CN104485000B - A kind of floating car data source efficiency analysis processing method - Google Patents

Abstract

The present invention relates to a kind of floating car data source efficiency analysis processing method, comprise the following steps: calculate conceptual data effectiveness and time data effectiveness；Determine shortage of data degree, and carry out the supplementary process of missing data according to disappearance degree；Calculate data traffic effectiveness；Determine often group data validity result successively, and determine data exception grade according to this result；Calculate cycle data validity result, determine cycle data effectiveness grade according to cycle data validity result, and according to data exception grade and the cycle data corresponding processing mode of effectiveness hierarchical selection.The present invention achieves by combining floating car data source quantity and statistic of attribute feature with the method for less parameters identification floating car data source effectiveness, reduces the complexity that floating car data source processes, and practice effect is good, workable；Meanwhile, the present invention ensure that, by choosing project mode multi-source data processing mode, the high efficiency and reliability that multi-source data processes.

Description

A kind of floating car data source efficiency analysis processing method

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 effectiveness Analysis and processing method.

Background technology

Floating car data source is to apply wider a kind of traffic data collection data source in municipal intelligent traffic information gathering, Owing to it is relative to other fixed detector data sources accurately and effectively Data Detection result, bigger coverage and more The advantage such as deployment conditions is at home and abroad widely applied flexibly.Treatment Analysis is carried out also by Floating Car is gathered data Obtain the mainstream research direction that urban road traffic state information result is current intelligent transportation field.

But, floating car data source the most also has certain inherent shortcoming.Due to the inherent characteristics of Floating Car dispersibility, Original Floating Car gathers data and typically has the shortcomings such as data noise is high, subregion deletion condition is serious, stability is not enough, makes A certain degree of congenital defect must be there is based on gained floating car data after this kind of original data processing.Therefore, single floating is used The intelligent transportation system of motor-car data source often has bigger error, needs to combine other detection data sources and comes gathering data It is adjusted improving.This process is exactly a pith of multi-source data treatment technology.

The most domestic to multi-source traffic data treatment technology, especially floating car data source gathers data and other data sources The technical research of the multi-source data treatment technology gathering data the most still stays in theoretical research stage, it is possible to be applied to reality The application to engineering practice research of intelligent transportation system is less, and does not show the effectiveness degree of data source to traffic number Positive impact according to analyzing and processing.It practice, the effectiveness of traffic data is the calculating to road traffic index direct shadow The key factor rung.Correct traffic data reliably can draw objective effective traffic index result, and the traffic number of exception According to then producing part the deviation even result of gross error.Show according to the analysis result of a large amount of traffic datas, traffic number Two aspects are comprised: the completeness of (1) data bulk according to effectiveness；(2) reliability of the quality of data.Meet the most simultaneously this two The traffic data of the requirement of individual aspect is only effective data.Indented material also exists obvious deficiency, reduce with The accuracy of the transport information of the intelligent transportation system based on floating car data source and reliability.Therefore, design one is floated Car data source efficiency analysis processing method is the most necessary.

Summary of the invention

The present invention is to overcome above-mentioned weak point, it is therefore intended that provide a kind of floating car data source efficiency analysis to process Method, the method, with unit section for processing unit, is processed by default process cycle timing, calculates conceptual data effective Property with time data effectiveness；Determine shortage of data degree, and carry out the supplementary process of missing data according to disappearance degree；Calculate number According to flow effectiveness；Determine often group data validity result successively, and determine data exception grade according to this result；The calculating cycle Data validity result, determines cycle data effectiveness grade according to cycle data validity result, and according to data exception etc. Level and the cycle data corresponding processing mode of effectiveness hierarchical selection；Achieve with less parameters identification floating car data source effective Property method, operation efficiency is high, workable, it is ensured that high efficiency that multi-source data processes and reliability.

