CN114758528B - Airport terminal area capacity prediction method based on service resource supply and demand balance - Google Patents
Airport terminal area capacity prediction method based on service resource supply and demand balance Download PDFInfo
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Abstract
The invention discloses a method for predicting the capacity of an airport terminal area based on service resource supply and demand balance, which comprises the following steps: step one, constructing a flight task service probability matrix; step two, calculating expected flight values of various aircrafts in different airlines; step three, calculating an expected value of the required service time in the terminal area; step four, calculating the expected maximum service time length; step five, constructing a terminal area capacity calculation model; and step six, correcting the terminal area capacity calculation model to obtain a terminal area capacity calculation model based on the balance of supply and demand of service resources. The invention provides a terminal area capacity calculation model based on a service resource view angle, which can effectively predict the hour capacity of an airport terminal area, or can calculate the maximum number of aircrafts which can simultaneously receive air traffic service by a sector under the condition of known capacity, thereby providing theoretical basis and reference for the sector division of the terminal area and the traffic alarming work.
Description
Technical Field
The invention relates to an airport terminal area capacity prediction method based on service resource supply and demand balance.
Background
Airport terminal areas are important coupling areas of air transport networks, and capacity limitation and insufficient service guarantee capability are main reasons for delay generation and propagation. Air traffic flow management is the most effective scheme for reducing delay, ensuring air traffic safety and relieving the workload of controller workers at present. In the implementation of air traffic flow management, accurate and effective assessment of terminal areas is a major task and an early stage of operation. Therefore, the research of the terminal area capacity assessment algorithm has important significance for safe, stable and efficient operation and management of airports and airspace.
At present, research on terminal area capacity assessment methods at home and abroad is mainly divided into three types: mathematical model construction and calculation, computer quick simulation and controller load analysis method. Overseas, mitchell J et al have studied the airspace channel maximum capacity model by using a maximum flow minimum cut method by analyzing the geometric shape and random weather model of the sector airspace; the Janic M and the like perform capacity assessment based on the workload of the controllers, and study the influence of the Janic M on the airspace capacity from three angles of control programs, interval regulations and service rules; after redefining the route structure, kageyama K et al uses computer simulation techniques to model a controller-based workload capacity assessment model. In China, dong Xiangning and the like analyze the assessment method of the workload of the manager, and a new terminal area capacity assessment model is built by improving the capacity assessment model of the workload of the manager; li Yinfeng and the like establish a terminal area capacity influence mechanism analysis model under a multi-operation scene based on a blocking flow network theory and method; shen Linan and the like add delay level as an influence factor in terminal capacity evaluation, and establish a mathematical calculation model between delay time and aircraft number; yellow sea cleaning and the like utilize the maximum flow minimum cut theory and an improved genetic algorithm to compare and analyze the capacity of a terminal area under military operation. Peng Ying et al combine deep learning with terminal area traffic prediction to propose a multiple input deep learning model that takes weather characteristics into account. The influence of convection weather is fully considered by the mao-citizen and the like, and a terminal area prediction model based on random forests is constructed.
The traditional terminal area capacity mathematical model is constructed by taking one or more influence factors as constraint conditions, and setting corresponding objective functions so as to realize capacity calculation. Different mathematical models consider different influencing factors, and have large differences among the models. Although the calculation accuracy of the terminal area capacity in a specific scene is improved to some extent, the complexity of the operation in the capacity estimation process is also increased. Among model variables, most of the existing mathematical models use random factors such as weather as constraint variables. Less consideration is given to the service resource supply and demand balance, and the influence of the service resource on the capacity is ignored.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides an airport terminal area capacity prediction method based on service resource supply and demand balance.
The technical scheme adopted for solving the technical problems is as follows: an airport terminal area capacity prediction method based on service resource supply and demand balance comprises the following steps:
step one, constructing a flight task service probability matrix;
step two, calculating expected flight values of various aircrafts in different airlines;
step three, calculating an expected value of the required service time in the terminal area;
step four, calculating the expected maximum service time length;
step five, constructing a terminal area capacity calculation model;
and step six, correcting the terminal area capacity calculation model to obtain a terminal area capacity calculation model based on the balance of supply and demand of service resources.
