Optimal configuration method of space-time resources on air routes

Abstract

The present invention belongs to the technical field of airway traffic allocation, and specifically relates to a method for optimizing the configuration of airway spatiotemporal resources. The method for optimizing the configuration of airway spatiotemporal resources comprises: step S1: inputting airway information; step S2: using the FW algorithm to solve the optimal configuration scheme of the system and outputting guidance information; step S3: solving the optimal travel scheme based on the guidance information; step S4: calculating the error between the guidance information and the optimal travel scheme, and terminating if it is within a preset range; otherwise, returning to step S2. The present invention adopts a traffic allocation model based on Stackelberg game to obtain an optimal configuration scheme in which the flow of each section is relatively balanced and in a balanced state, which can avoid the problem of uneven allocation of airway resources and alleviate the conflict between air traffic control and airlines.

Description

Technical Field

[0001] The present invention belongs to the technical field of airway traffic allocation, and in particular relates to an airway spatiotemporal resource optimization configuration method. Background Art

[0002] As the aviation industry grows and develops, competition for limited airway space and time resources between airlines and air traffic control (ATC) has intensified, and problems caused by improper allocation of airway resources have become increasingly prominent. In 2019, national passenger airlines operated a total of 4.6111 million flights, of which 3.7652 million were regular, for an average on-time rate of 81.65%. Among the non-normal reasons, weather accounted for 46.49%, airlines accounted for 18.91%, and air traffic control accounted for 1.43%. The average national passenger flight delay was 14 minutes.

[0003] Uncertain factors such as weather and airline preferences severely disrupt flight operations, impacting airway capacity and causing costly flight delays. Furthermore, the allocation of space and time resources along air routes requires joint decision-making by air traffic control and airlines. In the face of these uncertainties, airway capacity declines, air traffic flows become disrupted, and flight safety is impacted.

[0004] Therefore, a method for optimizing the configuration of space-time resources of routes is needed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing the configuration of space-time resources of a route.

[0006] In order to solve the above technical problems, the present invention provides a method for optimizing the configuration of route spatiotemporal resources, including: step S1: inputting route information; step S2: using the FW algorithm to solve the optimal configuration plan of the system and outputting guidance information; step S3: solving the optimal travel plan based on the guidance information; step S4: calculating the error between the guidance information and the optimal travel plan, and terminating if it is within a preset range; otherwise, returning to step S2.

[0007] The beneficial effect of the present invention is that, in the process of optimizing the configuration of the space-time resources of the route of the present invention, the air traffic control provides guidance information of the system optimal configuration plan, the airline provides the user optimal configuration plan, and the optimization configuration method provides the game optimal configuration plan; wherein the airline refers to the system optimal configuration plan given by the air traffic control when submitting the user optimal configuration plan, and when the air traffic control provides the next round of system optimal configuration plan, it also refers to the user optimal configuration plan given by the airline. The two interact with each other, continuously engage in game, and finally provide the game optimal configuration plan in the Stackelberg game equilibrium state.

[0008] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0009] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0011] Figure 1 Schematic diagram of a method for optimizing route spatiotemporal resource configuration according to an embodiment of the present invention;

[0012] Figure 2 FIG. 4 is a flow chart of the FW algorithm according to an embodiment of the present invention.

[0013] Figure 3 It is a route diagram in an application scenario;

[0014] Figure 4 It is a simplified route diagram;

[0015] Figure 5 It is a flow configuration diagram;

[0016] Figure 6 It is a route resource configuration diagram. DETAILED DESCRIPTION

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] Example

[0019] like Figure 1 As shown, this embodiment provides a method for optimizing the configuration of route spatiotemporal resources, which is characterized by comprising:

[0020] Step S1: input route information;

[0021] Step S2: Use the FW algorithm to solve the optimal configuration solution of the system and output guidance information;

[0022] Step S3: solving the optimal travel plan based on the guidance information;

[0023] Step S4: Calculate the error between the guidance information and the optimal travel plan. If it is within a preset range, terminate; otherwise, return to step S2.

[0024] In this embodiment, optionally, inputting route information in step S1 includes:

[0025] Initial impedance of each segment in represents the initial impedance of segment a, a∈A, A is the set of segments in the route network;

[0026] Traffic demand between OD pairs {q rs}, where q rs It represents the traffic demand of the OD pair with the starting point r and the ending point s, r∈R,s∈S, R is the set of starting nodes that generate traffic, and S is the set of ending nodes that attract traffic;

[0027] Route capacity a}, a∈A;

[0028] Travel costs for each flight segment a}, a∈A; and

[0029] q rs and u a is a known data, impedance and traffic capacity C a It is obtained from the following formula:

[0030] minZ=∑ a∈A x a u a =∑ a∈A x a (λ1t a +λ2γC a );

[0031]

[0032] in,

[0033] Z is the total cost of the route network system;

[0034] x a is the flow on segment a, obtained by 0-1 traffic assignment based on traffic demand and impedance;

[0035] λ1 refers to the weight of the pilot's choice of travel time or distance, and λ2 refers to the weight of the route capacity. The different values ​​of λ1 and λ2 represent the traffic flow distribution pattern of the route network, where 0≤λ1 and λ2≤1;

