Airport terminal area entry and departure traffic flow evaluation method, device and equipment

Abstract

The invention provides an airport terminal area arrival and departure traffic flow evaluation method, device and equipment, and relates to the technical field of air traffic transportation, and the method comprises the steps: obtaining the flight data of a target flight of an airport terminal area in a first target time period; the target flight is an approach flight or a departure flight; re-sampling the flight path data to obtain feature vectors of the target flights; according to the feature vector of each target flight, flight trajectory clustering is carried out, a plurality of clusters are obtained, and each cluster corresponds to one flight trajectory mode; determining a traffic flow time sequence corresponding to each flight trajectory mode; constructing a multiple recursive network by using the traffic flow time sequence; and calculating an average edge overlapping value of the multi-recursive network, wherein the average edge overlapping value is used for quantitatively evaluating the synchronism of traffic flow fluctuation. According to the invention, traffic flow evaluation can be carried out in multiple directions, and entrance traffic flow evaluation and departure traffic flow evaluation can be carried out on an airport terminal area.

Description

Technical Field

[0001] The present invention relates to the field of aviation transportation technology, and in particular to a method, device and equipment for evaluating arrival and departure traffic flow in an airport terminal area. Background Art

[0002] During peak hours of flight traffic, improving the synchronization of incoming or outgoing traffic in all directions is conducive to improving the operating efficiency of the airport. Improving the synchronization of traffic flow means optimizing the scheduling of flight flows within a specific airport terminal area so that each flight can take off or arrive and land at the same time as much as possible while ensuring safety, so as to reduce delays and improve operating efficiency.

[0003] However, the current synchronization analysis of traffic flow fluctuations over time mainly focuses on a single direction. An airport terminal area contains one or more airports, and an airport has one or more directions (approach direction or departure direction). Therefore, the existing technology cannot realize the evaluation of approach traffic flow and departure traffic flow in the airport terminal area. Summary of the invention

[0004] In view of this, a method, device and equipment for evaluating arrival and departure traffic flow in an airport terminal area are provided to solve the problem that the existing technology cannot realize arrival traffic flow evaluation and departure traffic flow evaluation in an airport terminal area.

[0005] The present invention adopts the following technical solution: In a first aspect, the present invention provides a method for evaluating arrival and departure traffic flow in an airport terminal area, comprising: Acquire flight data of a target flight in a first target time period at an airport terminal area; the target flight is an approaching flight or a departing flight, the flight data includes identity data and flight trajectory data, the flight trajectory data includes target flight parameters corresponding to different acquisition timestamps, the target flight parameters include longitude and latitude, and, when the target flight is an approaching flight, the flight data also includes an actual landing time, and when the target flight is a departing flight, the flight data also includes an actual take-off time; Resampling the flight trajectory data to obtain a feature vector of each target flight; the feature vectors of each target flight have the same dimension; Based on a preset clustering algorithm, flight trajectory clustering is performed according to the feature vectors of each target flight to obtain a plurality of clusters, each of which corresponds to a flight trajectory mode; Based on the flight data, a traffic flow time series corresponding to each flight trajectory mode is determined, wherein the traffic flow time series includes N elements, Nis equal to the ratio of the duration of the first target time period to the preset unit duration, the element represents the number of flights that land or take off in the corresponding second target time period according to the corresponding flight trajectory mode, and the duration of the second target time period is equal to the preset unit duration; Using the traffic flow time series, constructing a multiple recursive network; An average edge overlap value of the multiple recursive networks is calculated, and the average edge overlap value is used to quantitatively evaluate the synchronization of traffic flow fluctuations.

[0006] Optionally, before resampling the flight trajectory data to obtain the feature vectors of each target flight, the airport terminal area arrival and departure traffic flow evaluation method of the present invention further includes: The flight trajectory data is preprocessed to remove noise data and abnormal data in the flight trajectory data.