Compared with the prior art, the invention has the following positive effects:
in order to effectively predict the hour capacity of the airport terminal area or calculate the maximum number of aircrafts which can simultaneously receive air traffic services by a sector under the condition of known capacity, the invention provides a terminal area capacity calculation model based on a service resource view angle. Firstly, according to the configuration of the terminal area route network, traffic flow characteristics and airspace security guarantee capability requirements, the supply and demand relationship between the terminal area capacity and the service resource consumption is analyzed. Then, a flight service probability matrix is defined, and a terminal area demand service time model and a maximum available service time model are established. And constructing a terminal area capacity prediction model based on the service resource supply and demand balance. And finally, verifying the validity of the model through simulation. The example calculation result shows that the difference between the limit capacity and the correction capacity obtained by the model calculation result and the simulation result is less than 0.3 frames/hour. The results obtained by the two are consistent with the actual running conditions, thereby proving the validity and reliability of the model. When the capacity is known, the maximum number of aircrafts simultaneously receiving service in the terminal area at busy moment can be calculated by using the model of the invention, thereby providing theoretical basis and reference for the division of the terminal area and the traffic alarming work.
Drawings
The invention will now be described by way of example and with reference to the accompanying drawings in which:
FIG. 1 is a terminal area service flow model;
FIG. 2 is a block diagram of a terminal area of an airport;
FIG. 3 is a terminal area capacity calculation result;
FIG. 4 is a flow chart of random aircraft flow generation;
FIG. 5 is a flow chart of a numerical simulation capacity assessment;
FIG. 6 is a simulation result of a terminal area capacity value based on Monte Carlo;
FIG. 7 is a diagram showing a simulation result distribution;
fig. 8 is a maximum safety handling aircraft number versus capacity relationship.
Detailed Description
The invention establishes an airport terminal area capacity prediction mathematical calculation model based on service resource supply and demand balance by considering the supply and demand relation of air traffic service resources based on the airport terminal area operation environment and the air traffic flow operation characteristics. In the verification analysis, the capacity of the airport terminal area is calculated by statistical data such as the traffic flow distribution of the entering and exiting of the airport terminal area, the model ratio of the air route and the like. Meanwhile, based on the Monte Carlo numerical simulation method, the effectiveness and reliability of the mathematical calculation model are further verified.
1. Terminal area capacity and service resources
The airport terminal area is an origin-destination transition area in the whole air transportation process, and plays a role in connecting an air route with an airport. The capacity of the airspace refers to the maximum number of aircrafts that can be served per unit time under the influence of variable factors (aircraft configuration, human factors, weather factors, etc.) under the influence of a certain system structure (airspace structure, flight procedure, etc.), regulation rules, and safety level. The premise of obtaining the maximum capacity is that the aircraft continuously entering the terminal area leaves the airspace in time after being served, so that the service resources of the area are released. In the terminal area, demand refers to aircraft within the terminal area that perform regulatory services, supply refers to airline resources and regulatory service resources. The service flow model of the termination area is shown in fig. 1.
The airspace of the terminal region includes an access controlled airspace and a tower controlled airspace, which is divided by 2 control boundaries, as shown in fig. 1. The control boundary is a virtual space boundary, and represents the current space domain service resource occupation and released control handover position. When the aircraft enters the controlled area, the aircraft is indicated to start occupying part of service resources of the area; when an aircraft leaves the regulatory region through service, it indicates that the resources occupied by the aircraft are released immediately. The two virtual boundaries thus constitute the policing service handoff boundary.
The resource consumption in the flying process is not only the occupation of fixed resources such as a navigation way, but also the occupation of control service resources. The need for regulatory services arises from the mission of aircraft into terminals, which are physically and psychologically stressed while they provide services. The operating pressure on the body can be converted into time, and the self pressure and the requirement of the control task are relieved through the consumption of time. While psychological stress determines the number of aircraft that the controller can safely handle in a busy situation, this value is fixed for a certain period of time, and its magnitude is mainly determined by the workload that the controller can carry and the number of aircraft in the terminal area. According to civil aviation air traffic management rules, the number of aircraft that the sector receives air traffic control services at the same time must not exceed the number that can be safely handled in busy situations. Therefore, the higher the comprehensive capacity of the controller, the more aircraft can be serviced in a busy situation, the larger the air traffic flow which can be accommodated, and the higher the upper limit of the capacity of the terminal area.
