A method and system for optimizing taxiway paths to reduce potential conflicts in a flight area

Abstract

The application relates to a taxiway path optimization method and system for reducing potential conflicts in a flight area, belonging to the technical field of airport scheduling methods, which considers potential conflicts between aircrafts and aircrafts and between aircrafts and ground service vehicle routes, and constructs a mixed integer quadratic programming model for aircraft taxiway path optimization, with a main target of minimizing potential conflicts between aircrafts and aircrafts and between aircrafts and vehicles, and a secondary target of minimizing ground service vehicle driving distances; the aircraft taxiway path optimization model is solved, and an optimal solution is output, including aircraft taxiing paths and ground service vehicle service paths, types and quantities of potential conflicts, and the results can be used for reanalysis by adjusting parameters, thereby providing reference for airport aircraft and vehicle scheduling decision makers. Compared with the prior art, the application considers the safety of ground traffic and the convenience of passengers, and optimizes the allocation of aircraft landing and taking-off aprons and runways and the taxiing paths in an all-round way, which helps to guarantee the safety of taxiways and improve the operation efficiency of airports.

Description

Technical Field

[0001] This invention relates to the field of airport planning and scheduling technology, and in particular to a taxiway path optimization method and system for reducing potential conflicts in the flight area. Background Technology

[0002] Airport ground operations, as a crucial link in the overall airport system's operation, directly determine the safety and efficiency of airport operations. Many airports are large in scale, with numerous and complex taxiways, resulting in long aircraft taxiing times, heavy workloads for ground service vehicles, and an increased potential for conflicts between aircraft and ground vehicles. These conflicts not only create safety hazards but also consume significant time, manpower, and financial resources to resolve, impacting subsequent aircraft taxiing. Since aircraft taxi along specific routes, conflicts between aircraft and between aircraft and vehicles can be avoided by scientifically and rationally allocating boarding gates and planning aircraft taxiing routes.

[0003] Existing research mainly focuses on the taxiing scheduling problem for aircraft with given arrival / departure times, determining taxiing routes, and directing different aircraft to pass through the same location at different times. Taxiing scheduling models can be classified according to their objectives, including total taxiing time, delay, and energy consumption. Tjahjono et al. (2014) identified conflicts between taxiing routes and considered the delay time caused by avoiding conflicts. To minimize the total delay caused by airport ground traffic, Cheng et al. (2012) proposed an adaptive genetic algorithm to optimize aircraft departure order and time. Benlic et al. (2016) proposed a method to simultaneously determine taxiing routes and aircraft arrival and departure sequences on the runway. With the global trend of green airport development, airport energy conservation and emission reduction goals have received increasing attention. For example, Yv et al. (2017) considered the additional fuel costs caused by unnecessary deceleration-acceleration processes. Li et al. (2019) established an aircraft taxiing route planning model that considers distance, turning time, and collision avoidance, aiming to reduce carbon emissions throughout the taxiing process. Soltani et al. (2020) considered the avoidance of collisions and conflicts in taxiing routes, aiming to minimize fuel consumption for aircraft and towing vehicles.

[0004] In recent years, a growing trend towards multi-objective models has been observed in an increasing number of studies. For example, Marin and Codina (2008) extended the single-objective model proposed by Marin (2006) to consider other objectives, such as conflicts between different taxiing routes and flight throughput. Ravizza et al. (2013) analyzed the trade-off between total taxiing time and fuel consumption in conflict-free taxiing path planning. Weiszer et al. (2015) optimized the speed curve of taxiing aircraft with two objectives: taxiing time and fuel consumption. Jiang et al. (2015) established a bi-objective model aimed at minimizing the total length of taxi routes and reducing aircraft delays. Their models can guarantee continuous taxiing without conflicts. Jiang et al. (2023) proposed a two-layer model: the upper layer reduces carbon emissions and conflicts by allocating routes for arriving / departing aircraft, while the lower layer reduces waiting time and conflicts by scheduling aircraft departure times.