The present invention is to reach above-mentioned purpose by the following technical programs: a kind of efficiency analysis process side, floating car data source Method, described floating car data source gathers data and detects parameter including at least road-section average travel speed with Floating Car flow two, Described Floating Car flow is a Floating Car collection period C_FCThe Floating Car quantity in this unit section is passed through, including such as in time period Lower step:

(1) process cycle C is preset_TPI, acquiring unit section history floating car data carries out statistical analysis, calculates Floating Car The conceptual data effectiveness Q of data source_T；

(2) are divided into concern period and other periods all times of one day, according to when often organizing floating vehicle data acquisition Period belonging between calculates the time data effectiveness Q in floating car data source_P；

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

(4) calculating processes cycle C successively_TPIThe data traffic often organizing non-disappearance floating car data in interior step (3) is effective Property Q_F；

(5) according to result of calculation Q_T、Q_P、Q_FCalculating processes cycle C successively_TPIThe most often organize the data of non-disappearance floating car data Effectiveness V_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 week in interior floating car data source Phase data validity V_TC, according to result of calculation V_TCDetermine cycle data effectiveness grade.

As preferably, according to the data exception grade of step (5) and the cycle data effectiveness grade of step (6) to often group Floating car data selects multi-source data processing mode to process.

As preferably, described step (1) conceptual data effectiveness 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_SThe effective traffic data amount that can gather in effective time length in one day for section, α_TFor totally having Effect property coefficient, T is the effective time length in a day, and unit is minute；h_FFor the data acquiring frequency in floating car data source, C_FC It it is a floating vehicle data acquisition cycle；Q_TFor conceptual data effectiveness, span is [0,1], takes when result of calculation is more than 1 Value is 1, and D is the statistics natural law of historical statistical data, N_TiIt it is the traffic data amount of i-th day actual acquisition.

As preferably, it is as follows that described step (2) calculates time data effectiveness 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_SiThe effective traffic data amount that can gather within the i-th period for section, α_PFor effectiveness at times Coefficient, T_iFor the time span of i-th period, unit is minute；Q_PiFor the data validity in the i-th period, span For [0,1], when result of calculation is more than 1, value is 1；Q_PFor section time data effectiveness, span is [0,1], and k is for paying close attention to Period number, q₀Effectiveness for other periods.

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

1) the actual Floating Car in calculating processes cycle time gathers data volume N_C；

2) relatively actual Floating Car gathers data volume and gathers data volume N with Ideal float car_RSize, N_R=C_TPI·h_F；

3) if there being N_C≥N_R, the most there is not shortage of data, it is not necessary to carrying out missing data and supplement process, missing data is mended Fill process to terminate；

4) if there being μ N_R≤N_C＜ N_R, then there is slight shortage of data, use other data source data to supplement the disappearance moment Floating car data；If there being N_C＜ μ N_R, then there is serious shortage of data, Floating Car in collection period gathered data and uses completely Replace with other data source data in the time period；Wherein, μ is disappearance coefficient；

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

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

$Q_F=Q_{F0}+{\mathrm{\α}}_F\·{\log }_{F_A}f$

Wherein, F_AFor with reference to Floating Car flow, rounding downwards, C_FCDFor city Floating Car sum, C_RFor unit section, city Sum；Q_FFor section data traffic effectiveness, span is [Q_F0, 1], when result of calculation is more than 1, value is 1；Q_F0On the basis of Floating Car is passed through data validity；α_FFor data traffic effectiveness coefficient；F is Floating Car flow.

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

Q_TP=min (Q_T,Q_P)

V_T=[α_VQ_F+(1-α_V)Q_TP] × 100%

Wherein, Q_TPFor statistics traffic data with reference to effectiveness, 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, slight Extremely, severe is abnormal；Wherein data valid more than 60% for normal level；Data valid is less than or equal to 60% and big In 20% for mile abnormality；Data valid less than or equal to 20% for severely subnormal.

