CN104485000A - Method for analyzing and processing effectiveness of probe vehicle data source - Google Patents

Abstract

The invention relates to a method for analyzing and processing the effectiveness of a probe vehicle data source. The method includes the following steps: calculating the effectiveness of overall data and the effectiveness of time-shared data; determining the data missing degree, and carrying out missed data supplementing according to the missing degree; calculating the effectiveness of data flow; sequentially determining results of the effectiveness of the data, and determining data anomaly grades according to the results; calculating results of the effectiveness of periodical data, determining the grades of the effectiveness of the periodical data according to the results of the effectiveness of the periodical data, and selecting corresponding processing modes according to the data anomaly grades and the grades of the effectiveness of the periodical data. According to the method, the method that the effectiveness of the probe vehicle data source is identified with fewer parameters is achieved in cooperation with the number and quality statistical characteristics of the probe vehicle data source, the complexity for processing the probe vehicle data source is lowered, the practice effect is good, and operability is high; meanwhile, the high multi-source data processing efficiency and the multi-source data processing reliability are guaranteed in a scheme-selection type multi-source data processing mode.

Description

A kind of floating car data source efficiency analysis disposal route

Technical field

The present invention relates to municipal intelligent traffic technical field of data processing, particularly relate to a kind of floating car data source efficiency analysis disposal route.

Background technology

Wider a kind of traffic data collection data source is applied in floating car data source in municipal intelligent traffic information acquisition, due to its relative to other fixed detector data sources more accurately and effectively Data Detection result, larger coverage and more flexibly the advantage such as deployment conditions be at home and abroad widely applied.By carrying out Treatment Analysis to Floating Car image data and obtaining the mainstream research direction that urban road traffic state information result is current intelligent transportation field.

But floating car data source equally also has certain inherent shortcoming.Due to the inherent characteristics of Floating Car dispersiveness, original Floating Car image data generally has the shortcomings such as data noise is high, subregion deletion condition is serious, stability is not enough, makes based on gained floating car data existence inadequate natural endowment to a certain degree after this kind of original data processing.Therefore, adopt the intelligent transportation system in single floating car data source often to have larger error, need to detect data source in conjunction with other and adjustment improvement is carried out to image data.This process is exactly a pith of multi-source data treatment technology.

Domestic to multi-source traffic data treatment technology at present, especially the technical research of the multi-source data treatment technology of floating car data source image data and other data source image data still stays in theoretical research stage mostly, the application to engineering practice research that can be applied to actual intelligent transportation system is less, and does not show the positive impact of validity degree on traffic data analyzing process of data source.In fact, the validity of traffic data is the key factor calculating of road traffic index being had to directly impact.Correct traffic data reliably can draw objective effective traffic index result, and the traffic data of exception then can generating portion the deviation even result of gross error.Show according to the analysis result of a large amount of traffic data, traffic data validity comprises two aspects: the completeness of (1) data bulk; (2) reliability of the quality of data.The traffic data of the requirement simultaneously meeting these two aspects is only had to be only effective data.Indented material also exists obvious deficiency, reduces accuracy and the reliability of the transport information of the intelligent transportation system based on floating car data source.Therefore, it is very necessary for designing a kind of floating car data source efficiency analysis disposal route.

Summary of the invention

The present invention overcomes above-mentioned weak point, object is to provide a kind of floating car data source efficiency analysis disposal route, the method, processes by the treatment cycle timing preset, calculated population data validity and time data validity for process unit with unit section; Determine shortage of data degree, and carry out the supplementary process of missing data according to disappearance degree; Calculate data traffic validity; Determine successively often to organize data validity result, and according to this result determination data exception grade; Computation period data validity result, according to cycle data validity result determination cycle data validity grade, and the processing mode corresponding to cycle data validity hierarchical selection according to data exception grade; Achieve with the method for less parameters identification floating car data source validity, operation efficiency is high, workable, ensure that high efficiency and the reliability of multi-source data process.