[0007] Optionally, resampling the flight trajectory data to obtain a feature vector of each target flight specifically includes: For each of the target flights, according to the flight trajectory data of the target flight, the relative longitude corresponding to each of the longitudes of the target flight with the longitude of the center point of the airport terminal area as the zero point is calculated, and the relative latitude corresponding to each of the latitudes of the target flight with the latitude of the center point of the airport terminal area as the zero point is calculated; Normalizing the acquisition timestamp of the target flight; Determine whether the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to a preset number of coordinate points, wherein the geographic coordinate points include relative longitude and relative latitude corresponding to the same acquisition timestamp; If the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to the preset number of coordinate points, selecting the preset number of geographic coordinate points from the flight trajectory data of the target flight, and using the selected geographic coordinate points to construct a feature vector of the target flight; If the number of geographic coordinate points in the flight trajectory data of the target flight is less than the preset number of coordinate points, the flight trajectory data of the target flight is interpolated so that the flight trajectory data after the interpolation processing contains the preset number of geographic coordinate points, and the feature vector of the target flight is constructed using each geographic coordinate point in the flight trajectory data after the interpolation processing.

[0008] Optionally, the preset clustering algorithm is a DBSCAN clustering algorithm.

[0009] Optionally, determining the traffic flow time series corresponding to each of the flight trajectory patterns based on the flight data specifically includes: For each of the flight trajectory patterns, the first target time period is divided based on the preset unit duration to obtain N the second target time period; sequentially counting the number of flights that land or take off according to the flight trajectory pattern within each of the second target time periods; The number of each flight is used to construct a traffic flow time series corresponding to the flight trajectory pattern.

[0010] Optionally, the using the traffic flow time series to construct a multiple recursive network includes: Based on the time delay embedding technology, recursive network extraction is performed on each of the traffic flow time series to obtain the recursive network of each of the traffic flow time series; Using each of the recursive networks, the multiple recursive networks are constructed.

[0011] In a second aspect, the present invention further provides an airport terminal area arrival and departure traffic flow evaluation device, comprising: an acquisition module, configured to acquire flight data of a target flight in a first target time period at an airport terminal area; the target flight is an approaching flight or a departing flight, the flight data includes identity data and flight trajectory data, the flight trajectory data includes target flight parameters corresponding to different acquisition timestamps, the target flight parameters include longitude and latitude, and, when the target flight is an approaching flight, the flight data also includes an actual landing time, and when the target flight is a departing flight, the flight data also includes an actual take-off time; A resampling module, used for resampling the flight trajectory data to obtain a feature vector of each target flight; the feature vectors of each target flight have the same dimension; A clustering module, for clustering flight trajectories based on a preset clustering algorithm and according to the feature vectors of each target flight, to obtain a plurality of clusters, each of which corresponds to a flight trajectory mode; A determination module is used to determine the traffic flow time series corresponding to each flight trajectory mode based on the flight data, wherein the traffic flow time series includes N elements, N is equal to the ratio of the duration of the first target time period to the preset unit duration, the element represents the number of flights that land or take off in the corresponding second target time period according to the corresponding flight trajectory mode, and the duration of the second target time period is equal to the preset unit duration; A construction module, used to construct a multiple recursive network using the traffic flow time series; The calculation module is used to calculate the average edge overlap value of the multiple recursive networks, and the average edge overlap value is used to quantitatively evaluate the synchronization of traffic flow fluctuations.

[0012] In a third aspect, the present invention further provides an airport terminal area arrival and departure traffic flow evaluation device, comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the airport terminal area arrival and departure traffic flow evaluation method as described above.

[0013] The present invention adopts the above technical scheme, a method for evaluating the arrival and departure traffic flow in the airport terminal area, comprising: obtaining flight data of a target flight in the airport terminal area within a first target time period; the target flight is an arrival flight or a departure flight, the flight data includes identity data and flight trajectory data, the flight trajectory data includes target flight parameters corresponding to different acquisition timestamps, the target flight parameters include longitude and latitude, and, when the target flight is an arrival flight, the flight data also includes an actual landing time, and when the target flight is a departure flight, the flight data also includes an actual take-off time; resampling the flight trajectory data to obtain a feature vector of each of the target flights; the dimension of the feature vector of each of the target flights is the same; based on a preset clustering algorithm, flight trajectory clustering is performed according to the feature vector of each of the target flights to obtain multiple cluster clusters, each of the cluster clusters corresponds to a flight trajectory pattern; based on the flight data, a traffic flow time series corresponding to each of the flight trajectory patterns is determined, and the traffic flow time series contains N elements, N is equal to the ratio of the duration of the first target time period to the preset unit duration, the element represents the number of flights that land or take off in the corresponding second target time period according to the corresponding flight trajectory mode, and the duration of the second target time period is equal to the preset unit duration; using the traffic flow time series, constructing a multiple recursive network; calculating the average edge overlap value of the multiple recursive network, and the average edge overlap value is used to quantitatively evaluate the synchronization of traffic flow fluctuations.