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

1) floating car data outside deferrization loses supplementary data in the selection process cycle, calculates initial validity；At the beginning of calculating The formula of beginning effectiveness is as follows:

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

Wherein, V_T0For cycle data initial validity；K is the Floating Car number outside deferrization loses supplementary data in the process cycle According to number；V_TiEffectiveness for i-th floating car data；

2) floating car data outside deferrization loses supplementary data in the traversal processing cycle, selects to meet data validity condition Data, get rid of and be unsatisfactory for the data of condition；Described data validity condition is

$|V_{\mathrm{Ti}}-V_{T0}|\≤\sqrt{{\mathrm{\σ}}_0^2+{\mathrm{\ξ}}^2}$

${\mathrm{\σ}}_0=\sqrt{\frac{1}{k-1}\underset{i=1}{\overset{k}{\mathrm{\Σ}}}{(V_{\mathrm{Ti}}-V_{T0})}^2}$

Wherein, ξ is correction factor；σ₀For data valid interval；

3) by step 2) in select floating car data with disappearance supplementary data combination, calculate cycle availability；Described meter The formula calculating cycle availability is as follows:

$V_{\mathrm{TC}}=(\frac{1}{m}\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{V}_{\mathrm{Tj}}-\mathrm{\ξ})\×100\%$

Wherein, V_TCIt it is cycle data validity result；M is data amount check；V_TjEffectiveness for jth data.

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

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

(1) keep constant, i.e. maintain floating car data source to gather data and do not change；

(2) multisource data fusion, will floating car data source collection data and other data sources collection data melt in the same time Conjunction processes；

(3) he replaces at source data, other data sources will gather data replacement floating car data source collection data in the same time.

As preferably, described multisource data fusion uses average amalgamation mode to carry out calculating fusion.

As preferably, the selection scheme of described multi-source data processing method is as shown in table 1 below:

Table 1

The beneficial effects of the present invention is: (1) by combine floating car data source totally, at times data statistical characteristics with Real time data feature, it is achieved that with the method for less parameters identification floating car data source effectiveness, operation efficiency is high, operability By force；(2) it is capable of the traffic data under the conditions of more structurally sound multi-data source and processes application further, in turn ensure that multi-source number According to the high efficiency processed and reliability.

Accompanying drawing explanation

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

Detailed description of the invention

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 it is shown in figure 1, a kind of floating car data source efficiency analysis processing method, the method is with unit section For processing unit, process by default process cycle timing；Described unit section is the urban road through pavement section A section in transportation network；There are several road traffics comprising floating car data source in described unit midblock administration Gather data source；Described floating car data is the Floating Car characteristic through pretreatment, and often group Floating Car characteristic comprises Road-section average travel speed and two parameters of Floating Car flow, Floating Car flow is that Floating Car collection period is interior through this unit road The Floating Car quantity of section.

The present invention with 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 is Example, 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 effective time section, effective time length T=14 × 60=840min；With early, time period of evening peak (i.e. morning peak is for 07: 00～09:00 period of two hours, evening peak is 17:00～the 19:00 period of two hours) for paying close attention to the period, during concern Section number k=2.

Set collective effectiveness coefficient as α_T=0.8, timesharing effectiveness coefficient is α_P=0.8, other lime period availability q₀= 1, lack coefficient μ=0.3, data on flows effectiveness factor alpha_F=0.1, benchmark Floating Car is passed through data validity Q_F0=0.5, repair Positive coefficient ξ=0.01.Meanwhile, it is 1/2/3/ according to section significance level by unit pavement section in the main city zone road network of Hangzhou 4 four grades, according to different section grade configuration traffic data coefficient of efficiency α_VIt is worth as shown in table 2 below:

Section grade	1	2	3	4
					αV	0.7	0.6	0.5	0.4

Table 2

Step 1, takes this section history floating car data of continuous 30 days and carries out statistical analysis, and calculating section conceptual data has Effect property can obtain

$Q_T=\frac{\underset{i=1}{\overset{D}{\mathrm{\Σ}}}{N}_{\mathrm{Ti}}}{D\·N_S}=\frac{\underset{i=1}{\overset{30}{\mathrm{\Σ}}}{N}_{\mathrm{Ti}}}{30\×0.8\×840\×1}=0.73$

Wherein N_TiIt is the traffic data quantity of i-th day actual acquisition within 07:00～the 21:00 time period in 30 days, by counting Obtain according to historical data statistics in storehouse.