2. Calculation model structure
2.1 model assumptions
The present invention is directed to the entrance and exit field capacity of a single field termination region. There are many limiting factors affecting the capacity of the terminal area, and there are different definitions of the capacity of the terminal area from the standpoint of different limiting factors. The computational model built according to the present invention is defined as follows: by balancing service resource supply and demand on an acceptable level of regulatory service supply, the terminal area can provide a maximum number of aircrafts for service per unit time. In the previous study, part of influence factors are taken as one of constraint conditions, and the calculation model is more close to the actual running scene through the construction of the mathematical model. However, various influencing factors belong to potential risks in operation, the occurrence probability of the influencing factors has randomness, and the complexity of the terminal area structure also limits the accuracy of the model result. The invention simplifies the limiting factors and the operation modes, introduces the supply and demand relation of the service resources of the terminal area, and establishes the mathematical model of the terminal area capacity calculation under the balance of the service resource supply. To build the model, the following assumptions are made:
1) Any two incoming and outgoing aircrafts are provided with a minimum flight interval larger than that used in a control area.
2) The terminal area cannot start the service of another aircraft if the aircraft that has started the service has not finished the service.
3) The aircraft service requests to the terminal area are continuous and the number of aircraft in the terminal area that service remains no greater than the safe handling aviation number.
4) Each aircraft flies according to the planned route without deviating from and changing the planned route.
5) The aircrafts in the same class have the same flight time in the same route.
2.2 model construction
Assuming that the airport terminal area has Capacity, each route in the area is connected to a route and a control handoff point. There are i routes in the terminal area, denoted as R i 。
Respectively represent the aircraft f j Into the termination region and out of the termination region. />
Aircraft f j The terminal area service program is as follows:
the terminal zone is controlled to pass through the handover point to enter the control zone at any time from outside the terminal zone
Starting to occupy part of service resources at any moment and along the planned route R i Flying->
At the moment fly over another point of control transfer to leave the control zone, at the moment f j The occupied service resources are released immediately. Three types of mathematical models are respectively established for distinguishing different situations of service resource supply and demand: demand model, supply model, and capacity solution model.
1) Demand model
The subject object of the demand model is an aircraft that enters the terminal area to service. The accumulated approach and departure selection model and the terminal area route utilization rate of the aircraft operation are respectively as follows:
a 1 +a 2 +...+a n ＝∑a n ＝1,(n<i) (2)
d 1 +d 2 +...+d i-n ＝∑d i-n ＝1,(n<i) (3)
in the formulas (1) to (3): a is an approach aircraft f ARR The ratio of the total flow is D is the off-site aircraft f DEP Accounting for the proportion of the total flow. a, a n Indicating the utilization rate of the nth approach route, d i-n The utilization rate of the ith-nth off-site route is used; wherein the i routes of the terminal area have n approach routes, i-n departure routesThe field route and the entering and exiting field route are marked in sequence.
Aircraft f to serve all j Dividing into k classes, the proportion I of each aircraft in different routes i,k The method comprises the following steps:
I i,1 +I i,2 +...+I i,k ＝∑I i,k ＝1 (4)
in the formula (4): i i,k And the method shows the proportion of the kth aircraft in the total service rack of the route in the ith terminal area service route.
And constructing a flight task service probability matrix P through the numerical calculation result.
In formula (5): s is S i Representing the probability that the aircraft will select the ith terminal area service route. In formula (6), p ik Representing the proportion of the kth aircraft of the ith route to the total service rack of the terminal area.
Aircraft f j Through the termination region will generate
Two moments. Therefore, the expected flight value T of various aircrafts in different airlines can be calculated through the time statistics generated by the aircrafts i k ：
Wherein T is i k Representing the expected value of flight of the kth aircraft in the ith route, f i k A kth class of aircraft representing flight services on an ith route,
the time when the kth aircraft, which is in flight service on the ith route, enters the terminal area and leaves the terminal area are shown, respectively.
Expected value of demand service time E (T) i k ) The calculation model is as follows:
E(T i k )＝∑p ik T i k (8)
2) Supply model
In order to reproduce the supply and demand relation of terminal area service resources, the invention models a supply model, and the expected maximum available service time length T can be obtained through calculation ser The method comprises the following steps:
T ser ＝s max T cal (9)
wherein: s is(s) max The maximum number of aircraft that can be safely handled if the controller is busy; t (T) cal The length of time (typically 60 minutes) is calculated for the capacity.