[0014] Based on this, this solution obtains multiple clusters by clustering the flight trajectory data of each incoming flight or departing flight, each of which corresponds to a flight trajectory pattern, that is, when the flight trajectories are the same or similar, the corresponding flight trajectory data will be classified into the same cluster. Then, based on the flight data, the traffic flow time series corresponding to each flight trajectory pattern is determined. Finally, the traffic flow time series is used to construct a multiple recursive network, and the average edge overlap value of the multiple recursive network is calculated. In this way, the present invention can evaluate traffic flow in multiple directions, and further the present invention can realize the evaluation of incoming traffic flow and departing traffic flow in the airport terminal area, and the evaluation results are more comprehensive and detailed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a method for evaluating arrival and departure traffic flow in an airport terminal area provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of an airport terminal area arrival and departure traffic flow evaluation device provided by an embodiment of the present invention; Figure 3 It is a structural schematic diagram of an airport terminal area arrival and departure traffic flow evaluation device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0018] During peak hours of flight traffic, improving the synchronization of incoming or outgoing traffic in all directions is conducive to improving the operating efficiency of the airport. Improving the synchronization of traffic flow means optimizing the scheduling of flight flows within a specific airport terminal area so that each flight can take off or arrive and land at the same time as much as possible while ensuring safety, so as to reduce delays and improve operating efficiency.

[0019] However, the current synchronization analysis of traffic flow fluctuations over time mainly focuses on a single direction. An airport terminal area contains one or more airports, and an airport has one or more directions (approach direction or departure direction). Therefore, the existing technology cannot realize the evaluation of approach traffic flow and departure traffic flow in the airport terminal area.

[0020] Based on this, in order to realize traffic flow evaluation in multiple directions, and then realize approaching traffic flow evaluation and departing traffic flow evaluation of the airport terminal area, the present invention provides an airport terminal area approaching and departing traffic flow evaluation method, device and equipment.

[0021] It should be noted that the method of the present invention can be used to perform incoming traffic flow evaluation or outgoing traffic flow evaluation, and the specific execution processes of the incoming traffic flow evaluation and the outgoing traffic flow evaluation are the same or similar. This specification takes the incoming traffic flow evaluation as an example for explanation, and those skilled in the art can easily think of the specific execution process of the outgoing traffic flow evaluation based on the specific execution process of the incoming traffic flow evaluation.

[0022] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0023] Figure 1 FIG. 1 is a flow chart of a method for evaluating the arrival and departure traffic flow of an airport terminal area provided by an embodiment of the present invention. Figure 1 As shown in the figure, the airport terminal area arrival and departure traffic flow evaluation method includes: Step 101: Acquire flight data of a target flight in a first target time period at an airport terminal area; the target flight is an approaching flight or a departing flight, the flight data includes identity data and flight trajectory data, the flight trajectory data includes target flight parameters corresponding to different acquisition timestamps, the target flight parameters include longitude and latitude, and, when the target flight is an approaching flight, the flight data also includes an actual landing time, and when the target flight is a departing flight, the flight data also includes an actual take-off time.

[0024] Specifically, the airport terminal area may be the terminal area of ​​Shanghai Airport or other airport terminal areas. The terminal area of ​​Shanghai Airport includes two airports, namely Shanghai Pudong International Airport and Shanghai Hongqiao International Airport. This specification takes the terminal area of ​​Shanghai Airport as an example for explanation. The first target time period may be from 6:00 to 24:00 on a certain day.

[0025] When evaluating the arrival traffic flow of the airport terminal area, the target flights are the arrival flights; when evaluating the departure traffic flow of the airport terminal area, the target flights are the departure flights.

[0026] The identity data may specifically include the flight number and the aircraft registration number. In addition, the identity data may specifically include an ID corresponding to the flight number.

[0027] The flight trajectory data may be collected at a fixed collection frequency, for example, during the first target time period, the target flight parameters of each target flight are collected every five minutes.

[0028] In addition, when the target flight is an incoming flight, the flight data also includes the landing airport, and when the target flight is a departing flight, the flight data also includes the departure airport.

[0029] Step 102: resample the flight trajectory data to obtain a feature vector of each target flight; the feature vectors of each target flight have the same dimension.

[0030] In actual application, the flight speeds of different target flights may be different, and the data collection duration of the present invention is fixed, which is the first target time period. Therefore, the number of samples of different target flights in the collected flight trajectory data may be different.

