CN107564284B - A kind of traffic based on WIFI signal detection is passed unimpeded grade forecast system - Google Patents
A kind of traffic based on WIFI signal detection is passed unimpeded grade forecast system Download PDFInfo
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Abstract
The invention discloses a kind of traffic based on WIFI signal detection to pass unimpeded grade forecast system, including multiple sub-networks, each sub-network includes a host and several extension sets, the wind turbine is based on WIFI agreements and passes through wireless passive perceptual model, acquire the broadcast data packet that the mobile device based on WIFI signal agreement is sent in ambient enviroment, and screen the wherein data packet with mobile terminal device id information and retrieved, it is uploaded to host after stamping extension set label；The host by mobile phone to data carry out unified storage and stamp time tag, and be uploaded in data server and store；By carrying out macroscopical wagon flow flow, traffic cross-sectional flow, the average predicting travel time of polymerization, and then the prediction of the grade that passes unimpeded of road traffic to data.The present invention is based on the unique ID of portable mobile terminal, gathered data timestamp and detection device location information, the data mining and analysis to mobile terminal gathered data are realized, can be applied to traffic and pass unimpeded the prediction of grade.
Description
Technical field
The invention belongs to the improvement of development of Mobile Internet technology more particularly to macro-traffic information monitoring algorithm.
Background technology
Traffic flow data is the important information source of traffic operation dispatching and command system, can be command scheduling, the magnitude of traffic flow
Control and traffic guidance provide decision-making foundation.There are many existing Traffic flow detecting technologies, can be divided into contact according to mounting means
Formula detection mode and non-contact detection mode.Wherein contact measurement technology includes piezoelectricity, pressure pipe detection and loop coil
Detection.The major defect of this technology is that vehicle causes the service life of detector shorter rolling for road, is detected laying
When device, the road surface that suspends traffic, destroys is needed, therefore more difficult, use cost height is gone along with sb. to guard him in installation.Non-contact detection technology master
To be wave frequency detection and video detection.Wave frequency detection is divided into microwave, ultrasonic wave and three kinds infrared etc..Non-contact detection device can lead to
Holder installation is crossed, easy to maintain, service life is long, major defect is easily to be influenced by weather and outdoor conditions, and it is suitable that there are environment
The problems such as answering property is not strong, volume of transmitted data is big, Detection accuracy is not high and cost is higher.
With the rapid development of China's highway network, freeway traffic flow detects application demand and increases severely.In highway network
In, traffic flow information is equally important, and by flow information, highway network administrative department can understand the reality in each section in real time
When vehicle fleet size information, intuitive road network vehicle load amount is provided, accurate data is provided for the scheduling and integrated planning of road network.
But highway network has that some are special, such as highway power supply is inconvenient, information transmission is difficult, with
And fail to lay all kinds of detectors etc. in advance in process of construction, it can not accomplish concentrated type monitoring and management, need to existing detection
Device is further designed and is improved.
Invention content
In view of the problems of the existing technology, the present invention provides a kind of traffic based on WIFI signal detection to pass unimpeded grade
Forecasting system, the present invention is based on the macroscopic views of the unique ID of portable mobile terminal, gathered data timestamp and detection device location information
Traffic information detection algorithm realizes data mining and analysis to mobile terminal gathered data, can be applied to traffic cross-sectional flow
Detection.
