CN107689153B - A kind of traffic cross-sectional flow prediction algorithm based on WIFI signal - Google Patents
A kind of traffic cross-sectional flow prediction algorithm based on WIFI signal Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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Abstract
The invention discloses a kind of traffic cross-sectional flow prediction algorithm based on WIFI signal, prediction device systems are deployed with along traffic route, the prediction device systems include multiple sub-networks, each sub-network includes a host and several extension sets, the extension set is based on WIFI agreement and passes through wireless passive perceptual model, acquire the broadcast data packet that the mobile device in ambient enviroment based on WIFI signal agreement is sent, 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 by being analyzed data to obtain road section flow velocity assessment prediction result.The present invention realizes the data mining and analysis to mobile terminal acquisition data, can be applied to the detection of traffic cross-sectional flow.
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 prediction based on WIFI signal that the present invention provides a kind of
Algorithm, the present invention is based on the unique ID of portable mobile terminal, the macro-traffic of acquisition data time stamp and detection device location information
Infomation detection algorithm is realized to the data mining and analysis of mobile terminal acquisition data, can be applied to the pre- of traffic cross-sectional flow
It surveys.
In order to solve the above technical problems, the invention adopts the following technical scheme：
A kind of traffic cross-sectional flow prediction algorithm based on WIFI signal, is deployed with prediction device systems along traffic route,
The prediction device systems include multiple sub-networks, and each sub-network includes a host and several extension sets, the extension set base
Pass through wireless passive perceptual model in WIFI agreement, acquires what the mobile device in ambient enviroment based on WIFI signal agreement was sent
Broadcast data packet, and screen the wherein data packet with mobile terminal device id information and retrieved, it stamps on after extension set label
Host is reached, the data being collected into are carried out unified storage and stamp time tag by host, and are uploaded in data server and are deposited
Storage, and by being analyzed data to obtain road section flow velocity assessment prediction result.
Further, distance is d between the adjacent extension set, and the signal covering radius of single extension set is r, and d > 2r.
Further, the road section flow velocity assessment prediction includes the following steps：
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 the number of subnet, and M indicates 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- Δ t) to be analyzeds',DisFor 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 are ranked up according to mobile terminal device ID number, establish data matrix
{Tower(i,s),t}；
Step 5：The ID data list of foundation is 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 subsequent 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 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 shape
Condition, 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 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 screening according to above data, 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 flow velocity prediction neural network：
(T+ Δ t) indicates the traffic flow speed in the covered section of jth work song network in the Δ t period 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 network, the extension set number where m, n are indicated in host subnet network,At the time of indicating that ID-k occurs on m-th of extension set in sub-network Tower (j), 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 approximate fits function；I is the sum of extension set in the subnet；
Step-up error amountSetting self feed back iterative steps are C, and stopping criterion for iteration is arrangedAccording to the error amount of outputBy adjusting the value of weight A (j) and B (j), so that (T+ Δ 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.
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.
Beneficial effect：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 of the traffic throughput monitor system of the present invention based on WIFI signal disposes schematic diagram.
Fig. 2 is the flow diagram of the traffic cross-sectional flow prediction 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 prediction 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 prediction algorithm of the present invention based on WIFI signal
The process schematic of real value；
Fig. 5 is the data acquired the present invention is based on the road detection system of WIF signal and the comparison diagram of coil truthful data.
Fig. 6 is the enlarged drawing of encircled portion in Fig. 5.
