CN109767291A - Shared parking method towards elasticity parking incentive mechanism - Google Patents
Shared parking method towards elasticity parking incentive mechanism Download PDFInfo
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Abstract
The invention proposes a kind of shared parking methods towards elasticity parking incentive mechanism; the following steps are included: obtaining individual activity travel behaviour feature and mode from the individual travel behaviour track of berth owner; correlation analysis is carried out, the influence factor for influencing berth owner trip decision-making is obtained；Duration is bidded as situational variables using parking, and each influence factor is independent variable, constructs Cox risk model；The sub- variable of each influence factor is analyzed by significance test and collinearity diagnostics and is screened；The factor after screening is analyzed using Sensitivity Analysis Method, each factor is obtained and bids the affecting laws of behavior evolution to berth owner；The value of corresponding factor is adjusted, berth owner is improved and participates in the probability bidded, the sold berth that will bid shares to other car owners for having demand.This method comprehensively considers each factor and bids the affecting laws of behavior evolution to shared berth, can effectively reinforce the implementation result of elasticity parking incentive mechanism, improve the turnover rate in berth.
Description
Technical field
The invention belongs to parking planning field is shared in traffic programme, and in particular to a kind of towards elasticity parking incentive mechanism
Shared parking method.
Background technique
Elasticity parking incentive mechanism be it is a kind of parking position is actively shared by economic benefit excitation car trip person, and
Other Green Travel modes are selected to complete the shared parking strategy of commuter.The commuting motorist for possessing berth passes through proposition
It bids application, the commuting berth parking rights of certain period is transferred into parking lot management side, manager's selection, which accepts or rejects, bids,
The successfully commuting motorist that bids should select other greens, the trip mode (such as public transport, bicycle or walking) of low-carbon complete
At commuting and obtain economic compensation.Parking lot management side launches the parking rights again into parking demand market, realizes shared stop
Vehicle.
With the further growth of urban road vehicle, elasticity parking incentive mechanism is expected to stopping at release to a certain extent
Difficult problem.But in actual implementation its effect is unsatisfactory, berth turnover rate is not high, is primarily due to current implementation master
Related to excitation density, excitation density is high, and the enthusiasm that berth owner participates in shared parking is just high, and excitation density is low, ginseng
It is just low with degree, in order to attract the motorists for more possessing berth be actively engaged in parking position it is shared in, investment is more certainly
Arousal effect is better, but excitation density setting is excessively high will to dramatically increase operation cost, in terms of the comprehensive benefit finally obtained
Consider not being preferred plan.Although also there is researcher to propose the calculation method of motivation benefit according to game theory, also only limit to
In the research of the single dimension.In fact, it is to be related to the scheme of many aspects that the berth towards elasticity parking incentive mechanism is shared,
From the perspective of selling berth, whether berth owner selects to drive to go on a journey and may be influenced by multiple factors, such as distance, road
The influence of the objective condition such as condition, time, weather, and how to excavate the relationship between these factors and and then targetedly into
Row improves, and to reinforce the implementation result of elastic parking mechanism, improves berth turnover rate, currently still belongs to a unknown project.
Summary of the invention
Goal of the invention: in view of the deficiencies of the prior art, the present invention proposes a kind of towards the shared of elasticity parking incentive mechanism
Parking method can effectively reinforce the implementation result of elasticity parking incentive mechanism, improve the turnover rate of parking position.
Technical solution: the shared parking method of the invention towards elasticity parking incentive mechanism, comprising the following steps:
(1) the individual travel behaviour track for obtaining berth owner, through machine learning algorithm from travel behaviour track
Individual activity travel behaviour feature and mode are obtained, every travel behaviour feature is subjected to correlation as observational variable with mode
Analysis obtains the influence factor for influencing berth owner trip decision-making；
(2) bid request for obtaining berth owner, bids duration as situational variables using parking, obtains in step 1
Each influence factor be independent variable, construct Cox risk model, be fitted the coefficient of each variable；
(3) the sub- variable of each influence factor is analyzed by significance test and collinearity diagnostics and screened；
(4) according to the Coefficient Fitting of model as a result, obtain the Cox risk forecast model bidded of parking, and sensitivity is applied
Analytic approach analyzes the influence factor after screening, obtain each factor to berth owner bid behavior evolution influence advise
Rule；
(5) state between supply and demand based on current Berth number bids the influence of behavior evolution to berth owner according to each factor
Rule adjusts the value of corresponding factor, improves berth owner and participates in the probability bidded, the sold berth that will bid shares to
Other have the car owner of demand.
