CN110363483A - A kind of expansion sample check method based on shared platform shipping trip data - Google Patents
A kind of expansion sample check method based on shared platform shipping trip data Download PDFInfo
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Abstract
A kind of expansion sample check method based on shared platform shipping trip data provided in an embodiment of the present invention, it is related to freight traffic management statistical technique field, the expansion sample check method combination shared platform shipping trip data, the Objective for carrying out ship data extracts, and initial data is started the cleaning processing, it corrects perfect, on this basis, complete the expansion sample research of shipping investigation, and intersection check is carried out to sample data are expanded using macroscopical scalar, it determines and expands sample error, reduce the deviation that sample result and practical shipping investigation are expanded in shipping to the greatest extent, the scientific and reasonable of sample result is expanded in shipping, the shipping trip characteristics accurately presented.
Description
Technical field
The present invention relates to freight traffic management statistical technique fields, are gone on a journey in particular to one kind based on shared platform shipping
The expansion sample check method of data.
Background technique
In the prior art, in terms of resident trip survey and vehicle possess distribution, there is corresponding expansion sample check method.
Involved data include door-to-door survey data, such as family information, personal information and personal trip information, also relate to open air
Survey data, such as vehicle flow and cabin factor survey data, bus passenger flow survey data and track passenger flow investigation data, these are big
Part needs manually to be acquired.
Currently without the expansion sample check method for being directed to shipping trip data, the data as involved by freight traffic management and residence
People's trip survey and vehicle possess distribution and there is very big difference, and therefore, it is difficult to the expansion sample check method progress according to these two aspects
Processing needs to design a kind of completely new expansion sample check method.
Summary of the invention
The embodiment of the present invention is to provide a kind of expansion sample check method based on shared platform shipping trip data, can
Alleviate the above problem, expansion sample is carried out to the national volume of goods transported and volume of the circular flow distribution situation and is analyzed, and utilization each province and city volume of production and marketing data,
The macro-datas such as each province's freight classification transport ratio, each province's car ownership carry out check amendment.
In order to alleviate above-mentioned problem；The technical solution that the embodiment of the present invention is taken is as follows:
A kind of expansion sample check method based on shared platform shipping trip data provided in an embodiment of the present invention, comprising:
S1, using shared platform shipping trip data as target sample, pass sequentially through three phases, sampled step by step, most
Platform sampling samples are obtained eventually, determine platform sampling samples capacity；
S2, data prediction classify to platform sampling samples, including lorry classification and freight classification；
S3, variance analysis and amendment carry out missing data completion to platform sampling samples, and export missing data part
Expand all；
S4, on the basis of data prediction and variance analysis, expand spline coefficient according to determining between OD pairs of different provinces, and root
Expansion sample is carried out to platform sampling samples according to the expansion spline coefficient；
S5, expand the check of sample data, including
S51, using each province distribution of goods class year volume of production and marketing macro-data to expand sample after each province, each goods class, each volume of goods transported into
Row is checked, and carries out error analysis:
Wherein, j expression jth kind cargo type, j=1,2 ..., 17, volume of production and marketing macro-data is counted from cargo volume of production and marketing
Mechanism, volume of goods transported expansion sample data are the data in the platform sampling samples after expanding sample；
S52, each province lorry type after expanding sample is checked using each province year car ownership macro-data, is gone forward side by side
Row error analysis:
Wherein, l expression l kind lorry type, l=1,2,3,4；Car ownership macro-data is united from car ownership
Gauge body, goods stock expansion sample data are the data in the platform sampling samples after expanding sample；
S53, divide fuel type occupation rate of market data to check fuel type using each province, and carry out error point
Analysis:
Wherein, y expression y kind fuel type, y=1,2,3；Fuel type occupation rate of market macro-data is divided to come from fuel oil
Type market occupation rate statistical organization, divide fuel type goods stock expand sample data be expand sample after platform sampling samples in number
According to；
S54, it is investigated according to comprehensive traffic and expands sample check successful experience, within the scope of acceptable error, i.e., mean error exists
10% expands sample data hereinafter, output is complete.
In embodiments of the present invention, in conjunction with shared platform shipping trip data, the Objective for carrying out ship data is extracted, and
Initial data is started the cleaning processing, correct it is perfect, on this basis, complete shipping investigation expansion sample research, and using macroscopic view
Scalar carries out intersection check to sample data are expanded, and determines and expands sample error, reduces shipping to the greatest extent and expands sample result and practical goods
The deviation of investigation is transported, scientific and reasonable, the shipping trip characteristics accurately presented of sample result are expanded in shipping.
Optionally, step S1 is specifically included:
S11, first stage sampling, using stratified sampling method, in the way of time layering and region layering, with shared flat
Platform shipping trip data carries out stratified sampling for target sample, obtains first stage sample, and calculate first stage sample
Capacity n1；
S12, second stage sampling, using equal proportion sampling, according to cargo type, according to equal proportion principle, from first
Several unit composition second stage samples are directly extracted in stage sample, and calculate second stage sample size n sample range2；
S13, phase III sampling, using method of random sampling, are directly extracted from second stage sample according to randomly assigne
For several samples as phase III sample, phase III sample is platform sampling samples, calculates phase III sample size
n3, and as platform sampling samples capacity.