[0031] Based on this, in order to make the sample numbers of different target flights consistent, the present invention resamples the flight trajectory data to obtain the feature vectors of each target flight, wherein the dimension of the feature vectors of each target flight is the same.

[0032] In the embodiment of the present invention, resampling the flight trajectory data to obtain the feature vector of each target flight may specifically include: (1) For each target flight, based on the flight trajectory data of the target flight, calculate the relative longitude of each longitude of the target flight with the longitude of the center point of the airport terminal area as the zero point, and calculate the relative latitude of each latitude of the target flight with the latitude of the center point of the airport terminal area as the zero point.

[0033] Specifically, the flight trajectory data of any target flight includes the longitude and latitude corresponding to different collection timestamps. For each longitude of the target flight, the longitude is subtracted from the longitude of the center point of the airport terminal area to obtain the relative longitude of the longitude, and, for each latitude of the target flight, the latitude is subtracted from the latitude of the center point of the airport terminal area to obtain the relative latitude of the latitude.

[0034] (2) Normalize the acquisition timestamps of the target flights and place them in the interval [0,1].

[0035] (3) Determine whether the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to the preset number of coordinate points, where the geographic coordinate points include the relative longitude and relative latitude corresponding to the same acquisition timestamp.

[0036] Specifically, the number of preset coordinate points is set by relevant traffic flow evaluators, and can be an integer between 10 and 20. For example, the number of preset coordinate points is equal to 12.

[0037] (4) If the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to the preset number of coordinate points, select the preset number of geographic coordinate points from the flight trajectory data of the target flight, and use the selected geographic coordinate points to construct the feature vector of the target flight. It can be seen that each feature vector represents a flight trajectory and also corresponds to a target flight.

[0038] Specifically, when the number of geographic coordinate points in the flight trajectory data of the target flight is equal to the preset number of coordinate points, all geographic coordinate points in the flight trajectory data of the target flight are selected to construct the feature vector of the target flight.

[0039] When the number of geographic coordinate points in the flight trajectory data of the target flight is greater than the preset number of coordinate points, the preset number of geographic coordinate points can be evenly selected from the geographic coordinate points in the flight trajectory data of the target flight to construct the feature vector of the target flight.

[0040] The expression of the final constructed feature vector is as follows: ...... (1) in, The eigenvectors are The relative longitude and latitude of the first geographical coordinate point in all corresponding geographical coordinate points. The same applies to other elements, for example, The eigenvectors are The first of all the corresponding geographic coordinate points The relative longitude and latitude of a geographical coordinate point. Equal to the number of preset coordinate points.

[0041] (5) If the number of geographic coordinate points in the flight trajectory data of the target flight is less than the preset number of coordinate points, the flight trajectory data of the target flight is interpolated so that the interpolated flight trajectory data contains the preset number of geographic coordinate points, and the feature vector of the target flight is constructed using each geographic coordinate point in the interpolated flight trajectory data. The interpolation method may be linear interpolation.

[0042] Step 103: Based on a preset clustering algorithm, flight trajectories are clustered according to the feature vectors of each target flight to obtain a plurality of clusters, each of which corresponds to a flight trajectory pattern.

[0043] Specifically, the preset clustering algorithm may be the current DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and the number of clusters may be 3, 4 or 5 to ensure that the subsequent synchronization evaluation of traffic flow obtains effective results.

[0044] After obtaining the feature vectors of each target flight, the clustering results (i.e., multiple clusters) are obtained by adjusting the parameters MinPts and ε in DBSCAN. MinPts represents the minimum number of neighbors for an object to be considered a core object in its neighborhood. ε represents the size of the neighborhood radius of the object, which is used to determine the neighborhood range of the object. It should be noted that DBSCAN is a clustering algorithm in the prior art. Therefore, it is easy for those skilled in the art to cluster flight trajectories based on the preset clustering algorithm and the feature vectors of each target flight, and it will not be repeated here.

[0045] Step 104: Based on the flight data, determine the traffic flow time series corresponding to each flight trajectory mode, and the traffic flow time series includes N elements, N It is equal to the ratio of the duration of the first target time period to the preset unit duration. The element represents the number of flights that land or take off according to the corresponding flight trajectory mode within the corresponding second target time period. The duration of the second target time period is equal to the preset unit duration.

