CN105355047B - The Data Fusion method of many Vehicle Detection source dynamic time granularities - Google Patents

The Data Fusion method of many Vehicle Detection source dynamic time granularities Download PDF

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CN105355047B
CN105355047B CN201510736033.7A CN201510736033A CN105355047B CN 105355047 B CN105355047 B CN 105355047B CN 201510736033 A CN201510736033 A CN 201510736033A CN 105355047 B CN105355047 B CN 105355047B
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data
detection
time
source
traffic
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CN201510736033.7A
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CN105355047A (en
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于德新
邢雪
林赐云
张伟
杨庆芳
周户星
王薇
郑黎黎
田秀娟
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吉林大学
山东高速股份有限公司
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    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/012Measuring and analyzing of parameters relative to traffic conditions based on the source of data from other sources than vehicle or roadside beacons, e.g. mobile networks

Abstract

The invention discloses a kind of Data Fusion method of many Vehicle Detection source dynamic time granularities, belong to urban transportation data analysis technique field, it includes being spaced extraction time radix by detection data acquisition time;To detection data loading spatial information and the data structure of generalized treatment traffic data;Data mutuality degree, device data precision and the environmental sensitivity index for evaluating detection source obtain detection source degree of belief coefficient;By position in detection zone mobile terminal register data dynamic change update obtain time granularity application demand;Traffic parameter fusion is carried out to many detection sources according to application demand and detection source degree of belief.The present invention is mutually agreed with the trend that urban transportation data processing develops with big data using the traffic data combination mobile terminal locations data of registering that pedestal sensor is obtained, changing dynamic fusion data according to time granularity can effectively improve speed, have the advantages that acquisition computing is quick, reduce storage redundancy.

Description

The Data Fusion method of many Vehicle Detection source dynamic time granularities

Technical field

The invention belongs to urban transportation data analysis technique field, more particularly to a kind of many Vehicle Detection sources dynamic time grain The Data Fusion method of degree.

Background technology

With the rise of smart city, the various roads sensor such as earth magnetism wagon detector, induction coil, vehicle flowrate radar The pattern of common gather data in real time is gradually popularized.On the other hand with data collection and data storage ability it is continuous Enhancing, the particularly mobile terminal client terminal user such as the popularization of individual mobile terminal and microblogging, wechat gradually increases, shifting therein Dynamic terminal positioning technology provides the data source of magnanimity for traffic data collection.Under the propulsion of these two aspects technology, new Traffic data collection pattern and traditional data drainage pattern the data space shortage that interweaves lower jointly associate, and cause actual friendship Logical application aspect is made slow progress the bottleneck as Modern City Traffic data processing.

Traffic data fusion refers to that the traffic data of multisensor is comprehensive under certain criterion, is determined with the traffic needed for completing Plan and the transport information processing procedure assessed and carry out.The transport information that multiple sensors are gathered is carried out into effective integration, not only Cost performance, the degree of accuracy and the reliability of acquisition of information can be effectively improved, and single information source can be avoided to fail and cause Judgement and decision error.According to the position of each Loop detector layout in current domestic communication data fusion method, to including upstream The roadway element in intersection and section carries out traffic data fusion by certain hour granularity, during such transport information handling and Storage redundancy is excessive, and cannot ensure practical application effect.

Traditional road traffic fusion method is remained in many deficiencies under new traffic data collection pattern, and its is main Performance number exists:Time granularity is set as definite value in current data fusion method, expends time and memory space;Information processing is only Public presupposed information demand aspect is rested on, and cannot be formed with the change of regional traffic customer volume and then dynamic change time grain Degree carries out traffic data fusion;For the number that the magnanimity location information that individual mobile terminal is produced does not effectively become traffic characteristic According to foundation.