[0005] The solution disclosed in Chinese patent application CN202010349765.1 determines traffic conflicts and updates route planning by calculating in real time the arrival times of vehicles and aircraft at various path conflict points under the current task. Chinese patent application CN201911095648.0 sets up multiple conflict agents at multiple location nodes in the airport. These agents use conflict detection algorithms to determine whether there are taxiing conflicts between two or more aircraft based on the position, speed, and timestamp information of different aircraft received from multiple transit messages. Existing technical solutions mostly rely on real-time speed and distance data to determine the risk of conflict, lacking early planning capabilities, and only consider conflicts between aircraft, without taking airport ground service vehicles into account.

[0006] In summary, most current research focuses on optimizing the total taxiing time of aircraft and ground support vehicles, as well as delays and energy consumption. There is relatively little research on optimizing the conflict between aircraft in the airfield and airport ground support vehicles from the perspective of ensuring airport airfield safety. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a taxiway path strategic optimization scheduling method and system to reduce potential conflicts in the flight area. This method optimizes the allocation of parking aprons and runways and their taxiways for aircraft arriving at / departing from the airport and outputs static scheduling paths for aircraft and vehicles.

[0008] The objective of this invention can be achieved through the following technical solutions:

[0009] As a first aspect of the present invention, a taxiway path optimization method for reducing potential conflicts in the airfield is provided, the method comprising the following steps:

[0010] Read airport road network data, initialize the number of arriving aircraft at runway nodes and the number of departing aircraft at apron nodes, and construct a directed network of the airport flight area.

[0011] Identify potential conflicts between aircraft taxiing arcs and between aircraft taxiing arcs and vehicle driving paths, and generate multiple alternative taxiing paths for aircraft landing and takeoff.

[0012] A mixed-integer quadratic programming model for optimizing aircraft taxiway paths is constructed. The primary objective is to minimize potential conflicts between aircraft and between aircraft and vehicles, and the secondary objective is to minimize the travel distance of ground service vehicles.

[0013] Solve the aircraft taxiway path optimization model and output the optimal solution, including the aircraft taxiway path, ground support vehicle service path, and the type and number of potential conflicts.

[0014] As a second aspect of the present invention, a taxiway path optimization system for reducing potential conflicts in the airfield is also provided. The system is used to implement the taxiway path optimization method described above and includes the following modules:

[0015] Basic data input module: Reads airport road network data, including taxiway nodes, apron nodes, runway nodes, service routes of airport ground service vehicles, and capacity of each apron node; initializes the number of arriving aircraft at runway nodes and the number of departing aircraft at apron nodes; and constructs a directed network of the airport flight area.

[0016] Basic data processing module: Identifies potential conflict points and potential conflicts between aircraft and aircraft, and between aircraft and vehicles; generates a set of alternative aircraft taxiing paths; and generates several taxiing paths for aircraft landing and takeoff based on the path generation algorithm for each pair of landing runway and apron nodes, and between each pair of apron nodes and takeoff runways.

[0017] The path optimization solution module constructs a mixed integer quadratic programming model for aircraft taxiway path optimization. The primary objective is to minimize potential conflicts between aircraft and between aircraft and vehicles, and the secondary objective is to minimize the travel distance of ground service vehicles. The output is the optimal solution, which includes the aircraft taxiway path and the ground service vehicle path, as well as the type and number of potential conflicts.

[0018] Parameter adjustment and analysis module: Based on the output results, adjust and optimize the parameters, and then return to the path optimization solution module for solution.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] This invention provides a taxiway path optimization scheme to reduce potential conflicts in the airfield. From the perspective of ensuring airport airfield safety and improving airport ground service vehicle efficiency, it constructs an aircraft taxiway path optimization model to generate aircraft taxiways between runways and aprons, as well as corresponding ground service vehicle routes. The primary objective is to minimize potential conflicts between aircraft and between aircraft and vehicles, while the secondary objective is to minimize the travel distance of ground service vehicles. This method comprehensively optimizes the allocation of aircraft aprons and runways and their taxiways. It helps ensure taxiway safety and improve airport operational efficiency. The method can also be re-analyzed based on the results after parameter adjustments, providing a reference for airport aircraft and vehicle scheduling decision-makers. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a taxiway path optimization method for reducing potential conflicts in the flight area according to the present invention.