In embodiments of the present invention, stratified sampling method, equal proportion sampling and method of random sampling three are successively used step by step
Stage sampling method, can be good at matching that respondent's scale that shipping between OD pairs of city is investigated is big, spy that the field of investigation is wide
Property, take into account scientific and operability.
Optionally, first stage sample size n sample range in step S111It is calculated according to formula (1) or formula (2)
In formula, N is a period of time total sample size in shared platform shipping trip data, and t represents degree of probability Za/2,It is
Average variance in group, Δ represent limit error,Represent into several average intra-class variances.
Optionally, in stratified sampling, the number of units in sample that each layer should extract is allocated using method of equal proportion, calculation formula
Are as follows:
mi=n1Ni/N (1-3)
In formula, miThe sample number that should be extracted for i-th layer, NiFor i-th layer of total sample number.
Optionally, second stage sample size n sample range in step S122It is calculated according to formula (4)
n2=n1t2P(1-P)/n1Δ2+t2P(1-P) (1-4)
In formula, P (1-P) is expressed as several variances.
Optionally, phase III sample size n sample range in step S133Calculation method be according to interval estimation theory, prior
When requirement clearly to estimator, counter to push away parsing and obtain required sample size, which includes two kinds:
The first determines sample size by absolute precision:
Assuming that given absolute precision λ, that is, require
Under 1- α confidence level, meet
I.e.
Compare interval estimation as a result, obtaining:
In formula, u1-α/2It is N (0,1) distributionQuantile,It is estimationMean-squared departure, S2It is overall
Variance；
Second by relative accuracy decision sample size:
Given relative accuracy ε, i.e.,
Under 1- α confidence level, meet
Compare interval estimation as a result, obtaining:
Optionally, step S3 is specifically included:
S31, the platform sampling samples after data prediction are divided to month and carry out point province OD by cargo type and are analyzed, looked into
It sees between the OD of each province and lacks type of merchandize；
S32, in conjunction with province year cargo volume of production and marketing data of respectively setting out, the monthly cargo type highway freight ratio in each province,
Determine whether each moon excalation type of merchandize in each province is abnormal；
S33, it is directed to abnormal type of merchandize, missing cargo is augmented in the OD of province, to be saved involved by the exception cargo
Part highway OD freight volume accounts for the ratio of shipping total amount in province involved by the exception cargo, carries out OD points to the missing cargo type volume of goods transported
Solution generates the data list comprising province of setting out, arrival province, cargo type, shipping total amount；
S34, vehicle, lorry self weight, truckload ratio are corresponded in conjunction with the abnormal cargo of such in platform sampling samples, to goods
Fortune total amount is further decomposed, and is generated comprising province of setting out, is reached province, cargo type, shipping total amount, vehicle, lorry self weight, goods
Vehicle-mounted heavy data list；
S35, the city OD volume of goods transported is determined using year interurban trucking total amount, Expressway Road flow etc., in city OD
Shipping total amount is decomposed in dimension, generates comprising province of setting out, city of setting out, reach province, arrival city, cargo class
Type, shipping total amount, vehicle, lorry self weight, the data list of truckload；
S36, using platform sampling samples point vehicle and divide fuel type proportion, decompose goods by vehicle, fuel type
Freight volume generates comprising province of setting out, city of setting out, reaches province, arrival city, cargo type, shipping total amount, vehicle, fuel oil
The data list of type；
S37, according to freight all kinds annual charging ratio in each province in platform sampling samples, pass through truckload and average dress
Load rate obtains goods weight, and shipping total amount is decomposed into goods weight and cargo transport pass, generates comprising province of setting out, sets out
City reaches province, reaches city, cargo type, shipping total amount, vehicle, fuel type, goods weight, cargo transport pass
Data list；
S38, expansion all for exporting missing data part.
Optionally, step S4 is specifically included:
S41, it determines and expands spline coefficient formula
K=k0*kcargo*kvehicle*kfuel (4-1)
Wherein, koTo expand sample initial coefficients, kcargoFor cargo coefficient of variation, kvehicleVehicle fluctuation is corresponded to for each cargo class
Coefficient, kfuleTo divide vehicle fuel type coefficient of variation；
S42, k is determinedo
ko=Q/q (4-2)
Wherein, Q is OD to the year macroscopic view volume of goods transported, is obtained from cargo volume of production and marketing statistical organization, q is platform sampling samples data
In the year volume of goods transported；
S43, k is determinedcargo
kcargo=qcargo/qr (4-3)
Wherein, qcargoFor the moon volume of goods transported of the platform sampling samples r month class cargo between OD pairs, qrFor platform sampling sample
The r volume of goods transported month in and month out between OD pairs in this；
S44, k is determinedvehicle
kvehicle=qvehicle/qr (4-4)
Wherein, qvehicleThe volume of goods transported of the vehicle between OD pairs is corresponded to for r month class cargo in platform sampling samples；
S45, k is determinedfule
kfule=qfuel/qr (4-5)
Wherein, qfuelFor the volume of goods transported of the r month fuel type between OD pairs in platform sampling samples；
S46, it is calculated and is exported according to formula (4-1) and expand spline coefficient K, sampled using revised expansion spline coefficient K to platform
Sample carries out expansion sample.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, the embodiment of the present invention is cited below particularly, and match
Appended attached drawing is closed, is described in detail below.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is the expansion sample check method flow chart of the present invention based on shared platform shipping trip data；
Fig. 2 is the reasoning flow figure for expanding spline coefficient in the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.The present invention being usually described and illustrated herein in the accompanying drawings is implemented
The component of example can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed
The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without creative efforts belongs to the model that the present invention protects
It encloses.