The content of the invention

In order to time granularity fixation causes consuming time and memory space in overcoming the shortcomings of data fusion, the present invention is provided A kind of Data Fusion method of many Vehicle Detection source dynamic time granularities.The method is with urban highway traffic detection data Research object, the time-space attribute according to detection data extracts Uniform data format, is signed by detection zone mobile terminal APP positions Determine traffic application demand to data dynamic change, and then information source degree of belief coefficient is pressed to the data for detecting source in detection zone more Traffic parameter fusion is carried out, the flexible analyzing and processing of urban transportation data is realized.

Dynamic time granularity Fusion Module of the present invention comprising Vehicle Detection data generalization processing module with traffic data, should Method is comprised the following steps that:

(1), Vehicle Detection data generalization treatment

The treatment of Vehicle Detection data generalization is main to realize multi-source ground fundamental mode traffic detector normalization of data structure;Its treatment Process mainly includes the time interval feature extraction of multi-source detection data and two steps of data structure generalized of traffic data:

I) time interval of gathered data, the extraction time radix on the premise of data precision is ensured are extracted;Examined to obtaining Temporally radix carries out data quality control to survey device data;

Ii space position parameter) is extracted for each Vehicle Detection source, spatial information loading is carried out to detection data, and it is right The generalized data structure of traffic data;

According to above-mentioned basic implementing procedure, the Vehicle Detection data generalization module specific implementation is comprised the following steps:

Step 1, traffic parameter is extracted to data record, and by Import data records database;

Step 2, record adjacent to same detection source historical data extract the acquisition time interval of data;

Step 3, the average of the desired value for extracting multi-source detection data acquisition time interval are time radix, i.e., in the time period [ts, te] in, data record m kind time interval extraction time radixes tbFor Wherein pikIt is i-th K-th acquisition time interval of delta t in detection sourcekThe probability of appearance, riFor the gathered data amount in i-th detection source accounts for all detections The ratio of data, n is detection source number in region.

Step 4, with time radix be standard adjustment record time interval, and by after adjustment time interval repair missing data Record

The spatial information of step 5, over the ground fundamental mode detector, obtain traffic flow parameter unified field DLatitude, DLongitude, LaneNo, TimeStamp, Flow, Speed, Occ, wherein DLatitude are detector location latitude, DLongitude is detector location longitude, and LaneNo is numbered for road track, and TimeStamp is detection time label, Flow It is the magnitude of traffic flow that detection is obtained, Speed is the vehicle location speed that detection is obtained, and Occ is the time occupancy that detection is obtained.

According to Vehicle Detection data generalization of the present invention process particular technique embodiment, the traffic detector data when Between spaced features be for analysis collection Vehicle Detection data reliability and accuracy, correspondence obtain data time interval category Property, specifically include:1) acquisition time interval:It refer to the time interval in traffic detector detection data acquisition time;2) time Radix:It refer to the desired value of Vehicle Detection data collection interval;3) time granularity:Refer to multi-source traffic data fusion when Between interval setting, the time granularity of this method depends on the data volume change of registering of mobile terminal APP positions in corresponding region and determines Transport need.

(2), the dynamic time granularity fusion of traffic data

The dynamic time granularity fusion part of traffic data mainly includes that detection source Trust Values Asses module, position are registered number According to three parts of traffic parameter Fusion Module for updating statistical module and dynamic time granularity.Wherein detection source degree of belief is commented Estimate module and position registers that to update statistical module be the forerunner of the traffic parameter Fusion Module of dynamic time granularity, Liang Zhewei to data Implementation relation in parallel:

I) source Trust Values Asses module is detected:To detect the historical data degree of correlation factor in source, device data dilution of precision, The aspect qualitative assessment such as Environmental Factors, comprehensively obtains detection source Trust Values Asses coefficient;

Ii) position register data update statistical module:Mobile terminal APP positions are registered data filtering, reject traffic ginseng Record and repeat to record with person's positional information abnormity, and travel through mobile terminal APP in region and register position data, statistics dynamic becomes Change and update;

Iii) the traffic parameter Fusion Module of dynamic time granularity:By position register data variation obtain time granularity should With demand, traffic parameter fusion is carried out to many detection sources according to application demand and detection source degree of belief.