[0022] Figure 2 Satellite image of Pudong Airport, as shown in the specific embodiment of the present invention;

[0023] Figure 3 This is a road network map of Pudong Airport, as shown in a specific embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram illustrating the identification of conflict point types in this invention;

[0025] Figure 5 This is a detailed schematic diagram illustrating the identification of potential conflict points in this invention;

[0026] Figure 6 This is a schematic diagram of the path optimization results of the present invention;

[0027] Figure 7 This is a schematic diagram of the path optimization results after adjusting the intersection parameters according to the present invention;

[0028] Figure 8 This is a schematic diagram of the path optimization results after adjusting the secondary target weight parameters according to the present invention;

[0029] Figure 9 This is a schematic block diagram of a taxiway path optimization system for reducing potential conflicts in the flight area according to the present invention. Detailed Implementation

[0030] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0031] Example 1

[0032] like Figure 1As shown, a taxiway path optimization method to reduce potential conflicts in the airfield specifically includes the following steps:

[0033] S1. Basic data input: Read airport road network data, including taxiway nodes, apron nodes, runway nodes, service routes of airport ground service vehicles, and capacity of each apron node; initialize the number of arriving aircraft at runway nodes and the number of departing aircraft at apron nodes; and construct the directed network of the airport flight area.

[0034] S2. Basic Data Processing: Identify potential conflict points, identify potential conflicts between aircraft taxiing arcs and between aircraft taxiing arcs and vehicle driving paths; generate a candidate set of aircraft taxiing paths, and generate several aircraft landing and takeoff taxiing paths for each pair of landing runway and apron nodes, and between each pair of apron nodes and takeoff runways, based on Yen's algorithm.

[0035] S3. Path optimization solution: Construct a mixed integer quadratic programming model for aircraft taxiway path optimization. The primary objective is to minimize potential conflicts between aircraft and between aircraft and vehicles. The secondary objective is to minimize the travel distance of ground service vehicles. Output the optimal solution, including the aircraft taxiway path and the ground service vehicle path, as well as the type and number of potential conflicts.

[0036] S4. Visual output of results: Display the optimized aircraft and vehicle routes in the airport road network and mark the areas with a large number of conflict points.

[0037] S5. Parameter Adjustment Analysis: Based on the output results, the parameters can be adjusted and optimized, and then the solution can be obtained by returning to the S3 path optimization model.

[0038] This embodiment will use Shanghai Pudong International Airport as an example to illustrate the specific solutions for each step of the route optimization:

[0039] S1. The specific process for inputting basic data is as follows:

[0040] Step 1.1: Read the road network data of Shanghai Pudong International Airport, including taxiway nodes, apron nodes, runway nodes, service routes of airport ground service vehicles, capacity of each apron node, and the number of aircraft taxi routes X to be generated. Here, X = 20.

[0041] Step 1.2: Initialize the number of arriving aircraft at the runway nodes and the number of departing aircraft at the apron nodes. Here, we initialize that there is 1 aircraft that needs to depart from each apron and 20 aircraft that need to land on each of the two landing runways.