Please refer to Fig. 1, a kind of expansion sample check side based on shared platform shipping trip data provided in an embodiment of the present invention
Method, comprising:
S1, research cost and data required precision are considered, shipping investigation mainly uses sample investigation side between OD pairs of city
Method, the respondent's scale investigated in view of shipping between OD pairs of city is big, the field of investigation is wide, to take into account in the selection of investigation method
Scientific and operability, comprehensively considers multi-party factor, and to shared platform shipping trip data (such as O2O shipping platform parent
Database) in correlation attribute information analyzed after, determine and generally use three stage sampling methods, therefore with shared platform
Shipping trip data is target sample, passes sequentially through three phases, is sampled step by step, finally obtains platform sampling samples, really
Fixed platform sampling samples capacity；
S2, data prediction classify to platform sampling samples, including lorry classification and freight classification；
Wherein freight classification is carried out according to J/T19-2001 " Transportation Commodity Classification and code ", and 17 kinds of cargos are obtained
Type, shown in freight classification situation table 1:
Table 1
| Number | Cargo type | Number | Cargo type |
| 1 | Agriculture, forestry, animal husbandry and fishery product | 10 | Mineral construction material |
| 2 | Light industry, medical product | 11 | Coal and product |
| 3 | Grain | 12 | Timber |
| 4 | Non-metallic ore | 13 | Cement |
| 5 | Fertilizer and pesticide | 14 | Salt |
| 6 | Steel | 15 | Petroleum, natural gas and its product |
| 7 | Industrial chemicals and product | 16 | Non-ferrous metal |
| 8 | Mechanical equipment, electric appliance | 17 | Other |
| 9 | Metallic ore |
Lorry is classified as the different brands being collected into from commercial websites such as quotient's vehicle nets (https: //www.cn357.com/)
Wagon Tech parametric statistics data combine collected external data by vehicle commander, load-carrying, self weight to goods according to lorry brand field
Vehicle is classified, and lorry classification situation is shown in Table 2, and is omitted here the data such as vehicle commander, load-carrying, self weight:
Table 2
| Number | Lorry type | Number | Lorry type |
| 1 | Dumper | 4 | Light-duty Vehicle |
| 2 | Common in-vehicle | ||
| 3 | Tractor |
S3, variance analysis and amendment carry out missing data completion to platform sampling samples, and export missing data part
Expand all；
S4, on the basis of data prediction and variance analysis, expand spline coefficient according to determining between OD pairs of different provinces, and root
Expansion sample is carried out to platform sampling samples according to the expansion spline coefficient；
S5, expand the check of sample data, including
S51, using each province distribution of goods class year volume of production and marketing macro-data to expand sample after each province, each goods class, each volume of goods transported into
Row is checked, and carries out error analysis:
Wherein, j expression jth kind cargo type, j=1,2 ..., 17, volume of production and marketing macro-data is counted from cargo volume of production and marketing
Mechanism, volume of goods transported expansion sample data are the data in the platform sampling samples after expanding sample；
S52, each province lorry type after expanding sample is checked using each province year car ownership macro-data, is gone forward side by side
Row error analysis:
Wherein, l expression l kind lorry type, l=1,2,3,4；Car ownership macro-data is united from car ownership
Gauge body, goods stock expansion sample data are the data in the platform sampling samples after expanding sample；
S53, divide fuel type occupation rate of market data to check fuel type using each province, and carry out error point
Analysis:
Wherein, y expression y kind fuel type, y=1,2,3；Fuel type occupation rate of market macro-data is divided to come from fuel oil
Type market occupation rate statistical organization, divide fuel type goods stock expand sample data be expand sample after platform sampling samples in number
According to table 3 is to show three kinds of fuel types；
Table 3
| Number | Fuel type |
| 1 | Diesel oil |
| 2 | Gasoline |
| 3 | Natural gas |
S54, it is investigated according to comprehensive traffic and expands sample check successful experience, within the scope of acceptable error, i.e., mean error exists
10% hereinafter, output includes the complete expansion sample data of error, can refer to table 5, table 6, data shown in table 7.
During expansion sample data of the invention are checked, each province distribution of goods class year, volume of production and marketing macro-data was mainly from China
Highway index net, the annual national economy and social development statistical communique that each province statistics bureau, statistical information net are issued, each province and city
The annual and monthly Economic Operation of Committee of Development and Reform's website orientation, each province and city work, agriculture data release plan, each province and city count year
Mirror；Each province year car ownership macro-data is mainly from International Statistical office and China Association for Automobile Manufacturers；Each province point combustion
Oil type occupation rate of market data are mainly from Chinese fuel oil market annual report.
