CN107564282B - A kind of traffic cross-sectional flow detection method based on WIFI signal - Google Patents
A kind of traffic cross-sectional flow detection method based on WIFI signal Download PDFInfo
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Abstract
The invention discloses a kind of traffic cross-sectional flow detection algorithm based on WIFI signal, including multiple sub-networks, each sub-network includes a host and several extension sets, the extension set passes through wireless passive perceptual model, acquisition is by mobile terminal device based on the Wifi agreement broadcast data packet that environment is sent around at random, and it screens the wherein data packet with mobile terminal device id information and is retrieved, host is uploaded to after stamping extension set label, the data being collected into are carried out unified storage and stamp time tag by host, and it is uploaded in data server and stores, and traffic cross-sectional flow is detected by data analysis.The present invention is realized to the data mining and analysis of mobile terminal acquisition data, 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
Technical field
The invention belongs to the improvement of development of Mobile Internet technology more particularly to macro-traffic information monitoring algorithm.
Background technique
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
It when device, needs to suspend traffic, destroy road surface, therefore more difficult, use cost height is gone along with sb. to guard him in installation.Non-contact detection technology master
It 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
Bracket installation is crossed, easy to maintain, long service life, major defect is the influence vulnerable to 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, provides accurate data for the scheduling and integrated planning of road network.
But highway network there is a situation where some 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.
Summary of the invention
In view of the problems of the existing technology, the traffic cross-sectional flow detection based on WIFI signal that the present invention provides a kind of
Algorithm.
The present invention is based on the macroscopic views of the unique ID of portable mobile terminal, acquisition data time stamp and detection device location information to hand over
Communication breath detection algorithm, realizes to the data mining and analysis of mobile terminal acquisition data, can be applied to traffic cross-sectional flow
Detection.
In order to solve the above technical problems, the invention adopts the following technical scheme:
A kind of traffic cross-sectional flow detection algorithm based on WIFI signal disposes detection device system, institute along traffic route
Stating detection device system includes multiple sub-networks, and each sub-network includes a host and several extension sets, and the extension set is (single
Detection device) by wireless passive perceptual model, by mobile terminal device, based on Wifi agreement, environment is sent out around at random for acquisition
The broadcast data packet sent, and screen the wherein data packet with mobile terminal device id information and retrieved, stamp extension set mark
Host is uploaded to after label, the data being collected into are carried out unified storage and stamp time tag by host, and are uploaded to data service
It stores in device, and traffic cross-sectional flow is detected by data analysis.
Further, the data analysis the following steps are included:
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 indicates the number of subnet, and M indicates the extension set number in j-th of subnet；
Step 2: data being sliced and extract section S and complete period (the data D of T- Δ t) to be analyzeds',D in formulaisFor the collected data of extension set that number is i under s subnet；
Step 3: spatial match is carried out with corresponding practical section S to the sub-network of deployment, 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 being ranked up according to mobile terminal device ID number, establish data matrix
{Tower(i,s),t}；
Step 5: the ID data list of foundation being classified by the number for appearing in different data matrix: in the time
In section Δ t, what mobile terminal ID only occurred in the single extension set of sub-network, the corresponding data of mobile terminal ID are individually extracted
Carry out subsequent efficiency analysis；For 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: 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 subsequent efficiency analysis: 1, within the Δ t time, mobile terminal ID is individually dividing
Repeat in the data matrix TOWER of machine, and above situation occur in multiple mobile terminal ID, mark corresponding road section is congestion
Situation, and such ID data is denoted as valid data；2, within the Δ t time, do not find mobile terminal ID in single extension set
Repeat in data matrix TOWER, then traverses whether the sub-network before and after sub-network corresponding road section S identical ID occurs
Number, if do not occurred, using the ID data as noise data processing, if occurred in other sub-networks, identify into having
Imitate data list；
Step 7: repeating step 4~6 until data processing finishes；