Preferably, correlation analysis is carried out using every travel behaviour feature and mode as observational variable in the step 1,
Obtain influence berth owner trip decision-making influence factor include:
Using the Pearson correlation coefficient in SPSS Statistics 22.0 to every individual activity travel behaviour feature
Correlation analysis is carried out with mode, formula is as follows:
ρX,YIndicate that degree of strength linearly related between variable X and Y, cov (X, Y) are covariance, σX·σYFor standard deviation；
According to calculated result, the variable by coefficient lower than specified value value is rejected, and is obtained berth owner's activity trip and is determined
The influence factor of plan, including following five class: area characteristic, schedule peace are lived in social economy's attribute, extraneous trip weather, duty
Row, parking bidding decision, trip mode decision.
Preferably, the step 2 building Cox risk model is as follows:
In formula, u=(u1,u2,...,un)TIndicate each influence factor；h0It (t) is baseline risk function, β=(β1,
β2,...,βn)TIt is the corresponding regression coefficient vector of each influence factor.
Preferably, in the step 3, the sub- variable that significance test value is greater than 0.1 is rejected by significance test first,
Then the sub- variable that variance inflation factor is greater than 5 is rejected in collinearity diagnostics, is fitted to obtain variance inflation by multiplex screening
Factor of the factor less than 5, including it is gender, family size, electric vehicle quantity, family's Locational Entropy, unit Locational Entropy, Commuting Distance, competing
The valence amount of money, total degree of bidding, the parameter as Cox risk model.
Preferably, the Cox risk forecast model bidded that stops in the step 4 is as follows:
Wherein,μiFor
Variable after being screened in step 3.
The utility model has the advantages that
1, the present invention ensure that policies and mechanisms reality using the continuous action effect of elasticity parking incentive mechanism as point of penetration
The sustainability applied.In addition, present invention firstly provides be considered as motorist's parking behavior of bidding to carry in one group of daily behavior
Status switch, and introduce theory of survival analysis and bid the birth and death process of lasting behavior to portray parking.Due to behavior of bidding of stopping
Have survival properties, and in the presence of lasting survival technology of bidding is influenced, if berth owner completes on some working day
It bids behavior, i.e., it is considered that the parking of the individual, which is bidded, is in " survival condition ", if berth owner exists from viability theory
Some working day, which stopped, bids, then it is assumed that the behavior is in " dead state ", it can be seen that viability theory can solve well
Parking is released to bid the evolution condition of wish and behavior.
2, the present invention is bidded behavior evolution model by constructing the motorist based on Cox risk ratio, is divided using sensitivity
Analysis method probes into multiple key factors and bids the Influencing Mechanism of behavior duration variation to motorist, and then provides towards elasticity
The berth sharing method for incentive mechanism of stopping, the analysis of various dimensions and method for digging can fully grasp elasticity parking excitation
The element of mechanism effectively promotes its implementation result.
Detailed description of the invention
Fig. 1 is the flow chart of shared parking method according to the present invention；
Fig. 2 is the relative risk prediction result figure of behavior duration of being bidded according to the parking of the embodiment of the present invention.
Specific embodiment
In order to more clearly understand technical solution of the present invention, major technique design of the invention is introduced first.
Survival analysis (Survival Analysis) is according to provided by individual or group with information, to one or
Multiple survival conditions carry out statistical inference.Wherein so-called " means of subsistence " refers to the time for describing that some event or state occur
Data.From time dimension, possess the shared parking stall that the commuting motorist (hereinafter referred to as berth owner) in berth is initiated
It bids and belongs to a kind of " voluntary " behavior of sequence variation at any time.Broadly it is considered that it is berth owner in day that parking, which is bidded,
A kind of " state " of entrained decision predisposition, this state may continue with the owner of some berth in often activity trip
For a period of time, this berth owner also can routinely participate in parking bidding decision and within the period.
The present invention regards a kind of lasting shape for having survival properties as from the angle of individual, by the parking behavior of bidding
State, and there is the key factor for the duration that influences to bid, and then application theory of survival analysis method explains berth owner
Parking is bidded the evolution condition of wish and behavior, and using the continuous action effect of elasticity parking incentive mechanism as point of penetration, structure
The Cox risk model based on theory of survival analysis is built, individual is bidded into initiation behavior and duration of bidding sees dependent variable as,
It parses multiple key factors to bid the Influencing Mechanism of behavior duration variation to berth owner, and then adjusts influence factor
Value participates in the probability bidded to improve berth owner, and the sold berth that will bid shares to other car owners for having demand,
Improve berth turnover rate.
