CN102831487A - Flow predicted result verification method based on historical scheduled flight running data analysis - Google Patents
Flow predicted result verification method based on historical scheduled flight running data analysis Download PDFInfo
- Publication number
- CN102831487A CN102831487A CN2012102877112A CN201210287711A CN102831487A CN 102831487 A CN102831487 A CN 102831487A CN 2012102877112 A CN2012102877112 A CN 2012102877112A CN 201210287711 A CN201210287711 A CN 201210287711A CN 102831487 A CN102831487 A CN 102831487A
- Authority
- CN
- China
- Prior art keywords
- flight
- historical
- time
- data
- telegram
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a flow predicted result verification method based on historical scheduled flight running data analysis, which can estimate influence of accuracy of takeoff time in a flight plan to track prediction accuracy. The flow predicted result verification method comprises the steps of: determining if a relatively great difference exists between the takeoff time in an FPL before the takeoff of a scheduled flight and the practical takeoff time in a practical running process through statistical analysis on historical telegram data of civil aviation; then providing three evaluation models and making preparation for drive data in advance to perform targeted research; and finally, introducing a current telegram and a future track for synchronous playing and performing accuracy verification on a flow predicted result.
Description
Technical field
The invention belongs to communication network field, particularly a kind of method towards the centralized management of SkyWAN satellite communication system flexible network.
Background technology
Air traffic control has two targets from essence: air traffic safety and air traffic efficient.Whole air traffic control industry and even whole aviation industry all around how better to realize above-mentioned two targets are being organized work and are being moved.Particularly,, want well to solve above-mentioned contradiction, accomplish high as far as possible lifting air traffic efficient under the prerequisite that guarantees air traffic safety, just must improve the determinacy of transport air flow for the blank pipe industry.So-called air traffic determinacy just is meant that whole flight course is at each set of data state of programming phase and the difference degree between the actual conditions of execute phase.This difference degree is low more, just means that the order of aviation running is strong more, and then its controllability is just strong more with the property planned, air traffic safety so and air traffic efficient just are protected more easily; Otherwise this difference degree is high more, the inherent order that air traffic control system (ATCS) then is described a little less than, standardization level is low, the chance that potential safety hazard and inefficiency occur will be a lot of greatly.
How to assess traffic flow forecasting result's accuracy, how to confirm the problem that occurs in the volume forecasting process through a kind of appraisal procedure of verifying, this is to follow groping in the method for predicting research process always and attempt a difficult problem of seeking to find.
Air traffic prediction at present is the prediction to the traffic flow of certain observation station after following a period of time; After receiving that the navigator plans newspaper (FPL); The plan track that the volume forecasting module draws after FPL is handled carries out traffic flow and calculates; Received after report quickly (DEP) the plan track has been revised; Simultaneously modified flow rate predicts the outcome, because of the difference of the departure time among the departure time among the FPL and the DEP will obviously cause departing from of process point time; Existing method for predicting does not consider that the difference of the E.T.D(estimated time of departure) among the FPL and actual time of departure before the airborne vehicle flight is to predicting flight course track and volume forecasting result's influence.
Therefore, need a kind of new technical scheme to address the above problem.
Summary of the invention
To above-mentioned existing in prior technology problem and shortage, the present invention provides a kind of volume forecasting result verification method based on historical flight Operational Data Analysis.