Step 8: merger will be carried out by step 5 and the 6 resulting valid data of processing, and according to data matrix TOWER institute
The chronological order of position and appearance in corresponding physical space, and ID data matrix divided into it is two-way, and to each list
Subsequent processing is carried out to data；
Step 9: in T- Δ tT- Δ t to T time section, unidirectional valid data total amount is V, V=D { ID }, and by following
Method carries out the detection of practical macroscopical section flow:
(1) firstly, introducing the weight Δ (x) of the speed of each ID for participating in calculating, and extension set number is obtained by following formula
Mean flow rate v ' (n-m) between m and n, such as following formula: (because the rate results in each section are had by what the section detected
ID is imitated to obtain, and there are real offsets by the actual physical location L of each ID, are acquired by extension set and stamped the ID of timestamp information
Temporal information correspond to the actual physical location of the ID and use two o'clock when the use of physical location where extension set being calculating benchmark
Between range difference Ln-Lm(m < n) calculates resulting speed and obtains deviation, need to be modified it as fare register)
In formula, v ' (n-m) is that the mean flow rate that m is calculated to n extension set (is compiled to pass through number in j-th of subnet
Mean flow rate between number m and n extension set), LnFor the physical location for the extension set that number is n in j-th of subnet；For k-th of ID number
By the extension set moment collected that number is n in the subnet；Δ (k) institute in the valid data for calculating data for k-th of ID number
The weighted value accounted for, and
(2) due to due to detectors such as reference systems such as coil, video, microwave, being only capable of in practical automatic network learning process
Enough characterizations pass through vehicle flowrate, the flow velocity etc. of section, belong to 2-D data form.And this example algorithm, based on detection device adopt
With three-dimensional distribution, the flow velocity in sub-network between any two extension set can be measured in its coverage area.But in view of existing
Referential, which is unable to satisfy, calibrates the flow velocity that section is arbitrarily covered in the sub-network, simultaneously because the principle of the system itself limits
(can not be accurately positioned signal physical location, can only be with Range Representation) there is a certain error for flow relocity calculation, therefore quasi- by appointing
Meaning covering LmTo LnThe L in sectionm'To Ln'Section carries out summation amendment, introduces ω (n ', m ') with this and is used as any Lm'To Ln'Road
The weight coefficient matrix of section,
In formula, j is subnet number, and i is the sum of extension set in the subnet, and n, m, n ', m ' is extension set volume in jth work song net
Number, and n > m；ω (n ', m ') indicates to be that (n ', m ') extension set number group calculates L by numbern'To L 'mGroup knot when average speed
Weight coefficient matrix shared by fruit；
(3) finally, being calculated by the following formula to obtain L under the subnetmTo LnMean flow rate v (the L in sectionn-Lm),
Further, the allocation plan of weight Δ (x) described in step 9 and ω (n ', m ') are learnt by self feed back, are passed through
Successive ignition approaches approximate fits and obtains, the specific steps are as follows:
Firstly, the initial value of Δ (x) He ω (n ', m ') is set separately, the initial value Vo of output cross-sectional flow is calculated；
Then, by by the initial value v of cross-sectional flow0Self feed back study is carried out with bias reference system S, passes through successive ignition
It calculates amendment weighting function value and carries out approximate fits, obtain approximate fits function F (), the judgment basis of self feed back study is S/
V sets the termination range of S/v, when the value of S/v, which meets, terminates range, iterative fitting completes and export Δ (x) and ω (n ',
M ') allocation plan.
Further, the S of bias reference system described in step 9 is the data that coil checker or radar detector obtain, and
As algorithm training sample or effective reference unit.
Further, the value range of the judgment basis S/v of the self feed back study is 0.99≤S/v≤1.01.
Further, distance is d between the adjacent extension set, and the signal covering radius of single extension set is r, and d > 2r.
The utility model has the advantages that the present invention provides one kind based on the unique ID of portable mobile terminal, acquisition data time stamp and detection
The macro-traffic infomation detection algorithm of device location information is realized to the data mining and analysis of mobile terminal acquisition data, is
Magnitude of traffic flow detector and detection 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.
Detailed description of the invention
Fig. 1 is that the road deployment principle of the traffic cross-sectional flow detection algorithm of the present invention based on WIFI signal is illustrated
Figure.
Fig. 2 is the flow diagram of the traffic cross-sectional flow detection algorithm of the present invention based on WIFI signal.