Technical solution of the present invention is described in detail with reference to the accompanying drawing.
As described in the background art, elasticity parking incentive mechanism relates generally to two main bodys: parking management side and bidding asks
The person of asking (namely berth owner), Thoughts, which are berth owners, initiates bid request, parking management side to parking management side
Receiving is bidded or is refused.Illustrate method process of the invention with the angle of parking management side below, but the work of berth owner
Make process and their interactive processes between the two, can also clearly obtain from the embodiment below in conjunction with attached drawing
Know.Referring to Fig.1, a kind of shared parking method towards elasticity parking incentive mechanism of the invention, comprising the following steps:
Step 1 obtains individual activity travel behaviour feature and mode from the individual travel behaviour track of berth owner, and
Correlation analysis is carried out, the influence factor for influencing berth owner trip decision-making is obtained.
In the specific implementation, personal information can be obtained by carrying out investigation to berth owner, as gender, family advise
Mould, residence, inhabitation duration, family possess quantity of the vehicles etc., can also be by obtaining such letter from Relational database
Breath breath and is analyzed critical field and is obtained for example, being swashed using spiders from the website of statistical department to win the confidence.Together
When, its daily trip behavior rail can be obtained from the intelligent mobile terminal of berth owner or the GPS module of in-vehicle navigation apparatus
Mark excavates the trip information of high quality in conjunction with machine learning algorithm using the intrinsic space-time structure of GPS track data, by instrument
The time location data of device record are converted into cognizable semantic information, obtain individual activity travel behaviour feature and mode, make
For the candidate variables collection of the influence factor of trip decision-making.
When carrying out correlation analysis, using the Pearson correlation coefficient in SPSS Statistics 22.0 in embodiment
(Pearson correlation coefficient) to every observational variable (individual activity travel behaviour feature and mode) into
Row correlation analysis, formula are as follows:
Pearson correlation coefficient between two variables is defined as the quotient of covariance and standard deviation between two variables, it
Linearly related strong and weak degree between two variables is described.The value of ρ is between -1 and+1, if ρ > 0, shows two variables
It is to be positively correlated；If ρ < 0, show that two variables are negatively correlated.The absolute value of ρ shows that more greatly correlation is stronger, if ρ=0, shows
It is not linearly related between two variables.
Select 0.25 in the individual activity travel behaviour feature and each variable of mode of above-mentioned acquisition, in embodiment is for judgement
No relevant standard, reject related coefficient lower than standard value variable simultaneously, obtain berth owner's activity trip decision-making
Influence factor, be classified as following five class: area characteristic, schedule peace are lived in social economy's attribute, extraneous trip weather, duty
Row, parking bidding decision, trip mode decision.
Step 2, the bid request for obtaining berth owner, bid duration as situational variables using parking, each influence
Factor is independent variable, constructs Cox risk model, is fitted the coefficient of each variable.
From the bid request message that berth owner initiates, it can obtain to bid according to the timestamp of each event and apply
The timing node of each behavior in journey, duration of bidding are the time span initiated request between revocation request event.
Cox risk model is a kind of semi-parametric regression model, does not need duration to be sought survival when carrying out each parameter Estimation
(i.e. berth owner parking bid duration) obeys certain distribution form, and furthermore the model can be with survival condition result
A length of dependent variable when (persistent state of bidding) and existence, while regression fit is carried out to numerous influence factors, it specifically stops competing
The risk function of valence duration t can indicate are as follows:
In formula, u=(u1,u2,...,un)TIndicate each influence factor；h0It (t) is baseline risk function, β=(β1,
β2,...,βn)TIt is the corresponding regression coefficient vector of each influence factor, it can be seen that carry out the advantage of Cox risk model hypothesis
It is to ensure that the model baseline duration t that only bids with parking is related, it is unrelated with each influence factor u, and exponential part is then only
It is related with influence factor u, it is unrelated with the parking duration t that bids.
In embodiment to 23 berth owner 841 parking bid behavior sample carry out single factor test Cox regression analysis, tool
The Regression Analysis Result of body is as shown in table 1, and index is all common counter in regression analysis in table.
1 single factor test Cox risk model Regression Analysis Result of table
Step 3 is analyzed the sub- variable of each influence factor by significance test and collinearity diagnostics and is screened.