Step 2, calculates this section timesharing effectiveness according to paying close attention to the period.Morning peak period T₁=evening peak period T₂= 120min, has N_S1=N_S2=0.8 × 120 × 1=96, then timesharing effectiveness in section may be calculated

Step 3, in calculating in real time, 6min section cycle time being currently needed for carrying out data process is 08:10～08:16 (comprise 08:10:00, but do not comprise 08:16:00), 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 current time index and calculate shortage of data degree in cycle time, in the Index for Calculation cycle at current slot place 4 groups of data, i.e. N are had in time_C=4 >=0.3 × 6, there is low volume data disappearance in current slot, by the data in disappearance moment Supplementing by this section other data source data in the same time, after completing to supplement, in the cycle, real time data is as shown in table 4 below, and disappearance is supplemented The effectiveness of data is 50%.

Acquisition time	Speed	Traffic flow	Whether it is 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 supplement after process completes successively in the calculating process cycle floating car data source often organize non-disappearance The data traffic effectiveness of data.Total amount is possessed in Hangzhou Floating Car (generally taxi) at present is about 11000, and receives Road network section, the urban district sum entering intelligent transportation system is about 4000, then with reference to Floating Car flow be

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

Q_F=0.5+0.1 × log₂3=0.6585；

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

Step 5, passes through Q_T、Q_P、Q_FNon-missing data effectiveness determine floating according to result of calculation in the calculating cycle successively Car data source data exception level.Wherein, statistics traffic data with reference to effectiveness 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 effectiveness of the non-missing data of Article 1 For

V_T=[α_VQ_F+(1-α_V)Q_TP] × 100%=65.51%；

May determine that this data exception grade is normal according to the above-mentioned validity result of table 5 below.

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

Table 5

In like manner, the effectiveness that can calculate other non-missing datas successively is respectively 74%, and 62%, 56%, corresponding is different Often grade is respectively normal, normally, and mile abnormality.

Step 6, determines Floating Car cycle data effectiveness, comprises the following steps:

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

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

(2) calculating data valid interval is σ₀=0.075213, according toRule sentence successively Whether disconnected each non-missing data meets condition, retains the non-missing data meeting this condition；

(3) extraction step (2) select floating car data and 4 kinds of table lack supplementary data, as shown in table 6 below:

Acquisition time

Effectiveness

Whether it is 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

Calculating cycle availability is

$V_{\mathrm{TC}}=(\frac{1}{m}\underset{j=1}{\overset{m}{\mathrm{\Σ}}}{V}_{\mathrm{Tj}}-\mathrm{\ξ})\×100\%=55.88\%;$

May determine that cycle data effectiveness grade is B according to table 7 below.

Table 7

The cycle data effectiveness grade of the data exception grade according to step (5) and step (6) is to often organizing Floating Car number Process according to selecting multi-source data processing mode.Described multi-source data processing mode mainly includes following three kinds:

(1) keep constant, i.e. maintain floating car data source to gather data and do not change；

(2) multisource data fusion, will floating car data source collection data and other data sources collection data melt in the same time Conjunction processes；

(3) he replaces at source data, other data sources will gather data replacement floating car data source collection data in the same time.

Corresponding process can be selected with cycle data effectiveness rating calculation result according to floating car data exception level Scheme carries out multi-source data processing mode, shown in table 8 specific as follows:

Table 8

Successively the non-missing data in floating car data source in the present treatment cycle of table 3 is further processed, wherein 08: 10:00,08:11:01,08:12:03 time data exception level is normally, and corresponding processing scheme is " keeping constant ", can Not deal with.08:14:01 time data exception level is mile abnormality, and cycle data effectiveness grade is B, corresponding place Reason scheme is " multisource data fusion ".The embodiment of the present invention selects basic average amalgamation mode as data fusion mode, obtains Taking other data sources in the same time and gather data, this data source has speed=29.70, then after using average amalgamation mode to calculate fusion Data are The all data in this cycle floating car data source are disposed, this cycle data Process terminates, and starts the multi-source data in a new cycle and processes.

