CN103761585B - Continue transport Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method on airport - Google Patents
Continue transport Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method on airport Download PDFInfo
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Abstract
A kind of design of transport Dynamic Matching, the transport power monitoring and early warning and intelligent dispatching method of being continued the invention discloses airport, the technical problem of solution is directed to the complexity of existing conventional flow analysis and Forecasting Methodology problem high and actual operation inconvenience.It mainly includes：Information gathering is put in storage；Data analysis is carried out according to current real time information and arranges replacement analysis table；By passenger, taxi and flight real time information table comparative analysis table data, analytical table is met the taxi and Customer information of real time information as information of forecasting to the full extent；Contrast passenger and taxi information judge whether taxi breach occur；Predict that notch value judges whether to send early warning according to taxi, if it is send early warning and provide schedule information：Taxi breach quantity.The present invention goes statistical analysis initial data from the angle of weather pattern, flight quantity and date type, can effectively help airdrome control administrative staff to manage and coordinate taxi transport power, it is to avoid airdrome control personnel predict the inaccuracy brought by rule of thumb.
Description
Technical field
The invention belongs to traffic flow forecasting technical field, and in particular to continue transport Dynamic Matching, transport power on a kind of airport
The design of monitoring and early warning and intelligent dispatching method.
Background technology
Current taxi is the important means of transportation that continues in the Capital Airport, and its transport power allotment problem is influence system service matter
The key factor of amount, for intellectuality allotment taxi transport capacity resource, maintains the taxi transport power dynamic equilibrium of supply and demand, it is necessary to analyze
And predict the arrival rule of cab-getter and taxi, i.e. ridership and hire out vehicle flowrate rule.Researcher has carried
Go out many flow analyses and Forecasting Methodology, thus how to have chosen suitable flow analysis and Forecasting Methodology, set up corresponding number
It is to solve intelligent allotment taxi transport power to learn model, maintains the key of taxi dynamic equilibrium.
In practical application, in order to realize flow analysis and set up Forecasting Methodology, it is necessary to the mass data to being collected into is entered
The treatment such as row arrangement, processing, merger, classification, calculating and storage, produce new data, to reflect things or essence of phenomena, spy
Seek peace inner link, it is exactly data base management system to complete these tasks, it is a layer be located between user and operating system
Data management software.Database setting up, with and be just managed collectively by data base management system when safeguarding, be uniformly controlled, it
Allow users to easily define data and peration data, and ensure that security, integrality and multi-user's logarithm of data
According to concurrently use and break down after data recovery.
Domestic and international researcher is continued to develop according to the characteristic of network traffics and propose successively many models, is such as based on returning certainly
Return or autoregressive moving average linear prediction model and based on wavelet analysis, neutral net, chaology, SVMs side
The Nonlinear Prediction Models of method.But newest research shows, the flow in real network have long range dependent, self similarity, single point
The multifrequency nature such as shape and many points of shapes, these features that conventional single model can not be completely to network traffics are taken into account, therefore can
Flow is predicted with after by the way that different single models is combined.Existing combination forecasting can substantially return
It is following several classes：Wavelet transformation is combined with neutral net, and wavelet transformation and disposal vector machine are combined, wavelet transformation and gray model
With reference to.But existing Mathematical Modeling is all too complicated, do not account for the operability of practical application, implement it is difficult,
The present actual prediction mode in airport mostly according to the Empirical rules of relevant staff, with very big subjectivity.
The content of the invention
The technical problems to be solved by the invention be directed to existing conventional flow analysis and Forecasting Methodology complexity it is high and
The problem of actual operation inconvenience proposes a kind of airport and continues transport Dynamic Matching, transport power monitoring and warning and intelligent allocation side
Method.