In order to solve the above technical problems, present invention employs following technical schemes：
A kind of traffic based on WIFI signal detection is passed unimpeded grade forecast system, and detection device, institute are disposed in traffic route
It states detection device and forms multiple sub-networks, each sub-network includes a host and several extension sets, and the extension set is based on WIFI
Agreement acquires the broadcast number that the mobile device based on WIFI signal agreement is sent in ambient enviroment by wireless passive perceptual model
It according to packet, and screens the wherein data packet with mobile terminal device id information and is retrieved, master is uploaded to after stamping extension set label
Machine；The data being collected into are carried out unified storage and stamp time tag by the host, and are uploaded in data server and are stored；
By carrying out macroscopical wagon flow flow, traffic cross-sectional flow, the average predicting travel time of polymerization to data, and then determine road traffic
The grade that passes unimpeded；Wherein described macroscopical wagon flow flow, traffic cross-sectional flow, the average predicting travel time of polymerization include following step
Suddenly：
Step 1：The data D of complete period is acquired by extension set,Wherein, DijIt indicates j-th
No. i-th extension set data of subnet, N indicate that the number of subnet, M indicate the extension set number in j-th of subnet；
Step 2：Data are sliced and extract section S and complete period (the data D of T- Δs t) to be analyzeds',DisFor the collected data of extension set that number is i under s subnets；
Step 3：Spatial match is carried out to the sub-network of deployment and corresponding practical section S, obtain sub-network host with it is corresponding
The extension set deployment scenario list of the number information in section and the sub-network；
Step 4：The data of each extension set acquisition are ranked up according to mobile terminal device ID number, establish data matrix
{Tower(i,s),t}；
Step 5：The ID data lists of foundation are classified by the number for appearing in different data matrix：For in the time
In section Δ t, what mobile terminal ID only occurred in the single extension set of sub-network, to the corresponding data of mobile terminal ID individually extract into
The follow-up efficiency analysis of row；It, should in period Δ t, mobile terminal ID occurs in sub-network two or more extension set
The corresponding data of mobile terminal ID are directly as valid data
Step 6：For in period Δ t, mobile terminal ID is only in the single extension set appearance of sub-network, to movement end
ID corresponding data in end, which are individually extracted, carries out follow-up efficiency analysis：1, within the Δ t times, mobile terminal ID is individually dividing
Repeat in the data matrix TOWER of machine, and case above occur in multiple mobile terminal ID, mark corresponding road section is congestion shape
Condition, and such ID data is denoted as valid data；2, within the Δ t times, do not find mobile terminal ID in single extension set number
According to repeating in matrix TOWER, then traverse whether the sub-network before and after sub-network corresponding road section S identical ID number occurs,
If do not occurred, using the ID data as noise data processing, if occurred in other sub-networks, identify into significant figure
According to list；
Step 7：Step 4~6 are repeated until data processing finishes；
Step 8：For non-congestion situation, after being screened according to data above, the associated number of mobile terminal ID number is re-established
According to matrix { Tower ' (i, s), t }；
Step 9：According to the relationship between the time t and Tower in data matrix { Tower ' (i, s), t }, by data into
One step is divided into bi-directional data matrix DLAnd DR, and approximation computation is carried out to it；
Step 10：Establish macroscopical wagon flow flow, traffic cross-sectional flow, the prediction neural network of polymerization average hourage：
T is (after T+ Δs t) the expression T moment in the Δ t periods when vehicle prediction travelling in the covered section of jth work song network
Between；∑ j is the total amount of the effective ID of mobile terminal of the direction of jth work song network, point in host subnet network where m, n are indicated
Machine is numbered,At the time of indicating that ID-k occurs in sub-network Tower (j) on m-th of extension set, a (k) indicates weight,A (j) and B (j) indicates weight regulatory factor；T (j) indicates the mileage time average of j-th of Tower；F
() is macroscopical wagon flow flow approximate fits function；G () is the approximate fits function of traffic cross-sectional flow；P () is polymerization
The approximate fits function of average hourage；I is extension set sum under subnet.
Step 11：The saturation volume in section is set as V0, which is v0, which is D, and is set
Traffic is passed unimpeded grade, is shown below：
By the way that by V, (((T+ Δ t) are compared with above formula, to confirm current smooth in the section by T+ Δs t), t by T+ Δs t), v
Row grade.
Further, by output result V (T+ Δs t), v (T+ Δ t) and t (T+ Δ t) and the coil actual value in step 10
It is compared, obtains the margin of error
In formula, S is coil actual value；
Then, setting self feed back iterative steps are C, and stopping criterion for iteration is arrangedAccording to the mistake of output
DifferencePass through the value for adjusting weight A (j) and B (j) so that V (T+ Δ t) approximate algorithm training sample S, while by output data
Initial value of the value of weight A (j) and B (j) as allocation plan as data weighting in fitting function next time.
Further, the S of algorithm training sample described in step 10 is the data that coil checker or radar detector obtain,
And as effective reference unit.
Further, distance is d between the adjacent extension set, and the signal covering radius of single extension set is r, and d > 2r.
