CN102737284A - Application of multiple linear regression method in passenger flow forecast based on SAS (Sequence Retrieval System) - Google Patents
Application of multiple linear regression method in passenger flow forecast based on SAS (Sequence Retrieval System) Download PDFInfo
- Publication number
- CN102737284A CN102737284A CN2012101647429A CN201210164742A CN102737284A CN 102737284 A CN102737284 A CN 102737284A CN 2012101647429 A CN2012101647429 A CN 2012101647429A CN 201210164742 A CN201210164742 A CN 201210164742A CN 102737284 A CN102737284 A CN 102737284A
- Authority
- CN
- China
- Prior art keywords
- passenger flow
- sas
- linear regression
- multiple linear
- utilization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses application of a multiple linear regression method in passenger flow forecast based on an SAS (Sequence Retrieval System). The application comprises the following steps of: firstly, collecting factors influencing a passenger flow volume; secondly, in an REG process of the SAS, defining a selection criteria of variables by virtue of ADJRSQ and CP options; thirdly, comparing and determining a model, wherein the SAS can respectively give out ADJRSQ and CP values of manual experience selection method and stepwise selection method through the second step; fourthly, calculating a predicted value of the passenger flow volume, and taking the influencing factors determined in the third step and regression coefficients corresponding to the influencing factors in a formula to obtain the predicted value of the passenger flow volume. The application of the multiple linear regression method serves as a new solution for a passenger transport department of a local railway administration to achieve the prediction of the total passenger flow volume, so that the prediction process of the passenger flow volume is more scientific, the whole predication process is traceable, and the predication accuracy judgment is measurable.
Description
Technical field
The present invention relates to utilization multiple linear regression method, be specifically related to based on the utilization of the multiple linear regression method of SAS system at passenger flow estimation based on the SAS system.
Background technology
At present, the present situation in high-speed railway train passenger flow estimation field presents following some problem:
1. so far, going back the statistical method of the fairly perfect utilization of neither one realizes predicting for the volume of the flow of passengers in future.
2. mostly the process of passenger flow estimation is to rely on subjective experience to carry out artificial judgement, does not have trackability.
3. the accuracy of passenger flow estimation is passed judgment on and is not realized quantification property.
Use statistical method to carry out passenger flow statistics and can formulate the optimum starting scheme of train for the variation tendency that the Passenger Transport Department door knob is held market trend, passenger flow forecast amount, satisfy the demand of passenger transport market to greatest extent with analyzing not only; Simultaneously also can analysis foundation be provided, when satisfying demand of passenger transport, realize maximize revenue to reach for the decision-making of the passenger traffic marketing program of local road bureau.
In the category of mathematical statistics, multiple linear regression method is the statistical method of linear dependence between a dependent variable of research and a plurality of independent variable.With y is dependent variable;
is k independent variable, and then model can be written as:
（1）
Wherein,
is constant term;
is the regression coefficient of y to x,
be the stochastic error item.
Summary of the invention
Fundamental purpose of the present invention is, the prediction that solves the passenger flow total amount for the passenger traffic department of Local Railways Administration provides a kind of new solution, makes more science of passenger flow estimation process, and the whole prediction process has trackability, and forecasting accuracy is passed judgment on has the property of quantification.
The apprizing system of appearance treatment plant reaches above-mentioned technique effect for realizing above-mentioned technical purpose, and the present invention realizes through following technical scheme:
A kind of multiple linear regression method based on the SAS system may further comprise the steps in the utilization of passenger flow estimation:
The influence factor of the step 1) volume of the flow of passengers is collected;
Step 2) the REG process of SAS, the filter criteria of utilization ADJRSQ and CP option regulation variable;
The comparison of step 3) model with confirm that through step 2, the SAS system can provide artificial experience screening method and the progressively ADJRSQ and the CP value of screening method respectively;
Step 4) is calculated volume of the flow of passengers predicted value; Bring determined factor of influence of step 3 and the pairing regression coefficient of each factor of influence into formula
(1), can obtain the passenger flow value that to predict.