[0046] Specifically, based on the flight data, determining the traffic flow time series corresponding to each flight trajectory pattern may include: (1) For each flight trajectory mode, the first target time period is divided based on the preset unit duration to obtain N A second target time period.

[0047] In a specific example, assuming that the first target time period is from 6:00 to 24:00 on June 1, 2024, and the preset unit time length is 5 minutes, then N =18 hours / 5 minutes=216.

[0048] (2) Count the number of flights that land or take off according to the flight trajectory pattern in each second target time period.

[0049] Continuing with the above example, assume that the first target time period is from 6:00 to 24:00 on June 1, 2024, and the preset unit time length is 5 minutes. Also, this example is an evaluation of the incoming traffic flow in the airport terminal area. In this scenario, for the first second target time period (i.e., from 6:00 on June 1, 2024 to 6:05 on June 1, 2024), according to the actual landing time of each target flight, the number of target flights landing from any airport in the airport terminal area according to the flight trajectory mode (i.e., the currently targeted flight trajectory mode) during the time period from 6:00 on June 1, 2024 to 6:05 on June 1, 2024 is counted.

[0050] The same is true for the second second target time period (i.e., 6:05 on June 1, 2024 to 6:10 on June 1, 2024) and other second target time periods. Finally, the number of flights that landed according to the flight trajectory pattern in each second target time period is obtained.

[0051] (3) Use the number of flights to construct the traffic flow time series corresponding to the flight trajectory pattern.

[0052] The expression of traffic flow time series is as follows: ...... (2) in, , Represents the total number of flight trajectory modes; Indicated in During the second target time period, The number of flights landing or taking off for each flight trajectory pattern.

[0053] In a specific example, assume that the total number of flight trajectory patterns in the airport terminal area is 3 (i.e. ), the first target time period is from 8:00 to 9:00 on June 1, 2024 (duration is 1 hour), and the preset unit time is 5 minutes. This example is to evaluate the traffic flow in the airport terminal area. Therefore, it can be calculated N =12, the traffic flow time series obtained by the above scheme is as follows:

[0054]

[0055]

[0056] Among them, each value in the brackets is the number of flights corresponding to the second target time period.

[0057] Step 105: Use the traffic flow time series to construct a multiple recursive network.

[0058] In the embodiment of the present invention, a multiple recursive network is constructed using the traffic flow time series, which may specifically include: (1) Based on the time delay embedding technology, a recurrent network extraction is performed on each traffic flow time series to obtain the recurrent network (RN) of each traffic flow time series.

[0059] Specifically, The traffic flow time series represented by can be transformed into its phase space by time delay embedding method The corresponding calculation formula is as follows: ...... (3) in, represents the embedding delay, represents the embedding dimension.

[0060] for , the adjacency matrix A of RN is defined as: ...... (4) in, , , for The length of ; Θ(·) is the unit step function; ||·|| is the Euclidean norm; δ t1,t2 Kronecker δ Function (if ,but δ t1,t2 =1, if ,but δ t1,t2 =0); To set the threshold, you can take a value such as 1 or 2.

[0061] Based on the time delay embedding technology, the recursive network extraction is performed on any traffic flow time series. The process of obtaining the recursive network of the traffic flow time series is as follows: (a) Each element in the traffic flow time series is regarded as a node in the recursive network.

[0062] (b) When nodes are close to each other in space (i.e., their corresponding spatial vectors are adjacent), there is an edge between them.

[0063] (c) Matrix A represents the recursive network. Specifically, if the node and nodes interconnected, then Set to 1 if yes, otherwise set to 0.

[0064] In a specific example, suppose , , The traffic flow time series is the aforementioned , the recursive network of the traffic flow time series (i.e., the matrix A ) is calculated as follows: First, due to , , therefore, the phase space of the traffic flow time series can be obtained for: ,for and The Euclidean distance between the two is calculated to be approximately 1.73, which is greater than the set threshold. ,therefore, . Use the same method to calculate the matrix A The other elements in, finally, the resulting matrix A as follows: [[1 0 0 0 0 0 0 0 0 0] [0 1 0 0 0 0 0 0 0 0] [0 0 1 0 0 0 0 0 0 0] [0 0 0 1 0 0 0 0 0 0] [0 0 0 0 1 0 0 0 0 1] [0 0 0 0 0 1 0 0 0 0] [0 0 0 0 0 0 1 0 0 0] [0 0 0 0 0 0 0 1 0 0] [0 0 0 0 0 0 0 0 1 0] [0 0 0 0 1 0 0 0 0 1]] Among them, the matrix A In the first row and second column, 0 is ,correspond and .matrix A The same goes for the other elements in .