Advantageous effect：The present invention provides one kind being based on the unique ID of portable mobile terminal, gathered data timestamp and detection
The macro-traffic infomation detection algorithm of device location information realizes data mining and analysis to mobile terminal gathered data, is
Magnitude of traffic flow detector and detecting system based on WIFI signal provide the excavation of the depth based on the type data and the magnitude of traffic flow
Detection algorithm is realized, has been filled up application blank of the type data in terms of Vehicle Detection, has been promoted the development in wisdom traffic field.
Description of the drawings
Fig. 1 is that the road of the traffic flow detection system of the present invention based on WIFI signal disposes schematic diagram.
Fig. 2 is that the traffic of the present invention based on WIFI signal detection is passed unimpeded the flow diagram of grade forecast system.
Fig. 3 is that the traffic of the present invention based on WIFI signal detection is passed unimpeded the noise reductions of valid data in grade forecast system
Screening process schematic diagram；
Fig. 4 is that the traffic of the present invention based on the WIFI signal detection data approximate fits in grade forecast system that pass unimpeded are forced
The process schematic of nearly actual value；
Fig. 5 is the deployment schematic diagram of traffic flow detection system in the embodiment of the present invention；
Fig. 6 is the comparison figure of the output valve and coil actual value of forecasting system of the present invention；
Fig. 7 is the ratio versus time curve of the output valve and coil actual value of forecasting system of the present invention；
Fig. 8 be the embodiment of the present invention in road pass unimpeded grade illustrate distribution map.
Specific implementation mode
Below in conjunction with the accompanying drawings and with specific embodiment, the present invention is furture elucidated.It should be understood that these embodiments are only used for
It the bright present invention rather than limits the scope of the invention, after having read the present invention, those skilled in the art are to of the invention
The modification of various equivalent forms falls within the application range as defined in the appended claims.
As shown in Figure 1, in inventive network, comprehensive reference current moment target road section and approach way changes in flow rate are moved
Dynamic Termination ID unit mileage hourage variation, inputs historical data, by fitting function, realizes T+ time Δt traffic flow speeds
Prediction v (T+ Δ t), and algorithm training is carried out dependent on referential S, feedback Rule of judgment is v/S, can be according to precision of prediction needs
The ratio domain value range is adjusted, when v/S resulting values are in domain value range, data fitting operations are completed in expression, are formed with
Imitate cross-sectional flow prediction data.Specifically：
Predict device systems deployment way：As shown in Figure 1, Tower (j) indicates j-th of subnet in disposed road network
Host, Tower (i, j) indicate i-th of extension set in j-th of subnet.Each sub-network includes a host and several extension sets,
Extension set quantity can suitably increase and decrease according to road network condition, sub-network maximum coverage range 2Km, single extension set estimation range maximum radius
250m, user can adjust single extension set estimation range according to practical road conditions condition, between extension set distance d settings only need to be more than twice
Extension set estimation range, equally can according to practical road conditions condition carry out flexible modulation.Sub-network deployment density can be according to practical friendship
Logical environmental management demand is disposed.
