CN109345136A - Vehicle survival curve model optimization method based on China's motor vehicle resignation system - Google Patents
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Abstract
The vehicle survival curve model optimization method based on China's motor vehicle resignation system that the invention discloses a kind of, this method comprises: on the basis of Weibull distribution survival curve model, the characteristics of carrying out Life cycle record to bicycle using China's motor vehicle resignation system, establishes the characteristic parameter new car increment n of curve model0,j(k), learies si,j(k+i) direct calibration method, so that optimization is suitable for the vehicle survival curve model of China's national situation.Compared with prior art, the advantage of the invention is that taking full advantage of the data characteristics that China's motor vehicle resignation system business datum carries out Life cycle record to bicycle, realize the accurate acquisition to vehicle survival law characteristic parameter, compared with existing indirect calibration method, the present invention has higher reliability；Due to inventing the implementation front and back stage that related business datum time span includes the work of China's Motor Vehicle Pollution Prevention, this Optimized model can reflect influence of the vehicle management and control measures to vehicle survival curve.
Description
Technical field
The present invention relates to model optimization technologies, and in particular to a kind of vehicle survival based on China's motor vehicle resignation system is bent
Line model optimization method.
Background technique
Vehicle survival rule is vehicle safety and emission performance assessment, the important foundation for scrapping policy making, is grasped current
Vehicle survival rule is the element task of macroscopical automobile pollution control.
The survival curve model indicated is usually distributed with Weibull probability to describe vehicle survival rule, vehicle survival curve
Refer to the survival probability change curve in vehicle-mounted age, can intuitively reflect vehicle survival rule.The key input parameter of the model
Include: each vehicle the new car increment in each vehicle age/time, each vehicle each vehicle age/time learies.Current research is limited
In imperfect, skimble-scamble historical data, to the calibration of two key input parameters of model use substitution, it is counter push away etc. it is indirect
Method, error are larger；On the other hand, since the data of domestic correlative study count before 2010 more, and China nearly 20
A series of measures is taken over year during Motor Vehicle Pollution Prevention, quickening is eliminated old, high emission vehicle, changed
The survival law characteristic of China's vehicle makes the input parameter of survival curve model change.When preceding because of China's communications and transportation body
The management system of system constantly improve, and mature automotive business information resignation system and historical data abundant is formd, for me
Key input parameter calibration in state's vehicle survival law study provides the data basis of optimization method.Therefore, instantly to me
The research of state's vehicle survival rule has practical significance.
The determination of motor vehicle survival curve needs the data accumulation of many years.The starting of western developed country auto industry is more early,
It just experienced the stage that motor vehicle liquidation amount improves rapidly after World War II, carried out the research of more automobile survival rule aspect.
The early stage research of survival curve is concentrated mainly on to the existence of survey region vehicle and superseded law characteristic, with the acquisition of survival curve
Method, approximating method and signature analysis are principal concern.Nineteen ninety-five, Zachariadis etc. are simulated using Weibull distribution
The survival rate of motor vehicle.De passes through the comparison to four kinds of survival distributions fitting effects, it is indicated that Weibull Distribution effect is excellent
In Exponential Model effect, wherein optimal with the Weibull Distribution effect for meeting the distribution of gamma isomery.Chen etc. is with prestige
Premised on boolean's distribution, the survival curve deduction method based on a small amount of observation data is constructed.Currently, vehicle is deposited in many researchs
Curve living predicts fleet's technical level of survey region future scene as important foundation research, for support
Decision, emission reduction effect assessment and the assessment of energy policy implementation result are replaced or scrapped to vehicle, constructs a variety of energy on this basis
Source consumption or environment influence to calculate assessment models.Chen etc. scraps making policies from country and the displacement of resident's personal vehicles is selected as
Research purpose, it is bent with the survival feature that fleet's situation in this state of Illinois county Du Peiji constructs different fuel type of vehicle
Line；Survival curve is applied to be distributed 2015~2035 years fleet's technical levels in Ireland pre- with each vehicle sales volume by Alam etc.
It surveys, and is combined with discharge Calculating model, for the effect of a variety of reduction of greenhouse gas discharge policies is assessed in simulation.