Basic implementing procedure of the invention, the detection source Trust Values Asses module specific implementation includes following step Suddenly:

Step 1, section n Data Detection source to be fused is determined, to i-th detection source data xiCalculate and j-th inspection Survey source data xjThe discrete data correlation coefficient r in one dayijFor

Wherein xik It is k-th data of time radix, x in i-th detection source data one dayjkIt is k-th time in j-th detection source data one day The data of radix.

Step 2, the Data mutuality degree factor C for detecting source for i-th according to calculating in detection source discrete data coefficient correlationiForWherein rijIt is i-th detection source data xiCalculate and j-th detection source data xjCoefficient correlation.

Step 3, to i-th detection source computing device data precision factors Ai, andWherein aiIt is such detection source Innate detection precision.

Step 4, according to i-th detection source characteristic assignment Environmental Factors Si, and

Step 5, calculate i-th independent detection source Trust Values Asses coefficient ωi, andWherein CiIt is i-th The detection source data degree of correlation factor, AiIt is i-th device data dilution of precision in detection source, SiIt is i-th environment shadow in detection source Ring the factor.

According to Trust Values Asses module particular technique embodiment in detection source of the present invention, detection source Trust Values Asses are To including detecting the transport information that source obtains same section, from Data mutuality degree, three sides of equipment accuracy and environmental sensitivity index more The quantitative overall merit in face detects the degree of belief in source, is embodied as including:1) the multi-source data degree of correlation factor:Refer to that certain section is treated The related proportionality coefficient of other class detector datas of the data that the traffic detector of evaluation is measured and same section;2) device data precision The factor:It refer to the proportionality coefficient of certain Vehicle Detection equipment innate detection precision and all kinds of detector precision ratios;3) ambient influnence The factor:It refer to the influence proportionality coefficient of certain traffic detector testing result under conditions of environmental change.

Basic implementing procedure of the invention, the position register data update statistical module specific implementation include it is following Step:

Step 1, information data of being registered to mobile terminal APP positions filtering, conversion and loading, reject traffic participant position Information abnormity records and repeats to record.To obtain unified field Venue id, Venue of traffic participant positional information Name, Latitude, Longitude, Checked UserID, wherein Venue id are to register data number, Venue position Name is register position longitude, Checked to register location name, Latitude to register position latitude, Longitude UserID for position register data source mobile terminal numbering;

Step 2, all adjacent detection intersections are linked to be triangle, make the perpendicular bisector on each side of triangle, then Some perpendicular bisectors around each intersection surround Thiessen polygon.Unique intersection is encompassed within unique Tyson In polygon.Initialization δ is 1, and wherein δ is the time radix multiple in dynamic time cycle.

Step 3, [the t-2 δ * t before current time tb,t-δ*tb] interval and [t- δ * tb, t] and it is interval interior, judge that position is marked In polygonal region belonging to signing, and count position in each region and register the number m of data0And m1

Step 4, with current statistic cycle [t- δ * tb, t] and measurement period [t-2 δ * tb,t-δ*tb] in position register number According to variation delta m=| m1-m0| with upper limit threshold Mmax, lower threshold MminCompare;If Δ m<Mmin, then δ=δ+1 simultaneously repeats to walk Rapid 3;If Δ m>Mmax, then δ=δ -1 and repeat step 3;If Δ m ∈ [Mmin,Mmax], it is determined that dynamic time granularity is δ times Time radix.

According to position of the present invention register data update statistical technique embodiment, the filtering refer to traffic participant position Confidence breath rejecting abnormalities record and repetition are recorded.