Predict device data acquisition flow：Single pre- measurement equipment, that is, extension set uses TI by wireless passive perceptual model
CC3XXXX family chips detector, by be based on wifi agreements acquisition by mobile terminal device at random around environment send out
WIFI broadcast data packets are sent, and screens the wherein data packet with equipment id information and is retrieved.It stamps on after extension set label
Reach host, the data being collected into are carried out unified storage and stamp time tag by host, and be uploaded in data server into
Row storage, is waited to be analyzed.As shown in Fig. 2, inventive algorithm principle process is specifically described：
Step 1：Acquiring complete period, (partial data of T- Δs t), partial data are expressed asWherein DijIndicate No. i-th extension set data of j-th of subnet；
Step 2：Data are sliced, and extraction and analysis section S and the period (partial data of T- Δs t)
Step 3：Physical spatial location maps, itself and practical section S are carried out space by corresponding disposed sub-network
Match, each sub-network host carries the corresponding number information of corresponding road section and extension set deployment scenario list；
Step 4：Gathered data is ranked up according to its ID number, and establishes data matrix { Tower (i, S), t }；
Step 5：As shown in figure 3, the ID data lists of foundation are divided by the number appeared in different Tower
The ID only occurred in a Tower is individually extracted and is analyzed by class, the ID numbers that will occur in more than two Tower
According to being classified as one kind；
Step 6：The ID only occurred in a Tower is analyzed, situation is divided into following two：1, in Δ t
Between in section, repeat the ID in the Tower, and this kind of situation largely occurs, having a great deal of ID, this occurs, then
Indicate that congestion occurs in the section, which is valid data；If 2, not finding ID weights in the Tower in the Δ t periods
It appears again existing, which is rejected；
Step 7：Repetition step 4,5,6, confirm exhaustive data；
Step 8：For under incomplete jam situation, after deleting choosing according to data above, the associated data of ID number are re-established
Matrix { Tower (i, S), t }；
Step 9：According to the relationship of time t and Tower in data matrix { Tower (i, S), t }, data are further classified
For bi-directional data matrix：DLAnd DR, and it is calculated；
Step 10：As shown in figure 4, flow, flow velocity, the prediction neural network of hourage are established, it was found from figure：
ID_X indicates effective ID data under corresponding Tower；
T_X indicates the mileage time of each ID under the conditions of corresponding Tower；
Σ (j) indicates the summation of ID quantity；
T (j) indicates that the mileage time under corresponding Tower takes mean value；
A (j), B (j) are weight regulatory factor；
F () indicates approximate fits function；
Z indicates the feedback factor under self study；
Bias reference system S indicates effective reference unit, such as coil checker data, radar detector data, uses
It is trained in algorithm；(T+ Δs t) indicates the traffic flow speed in the covered sections Tower (j) under prediction future T+ time Δts to v.Specifically
Algorithm is as follows：
In formula：
(T+ Δs t) indicates the traffic flow speed in the covered section of jth work song network in the Δ t periods after the T moment to v；∑ j is the
The total amount of the effective ID of mobile terminal of the direction of j work song networks, m, n indicate the extension set number in the host subnet network of place,At the time of indicating that ID-k occurs in sub-network Tower (j) on m-th of extension set, a (k) indicates weight,A (j) and B (j) indicates weight regulatory factor；T (j) indicates the mileage time average of j-th of Tower；F
() is macroscopical wagon flow flow approximate fits function；G () is the approximate fits function of traffic cross-sectional flow；P () is polymerization
The approximate fits function of average hourage.
Step-up error amountSetting self feed back iterative steps are C, and stopping criterion for iteration is arrangedAccording to the error amount of outputBy the value for adjusting weight A (j) and B (j) so that (T+ Δs t) approaches calculation to V
The value of method training sample S, final network output weight A (j) and B (j) are as allocation plan.Algorithm described in step 10 trains sample
This S is the data that coil checker or radar detector obtain, and as effective reference unit.
Traffic pass unimpeded grade classification prediction algorithm rely on to traffic current flow, flow velocity and the prediction of hourage,
Foundation is passed unimpeded level data matrix { flow；Flow velocity；Hourage }.User can according to itself handling characteristics by flow saturation degree,
Flow velocity mean value, hourage or three arbitrarily combine.Due to the forecasting system deployment can deployment density it is big, result of calculation is opposite
Accurately, data irrelevance is can be controlled within 10%, therefore grade that traffic can be passed unimpeded further is segmented, in existing 3-4
Expanded on the basis of grade, to more accurately grasp high-speed transit state, to prevent in time and management and control.Pass unimpeded grade
Classification：If certain section theory saturation volume is V, speed limit S, section mileage is D, theory a length of T when current.Section is classified as 6 grades,
Grade classification list can be established as：
| The grade that passes unimpeded (Lv) | Pass through flow v | Pass through average rate s | Pass through duration t |
| Dark green (Lv6) | v<20%V | s>80%S | t<(D/(0.8xS)) |
| Light green (Lv5) | 20%V<v<60%V | s>60%S | t<(D/(0.6xS)) |
| Pale yellow (Lv4) | 40%V<v<80%V | 40%S<s<60%S | (D/(0.6xS))<t<(D/0.4xS) |
| Deep yellow (Lv3) | 15%V<v<60%V | 15%S<s<40%S | (D/(0.4xS))<t<(D/0.15xS) |
| Red (Lv2) | 5%V<v<15%V | 5%S<s<15%S | (D/(0.15xS))<t<(D/0.05xS) |
| Dark red (Lv1) | v<5%V | s<5%S | t>(D/(0.05xV)) |
Traffic is passed unimpeded grade forecast, simply to illustrate：
Initialization condition：
Setting Tower (j) network where section currently pass unimpeded grade L, the flow v that passes by, the flow velocity s that passes by, pass by duration t.
