’s layout: different thresholds are considered. Delays of I_09_09 and I_27_27 are greater than I_05_27 because it is necessary to wait for the arriving aircraft to cover the entire runway before being able to free it. of the runway’s layout: it is necessary to wait for the arriving aircraft to cover the entire runway I_ 09_23 crosses RWY 23, where the arrivals are expected: this means that a departing aircraft on RWY before being able to free it. I_ 09_23 crosses RWY 23, where the arrivals are expected: this means that 09 should wait for the arriving aircraft on RWY 23 to clear at least half runway: such conditions justify a departing aircraft on RWY 09 should wait for the arriving aircraft on RWY 23 to clear at least half the high average delay values. Finally, I_05_05 and I_23_23 give the worst performance because of runway: such conditions justify the high average delay values. Finally, I_05_05 and I_23_23 give the double crossings. With the same trac increase (30%), the I_05_05 configuration has the highest delay worst performance because of double crossings. With the same traffic increase (30%), the I_05_05 (i.e., more than 18 min) and does not satisfy the 10-min limit threshold at di erent times of the day. configuration has the highest delay (i.e., more than 18 min) and does not satisfy the 10-min limit Therefore, a trac increase of 30% is not sustainable for I_05_05; the same is for I_23_23 (maximum threshold at different times of the day. Therefore, a traffic increase of 30% is not sustainable for average delay 15 min). The scenario that best supports this increase in terms of delays is I_05_27 I_05_05; the same is for I_23_23 (maximum average delay 15 min). The scenario that best supports (maximum average delay almost equal to that of CL_00). I_27_27 and I_09_09 tend to remain below the this increase in terms of delays is I_05_27 (maximum average delay almost equal to that of CL_00). limit threshold (maximum average delay of 11 min and 10.5 min, respectively). However, I_09_09 has I_27_27 and I_09_09 tend to remain below the limit threshold (maximum average delay of 11 min a slightly lower average delay (i.e., 10.5 min) than I_27_27 as the arrivals can take the quick exits on the and 10.5 min, respectively). However, I_09_09 has a slightly lower average delay (i.e., 10.5 min) than runway, leaving the runway free faster than I_27_27. Finally, I_09_23 has a maximum average delay I_27_27 as the arrivals can take the quick exits on the runway, leaving the runway free faster than (13 I_27 min), _27. Fi higher nally, than I_09the _23other has a critical maxim scenarios um averI_05_05, age dela I_27_27, y (13 mand in), I_09_09. higher than the other critical In all the examined scenarios, the highest delays are during morning hours, but important scenarios I_05_05, I_27_27, and I_09_09. di erIn ences all ar the e in exterms amined of scena throughput rios, th of e CL00 highest and del CL30 ays (Figur are duri e 8n a,b, g m respectively). orning hours, but important In Figure 8a, all scenarios have the same throughput trend: CL_00 does not report increases, differences are in terms of throughput of CL00 and CL30 (Figure 8a,b, respectively). and, consequently, it is expected that the behavior of each scenario is almost similar. Only scenarios I_05_05 and I_23_23 deviate from other throughput curves because they cannot support the same number of movements of others. It requires moving some movements to the following time slots with a consequent increase in delays and deviation of the movement curves compared to the standard trend. The other four scenarios could eciently manage the movements even after the 30% increase. Average delay (min) Infrastructures 2020, 5, 111 11 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 11 of 15 00:00:00 05:45:36 11:31:12 17:16:48 23:02:24 04:48:00 CL00 I_05_27 CL00 I_23_23 CL00 I_09_23 CL00 I_09_09 