Basic implementing procedure of the invention, the traffic parameter Fusion Module specific implementation bag of the dynamic time granularity Include following steps:

Section is in dynamic time cycle [t, t+ δ * t in step 1, same detection zone Thiessen polygonb] in n detector Weight matrix W={ the ω of degree of belief12...ωn}T, wherein ωiRepresent i-th detector data measured in fusion process Weight ratio.

Step 2, in dynamic time cycle [t, t+ δ * tb] interior to i-th common n in detection sourceiIndividual data calculate i-th it is only The information fusion in vertical detection sourceWherein xaIt is the traffic data of i-th detector acquisition to be fused.

Step 3, in dynamic time cycle [t, t+ δ * tb] in the information fusion knot that obtains of various traffic information collecting methods It is really:WhereinI-th information fusion result in independent detection source.

Beneficial effects of the present invention:

1) traffic data combination mobile terminal APP positions are obtained using pedestal sensor and registers data to urban transportation data The trend developed with big data is processed mutually to agree with.

2) class statistic that mobile terminal APP registers position, is existed by position number change of registering to time granularity demand Line is divided.

3) changing dynamic data combining according to time granularity can effectively improve speed, with obtaining that computing is quick, reduce and deposit Store up the advantage of redundancy.

Brief description of the drawings

Fig. 1 is flow chart of the invention.

Fig. 2 is many source detection apparatus acquisition interval characteristic patterns of the invention.

Fig. 3 is intersection Thiessen polygon organigram of the invention.

Fig. 4 is flow chart of the judgement location tags of the invention in Thiessen polygon region.

Specific embodiment

Based on the fundamental mode traffic detector data by multi-source ground of the invention, the mobile terminal that additional transport participant participates in APP dynamic positions are registered data, it is proposed that a kind of to be dynamically determined the section multi-source traffic number of time granularity with position data of registering According to method for amalgamation processing.Below in conjunction with accompanying drawing and embodiment explanation technical scheme.

As shown in figure 1, the flow of embodiment specifically includes following steps:

Step 1:Extract example section geomagnetism detecting device, induction coil detector and video monitor acquisition time interval simultaneously Extraction time radix.From example section geomagnetism detecting device (DE537919498), video monitor (DC00004838) and the line of induction Circle detector (COIL1518017) is in time period [ts, te] in various detectors historical data time interval feature, such as Fig. 2 institutes Show.

The flow of the step extraction time radix specifically includes following steps:

1. traffic parameter is extracted to multi-source data record and imports database, and adjust data field DetectorID, LaneNo, TimeStamp, Flow, Speed, Occ, wherein DetectorID are numbered for detector, and LaneNo is compiled for road track Number, TimeStamp is detection time label, and Flow is the magnitude of traffic flow that detection is obtained, and Speed is the vehicle location that detection is obtained Speed, Occ is the time occupancy that detection is obtained.

2. to historgraphic data recording extraction time sequence, then traffic data time series the finite aggregate { (t for gathering1,o1), (t2,o2),…,(tn,on) meet ts≤ti≤teAnd ti<ti+1(i=1,2 ..., n-1), oiIt is correspondence time tiTraffic parameter Collection includes magnitude of traffic flow Flow, vehicle location speed Speed, time occupancy Occ, calculates the time period [ts, te] gathered data Time interval { Δ tk|Δtk=ti+1-tiAnd i=1,2 ..., n-1 };

3. multi-source detection data acquisition time interlude radix is calculated, i.e., in time period [ts, te] in, data record M kind time interval extraction time radixes tbForWherein pikIt is k-th collection in i-th detection source Time interval Δ tkThe probability of appearance, riThe ratio of all detection datas is accounted for for the gathered data amount in i-th detection source, n is region Interior detection source number.

Step 2, by various regions fundamental mode Vehicle Detection source extract space position parameter DLatitude, DLongitude, to detection Data carry out spatial information loading obtain unified field DetectorID, DLatitude of traffic data, DLongitude, LaneNo, TimeStamp, Flow, Speed, Occ, wherein DLatitude are detector location latitude, and DLongitude is inspection Survey device position longitude.