3) Capacity calculation model
The premise of calculating the capacity of the terminal area is that the supply and demand of the service resources are balanced, namely the maximum available service time is more than or equal to the expected required service time. From a number perspective, i.e. the number of aircraft that are simultaneously serviced by the terminal area, should not exceed the number of security treatments that can be handled by the controller in busy situations. The capacity calculation model can be expressed by the following equation:
T ser ≥E(T i k )Capacity (10)
considering that the provided policing service cannot be kept in a busy state, it is necessary to add a utilization coefficient u to limit it. The modified calculation model of the terminal area capacity based on the service resource supply and demand balance is as follows:
uT ser ＝E(T i k )Capacity (11)
wherein: u is a utilization coefficient representing a time probability that the number of aircraft simultaneously serviced by the terminal area per unit time does not exceed the number of safety processes in the busy situation. When u is 1, the calculated capacity is the limit capacity; when u is 0, 1), the calculated capacity is the corrected capacity.
3. Model verification and analysis
3.1 example calculation
And selecting a certain airport terminal area as a calculation object to perform capacity calculation. The terminal area of the selected time period adopts program control and related approach running modes, and 6 approach and departure routes are shared in the area, wherein 3 standard instrument approach routes and 3 standard instrument departure routes are shared. And classifying the machine types in the terminal area into 3 types according to the analysis of the field acquisition data. The take-off and landing ratio of the airport is 6:4, a step of; the ratio of the entrance and exit procedures KAGAK, TEPOD, ELPUN is 3:17:80; the different approach and departure program model distributions and their flight times are shown in table 1 below.
TABLE 1 Inlet and Exit Programming model distribution and time of flight
The maximum number of safely handled aircraft in the terminal area is not more than 4 under normal conditions and the condition that the controller is busy. Thus supplying model parameters s max Taking 4, T cal Set to 60. The method can be calculated as follows:
T ser ＝s max T cal ＝4×60＝240min
considering that typically the average workload of the controller should be below 70% of the highest load, the utilization factor u is set to 0.7 in the calculation. The mathematical calculation model calculates the following:
when the utilization coefficient is not limited:
uT ser ＝E(T i k )Capacity＝(0.408×11.2+0.072×14.4+0.086×9+0.016×11.5+0.010×6.5+0.005×8+0.003×7+0.272×7.8+0.048×10.1+0.057×6+0.011×7.7+0.006×12.3+0.004×15.8+0.002×22.2)Capacity
i.e. 240 xu= 9.905 ×capacity
When the utilization coefficient is different, the calculated capacity results are different. The larger u, the greater the workload of the controller. The Capacity increases with increasing u in fig. 3. The workload in the termination region ranges from 0.1 to 1 according to the value range of u. According to different statistics results of each control unit on the workload at the busy moment, the values of u are different. The utilization coefficient is 0.7 under the condition that the workload of the controller of the selected terminal area is busy, and the calculated operation proposal capacity is 17 frames/hour.
3.2 model verification
In order to verify the accuracy of the model calculation results, a numerical simulation program is established herein using the monte carlo method. And performs capacity estimation on the airport terminal using the program. The generation of random aircraft flow is a critical step in the simulation process. The process of random aircraft flow generation in the terminal area is simulated by cycling through multiple experiments by using the Monte Carlo method. This approach both ensures that the generation of random aircraft flow is satisfactory to meet the actual operating conditions and also makes each cycle random. The specific production flow is shown in fig. 4.
Based on the random aircraft flow generation method, the time accumulation simulation is carried out according to the airspace resource occupation time calculated by the aircraft. The aircraft flow generation results in the simulation model are random. To eliminate the random factor, the calculation results for each cycle are averaged. The running flow of the simulation program is shown in fig. 5, and the specific simulation steps are as follows:
1) Aircraft are randomly generated within the termination region. In the simulation, each aircraft needs to randomly generate the aircraft running in the air domain according to the take-off and landing ratio, the entering and leaving program proportion and the different entering and leaving program model proportions of the airport. And meanwhile, obtaining the flight distance and the flight speed according to the selected entering and exiting programs and the model, so as to obtain the flight time of the aircraft. N aircrafts are generated for the first time in each circulation, and the condition that the command of the n aircrafts is not exceeded at the same time is satisfied, namely, the limit condition that the number of aircrafts in the terminal area is n is kept at any time.