It is the specific embodiment of the present invention and the know-why used described in Yi Shang, if conception under this invention institute Make change, function produced by it still without departing from description and accompanying drawing contained spiritual time, must belong to the present invention's Protection domain.

Claims (11)

1. a floating car data source efficiency analysis processing method, it is characterised in that comprise the steps:

(1) process cycle C is preset_TPI, acquiring unit section history floating car data carries out statistical analysis, calculates floating car data The conceptual data effectiveness Q in source_T；

(2) are divided into concern period and other periods all times of one day, according to often organizing floating vehicle data acquisition time institute Belong to the time data effectiveness Q in period calculating floating car data source_P；

(3) compared by calculating and determine process cycle C_TPIThe shortage of data degree in interior floating car data source, enters according to disappearance degree Row missing data supplements and processes；

(4) calculating processes cycle C successively_TPIThe data traffic effectiveness Q often organizing non-disappearance floating car data in interior step (3)_F；

(5) according to result of calculation Q_T、Q_P、Q_FCalculating processes cycle C successively_TPIThe data the most often organizing non-disappearance floating car data are effective Property V_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 periodicity in interior floating car data source According to effectiveness V_TC, according to result of calculation V_TCDetermine cycle data effectiveness grade.

A kind of floating car data source the most according to claim 1 efficiency analysis processing method, it is characterised in that according to step Suddenly the data exception grade of (5) selects multi-source data with the cycle data effectiveness grade of step (6) to often organizing floating car data Processing mode processes.

A kind of floating car data source the most according to claim 1 and 2 efficiency analysis processing method, it is characterised in that institute State step (1) conceptual data effectiveness computing formula as follows:

N_S=α_T×(T·h_F)

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

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

Wherein, N_SThe effective traffic data amount that can gather in effective time length in one day for section, α_TFor collective effectiveness Coefficient, T is the effective time length in a day, and unit is minute；h_FFor the data acquiring frequency in floating car data source, C_FCIt is one The individual floating vehicle data acquisition cycle；Q_TFor conceptual data effectiveness, span is [0,1], and when result of calculation is more than 1, value is 1, D is the statistics natural law of historical statistical data, N_TiIt it is the traffic data amount of i-th day actual acquisition.

A kind of floating car data source the most according to claim 1 and 2 efficiency analysis processing method, it is characterised in that institute State step (2) calculating time data effectiveness computing formula as follows:

N_Si=α_P×(T_i·h_F)

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

Wherein, N_SiThe effective traffic data amount that can gather within the i-th period for section, α_PFor effectiveness system at times Number, T_iFor the time span of i-th period, unit is minute；h_FData acquiring frequency for floating car data source；Q_PiIt is i-th Data validity in the individual period, span is [0,1], and when result of calculation is more than 1, value is 1；N_PijFor the i-th time period The traffic data amount of interior jth sky actual acquisition；D is the statistics natural law of historical statistical data；Q_PFor section time data effectiveness, Span is [0,1], and k is the period number paid close attention to, q₀Effectiveness for other periods.

A kind of floating car data source the most according to claim 1 and 2 efficiency analysis processing method, it is characterised in that institute State step (3) and determine shortage of data degree, carry out missing data and supplement the step of process and include:

1) the actual Floating Car in calculating processes cycle time gathers data volume N_C；

2) relatively actual Floating Car gathers data volume and gathers data volume N with Ideal float car_RSize, N_R=C_TPI·h_F, wherein, h_FData acquiring frequency for floating car data source；

3) if there being N_C≥N_R, the most there is not shortage of data, it is not necessary to carrying out missing data and supplement process, missing data supplements place Reason terminates；