Then, Tower (j-1) to Tower (j+1) section, data are obtained.It is that direction is discussed that this, which sentences j-1 to the directions j+1,.
Foundation is passed unimpeded grade if-else lists：
If L (j+1) >=L (j), Tower (j) sections, the flow v (j) that passes by cannot be accumulated in the direction Tower (j+1)；
If L (j-1) >=L (j), Tower (j) sections, the flow v (j) that passes by are obtained by the directions (j-1) Tower to Tower (j)
Less than accumulation, then Tower (j) sections, flow will not be accumulated and be released, and L (j) grades are on the rise；
Else L (j-1) < L (j), Tower (j) section, the flow v (j) that passes by is by the directions (j-1) Tower to Tower (j)
It is accumulated；
If Tower (j+1) directional flow release >=Tower (j-1) directional flow accumulates, then Tower (j) sections, and flow is not
It can accumulate and be released, L (j) grades are on the rise；
Else Tower (j+1) directional flow discharges the accumulation of < Tower (j-1) directional flow, then Tower (j) sections, flow
Accumulation, L (j) grades have downward trend；
Else L (j+1) < L (j), Tower (j) section, the flow v (j) that passes by are accumulated in the direction Tower (j+1), L
(j) grade has downward trend.
The grade forecast basic principle explanation that passes unimpeded above, specific implementation need in view of inflow and outflow flow proportional and
Current road segment grade is specifically assessed.
It is tested with the supreme sea G42 highways section in Beijing, is handed in the road both sides installation and deployment present invention below
Through-flow amount detection systems.If Fig. 5 is detected device deployment in road, monitoring reception ambient enviroment passes through the mobile device on vehicle
The WIFI signal broadcast data packet sent out.As shown in Figure 6, detector receive data after this system algorithm noise reduction process with
The flow actual value of coil acquisition is compared, almost the same.As can be seen from Figure 7, the flow output valve of forecasting system of the present invention with
Coil acquires the ratio of flow actual value, changes over time and keeps highly consistent, it is only necessary to make constant compensation；It was with 5 minutes
For the detector calculating speed of chronomere with Coil Detector speed deviation under the premise of not doing any compensation, basic holding is steady
It is fixed, by the way that the further of data is excavated and advanced optimized to experiment, speed deviations amount can be effectively controlled, further
Improve Accuracy of Velocity Calculation.As shown in figure 8, passing unimpeded level diagram for the road that detecting system in the present embodiment is predicted.
Claims (4)
- The grade forecast system 1. a kind of traffic based on WIFI signal detection is passed unimpeded disposes detection device in traffic route, described Detection device forms multiple sub-networks, and each sub-network includes a host and several extension sets, and the extension set is assisted based on WIFI View acquires the broadcast data that the mobile device based on WIFI signal agreement is sent in ambient enviroment by wireless passive perceptual model Packet, and screen the wherein data packet with mobile terminal device id information and retrieved, it is uploaded to host after stamping extension set label； The data being collected into are carried out unified storage and stamp time tag by the host, and are uploaded in data server and are stored；It is logical It crosses and macroscopical wagon flow flow, traffic cross-sectional flow, the average predicting travel time of polymerization is carried out to data, and then determine road traffic Pass unimpeded grade；Wherein described macroscopical wagon flow flow, traffic cross-sectional flow, the prediction of polymerization average hourage include following step Suddenly：Step 1：The data D of complete period is acquired by extension set,Wherein, DijIndicate j-th of subnet No. i-th extension set data, N indicate that the number of subnet, M indicate the extension set number in j-th of subnet；Step 2：Data are sliced and extract section S and complete period (the data D ' of T- Δs t) to be analyzeds,DisFor the collected data of extension set that number is i under s subnets；Step 3：Spatial match is carried out with corresponding practical section S to the sub-network of deployment, obtains sub-network host and corresponding road section Number information and the sub-network extension set deployment scenario list；Step 4：The data of each extension set acquisition are ranked up according to mobile terminal device ID number, establish data matrix { Tower (i,s),t}；Step 5：The ID data lists of