2) And judging the disappearance of the airplane in the terminal area. Over time, the aircraft flies according to the selected approach and departure procedure until one of the aircraft first reaches the destination. The aircraft was counted from the first disappearance until the cumulative time was 1 hour. The simulation operation end point is as follows: the incoming aircraft arrives at the intersection point of the incoming and tower control, or the outgoing aircraft arrives at the intersection point of the incoming and regional control.
3) And setting simulation circulation rules. After the aircraft reaching the destination disappeared, 1 aircraft was randomly generated. This keeps n aircraft in the terminal area. And (5) repeating the step (2) and the step (3) until the accumulated time is 1 hour. And counting the total number of frames of the airplane in the current circulation, and removing the airplane flow in the current circulation.
4) Overall circulation rule setting. Repeating the steps 1 to 3, and carrying out arithmetic average on the result after the set total cycle times are reached. The arithmetic average is the terminal area numerical simulation capacity.
In the present simulation, the total number of cycles k of the simulation is set to 1000. The data such as the flow distribution of the incoming and outgoing fields, the route selection proportion of the terminal area, the flight time and the like utilized in the simulation are the same as the data used in the calculation of the model. The final simulation result is shown in fig. 6 by taking the arithmetic average value of the terminal area limit capacity after 1000 simulations.
As can be seen from fig. 6 and 7, the results of each cycle were different and fluctuated up and down between 24 frames/hour. This indicates that the data such as the ordering of flights and model parameters in a unit time has a certain influence on the capacity. In the simulation process, the maximum value is 29 frames/hour, and the minimum value is 20 frames/hour. Considering that a single cycle has randomness and the number of flights in a terminal area always kept full, the arithmetic average value of all cycle results is taken as the limit capacity of numerical simulation.
3.3 comparative analysis of results
And evaluating the terminal area capacity of the airport by using the two methods and the empty pipe simulator experiment. In the current operation mode, the results of the two methods are similar to the experimental results of the empty pipe simulator, and the correction capacity is 17 frames/hour. The limit capacity difference in table 2 below was 0.291 rack times/hour, and the corrected capacity difference was 0.203 rack times/hour. In conventional capacity evaluations, the values after the decimal point are typically fixed using a "rounding" approach. The difference between the mathematical model and the numerical simulation is less than 0.3. Thus, the result of the mathematical calculation model can be verified to have a certain validity.
TABLE 2 Capacity evaluation results Table Tab.2Results of Capacity Evaluation
3.4 application extension
The capacity is currently considered constant by most scholars in the study of airport traffic management. In practice airport capacity will vary as the operating environment varies. The controller flow alarming mode is mainly divided into flight flow alarming and workload percentage alarming. If the normal method is adopted, 70% of the workload is used as an alarm index, a large amount of data statistics and calculation are needed for the alarm mode to obtain a relatively accurate result, and the alarm mode is unfavorable for timely development of alarm work. The time period and the flight flow characteristics may vary, resulting in different controller workloads and numbers of maximally safe handling aircraft. The time-varying nature of air traffic also allows the maximum number of safely handled aircraft that can be serviced by the controller to be varied from time to time under safe workload.
Therefore, in order to integrate the workload factors with the flow alert job, the computational model built by the present invention is improved. And on the premise of determining the workload of the inner pipe manufacturer, the structure of the terminal area and the flight flow characteristics in a period, calculating the maximum safe processing aircraft quantity at the busy moment of the controller. The relationship between maximum safe handling aircraft number and capacity at busy hours is:
assume that the current peak hour capacity of the termination area is 17 frames/hour. By changing the utilization coefficient step by step and reducing the peak hour capacity, the law between the maximum safe handling aircraft quantity and capacity at busy hours is obtained. Under 4 utilization coefficientsThe maximum number of safety-treated aircraft is positively correlated with capacity, and the result is shown in FIG. 8, where the maximum number of safety-treated aircraft is s max There is a decreasing trend as the capacity of the termination area decreases. The bottleneck of the traffic of the terminal area is transferred to the service capability of the controller under the condition that the external overall environment is not changed. According to the rules, the scheduling scheme of the controller under different flow conditions can be specifically formulated. And the flow control strategy of the terminal area under different running states can be formulated according to the service capacity of the terminal area.