CL00 I_27_27 CL00 I_05_05 (a) 00:00:00 05:45:36 11:31:12 17:16:48 23:02:24 04:48:00 CL30 I_05_27 CL30 I_23_23 CL30 I_09_23 CL30 I_09_09 CL30 I_27_27 CL30 I_05_05 (b) Figure 8. Comparison of throughput for all scenarios (a) CL00; (b) CL30. Figure 8. Comparison of throughput for all scenarios (a) CL00; (b) CL30. Finally In Figure , the 8a,authors all scena have rios h compar ave the ed sam values e through of delays put trend: and CL movements _00 does nobtained ot report ifor ncrea saturation ses, and, clones conseqof ueeach ntly, scenario it is expe (Figur cted e th 9a ). t the behavior of each scenario is almost similar. Only scenarios I_05_0 The 5 a clones nd I_2that 3_23 cause devia saturation te from oon ther each thro scenario ughput ar curv e die s er b ent: ecau CL50 se thfor ey ca I_05_27, nnot suppo CL10rt for th I_09_23, e same CL40 number for oI_27_27, f movemen CL30 ts offor othI_09_09, ers. It reqCL20 uires m for ov I_23_23, ing some and mov CL00 emenfor ts to I_05_05. the folloTher wing efor time, e sl the otsmost with performing a consequen scenarios t increase arie nI_05_27, delays a I_27_27, nd deviand ation I_09_09 of the because movement they cur have ves higher compa per red centages to the st of an tra da  rd c incr trenease d. The within otherthe four thr scena eshold riodelay s could limit. efficAmong iently ma these, nage I_05_27 the mov has ement thes e lowest ven adelay fter thand e 30% the in highest crease. increase, Finalbut ly, th it e is a aut mixed hors h configuration ave compared and valhas ues the of d disadvantage elays and moof ver m unway ents ob cr ta ossing. ined foParticularly r saturation, for clon I_27_27, es of each sce it is possi narioble (Fito gure reach 9). a higher increase compared to I_09_09, but it involves a greater delay. The choice of the most advantageous scenario between them depends on a strategic decision: the airport manager will opt for I_27_27 if he prefers having more trac; on the other hand, he will opt for I_09_09 if he prefers having lower delay values. I_09_23 has rather low delays, but the maximum trac increase value is equal to 10%: a limited number of movements can be accepted in order not to Throughput Throughput Infrastructures 2020, 5, x FOR PEER REVIEW 12 of 15 CL50 I_05_27 CL10 I_09_23 CL40 I_27_27 CL30 I_09_09 CL20 I_23_23 2 CL0 I_05_05 Infrastructures 2020, 5, 111 12 of 15 exceed the delay. Due to runway crossing, both I_05_05 and I_23_23 have quite high delays and rather HOURS Infrastructures 2020, 5, x FOR PEER REVIEW 12 of 15 limited trac increases. 12 Figure 9. Comparison of saturation capacity average delay for each scenario. The clones that cause saturation on each scenario are different: CL50 for I_05_27, CL10 for I_09_23, CL40 for I_27_27, CL30 for I_09_09, CL20 for I_23_23, and CL00 for I_05_05. Therefore, the most performing scenarios are I_05_27, I_27_27, and I_09_09 because they have higher percentages CL50 I_05_27 CL10 I_09_23 of traffic increase within the threshold delay limit. Among these, I_05_27 has the lowest delay and CL40 I_27_27 the highest increase, but it is a mixed configuration and has the disadvantage of runway crossing. CL30 I_09_09 CL20 I_23_23 Particularly, for I_27_27, it is possible to reach a higher increase compared to I_09_09, but it involves 2 CL0 I_05_05 a greater delay. The choice of the most advantageous scenario between them depends on a str ategic decision: the airport manager will opt for I_27_27 if he prefers having more traffic; on the other hand, he will opt for I_09_09 if he prefers having lower delay values. I_09_23 has rather low delays, but the maximum traffic increase value is equal to 10%: a limited number of movements can be accepted in HOURS order not to exceed the delay. Due to runway crossing, both I_05_05 and I_23_23 have quite high Figure 9. Comparison of saturation capacity average delay for each