CN107564283A - A kind of macroscopical wagon flow flow detection algorithm based on WIFI signal - Google Patents
A kind of macroscopical wagon flow flow detection algorithm based on WIFI signal Download PDFInfo
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Abstract
The invention discloses a kind of macroscopical wagon flow flow detection algorithm based on WIFI signal, detection device system is disposed along traffic route, the detection device system includes multiple sub-networks, each sub-network includes a main frame and several extension sets, the extension set (single detection device) passes through wireless passive perceptual model, gather the broadcast data bag sent at random to surrounding environment based on Wifi agreements from mobile terminal device, and screen the wherein packet with mobile terminal device id information and retrieved, main frame is uploaded to after stamping extension set label, the data being collected into are carried out unified storage and stamp time tag by main frame, and it is uploaded in data server and stores, and assessment detection is carried out to macroscopical wagon flow flow by data analysis.
Description
Technical field
The invention belongs to development of Mobile Internet technology, more particularly to the improvement of 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.Existing Traffic flow detecting technology has a variety of, 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
, it is necessary to suspend traffic, destroy road surface during device, 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 infrared etc. three kinds.Non-contact detection device can lead to
Support installation is crossed, easy to maintenance, service life length, its major defect is easily to be influenceed by weather and outdoor conditions, environment be present and fits
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 detection application demand 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, there is provided intuitively road network vehicle load amount, 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 inconvenience, information transfer difficulty, with
And fail to lay all kinds of detectors etc. in advance in process of construction, concentrated type monitoring can not be accomplished and managed, it is necessary to existing detection
Device is further designed and improved.
The content of the invention
The problem of existing for prior art, the invention provides a kind of macroscopical wagon flow flow detection based on WIFI signal
Algorithm, the macro-traffic of the invention based on the unique ID of portable mobile terminal, gathered data timestamp and detection device positional information
Infomation detection algorithm, have power supply convenient, facilitate dense deployment, monitoring and management, algorithm optimization data are kept with True Data
It is highly consistent, accurate Traffic Information data can be provided.
In order to solve the above technical problems, present invention employs following technical scheme：A kind of macroscopical car based on WIFI signal
Flow detection algorithm is flowed, disposes detection device system along traffic route, the detection device system includes multiple sub-networks, each
Sub-network includes a main frame and several extension sets, and the extension set (single detection device) is adopted by wireless passive perceptual model
Collect the broadcast data bag sent at random to surrounding environment based on Wifi agreements from mobile terminal device, and screen wherein with shifting
The packet of dynamic terminal device id information is retrieved, and main frame, the data that main frame will be collected into are uploaded to after stamping extension set label
Carry out unified storage and stamp time tag, and be uploaded in data server and store, and by data analysis to macroscopical wagon flow
Flow carries out assessment detection.
Further, the analysis of the data comprises the following steps：
Step 1：The data D of complete period is gathered by extension set,Wherein, DijRepresent j-th
No. i-th extension set data of subnet；N represents the number of subnet, and M represents the extension set number in j-th of subnet；
Step 2：Data are cut into slices and extract section S and complete period (T- Δs t) data D' to be analyzeds,D in formulaisThe data collected by the extension set that numbering is i under s subnets；
Step 3：Spatial match is carried out to the sub-network of deployment and corresponding actual section S, obtain sub-network main frame 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 collection 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 pieces of information matrix：For in the time
In section Δ t, what mobile terminal ID only occurred in the single extension set of sub-network, data corresponding to mobile terminal ID are individually extracted into
The follow-up efficiency analysis of row；For in period Δ t, mobile terminal ID occurs in sub-network two and above extension set, should
Data are directly as valid data corresponding to mobile terminal ID；
Step 6：For in period Δ t, mobile terminal ID is whole to the movement only in the single extension set appearance of sub-network
Data corresponding to the ID of end, which are individually extracted, carries out follow-up efficiency analysis：1st, when within the Δ t times, mobile terminal ID is at single point
Repeat in the data matrix TOWER of machine, and there is case above in multiple mobile terminal ID, mark corresponding road section is congestion shape
Condition, and such ID data is designated as valid data；2nd, when within the Δ t times, not finding mobile terminal ID in single extension set number
According to repeating in matrix TOWER, then travel through whether the sub-network before and after the 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：Repeat step 4~6 finishes until data processing；
Step 8：Valid data by step 5 and 6 processing gained are subjected to merger, and according to data matrix TOWER institutes
The time order and function order of position and appearance in corresponding physical space, and ID data matrixes are divided into it is two-way, and to each list
Subsequent treatment is carried out to data；
Step 9：Within moment T- Δ t to moment T period, unidirectional valid data total amount is V, V=D { ID }, and is led to
Cross the detection that following methods carry out actual macroscopical section flow：
In formula, F () is the fitting function of effective ID data, and x is extension set sum in subnet, and i is extension set numbering in subnet；
Feedback Rule of judgment is setS is Algorithm for Training sample, and it is C to set feedback iteration number, sets iteration
End condition isWhenData fitting is completed when eligible, exports section data on flows.