[0042] Step 1.3: Construct two taxiway nodes by connecting two opposite directions of a taxiway segment, and construct a parking apron node by connecting multiple parking stands in close proximity. The aircraft capacity of each parking apron node is equal to the number of parking stands. Each parking apron node has a fixed ground support vehicle service route. Construct a directed network for the airport's flight area, such as... Figure 3 As shown;

[0043] S2. The specific process for basic data processing is as follows:

[0044] Step 2.1: Identify potential conflict points: Due to the long wingspan of aircraft, a conflict will inevitably occur when two aircraft enter the same intersection simultaneously. Conflicts between aircraft and between aircraft and vehicles are categorized into five potential conflict types:

[0045] A. Potential head-on collision, i.e., the taxiing arcs of two aircraft belong to the same intersection, one arc leaves from one taxiway node, while the other arc enters the same taxiway node;

[0046] B. Potential rear-end collision, where the taxiing arcs of two aircraft belong to the same intersection, and the two arcs leave from different taxiway nodes and enter the same taxiway node;

[0047] C. Potential intersection conflict, which means that the taxiing arcs of two aircraft belong to the same intersection, and the two arcs leave from different taxiway nodes and enter different taxiway nodes;

[0048] D. Potential pushback conflict, which is the potential conflict that may occur when the taxiing arc of one aircraft leaves the apron node and the taxiing arc of another aircraft.

[0049] E. Potential vehicle conflicts, which are conflicts that may arise between the taxiing arc of an aircraft and the path of a ground support vehicle.

[0050] Figure 4 The diagrams illustrate five types of real-world conflict. Figure 5 This diagram illustrates the potential conflicts between aircraft taxiing arcs and between aircraft taxiing arcs and vehicle travel paths. Taxiways and apron areas are numbered accordingly. Table 1 shows the breakdown. Figure 5 Summarize the potential conflict types in the process;

[0051] Table 1

[0052]

[0053]

[0054] Step 2.2: Generate a set of candidate aircraft taxiing paths: For each pair of landing runways and apron nodes, and between each pair of apron nodes and takeoff runways, generate X (X=20) taxiing paths for aircraft landing and takeoff according to Yen's algorithm, in preparation for subsequent path optimization selection;

[0055] S3. The specific process for solving path optimization is as follows:

[0056] Step 3.1: The objective function of the path optimization model includes the following:

[0057] (1) Considering the need to improve the safety of the flight area, the main objective of the mixed-integer quadratic programming model for aircraft taxiway path optimization is to minimize potential conflicts between aircraft and between aircraft and vehicles:

[0058]

[0059] Among them, u a，b The weights representing the aircraft and potential aircraft collisions between arcs a and b are set here by u. a，b =1; if there is no conflict, then u a，b =0; v a，h The weight representing the potential conflict between arc a and the vehicle's travel path h is set here to v. a，h =1; if there is no conflict, then v a，h =0; y a This indicates that if arc a is used, then y a =1, otherwise y a =0; z h This indicates that if road segment h is used, then z h =1, otherwise z h =0; A is the set of arcs (the trajectory of the aircraft); H is the set of vehicle paths.

[0060] (2) Considering the need to improve the efficiency of ground support vehicle services, the secondary objective of the objective function of the constructed mixed-integer quadratic programming model for aircraft taxiway path optimization is to minimize the travel distance of ground support vehicles:

[0061] MINIMIZE f2

[0062] f2 = Z p∈P (d p ×∑ s∈S (m s，p +n p，s ))

[0063]

[0064]

[0065] Where, d p x represents the distance traveled by ground support vehicles from apron node p to the terminal building; r This represents the number of aircraft assigned to route r; m s,p This represents the number of aircraft originating from runway node s and destined for apron node p; n p，s This represents the number of aircraft originating from apron node p and destined for runway node s; N is the set of nodes (taxiway segments, runways, and aprons); S is the set of runway nodes. P is the set of apron nodes.

[0066] (3) Using linear weighting, the two objectives are combined into a single objective for weighted solution. The overall objective function is:

[0067] MINIMIZE f1+ε×f2

[0068] In the formula, ε represents the weight assigned to the secondary objective. Here, ε is set to 0.001.