According to single factor test Cox risk model Regression Analysis Result, some variables are insufficient due to the correlation with its dependent variable
Or not significant correlation, it should be removed when constructing structural equation model, therefore significance test first is carried out to outcome variable, i.e.,
One is made it is assumed that then judging whether this hypothesis is reasonable, judges totality using sample information to the parameter of variable in advance
Truth and null hypothesis whether have significant difference.In addition, if Multiple factors exist aobvious in the fitting of Cox risk model
The multicollinearity of work can then be such that model parameter estimation is distorted or be difficult to estimate accurately, therefore need to carry out synteny to These parameters
Diagnosis, screens and arranges satisfactory variable, then these factors are brought into Cox risk model parameter fitting.
Correlated variables is screened by significance test value, value, that is, probability of conspicuousness is usually indicated with p, is reflected a certain
A possibility that event occurs size.Statistics is according to the obtained p value of significance test method, the difference being meant that between sample
Probability caused by sampling error is generally significant with p < 0.1.As it can be seen from table 1 removing " activity schedule ", other are each
Classification has sub- variable of the significance test value less than 0.1, it can think that this little variable belongs to significant variable, and for aobvious
Sub- variable of the work property test value greater than 0.1 is then regarded as not significant, it is necessary to be rejected.In addition, being carried out to These parameters conllinear
Property diagnosis, the results are shown in Table 2.
2 collinearity diagnostics result of table
From 2 first row of table can be seen that in initial regression fit scheme " highest degrees Fahrenheit ", " minimum degrees Fahrenheit ",
There is significant multicollinearity between " average degrees Fahrenheit " and " dew point " this four factors, lead to its variance inflation factor
(VIF, Variance Inflation Factor) value is greater than 5.In order to ensure the validity of parameter fitting, model is only by variance
The smallest " dew point " item of expansion factor is included in subsequent modeling process.Similarly, for " currently bidding accumulative ", " bid successfully time
Multicollinearity feature between number " and " total degree of bidding " these three factors, Estimating The Model Coefficients process, which also only retains, " bids
Total degree ".2 secondary series of table is to carry out a test for multi-collinearity again for the factor after screening for the first time, as a result, it has been found that screening
Factor afterwards meets synteny examination requirements (i.e. VIF value is less than 5), therefore brings these factors into Cox risk model ginseng
In number fitting, the results are shown in Table 3, according to significance test, has 8 factors all to bid behavior to parking in 10 key factors
Different degrees of influence is generated, wherein household size situation is the most significant to the influence degree for behavior of bidding of stopping, opposite to endanger
Dangerous rate reaches 1.446.
The multifactor Cox risk model Regression Analysis Result of table 3
Step 4, according to the Coefficient Fitting of model as a result, obtain the Cox risk forecast model that parking is bidded, and application is sensitive
Degree analytic approach the factor after screening is analyzed, obtain each factor to berth owner bid behavior evolution influence advise
Rule.
The Cox risk model expression formula for deriving that berth owner parking is bidded by above-mentioned fitting result is as follows:
In formula, the lower label of fitting parameter and influence factor respectively corresponds the number in table 3, and h0(t) functional value can
Can be calculated according to the following formula:
It bids the influence evolution mechanism of duration further to parse key factor to berth owner parking, embodiment
In using the formula as prediction model, it is assumed that berth owner is a single woman driver, and family is without other electrical salf-walkings
Vehicle can be used as the vehicles of substitution and Commuting Distance is 20 kilometers, then can be from duty settlement position by Cox risk model formula
Entropy and economic incentives variation to her parking bid behavior carry out sensitivity analysis.
It, should when adjusting another party's Locational Entropy and being changed to 5 from 3 when Fig. 2 (a) is that family or unit Locational Entropy are initially set to 3
Motorist's group (meeting the motorist that regional conditions are lived in same duty) parking is bidded the relative risk result of variations of duration.When
Family position entropy from 3 be changed to 5 when, which will be gradually decrease to 27.79% from 39.6%；And when unit position entropy from
3 when being changed to 5, which will be gradually decrease to 30.29% from 39.6%.It can be said that improving berth owner duty settlement
The infrastructure services level of position can effectively assist the implementation result of elasticity parking incentive mechanism, when section position entropy energy is lived in duty
2 points are promoted, then the duration that berth owner participates in that parking is bidded will promote about 10% to 15%.It can from Fig. 2 (b)
Out, if elasticity parking incentive mechanism bid amount is from when adjusting to 30 yuan for 20 yuan, berth owner parking is bidded behavior interruption
Risk will be reduced to 16.25% from 39.6%.If this is because when parking is bidded berth owner can submit it is higher competing
Valence application volume, so that it may more effectively liquidate and select psychology Trip Costs caused by other vehicles, and then constantly swash
Encourage they participate in parking position it is shared in.Fig. 2 (c) is that the total degree that berth owner participates in bidding is held with the parking behavior of bidding
The corresponding relationship of continuous property, expressed by meaning be consistent with investigation actual result: i.e. berth owner, which participates in stopping, shares
Initiative is higher, and the number that participation parking is bidded is more, and the relative risk that the parking behavior of bidding is interrupted is also lower.