scenario. delays and rather limited traffic increases. Figure 9. Comparison of saturation capacity average delay for each scenario. Having regard to the average results obtained for the saturation clone of each scenario, Figure Having regard to the average results obtained for the saturation clone of each scenario, Figure 10 1 compar 0 compa esre the s th thr e th oughput roughpu output. t output. The clones that cause saturation on each scenario are different: CL50 for I_05_27, CL10 for I_09_23, CL40 for I_27_27, CL30 for I_09_09, CL20 for I_23_23, and CL00 for I_05_05. Therefore, the most performing scenarios are I_05_27, I_27_27, and I_09_09 because they have higher percentages of traffic increase within the threshold delay limit. Among these, I_05_27 has the lowest delay and the highest increase, but it is a mixed configuration and has the disadvantage of runway crossing. Particularly, for I_27_27, it is possible to reach a higher increase compared to I_09_09, but it involves CL50 I_05_27 a greater delay. The choice of the most advantageous scenario between them depends on a str ategic CL10 I_09_23 CL40 I_27_27 decision: the airport manager will opt for I_27_27 if he prefers having more traffic; on the other hand, CL30 I_09_09 he will opt for I_09_09 if he prefers having lower delay values. I_09_23 has rather low delays, but the CL20 I_23_23 maximum traffic increase value is equal to 10%: a limited number of movements can be accepted in CL0 I_05_05 order not to exceed the delay. Due to runway crossing, both I_05_05 and I_23_23 have quite high delays and rather limited traffic increases. Having regard to the average results obtained for the saturation clone of each scenario, Figure 10 compares the throughput output. HOURS Figure 10. Comparison of saturation capacity throughput for each scenario. Figure 10. Comparison of saturation capacity throughput for each scenario. The throughput analysis highlights that I_05_27 has the highest number of hourly movements: it complies with the discussed data in Figure 9. Finally, the Pareto frontiers of the examined saturation The throughput analysis highlights that I_05_27 has the highest number of hourly movements: scenarios are in Figure 11. it complies with the discussed data in Figure 9. Finally, the Pareto frontiers of the examined saturation I_05_27 is the scenario that ensures the higher number of hourly movements (i.e., 22 with balanced CL50 I_05_27 scenarios are in Figure 11. CL10 I_09_23 conditions); both I_27_27 and I_09_09 allow 20 hourly movements with balanced conditions: they have CL40 I_27_27 a similar frontier. Both runway layout and aeronautical procedures a ect the maximum hourly capacity CL30 I_09_09 of other scenarios: in balanced conditions, I_23_23, I_05_05, and I_09_23 have the same maximum CL20 I_23_23 number of allowable movements per hour (i.e., 16), but with decreasing performances in unbalanced CL0 I_05_05 conditions (when departures are given priority 12, 11, and 10, respectively; when arrivals are given priority 12, 10, and 7, respectively). HOURS Figure 10. Comparison of saturation capacity throughput for each scenario. The throughput analysis highlights that I_05_27 has the highest number of hourly movements: it complies with the discussed data in Figure 9. Finally, the Pareto frontiers of the examined saturation scenarios are in Figure 11. Average delay (min) Average delay (min) Average throughput Average throughput 2 01:00:00 2 01:00:00 2 01:00:00 2 01:00:00 2 01:30:00 2 01:30:00 2 01:40:00 2 01:40:00 2 02:00:00 2 02:00:00 2 02:30:00 2 02:30:00 2 02:20:00 2 02:20:00 2 03:00:00 2 03:00:00 2 03:00:00 2 03:00:00 2 03:30:00 2 03:30:00 2 03:40:00 2 03:40:00 2 04:00:00 2 04:00:00 2 04:30:00 2 04:30:00 2 04:20:00 2 04:20:00 2 05:00:00 2 05:00:00 2 05:00:00 2 05:00:00 2 05:30:00 2 05:30:00 2 05:40:00 2 05:40:00 2 06:00:00 2 06:00:00 2 06:20:00 2 06:20:00 2 06:30:00 