Fig. 3 is the noise reduction screening of valid data in the traffic cross-sectional flow detection algorithm of the present invention based on WIFI signal
Flow diagram；
Fig. 4 is that data approximate fits approach very in the traffic cross-sectional flow detection algorithm of the present invention based on WIFI signal
The process schematic of real value；
Fig. 5 is the deployment schematic diagram of Beijing to Shanghai section detection system in the embodiment of the present invention；
Fig. 6 is that lower No. 31 experimental groups of unit 5-minute data amount measure cross-sectional flow and coil checker measures pair of flow velocity
Than figure；
The comparison diagram of ratio between the flow velocity that Fig. 7 is No. 21 experimental groups, No. 31 experimental groups, coil checkers measure respectively.
Specific embodiment
With reference to the accompanying drawing and with specific embodiment, the present invention is furture elucidated.It should be understood that these embodiments are only used for
It is bright the present invention rather than limit the scope of the invention, after the present invention has been read, 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, the present invention includes multiple sub-networks, each subnet and practical section S are spatial match mapping relations,
The detection device that each subnet section S is disposed includes several extension sets and at least one host；Extension set is as basic sensor
And based on 802.1b agreement (WIFI agreement) for the mobile device hair in monitoring reception ambient enviroment based on WIFI signal agreement
The broadcast data packet sent, and the broadcast data packet is sent to data processing module, the data processing module is to broadcast data
Packet carries out deleting choosing and reading, obtains the ID number of mobile device, and the data numbered including ID number and extension set module are transmitted to master
Machine；Host is then stamped stored after timestamp after be uploaded to remote server.The present invention is to be received based on extension set sensor
To the data packet comprising ID number carry out detection calculating, obtain the traffic cross-sectional flow of corresponding road section.Specifically:
Detection device deployment way of the present invention: in Fig. 1, Tower (j) indicates the sub- host of jth in disposed road network
Machine, Tower (i, j) indicate i-th of extension set in j-th of subnet.Each sub-network includes a host and several extension sets, is divided
Machine quantity can suitably increase and decrease according to road network condition, sub-network maximum coverage range 2Km, and single extension set signal cover is maximum
Radius 250m, user can adjust single extension set estimation range according to actual road conditions condition, and distance d setting only needs to be greater than between extension set
Twice of extension set estimation range equally can carry out flexible modulation according to actual road conditions condition.Sub-network deployment density can be according to reality
Border traffic environment regulatory requirement is disposed.
Detection device data acquisition flow: the standby i.e. extension set of single detection passes through wireless passive perceptual model, i.e., using TI's
The detector of CC3XXXX family chip, by based on the acquisition of wifi agreement, by mobile terminal device, environment is sent around at random
WIFI broadcast data packet, and screen the wherein data packet with equipment id information and retrieved.It is uploaded after stamping extension set label
To host, the data being collected into are carried out unified storage and stamp time tag by host, and are uploaded in data server and are carried out
Storage, is waited to be analyzed.Inventive algorithm principle process is specifically described:
Step 1: (partial data of T- Δ t), partial data are expressed as acquisition complete period
Wherein DijIndicate No. i-th extension set data of j-th of subnet；
Step 2: data being sliced, and extraction and analysis section S and the period (partial data of T- Δ t)Step 3: physical spatial location mapping, corresponding disposed sub-network, by its with practical section S into
Row spatial match, each sub-network host have the corresponding number information of corresponding road section and extension set deployment scenario list；
Step 4: acquisition data being ranked up according to its ID number, and establish data matrix { Tower (i, s), t }；
Step 5: the ID data list of foundation is classified by the number appeared in different Tower, it will be only one
The ID occurred in a Tower, which is individually extracted, to be analyzed, and the ID data occurred in more than two Tower are classified as one kind；
Step 6: the ID only occurred in a Tower being 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 weight in the Tower in the Δ t period
It appears again existing, which is rejected, as shown in Figure 3.