The present invention achieves the above object by the following technical programs: a kind of floating car data source efficiency analysis disposal route, described floating car data source image data at least comprises road-section average travel speed and Floating Car flow two detected parameters, and described Floating Car flow is a Floating Car collection period C _fCthrough the Floating Car quantity in this unit section in time period, comprise the steps:

(1) treatment cycle C is preset _tPI, acquiring unit section history floating car data carries out statistical study, calculates the conceptual data validity Q in floating car data source _t;

(2) one day be divided into if having time is paid close attention to period and other periods, according to the time data validity Q often organizing period calculating floating car data source belonging to the floating vehicle data acquisition time _p;

(3) compared by calculating and determine treatment cycle C _tPIthe shortage of data degree in interior floating car data source, carries out missing data according to disappearance degree and supplements process;

(4) computing cycle C successively _tPIthe data traffic validity Q often organizing non-disappearance floating car data in interior step (3) _f;

(5) according to result of calculation Q _t, Q _p, Q _fcomputing cycle C successively _tPIinside often organize the data validity V of non-disappearance floating car data _t, according to result of calculation V _tdetermine floating car data source data exception level;

(6) V is passed through _tpresent treatment cycle C is calculated with step (3) missing data result _tPIthe cycle data validity V in interior floating car data source _tC, according to result of calculation V _tCdetermine cycle data validity grade.

As preferably, multi-source data processing mode is selected to process according to the data exception grade of step (5) and the cycle data validity grade of step (6) to often organizing floating car data.

As preferably, described step (1) conceptual data validity computing formula is as follows:

N _S＝α _T×(T·h _F)

$Q_T=\frac{\underset{i=1}{\overset{D}{\mathrm{\Σ}}}{N}_{\mathrm{Ti}}}{D\·N_S}$

$h_F=\frac{1}{C_{\mathrm{FC}}}$

Wherein, N _sfor effective traffic data amount that section can gather in length in effective time in a day, α _tfor collective effectiveness coefficient, T is length effective time in a day, and unit is minute; h _ffor the data acquiring frequency in floating car data source, C _fCit is a floating vehicle data acquisition cycle; Q _tfor conceptual data validity, span is [0,1], the statistics number of days of value to be 1, D be historical statistical data when result of calculation is more than 1, N _tiit is the traffic data amount of i-th day actual acquisition.

As preferably, it is as follows that described step (2) calculates time data validity computing formula:

N _Si＝α _P×(T _i·h _F)

$Q_{\mathrm{Pi}}=\frac{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{N}_{\mathrm{Pij}}}{D\·N_{\mathrm{Si}}}$

Wherein, N _sifor effective traffic data amount that section can gather within i-th period, α _pfor validity coefficient at times, T _ibe the time span of i-th period, unit is minute; Q _pibe the data validity in i-th period, span is [0,1], and when result of calculation is more than 1, value is 1; Q _pfor section time data validity, span is [0,1], and k is the period number paid close attention to, q ₀for the validity of other periods.

As preferably, described step (3) determines shortage of data degree, carries out the step that missing data supplements process and comprises:

1) the actual Floating Car image data amount N in computing cycle length _c;

2) actual Floating Car image data amount and Ideal float car image data amount N _rsize, N _r=C _tPIh _f;

3) if there is N _c>=N _r, then there is not shortage of data, do not need to carry out missing data and supplement process, missing data supplements process to be terminated;

4) if there is μ N _r≤ N _c< N _r, then there is slight shortage of data, use other data source data to supplement the floating car data in disappearance moment; If there is N _c< μ N _r, then there is serious shortage of data, Floating Car image data in collection period replaced by other data source data in the same time period completely; Wherein, μ is disappearance coefficient;

5) validity of the data after supplementary process is defined as 50%, missing data supplements process to be terminated.