2 06:30:00 2 07:00:00 2 07:00:00 2 07:00:00 2 07:00:00 2 07:30:00 2 07:30:00 2 07:40:00 2 07:40:00 2 08:00:00 2 08:00:00 2 08:20:00 2 08:20:00 2 08:30:00 2 08:30:00 2 09:00:00 2 09:00:00 2 09:00:00 2 09:00:00 2 09:30:00 2 09:30:00 2 09:40:00 2 09:40:00 2 10:00:00 2 10:00:00 2 10:20:00 2 10:20:00 2 10:30:00 2 10:30:00 2 11:00:00 2 11:00:00 2 11:00:00 2 11:00:00 2 11:30:00 2 11:30:00 2 11:40:00 2 11:40:00 2 12:00:00 2 12:00:00 2 12:20:00 2 12:20:00 2 12:30:00 2 12:30:00 2 13:00:00 2 13:00:00 2 13:00:00 2 13:00:00 2 13:40:00 2 13:40:00 2 13:30:00 2 13:30:00 2 14:20:00 2 14:20:00 2 14:00:00 2 14:00:00 2 14:30:00 2 14:30:00 2 15:00:00 2 15:00:00 2 15:00:00 2 15:00:00 2 15:40:00 2 15:40:00 2 15:30:00 2 15:30:00 2 16:20:00 2 16:20:00 2 16:00:00 2 16:00:00 2 16:30:00 2 16:30:00 2 17:00:00 2 17:00:00 2 17:00:00 2 17:00:00 2 17:40:00 2 17:40:00 2 17:30:00 2 17:30:00 2 18:20:00 2 18:20:00 2 18:00:00 2 18:00:00 2 19:00:00 2 19:00:00 2 18:30:00 2 18:30:00 2 19:00:00 2 19:00:00 2 19:40:00 2 19:40:00 2 19:30:00 2 19:30:00 2 20:20:00 2 20:20:00 2 20:00:00 2 20:00:00 2 21:00:00 2 21:00:00 2 20:30:00 2 20:30:00 2 21:40:00 2 21:40:00 2 21:00:00 2 21:00:00 2 21:30:00 2 21:30:00 2 22:20:00 2 22:20:00 2 22:00:00 2 22:00:00 2 23:00:00 2 23:00:00 2 22:30:00 2 22:30:00 2 23:40:00 2 23:40:00 2 23:00:00 2 23:00:00 2 23:30:00 2 23:30:00 3 00:00:00 3 00:00:00 Infrastructures 2020, 5, 111 13 of 15 Infrastructures 2020, 5, x FOR PEER REVIEW 13 of 15 I_05_05 I_09_23 I_23_23 I_09_09 I_27_27 I_05_27 0 5 10 15 20 Departures Figure 11. The Pareto frontier of the examined scenarios. Figure 11. The Pareto frontier of the examined scenarios. 4. Conclusions I_05_27 is the scenario that ensures the higher number of hourly movements (i.e., 22 with The airside capacity is the attitude to manage air trac at take-o and landing movements balanced conditions); both I_27_27 and I_09_09 allow 20 hourly movements with balanced with safety and within acceptable delays. It can be assessed with di erent five levels of analysis: conditions: they have a similar frontier. Both runway layout and aeronautical procedures affect the simplistic and no longer updates methods attempt to describe real situations by approximating them maximum hourly capacity of other scenarios: in balanced conditions, I_23_23, I_05_05, and I_09_23 to predefined configurations (levels 1 and 2); analytical capacity models (level 3) permit to analyze have the same maximum number of allowable movements per hour (i.e., 16), but with decreasing small size airports and some more information in inputs; most recent simulation techniques (levels 4 performances in unbalanced conditions (when departures are given priority 12, 11, and 10, and 5) allow extremely detailed analyses. respectively; when arrivals are given priority 12, 10, and 7, respectively). In the present study, a fast time simulation model has been built to assess the airside capacity of an international airport whose layout is composed of two crossing runways. The obtained results, 4. Conclusions which depend on the input data (e.g., geometry, aeronautical procedures, ground handling processes, The airside capacity is the attitude to manage air traffic at take-off and landing movements with anemometric conditions, fleet mix composition), allow the airport manager to identify the most critical safety and within acceptable delays. It can be assessed with different five levels of analysis: simplistic configurations in terms of delay and number of movements managed in 1 h having regard to the and no longer updates methods attempt to describe real situations by approximating them to current trac volume. Six scenarios have been considered: each one refers to the busiest volume predefined configurations (levels 1 and 2); analytical capacity models (level 3) permit to analyze small in the reference year under di erent weather and usage conditions. On the other hand, the