CN112257748A - Restricted airspace unit identification method based on DBSCAN clustering algorithm - Google Patents
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Abstract
The invention discloses a restricted airspace unit identification method based on a DBSCAN clustering algorithm, which comprises the steps of firstly defining an airspace unit; secondly, collecting flight actual operation data and corresponding airway information, and matching the airway information with longitude and latitude coordinate data of an airspace unit to construct an airway network model; then, flight delay data are calculated, data are down-sampled and are associated with the airspace unit to obtain flight delay characteristic data; and finally, constructing an identification model of the restricted airspace unit based on a DBSCAN clustering algorithm by combining an airway network model and flight delay characteristics. The method can identify the limited airspace unit and the limited start-stop time, and is favorable for solving the randomness caused by events such as severe weather, air traffic control and the like. The starting time and the ending time of the limited airspace are determined, the direct delay of the flight can be separated from the delay caused by propagation, the propagation mechanism of the flight delay is further explored, and the method has important significance for improving the accuracy of flight delay prediction.
Description
Technical Field
The invention relates to a restricted airspace unit identification method based on a DBSCAN clustering algorithm, and belongs to the technical field of air traffic management.
Background
With the rapid development of civil aviation, the problem of flight delay becomes an important subject of an air transportation system, and the aviation industry suffers from economic loss all the time. According to the data of the united states traffic statistics Bureau (BTS), there was a flight delay of over 20% in 2018. In china, the rate of flight abnormality is 80.13%, which means that flights are delayed by more than 85 thousands of times in 2018. There are many factors that cause flight delay in actual operation, such as airline operation management factors, weather factors, air management flow control, military activities, etc., which may cause the air space units to be restricted to different degrees, thereby causing flight delay. While the occurrence and occurrence of these influencing factors are highly random and uncertain, when these factors, such as bad weather, suddenly appear on the flight, the flight will be delayed even if the airport is not restricted in its range and its vicinity. Furthermore, the delay of a preorder flight can also cause the delay of a plurality of subsequent flights, so the delay of the flight presents a chaotic or nonlinear development trend, and the accurate prediction of the flight delay has certain difficulty.
Many scholars are currently conducting a great deal of research on flight delay prediction. However, as mentioned above, flight delays are affected by many factors, which have some burstiness and randomness. Therefore, flight delays appear chaotic or nonlinear and are difficult to predict accurately. Also, a flight delay may cause a subsequent flight delay. Although learners consciously separate direct delays from propagation-induced delays, no effective method has been found to quantitatively distinguish between the two. When an event causing flight delay occurs, the airspace unit is restricted, causing flight delay. The identification of the restricted airspace unit and the restricted start-stop time is helpful for solving the randomness of delay events caused by severe weather, air traffic control and the like. The limited starting time and the limited ending time are determined, the direct delay and the delay caused by propagation can be separated, the propagation mechanism of the flight delay is further explored, and the method has important significance for improving the accuracy of flight delay prediction.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method for identifying the restricted airspace unit based on the DBSCAN clustering algorithm is characterized by counting the accumulated delay time of each airspace unit in each time period, matching the accumulated delay time with longitude and latitude coordinates of the airspace unit, establishing a characteristic matrix, and performing DBSCAN clustering on each time period to identify the restricted airspace unit.
The invention adopts the following technical scheme for solving the technical problems:
a restricted airspace unit identification method based on a DBSCAN clustering algorithm comprises the following steps:
step 1, defining an airspace unit to be an airport and a waypoint, and defining a restricted airspace unit to be an airspace unit with reduced capacity caused by weather, military activities and air traffic control factors;
step 2, collecting historical flight data and chart data, extracting airway data from the historical flight data, and constructing an airway network model by combining longitude and latitude coordinates of each airspace unit in the chart data;
step 3, dividing one day into 96 time periods at intervals of 15 minutes, extracting airspace delay cumulative association characteristics including time sequences from historical flight data according to the time period sequence, and establishing an airspace unit data set including time characteristics, space characteristics and delay cumulative association characteristics according to the airspace delay cumulative association characteristics;
step 4, clustering an airspace unit data set comprising time characteristics, space characteristics and delay accumulated correlation characteristics by adopting a density-based DBSCAN clustering algorithm, and identifying an airspace limited unit and delay characteristics from all airspace units by combining historical flight data, wherein the method comprises the following steps: the number, location, and limited start-stop time of the airspace-limited units.