Optionally, step S1 is specifically included:
S11, first stage sampling, using stratified sampling method, in the way of time layering and region layering, with shared flat
Platform shipping trip data carries out stratified sampling for target sample, obtains first stage sample, and calculate first stage sample
Capacity n1；
Time layering is to be divided into whole year 12 months according to calendar month operating, and region layering is according to China mainland
The whole nation is divided into 31 areas to operate by area's administrative division；
S12, second stage sampling, using equal proportion sampling, according to cargo type, according to equal proportion principle, from first
Several unit composition second stage samples are directly extracted in stage sample, and calculate second stage sample size n sample range2；
S13, phase III sampling, using method of random sampling, are directly extracted from second stage sample according to randomly assigne
For several samples as phase III sample, phase III sample is platform sampling samples, calculates phase III sample size
n3, and as platform sampling samples capacity.
In embodiments of the present invention, stratified sampling method, equal proportion sampling and method of random sampling three are successively used step by step
Stage sampling method, can be good at matching that respondent's scale that shipping between OD pairs of city is investigated is big, spy that the field of investigation is wide
Property, take into account scientific and operability.
Optionally, first stage sample size n sample range in step S111It is calculated according to formula (1) or formula (2)
In formula, N is a period of time total sample size in shared platform shipping trip data, and t represents degree of probability Za/2,It is
Average variance in group, Δ represent limit error,Represent into several average intra-class variances.
Optionally, in stratified sampling, the number of units in sample that each layer should extract is allocated using method of equal proportion, calculation formula
Are as follows:
mi=n1Ni/N (1-3)
In formula, miThe sample number that should be extracted for i-th layer, NiFor i-th layer of total sample number.
Optionally, second stage sample size n sample range in step S122It is calculated according to formula (4)
n2=n1t2P(1-P)/n1Δ2+t2P(1-P) (1-4)
In formula, P (1-P) is expressed as several variances.
Optionally, phase III sample size n sample range in step S133Calculation method be according to interval estimation theory, prior
When requirement clearly to estimator, counter to push away parsing and obtain required sample size, which includes two kinds:
The first determines sample size by absolute precision:
Assuming that given absolute precision λ, that is, require
Under 1- α confidence level, meet
I.e.
Compare interval estimation as a result, obtaining:
In formula, u1-α/2It is N (0,1) distributionQuantile,It is estimationMean-squared departure, S2It is overall
Variance；
Second by relative accuracy decision sample size:
Given relative accuracy ε, i.e.,
Under 1- α confidence level, meet
Compare interval estimation as a result, obtaining:
The successful experiences such as China's Urban Residential Trip survey sampling, volume of road freight survey sampling are used for reference, it is specified that originally
For inventive method under 95% confidence level, the limit relative error range of sampling aim parameter estimation is 10% to 15%.Therefore root
The sampling samples capacity formula of upper three stage samplings method can successively calculate shipping city OD in last the method for the present invention accordingly
To sample size needed for expanding sample investigation.Sampling samples amount size is finally shown in the form of by the month OD volume of goods transported, is with 2018
Example, as shown in table 4, sample sample size is about ten thousand tons of volumes of goods transported of xxx, and sampling rate is about xxx%, this shipping survey sampling rate
Close to theoretical value, meet the sampling rate requirement under Conditions of General Samples.
Table 4
| Month | Sample size (ten thousand tons) | Month | Sample size (ten thousand tons) |
| In January, 2018 | Xxx ten thousand | In July, 2018 | Xxx ten thousand |
| 2 months 2018 | Xxx ten thousand | In August, 2018 | Xxx ten thousand |
| In March, 2018 | Xxx ten thousand | In September, 2018 | Xxx ten thousand |
| In April, 2018 | Xxx ten thousand | In October, 2018 | Xxx ten thousand |
| In May, 2018 | Xxx ten thousand | In November, 2018 | Xxx ten thousand |
| In June, 2018 | Xxx ten thousand | In December, 2018 | Xxx ten thousand |
Optionally, step S3 is specifically included:
S31, the platform sampling samples after data prediction are divided to month and carry out point province OD by cargo type and are analyzed, looked into
It sees between the OD of each province and lacks type of merchandize；
S32, in conjunction with province year cargo volume of production and marketing data of respectively setting out, the monthly cargo type highway freight ratio in each province,
Determine whether each moon excalation type of merchandize in each province is abnormal；
S33, it is directed to abnormal type of merchandize, missing cargo is augmented in the OD of province, to be saved involved by the exception cargo
Part highway OD freight volume accounts for the ratio of shipping total amount in province involved by the exception cargo, carries out OD points to the missing cargo type volume of goods transported
Solution generates the data list comprising province of setting out, arrival province, cargo type, shipping total amount；
S34, vehicle, lorry self weight, truckload ratio are corresponded in conjunction with the abnormal cargo of such in platform sampling samples, to goods
Fortune total amount is further decomposed, and is generated comprising province of setting out, is reached province, cargo type, shipping total amount, vehicle, lorry self weight, goods
Vehicle-mounted heavy data list；
S35, the city OD volume of goods transported is determined using year interurban trucking total amount, Expressway Road flow etc., in city OD
Shipping total amount is decomposed in dimension, generates comprising province of setting out, city of setting out, reach province, arrival city, cargo class
Type, shipping total amount, vehicle, lorry self weight, the data list of truckload；
S36, using platform sampling samples point vehicle and divide fuel type proportion, decompose goods by vehicle, fuel type
Freight volume generates comprising province of setting out, city of setting out, reaches province, arrival city, cargo type, shipping total amount, vehicle, fuel oil
The data list of type；
S37, according to freight all kinds annual charging ratio in each province in platform sampling samples, pass through truckload and average dress
Load rate obtains goods weight, and shipping total amount is decomposed into goods weight and cargo transport pass, generates comprising province of setting out, sets out
City reaches province, reaches city, cargo type, shipping total amount, vehicle, fuel type, goods weight, cargo transport pass
Data list；
S38, expansion all for exporting missing data part.