The present invention solve its technical problem use technical scheme be：Transport Dynamic Matching, transport power monitoring continue in advance in airport
Alert and intelligent allocation method, specifically includes：
S1, information gathering storage：Passengers quantity on the airport taxiways platform that real-time collecting counts in each time interval
And be input in the REAL_SPHH in database；Collect taxi wait, supplement and amount shipped out and be input in database
In REAL_THH, port flight quantity and the REAL_FLIGHTHH being input in database are collected into；
S2, according to current real time information, date type, weather pattern and to port flight number of levels, query analysis table：
Whether STA_HHSPT_BY_HWF has corresponding record；
S3, data analysis：Real-time number in REAL_SPHH, REAL_WHH, REAL_THH and REAL_FLIGHTHHT
According to four analytical tables updated the data in storehouse：STA_HHSPT_BY_HWF、STA_AVG_HHSPT_BY_H、STA_AVG_HHSPT_
W、STA_AVG_HHSPT_F；
S4, according to the data meter in passenger, taxi and flight real time information table and analytical table STA_HHSPT_BY_HWF
Calculate taxi breach quantity；
If S5, in S4, there is no the information of matching in analytical table STA_HHSPT_BY_HWF, then according to passenger, hire out
Phase in car and flight real time information table difference analytical table STA_HHSPT_BY_H, STA_HHSPT_BY_W, STA_HHSPT_BY_F
The taxi and Customer information of condition are answered, and by the taxi of minimum number and the passenger-seat information of forecasting of maximum quantity, and
Calculate taxi breach quantity；
S6, judged whether to send early warning according to taxi notch value, if it is send early warning and provide schedule information：Go out
Hire a car breach quantity；Otherwise return to S1.
Beneficial effects of the present invention：In order to the complexity for solving conventional flow analysis and Forecasting Methodology is high and actual operation
The problem of inconvenience, because algorithm will use a large amount of historical datas, and based on the analysis to historical data to ridership and taxi
Vehicle flowrate is made a prediction, and these historical datas are disorderly and unsystematic and with dispersiveness, thus the present invention using database to going through
History data do statistical analysis, find rule therein.Have one under equivalent environment in view of ridership and taxi vehicle flowrate
The reproducibility of degree is determined, so the present invention goes statistical analysis original from the angle of weather pattern, flight quantity and date type
Data.With real-time conditions comparison database correlated condition, using the historical data under the same terms as predicted value, can be effectively
Help airdrome control administrative staff management and coordinate taxi transport power, it is to avoid airdrome control personnel predict that brings is forbidden by rule of thumb
True property.
Brief description of the drawings
Fig. 1 is to continue transport Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method on the airport of the embodiment of the present invention
FB(flow block)；
Fig. 2 continues in transport Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method for the airport of the embodiment of the present invention
Four real time information data tables described in S2
Fig. 3 is to continue transport Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method on the airport of the embodiment of the present invention
Data acquisition and typing module；
Fig. 4 is to continue transport Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method on the airport of the embodiment of the present invention
Date classification modular structure；
Fig. 5 is to continue transport Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method on the airport of the embodiment of the present invention
Data update module.
Specific embodiment
The invention will be further elaborated with specific embodiment below in conjunction with the accompanying drawings.
Continued for the airport of the embodiment of the present invention as shown in Figure 1 and transport Dynamic Matching, transport power monitoring and warning and intelligent allocation
The FB(flow block) of method, it is specifically included,
S1, create database and design real time information and data analytical data.According to algorithm, it is necessary to design five realities
When information data table and four analytical datas.Data table name and row name as shown in table 1, wherein real time information data table be for
Store the information of real-time collecting, i.e. real-time weather, real time date type, take in real time the passenger of taxi, taxi information and
Flight number of levels；By certain rule, that is, behind data update, in all real time information data tables of confluence analysis
Data obtain correlated results, by the storage of these correlated results in analytical data.
Table 1：Tables of data
Wherein：HH is represented：Half hour, refer to that predicted time is spaced, and certain predicted time interval can be different value；
DTTM：Datetime is represented, in a few days the phase accurate second, such as 2013080121:39:00, and TIME, the time is only represented, also it is accurate to
Second, such as：21:39:00.But every interval of the DTTM or TIME of record is all spaced with predicted time, the present invention is
30 minutes.Customer information refers to the passengers quantity that each time interval takes taxi；Between taxi information refers to each time
Every it is inner it is to be shipped, supplement and send taxis quantity.
S2, data acquisition：By real-time collecting to information (weather condition of current time interval, date type, flight quantity
Rank, passengers quantity (NP) and taxis quantity (NT)) it is entered into respectively in corresponding five real time information data tables, while more
New four analytical datas.
Table 2：Weather pattern is classified
Taking off, land and each stage of airflight can all be influenceed by meteorological condition, wind, temperature, gas
Pressure is all the important meteorological element of influence flight.Surface wind can directly affect the manipulation of aircraft, and upper-level winds can influence aircraft in boat
Flying speed and volume read-out on line.Temperature just, can change engine thrust, influence airspeed-indicator, the ground run distance that rises and falls etc.