[0069] Step 3.2: The constraints of the path optimization model include the following:

[0070] ∑ p∈P m s,p =f s , s∈S——(1)

[0071] ∑ s∈S n p,s =g p , p∈P——(2)

[0072] Σ s∈S m s,p ≤c p , p∈P——(3)

[0073]

[0074] ∑ p∈P (σ h，p ×∑ s∈S (m s，p +n p，s )≤M×z h , h∈H——(5)

[0075]

[0076] y a , z h ∈{0, 1}, a∈A, h∈H——(7)

[0077] Where N is the set of nodes (taxiway segments, runways, and aprons), and S is the set of runway nodes. P is the set of apron nodes. A is the set of arcs (the trajectory of the aircraft); H is the set of vehicle paths; f represents the set of candidate aircraft taxi routes between runway s and apron node p during arrival (departure); s g represents the number of aircraft originating from runway node s; p c represents the number of aircraft originating from apron node p; p δ represents the capacity (i.e., the number of parking positions) of apron node p; a，r If arc a is contained in the aircraft path r, then δ a，r =1, otherwise δ a，r =0; σ h,p If an aircraft occupies the apron node p and requires vehicle service, and the corresponding vehicle travel segment is h, then σ h,p =1, otherwise σ h,p =0;

[0078] Among them, constraints (1) and (2) ensure that the needs of aircraft arrival and departure are met, (3) is the capacity limit of the parking area, (4) indicates that the trajectory is used only when the aircraft is traveling on the route, where M is a large positive value, (5) indicates that the vehicle travel section is only in use when it is occupied by vehicles, and (6) and (7) represent the non-negative and binary types of the decision variables, respectively.

[0079] S4. The specific process for visualizing and outputting the results is as follows:

[0080] Step 4.1: Output the path optimization results, including the objective function results, the optimized aircraft taxiing path, the optimized vehicle driving path, all types of conflicts and their locations;

[0081] Step 4.2: Display the optimized aircraft and vehicle routes in the airport road network, including the taxiing routes of aircraft from the runway to the apron and the routes from the apron to the runway, as well as the driving routes of vehicles to the apron to serve aircraft.

[0082] Step 4.3: Mark the areas with a large number of conflict points. In the road network, you can circle the intersections with the most conflict points.

[0083] Figure 6 This is a schematic diagram of the path optimization results of the present invention, including the output aircraft arrival taxiway path, aircraft departure taxiway path, ground support vehicle service path, and marking intersections A and B as the two areas with the most conflict points.

[0084] S5. The specific process for parameter adjustment analysis is as follows:

[0085] Step 5.1: Adjust the conflict weights of different intersections. Here, the conflict weight of intersection A, which has the most conflict points in the visualization output, is adjusted from 1 to 2. After adjusting the parameters, return to the path optimization model to solve the problem. The results are as follows. Figure 7 As shown, Figure 7 This is a schematic diagram of the path optimization results after adjusting the intersection parameters according to the present invention. The number of conflicts at intersection A is reduced.

[0086] Step 5.2: Adjust the weight of the secondary objective. In the path optimization model, adjust the weight of the secondary objective from 0.001 to 1000, making minimizing the vehicle path the primary objective. After adjusting the parameters, return to the path optimization model for solution. The results are as follows: Figure 8 As shown, Figure 8 This is a schematic diagram of the path optimization results after adjusting the secondary target weight parameters according to the present invention;

[0087] Step 5.3: Adjust the conflict weights for different conflict types. The conflict weights for the five potential conflict types can be adjusted. Tables 2 and 3 summarize the schemes for adjusting the conflict weights of potential conflict types and the corresponding output results for the number of conflicts:

[0088] Table 2

[0089] plan Adjusting the conflict weights of potential conflict types A The weight of potential adversary conflicts has been adjusted from 1 to 2. B The weight of potential rear-end collisions has been adjusted from 1 to 2. C The potential cross-conflict weight is adjusted from 1 to 2. D The potential rollout conflict weight has been adjusted from 1 to 2. E The weight of potential vehicle conflict has been adjusted from 1 to 2.