Optionally, as shown in Fig. 2, step S4 is specifically included:
S41, it determines and expands spline coefficient formula
K=k0*kcargo*kvehicle*kfuel (4-1)
Wherein, koTo expand sample initial coefficients, kcargoFor cargo coefficient of variation, kvehicleVehicle fluctuation is corresponded to for each cargo class
Coefficient, kfuleTo divide vehicle fuel type coefficient of variation；
S42, k is determinedo
ko=Q/q (4-2)
Wherein, Q is OD to the year macroscopic view volume of goods transported, is obtained from cargo volume of production and marketing statistical organization, q is platform sampling samples data
In the year volume of goods transported；
S43, k is determinedcargo
kcargo=qcargo/qr (4-3)
Wherein, qcargoFor the moon volume of goods transported of the platform sampling samples r month class cargo between OD pairs, qrFor platform sampling sample
The r volume of goods transported month in and month out between OD pairs in this；
S44, k is determinedvehicle
kvehicle=qvehicle/qr (4-4)
Wherein, qvehicleThe volume of goods transported of the vehicle between OD pairs is corresponded to for r month class cargo in platform sampling samples；
S45, k is determinedfule
kfule=qfuel/qr (4-5)
Wherein, qfuelFor the volume of goods transported of the r month fuel type between OD pairs in platform sampling samples；
S46, it is calculated and is exported according to formula (4-1) and expand spline coefficient K, sampled using revised expansion spline coefficient K to platform
Sample carries out expansion sample.
Table 5, table 6, table 7 are expansion sample result few examples of the invention:
5 2018 years each province volumes of goods transported of table and error statistics (unit: ten thousand tons)
| Province | January | Error | 2 months | Error | March | Error | April | Error | May | Error | June | Error |
| Shanghai | 3211.3 | 0.018 | 2851.268 | 0.052935 | 3406.652 | 0.065666 | 3124.757 | 0.075284 | 3157.018 | 0.077282 | 2914.982 | 0.153695 |
| Yunnan | 7915.33 | 0.025 | 6899.404 | 0.112415 | 11076.1 | 0.027709 | 11489.77 | 0.013858 | 9718.482 | 0.114475 | 9855.58 | 0.023075 |
| The Inner Mongol | 8816.99 | 0.067 | 6249.132 | 0.062227 | 11307.45 | 0.025412 | 10782.97 | 0.049896 | 12119.18 | 0.070617 | 12651.41 | 0.060751 |
| Beijing | 1140.64 | 0.013 | 982.7772 | 0.142974 | 1203.705 | 0.024338 | 1686.356 | 0.070355 | 1525.709 | 0.180435 | 2033.351 | 0.001303 |
| Jilin | 2940.86 | 0.014 | 1322.213 | 0.017234 | 2816.543 | 0.040637 | 3883.882 | 0.006725 | 4198.772 | 0.008152 | 4252.901 | 0.005431 |
| Sichuan | 11657.42 | 0.003 | 6726.278 | 0.09377 | 11252.39 | 0.061641 | 12318.11 | 0.041312 | 13566.69 | 0.08759 | 12694.13 | 0.003377 |
| Tianjin | 2233.93 | 0.084 | 2139.081 | 0.137404 | 2690.167 | 0.04566 | 2936.785 | 0.039232 | 2801.818 | 0.129624 | 2983.762 | 0.073142 |
| Ningxia | 1891.34 | 0.183 | 1616.389 | 0.035642 | 2317.745 | 0.078203 | 2981.495 | 0.034716 | 3269.576 | 0.038973 | 2684.336 | 0.17571 |
| Anhui | 15186.48 | 0.125 | 9952.966 | 0.135541 | 23917.2 | 0.004649 | 22388.61 | 0.072152 | 24926.26 | 0.014793 | 20659.39 | 0.118377 |
| Shandong | 15621.1 | 0.065 | 13830.78 | 0.029587 | 22968.77 | 0.088957 | 23310.59 | 0.074576 | 25443.04 | 0.051997 | 24663.48 | 0.05938 |
| Shanxi | 8049.51 | 0.017 | 5178.149 | 0.057907 | 5700.926 | 0.090525 | 8129.013 | 0.044038 | 8626.031 | 0.056569 | 10005.893 | 0.012784 |
| Guangdong | 17966.8 | 0.04 | 21319.08 | 0.014865 | 20722.69 | 0.047451 | 22610.25 | 0.006358 | 24309.85 | 0.023853 | 23229.32 | 0.031842 |
| Guangxi | 10044.3 | 0.05 | 6242.163 | 0.039543 | 10249.41 | 0.036937 | 11212.44 | 0.013428 | 11729.32 | 0.048228 | 10827.56 | 0.048436 |
| Xinjiang | 4404.07 | 0.08 | 1981.463 | 0.075973 | 6188.494 | 0.056962 | 6301.438 | 0.089434 | 6249.571 | 0.01287 | 5529.859 | 0.275439 |
| Jiangsu | 7079.25 | 0.042 | 7199.469 | 0.098414 | 10991.54 | 0.0287 | 11005.35 | 0.024 | 10739.31 | 0.114504 | 10739.29 | 0.035357 |
| Jiangxi | 10230.62 | 0.02 | 5176.989 | 0.082482 | 11025.81 | 0.014891 | 11030.74 | 0.011809 | 9432.008 | 0.081424 | 10568.91 | 0.010322 |
| Hebei | 12444.94 | 0.081 | 7360.87 | 0.102723 | 15493.69 | 0.021577 | 15186.77 | 0.160154 | 17524.06 | 0.13815 | 17428.63 | 0.082128 |