As preferably, described step (4) non-disappearance floating car data flow validity computing formula is as follows:

$Q_F=Q_{F0}+{\mathrm{\&alpha;}}_F\&CenterDot;{\log }_{F_A}f$

Wherein, F _afor reference Floating Car flow, round downwards, C _fCDfor city Floating Car sum, C _rfor unit section, city sum; Q _ffor section data traffic validity, span is [Q _f0, 1], when result of calculation is more than 1, value is 1; Q _f0for benchmark Floating Car is passed through data validity; α _ffor data traffic validity coefficient; F is Floating Car flow.

As preferably, in described step (5) treatment cycle, the data validity computing formula of non-disappearance floating car data is as follows:

Q _TP＝min(Q _T,Q _P)

V _T＝[α _VQ _F+(1-α _V)Q _TP]×100％

Wherein, Q _tPfor statistics traffic data is with reference to validity, V _tfor current time floating car data validity result; α _vfor the coefficient of efficiency of traffic data.

As preferably, described step (5) floating car data source data exception level is divided into Three Estate: normal, mile abnormality, severe are abnormal; Wherein data effective value be greater than 60% for normal level; Data effective value be less than or equal to 60% and be greater than 20% for mile abnormality; Data effective value be less than or equal to 20% for severely subnormal.

As preferably, the calculation procedure of described step (6) Floating Car cycle data validity comprises:

1) select the floating car data except disappearance supplementary data in treatment cycle, calculate initial validity; The formula calculating initial validity is as follows:

$V_{T0}=\frac{1}{k}\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}{V}_{\mathrm{Ti}};$

Wherein, V _t0for cycle data initial validity; K is the floating car data number in treatment cycle except disappearance supplementary data; V _tibe the validity of i-th floating car data;

2) floating car data in the traversal processing cycle except disappearance supplementary data, selects the data meeting data validity condition, gets rid of the data do not satisfied condition; Described data validity condition is

$|V_{\mathrm{Ti}}-V_{T0}|\&le;\sqrt{{\mathrm{\&sigma;}}_0^2+{\mathrm{\&xi;}}^2}$

${\mathrm{\&sigma;}}_0=\sqrt{\frac{1}{k-1}\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}{(V_{\mathrm{Ti}}-V_{T0})}^2}$

Wherein, ξ is correction factor; σ ₀for data valid interval;

3) by step 2) in select floating car data with disappearance supplementary data combine, computation period validity; The formula of described computation period validity is as follows:

$V_{\mathrm{TC}}=(\frac{1}{m}\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{V}_{\mathrm{Tj}}-\mathrm{\&xi;})\&times;100\%$

Wherein, V _tCit is cycle data validity result; M is data amount check; V _tjfor the validity of a jth data.

As preferably, described step (6) Floating Car cycle data is divided into A, B, C, D tetra-grades according to cycle data validity value result; Wherein cycle data effective value be less than 30% for A grade; Cycle data effective value be more than or equal to 30% and be less than 60% for B grade; Cycle data effective value be more than or equal to 60% and be less than or equal to 75% for C grade; Cycle data effective value be greater than 75% for D grade.

As preferably, described multi-source data processing mode comprises following three kinds of modes:

(1) remain unchanged, namely maintain floating car data source image data and do not do to change;

(2) multisource data fusion, is about to floating car data source image data and other data source image data fusion treatment in the same time;

(3) he replaces at source data, is about to other data source image data in the same time and replaces floating car data source image data.

As preferably, described multisource data fusion adopts average amalgamation mode to carry out calculatings fusion.

As preferably, described multi-source data disposal route select scheme as shown in table 1 below:

Table 1

Beneficial effect of the present invention is: (1) by conjunction with floating car data source totally, data statistical characteristics and real time data feature at times, achieve with the method for less parameters identification floating car data source validity, operation efficiency is high, workable; (2) traffic data that can realize under more reliable multi-data source condition processes application further, in turn ensure that high efficiency and the reliability of multi-source data process.

Accompanying drawing explanation

Fig. 1 is the FB(flow block) of the inventive method.