For realizing above-mentioned purpose, the volume forecasting result verification method that the present invention is based on historical flight Operational Data Analysis can adopt following technical scheme:
A kind of volume forecasting result verification method based on historical flight Operational Data Analysis through the navigator being planned report FPL and the time of rising in advance and the real error condition analysis that plays the time of rising among the DEP that reports quickly, is set up three kinds of evaluation profiles, is respectively:
Actual pattern: the receiving course of civil aviaton's telegram and data in the true reappearance actual moving process, temporal frequency is with actual consistent;
Idealized model: the E.T.D(estimated time of departure) in the FPL of the civil aviaton newspaper that receives is consistent with the actual time of departure, and temporal frequency is with actual consistent;
Empirical mode: the E.T.D(estimated time of departure) in the FPL of civil aviaton that the receives newspaper is experience departure time of this flight in history, and temporal frequency is consistent with reality;
The data of idealized model and empirical mode need True Data is carried out analysis and arrangement, and it is critical process that data are in advance prepared,
Actual pattern: enroll historical civil aviaton telegram data, comprise FPL, DEP, CNL;
Idealized model: enroll historical civil aviaton telegram data; Comprise FPL, DEP, CNL; With the anti-E.T.D(estimated time of departure) of inserting in the corresponding flight FPL newspaper of flight actual time of departure in the DEP message, set E.T.D(estimated time of departure) and actual time of departure bias free in civil aviaton's plan newspaper;
Empirical mode: enroll historical civil aviaton telegram data, comprise FPL, DEP, CNL etc., to history repeatedly the actual time of departure of identical flight carry out weighted mean, draw the experience departure time, instead insert the E.T.D(estimated time of departure) in the flight FPL newspaper;
This method comprises the steps:
Step 1: historical data Play Control primary module is confirmed historical data reproduction time scope and broadcasting speed;
Step 2: historical flight path data playback control module loads the historical flight path information of admission, and according to start time of the broadcast zero-time position history radar track of historical flight path data demand;
Step 3: the historical telegram transmit control module that flies loads corresponding historical flight telegram data according to evaluation profile, according to the start time stamp of the broadcast zero-time position history flight telegram of setting;
Step 4: historical data Play Control primary module regularly sends the different time control information to historical flight path data playback control module and historical flight telegram transmit control module, and the reproduction time of flight telegram is early than the reproduction time of flight path;
Step 5: historical flight path data playback control module meets the flight path information of time conditions according to the search of the time control information of historical data Play Control primary module, to plan track correcting module and traffic statistics module transmission flight path information;
Step 6: historical flight telegram playing module is according to the time of historical data Play Control primary module, the historical flight of playback telegram;
Step 7: plan trajectory predictions module is planned newspaper according to the navigator who plays and is planned trajectory predictions, and the E.T.D(estimated time of departure) during the navigator plans to report is the start time of prediction locus; Under actual pattern and empirical mode, received report quickly after, start time of prediction locus has been modified to the actual time of departure in reporting quickly; Plan track correcting module is revised prediction locus according to historical flight path data; The volume forecasting module is carried out future transportation stream to the plan track and is calculated;
Step 8: the traffic statistics module changes the traffic flow statistics of historical juncture to historical flight path;
Step 9: compare synchronization volume forecasting result and traffic statistics result.
Preferably, the verifying software module is provided, in order to realize that the assessment of plan forecast track is divided the verifying software module, this verifying software module comprises:
Historical data Play Control primary module: time range and playback rate information that output is play, and with identical speed output time information;
Historical flight path data playback control module: control historical flight path data time range and playback rate as requested and carry out data output;
Historical flight telegram transmit control module: the historical flight of control telegram time range and playback rate as requested carries out data output;
Plan trajectory predictions module: the flight telegram is handled, formed flight plan, and produce the plan forecast track based on flight plan;
Plan track correcting module: the plan track of prediction is revised based on the flight path data;
Volume forecasting module: carry out future transportation stream statistics according to the plan forecast track;
Traffic statistics module: carry out the current time traffic flow statistics according to the flight path data.
Preferably; The method that obtains assessment result is: volume forecasting and traffic statistics result's difference in the recording process in using the process of various evaluation profiles; And through inserting the numerical value that display frame shows volume forecasting of same time range location point and traffic statistics; Comparing result directly perceived is reached a conclusion.
Preferably, before setting up three kinds of evaluation profiles, also have step and the volume forecasting processed steps of handling flight telegram data;
The step of handling flight telegram data comprises: flight telegram processing module forms accurate flight planning to carrying out data parsing from the flight telegram of moving on-the-spot admission; The flight planning processing module is aimed at flight planning and is carried out line of flight decomposition and the four-dimensional track reckoning of flight overall process; The volume forecasting module is carried out traffic flow statistics according to the four-dimensional track of schedule flight overall process;
The volume forecasting processed steps comprises: at first the long-term flight telegram data of history are resolved, the telegram of will flying is handled encapsulation in a different manner, and carries out playback with different patterns, the historical flight path after following a period of time of broadcast simultaneously; Carry out the schedule flight trajectory predictions according to flight telegram data; Prediction locus is carried out synchronized transmission with historical flight path identical time and speed; The traffic statistics module is added up according to radar track; The volume forecasting module is added up according to prediction locus, and volume forecasting result and traffic statistics result in the more same time range analyze the correctness that flow predicts the outcome.