As a preferred embodiment of the present invention, the specific process of step 2 is as follows:
step 21, collecting historical flight data and chart data, wherein the historical flight data comprises flight numbers, predicted taking-off and landing time of flights, actual taking-off and landing time, taking-off and landing airports, used routes and route points on the routes, and the chart data comprises longitude and latitude coordinates of an airspace unit;
and step 23, associating the take-off airport, the landing airport and the waypoints on the air routes with the corresponding longitude and latitude coordinates, namely associating the airspace units on the air routes with the corresponding longitude and latitude coordinates, and constructing an air route network model.
As a preferred embodiment of the present invention, the specific process of step 3 is as follows:
step 31, counting flight delays for take-off airports and route points on the routes according to departure delay time of flights, counting flight delays for landing airports according to landing delay time of the flights, and regarding the departure delay time or the landing delay time as the flight delays when the departure delay time or the landing delay time is more than 15 minutes;
step 32, dividing one day into 96 time periods at intervals of 15 minutes, and counting flight delays of each time period passing through each airspace unit according to the time period sequence, so as to count the accumulated flight delays of each airspace unit of each time period, namely the airspace delay accumulated correlation characteristics comprising the time sequence;
and step 33, establishing an airspace unit data set comprising the time characteristics, the space characteristics and the delay accumulated correlation characteristics by taking the time period corresponding to the flight passing through each airspace unit as the time characteristics and the longitude and latitude coordinates of each airspace unit as the space characteristics.
As a preferred embodiment of the present invention, step 31 is to count flight delays for departure airport and waypoint on the route according to departure delay time of the flight, and count flight delays for landing airport according to approach delay time of the flight, specifically:
the flight delay of the takeoff airport is equal to the actual takeoff time of the departure flight minus the predicted takeoff time;
flight delay at the waypoint is equal to flight delay at the take-off airport;
the flight delay to land at the airport is equal to the actual landing time of the incoming flight minus the predicted landing time.
As a preferred embodiment of the present invention, the step 32 of accumulating flight delays specifically includes: the sum of flight delays for all flights that pass through a space domain unit for a certain period of time.
As a preferred embodiment of the present invention, the specific process of step 4 is as follows:
step 41, based on the airway network model established in step 2, the restricted airspace unit identification model established by combining the time characteristic, the space characteristic and the delay accumulated correlation characteristic is as follows:
wherein D isTA restricted space domain unit identification model representing the T-th time segment, T-1, …,96,
latitude and longitude of the 1 st airspace unit,
respectively, the latitude and longitude of the 2 nd airspace unit,
respectively the latitude and longitude of the kth airspace unit, k is the number of the airspace units on all the routes,
are respectively a space domain unit U1、U2、UkAccumulated flight delay at the Tth time period;
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the method and the device identify the airspace unit which is limited in the airspace, identify the starting time and the stopping time of the airspace unit which is limited, and are favorable for solving the randomness caused by events such as severe weather, air traffic control and the like. The method has important significance for deeply exploring the propagation mechanism of flight delay and finely analyzing the influence of one flight delay on the subsequent flights, and can further improve the accuracy of flight delay prediction.
Drawings
FIG. 1 is a schematic diagram of a restricted airspace unit identification method based on a DBSCAN clustering algorithm.
FIG. 2 is a schematic view of a model of an airway network.
FIG. 3 is a graph showing the results of restricted airspace unit identification in example 2017, month 7, day 17, 22:15: 00-22: 29: 59.
FIG. 4 is a restricted airspace unit distribution diagram from 22:15:00 to 22:29:59 on 17.7.7.2017.
FIG. 5 is a thermodynamic distribution diagram of a restricted severe airspace unit in example 2017, 7, 17.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, the present invention calculates departure delays and arrival delays for flights by mining historical flight data, and associates each flight with the route information it uses. Then, coordinate information of each airport and waypoint is collected, and historical flight data is classified and grouped according to the time series and the origin airport. And counting the accumulated delay time and delay time of each airspace unit, matching the average delay time of each time period with the coordinates of each airspace unit, and establishing a characteristic matrix. And finally, carrying out DBSCAN clustering on each time slot to identify the restricted airspace unit. The method comprises the following concrete steps:
step 1, defining the airspace unit mentioned in the invention. The airspace unit in the invention refers to an airway point and an airport; the restricted airspace unit refers to an airspace unit with reduced capacity caused by factors such as weather, military activities, air management flow control and the like.