Embodiment

Below in conjunction with specific embodiment, the present invention is described further, but protection scope of the present invention is not limited in this:

Embodiment: as shown in Figure 1, a kind of floating car data source efficiency analysis disposal route, the method, processes by the treatment cycle timing preset for process unit with unit section; Described unit section is a section in the urban road traffic network of pavement section; There are several road traffic image data sources comprising floating car data source in described unit midblock administration; Described floating car data is through pretreated Floating Car characteristic, often organizes Floating Car characteristic and comprises road-section average travel speed and Floating Car flow two parameters, and Floating Car flow is the Floating Car quantity through this unit section in Floating Car collection period.

The present invention is for a unit section (temmoku hill path (various schools of thinkers garden road-sunken flower-bed road)) in the main city zone road network of Hangzhou, and this section data processing cycle is C _tPI=6min, the floating vehicle data acquisition cycle is C _fC=1min, with 07:00 ~ 21:00 for section effective time, effective time length T=14 × 60=840min; With the time period (namely morning peak is 07:00 ~ 09:00 period of two hours, and evening peak is 17:00 ~ 19:00 period of two hours) of morning, evening peak for paying close attention to the period, pay close attention to period number k=2.

Setting collective effectiveness coefficient is α _t=0.8, timesharing validity coefficient is α _p=0.8, other lime period availability q ₀=1, disappearance coefficient μ=0.3, data on flows validity factor alpha _f=0.1, benchmark Floating Car is passed through data validity Q _f0=0.5, correction factor ξ=0.01.Meanwhile, be 1,/2/,3/4 4 grades according to section significance level by unit pavement section in the main city zone road network of Hangzhou, according to different section grade configuration traffic data coefficient of efficiency α _vbe worth as shown in table 2 below:

Section grade	1	2	3	4
					α V	0.7	0.6	0.5	0.4

Table 2

Step 1, gets this section history floating car data of continuous 30 days and carries out statistical study, and calculating section conceptual data validity can obtain

$Q_T=\frac{\underset{i=1}{\overset{D}{\mathrm{\&Sigma;}}}{N}_{\mathrm{Ti}}}{D\&CenterDot;N_S}=\frac{\underset{i=1}{\overset{30}{\mathrm{\&Sigma;}}}{N}_{\mathrm{Ti}}}{30\&times;0.8\&times;840\&times;1}=0.73$

Wherein N _tibe the traffic data quantity of i-th day actual acquisition within 07:00 ~ 21:00 time period in 30 days, added up obtaining by historical data in database.

Step 2, calculates this section timesharing validity according to the concern period.Morning peak period T ₁=evening peak period T ₂=120min, has N _s1=N _s2=0.8 × 120 × 1=96, then timesharing validity in section may be calculated

Step 3, in calculating in real time, current 6min section cycle length of carrying out data processing that needs is 08:10 ~ 08:16 (comprises 08:10:00, but do not comprise 08:16:00), and in the acquisition cycle, floating car data is as shown in table 3 below:

Acquisition time	Speed	Traffic flow
			08:10:00	36.70	3
08:11:01	17.10	8
			08:12:03	30.00	2
08:14:01	46.30	1

Table 3

Calculate shortage of data degree in the current time index computation period time, within the index computation period time at current slot place, have 4 groups of data, i.e. N _c=4>=0.3 × 6, there is low volume data disappearance in current slot, by the data in disappearance moment with this section in the same time other data source data supplement, to complete after supplementing real time data in the cycle as shown in table 4 below, the validity of disappearance supplementary data is 50%.

Acquisition time	Speed	Traffic flow	Whether be missing data
				08:10:00	36.70	3	N
08:11:01	17.10	8	N
				08:12:03	30.00	2	N
08:13:00	28.00	—	Y
				08:14:01	46.30	1	N
08:15:00	33.50	—	Y

Table 4

Step 4, missing data after supplementing and having processed successively in the computing cycle floating car data source often organize the data traffic validity of non-missing data.It is about 11000 that total amount is possessed in current Hangzhou Floating Car (being generally taxi), and the road network section, urban district including intelligent transportation system in adds up to about 4000, then with reference to Floating Car flow be

The data traffic validity that can calculate the non-missing data of Article 1 is

Q _F＝0.5+0.1×log ₂3＝0.6585；

The data traffic validity that can calculate other non-missing datas by that analogy is successively respectively 0.8,0.6,0.5.