Technical scheme of the present invention has following advantage:
The present invention can assess the influence of the accuracy of the departure time in the flight planning to the trajectory predictions accuracy.
At first, the present invention has confirmed that through the statistical study to historical civil aviaton telegram data the navigator before the flight takeoff plans to report the departure time and actual time of departure among the FPL to exist than big-difference in the actual moving process.
Secondly, the invention provides three kinds of evaluation profiles, and driving data is prepared in advance, carried out research targetedly;
At last, introduce current telegram and following flight path synchronous playing, flow is predicted the outcome carry out the accuracy checking.
Description of drawings
Fig. 1 is the synoptic diagram of flight telegram treatment scheme among the present invention.
Fig. 2 compares the synoptic diagram of flow process for volume forecasting result among the present invention.
Fig. 3 is the synoptic diagram of verifying software Module Division among the present invention.
Fig. 4 is the synoptic diagram of assessment result contrast among the present invention.
Embodiment
Below in conjunction with accompanying drawing and embodiment; Further illustrate the present invention; Should understand following embodiment only be used to the present invention is described and be not used in the restriction scope of the present invention; After having read the present invention, those skilled in the art all fall within the application's accompanying claims institute restricted portion to the modification of the various equivalent form of values of the present invention.
Terminological interpretation:
Plan track: plan forecast track; According to information such as the landing airport of flight planning, type, flight air route, control orders; The flight path of airbound target that infer to generate from the initial position state to the end position state, comprise longitude, latitude, highly, time, speed etc.
AFTN: civil aviaton's flight telegram, through the message relevant of AFTN transmission with schedule flight.
FPL: the plan newspaper that navigates,, send according to the flight plan data that aircraft operation people or its procurator submit to by air traffic service unit to the telegram of all relevant air traffic service units down an airway.
DEP: work reporting quickly, be used to circulate a notice of the telegram of aircraft takeoff time.
CNL: the cancellation newspaper is used for the telegram that the circular plan is cancelled.
The experience departure time: the big probability departure time of repeatedly flying and summing up according to historical unit-frame flight.
Volume forecasting: to the estimation and the statistics of number of vehicles in following a period of time, a certain spatial domain scope.
The present invention provides a kind of volume forecasting result verification method based on historical flight Operational Data Analysis.
The source data of volume forecasting is a flight telegram (AFTN telegram), and before carrying out the volume forecasting processing, flight telegram data must be through the processing of flight telegram, flight planning processing module, and flow chart of data processing is as shown in Figure 1.
Procedure declaration:
Flight telegram processing module forms accurate flight planning to carrying out data parsing from the flight telegram of moving on-the-spot admission;
The flight planning processing module is aimed at flight planning and is carried out line of flight decomposition and the four-dimensional track reckoning of flight overall process;
The volume forecasting module is carried out traffic flow statistics according to the four-dimensional track of schedule flight overall process.
Purport of the present invention is that the flight planning service data that draws after the schedule flight telegram is handled is analyzed; Use the certain mathematical method to promote the authenticity of plan service data; Thereby improve the accuracy of the four-dimensional track of flight planning, from the angle checking volume forecasting result of source data.
Volume forecasting result's decision process is as shown in Figure 2; At first the long-term flight telegram data of history are resolved; The telegram of will flying is handled encapsulation in a different manner, and carries out playback with different patterns, plays simultaneously following a period of time (with the same time range of analyzing of flight telegram; Play start time can artificially be provided with, and generally is made as 1 hour) after historical flight path; Carry out the schedule flight trajectory predictions according to flight telegram data; Prediction locus is carried out synchronized transmission with historical flight path identical time and speed; The traffic statistics module is added up according to radar track; The volume forecasting module is added up according to prediction locus, and volume forecasting result and traffic statistics result in the more same time range analyze the correctness that flow predicts the outcome.
Mode-definition
Through the navigator being planned report FPL and the time of rising in advance and the real error condition analysis that plays the time of rising among the DEP that reports quickly, set up three kinds of evaluation profiles, be respectively:
Actual pattern: the receiving course of civil aviaton's telegram and data in the true reappearance actual moving process, temporal frequency is with actual consistent.
Idealized model: the E.T.D(estimated time of departure) in the FPL of the civil aviaton newspaper that receives is consistent with the actual time of departure, and temporal frequency is with actual consistent.
Empirical mode: the E.T.D(estimated time of departure) in the FPL of civil aviaton that the receives newspaper is experience departure time of this flight in history, and temporal frequency is consistent with reality.