Further, the S of Algorithm for Training sample described in step 9 is the data that coil checker or radar detector obtain,
And it is used as effective reference unit.
Beneficial effect：The invention provides one kind to be 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 the data mining and analysis to mobile terminal gathered data, is
Magnitude of traffic flow detector and detecting system based on WIFI signal provide depth excavation and the magnitude of traffic flow based on the type data
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.
Brief description of the drawings
Fig. 1 is the deployment principle schematic of the traffic flow detection system of the present invention based on WIFI signal；
Fig. 2 is the logic flow schematic diagram of macroscopical wagon flow flow detection algorithm of the present invention；
Fig. 3 is the sorting technique schematic diagram of valid data in macroscopical wagon flow flow detection algorithm of the present invention；
Fig. 4 is the optimization process schematic diagram of original valid data in macroscopical wagon flow flow detection algorithm of the present invention；
The road deployment schematic diagram of vehicle flow detecting system in Fig. 5 embodiment of the present invention；
Fig. 6 is the contrast curve of detector initial data of the present invention, optimization data and training sample data；
Fig. 7 is the enlarged drawing of ellipse encircled portion in Fig. 6；
Ratio compares figures of the Fig. 8 between No. 21 experimental groups, No. 31 experimental groups and training sample data.
Embodiment
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
The bright present invention rather than limitation the scope of the present invention, after the present invention has been read, those skilled in the art are to the present invention's
The modification of the various equivalent form of values falls within the application appended claims limited range.
As shown in figure 1, dispose schematic diagram for the traffic flow detection system based on WIFI signal and detection sub-network network.This hair
It is bright to provide a kind of transport information detection algorithm of the data collected by the system.
As shown in Fig. 2 the traffic flow detection system operation principle of the invention based on WIFI signal is as follows：
Detection device system deployment mode：In figure, Tower (j) represents j-th of sub-network host computer in disposed road network,
Tower (i, j) represents i-th of extension set in j-th of subnet.Each sub-network includes a main frame 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 detection range maximum radius
250m, user can adjust single extension set detection range according to actual road conditions condition, between extension set distance d settings only need to be more than twice
Extension set detection range, equally can according to actual road conditions condition carry out flexible modulation.Sub-network deployment density can be according to actual friendship
Logical environmental management demand is disposed.
Detection device data acquisition flow：Single detection device is extension set by wireless passive perceptual model, is gathered by moving
The broadcast data bag that dynamic terminal device is sent to surrounding environment at random, and screen the wherein packet with equipment id information and enter
Row retrieval.Main frame is uploaded to after stamping extension set label, the data being collected into are carried out unified storage and stamp time tag by main frame,
And be uploaded in data server and stored, wait to be analyzed.
As in Figure 2-4, section flow detection algorithm basic procedure of the present invention is as follows：
Step 1：Gathering complete period, (T- Δs t) partial data, partial data are expressed as
Wherein DijRepresent No. i-th extension set data of j-th of subnet；
Step 2：Data are cut into slices, and extraction and analysis section S and period (T- Δs t) partial data
Step 3：Physical spatial location maps, and corresponding disposed sub-network, itself and actual section S is carried out into space
Match somebody with somebody, each sub-network main frame 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：The ID data lists of foundation are classified by the number appeared in different Tower, will be only one
The ID occurred in individual Tower, which is individually extracted, to be analyzed, and the ID data occurred in two or more Tower are classified as into one kind；
Step 6：Analyze, situation is divided into following for only occurring in the ID in a Tower in period Δ t
Two kinds：1st, within the Δ t periods, the ID is repeated in the Tower, and this kind of situation largely occurs, and has a great deal of ID
This occurs, then it represents that congestion occurs in the section, and the ID is valid data；If the 2nd, in the Δ t periods, this is not found
ID repeats in the Tower, then travels through subnet before and after the S of the section, search whether identical ID number occur, if do not gone out
Existing, this data is handled as noise data, if occurring in other subnets, stamps mark, and be included into valid data
List；
Step 7：Repeat step 4,5,6, until data processing finishes；
Step 8：The valid data of processing gained in step 5 and 6 are subjected to merger, and according to Tower in physical space
Position and appearance time t sequencing, be two-way (track has directionality) by ID data separations, and to each list
Handled to data；
Step 9：In T- Δs t to T time section, unilateral direction valid data total amount is V=D { ID }, using following methods
Actual macroscopical section flow is assessed, as shown in Figure 4：
By that can be obtained in figure, ID_X represents effective ID data, and F () represents approximate fits function, and bias reference system S is indicated
Imitate reference unit, such as coil checker data, radar detector data.Cumulative ID data, and pass through approximate fits letter
Number F (), is fitted, and feedback Rule of judgment isIt can need to enter the ratio domain value range according to accuracy of detection
Row regulation, when S/V income values are in domain value range, such asRepresent to complete data fitting operations, formed effective
Section data on flows.