Step 5, passes through Q _t, Q _p, Q _fnon-missing data validity according to result of calculation determination floating car data source data exception level in computation period successively.Wherein, adding up traffic data with reference to validity is

Q _TP＝min(Q _T,Q _P)＝min(0.73,0.65)＝0.65；

Road section selected grade is 2, has traffic data coefficient of efficiency α _v=0.6, then the validity of the non-missing data of Article 1 is

V _T＝[α _VQ _F+(1-α _V)Q _TP]×100％＝65.51％；

Can judge that this data exception grade is normal according to the above-mentioned validity result of following table 5.

Data exception grade	Normally	Mile abnormality	Severely subnormal
				Data validity value	(60％,100％]	(20％,60％]	[0,20％]

Table 5

In like manner, the validity that can calculate other non-missing datas is successively respectively 74%, 62%, 56%, and corresponding exception level is respectively normal, normally, and mile abnormality.

Step 6, determine Floating Car cycle data validity, comprise the following steps:

(1) non-missing data shown in option table 3, calculating initial period validity is

$V_{T0}=\frac{1}{k}\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}{V}_{\mathrm{Ti}}=0.643775;$

(2) calculating data valid interval is σ ₀=0.075213, according to rule judge whether each non-missing data satisfies condition successively, retain and meet the non-missing data of this condition;

(3) floating car data and table 4 kind is selected to lack supplementary data in extraction step (2), as shown in table 6 below:

Acquisition time

Validity

Whether be missing data

08:10:00	65.51％	N
			08:12:03	62％	N
08:13:00	50％	Y
			08:15:00	50％	Y

Table 6

Computation period validity is

$V_{\mathrm{TC}}=(\frac{1}{m}\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{V}_{\mathrm{Tj}}-\mathrm{\&xi;})\&times;100\%=55.88\%;$

Can judge that cycle data validity grade is B according to following table 7.

Table 7

Multi-source data processing mode is selected to process according to the data exception grade of step (5) and the cycle data validity grade of step (6) to often organizing floating car data.Described multi-source data processing mode mainly comprises following three kinds:

(1) remain unchanged, namely maintain floating car data source image data and do not do to change;

(2) multisource data fusion, is about to floating car data source image data and other data source image data fusion treatment in the same time;

(3) he replaces at source data, is about to other data source image data in the same time and replaces floating car data source image data.

Corresponding processing scheme can be selected to carry out multi-source data processing mode, shown in table 8 specific as follows according to floating car data exception level to cycle data validity rating calculation result:

Table 8

In the present treatment cycle of his-and-hers watches 3 successively, the non-missing data in floating car data source is further processed, wherein 08:10:00,08:11:01,08:12:03 time data exception level is normally, corresponding processing scheme is " remaining unchanged ", can not deal with.08:14:01 time data exception level is mile abnormality, and cycle data validity grade is B, and corresponding processing scheme is " multisource data fusion ".The embodiment of the present invention selects basic average amalgamation mode as data fusion mode, and obtain other data source image data in the same time, this data source has speed=29.70, then after adopting average amalgamation mode to calculate fusion, data are the all data processings in this cycle floating car data source are complete, and the process of this cycle data terminates, and start the multi-source data process in a new cycle.

The know-why being specific embodiments of the invention and using described in above, if the change done according to conception of the present invention, its function produced do not exceed that instructions and accompanying drawing contain yet spiritual time, must protection scope of the present invention be belonged to.