Data are prepared
The data of idealized model and empirical mode need True Data is carried out analysis and arrangement, and it is critical process that data are in advance prepared.
Actual pattern: enroll historical civil aviaton telegram data, comprise FPL, DEP, CNL (cancellation newspaper) etc.
Idealized model: enroll historical civil aviaton telegram data; Comprise FPL, DEP, CNL (cancellation newspaper) etc.; With the anti-E.T.D(estimated time of departure) of inserting in the corresponding flight FPL newspaper of flight actual time of departure in the DEP message, set E.T.D(estimated time of departure) and actual time of departure bias free in civil aviaton's plan newspaper.
Empirical mode: enroll historical civil aviaton telegram data, comprise FPL, DEP, CNL (cancellation newspaper) etc., to history repeatedly the actual time of departure of identical flight carry out weighted mean, draw the experience departure time, instead insert the E.T.D(estimated time of departure) in the flight FPL newspaper.
The division of verifying software Module Division
In order to realize that the assessment of plan forecast track is divided the verifying software Module Division here, as shown in Figure 3.
Historical data Play Control primary module: time range and playback rate information that output is play, and with identical speed output time information;
Historical flight path data playback control module: control historical flight path data time range and playback rate as requested and carry out data output;
Historical flight telegram transmit control module: the historical flight of control telegram time range and playback rate as requested carries out data output;
Plan trajectory predictions module: the flight telegram is handled, formed flight plan, and produce the plan forecast track based on flight plan;
Plan track correcting module: the plan track of prediction is revised based on the flight path data;
Volume forecasting module: carry out future transportation stream statistics according to the plan forecast track;
Traffic statistics module: carry out the current time traffic flow statistics according to the flight path data.
The use implementation step of pattern is following:
Step 1: historical data Play Control primary module is confirmed historical data reproduction time scope and broadcasting speed; (historical flight path is provided with respectively with historical flight telegram reproduction time, and historical flight telegram reproduction time needs early than historical flight path reproduction time)
Step 2: historical flight path data playback control module loads the historical flight path information of admission, and according to start time of the broadcast zero-time position history radar track of historical flight path data demand;
Step 3: the historical telegram transmit control module that flies loads corresponding historical flight telegram data according to evaluation profile, according to the start time stamp of the broadcast zero-time position history flight telegram of setting;
Step 4: historical data Play Control primary module regularly sends the different time control information to historical flight path data playback control module and historical flight telegram transmit control module; The reproduction time of flight telegram is early than the reproduction time of flight path; The start time that the example flight path is play is 08:00, and the start time that the flight telegram is play is 06:00;
Step 5: historical flight path data playback control module meets the flight path information of time conditions according to the search of the time control information of historical data Play Control primary module, to plan track correcting module and traffic statistics module transmission flight path information;
Step 6: historical flight telegram playing module is according to the time of historical data Play Control primary module, the historical flight of playback telegram;
Step 7: plan trajectory predictions module is planned newspaper based on the navigator who plays and is planned trajectory predictions, and the E.T.D(estimated time of departure) during the navigator plans to report is the time started of prediction locus; Under actual pattern and empirical mode, received report quickly after, time started of prediction locus has been modified to the actual time of departure in reporting quickly; Plan track correcting module is revised prediction locus based on historical flight path data; The volume forecasting module is carried out future transportation stream to the plan track and is calculated;
Step 8: the traffic statistics module changes the traffic flow statistics of historical juncture to historical flight path;
Step 9: compare synchronization volume forecasting result and traffic statistics result.
The assessment result explanation
Volume forecasting and traffic statistics result's difference in the recording process in using the process of various evaluation profiles; And can be through inserting the numerical value that display frame shows volume forecasting of same time range location point and traffic statistics; Comparing result directly perceived draws to draw a conclusion, and is as shown in Figure 4.The longitudinal axis is a Sortie among Fig. 4, is illustrated under the different mode volume forecasting and the numerical value of traffic statistics in identical time range.From figure, can clearly find out closing to reality departure time E.T.D(estimated time of departure) among the telegram FPL of civil aviaton, with the authenticity that improves the volume forecasting result.