Fig. 7 is the comparison diagram that inventive algorithm exports result cross-sectional flow
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, 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 speed
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 value is 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：In Fig. 1, Tower (j) indicates j-th of sub-network host computer in disposed road network,
Tower (i, j) indicates 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 actual road conditions condition, and distance d setting only needs to be greater than twice between extension set
Extension set estimation range, equally can according to actual 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 process：Single pre- measurement equipment, that is, extension set passes through wireless passive perceptual model, that is, uses TI
CC3XXXX family chip detector, by based on wifi agreement acquisition by mobile terminal device at random around environment send out
WIFI broadcast data packet is 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.Inventive algorithm principle process is specifically described：
Firstly, the data D of complete period is acquired by extension set,Wherein, DijTable
Show No. i-th extension set data of j-th of subnet, N indicates the number of subnet, and M indicates 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- Δ t) to be analyzeds',
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 are ranked up according to mobile terminal device ID number, establish data matrix
{Tower(i,s),t}；
Step 5：The ID data list of foundation is 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 subsequent 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 subsequent efficiency analysis：1, within the Δ t time, mobile terminal ID is individually dividing
Repeat in the data matrix TOWER of machine, mark corresponding road section is congestion, and the ID data are denoted as valid data；
2, within the Δ t time, do not find that mobile terminal ID repeats in single extension set data matrix TOWER, then traversal should
Whether the sub-network before and after sub-network corresponding road section S there is identical ID number, if do not occurred, using the ID data as noise
Data processing identifies if occurred in other sub-networks into valid data list；
Step 7：Step 4~6 are repeated until data processing finishes；
Step 8：For non-congestion situation, after screening according to above data, 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：Flow velocity prediction neural network is established, as shown in Figure 4：
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
In algorithm training；(T+ Δ t) indicates the traffic flow speed in the covered section Tower (j) under prediction future T+ time Δt to v.Specifically
Algorithm is as follows：
In formula：
(T+ Δ t) indicates the traffic flow speed in the covered section of jth work song network in the Δ t period 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 network, the extension set number where m, n are indicated in host subnet network,At the time of indicating that ID-k occurs on m-th of extension set in sub-network Tower (j), 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.
Step-up error amountSetting self feed back iterative steps are C, and stopping criterion for iteration is arrangedAccording to the error amount of outputBy adjusting the value of weight A (j) and B (j), so that (T+ Δ 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.
It is tested below with the supreme sea G42 highway section in Beijing, is handed in the road both sides installation and deployment present invention
Through-flow amount detection systems.As shown in figure 5, true for the data and coil acquired the present invention is based on the road detection system of WIF signal
The comparison diagram of real data.Fig. 6 is the enlarged drawing of encircled portion in Fig. 5.From Figures 5 and 6 it is found that being screened by inventive algorithm noise reduction
No. 31 machine data afterwards have high consistency compared with coil truthful data (correlation data), have excellent performance, it was demonstrated that the present invention
Feasibility and data accuracy.
Fig. 7 is the comparison diagram that this algorithm exports result cross-sectional flow, from figure it is found that output result and corresponding road section to it is corresponding when
Groove circle flow speed data is that height is consistent.
Claims (3)
1. a kind of traffic cross-sectional flow prediction algorithm based on WIFI signal, is deployed with prediction device systems, institute along traffic route
Stating prediction device systems includes multiple sub-networks, and each sub-network includes a host and several extension sets, and the extension set is based on
WIFI agreement passes through wireless passive perceptual model, acquires the wide of the mobile device transmission in ambient enviroment based on WIFI signal agreement
Unicast packets, and screen the wherein data packet with mobile terminal device 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 stored,
And by being analyzed data to obtain road section flow velocity assessment prediction result；
The road section flow velocity assessment prediction includes the following steps：
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 the number of subnet, and M indicates 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- Δ t) to be analyzeds',DisFor 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, 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 list of foundation is 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 data
Step 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 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：Step 4~6 are repeated until data processing finishes；
Step 8：For non-congestion situation, after screening according to above data, 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 flow velocity prediction neural network：
(T+ Δ t) indicates the traffic flow speed in the covered section of jth work song network in the Δ t period after the T moment to v；∑ j is jth number
The total amount of the effective ID of the mobile terminal of the direction of sub-network, the extension set number where m, n are indicated in host subnet network,At the time of indicating that ID-k occurs on m-th of extension set in sub-network Tower (j), 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 approximate fits function；I is the sum of extension set in the subnet；
Step-up error amountSetting self feed back iterative steps are C, and stopping criterion for iteration is arranged
According to the error amount of outputBy adjusting the value of weight A (j) and B (j), so that v (T+ Δ t) approximate algorithm training sample S, most
The value of whole network output weight A (j) and B (j) are as allocation plan.
2. the traffic cross-sectional flow prediction algorithm based on WIFI signal according to claim 1, it is characterised in that：It is described adjacent
Distance is d between extension set, and the signal covering radius of single extension set is r, and d > 2r.
3. the traffic cross-sectional flow prediction algorithm based on WIFI signal according to claim 1, it is characterised in that：In step 10
The algorithm training sample S is the data that coil checker or radar detector obtain, and as effective reference unit.
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