And 2, collecting historical flight data and chart data. And extracting the round-trip route information among the target airports from the historical flight data, and constructing a route network model by combining longitude and latitude coordinates of route points in the chart. The method specifically comprises the following steps:
and (2.1) collecting historical flight data and chart data. The historical flight data comprises information such as scheduled flight take-off/landing time, actual take-off/landing time, a departure/landing airport, a flight number, a used airway and the like;
(2.2) grouping the historical flight data according to the take-off and landing airports, and extracting the route data used by the round-trip flights between the airports;
and (2.3) correlating the airway data with longitude and latitude coordinate data of the airspace unit to construct and visualize an airway network model. Taking flight data of four return routes of Beijing capital airport (ZBAA), Shanghai Pudong airport (ZSPD), Guangzhou white cloud airport (ZGGG) and Chengdu double-flow airport (ZUUU) as an example, grouping is carried out according to a take-off airport and a landing airport, and route information used by each group is extracted. Wherein the waypoints are represented by the waypoints through which it passed. The longitude and latitude coordinates of each point of 12 total routes of 4 target airports are marked in a rectangular plane coordinate system, and then all path points (route points and airports) are connected according to the flying sequence of the aircraft, so that the route schematic diagram shown in fig. 2 can be formed.
And 3, performing correlation analysis on the flight delay characteristics and the airspace unit, and providing support for identifying the airspace limited unit information from historical data. Cleaning and resampling historical flight time data, performing cumulative association on flight delay data of each time interval and an air space unit which is expected to be used by the flight delay data, extracting an air space delay cumulative association characteristic comprising a time sequence, and creating an air space unit data set based on the time characteristic, the space characteristic (longitude/latitude) and the delay cumulative association characteristic. The method specifically comprises the following steps:
(3.1) calculating the take-off and landing delay of each flight, wherein the take-off/landing delay time is more than 15 minutes in the invention and is regarded as the flight delay;
(3.2) associating the delay characteristics of the flights with an airspace unit, selecting accumulated delay time as the characteristics to study, counting departure delays of take-off airports, routes and waypoints according to the flights, and counting landing airports according to landing delays of the flights;
(3.3) cleaning and resampling historical flight time data, dividing one day into 96 time periods at intervals of 15 minutes, performing cumulative association on the flight delay data of each time interval and the air space units which are expected to be used, and counting the cumulative flight delay of each air space unit of each time period, namely the delay cumulative association characteristic;
and (3.4) taking the time period corresponding to the flight passing through each airspace unit as a time characteristic, taking the longitude and latitude coordinates of each airspace unit as a space characteristic, and creating an airspace unit data set based on the time characteristic, the space characteristic (longitude/latitude) and the delay accumulated correlation characteristic.
Step 4, clustering the airspace unit data sets including time characteristics, spatial characteristics (longitude/latitude) and delay accumulated correlation characteristics by adopting a density-based DBSCAN clustering algorithm, and identifying airspace limited units and delay characteristics from all airspace units by combining the forecast and actual take-off and landing information of historical flights, wherein the method comprises the following steps: the number, location, and limited start-stop time of the airspace-limited units. The method specifically comprises the following steps:
(4.1) based on the airway network model established in the second step, establishing a restricted airspace unit identification model by combining time characteristics, space characteristics (longitude/latitude) and delay accumulated correlation characteristics, and establishing a characteristic matrix D of T time periodTComprises the following steps:
in the formula:
is a space domain unit UkThe latitude of (d);
is a space domain unit UkLongitude; t is a time period;
is a space domain unit UkCumulative delay time at time period T;
(4.2) clustering the restricted airspace unit identification model by using a DBSCAN clustering algorithm to identify the restricted airspace unit;
and (4.3) outputting the recognition result of each time interval and visualizing the result. FIG. 3 is a graph showing the results of unit identification of restricted airspace between the 4 airports at 17 days 22:15: 00-22: 29:59 in 7/2017. To more intuitively see the distribution of restricted waypoints in the airspace, the restricted airspace unit distribution map shown in FIG. 4 is plotted. Comprehensively analyzing the identification results of all flights in all 96 time intervals of 17 days in 7 months and 17 days in 2017, counting the limited times of the airspace unit, and drawing a thermodynamic distribution diagram shown in figure 5, wherein 0-55 in the diagram represent the limited times of the airspace unit in 96 time intervals in one day. In 2017, 7-17 months, the limited number of times was greatest for Chengdu Shufu airports, followed by Beijing capital airport, Guangzhou white cloud airport, and Shanghai Pudong airport; for waypoints, waypoints with the limited number of times in the first five days of the day are ONEBA, SUBUL, BOBAK, JTG and WFX in sequence.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (6)
1. A restricted airspace unit identification method based on a DBSCAN clustering algorithm is characterized by comprising the following steps:
step 1, defining an airspace unit to be an airport and a waypoint, and defining a restricted airspace unit to be an airspace unit with reduced capacity caused by weather, military activities and air traffic control factors;
step 2, collecting historical flight data and chart data, extracting airway data from the historical flight data, and constructing an airway network model by combining longitude and latitude coordinates of each airspace unit in the chart data;
step 3, dividing one day into 96 time periods at intervals of 15 minutes, extracting airspace delay cumulative association characteristics including time sequences from historical flight data according to the time period sequence, and establishing an airspace unit data set including time characteristics, space characteristics and delay cumulative association characteristics according to the airspace delay cumulative association characteristics;
step 4, clustering an airspace unit data set comprising time characteristics, space characteristics and delay accumulated correlation characteristics by adopting a density-based DBSCAN clustering algorithm, and identifying an airspace limited unit and delay characteristics from all airspace units by combining historical flight data, wherein the method comprises the following steps: the number, location, and limited start-stop time of the airspace-limited units.