Further, the REG procedural implementation method of SAS is:
Utilization PROC statement regulation begins the REG process and specifies the data set name that will analyze in the SAS system; Then, the dependent variable of regulation regression model and all independents variable that will consider are screened the factor that influences the volume of the flow of passengers with artificial experience under the MODEL statement.
Further, the REG procedural implementation method of SAS is:
Set SELECTION=STEPWISE, use the progressively screening method screening relative influence factor.
Further, ADJRSQ and CP bigger model of value all in the step 3).
The invention has the beneficial effects as follows:
1, the multiple linear regression method based on the SAS system of the present invention is predicted the volume of the flow of passengers on ordinary days, and utilization is got up simple.It does not relate to historical volume of the flow of passengers information, so the collection of data is fairly simple.Especially under the situation that lacks historical data, the method and additive method are compared (like time series analysis method) and are seemed more simple and easy to do.
2, the multiple linear regression method based on the SAS system of the present invention is predicted the volume of the flow of passengers on ordinary days, can realize the adjustment of parameter.The decision maker can select the regression vectors of adjustment linear regression according to the practical experience of oneself.Imbody is both ways: the first, the screening technique of variable can manual adjustment.The second, the filter criteria of variable can manual adjustment.
3, the multiple linear regression method based on the SAS system of the present invention predicts that to the volume of the flow of passengers on ordinary days process is science more, and the whole prediction process has trackability, and forecasting accuracy is passed judgment on has the property of quantification.
In sum, the multiple linear regression method that the present invention is based on the SAS system is for realizing that passenger flow estimation provides a kind of simple and reliable method.Obvious improvement being arranged technically, and have tangible good effect, really is a novelty, progress, practical new.
Embodiment
To combine embodiment below, specify the present invention.
A kind of multiple linear regression method based on the SAS system may further comprise the steps in the utilization of passenger flow estimation:
The influence factor of the step 1) volume of the flow of passengers is collected; Through expert consulting, the factor that influences volume of the flow of passengers size can reduce as shown in the table:
| Influence factor | Unit |
| Attention degree in red-letter day (subjectivity) | --- |
| Vacation length | My god |
| Distance | Km |
| The journey time | Minute |
| GDP | Hundred million yuan |
| Revenue from domestic tourism | Hundred million yuan |
| Urban population | Ten thousand people |
| Work as monthly mean temperature | Degree centigrade |
| Annual power consumption | Hundred million kilowatt hours |
| The private car recoverable amount | Ten thousand intact |
| The mobile phone number | Ten thousand families |
| The of that month volume of the flow of passengers | The people |
| Tertiary industry practitioner | Ten thousand people |
| The practitioner | Ten thousand people |
| Per capita disposable income | Unit |
As, estimating that through official the population total amount of China in 2011 is 1,347,220,000 people, GDP is 440359.9 hundred million yuan, and it is 53275 that passenger train has quantity, and urban and rural residents' annual income per capita are 13910 yuan.
Step 2) the REG process of SAS.This process has two methods to realize.First method is that utilization PROC statement regulation begins the REG process and specifies the data set name that will analyze in the SAS system.Then, the dependent variable of regulation regression model and all independents variable that will consider under the MODEL statement, the method is screened the factor that influences the volume of the flow of passengers with artificial experience.Second method is to set SELECTION=STEPWISE, uses the progressively screening method screening relative influence factor.These two kinds of methods are all used the filter criteria of ADJRSQ and CP option regulation variable.
The comparison of step 3) model is with definite.Through step 2, the SAS system can provide artificial experience screening method and the progressively ADJRSQ and the CP value of screening method respectively.Generally speaking, get ADJRSQ and the bigger model of CP value.
Step 4) is calculated volume of the flow of passengers predicted value.Bring determined factor of influence of step 3 and the pairing regression coefficient of each factor of influence into formula
(1), can obtain the passenger flow value that to predict.As, the factor of influence that step 1 is collected respectively goes on foot through above, and calculating Shanghai Railway Bureau's overall situation passenger traffic volume in 2011 is 32705.61 ten thousand people.
The foregoing description just is to let the one of ordinary skilled in the art can understand content of the present invention and enforcement according to this in order technical conceive of the present invention and characteristics to be described, to be its objective is, can not limit protection scope of the present invention with this.The variation or the modification of every equivalence that the essence of content has been done according to the present invention all should be encompassed in protection scope of the present invention.