[0036] γ is the limit capacity coefficient, which takes a value range of [1,∞) to adjust the critical capacity, that is, the maximum range within which the flight segment is allowed to exceed its capacity; in this embodiment, γ is set to 1;

[0037] The traffic flow on the kth path of the OD pair with the starting point r and the end point s can be obtained through traffic distribution, k∈K rs , K rs is the set of all paths connecting OD to rs;

[0038] is a path-related variable, i.e., a 0-1 variable. If segment a is on the kth path between ODs with a starting point of r and a terminal of s, then on the contrary

[0039] like Figure 2 As shown, in this embodiment, optionally, the step of using the FW algorithm to solve the optimal configuration solution of the system in step S2 includes:

[0040] Step S21: Initialization operation, through 0-1 traffic allocation, obtain

[0041] Step S22: Update the impedance on the flight segment. Among them, t a (x a ) is the impedance function of the road section with flow as the independent variable; n is the number of iterations when solving the optimal configuration scheme of the system, n≥1;

[0042] Step S23: Find the iteration direction and use the updated Perform 0-1 traffic allocation again to obtain a set of traffic flows

[0043] Step S24: Determine the iteration step size and use the bisection method to solve λ that satisfies the following equation:

[0044]

[0045] Step S25: Determine a new iteration starting point,

[0046]

[0047] Step S26: Check convergence, if satisfied Where ε1 is the given error limit, If the solution is found, the calculation ends; otherwise, set n=n+1 and return to step S22.

[0048] In this embodiment, optionally, solving the optimal travel plan according to the guidance information in step S3 includes:

[0049] Step S31: Calculate the probability of the airline selecting flight segment p after m iterations using the following formula:

[0050]

[0051]

[0052]

[0053] in, represents the probability that the expected travel time of path p is minimized, Select a path p∈P for the airline between routes starting and ending at r and s rs The probability of The cost of path p between routes with r and s as the starting and ending points, σ ​​is the variance of the utility of all paths between routes with r and s as the starting and ending points; q rs is the traffic demand of the OD pair with the starting point r and the end point s; is the air traffic flow on the selected path p between the routes with r and s as the starting and ending points, which can be combined to represent the user's travel strategy;

[0054] Step S32: Use probability distribution to assign the traffic demand {q rs} is distributed to the path p between the paths, and the flow rate of the segment with the number of iterations being m is solved. This is the optimal travel plan decided by the airline after playing the game m times.

[0055] In this embodiment, optionally, in step S4, the error between the guidance information and the optimal travel plan is calculated. If the error is within a preset range, the process is terminated; otherwise, the process returns to step S2 and includes:

[0056] Assumed error limit And ε2>0, when the error between the guidance information given by the air traffic control and the optimal travel plan decided by the airline is within this range, the iteration is terminated; otherwise, let m=m+1 and return to step S2.

[0057] In this embodiment, optionally, setting m=m+1 and returning to step S2 includes:

[0058] Return to step S21, let As the initial impedance, the optimal configuration solution of the system is re-solved.

[0059] In one application scenario, based on the simulation data extraction, a single OD pair path with the starting point at ZBAA and the end point at ZSSS is selected. Figure 3 As shown, there are two alternative flight paths between ZBAA and ZSSS. The horizontal distance between flight path 1 and flight path 2 is the farthest at waypoints LYG and VEMEX. These two points determine different flight paths. To simplify understanding, the actual flight path diagram is simplified to Figure 4 .

[0060] like Figure 4 As shown in the figure, flight path 1 is ZBAA-LYG-ZSSS, flight path 2 is ZBAA-VEMEX-ZSSS, and flight path 3 is ZBAA-VEMEX-ZSSS. The traffic demand between the OD pairs can be calculated to obtain the initial impedance of the segment as {5, 9, 6, 7, 1}, the segment capacity is a1=32.5, a2=34.7, a3=33.5, a4=33, a5=32, and the path segment 0-1 relationship is [1 0 0 1 0; 0 1 0 0 1; 1 0 1 0 1].

[0061] Based on the Beckmann model for optimal space-time resource allocation, Matlab was used to program and solve the problem, and the flow distribution results and path impedance shown in the following table were obtained:

[0062] Table 5.1 Segment traffic

[0063]

[0064] Table 5.2 Flight path impedance

[0065]

[0066] The error value is 0.00048, and the number of iterations is 4. The experimental results show that the impedances of the three flight paths are basically equal, which meets the expected flow distribution balance result. Therefore, this result is the optimal configuration solution for the system.

[0067] The method for optimizing the time-space resource allocation of air routes of the present invention is used to perform programming and solve using MATLAB, and the flow distribution results and flight path impedance shown in the following table are obtained:

[0068] Table 5.3 Game segment traffic

[0069]

[0070] Table 5.4 Game flight path impedance

[0071]

[0072] The error value is 0.346, and the number of game iterations is 1. The experimental results show that the impedances of the three paths are basically equal, which meets the expected distribution equilibrium result. Therefore, this result is the optimal configuration solution of the game.