Claims (4)
1. volume forecasting result verification method based on historical flight Operational Data Analysis is characterized in that:
Through the navigator being planned report FPL and the time of rising in advance and the real error condition analysis that plays the time of rising among the DEP that reports quickly, set up three kinds of evaluation profiles, be respectively:
Actual pattern: the receiving course of civil aviaton's telegram and data in the true reappearance actual moving process, temporal frequency is with actual consistent;
Idealized model: the E.T.D(estimated time of departure) in the FPL of the civil aviaton newspaper that receives is consistent with the actual time of departure, and temporal frequency is with actual consistent;
Empirical mode: the E.T.D(estimated time of departure) in the FPL of civil aviaton that the receives newspaper is experience departure time of this flight in history, and temporal frequency is consistent with reality;
The data of idealized model and empirical mode need True Data is carried out analysis and arrangement, and it is critical process that data are in advance prepared,
Actual pattern: enroll historical civil aviaton telegram data, comprise FPL, DEP, CNL;
Idealized model: enroll historical civil aviaton telegram data; Comprise FPL, DEP, CNL; With the anti-E.T.D(estimated time of departure) of inserting in the corresponding flight FPL newspaper of flight actual time of departure in the DEP message, set E.T.D(estimated time of departure) and actual time of departure bias free in civil aviaton's plan newspaper;
Empirical mode: enroll historical civil aviaton telegram data, comprise FPL, DEP, CNL etc., to history repeatedly the actual time of departure of identical flight carry out weighted mean, draw the experience departure time, instead insert the E.T.D(estimated time of departure) in the flight FPL newspaper;
This method comprises the steps:
Step 1: historical data Play Control primary module is confirmed historical data reproduction time scope and broadcasting speed;
Step 2: historical flight path data playback control module loads the historical flight path information of admission, and according to start time of the broadcast zero-time position history radar track of historical flight path data demand;
Step 3: the historical telegram transmit control module that flies loads corresponding historical flight telegram data according to evaluation profile, according to the start time stamp of the broadcast zero-time position history flight telegram of setting;
Step 4: historical data Play Control primary module regularly sends the different time control information to historical flight path data playback control module and historical flight telegram transmit control module, and the reproduction time of flight telegram is early than the reproduction time of flight path;
Step 5: historical flight path data playback control module meets the flight path information of time conditions according to the search of the time control information of historical data Play Control primary module, to plan track correcting module and traffic statistics module transmission flight path information;
Step 6: historical flight telegram playing module is according to the time of historical data Play Control primary module, the historical flight of playback telegram;
Step 7: plan trajectory predictions module is planned newspaper according to the navigator who plays and is planned trajectory predictions, and the E.T.D(estimated time of departure) during the navigator plans to report is the start time of prediction locus; Under actual pattern and empirical mode, received report quickly after, start time of prediction locus has been modified to the actual time of departure in reporting quickly; Plan track correcting module is revised prediction locus according to historical flight path data; The volume forecasting module is carried out future transportation stream to the plan track and is calculated;
Step 8: the traffic statistics module changes the traffic flow statistics of historical juncture to historical flight path;
Step 9: compare synchronization volume forecasting result and traffic statistics result.
2. the volume forecasting result verification method based on historical flight Operational Data Analysis according to claim 1; It is characterized in that: the verifying software module is provided; In order to realize that the assessment of plan forecast track is divided the verifying software module, this verifying software module comprises:
Historical data Play Control primary module: time range and playback rate information that output is play, and with identical speed output time information;
Historical flight path data playback control module: control historical flight path data time range and playback rate as requested and carry out data output;
Historical flight telegram transmit control module: the historical flight of control telegram time range and playback rate as requested carries out data output;
Plan trajectory predictions module: the flight telegram is handled, formed flight plan, and produce the plan forecast track based on flight plan;
Plan track correcting module: the plan track of prediction is revised based on the flight path data;
Volume forecasting module: carry out future transportation stream statistics according to the plan forecast track;
Traffic statistics module: carry out the current time traffic flow statistics according to the flight path data.
3. the volume forecasting result verification method based on historical flight Operational Data Analysis according to claim 2; It is characterized in that: the method that obtains assessment result is: volume forecasting and traffic statistics result's difference in the recording process in using the process of various evaluation profiles; And through inserting the numerical value that display frame shows volume forecasting of same time range location point and traffic statistics; Comparing result directly perceived is reached a conclusion.