| Henan | 14331.35 | 0.097 | 5863.256 | 0.066302 | 12294.71 | 0.131381 | 13840.16 | 0.094857 | 15149.24 | 0.079658 | 16675.88 | 0.089837 |
| Zhejiang | 9544.46 | 0.023 | 8501.852 | 0.008957 | 9564.737 | 0.142426 | 13979.52 | 0.096819 | 13599 | 0.011912 | 12295.93 | 0.015377 |
| Hainan | 886.63 | 0.008 | 846.1853 | 0.001401 | 875.5046 | 0.000566 | 874.4422 | 0.002925 | 883.7024 | 0.001926 | 912.6837 | 0.003634 |
The table 6 2018 fraction of the year cargo type volume of goods transported (unit: ten thousand tons)
| Type of merchandize | January | 2 months | March | April | May | June | July | August | September | October | November | December |
| 0 | 0 | 8.5662 | 6.0251 | 0 | 0 | 0 | 0.74125 | 0 | 0.65214 | 0.70231 | 0 | |
| Other | 5621.562 | 2324.213 | 4119.692 | 4872.472 | 4258.014 | 6591.685 | 4613.553 | 5998.225 | 5009.2 | 5915.412 | 6194.12 | 4765.357 |
| Agriculture, forestry, animal husbandry and fishery product | 58515.34 | 49942.1 | 64051.92 | 76107.94 | 85497.55 | 73686.04 | 96018.51 | 90998.1 | 86677.88 | 90290.56 | 96751.7 | 79125.48 |
| Industrial chemicals and product | 8823.129 | 6698.368 | 10518.6 | 11726.56 | 11782.94 | 11136.98 | 11895.59 | 10556.57 | 11692.86 | 10369.53 | 11355.11 | 10632.82 |
| Non-ferrous metal | 239.0282 | 2891.939 | 1884.259 | 4693.669 | 7715.893 | 300.7447 | 5393.4614 | 3041.264 | 448.7136 | 3085.799 | 3269.311 | 433.7968 |
| Timber | 332.2003 | 587.5458 | 3307.728 | 2736.642 | 1110.842 | 2051.674 | 1356.406 | 4561.043 | 4162.762 | 4462.865 | 4771.937 | 4057.054 |
| Mechanical equipment, electric appliance | 23976.07 | 16399.21 | 32934.38 | 31965.93 | 28900.78 | 30525.42 | 39009.09 | 37576.5 | 38762.4 | 36940.86 | 40209.02 | 35007.18 |
| Cement | 5033.209 | 130.703 | 241.2424 | 256.5611 | 162.3674 | 5868.631 | 286.5022 | 439.2028 | 7415.718 | 448.6254 | 467.734 | 7355.141 |
| Coal and its product | 8738.482 | 5368.435 | 4967.678 | 8139.926 | 10047.36 | 11764.59 | 7379.881 | 5434.969 | 15564.84 | 5251.552 | 4389.864 | 14081.22 |
| Salt | 126.0978 | 148.3826 | 13.71945 | 129.9412 | 238.5026 | 141.404 | 46.30471 | 226.7375 | 251.2598 | 227.829 | 236.8429 | 237.421 |
| Petroleum, natural gas and its product | 858.4272 | 1595.792 | 3728.116 | 2955.872 | 1786.284 | 866.9424 | 1939.271 | 922.382 | 982.6431 | 921.7735 | 938.9019 | 946.4748 |
| Mineral construction material | 11949.08 | 4280.233 | 12968.92 | 11055.31 | 7648.643 | 14717.21 | 7142.843 | 22451.94 | 23877.23 | 22875.79 | 24575.53 | 23314.12 |
| Grain | 5730.27 | 1985.651 | 3695.262 | 4332.912 | 4127.161 | 7583.869 | 3383.778 | 4921.332 | 8128.476 | 5051.636 | 4566.817 | 7085.13 |
| Fertilizer and pesticide | 4948.104 | 2329.153 | 4716.197 | 5059.472 | 4698.946 | 6487.782 | 2571.95 | 3227.086 | 4594.529 | 3280.925 | 3707.559 | 4057.353 |
| Light industry, medical product | 78051.13 | 57375.72 | 91566.03 | 97409.49 | 101647.6 | 95336.08 | 86198.33 | 85195.21 | 96503.58 | 84940.71 | 91332.27 | 89727.94 |
| Metallic ore | 10396.89 | 5277.787 | 8846.799 | 10311.16 | 10932.99 | 13002.51 | 12674.9 | 8978.381 | 12424.09 | 9181.156 | 9159.759 | 11933.5 |
| Steel | 10595.63 | 11450.27 | 7866.942 | 10904.15 | 22213.63 | 12853.96 | 28839.51 | 30355.3 | 12483.53 | 30226.74 | 32529.56 | 10988.54 |
| Non-metallic ore | 8041.365 | 6631.87 | 10270.08 | 10273.53 | 10192.45 | 9578.625 | 7852.742 | 14482.96 | 14868.64 | 14886.95 | 15265.02 | 14172.22 |
7 2018 years vehicle statistics (pass) of table
| Lorry vehicle | January | 2 months | March | April | May | June | July | August | September | October | November | December |
| Common in-vehicle | 66109637 | 49677369 | 86821986 | 64473254 | 53411392 | 66872115 | 8926195 | 100312902 | 97588906 | 100312902 | 105487347 | 93493249 |
| Tractor | 54238809 | 39023598 | 66195318 | 77147167 | 82439645 | 63549596 | 11273848 | 76641739 | 84753024 | 76641739 | 83309291 | 80310032 |
| Dumper | 52369848 | 39014109 | 62356523 | 47257096 | 40854597 | 52861219 | 7497167 | 86608224 | 75225970 | 86608224 | 88230555 | 72433316 |
| Light-duty Vehicle | 13149939 | 10876550 | 18457486 | 13625979 | 11369862 | 13246276 | 1548695 | 22645435 | 19230245 | 22648435 | 22386589 | 18532760 |
The foregoing is only a preferred embodiment of the present invention, is not intended to restrict the invention, for the skill of this field