[0090] Table 3

[0091] Potential Conflict Types Original plan A B C D E Potential rivalry 6 4 5 6 8 6 Potential rear-end collision 31 31 23 30 39 34 Potential cross-conflict 2 2 4 1 3 3 Potential launch conflict 39 41 48 41 30 39 Potential vehicle conflict 13 13 16 13 12 10 total 91 92 96 91 92 92

[0092] Example 2

[0093] like Figure 9 As shown, as a second embodiment of the present invention, the present invention also provides a system for implementing the taxiway path optimization method described in the above embodiments, specifically including the following modules:

[0094] Basic data input module: Reads airport road network data, including taxiway nodes, apron nodes, runway nodes, service routes of airport ground service vehicles, and capacity of each apron node; initializes the number of arriving aircraft at runway nodes and the number of departing aircraft at apron nodes; and constructs a directed network of the airport flight area.

[0095] Basic data processing module: Identifies potential conflict points, identifies potential conflicts between aircraft taxiing arcs and between aircraft taxiing arcs and vehicle driving paths; generates a set of alternative aircraft taxiing paths, and generates several aircraft landing and takeoff taxiing paths for each pair of landing runway and apron nodes, and between each pair of apron nodes and takeoff runways, based on Yen's algorithm.

[0096] The path optimization solution module constructs a mixed integer quadratic programming model for aircraft taxiway path optimization. The primary objective is to minimize potential conflicts between aircraft and between aircraft and vehicles, and the secondary objective is to minimize the travel distance of ground service vehicles. The output is the optimal solution, which includes the aircraft taxiway path and the ground service vehicle path, as well as the type and number of potential conflicts.

[0097] The results visualization output module displays the optimized aircraft and vehicle routes on the airport road network and marks areas with a high number of conflict points.

[0098] The parameter adjustment and analysis module can adjust and optimize parameters based on the output results, and then return to the S3 path optimization solution module for solution.

[0099] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A taxiway path optimization method for reducing potential conflicts in the airfield, characterized in that, The method includes: Read the airport road network data, initialize the number of arriving aircraft at the runway nodes and the number of departing aircraft at the apron nodes, and construct the directed network of the airport flight area. Identify potential conflicts between aircraft taxiing arcs and between aircraft taxiing arcs and vehicle driving paths, and generate multiple alternative taxiing paths for aircraft landing and takeoff. A mixed-integer quadratic programming model for aircraft taxiing path optimization is constructed. The primary objective is to minimize potential conflicts between aircraft and between aircraft and vehicles, and the secondary objective is to minimize the travel distance of ground support vehicles. The specific objective function of the aircraft taxiing path optimization model is as follows: The main objective function of the mixed-integer quadratic programming model for aircraft taxiing path optimization is... It is about minimizing potential conflicts between aircraft and between aircraft and vehicles, specifically expressed as: in, Represents arc a Sum of arcs b The weight of potential conflicts between aircraft and aircraft, if there is no conflict, then ; Represents arc a and vehicle travel path h The weight of potential conflicts between them; if there is no conflict, then... ; Represents arc a Whether it is used; Indicates whether the vehicle travel segment h is used; A is the arc set, i.e., the set of aircraft trajectories; H is the vehicle path set. The secondary objective of the mixed-integer quadratic programming model for optimizing aircraft taxiing paths is... It minimizes the travel distance of ground support vehicles, specifically expressed as: in, Indicates the apron node p The distance traveled by ground support vehicles to the terminal building; Indicates the route allocation r The number of aircraft; Indicates originating from runway node s And specify the apron node p The number of aircraft; Indicates originating from the apron node. p And specify to the runway node s The number of aircraft; N is the set of nodes; S is the set of runway nodes, S⊆N; P is the set of apron nodes, P⊆N; By using linear weighting, the two objectives are combined into a single objective for weighted solution. The overall objective function is: Where, in the formula The weights of secondary objectives are determined; the aircraft taxiing path optimization model is solved, and the optimal solution is output, including the aircraft taxiing path, the ground support vehicle service path, and the type and number of potential conflicts.