[0065] (2) Use each recursive network to construct multiple recursive networks.

[0066] Specifically, the matrix of each recursive network A Substitute the following calculation formula to get the overall adjacency matrix of the multi-recursive network : ...... (5) in, I N The size is N The identity matrix of is the total number of recurrent networks.

[0067] Step 106: Calculate the average edge overlap value of the multiple recursive networks, where the average edge overlap value is used to quantitatively evaluate the synchronization of traffic flow fluctuations. The higher the value of , the stronger the correlation of traffic flow fluctuations, indicating that the synchronization of traffic flow time series changes in different flight trajectory patterns is stronger.

[0068] Specifically, the average edge overlap value of a multi-recursive network can be calculated using the following calculation formula: : ...... (6) in, Indicates Matrix elements in a recurrent network .

[0069] The solution process of formula (6) is as follows: Step 6.1: Initialize the numerator and denominator to 0 respectively.

[0070] Step 6.2: Traverse each pair of nodes and calculate the total number of connections.

[0071] Specifically, for each pair of nodes ,in, , perform the following steps until every pair of nodes is traversed: Step 6.2.1: Calculate the pair of nodes in all layers The total number of connections (continued from the previous example, When middle, , , of ( ) and ( ): ...... (7) in, Indicates Node in layer i and nodes j The connection relationship between Matrix elements in a recurrent network .

[0072] Step 6.2.2: Add the current numerator value to the total number of connections to obtain the updated numerator value, that is: numerator += total number of connections.

[0073] Update the denominator using the following formula: delta_sum={1, if total number of connections ≠ 0 0, if total number of connections = 0} Denominator +=delta_sum......(8) Step 6.3: Use the following formula to calculate : ...... (8) In the embodiment of the present invention, before resampling the flight trajectory data to obtain the feature vector of each target flight, the airport terminal area arrival and departure traffic flow evaluation method of the present invention may further include: The flight trajectory data is preprocessed to remove noise data and abnormal data in the flight trajectory data.

[0074] Specifically, the present invention can select noise data and abnormal data from the flight trajectory data by relevant traffic flow evaluators (i.e., technicians in this field), and delete these noise data and abnormal data from the flight trajectory data in response to the data deletion operation of the relevant traffic flow evaluators.

[0075] Here, in order to facilitate the relevant traffic flow evaluators to select noise data and abnormal data, the target flight parameters may include altitude, ground speed and vertical speed in addition to longitude and latitude. The parameters of altitude, ground speed and vertical speed are used to assist the relevant traffic flow evaluators to select noise data and abnormal data from the flight trajectory data. When the relevant traffic flow evaluators select noise data and abnormal data from the flight trajectory data, they will comprehensively consider the actual take-off time or actual landing time, altitude, ground speed and vertical speed of the target flight.

[0076] Based on a general inventive concept, the present invention also provides an airport terminal area arrival and departure traffic flow evaluation device. Figure 2 Schematic diagram of the structure of an airport terminal area arrival and departure traffic flow evaluation device provided by an embodiment of the present invention. Figure 2 As shown in the figure, the airport terminal area arrival and departure traffic flow evaluation device includes: The acquisition module 21 is used to obtain the flight data of the target flight in the airport terminal area within the first target time period; the target flight is an approaching flight or a departing flight, and the flight data includes identity data and flight trajectory data, the flight trajectory data includes target flight parameters corresponding to different acquisition timestamps, the target flight parameters include longitude and latitude, and, when the target flight is an approaching flight, the flight data also includes an actual landing time, and when the target flight is a departing flight, the flight data also includes an actual take-off time.

[0077] The resampling module 22 is used to resample the flight trajectory data to obtain the feature vector of each target flight; the dimension of the feature vector of each target flight is the same.

[0078] The clustering module 23 is used to cluster the flight trajectories based on the feature vectors of each target flight based on a preset clustering algorithm to obtain a plurality of clusters, each of which corresponds to a flight trajectory mode.

[0079] The determination module 24 is used to determine the traffic flow time series corresponding to each flight trajectory mode based on the flight data, and the traffic flow time series includes N elements, N It is equal to the ratio of the duration of the first target time period to the preset unit duration. The element represents the number of flights that land or take off according to the corresponding flight trajectory mode within the corresponding second target time period. The duration of the second target time period is equal to the preset unit duration.