current size airports and some more information in inputs; most recent simulation techniques (levels 4 and number of managed movements has been increased with 10% steps up to double it in order to explore 5) allow extremely detailed analyses. the technical or logistic feasibility of grown trac. The most performing configurations are I_05_27 In the present study, a fast time simulation model has been built to assess the airside capacity of (22 movements/hour), I_09_09 (20 movements/hour), and I_27_27 (20 movements/hour). However, an international airport whose layout is composed of two crossing runways. The obtained results, I_05_27 is less preferable than the others because it has an intersection between runways that slows which depend on the input data (e.g., geometry, aeronautical procedures, ground handling down the airplane path fluidity. Therefore, it allows a possible increase in trac and a lower delay processes, anemometric conditions, fleet mix composition), allow the airport manager to identify the value, but it cannot be considered the most performing because movements are less fluid than I_09_09 most critical configurations in terms of delay and number of movements managed in 1 h having and I_27_27. regard to the current traffic volume. Six scenarios have been considered: each one refers to the busiest volume in the reference year under different weather and usage conditions. On the other hand, the Author Contributions: Conceptualization, P.D.M. and L.M.; methodology, P.D.M.; software, L.M.; validation, L.M. and P.D.M.; formal analysis, L.M.; investigation, L.M.; data curation, P.D.M. and L.M.; writing—original draft current number of managed movements has been increased with 10% steps up to double it in order preparation, L.M.; review and editing, P.DM. and L.M.; supervision, P.D.M. All authors have read and agreed to to explore the technical or logistic feasibility of grown traffic. The most performing configurations the published version of the manuscript. are I_05_27 (22 movements/hour), I_09_09 (20 movements/hour), and I_27_27 (20 movements/hour). Funding: This research received no external funding. However, I_05_27 is less preferable than the others because it has an intersection between runways Acknowledgments: The authors sincerely thank Eng. Martina Possanzini for her support in building the FTS that slows down the airplane path fluidity. Therefore, it allows a possible increase in traffic and a model and Italian air navigation service provider (ENAV) for having granted the use of the software AirTOp. lower delay value, but it cannot be considered the most performing because movements are less fluid Conflicts of Interest: The authors declare no conflict of interest. than I_09_09 and I_27_27. Author Contributions: Conceptualization, P.D.M. and L.M.; methodology, P.D.M.; software, L.M.; validation, L.M. and P.D.M.; formal analysis, L.M.; investigation, L.M.; data curation, P.D.M. and L.M.; writing—original Arrivals Infrastructures 2020, 5, 111 14 of 15 References 1. International Civil Aviation Organization (ICAO). Advanced Surface Movement Guidance and Control Systems (A-SMGCS) Manual. Doc 9830 AN/452, First Edition. Available online: https://www.icao.int/ Meetings/anconf12/Document%20Archive/9830_cons_en[1].pdf (accessed on 9 October 2020). 2. International Civil Aviation Organization (ICAO). Manual on Global Performance of the Air Navigation System. Doc 9883, First Edition. Available online: http://www.aviationchief.com/uploads/9/2/0/9/92098238/ icao_doc_9883_-_manual_on_global_performance_of_air_navigation_system_-_1st_edition_-_2010.pdf (accessed on 9 October 2020). 3. Airports Council International (ACI). 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Journal

Infrastructures – Multidisciplinary Digital Publishing Institute

Published: Dec 4, 2020

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