China is still at an early stage in the data accumulation and progress of vehicle survival rule, since basic data lacks
It loses, Life cycle monitoring can not be carried out to vehicle, the key input parameter of survival curve model be obtained using difference
Connect method, such as anti-pushing manipulation, method of substitution.For example, Yang Fang etc. by taking Beijing's nineteen ninety-five as an example, is distributed by the vehicle age of survey,
Counter the new of each time is pushed away in conjunction with 1997~2002 years vehicle scrapping numbers in Beijing of the ownership of statistical yearbook, vehicle administration office of Beijing
Increase vehicle number (anti-pushing manipulation), obtains the survival curve of Beijing's motor bus, minibus；China Automotive Technology & Research Center is by China
2001~2010 years lorries of auto industry association and the sales volume of each subdivision vehicle are as new car amount (method of substitution), from China
The vehicle age distribution of 2001~2010 years lorries of the Ministry of Public Security and the ownership of each subdivision vehicle, lorries in 2010 and each subdivision vehicle
Situation is counter to push away each vehicle age learies (anti-pushing manipulation), to construct survival curve.These researchs reflect China 1990~2010 years
The local vehicle of part vehicle or urban survival rule, for the distribution estimation of vehicle age, security performance and environmental impact assessment
Play certain research supporting role.But due to its data source disunity, not perfect, classification of the different data source for vehicle
The difference of method, substitution calculate data and the difference of truth etc., will affect the reliability of model result.In addition, China
Started to promulgate a series of incentive measures with " automobile old for new service implementation method " for representative in 2009, changes depositing for vehicle
Feature living, and the domestic data overwhelming majority about survival curve research counted before 2010, lacked anti-in atmosphere pollution
Research conclusion after only policy is carried out energetically.
Summary of the invention
In view of the deficiencies of the prior art, the present invention is intended to provide a kind of vehicle survival based on China's motor vehicle resignation system
Curve model optimization method provides basic ginseng accurately to reflect the case where vehicle is survived for macroscopical automobile pollution control
It examines.
Past, difference, which is studied, used a variety of substitutions or projectional technique, such as due to basic data deficiency with " Chinese automobile
Industrial yearbook " announce sales data substitute each vehicle in the new car increment n in each vehicle age/time0,j(k), existing with China
The policy of scrapping calculates each vehicle in the learies s in each vehicle age/timei,j(k+i) etc..Due to being related to different data source, each data
Source is for the difference of the classification method of vehicle, substitution or calculates data and the difference of truth etc., will affect vehicle survival rule
Restrain the reliability of statistical result.
Therefore, the present invention is based on the business register information of China's motor vehicle resignation system, by retrospect bicycle from first
Secondary registration counts each vehicle in the survival condition in different vehicle ages to current state.This method makes full use of automotive business to step on
Remember information database to bicycle carry out Life cycle record data characteristics, avoid by multi-source data statistical method difference,
Substitution or the difference equal error for calculating data and truth.
Specifically, to achieve the above object, the present invention adopts the following technical scheme:
A kind of vehicle survival curve model optimization method based on China's motor vehicle resignation system, which is characterized in that described
Method includes
Automotive business register information is obtained, to obtain new car increment n0,j(k) and learies si,j(k+i)；
Vehicle survival curve model is constructed using Weibull probability distribution, the distribution and expression is as follows:
Wherein,Indicate vehicle j in the survival probability of vehicle age i；
bjFor equation undetermined coefficient, failure steepness (b is indicatedj> 1)；
TjFor equation undetermined coefficient, the service life of vehicle j is indicated；
K is the time；
Also, survival probabilityMeet following equation:
ni,j(k+i)=1-si,j(k+i) (3)
Wherein, n0,j(k) new car increment refers to k j vehicle new car number (vehicle age i=0)；
ni,j(k+i) refer to j vehicle vehicle in the vehicle number of k+i still normal use；
si,j(k+i) learies refer to the vehicle number that j vehicle vehicle is scrapped in k+i；
Therefore formula (1) converts are as follows:
Pass through the above i group new car increment n0,j(k), learies si,j(k+i), it is fitted according to formula (4), with minimum
Square law iteration determines the undetermined coefficient b for dividing vehicle curvilinear equation in error sum of squares minimumj、Tj, establish survival curve
Mathematical model.
The automotive business register information includes:
(1) time is registered for the first time: for calculating vehicle age and statistics new registration vehicle fleet；
(2) vehicle-state: for judging vehicle survival condition；
(3) type of vehicle: for judging type of vehicle.
New car increment n0,j(k) scaling method are as follows: with register for the first time the time, two attributes of type of vehicle be sentence
Fixed condition counts each vehicle in the registration vehicle number for the first time of each vehicle age (i.e. k≤first registration registration time < k+i).
Learies si,j(k+i) scaling method are as follows: sentenced with registering time, vehicle-state and type of vehicle for the first time
Fixed condition, statistics a variety of models each vehicle age (i.e. time < k+i is registered in k≤first registration) is in current state as the vehicle of cancellation
Number.
The beneficial effects of the present invention are:
This method carries out bicycle on the basis of Weibull distribution survival curve model, using China's motor vehicle resignation system
The characteristics of Life cycle records, establishes characteristic parameter new car increment, the direct calibration method of learies of curve model, from
And optimize the vehicle survival curve model for being suitable for China's national situation.