[0080] The construction module 25 is used to construct a multiple recursive network using the traffic flow time series.

[0081] The calculation module 26 is used to calculate the average edge overlap value of the multiple recursive networks, and the average edge overlap value is used to quantitatively evaluate the synchronization of traffic flow fluctuations.

[0082] Optionally, the airport terminal area arrival and departure traffic flow evaluation device of the present invention may also include: The preprocessing module is used to preprocess the flight trajectory data to remove noise data and abnormal data in the flight trajectory data.

[0083] Optionally, the resampling module 22 may be specifically used for: (1) For each target flight, based on the flight trajectory data of the target flight, calculate the relative longitude of each longitude of the target flight with the longitude of the center point of the airport terminal area as the zero point, and calculate the relative latitude of each latitude of the target flight with the latitude of the center point of the airport terminal area as the zero point.

[0084] (2) Normalize the acquisition timestamp of the target flight.

[0085] (3) Determine whether the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to the preset number of coordinate points, where the geographic coordinate points include the relative longitude and relative latitude corresponding to the same acquisition timestamp.

[0086] (4) If the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to the preset number of coordinate points, select the preset number of geographic coordinate points from the flight trajectory data of the target flight, and use the selected geographic coordinate points to construct a feature vector of the target flight.

[0087] (5) If the number of geographic coordinate points in the flight trajectory data of the target flight is less than the preset number of coordinate points, the flight trajectory data of the target flight is interpolated so that the interpolated flight trajectory data contains the preset number of geographic coordinate points, and the feature vector of the target flight is constructed using each geographic coordinate point in the interpolated flight trajectory data.

[0088] Optionally, the preset clustering algorithm is a DBSCAN clustering algorithm.

[0089] Optionally, the determination module 24 may be specifically used for: (1) For each flight trajectory mode, the first target time period is divided based on the preset unit duration to obtain N A second target time period.

[0090] (3) Count the number of flights that land or take off according to the flight trajectory pattern in each second target time period.

[0091] (3) Use the number of flights to construct the traffic flow time series corresponding to the flight trajectory pattern.

[0092] The building block 25 can be specifically used for: (1) Based on the time delay embedding technology, the recursive network extraction is performed on each traffic flow time series to obtain the recursive network of each traffic flow time series.

[0093] (2) Use each recursive network to construct multiple recursive networks.

[0094] Based on a general inventive concept, the present invention also provides an airport terminal area arrival and departure traffic flow evaluation device. Figure 3 Schematic diagram of the structure of an airport terminal area arrival and departure traffic flow evaluation device provided by an embodiment of the present invention. Figure 3 As shown, the airport terminal area arrival and departure traffic flow evaluation device 300 includes: at least one processor 310; and, A memory 330 is communicatively connected to at least one processor 310; wherein, The memory 330 stores instructions 320 executable by at least one processor 310. The instructions 320 are executed by at least one processor 310 so that the at least one processor 310 can implement the airport terminal area arrival and departure traffic flow evaluation method as described in the above embodiment.

[0095] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0096] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0097] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0098] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0099] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0100] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0101] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0102] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0103] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating arrival and departure traffic flow in an airport terminal area, characterized in that: include: Obtain flight data of a target flight in a first target time period at an airport terminal area; The target flight is an approaching flight or a departing flight, the flight data includes identity data and flight trajectory data, the flight trajectory data includes target flight parameters corresponding to different acquisition timestamps, the target flight parameters include longitude and latitude, and, when the target flight is an approaching flight, the flight data also includes an actual landing time, and when the target flight is a departing flight, the flight data also includes an actual take-off time; Resampling the flight trajectory data to obtain a feature vector of each target flight; the feature vectors of each target flight have the same dimension; Based on a preset clustering algorithm, flight trajectory clustering is performed according to the feature vectors of each target flight to obtain a plurality of clusters, each of which corresponds to a flight trajectory mode; Based on the flight data, a traffic flow time series corresponding to each flight trajectory mode is determined, wherein the traffic flow time series includes N elements, N is equal to the ratio of the duration of the first target time period to the preset unit duration, the element represents the number of flights that land or take off in the corresponding second target time period according to the corresponding flight trajectory mode, and the duration of the second target time period is equal to the preset unit duration; Using the traffic flow time series, constructing a multiple recursive network; An average edge overlap value of the multiple recursive networks is calculated, and the average edge overlap value is used to quantitatively evaluate the synchronization of traffic flow fluctuations.