2. The restricted airspace unit identification method based on the DBSCAN clustering algorithm according to claim 1, wherein the specific process of the step 2 is as follows:
step 21, collecting historical flight data and chart data, wherein the historical flight data comprises flight numbers, predicted taking-off and landing time of flights, actual taking-off and landing time, taking-off and landing airports, used routes and route points on the routes, and the chart data comprises longitude and latitude coordinates of an airspace unit;
step 22, extracting all pieces of airway data from historical flight data, wherein each piece of airway data comprises a flight number, a take-off and landing airport, an used airway and airway points on the airway;
and step 23, associating the take-off airport, the landing airport and the waypoints on the air routes with the corresponding longitude and latitude coordinates, namely associating the airspace units on the air routes with the corresponding longitude and latitude coordinates, and constructing an air route network model.
3. The restricted airspace unit identification method based on the DBSCAN clustering algorithm according to claim 1, wherein the specific process of the step 3 is as follows:
step 31, counting flight delays for take-off airports and route points on the routes according to departure delay time of flights, counting flight delays for landing airports according to landing delay time of the flights, and regarding the departure delay time or the landing delay time as the flight delays when the departure delay time or the landing delay time is more than 15 minutes;
step 32, dividing one day into 96 time periods at intervals of 15 minutes, and counting flight delays of each time period passing through each airspace unit according to the time period sequence, so as to count the accumulated flight delays of each airspace unit of each time period, namely the airspace delay accumulated correlation characteristics comprising the time sequence;
and step 33, establishing an airspace unit data set comprising the time characteristics, the space characteristics and the delay accumulated correlation characteristics by taking the time period corresponding to the flight passing through each airspace unit as the time characteristics and the longitude and latitude coordinates of each airspace unit as the space characteristics.
4. The restricted airspace unit identification method based on the DBSCAN clustering algorithm of claim 3, wherein the step 31 is to count flight delays for departure time of flights at departure airports and waypoints on the airway, and for arrival time of flights at landing airports, specifically:
the flight delay of the takeoff airport is equal to the actual takeoff time of the departure flight minus the predicted takeoff time;
flight delay at the waypoint is equal to flight delay at the take-off airport;
the flight delay to land at the airport is equal to the actual landing time of the incoming flight minus the predicted landing time.
5. The restricted airspace unit identification method based on the DBSCAN clustering algorithm as claimed in claim 3, wherein the step 32 of accumulated flight delay specifically comprises: the sum of flight delays for all flights that pass through a space domain unit for a certain period of time.
6. The restricted airspace unit identification method based on the DBSCAN clustering algorithm according to claim 1, wherein the specific process of the step 4 is as follows:
step 41, based on the airway network model established in step 2, the restricted airspace unit identification model of each time period established by combining the time characteristic, the space characteristic and the delay accumulated correlation characteristic is as follows:
wherein D isTA restricted space domain unit identification model representing the T-th time segment, T-1, …,96,
latitude and longitude of the 1 st airspace unit,
respectively, the latitude and longitude of the 2 nd airspace unit,
respectively the latitude and longitude of the kth airspace unit, k being on all the air routesThe number of spatial domain units is,
are respectively a space domain unit U1、U2、UkAccumulated flight delay at the Tth time period;
step 42, clustering the restricted airspace unit identification models of each time period by using a DBSCAN clustering algorithm, identifying the restricted airspace units from all the airspace units, and obtaining delay characteristics according to the restricted airspace units, wherein the delay characteristics comprise: the number, location, and limited start-stop time of the airspace-limited units.
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