Compared with the prior art, the advantages of the present invention are as follows take full advantage of China's motor vehicle resignation system business datum pair
Bicycle carries out the data characteristics of Life cycle record, realizes the accurate acquisition to vehicle survival law characteristic parameter, and existing
Indirect calibration method compare, the present invention have higher reliability；Due to inventing related business datum time span packet
Stage before and after the implementation of the work of Motor Vehicle Pollution Prevention containing China, therefore this Optimized model can reflect vehicle management and control measures to vehicle
The influence of survival curve.
Detailed description of the invention
Fig. 1 is using the vehicle survival curve model optimization side provided in this embodiment based on China's motor vehicle resignation system
Method and the prior art are to each vehicle survival curve fitting result comparison diagram in Guangdong Province.
Specific embodiment
In the present invention, vehicle survival curve refers to that the survival probability change curve in vehicle-mounted age, survival probability refer to often
The new car for entering market year excludes the ratio survived because a variety of causes is by natural selection.
In the following, being described further in conjunction with attached drawing and specific embodiment to the present invention:
This example is by taking Guangdong Province as an example, the year on the basis of 2014, wide based on the fitting of automotive business register information database
Dong Sheng divides vehicle vehicle survival curve, to a kind of vehicle survival curve model optimization method based on China's motor vehicle resignation system
It is described in detail.Include:
Vehicle survival curve model is constructed using Weibull probability distribution, the distribution and expression is as follows:
Wherein,Indicate vehicle j in the survival probability of vehicle age i；
bjFor equation undetermined coefficient, failure steepness (b is indicatedj> 1)；
TjFor equation undetermined coefficient, the service life of vehicle j is indicated；
K is the time.
Also, survival probabilityMeet following equation:
ni,j(k+i)=1-si,j(k+i) (3)
Wherein, n0,j(k) refer to k j vehicle new car number (vehicle age i=0)；
ni,j(k+i) refer to j vehicle vehicle in the vehicle number of k+i still normal use (surviving)；
si,j(k+i) refer to the vehicle number that j vehicle vehicle is scrapped in k+i.
Therefore formula (1) can convert are as follows:
Therefore, the determination of curve model need to obtain each vehicle in the new car increment n in each vehicle age/time first0,j(k)、
Learies s of each vehicle in each vehicle age/timei,j(k+i) two key input parameters.
Using the business register information of Guangdong Province's motor vehicle resignation system as data source in this example, which comes from Guangdong
The Ministry of Public Security of province, the business register information is including but not limited to following message:
(1) time is registered for the first time: for calculating vehicle age and statistics new registration vehicle fleet；
(2) vehicle-state: for judging vehicle survival condition.Wherein, vehicle condition is according to the Ministry of Public Security " GA24.4-2005
The 17th part of motor vehicle register information code: state of motor vehicle code " regulation, comprising normal (A), (E) two states are nullified, are used
In the survival condition for judging vehicle；
(3) type of vehicle: for judging type of vehicle.Wherein, " GA24.4-2005 is motor-driven according to the Ministry of Public Security for vehicle classification
The 4th part of vehicle register information code: type of vehicle code " regulation, choose motorbus (K1), middle bus (K2), small-sized visitor
Vehicle (K3), 4 class of microbus (K4).Wherein, being specifically defined for vehicle is shown in Table 1.
Table 1
Telecommunications databases are registered according to automotive business, in year on the basis of choosing 2014, count following index respectively:
(1) to register two time, type of vehicle attributes for the first time as decision condition, each time before statistics 2014
The vehicle number of the registration for the first time n of lower 4 kinds of vehicles (i.e. time < k+i is registered in k≤first registration)0,j(k)；
(2) to register time, vehicle-state and type of vehicle for the first time as decision condition, each year before statistics 2014
The lower 4 kinds of vehicles (i.e. time < k+i is registered in k≤first registration) of part are in current state as the vehicle number s of cancellationi,j(k+i)。
Pass through the above i group new car increment n0,j(k), learies si,j(k+i), it is fitted according to formula (4), with minimum
Square law iteration determines the undetermined coefficient b for dividing vehicle curvilinear equation in error sum of squares minimumj、Tj, establish survival curve
Mathematical model.
This example obtains all business handling data of 6560129 cars during data observation in total.According to the Ministry of Public Security
Vehicle classification rule, to each vehicle survival curve fitting result in Guangdong Province, and comparison such as table 2, Fig. 1 with part document.Its
In, the minimal error quadratic sum s of each vehicle matched curve is 0.71 × 10-3~7.66 × 10-3Between, there is good fitting
Effect.With indirect method calibration survival curve mode input parameter as a result, with based on complete it can be seen from the matched curve of K3
The legitimate reading that the business record data of life cycle count has certain deviation, and one kind proposed by the invention is based on
The vehicle survival curve model optimization method of China's motor vehicle resignation system, optimum results and legitimate reading are more close.