2. The airport terminal area arrival and departure traffic flow evaluation method according to claim 1 is characterized in that: Before resampling the flight trajectory data to obtain the feature vectors of each target flight, the method further includes: The flight trajectory data is preprocessed to remove noise data and abnormal data in the flight trajectory data.

3. The airport terminal area arrival and departure traffic flow evaluation method according to claim 1 is characterized in that: The resampling of the flight trajectory data to obtain the feature vector of each target flight specifically includes: For each of the target flights, according to the flight trajectory data of the target flight, the relative longitude corresponding to each of the longitudes of the target flight with the longitude of the center point of the airport terminal area as the zero point is calculated, and the relative latitude corresponding to each of the latitudes of the target flight with the latitude of the center point of the airport terminal area as the zero point is calculated; Normalizing the acquisition timestamp of the target flight; Determine whether the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to a preset number of coordinate points, wherein the geographic coordinate points include relative longitude and relative latitude corresponding to the same acquisition timestamp; If the number of geographic coordinate points in the flight trajectory data of the target flight is greater than or equal to the preset number of coordinate points, selecting the preset number of geographic coordinate points from the flight trajectory data of the target flight, and using the selected geographic coordinate points to construct a feature vector of the target flight; If the number of geographic coordinate points in the flight trajectory data of the target flight is less than the preset number of coordinate points, the flight trajectory data of the target flight is interpolated so that the flight trajectory data after the interpolation processing contains the preset number of geographic coordinate points, and the feature vector of the target flight is constructed using each geographic coordinate point in the flight trajectory data after the interpolation processing.

4. The airport terminal area arrival and departure traffic flow evaluation method according to claim 1 is characterized in that: The preset clustering algorithm is the DBSCAN clustering algorithm.

5. The airport terminal area arrival and departure traffic flow evaluation method according to claim 1 is characterized in that: Determining the traffic flow time series corresponding to each flight trajectory pattern based on the flight data specifically includes: For each of the flight trajectory patterns, the first target time period is divided based on the preset unit time length to obtain N the second target time period; sequentially counting the number of flights that land or take off according to the flight trajectory pattern within each of the second target time periods; The number of each flight is used to construct a traffic flow time series corresponding to the flight trajectory pattern.

6. The airport terminal area arrival and departure traffic flow evaluation method according to claim 1 is characterized in that: The method of using the traffic flow time series to construct a multiple recursive network includes: Based on the time delay embedding technology, recursive network extraction is performed on each of the traffic flow time series to obtain the recursive network of each of the traffic flow time series; Using each of the recursive networks, the multiple recursive networks are constructed.

7. An airport terminal area arrival and departure traffic flow evaluation device, characterized in that: include: An acquisition module, used to acquire flight data of a target flight in a first target time period at an airport terminal area; The target flight is an approaching flight or a departing flight, the flight data includes identity data and flight trajectory data, the flight trajectory data includes target flight parameters corresponding to different acquisition timestamps, the target flight parameters include longitude and latitude, and, when the target flight is an approaching flight, the flight data also includes an actual landing time, and when the target flight is a departing flight, the flight data also includes an actual take-off time; A resampling module, used for resampling the flight trajectory data to obtain a feature vector of each target flight; the feature vectors of each target flight have the same dimension; A clustering module, for clustering flight trajectories based on a preset clustering algorithm and according to the feature vectors of each target flight, to obtain a plurality of clusters, each of which corresponds to a flight trajectory mode; A determination module is used to determine the traffic flow time series corresponding to each flight trajectory mode based on the flight data, wherein the traffic flow time series includes N elements, N is equal to the ratio of the duration of the first target time period to the preset unit duration, the element represents the number of flights that land or take off in the corresponding second target time period according to the corresponding flight trajectory mode, and the duration of the second target time period is equal to the preset unit duration; A construction module, used to construct a multiple recursive network using the traffic flow time series; The calculation module is used to calculate the average edge overlap value of the multiple recursive networks, and the average edge overlap value is used to quantitatively evaluate the synchronization of traffic flow fluctuations.

8. An airport terminal area arrival and departure traffic flow evaluation device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the airport terminal area arrival and departure traffic flow evaluation method according to any one of claims 1 to 6.

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