2. The taxiway path optimization method for reducing potential conflicts in the flight area according to claim 1, characterized in that, The types of potential conflicts include: Potential head-on collision occurs when the taxiing arcs of two aircraft belong to the same intersection, with one arc leaving a taxiway node and the other arc entering that taxiway node. A potential rear-end collision occurs when the taxiing arcs of two aircraft belong to the same intersection, with the two arcs departing from different taxiway nodes and entering the same taxiway node. Potential intersection conflict occurs when the taxiing arcs of two aircraft belong to the same intersection, but the two arcs depart from different taxiway nodes and enter different taxiway nodes. Potential pushback conflict refers to the potential conflict that may occur when the taxiing arc of one aircraft leaves the apron node and the taxiing arc of another aircraft. Potential vehicle conflicts refer to the potential conflict between the taxiing arc of an aircraft and the service path of a ground support vehicle.

3. The taxiway path optimization method for reducing potential conflicts in the flight area according to claim 1, characterized in that, The specific steps for generating multiple alternative taxiing paths for aircraft landing and takeoff are as follows: For each pair of landing runways and apron nodes, and between each pair of apron nodes and takeoff runways, multiple taxiing paths for aircraft landing and takeoff are generated based on the path generation algorithm for subsequent path optimization selection.

4. The taxiway path optimization method for reducing potential conflicts in the flight area according to claim 1, characterized in that, The constraints of the objective function of the aircraft taxiing path optimization model include: Ensure that the arrival and departure requirements of aircraft are met; limit the capacity of parking areas; use the trajectory only when the aircraft is traveling on the route; use vehicle lanes only when they are occupied by vehicles; ensure the non-negativity of decision variables and ensure that decision variables are binary types.

5. The taxiway path optimization method for reducing potential conflicts in the flight area according to claim 1, characterized in that, The taxiway path optimization method also adjusts the optimization parameters based on the output results, specifically including: adjusting the conflict weights of different intersections, adjusting the weights of secondary objectives in the aircraft taxiway path optimization model, and adjusting the conflict weights of different conflict types. After adjusting the parameters, return to the aircraft taxiing path optimization model for solution.

6. A taxiway path optimization system for reducing potential conflicts in the airfield, characterized in that, The system is used to implement the taxiway path optimization method as described in any one of claims 1-5, and includes the following modules: Basic data input module: Reads airport road network data, including taxiway nodes, apron nodes, runway nodes, service routes of airport ground support vehicles, and capacity of each apron node; initializes the number of arriving aircraft at runway nodes and the number of departing aircraft at apron nodes; and constructs a directed network of the airport flight area. Basic data processing module: Identifies potential conflict points, and identifies potential conflicts between aircraft and aircraft, and between aircraft and vehicle paths; Generate a set of candidate aircraft taxiing paths. For each pair of landing runway and apron nodes, and between each pair of apron nodes and takeoff runway, generate several aircraft landing and takeoff taxiing paths according to the path generation algorithm. The path optimization solution module constructs a mixed integer quadratic programming model for aircraft taxiway path optimization. The primary objective is to minimize potential conflicts between aircraft and between aircraft and vehicles, and the secondary objective is to minimize the travel distance of ground support vehicles. The output is the optimal solution, which includes the aircraft taxiway path and the ground support vehicle service path, as well as the type and number of potential conflicts. Parameter adjustment and analysis module: Based on the output results, adjust and optimize the parameters, and then return to the path optimization solution module for solution.

7. A taxiway path optimization system for reducing potential conflicts in the flight area according to claim 6, characterized in that, The system also includes a result visualization output module for: The results of the path optimization are displayed, including the objective function results, the optimized aircraft taxiing path, the optimized vehicle driving path, and all types of conflicts and their locations. The optimized aircraft and vehicle routes are displayed in the airport road network, including the taxiing routes of aircraft from the runway to the apron and the routes of aircraft from the apron to the runway, as well as the driving routes of vehicles to the apron to serve aircraft. Mark the areas with a high number of conflict points, and mark the intersections with the most conflict points in the road network.

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