4) if there being μ N_R≤N_C＜ N_R, then there is slight shortage of data, use other data source data to supplement the floating of disappearance moment Motor-car data；If there being N_C＜ μ N_R, then there is serious shortage of data, Floating Car in collection period gathered data completely with simultaneously Between in section other data source data replace；Wherein, μ is disappearance coefficient；

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

A kind of floating car data source the most according to claim 1 and 2 efficiency analysis processing method, it is characterised in that institute The flow effectiveness computing formula stating step (4) non-disappearance floating car data is as follows:

$Q_F=Q_{F0}+{\mathrm{\α}}_F\·{\log }_{F_A}f$

Wherein, F_AFor with reference to Floating Car flow, rounding downwards, C_FCDFor city Floating Car sum, C_RFor unit section, city sum； Q_FFor section data traffic effectiveness, span is [Q_F0, 1], when result of calculation is more than 1, value is 1；Q_F0On the basis of Floating Car Current data validity；α_FFor data traffic effectiveness coefficient；F is Floating Car flow.

A kind of floating car data source the most according to claim 1 efficiency analysis processing method, it is characterised in that described step Suddenly in (5) process 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 with reference to effectiveness, V_TFor current time floating car data validity result；α_VFor traffic The coefficient of efficiency of data.

8. according to a kind of floating car data source efficiency analysis processing method described in claim 1 or 7, it is characterised in that institute State step (5) floating car data source data exception level and be divided into Three Estate: be normal, mile abnormality, severe are abnormal；Wherein count According to virtual value more than 60% for normal level；Data valid less than or equal to 60% and more than 20% for mile abnormality； Data valid less than or equal to 20% for severely subnormal.

A kind of floating car data source the most according to claim 1 efficiency analysis processing method, it is characterised in that described step Suddenly the calculation procedure of (6) Floating Car cycle data effectiveness includes:

1) floating car data outside deferrization loses supplementary data in the selection process cycle, calculates initial validity；Calculate and initially have The formula of effect property is as follows:

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

Wherein, V_T0For cycle data initial validity；K is the floating car data outside deferrization loses supplementary data in the process cycle Number；V_TiEffectiveness for i-th floating car data；

2) floating car data outside deferrization loses supplementary data in the traversal processing cycle, selects to meet the number of data validity condition According to, get rid of the data of the condition that is unsatisfactory for；Described data validity condition is

$|V_{Ti}-V_{T0}|\≤\sqrt{{\mathrm{\σ}}_0^2+{\mathrm{\ξ}}^2}$

${\mathrm{\σ}}_0=\sqrt{\frac{1}{k-1}\underset{i=1}{\overset{k}{\Σ}}{(V_{Ti}-V_{T0})}^2}$

Wherein, ξ is correction factor；σ₀For data valid interval；

3) by step 2) in select floating car data with disappearance supplementary data combination, calculate cycle availability；Described calculating week The formula of phase effectiveness is as follows:

$V_{TC}=(\frac{1}{m}\underset{j=1}{\overset{m}{\Σ}}{V}_{Tj}-\mathrm{\ξ})\×100\%$

Wherein, V_TCIt it is cycle data validity result；M is data amount check；V_TjEffectiveness for jth data.

A kind of floating car data source the most according to claim 1 efficiency analysis processing method, it is characterised in that described Step (6) Floating Car cycle data is divided into tetra-grades of A, B, C, D according to cycle data effectiveness value result；The wherein cycle Data valid less than 30% for A grade；Cycle data virtual value more than or equal to 30% and less than 60% for B grade； Cycle data virtual value more than or equal to 60% and less than or equal to 75% for C grade；Cycle data virtual value is more than 75% For D grade.

11. a kind of floating car data source according to claim 2 efficiency analysis processing methods, it is characterised in that described Multi-source data processing mode include following three kinds of modes:

(1) keep constant, i.e. maintain floating car data source to gather data and do not change；

(2) multisource data fusion, will gather at data and other data sources collection data fusion in floating car data source in the same time Reason；

(3) he replaces at source data, other data sources will gather data replacement floating car data source collection data in the same time.