As shown in figure 5, to be tested using this method G42 sections, two groups of experimental groups are deployed with figure is respectively
No. 21 and No. 31, and it is distributed in two-way road both sides.For 31 experimental groups with every 5 minutes for chronomere, to its in Shanghai extremely
Beijing direction and Beijing to Shanghai angle detecting quantity, the 1 kilometer of average rate and detection irrelevance progress with coil checker
Compare control.
As shown in Figures 6 and 7, the initial data of detecting system collection of the present invention based on WIFI signal is excellent by above-mentioned algorithm
Change the data on flows after output and keep highly consistent with training sample data；As it can be observed in the picture that No. 21 experimental groups and No. 31 experimental groups
Ratio keep highly consistent, ratio range is essentially 1, and ratio curve fluctuation is smaller, illustrates that detector of the present invention (mainly divides
Machine) deployed position on road influences little, and both sides can be deployed in, can also be deployed among the isolation flower bed of centre,
With dispose it is flexible the characteristics of；No. 21 experimental groups and No. 31 the experimental groups ratio with training sample data respectively, it is also basic to keep
It is consistent.
Claims (3)
1. a kind of macroscopical wagon flow flow detection algorithm based on WIFI signal, detection device system is disposed along traffic route, it is described
Detection device system includes multiple sub-networks, and each sub-network includes a main frame and several extension sets, extension set (the single inspection
Measurement equipment) by wireless passive perceptual model, gather from mobile terminal device and be based on Wifi agreements at random to surrounding environment transmission
Broadcast data bag, and screen and wherein retrieved with the packet of mobile terminal device id information, stamp extension set label
After be uploaded to main frame, the data being collected into are carried out unified storage and stamp time tag by main frame, and are uploaded to data server
Middle storage, and assessment detection is carried out to macroscopical wagon flow flow by data analysis.
2. macroscopical wagon flow flow detection algorithm based on WIFI signal according to claim 1, it is characterised in that：The data
Analysis comprise the following steps：
Step 1：The data D of complete period is gathered by extension set,Wherein, DijRepresent j-th of subnet
No. i-th extension set data；N represents the number of subnet, and M represents the extension set number in j-th of subnet；
Step 2：Data are cut into slices and extract section S and complete period (T- Δs t) data D ' to be analyzeds,D in formulaisThe data collected by the extension set that numbering is i under s subnets；
Step 3：Spatial match is carried out with corresponding actual section S to the sub-network of deployment, obtains sub-network main frame and corresponding road section
Number information and the sub-network extension set deployment scenario list；
Step 4：The data of each extension set collection 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 pieces of information 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 data corresponding to mobile terminal ID
Continuous efficiency analysis；For in period Δ t, mobile terminal ID occurs in sub-network two and above extension set, the movement
Data are directly as valid data corresponding to Termination ID；
Step 6：For in period Δ t, what mobile terminal ID only occurred in the single extension set of sub-network, to mobile terminal ID
Corresponding data, which are individually extracted, carries out follow-up efficiency analysis：1st, when within the Δ t times, mobile terminal ID is in single extension set
Repeat in data matrix TOWER, and there is case above in multiple mobile terminal ID, mark corresponding road section is congestion,
And such ID data is designated as valid data；2nd, when within the Δ t times, not finding mobile terminal ID in single extension set data square
Repeat in battle array TOWER, then travel through whether the sub-network before and after the 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, identify and arranged into valid data
Table；
Step 7：Repeat step 4~6 finishes until data processing；
Step 8：Valid data by step 5 and 6 processing gained are subjected to merger, and according to corresponding to data matrix TOWER
The time order and function order of position and appearance in physical space, and ID data matrixes are divided into it is two-way, and to each unidirectional number
According to progress subsequent treatment；
Step 9：Within T period, unidirectional valid data total amount is V, V=D { ID }, and is carried out by the following method actual grand
See the detection of section flow：
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In formula, F () is the fitting function of effective ID data, and x is extension set sum in subnet, and i is extension set numbering in subnet；
Feedback Rule of judgment is setS is Algorithm for Training sample, and it is C to set feedback iteration number, sets iteration ends
Condition isWhenData fitting is completed when eligible, exports section data on flows.
3. macroscopical wagon flow flow detection algorithm based on WIFI signal according to claim 1, it is characterised in that：In step 9
The Algorithm for Training sample S is the data that coil checker or radar detector obtain, and is used as effective reference unit.
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