4. the volume forecasting result verification method based on historical flight Operational Data Analysis according to claim 1 is characterized in that: before setting up three kinds of evaluation profiles, also have step and the volume forecasting processed steps of handling flight telegram data;
The step of handling flight telegram data comprises: flight telegram processing module forms accurate flight planning to carrying out data parsing from the flight telegram of moving on-the-spot admission; The flight planning processing module is aimed at flight planning and is carried out line of flight decomposition and the four-dimensional track reckoning of flight overall process; The volume forecasting module is carried out traffic flow statistics according to the four-dimensional track of schedule flight overall process;
The volume forecasting processed steps comprises: at first the long-term flight telegram data of history are resolved, the telegram of will flying is handled encapsulation in a different manner, and carries out playback with different patterns, the historical flight path after following a period of time of broadcast simultaneously; Carry out the schedule flight trajectory predictions according to flight telegram data; Prediction locus is carried out synchronized transmission with historical flight path identical time and speed; The traffic statistics module is added up according to radar track; The volume forecasting module is added up according to prediction locus, and volume forecasting result and traffic statistics result in the more same time range analyze the correctness that flow predicts the outcome.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2012102877112A CN102831487A (en) | 2012-08-13 | 2012-08-13 | Flow predicted result verification method based on historical scheduled flight running data analysis |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2012102877112A CN102831487A (en) | 2012-08-13 | 2012-08-13 | Flow predicted result verification method based on historical scheduled flight running data analysis |
Publications (1)
Publication Number | Publication Date |
---|---|
CN102831487A true CN102831487A (en) | 2012-12-19 |
Family
ID=47334608
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN2012102877112A Pending CN102831487A (en) | 2012-08-13 | 2012-08-13 | Flow predicted result verification method based on historical scheduled flight running data analysis |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN102831487A (en) |
Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105513431A (en) * | 2015-11-30 | 2016-04-20 | 中国民用航空厦门空中交通管理站 | Dynamic value determining method and system for practical operation standard of air traffic flow management |
CN106648757A (en) * | 2016-11-29 | 2017-05-10 | 维沃移动通信有限公司 | Data processing method of virtual reality terminal and virtual reality terminal |
CN106779245A (en) * | 2017-01-04 | 2017-05-31 | 中国民航信息网络股份有限公司 | Civil aviaton's needing forecasting method and device based on event |
CN108418848A (en) * | 2017-02-10 | 2018-08-17 | 阿里巴巴集团控股有限公司 | A kind of push of flight data, processing method and equipment |
CN111477033A (en) * | 2020-01-17 | 2020-07-31 | 上海眼控科技股份有限公司 | Traffic management method and device based on navigation volume change, electronic equipment and storage medium |
CN112686309A (en) * | 2020-12-29 | 2021-04-20 | 北京维艾思气象信息科技有限公司 | Aircraft test flight condition prediction method and device based on actual observation |
CN115469785A (en) * | 2018-10-19 | 2022-12-13 | 华为技术有限公司 | Timeline user interface |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101201978A (en) * | 2007-12-20 | 2008-06-18 | 四川川大智胜软件股份有限公司 | Method for predicting short-run air traffic flux based on real time radar and flight information |
EP1943571A1 (en) * | 2005-10-31 | 2008-07-16 | Honeywell International Inc. | System and method for performing 4-dimensional navigation |
CN101540113A (en) * | 2009-03-16 | 2009-09-23 | 民航数据通信有限责任公司 | Air traffic waypoint flow control method, server and system |
-
2012
- 2012-08-13 CN CN2012102877112A patent/CN102831487A/en active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
EP1943571A1 (en) * | 2005-10-31 | 2008-07-16 | Honeywell International Inc. | System and method for performing 4-dimensional navigation |
CN101201978A (en) * | 2007-12-20 | 2008-06-18 | 四川川大智胜软件股份有限公司 | Method for predicting short-run air traffic flux based on real time radar and flight information |
CN101540113A (en) * | 2009-03-16 | 2009-09-23 | 民航数据通信有限责任公司 | Air traffic waypoint flow control method, server and system |
Non-Patent Citations (2)
Title |
---|
徐肖豪 等: "空中交通流量统计和预测系统的设计与实现", 《中国民航学院学报》, vol. 23, no. 4, 25 August 2005 (2005-08-25) * |