For art personnel, the invention may be variously modified and varied.All within the spirits and principles of the present invention, made any to repair
Change, equivalent replacement, improvement etc., should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of expansion sample check method based on shared platform shipping trip data characterized by comprising
S1, using shared platform shipping trip data as target sample, pass sequentially through three phases, sampled step by step, it is final
To platform sampling samples, platform sampling samples capacity is determined；
S2, data prediction classify to platform sampling samples, including lorry classification and freight classification；
S3, variance analysis and amendment carry out missing data completion to platform sampling samples, and export the expansion sample of missing data part
Sample；
S4, on the basis of data prediction and variance analysis, expand spline coefficient according to determining between OD pairs of different provinces, and according to this
Expand spline coefficient and expansion sample is carried out to platform sampling samples；
S5, expand the check of sample data, including
S51, school is carried out to each province, each goods class, each volume of goods transported after expansion sample using each province distribution of goods class year volume of production and marketing macro-data
Core, and carry out error analysis:
Wherein, j expression jth kind cargo type, j=1,2 ..., 17, volume of production and marketing macro-data comes from cargo volume of production and marketing statistical machine
Structure, volume of goods transported expansion sample data are the data in the platform sampling samples after expanding sample；
S52, each province lorry type after expanding sample is checked using each province year car ownership macro-data, and is missed
Difference analysis:
Wherein, l expression l kind lorry type, l=1,2,3,4；Car ownership macro-data comes from car ownership statistical machine
Structure, goods stock expansion sample data are the data in the platform sampling samples after expanding sample；
S53, divide fuel type occupation rate of market data to check fuel type using each province, and carry out error analysis:
Wherein, y expression y kind fuel type, y=1,2,3；Fuel type occupation rate of market macro-data is divided to come from fuel type
Occupation rate of market statistical organization, divide fuel type goods stock expand sample data be expand sample after platform sampling samples in data；
S54, it is investigated according to comprehensive traffic and expands sample check successful experience, within the scope of acceptable error, i.e., mean error is 10%
Hereinafter, output is complete to expand sample data.
2. the expansion sample check method according to claim 1 based on shared platform shipping trip data, which is characterized in that step
Rapid S1 is specifically included:
S11, first stage sampling, using stratified sampling method, in the way of time layering and region layering, with shared platform goods
Fortune trip data carries out stratified sampling for target sample, obtains first stage sample, and calculate first stage sample size
n1；
S12, second stage sampling, using equal proportion sampling, according to cargo type, according to equal proportion principle, from the first stage
Several unit composition second stage samples are directly extracted in sample, and calculate second stage sample size n sample range2；
S13, phase III sampling, using method of random sampling, are directly extracted from second stage sample several according to randomly assigne
For sample as phase III sample, phase III sample is platform sampling samples, calculates phase III sample size n sample range3, and
As platform sampling samples capacity.
3. the expansion sample check method according to claim 2 based on shared platform shipping trip data, which is characterized in that step
First stage sample size n sample range in rapid S111It is calculated according to formula (1) or formula (2)
In formula, N is a period of time total sample size in shared platform shipping trip data, and t represents degree of probability Za/2,It is in group
Average variance, Δ represent limit error,Represent into several average intra-class variances.