4. The working principle of the invention
The invention provides a new terminal area capacity calculation model from the service point of view. When the supply model and the demand model are considered to be balanced, they are converted into time balance problems. The limit capacity of the terminal area is calculated under the condition that the terminal area is always kept at the maximum available service time constraint. On this basis, consideration of the workload of the manager is added, and the correction capacity of the terminal area is finally obtained as a final result. The concrete explanation is as follows:
(1) The invention expands the mathematical model verification through the actual data of a certain airport. The model has good applicability to capacity calculation of airport terminal areas. Compared with other mathematical models, the method has more universality in the statistical analysis of basic data and the modeling calculation of the terminal area operation rule.
(2) A numerical simulation model is built based on the Monte Carlo method, and the calculation result of the numerical model is verified. The experimental results show that the two results are well matched. The limit capacity value interval obtained by simulation is [20,29] frames/hour, and is normally distributed. The validity of the established capacity calculation model result is further verified. The model of the invention can accurately and reasonably calculate the capacity of the terminal area, and provides theoretical support for airport and terminal area flow management methods.
(3) By combining service resources with flow alarm work, the model of the invention can also deduce and calculate the maximum safe processing aircraft quantity at the busy moment of the controller in a reverse way.
Claims (9)
1. An airport terminal area capacity prediction method based on service resource supply and demand balance is characterized in that: the method comprises the following steps:
step one, constructing a flight task service probability matrix:
wherein S is i Representing the probability of the aircraft selecting the ith route, I i,k Representing the proportion of the kth aircraft to the total service rack of the ith route, p ik Representing the proportion of the kth aircraft of the ith route to the total service rack of the terminal area;
step two, calculating expected flight values of various aircrafts in different airlines;
step three, calculating an expected value of the required service time in the terminal area;
step four, calculating the expected maximum service time length;
step five, constructing a terminal area capacity calculation model;
and step six, correcting the terminal area capacity calculation model to obtain a terminal area capacity calculation model based on the balance of supply and demand of service resources.
2. The airport terminal area capacity prediction method based on service resource supply and demand balance of claim 1, wherein: the expected flight values of various aircrafts in different airlines are calculated according to the following formula:
in the method, in the process of the invention,
representing the expected value of flight of the kth aircraft in the ith route, f i k Class k aircraft representing flight services on the ith route, +.>
The time when the kth aircraft, which is in flight service on the ith route, enters and leaves the terminal area is shown.
3. The airport terminal area capacity prediction method based on service resource supply and demand balance of claim 1, wherein: the expected value E (T) of the required service time in the terminal area is calculated according to the following formula i k )：
E(T i k )＝∑p ik T i k
Wherein p is ik Representing the proportion of the kth aircraft of the ith route to the total service rack of the terminal area, T i k Representing the expected value of flight of the kth type of aircraft in the ith course.
4. The airport terminal area capacity prediction method based on service resource supply and demand balance of claim 1, wherein: the predicted maximum length of service available T is calculated according to the following formula ser ：
T ser ＝s max T cal
Wherein s is max For maximum number of aircraft that can be safely handled in case of busy controllers, T cal The length of time is calculated for the capacity.
5. The airport terminal area capacity predicting method based on service resource supply and demand balance of claim 4, wherein: t (T) cal The value was 60 minutes.
6. The airport terminal area capacity prediction method based on service resource supply and demand balance of claim 1, wherein: the termination area capacity calculation model is represented by the following formula:
T ser ≥E(T i k )Capacity
wherein T is ser To predict the maximum length of available service time, E (T i k ) The Capacity is the airport terminal area Capacity, which is the expected value of the demand service time in the terminal area.
7. The airport terminal area capacity predicting method based on service resource supply and demand balance of claim 6, wherein: the calculation model of the terminal area capacity based on the service resource supply and demand balance is as follows:
uT ser ＝E(T i k )Capacity
where u is a utilization coefficient and represents a time probability that the number of aircraft simultaneously served by the terminal area per unit time does not exceed the number of safety processes in the case of busy.
8. The airport terminal area capacity predicting method based on service resource supply and demand balance of claim 7, wherein: u is 0-1.
9. The airport terminal area capacity predicting method based on service resource supply and demand balance of claim 7, wherein: the maximum number of aircraft s that can be safely handled in the event of a busy controller is calculated according to the following formula max ：
Wherein T is cal The length of time is calculated for the capacity.
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