Claims (11)

1. a floating car data source efficiency analysis disposal route, is characterized in that, comprise the steps:

(1) treatment cycle C is preset _tPI, acquiring unit section history floating car data carries out statistical study, calculates the conceptual data validity Q in floating car data source _t;

(2) one day be divided into if having time is paid close attention to period and other periods, according to the time data validity Q often organizing period calculating floating car data source belonging to the floating vehicle data acquisition time _p;

(3) compared by calculating and determine treatment cycle C _tPIthe shortage of data degree in interior floating car data source, carries out missing data according to disappearance degree and supplements process;

(4) computing cycle C successively _tPIthe data traffic validity Q often organizing non-disappearance floating car data in interior step (3) _f;

(5) according to result of calculation Q _t, Q _p, Q _fcomputing cycle C successively _tPIinside often organize the data validity V of non-disappearance floating car data _t, according to result of calculation V _tdetermine floating car data source data exception level;

(6) V is passed through _tpresent treatment cycle C is calculated with step (3) missing data result _tPIthe cycle data validity V in interior floating car data source _tC, according to result of calculation V _tCdetermine cycle data validity grade.

2. a kind of floating car data source according to claim 1 efficiency analysis disposal route, it is characterized in that, selecting multi-source data processing mode to process according to the data exception grade of step (5) and the cycle data validity grade of step (6) to often organizing floating car data.

3. a kind of floating car data source according to claim 1 and 2 efficiency analysis disposal route, it is characterized in that, described step (1) conceptual data validity computing formula is as follows:

N _S＝α _T×(T·h _F)

$Q_T=\frac{\underset{i=1}{\overset{D}{\mathrm{\&Sigma;}}}{N}_{\mathrm{Ti}}}{D\&CenterDot;N_S}$

$h_F=\frac{1}{C_{\mathrm{FC}}}$

Wherein, N _sfor effective traffic data amount that section can gather in length in effective time in a day, α _tfor collective effectiveness coefficient, T is length effective time in a day, and unit is minute; h _ffor the data acquiring frequency in floating car data source, C _fCit is a floating vehicle data acquisition cycle; Q _tfor conceptual data validity, span is [0,1], the statistics number of days of value to be 1, D be historical statistical data when result of calculation is more than 1, N _tiit is the traffic data amount of i-th day actual acquisition.

4. a kind of floating car data source according to claim 1 and 2 efficiency analysis disposal route, is characterized in that, it is as follows that described step (2) calculates time data validity computing formula:

N _Si＝α _P×(T _i·h _F)

$Q_{\mathrm{Pi}}=\frac{\underset{j=1}{\overset{D}{\mathrm{\&Sigma;}}}{N}_{\mathrm{Pij}}}{D\&CenterDot;N_{\mathrm{Si}}}$

Wherein, N _sifor effective traffic data amount that section can gather within i-th period, α _pfor validity coefficient at times, T _ibe the time span of i-th period, unit is minute; Q _pibe the data validity in i-th period, span is [0,1], and when result of calculation is more than 1, value is 1; Q _pfor section time data validity, span is [0,1], and k is the period number paid close attention to, q ₀for the validity of other periods.

5. a kind of floating car data source according to claim 1 and 2 efficiency analysis disposal route, it is characterized in that, described step (3) determines shortage of data degree, carries out the step that missing data supplements process and comprises:

1) the actual Floating Car image data amount N in computing cycle length _c;

2) actual Floating Car image data amount and Ideal float car image data amount N _rsize, N _r=C _tPIh _f;

3) if there is N _c>=N _r, then there is not shortage of data, do not need to carry out missing data and supplement process, missing data supplements process to be terminated;

4) if there is μ N _r≤ N _c< N _r, then there is slight shortage of data, use other data source data to supplement the floating car data in disappearance moment; If there is N _c< μ N _r, then there is serious shortage of data, Floating Car image data in collection period replaced by other data source data in the same time period completely; Wherein, μ is disappearance coefficient;

5) validity of the data after supplementary process is defined as 50%, missing data supplements process to be terminated.