Rapid 7: repetition step 4,5,6 confirm exhaustive data；
Step 8: under incomplete jam situation, after deleting choosing according to above data, re-establishing the associated data of ID number
Matrix { Tower (i, s), t }；
Step 9: according to the relationship of time t and Tower in data matrix { Tower (i, s), t }, data further being classified
For bi-directional data matrix: DLAnd DR, and it is respectively calculated；
Step 10: because the rate results in each section are obtained by effective ID that the section detects, and each ID
There are real offsets by actual physical location L, and the temporal information for being acquired by extension set and being stamped the ID of timestamp information, which corresponds to, is somebody's turn to do
The actual physical location of ID uses the range difference L of point-to-point transmission when the use of physical location where extension set being calculating benchmarkn-Lm(m<n)
As fare register, calculates resulting speed and obtain deviation, need to be modified it.As introduced weighting function Δ in Fig. 4
(x).In addition, due to detectors such as reference systems such as coil, video, microwave, only can due in practical automatic network learning process
Characterization passes through vehicle flowrate, the flow velocity etc. of section, belongs to 2-D data form.And this example algorithm, based on detection device use
Three-dimensional distribution can measure the flow velocity in sub-network between any two extension set in its coverage area.But consider existing ginseng
Examining is to be unable to satisfy to calibrate the flow velocity for arbitrarily covering section in the sub-network, simultaneously because the principle of the system itself limits
(can not be accurately positioned signal physical location, can only be with Range Representation) there is a certain error for flow relocity calculation.Therefore it needs to pass through
To any covering LmTo LnThe L in sectionm'To Ln'Section carries out summation amendment, introduces ω (n ', m ') with this and is used as any Lm'To Ln'
The weight coefficient matrix in section.The mean flow rate in subnet section is calculated eventually by following formula,
In formula, v ' (n-m) is that the mean flow rate that m is calculated to n extension set (is compiled to pass through number in j-th of subnet
Mean flow rate between number m and n extension set), LnFor the physical location for the extension set that number is n in j-th of subnet；tnFor k-th of ID number
By the extension set moment collected that number is n in the subnet；Δ (k) institute in the valid data for calculating data for k-th of ID number
The weighted value accounted for；I is the sum of extension set in the subnet, n, m, n ', m ' be in jth work song net extension set number, and n > m；ω
(n ', m ') indicates to be that (n ', m ') extension set number group calculates L by numbern′To Lm′Power shared by this group of result when average speed
Weight coefficient matrix；ω (n ', m ') is any Lm'To Ln'The weight coefficient matrix in section.
Due to the update with data, over time can according to the road-section average flow velocity that Δ (x) and ω (n ', m ') are obtained
Offset error is gradually generated, needs to carry out it at this time to reset value allocation plan.Here, can be according to neural network certainly
Feedback learning carries out successive ignition approximate fits to weight coefficient matrix Δ (x) and ω (n ', m '), to obtain current slot
The value of interior weight coefficient Δ (x) and ω (n ', m '), as shown in Figure 4:
Flow relocity calculation neural network is established, according to ID data matrix, generates the flow velocity matrix V x of each ID.As drawn in Fig. 4
Enter weighting function Δ (x), weighting function and be 1, Matrix Calculating and rear output cross-sectional flow
By setting the initial value of weighting function Δ (x), and by bias reference system S (such as coil checker, radar detector etc.)
Self feed back study, amendment relevant parameter carry out approximate fits, and by approximate fits function F (), are fitted, feedback judgement
Condition is S/v, and when 0.99≤S/v≤1.01, indicates to complete data fitting operations, determines the value allocation plan of Δ (x).
Similarly, self feed back is carried out to ω (n ', m ') and learns to obtain immediate value under current slot.
As shown in figure 5, being tested below with the supreme sea G42 highway section in Beijing, in road both sides mounting portion
Affix one's name to traffic flow detection system of the present invention.
As shown in fig. 6, being to be detected and passed through this for 31 experimental groups with the every 5 minutes data for chronomere's acquisition
The result that the true flow velocity that section flow velocity and Coil Detector that invention algorithm is calculated obtain compares.As shown in fig. 7,
The comparison diagram that the carry out ratio Analysis of flow velocity and coil flow velocity that No. 21 experimental groups and No. 31 experimental groups measure is obtained.From figure
It is found that in terms of average speed, with 5 minutes detector calculating speeds for chronomere and Coil Detector speed deviation not
Under the premise of doing any compensation, kept stable.By the further excavation to data and experiment is advanced optimized, it can
Effectively to control speed deviations amount, Accuracy of Velocity Calculation is further increased.