4. the expansion sample check method according to claim 3 based on shared platform shipping trip data, which is characterized in that point
In layer sampling, the number of units in sample that each layer should extract is allocated using method of equal proportion, calculation formula are as follows:
mi=n1Ni/N (1-3)
In formula, miThe sample number that should be extracted for i-th layer, NiFor i-th layer of total sample number.
5. the expansion sample check method according to claim 3 based on shared platform shipping trip data, which is characterized in that step
Second stage sample size n sample range in rapid S122It is calculated according to formula (4)
n2=n1t2P(1-P)/n1Δ2+t2P(1-P) (1-4)
In formula, P (1-P) is expressed as several variances.
6. the expansion sample check method according to claim 5 based on shared platform shipping trip data, which is characterized in that step
Phase III sample size n sample range in rapid S133Calculation method be according to interval estimation theory, clearly estimator is wanted in advance
When asking, it is counter push away parsing obtain required sample size, the calculation method include two kinds:
The first determines sample size by absolute precision:
Assuming that given absolute precision λ, that is, require
Under 1- α confidence level, meet
I.e.
Compare interval estimation as a result, obtaining:
In formula, u1-α/2It is N (0,1) distributionQuantile,It is estimationMean-squared departure, S2It is population variance；
Second by relative accuracy decision sample size:
Given relative accuracy ε, i.e.,
Under 1- α confidence level, meet
Compare interval estimation as a result, obtaining:
7. the expansion sample check method according to claim 1 based on shared platform shipping trip data, which is characterized in that step
Rapid S3 is specifically included:
S31, the platform sampling samples after data prediction are divided to month and carry out point province OD by cargo type and are analyzed, checked each
Type of merchandize is lacked between the OD of province；
S32, in conjunction with province year cargo volume of production and marketing data of respectively setting out, the monthly cargo type highway freight ratio in each province, determine
Whether each moon excalation type of merchandize in each province is abnormal；
S33, it is directed to abnormal type of merchandize, missing cargo is augmented in the OD of province, with the public affairs of province involved by the exception cargo
Road OD freight volume accounts for the ratio of shipping total amount in province involved by the exception cargo, carries out OD decomposition to the missing cargo type volume of goods transported, raw
At the data list comprising province of setting out, arrival province, cargo type, shipping total amount；
S34, vehicle, lorry self weight, truckload ratio are corresponded in conjunction with the abnormal cargo of such in platform sampling samples, it is total to shipping
Amount further decomposition generates comprising province of setting out, reaches province, cargo type, shipping total amount, vehicle, lorry self weight, lorry load
The data list of weight；
S35, the city OD volume of goods transported is determined using year interurban trucking total amount, Expressway Road flow etc., in city OD dimension
On shipping total amount is decomposed, generate comprising set out province, city of setting out, reach province, reach city, cargo type, goods
Transport total amount, vehicle, lorry self weight, the data list of truckload；
S36, using platform sampling samples point vehicle and divide fuel type proportion, decompose the volume of goods transported by vehicle, fuel type,
Generate includes set out province, city of setting out, arrival province, arrival city, cargo type, shipping total amount, vehicle, fuel type
Data list；
S37, according to freight all kinds annual charging ratio in each province in platform sampling samples, pass through truckload and average charging ratio
Goods weight is obtained, shipping total amount is decomposed into goods weight and cargo transport pass, is generated comprising set out province, city of setting out
City reaches province, reaches city, cargo type, shipping total amount, vehicle, fuel type, goods weight, cargo transport pass
Data list；
S38, expansion all for exporting missing data part.
8. the expansion sample check method according to claim 1 based on shared platform shipping trip data, which is characterized in that step
Rapid S4 is specifically included:
S41, it determines and expands spline coefficient formula
K=k0*kcargo*kvehicle*kfuel (4-1)
Wherein, koTo expand sample initial coefficients, kcargoFor cargo coefficient of variation, kvehicleVehicle coefficient of variation is corresponded to for each cargo class,
kfuleTo divide vehicle fuel type coefficient of variation；
S42, k is determinedo
ko=Q/q (4-2)
Wherein, Q is OD to the year macroscopic view volume of goods transported, is obtained from cargo volume of production and marketing statistical organization, q is in platform sampling samples data
Year volume of goods transported；
S43, k is determinedcargo
kcargo=qcargo/qr (4-3)
Wherein, qcargoFor the moon volume of goods transported of the platform sampling samples r month class cargo between OD pairs, qrFor in platform sampling samples
The r volume of goods transported month in and month out between OD pairs；
S44, k is determinedvehicle
kvehicle=qvehicle/qr (4-4)
Wherein, qvehicleThe volume of goods transported of the vehicle between OD pairs is corresponded to for r month class cargo in platform sampling samples；
S45, k is determinedfule
kfule=qfuel/qr (4-5)
Wherein, qfuelFor the volume of goods transported of the r month fuel type between OD pairs in platform sampling samples；
S46, it is calculated and is exported according to formula (4-1) and expand spline coefficient K, using revised expansion spline coefficient K to platform sampling samples
Carry out expansion sample.
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