2 Guangdong Province's vehicle survival curve fitting result of table
1)aFrom document [64] (car survival probability model of Liu Sen, Zhu Xianglei, the Xu Guoqiang based on Weibull distribution
Study [J] industry and scientific and technological forum, 2012,11 (18): 122-124.)；
2)bFrom document [16], (Yang Fang, Yu Lei, Song Guohua wait Dynamic Vehicle age distributed model of the based on survival probability
[J] China Safety Science journal, 2005,15 (6): 24-27.)；
3)cFrom document [65], (Hao Han, Wang Hewu, Ou Yangminggao are waited in the China automobile survival law study [J]
State's science: technological sciences, 2011 (3): 301-305.), failure time disintegrates for 2765 totally from June, 2006 in April, 2010
The record of vehicle.
It will be apparent to those skilled in the art that can make various other according to the above description of the technical scheme and ideas
Corresponding change and deformation, and all these changes and deformation all should belong to the protection scope of the claims in the present invention
Within.
Claims (4)
1. a kind of vehicle survival curve model optimization method based on China's motor vehicle resignation system, which is characterized in that the side
Method includes
Automotive business register information is obtained, to obtain new car increment n0,j(k) and learies si,j(k+i)；
Vehicle survival curve model is constructed using Weibull probability distribution, the distribution and expression is as follows:
Wherein,Indicate vehicle j in the survival probability of vehicle age i；
bjFor equation undetermined coefficient, failure steepness (b is indicatedj> 1)；
TjFor equation undetermined coefficient, the service life of vehicle j is indicated；
K is the time；
Also, survival probabilityMeet following equation:
ni,j(k+i)=1-si,j(k+i) (3)
Wherein, n0,j(k) new car increment refers to k j vehicle new car number (vehicle age i=0)；
ni,j(k+i) refer to j vehicle vehicle in the vehicle number of k+i still normal use；
si,j(k+i) learies refer to the vehicle number that j vehicle vehicle is scrapped in k+i；
Therefore formula (1) converts are as follows:
Pass through the above i group new car increment n0,j(k), learies si,j(k+i), it is fitted according to formula (4), with least square
Method iteration determines the undetermined coefficient b for dividing vehicle curvilinear equation in error sum of squares minimumj、Tj, establish the number of survival curve
Learn model.
2. the vehicle survival curve model optimization method based on China's motor vehicle resignation system as described in claim 1, special
Sign is that the automotive business register information includes:
(1) time is registered for the first time: for calculating vehicle age and statistics new registration vehicle fleet；
(2) vehicle-state: for judging vehicle survival condition；
(3) type of vehicle: for judging type of vehicle.
3. the vehicle survival curve model optimization method based on China's motor vehicle resignation system as claimed in claim 2, special
Sign is, new car increment n0,j(k) scaling method are as follows: to register two time, type of vehicle attributes for the first time as judgement
Condition counts each vehicle in the registration vehicle number for the first time in each vehicle age.
4. the vehicle survival curve model optimization method based on China's motor vehicle resignation system as claimed in claim 2 or claim 3,
It is characterized in that, learies si,j(k+i) scaling method are as follows: sentenced with registering time, vehicle-state and type of vehicle for the first time
Fixed condition, statistics a variety of models each vehicle age are the vehicle number nullified in current state.
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| WO2020082683A1 (en) * | 2018-10-22 | 2020-04-30 | 中山大学 | Chinese motor vehicle registration system-based method for optimizing vehicle survival curve model |
| CN111143771A (en) * | 2019-12-12 | 2020-05-12 | 中山大学 | Motor vehicle remaining amount calculation method, system, device and medium |
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| CN108154275A (en) * | 2017-12-29 | 2018-06-12 | 广东数鼎科技有限公司 | Automobile residual value prediction model and Forecasting Methodology based on big data |
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| CN109345136A (en) * | 2018-10-22 | 2019-02-15 | 中山大学 | Vehicle survival curve model optimization method based on China's motor vehicle resignation system |
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| CN108053075A (en) * | 2017-12-27 | 2018-05-18 | 北京中交兴路车联网科技有限公司 | A kind of scrap-car Forecasting Methodology and system |
| CN108154275A (en) * | 2017-12-29 | 2018-06-12 | 广东数鼎科技有限公司 | Automobile residual value prediction model and Forecasting Methodology based on big data |
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| WO2020082683A1 (en) * | 2018-10-22 | 2020-04-30 | 中山大学 | Chinese motor vehicle registration system-based method for optimizing vehicle survival curve model |
| CN111143771A (en) * | 2019-12-12 | 2020-05-12 | 中山大学 | Motor vehicle remaining amount calculation method, system, device and medium |
| CN111143771B (en) * | 2019-12-12 | 2021-12-07 | 中山大学 | Motor vehicle remaining amount calculation method, system, device and medium |
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Application publication date: 20190215 |