Singly detect that data quality control flow of the source data based on time radix specifically includes following steps in the step:

If 1. uniqueThen data (ti, oi) be adapted to (oi);

If 2. multipleThen k evidence is set to data (ti_1, oi_1), (ti_2, oi_2) ..., (ti_k, oi_k), dataObtain generalized data

If 3. there is no (ti, oi)∈(nTb,(n+1)tb], then traffic data has continuity in time-domain, thus utilizes Front and rear two time cycle carries out the translation of time cycle data and repairs.If front and rear cycle data is set to data (ti-2, oi-2), (ti-1, oi-1) and (ti+1, oi+1), (ti+2, oi+2), dataObtain generalized data

Step 3, to being related to the historical data degree of correlation factor, device data dilution of precision, the Environmental Factors in detection source Etc. aspect qualitative assessment, comprehensively obtain detection source Trust Values Asses coefficient;

Step detection source Trust Values Asses flow specifically includes following steps:

1. section n Data Detection source to be fused is determined, to i-th detection source data xiCalculate and j-th detection source Data xjThe discrete data correlation coefficient r in one dayijFor

Wherein xik It is k-th data of time radix, x in i-th detection source data one dayjkIt is k-th time in j-th detection source data one day The data of radix.

2. the Data mutuality degree factor according to i-th detection source of calculating in detection source discrete data coefficient correlation isWherein rijIt is i-th detection source data xiCalculate and j-th detection source data xjCoefficient correlation.

3. to i-th detection source computing device data precision factorWherein aiIt is such detection source innate detection Precision.

4. according to i-th detection source characteristic assignment Environmental Factors SI,And

5. i-th independent detection source Trust Values Asses coefficient ω is calculatediForWherein CiIt is i-th detection The source data degree of correlation factor, AiIt is i-th device data dilution of precision in detection source, SiFor i-th detection source ambient influnence because Son.

Step 4, information data of being registered to mobile terminal APP positions conversion and filtering, and to mobile terminal in detection zone APP registers position data dynamic change update;

The step specifically includes following steps:

1. check position register data category information it is whether complete, the position data of registering for lacking attribute are deleted Remove, and to data according to criteria field Venue id, Venue name, Latitude, Longitude, Checked UserID Form is modified, and it is register location name, Latitude that wherein Venue id register data number, Venue name for position For position latitude of registering, Longitude are to register position longitude, Checked UserID for data source movement is registered eventually in position End numbering;

2. data of being registered to the repeatable position of identical Checked UserID carry out data merging treatment, reduce the superfluous of data Remaining.

3. all adjacent detection intersections are linked to be triangle, make the perpendicular bisector on each side of these triangles, then Some perpendicular bisectors around each intersection just surround Thiessen polygon, as shown in Figure 3.Unique intersection is forgiven In unique Thiessen polygon, and initialization time radix coefficient δ is 1.

4. a large amount of discrete location points of registering are collected in polygon, the δ times of time with time radix as measurement period, [t-2 δ * t before current time tb,t-δ*tb] interval and [t- δ * tb, t] and it is interval interior, judged belonging to location tags with scanning Beam Method Polygonal region, and count position and register the number m of data0And m1, flow is as shown in Figure 4.

5. with current statistic cycle [t- δ * tb, t] and measurement period [t-2 δ * tb,t-δ*tb] in position register data change Change amount Δ m=| m1-m0| with upper limit threshold Mmax, lower threshold MminCompare;If Δ m<Mmin, then 4. δ=δ+1 simultaneously repeats;If Δm>Mmax, then 4. δ=δ -1 simultaneously repeats;If Δ m ∈ [Mmin,Mmax], it is determined that dynamic time granularity is δ times of time base Number.