Step 5, the state between supply and demand based on current Berth number bid the shadow of behavior evolution to berth owner according to each factor
Rule is rung, the value of corresponding factor is adjusted, berth owner is improved and participates in the probability bidded, the sold berth that will bid is shared
There is the car owner of demand to other.
With the influence of extraneous various different factors, use of the berth owner group to each parking position different periods
Weigh valuation will having differences property, this otherness will finally be embodied in their bid amount.Policy maker or pipe
It is each according to the application analysis of bidding of berth owner after reason side obtains the individual travel behaviour feature and mode of berth owner
Influence factor bids the Evolution of behavior to berth owner, and it is poor can to live regional conditions for duty according to calculated result
Berth owner promotes the service level of their duty settlement position infrastructure；Most suitable elasticity can be found according to calculated result
Stop excitation density.Most suitable influence factor adjusted value, which is found, according to affecting laws had both guaranteed that every motorist participated in parking altogether
The enthusiasm enjoyed, and the influence due to caused by the operation cost of elastic excitation mechanism is alleviated, it will attract naturally more
More berth owners be actively engaged in parking position it is shared in, improve berth turnover rate.
Although the embodiment of the present invention has been disclosed as above, also it should be explained that, above embodiments are merely to illustrate this
The technical solution of invention, rather than the limitation of the implementation method to invention, such as according to different berth owner individuals, trip
The specific variable of the difference of track, acquisition can also be not quite similar, but this has no effect on implementation process of the invention.And this field
It is to be appreciated by one skilled in the art that still the dependency rule that refer to of the present invention or method can be modified and be filled；And
All do not depart from the technical solution and its improvement of the spirit and scope of the present invention, should all cover in claim model of the invention
It encloses.
Claims (5)
1. a kind of shared parking method towards elasticity parking incentive mechanism, which is characterized in that the described method comprises the following steps:
(1) the individual travel behaviour track for obtaining berth owner, is obtained from travel behaviour track by machine learning algorithm
Every travel behaviour feature is carried out correlation point as observational variable with mode by individual activity travel behaviour feature and mode
Analysis obtains the influence factor for influencing berth owner trip decision-making；
(2) bid request for obtaining berth owner bids duration as situational variables, obtained in step 1 respectively using parking
A influence factor is independent variable, constructs Cox risk model, is fitted the coefficient of each variable；
(3) the sub- variable of each influence factor is analyzed by significance test and collinearity diagnostics and screened；
(4) according to the Coefficient Fitting of model as a result, obtain the Cox risk forecast model bidded of parking, and sensitivity analysis is applied
Method analyzes the influence factor after screening, obtains each factor and bids the affecting laws of behavior evolution to berth owner；
(5) state between supply and demand based on current Berth number bids the affecting laws of behavior evolution to berth owner according to each factor,
The value of corresponding factor is adjusted, berth owner is improved and participates in the probability bidded, the sold berth that will bid shares to other
There is the car owner of demand.
2. the shared parking method according to claim 1 towards elasticity parking incentive mechanism, which is characterized in that the step
Correlation analysis is carried out using every travel behaviour feature and mode as observational variable in rapid 1, obtaining, which influences berth owner, goes out
The influence factor of row decision includes:
Using the Pearson correlation coefficient in SPSS Statistics 22.0 to every individual activity travel behaviour feature and mould
Formula carries out correlation analysis, and formula is as follows:
ρX,YIndicate that degree of strength linearly related between variable X and Y, cov (X, Y) are covariance, σX·σYFor standard deviation；
According to calculated result, the variable by coefficient lower than specified value value is rejected, and obtains berth owner's activity trip decision-making
Influence factor, including following five class: social economy's attribute, extraneous trip weather, duty are lived area characteristic, activity schedule, are stopped
Vehicle bidding decision, trip mode decision.