6. a kind of floating car data source according to claim 1 and 2 efficiency analysis disposal route, is characterized in that, the flow validity computing formula of described step (4) non-disappearance floating car data is as follows:

$Q_F=Q_{F0}+{\mathrm{\&alpha;}}_F\&CenterDot;{\log }_{F_A}f$

Wherein, F _afor reference Floating Car flow, round downwards, C _fCDfor city Floating Car sum, C _rfor unit section, city sum; Q _ffor section data traffic validity, span is [Q _f0, 1], when result of calculation is more than 1, value is 1; Q _f0for benchmark Floating Car is passed through data validity; α _ffor data traffic validity coefficient; F is Floating Car flow.

7. a kind of floating car data source according to claim 1 efficiency analysis disposal route, it is characterized in that, in described step (5) treatment cycle, the data validity computing formula of non-disappearance floating car data is as follows:

Q _TP＝min(Q _T,Q _P)

V _T＝[α _VQ _F+(1-α _V)Q _TP]×100％

Wherein, Q _tPfor statistics traffic data is with reference to validity, V _tfor current time floating car data validity result; α _vfor the coefficient of efficiency of traffic data.

8. a kind of floating car data source efficiency analysis disposal route according to claim 1 or 7, is characterized in that, described step (5) floating car data source data exception level is divided into Three Estate: normal, mile abnormality, severe exception; Wherein data effective value be greater than 60% for normal level; Data effective value be less than or equal to 60% and be greater than 20% for mile abnormality; Data effective value be less than or equal to 20% for severely subnormal.

9. a kind of floating car data source according to claim 1 efficiency analysis disposal route, is characterized in that, the calculation procedure of described step (6) Floating Car cycle data validity comprises:

1) select the floating car data except disappearance supplementary data in treatment cycle, calculate initial validity; The formula calculating initial validity is as follows:

$V_{T0}=\frac{1}{k}\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}{V}_{\mathrm{Ti}};$

Wherein, V _t0for cycle data initial validity; K is the floating car data number in treatment cycle except disappearance supplementary data; V _tibe the validity of i-th floating car data;

2) floating car data in the traversal processing cycle except disappearance supplementary data, selects the data meeting data validity condition, gets rid of the data do not satisfied condition; Described data validity condition is

$|V_{\mathrm{Ti}}-V_{T0}|\&le;\sqrt{{\mathrm{\&sigma;}}_0^2+{\mathrm{\&xi;}}^2}$

${\mathrm{\&sigma;}}_0=\sqrt{\frac{1}{k-1}\underset{i=1}{\overset{k}{\mathrm{\&Sigma;}}}{(V_{\mathrm{Ti}}-V_{T0})}^2}$

Wherein, ξ is correction factor; σ ₀for data valid interval;

3) by step 2) in select floating car data with disappearance supplementary data combine, computation period validity; The formula of described computation period validity is as follows:

$V_{\mathrm{TC}}=(\frac{1}{m}\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}{V}_{\mathrm{Tj}}-\mathrm{\&xi;})\&times;100\%$

Wherein, V _tCit is cycle data validity result; M is data amount check; V _tjfor the validity of a jth data.

10. a kind of floating car data source according to claim 1 efficiency analysis disposal route, is characterized in that, described step (6) Floating Car cycle data is divided into A, B, C, D tetra-grades according to cycle data validity value result; Wherein cycle data effective value be less than 30% for A grade; Cycle data effective value be more than or equal to 30% and be less than 60% for B grade; Cycle data effective value be more than or equal to 60% and be less than or equal to 75% for C grade; Cycle data effective value be greater than 75% for D grade.

11. a kind of floating car data source according to claim 2 efficiency analysis disposal routes, it is characterized in that, described multi-source data processing mode comprises following three kinds of modes:

(1) remain unchanged, namely maintain floating car data source image data and do not do to change;

(2) multisource data fusion, is about to floating car data source image data and other data source image data fusion treatment in the same time;

(3) he replaces at source data, is about to other data source image data in the same time and replaces floating car data source image data.