Deng.When temperature is higher than standard atmospheric temperature, can increase and take off ground run distance and rise the duration of ascent, reduce aircraft load-carrying
Amount.Air pressure can influence the flying height of aircraft.Because various regions air pressure often changes, the mistake of pressure altimeter instruction is often resulted in
Difference.Additionally, thunderstorm, low clouds, low visibility, low, atmospheric turbulance, aerial torrent, the weather phenomenon such as jolt, freeze all
Directly threaten flight safety.Therefore weather is divided into 4 classes by the present invention, as shown in table 2.At tables of data REAL_WHH (real-time weather)
In WEATHER values be corresponding classification：1、2、3、4.
In the different dates, passenger flows and taxi wagon flow have different features.Most obvious difference be exactly festivals or holidays,
The passenger on weekend and other dates and hire out wagon flow and have obvious difference because people typically can more select in festivals or holidays and
Weekend goes on a journey.In order to more accurately embody the traffic characteristic in different date types, the present invention will be divided into 14 types the date,
As shown in table 3.But according to following classification, there are two attributes the national legal festivals and holidays, and another attribute is week, this when
The date property is waited for festivals or holidays.Therefore, the internal structure of real time date sort module such as Fig. 2.
Table 3：Date classification
| Date | Type |
| Lunar calendar New Year's Eve is to first month of the lunar year the sixth day of lunar month | The Spring Festival |
| Gregorian calendar January 1 | New Year's Day |
| On Gregorian calendar April 4 to 6 | It is clear and bright |
| Gregorian calendar May 1 | International Labour Day |
| The lunar calendar is 5 at the beginning of 5 months | The Dragon Boat Festival |
| Lunar calendar August 15 | The Mid-autumn Festival |
| On Gregorian calendar October 1 to 7 | National Day |
| Monday | |
| Tuesday | |
| Wednesday | |
| Thursday | |
| Friday | |
| Saturday | |
| Sunday |
Because flight quantity is larger, if will result in classification too much according to one classification of each flight quantity, therefore will boat
Class quantity divide into several classes does not distinguish the classification that granularity is 5, such as：0~5 sortie flight is rank A, and 6~10 is rank
B ..., as shown in table 4.Certainly distinguishing granularity can be adjusted to other suitable values.
Table 4：Flight number of levels is classified
| Flight quantitative range | Flight number of levels |
| 0~5 | A |
| 6~10 | B |
| 11~15 | C |
| ...... | ...... |
S3, data update.Data replacement analysis tables of data according to real time data table.As shown in Figure 3 and Figure 4.
S4, prediction passenger and taxi vehicle flowrate.According to current information (date type, weather pattern, flight number of levels,
Current time) querying condition, whether query analysis table STA_AVG_HHSPT_BY_HWF have relative recording.
4.1 if so, then read out data AVG_SP, AVG_TAXI_WAIT of respective record, AVG_TAXI_OUT, AVG_
TAXI_ADD is used as the ridership in predicted time sectionWait taxi numberSend taxi number
And supplement taxi number
It is inquiry bar according to current information (date type, weather pattern, flight number of levels, current time) if 4.2 nothings
Part, inquires about in STA_AVG_HHSPT_BY_F, STA_AVG_HHSPT_BY_H, STA_AVG_HHSPT_BY_W tables of data respectively
Record, by the AVG_SP maximums in three analytical tables, AVG_TAXI_WAIT minimum values, AVG_TAXI_OUT minimum values, AVG_
TAXI_ADD minimum values are used as the ridership in predicted time sectionWait taxi numberSend taxi numberAnd supplement taxi number
Prediction ridership in S5, the ridership according to current time and taxi number and current slot, wait out
Hire a car number, send taxi number and supplement taxi number, calculate taxi notch value.
Wherein：μ is a constant value, refers to average each taxi carrying person numble.
S6, whether judge taxi notch value G beyond early warning threshold values λ, if beyond providing early warning, and be given dispatch command-
Need (G- λ) taxi.