Claims (4)
1. a kind of traffic cross-sectional flow detection method based on WIFI signal disposes detection device system along traffic route, described
Detection device system includes multiple sub-networks, and each sub-network includes a host and several extension sets, and the extension set passes through nothing
The passive perceptual model of line, acquisition is by mobile terminal device based on the Wifi agreement broadcast data that environment is sent around at random
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 host, and are uploaded in data server and are stored, and pass through
Data analysis detects traffic cross-sectional flow；
The analyses of the data the following steps are included:
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 indicates the number of subnet, and M indicates the extension set number in j-th of subnet；
Step 2: data being sliced and extract section S and complete period (the data D ' of T- Δ t) to be analyzeds,D in formulaisFor the collected data of extension set that number is i under s subnet；
Step 3: spatial match being 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 being ranked up according to mobile terminal device ID number, establish data matrix { Tower
(i,s),t}；
Step 5: the ID data list of foundation being classified by the number for appearing in different data matrix: 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 data；
Step 6: 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 subsequent efficiency analysis: 1, within the Δ t time, mobile terminal ID is in single extension set
Repeating in data matrix TOWER, and above situation 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 time, 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: repeating step 4~6 until data processing finishes；
Step 8: merger will be carried out by step 5 and the 6 resulting valid data of processing, and according to corresponding to data matrix TOWER
The chronological order of position and appearance in physical space, and ID data matrix divided into it is two-way, and to each unidirectional number
According to progress subsequent processing；
Step 9: in T- Δ t to T time section period in, unidirectional valid data total amount is V, V=D { ID }, and is passed through
Following methods carry out the detection of practical macroscopical section flow:
(1) firstly, introducing the weight Δ (x) of the speed of each ID for participating in calculating, and by following formula obtain extension set number be m with
Mean flow rate v ' (n-m) between n, such as following formula:
In formula, v ' (n-m) is the mean flow rate that m is calculated to n extension set, L to pass through number in j-th of subnetnIt is j-th
The physical location for the extension set that number is n in subnet；When collected by the extension set that number is n in the subnet for k-th of ID number
It carves；Δ (k) is k-th of ID number weighted value shared in the valid data for calculating data, and
(2) by any covering LmTo LnThe L in sectionm'To Ln'Section carries out summation amendment, introduces ω (n ', m ') conduct with this
Any Lm'To Ln'The weight coefficient matrix in section, and:
In formula, j is subnet number, and i is the sum of extension set in the subnet, and n, m, n ', m ' is extension set number in jth work song net,
And n > m；ω (n ', m ') indicates to be that (n ', m ') extension set number group calculates L by numbern'To Lm′This group of result institute when average speed
The weight coefficient matrix accounted for；
(3) finally, being calculated by the following formula to obtain L under the subnetmTo LnMean flow rate v (the L in sectionn-Lm),
2. the traffic cross-sectional flow detection method based on WIFI signal according to claim 1, it is characterised in that: in step 9
The allocation plan of the weight Δ (x) and ω (n ', m ') are learnt by self feed back, are approached approximate fits by successive ignition and are obtained
It arrives, the specific steps are as follows:
Firstly, the initial value of Δ (x) He ω (n ', m ') is set separately, the initial value v of output cross-sectional flow is calculated0；
Then, by by the initial value v of cross-sectional flow0Self feed back study is carried out with bias reference system S, is calculated by successive ignition
It corrects weighting function value and carries out approximate fits, obtain approximate fits function F (),
It then, is the foundation for judging feedback learning and terminating with the ratio S/v of the output valve S of bias reference system and cross-sectional flow v, and
The range of S/v is set in 0.99≤S/v≤1.01；
Finally, iterative fitting is completed and exports the distribution side of Δ (x) He ω (n ', m ') when the value of S/v meets and terminates range
Case.
3. the traffic cross-sectional flow detection method based on WIFI signal according to claim 2, it is characterised in that: in step 9
The bias reference system S is the data that coil checker or radar detector obtain, and as method training sample or effective base
Quasi- reference unit.
4. the traffic cross-sectional flow detection method based on WIFI signal according to claim 2, it is characterised in that: adjacent extension set
Between distance be d, the signal covering radius of single extension set is r, and d > 2r.
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