殷允楠: "基于历史航班运行数据分析的流量预测结果验证方法", 《中国优秀硕士学位论文全文数据库(工程科技Ⅱ辑)》, no. 06, 15 June 2011 (2011-06-15) * |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN105513431A (en) * | 2015-11-30 | 2016-04-20 | 中国民用航空厦门空中交通管理站 | Dynamic value determining method and system for practical operation standard of air traffic flow management |
CN105513431B (en) * | 2015-11-30 | 2017-12-26 | 中国民用航空厦门空中交通管理站 | The dynamic constant value method and system of ATFM practical operation standard |
CN106648757A (en) * | 2016-11-29 | 2017-05-10 | 维沃移动通信有限公司 | Data processing method of virtual reality terminal and virtual reality terminal |
CN106648757B (en) * | 2016-11-29 | 2021-02-02 | 维沃移动通信有限公司 | Data processing method of virtual reality terminal and virtual reality terminal |
CN106779245B (en) * | 2017-01-04 | 2020-09-04 | 中国民航信息网络股份有限公司 | Event-based civil aviation demand prediction method and device |
CN106779245A (en) * | 2017-01-04 | 2017-05-31 | 中国民航信息网络股份有限公司 | Civil aviaton's needing forecasting method and device based on event |
CN108418848A (en) * | 2017-02-10 | 2018-08-17 | 阿里巴巴集团控股有限公司 | A kind of push of flight data, processing method and equipment |
CN108418848B (en) * | 2017-02-10 | 2021-02-19 | 阿里巴巴集团控股有限公司 | Flight data pushing and processing method and equipment |
CN115469785A (en) * | 2018-10-19 | 2022-12-13 | 华为技术有限公司 | Timeline user interface |
CN115469785B (en) * | 2018-10-19 | 2023-11-28 | 华为技术有限公司 | Timeline user interface |
CN111477033A (en) * | 2020-01-17 | 2020-07-31 | 上海眼控科技股份有限公司 | Traffic management method and device based on navigation volume change, electronic equipment and storage medium |
CN111477033B (en) * | 2020-01-17 | 2021-07-27 | 上海眼控科技股份有限公司 | Traffic management method and device based on navigation volume, electronic equipment and storage medium |
CN112686309A (en) * | 2020-12-29 | 2021-04-20 | 北京维艾思气象信息科技有限公司 | Aircraft test flight condition prediction method and device based on actual observation |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Singh et al. | Deployment of autonomous trains in rail transportation: Current trends and existing challenges | |
CN102831487A (en) | Flow predicted result verification method based on historical scheduled flight running data analysis | |
CN100483477C (en) | Method for predicting short-run air traffic flux based on real time radar and flight information | |
US10678265B2 (en) | Revised speed advisory for an aircraft during flight based on holding time | |
US9262928B2 (en) | Prediction of flight path privacy | |
Beugin et al. | Simulation-based evaluation of dependability and safety properties of satellite technologies for railway localization | |
CN103093649B (en) | Method and system for inferring aircraft parameters | |
EP2902987B1 (en) | Method for modeling aircraft performance through adaptive aircraft performance models | |
CN104077928B (en) | Be applicable to the navigation of irregular flight outside air route dot position information update method | |
US10235892B1 (en) | Aircraft surface state event track system and method | |
US20180240348A1 (en) | Methods and systems for probabilistic spacing advisory tool (psat) | |
EP3716247A1 (en) | Method and system for detecting and avoiding loss of separation between vehicles and updating the same | |
US10930165B2 (en) | Enhanced flight efficiency through smart automation for on-board weather update | |
EP3716246A1 (en) | Method and system for detecting and avoiding loss of separation between vehicles | |
Tran et al. | Taxi-speed prediction by spatio-temporal graph-based trajectory representation and its applications | |
CN107544536B (en) | Method and system for performance-based reach and ordering and spacing | |
Rodríguez-Sanz et al. | Practical implementation of 4D-trajectories in air traffic management: system requirements and time windows monitoring | |
Saifutdinov et al. | An emulation oriented method and tool for test of ground traffic control systems at airports | |
Zheng et al. | Time-of-arrival taxi conformance monitoring for surface operations | |
US11482103B2 (en) | Control and manage traffic light system with VANET | |
Zamanipour et al. | New Advances in Intelligent Intersection Management with Connected and Automated Vehicle Technology | |
CA3024602C (en) | Control and manage traffic light system with vanet | |
Hutchinson et al. | Precise and reliable localization as a core of railway automation (Rail 4.0) | |
JP2020142745A (en) | Train operation control device and method for the same | |
CN109657340A (en) | A kind of 1090ES signal overlaps the estimation method of probability |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20121219 |