Claims (3)
1. a kind of airport is continued and transports Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method, it is characterised in that specific bag
Include：
S1. information gathering is put in storage, by real time data typing real time data table；
Passengers quantity and it is input in database on the airport taxiways platform that real-time collecting counts in each time interval
REAL_SPHH；The wait of real-time collecting taxi, supplement and amount shipped out and the REAL_THH being input in database；Receive in real time
Collect port flight quantity and the REAL_FLIGHTHH being input in database；Obtain real time date type and be input to database
In REAL_DHH；Obtain real-time weather type and the REAL_WHH being input in database；
The real-time weather type of acquisition is divided into 4 classes：
1st class includes heavy rain, floating dust, heavy snow, heavy or torrential rain, heavy rain, mist, thunder shower；
2nd class includes moderate rain or heavy rain, moderate snow, rain and snow mixed, moderate rain；
3rd class includes medium to small rain, medium to small snow, light rain, slight snow, shower；
4th class includes cloudy day, fine day；
S2. data analysis, replacement analysis tables of data is gone with real time data；
With the FLIGHT_NUM_ in the WEATHER and REAL_FLIGHTHH in DATETYPE, the REAL_WHH in REAL_DHH
CLASS is used as corresponding record is whether there is in querying condition inquiry STA_AVG_HHSPT_BY_HWF if having corresponding record
The state-of-the-art record of REAL_SPHH and REAL_THH updates the record for inquiring, with REAL_DHH if without recording accordingly
DATETYPE, REAL_WHH in WEATHER and REAL_FLIGHTHH in FLIGHT_NUM_CLASS and REAL_
The newly-built record of taxis quantity in passengers quantity and REAL_THH in SPHH；
S3. Data Matching, match query data in historical data base are gone according to current real time information；
According to current date type, weather pattern, flight number of levels, time inquiring analytical table STA_AVG_HHSPT_BY_
Whether HWF has relative recording；
If there is relative recording, data AVG_SP, AVG_TAXI_WAIT, AVG_TAXI_OUT, AVG_ of respective record are read out
TAXI_ADD as the ridership in predicted time section, wait taxi number, send taxi number and supplement taxi number；
If without relative recording, STA_ is inquired about respectively according to current date type, weather pattern, flight number of levels, time
Record in AVG_HHSPT_BY_F, STA_AVG_HHSPT_BY_H, STA_AVG_HHSPT_BY_W tables of data, by three analyses
AVG_SP maximums, AVG_TAXI_WAIT minimum values in table, AVG_TAXI_OUT minimum values, AVG_TAXI_ADD minimum values
As the ridership in predicted time section, taxi number is waited, taxi number is sent and supplements taxi number；
S4. taxi breach quantity is calculated；
S5. judge early warning, send dispatch command；
The REAL_SPHH is data table name, represents passengers quantity；The REAL_THH is data table name, represents taxi letter
Breath；The REAL_WHH is data table name, represents real-time weather；The REAL_DHH is data table name, represents date type；Institute
REAL_FLIGHTHH is stated for data table name, flight number of levels is represented；The STA_AVG_HHSPT_BY_F is data table name,
Represent the passenger under different flight number of levelss and taxi par；The STA_AVG_HHSPT_BY_H is tables of data
Name, represents the passenger under different date types and taxi par；The STA_AVG_HHSPT_BY_W is data table name,
Represent the passenger under different weather type and taxi par；The STA_AVG_HHSPT_BY_HWF is data table name,
Represent the passenger under different weather, date and flight number of levels and taxi par；
The DATETYPE is date type, WEATHER is weather pattern, FLIGHT_NUM_CLASS is flight number of levels,
AVG_SP is average passenger quantity, AVG_TAXI_WAIT is average waiting taxis quantity, AVG_TAXI_OUT is averagely to send
Taxis quantity, AVG_TAXI_ADD are average supplement taxis quantity.
2. airport according to claim 1 is continued and transports Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method, and it is special
Levy and be, the formula of the calculating taxi breach in step 4 is：
Taxi_Left (t+1)=(νT_in(t+1)+vT_wait(t))-(vP_in(t+1)+vP_wait(t))/α
Wherein：vP_waitT () represents the patronage for waiting at current time, vT_in(t+1) represent that subsequent time arrives at a station taxi
Predicted value, vP_in(t+1) represent that subsequent time arrives at a station the predicted value of passenger, vT_waitT () represents the taxi for waiting at current time
Quantity；α represents the average service number of each taxi.
3. airport according to claim 1 is continued and transports Dynamic Matching, transport power monitoring and early warning and intelligent dispatching method, and it is special
Levy and be, early warning is judged in step 5, issue concretely comprising the following steps for dispatch command：Breach early warning threshold values of hiring a car is set out for λ, when
During Wanted_Taxi (t) ＞ λ, early warning is provided, and send dispatch command：Wanted_Taxi (t)-λ taxi is needed, it is described
Wanted_Taxi (t) represents taxi notch value.
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