3. the shared parking method according to claim 1 towards elasticity parking incentive mechanism, which is characterized in that the step
Rapid 2 building Cox risk model is as follows:
H (t, u)=h0(t)eβu
In formula, u=(u1,u2,...,un)TIndicate each influence factor；h0It (t) is baseline risk function, β=(β1,β2,...,
βn)TIt is the corresponding regression coefficient vector of each influence factor.
4. the shared parking method according to claim 1 towards elasticity parking incentive mechanism, which is characterized in that the step
In rapid 3, the sub- variable that significance test value is greater than 0.1 is rejected by significance test first, is then picked in collinearity diagnostics
Except variance inflation factor is greater than 5 sub- variable, it is fitted to obtain factor of the variance inflation factor less than 5 by multiplex screening, including
Gender, family size, electric vehicle quantity, family's Locational Entropy, unit Locational Entropy, Commuting Distance, bid amount, total degree of bidding are made
For the parameter of Cox risk model.
5. the shared parking method according to claim 4 towards elasticity parking incentive mechanism, which is characterized in that the step
The Cox risk forecast model bidded that stops in rapid 4 is as follows:
Wherein,μiFor step 3
Variable after middle screening.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050216384A1 (en) * | 2003-12-15 | 2005-09-29 | Daniel Partlow | System, method, and computer program for creating and valuing financial instruments linked to real estate indices |
| CN102855773A (en) * | 2012-09-13 | 2013-01-02 | 东南大学 | Short-term prediction method for occupancy of effective parking space of parking lot |
| CN107299129A (en) * | 2016-04-15 | 2017-10-27 | 昆山市工业技术研究院小核酸生物技术研究所有限责任公司 | Circle nucleic acid as breast cancer biomarker application |
| CN107341968A (en) * | 2017-07-26 | 2017-11-10 | 李丹 | One kind parking shared system |
| CN107958610A (en) * | 2017-11-21 | 2018-04-24 | 同济大学 | A kind of function area of mixed usage parking stall predictor method shared based on berth |
| CN108133613A (en) * | 2017-12-19 | 2018-06-08 | 深圳先进技术研究院 | A kind of real-time release road-surface concrete Service Index method and system |
| CN108346087A (en) * | 2018-02-08 | 2018-07-31 | 南京交通职业技术学院 | Dynamic based on location information shares parking service system |
| CN109003128A (en) * | 2018-07-07 | 2018-12-14 | 太原理工大学 | Based on improved random forest public bicycles website Demand Forecast method |
-
2018
- 2018-12-19 CN CN201811558514.3A patent/CN109767291B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050216384A1 (en) * | 2003-12-15 | 2005-09-29 | Daniel Partlow | System, method, and computer program for creating and valuing financial instruments linked to real estate indices |
| CN102855773A (en) * | 2012-09-13 | 2013-01-02 | 东南大学 | Short-term prediction method for occupancy of effective parking space of parking lot |
| CN107299129A (en) * | 2016-04-15 | 2017-10-27 | 昆山市工业技术研究院小核酸生物技术研究所有限责任公司 | Circle nucleic acid as breast cancer biomarker application |
| CN107341968A (en) * | 2017-07-26 | 2017-11-10 | 李丹 | One kind parking shared system |
| CN107958610A (en) * | 2017-11-21 | 2018-04-24 | 同济大学 | A kind of function area of mixed usage parking stall predictor method shared based on berth |
| CN108133613A (en) * | 2017-12-19 | 2018-06-08 | 深圳先进技术研究院 | A kind of real-time release road-surface concrete Service Index method and system |
| CN108346087A (en) * | 2018-02-08 | 2018-07-31 | 南京交通职业技术学院 | Dynamic based on location information shares parking service system |
| CN109003128A (en) * | 2018-07-07 | 2018-12-14 | 太原理工大学 | Based on improved random forest public bicycles website Demand Forecast method |
Non-Patent Citations (3)
| Title |
|---|
| PARK等: "Survival analysis on the business types of small business using Cox"s proportional hazard regression model", 《JOURNAL OF THE KOREAN DATA & INFORMATION SCIENCE SOCIETY》 * |
| 刘松: "基于泊位共享的区域停车场规划研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 李仕来等: "基于SPSS因子分析的肝细胞癌预后因素的Cox比例风险回归模型", 《广西医学》 * |
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