foundation are classified by the number for appearing in different data matrix：For in period Δ In t, what mobile terminal ID only occurred in the single extension set of sub-network, after individually extracting progress to the corresponding data of mobile terminal ID Continuous efficiency analysis；For in period Δ t, mobile terminal ID occurs in sub-network two or more extension set, the movement The corresponding data of Termination ID are directly as valid dataStep 6：For in period Δ t, mobile terminal ID is only in the single extension set appearance of sub-network, to mobile terminal ID Corresponding data, which are individually extracted, carries out follow-up efficiency analysis：1, within the Δ t times, mobile terminal ID is in single extension set Repeating in data matrix TOWER, and case above occur in multiple mobile terminal ID, mark corresponding road section is congestion, And such ID data is denoted as valid data；2, within the Δ t times, do not find mobile terminal ID in single extension set data square Repeat in battle array TOWER, then traverses whether the sub-network before and after sub-network corresponding road section S identical ID number occurs, if Do not occur, then using the ID data as noise data processing, if occurred in other sub-networks, identifies and arranged into valid data Table；Step 7：Step 4~6 are repeated until data processing finishes；Step 8：For non-congestion situation, after being screened according to data above, the associated data square of mobile terminal ID number is re-established Battle array { Tower ' (i, s), t }；Step 9：It is according to the relationship between the time t and Tower in data matrix { Tower ' (i, s), t }, data are further It is divided into bi-directional data matrix DLAnd DR, and approximation computation is carried out to it；Step 10：Establish macroscopical wagon flow flow, traffic cross-sectional flow, the prediction neural network of polymerization average hourage：(vehicle in the covered section of jth work song network predicts hourage to t in the Δ t periods after T+ Δs t) the expression T moment；∑j For the total amount of the effective ID of mobile terminal of the direction of jth work song network, the extension set in host subnet network where m, n are indicated is compiled Number,At the time of indicating that ID-k occurs in sub-network Tower (j) on m-th of extension set, a (k) indicates weight,A (j) and B (j) indicates weight regulatory factor；T (j) indicates the mileage time average of j-th of Tower；F () is macroscopical wagon flow flow approximate fits function；G () is the approximate fits function of traffic cross-sectional flow；P () is polymerization The approximate fits function of average hourage；I is extension set sum under subnet；Step 11：The saturation volume in section is set as V0, which is v0, which is D, and sets traffic Pass unimpeded grade, is shown below：By the way that by V, (((T+ Δ t) are compared with above formula, are passed unimpeded to confirm that the section is current by T+ Δs t), t by T+ Δs t), v Grade.
- The grade forecast system 2. the traffic according to claim 1 based on WIFI signal detection is passed unimpeded, it is characterised in that：It will step (((T+ Δ t) are compared output result V in rapid with coil actual value, obtain the margin of error by T+ Δ t) and t by T+ Δs t), vIn formula, S is coil actual value；Then, setting self feed back iterative steps are C, and stopping criterion for iteration is arrangedAccording to the error amount of outputPass through the value for adjusting weight A (j) and B (j) so that V (T+ Δ t) approximate algorithm training sample S, while by output data weight Initial value of the value of A (j) and B (j) as allocation plan as data weighting in fitting function next time.
- The grade forecast system 3. the traffic according to claim 1 based on WIFI signal detection is passed unimpeded, it is characterised in that：Step The S of algorithm training sample described in 10 is the data that coil checker or radar detector obtain, and as effective reference list Member.
- The grade forecast system 4. the traffic according to claim 1 based on WIFI signal detection is passed unimpeded, it is characterised in that：It is described Distance is d between adjacent extension set, and the signal covering radius of single extension set is r, and d > 2r.
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| CN106920388A (en) * | 2015-12-24 | 2017-07-04 | 北京奇虎科技有限公司 | A kind of highway monitoring system and control method |
| CN106297277A (en) * | 2016-10-21 | 2017-01-04 | 合肥哦走信息技术有限公司 | A kind of based on intelligent terminal's positioning intelligent transportation system |
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