Step 5, change to attributes of being registered by position change are obtained to time granularity demand, to obtain the unification of traffic data Field DLatitude, DLongitude, LaneNo, TimeStamp, Flow, Speed, Occ, according to time granularity application demand Many detection sources are carried out with the fusion of traffic parameter field data.

Time granularity application demand specifically includes following to the fusion flow that many detection sources carry out traffic data in the step Step:

1. in same detection zone Thiessen polygon section in dynamic time cycle [t, t+ δ * tb] in n detector measure Weight matrix W={ the ω of data12...ωn}T, wherein ωiRepresent that i-th detector measures traffic parameter field data xi The shared weight in fusion process.

2. in dynamic time cycle [t, t+ δ * tb] interior to i-th common n in detection sourceiIndividual data calculate i-th independent detection The information fusion in sourceWherein xaIt is the traffic data of i-th detector acquisition to be fused.

4. in dynamic time cycle [t, t+ δ * tb] in the information fusion result that obtains of various traffic information collecting methods be:WhereinI-th information fusion result in independent detection source.

General principle of the invention be by the use of position register data as road traffic demand dynamic update foundation, enter And meet the requirement of novel traffic data acquisition, wherein to the number in many Vehicle Detection sources on the basis of detection source degree of belief is evaluated According to data processing is carried out under dynamic change time granularity, so as to realize the data dynamic fusion to many Vehicle Detection sources.

Claims (7)