[0073] Comparing the above two route resource allocation methods, we can get the following Figure 5 Flow configuration shown.

[0074] Table 5.5 Traffic configuration obtained by two algorithms

[0075]

[0076] Compared with the system optimal configuration scheme and the game optimal configuration scheme, it can be seen that the traffic of each segment in the game optimal configuration scheme is more balanced and in a balanced state, which can avoid the problem of uneven allocation of route resources and alleviate the conflict between air traffic control and airlines.

[0077] Calculated by multiplying the path flow and impedance Figure 6 The route resource configuration result is shown.

[0078] Table 5.6 Configuration results obtained by the two algorithms

[0079]

[0080] By comparing the system optimal configuration scheme and the game optimal configuration scheme, it can be found that the travel difficulty obtained by the game optimal configuration scheme is lower, which essentially reduces the difficulty of air traffic flow, thereby improving the efficiency of resolving problems when disagreements arise between air traffic control and airlines, and optimizing the route resource allocation method.

[0081] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a portion of code, and the module, program segment or a portion of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0082] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0083] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0084] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A method for optimizing the configuration of space-time resources of air routes, characterized in that: include: Step S1: input route information; Step S2: Use the FW algorithm to solve the optimal configuration solution of the system and output guidance information; Step S3: solving the optimal travel plan based on the guidance information; Step S4: Calculate the error between the guidance information provided by the air traffic control and the optimal travel plan decided by the airline. If it is within the preset range, terminate; otherwise, return to step S2.

2. The method for optimizing the allocation of route space-time resources according to claim 1, wherein: Inputting route information in step S1 includes: Initial impedance of each segment },in represents the initial impedance of segment a, ,A is the set of flight segments in the route network; Traffic demand between OD pairs { },in It represents the traffic demand of the OD pair with the starting point r and the end point s, , ,R is the set of starting nodes that generate traffic, and S is the set of ending nodes that attract traffic; Route Capacity }, ; Travel costs for each flight segment }, ;as well as and is a known data, impedance and traffic capacity It is obtained from the following formula: ； in, is the total cost of the route network system; is the flow on segment a, obtained by 0-1 traffic assignment based on traffic demand and impedance; Refers to the weight of the pilot's choice of travel time or distance, Refers to the weight of route capacity, The different values ​​represent the traffic flow distribution mode of the route network. ; is the limiting capacity factor, with a value range of To regulate critical capacity, that is, the maximum range by which a segment is allowed to exceed its capacity; is the traffic flow on the kth path of the OD pair with the starting point r and the end point s, , K rs is the set of all paths connecting OD to rs; is a path-related variable, i.e., a 0-1 variable. If segment a is on the kth path between ODs with a starting point of r and a terminal of s, then ,on the contrary .

3. The method for optimizing the allocation of route space-time resources according to claim 2, wherein: The step of using the FW algorithm to solve the optimal configuration solution of the system in step S2 includes: Step S21: Initialization operation, through 0-1 traffic allocation, obtain { }; Step S22: Update the impedance on the flight segment. ;in, is the impedance function of the road section with flow as the independent variable; n is the number of iterations when solving the optimal configuration solution of the system, n≥1; Step S23: Find the iteration direction and use the updated { }, perform 0-1 traffic allocation again and get a set of traffic flows { }; Step S24: Determine the iteration step size and use the bisection method to solve the following equation: : ； Step S25: Determine a new iteration starting point, ； Step S26: Check convergence, if satisfied ,in For a given error limit, If the solution is found, the calculation ends; otherwise, set n=n+1 and return to step S22.

4. The method for optimizing the allocation of route spatiotemporal resources according to claim 3, wherein: In step S3, solving the optimal travel plan according to the guidance information includes: Step S31: Calculate the probability of the airline selecting flight segment p after m iterations using the following formula: ; ； ； in, represents the probability that the expected travel time of path p is minimized, Select a flight path for an airline between routes starting and ending at r and s The probability of The path between routes with r and s as starting and ending points the cost, is the variance of all path utilities between routes starting and ending at r and s; is the traffic demand of the OD pair with the starting point r and the end point s; The selected path between the routes with r and s as the starting and ending points The air traffic flow on the network can be combined to represent the user's travel strategy; Step S32: Using probability distribution to allocate the traffic demand between OD pairs } is distributed to the path p between the paths, and the segment flow rate with the number of iterations m is solved { }, which is the optimal travel plan decided by the airline after playing the game m times.

5. The method for optimizing the allocation of route spatiotemporal resources according to claim 4, wherein: In step S4, the error between the guidance information and the optimal travel plan is calculated, and if it is within a preset range, the process is terminated; Otherwise, returning to step S2 includes: Assumed error limit , and ε2>0, when the error between the guidance information given by the air traffic control and the optimal travel plan decided by the airline is within this range, the iteration terminates; otherwise, let m=m+1 and return to step S2.

6. The method for optimizing the allocation of route space-time resources according to claim 5, wherein: The step of setting m=m+1 and returning to step S2 includes: Return to step S21, let as the initial impedance.

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