1. a kind of Data Fusion method of many Vehicle Detection source dynamic time granularities, is processed comprising Vehicle Detection data generalization Two parts are merged with the dynamic time granularity of traffic data, the method is comprised the following steps:
(1), Vehicle Detection data generalization treatment
Vehicle Detection data generalization processing procedure mainly includes time interval feature extraction and the traffic data of multi-source detection data Two steps of structure generalized;The time interval of gathered data, the extraction time base on the premise of data precision is ensured are extracted first Number;To obtaining detector data, temporally radix carries out data quality control;Space bit is extracted to each Vehicle Detection source afterwards Parameter is put, spatial information loading is carried out to detection data and is obtained the generalized data structure of traffic data, and then by data after generalized For the dynamic time granularity fusion of highway traffic data;
(2), the dynamic time granularity fusion of traffic data
The dynamic time granularity fusion part of traffic data mainly includes that detection source Trust Values Asses module, position register data more New statistical module and dynamic time granularity traffic parameter three parts of Fusion Module;
I) Trust Values Asses module:To detect the historical data degree of correlation factor in source, device data dilution of precision, ambient influnence because Sub- qualitative assessment, comprehensively obtains detection source Trust Values Asses coefficient;
Ii) position register data update statistical module:Mobile terminal APP positions are registered data filtering, reject traffic participant Positional information abnormity records and repeats to record, and travels through mobile terminal APP in region and register position data, and statistics dynamic change is more Newly;
Iii) dynamic time granularity traffic parameter Fusion Module:By position register data variation obtain time granularity application need Ask, traffic parameter fusion is carried out to many detection sources according to application demand and detector degree of belief.
2. the Data Fusion method of many Vehicle Detection source dynamic time granularities according to claim 1, its feature exists In:The time interval feature extracting method of multi-source detection data is comprised the following steps in the Vehicle Detection data generalization treatment:
Step 1, traffic parameter is extracted to data record, and by Import data records database;
Step 2, record adjacent to same detection source historical data extract the acquisition time interval of data;
Step 3, the average of the desired value for extracting multi-source detection data acquisition time interval are time radix, i.e., in time period [ts, te] in, data record m kind time interval extraction time radixes tbFor Wherein pikIt is i-th detection source In k-th acquisition time interval of delta tkThe probability of appearance, riFor the gathered data amount in i-th detection source accounts for all detection datas Ratio, n is detection source number in region;
Step 4, with time radix be standard adjustment record time interval, and by after adjustment time interval repair missing data remember Record;
The spatial information of step 5, over the ground fundamental mode detector, obtains the unified field of traffic flow parameter.
3. the Data Fusion method of many Vehicle Detection source dynamic time granularities according to claim 1, its feature exists In:Data structure generalized in the Vehicle Detection data generalization process part is to adjust data record by standard of time radix Time simultaneously repairs missing data record, and the spatial information of detector is loaded into detection data the unified word for obtaining traffic flow parameter Section.
4. the Data Fusion method of many Vehicle Detection source dynamic time granularities according to claim 1, its feature exists In:Detection source Trust Values Asses module and position are registered data renewal in the dynamic time granularity Fusion Module of the traffic data Statistical module parallel processing, for the traffic parameter Fusion Module of dynamic time granularity provides foundation.
5. the Data Fusion method of many Vehicle Detection source dynamic time granularities according to claim 1, its feature exists In:Detect that the method for source Trust Values Asses module includes following step in the dynamic time granularity fusion part of the traffic data Suddenly:
Step 1, section n Data Detection source to be fused is determined, to i-th detection source data xiCalculate and j-th detection source number According to xjThe discrete data correlation coefficient r in one dayijFor
Wherein xikIt is i-th K-th data of time radix, x in individual detection source data one dayjkIt is k-th time radix in j-th detection source data one day Data;
Step 2, the Data mutuality degree factor C for detecting source for i-th according to calculating in detection source discrete data coefficient correlationiFor
Wherein rijIt is i-th detection source data xiCalculate and j-th detection source data xjCoefficient correlation;
Step 3, to i-th detection source computing device data precision factors Ai, andWherein aiFor i-th detection source is consolidated There is accuracy of detection;
Step 4, according to i-th detection source characteristic assignment Environmental Factors Si, and
Step 5, calculate i-th independent detection source Trust Values Asses coefficient ωi, andWherein CiIt is i-th detection The source data degree of correlation factor, AiIt is i-th device data dilution of precision in detection source, SiFor i-th detection source ambient influnence because Son.
6. the Data Fusion method of many Vehicle Detection source dynamic time granularities according to claim 1, its feature exists In:The position register data update statistical module method comprise the following steps:
Step 1, information data of being registered to mobile terminal APP positions filtering, conversion and loading, reject traffic participant positional information Exception record and repetition are recorded, to obtain the unified field of traffic participant positional information;
Step 2, all adjacent detection intersections are linked to be triangle, make the perpendicular bisector on each side of these triangles, then Some perpendicular bisectors around each intersection just surround Thiessen polygon;Unique intersection is encompassed within unique Thailand Gloomy polygon, and it is 1 to initialize δ, wherein δ is the time radix coefficient for determining the time cycle;
Step 3, [the t-2 δ * t before current time tb,t-δ*tb] interval and [t- δ * tb, t] and interval interior, wherein tbIt is what is extracted Time radix, judges the polygonal region belonging to location tags, and counts position in each region and register the number m of data0With m1
Step 4, with current statistic cycle [t- δ * tb, t] and measurement period [t-2 δ * tb,t-δ*tb] in position register data change Change amount Δ m=| m1-m0| with upper limit threshold Mmax, lower threshold MminCompare;If Δ m<Mmin, then δ=δ+1 and repeat step 3; If Δ m>Mmax, then δ=δ -1 and repeat step 3;If Δ m ∈ [Mmin,Mmax], it is determined that dynamic time granularity for δ times when Between radix.
7. the Data Fusion method of many Vehicle Detection source dynamic time granularities according to claim 1, its feature exists In:The traffic parameter Fusion Module of the dynamic time granularity determines according to the evaluation weight matrix of each detector in detection zone Shared weight in data fusion process, by position register data update statistical module determine the time cycle, it is desirable to by same inspection The traffic data of many discrete time points collection in polytype detection source in region time period is surveyed by evaluating weights than weighting Mode carries out data fusion, and then obtains more accurate data result.
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