Claims (4)
1. one kind based on the utilization at passenger flow estimation of the multiple linear regression method of SAS system, it is characterized in that, may further comprise the steps:
The influence factor of the step 1) volume of the flow of passengers is collected;
Step 2) the REG process of SAS, the filter criteria of utilization ADJRSQ and CP option regulation variable;
The comparison of step 3) model with confirm that through step 2, the SAS system can provide artificial experience screening method and the progressively ADJRSQ and the CP value of screening method respectively;
Step 4) is calculated volume of the flow of passengers predicted value; Bring determined factor of influence of step 3 and the pairing regression coefficient of each factor of influence into formula
(1), can obtain the passenger flow value that to predict.
2. the multiple linear regression method based on the SAS system according to claim 1 is in the utilization of passenger flow estimation; It is characterized in that the REG procedural implementation method of SAS is: utilization PROC statement regulation begins the REG process and specifies the data set name that will analyze in the SAS system; Then, the dependent variable of regulation regression model and all independents variable that will consider are screened the factor that influences the volume of the flow of passengers with artificial experience under the MODEL statement.
3. the multiple linear regression method based on the SAS system according to claim 1 is characterized in that in the utilization of passenger flow estimation the REG procedural implementation method of SAS is: set SELECTION=STEPWISE, use the progressively screening method screening relative influence factor.
According to claim 1 or 2 or 3 described multiple linear regression methods based on the SAS system in the utilization of passenger flow estimation, it is characterized in that: ADJRSQ and CP bigger model of value all in the step 3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012101647429A CN102737284A (en) | 2012-05-25 | 2012-05-25 | Application of multiple linear regression method in passenger flow forecast based on SAS (Sequence Retrieval System) |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2012101647429A CN102737284A (en) | 2012-05-25 | 2012-05-25 | Application of multiple linear regression method in passenger flow forecast based on SAS (Sequence Retrieval System) |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102737284A true CN102737284A (en) | 2012-10-17 |
Family
ID=46992740
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2012101647429A Pending CN102737284A (en) | 2012-05-25 | 2012-05-25 | Application of multiple linear regression method in passenger flow forecast based on SAS (Sequence Retrieval System) |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102737284A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105654199A (en) * | 2015-12-30 | 2016-06-08 | 山东大学 | Bus line passenger flow prediction method |
| CN106779196A (en) * | 2016-12-05 | 2017-05-31 | 中国航天系统工程有限公司 | A kind of tourist flow prediction and peak value regulation and control method based on tourism big data |
| CN106910005A (en) * | 2017-01-17 | 2017-06-30 | 北京万相融通科技股份有限公司 | A kind of trend prediction of station volume of the flow of passengers and statistical analysis technique |
| CN110956089A (en) * | 2019-11-04 | 2020-04-03 | 李苗裔 | Historical block walking performance measuring method based on ICT technology |
| CN113033921A (en) * | 2021-04-28 | 2021-06-25 | 北京市交通信息中心 | Bus route passenger flow prediction method based on multivariate stepwise regression analysis |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101801004A (en) * | 2009-12-31 | 2010-08-11 | 哈尔滨工业大学 | Medium to long-term predication method in self-adaptive telephone traffic based on prior knowledge |
-
2012
- 2012-05-25 CN CN2012101647429A patent/CN102737284A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101801004A (en) * | 2009-12-31 | 2010-08-11 | 哈尔滨工业大学 | Medium to long-term predication method in self-adaptive telephone traffic based on prior knowledge |
Non-Patent Citations (2)
| Title |
|---|
| 覃章景，胡桂华，万为国: "《SAS系统中线性回归模型的选择》", 《荆州师范学院学报（自然科学版）》 * |
| 高惠璇: "《实用统计学方法与SAS系统》", 31 October 2001 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105654199A (en) * | 2015-12-30 | 2016-06-08 | 山东大学 | Bus line passenger flow prediction method |
| CN106779196A (en) * | 2016-12-05 | 2017-05-31 | 中国航天系统工程有限公司 | A kind of tourist flow prediction and peak value regulation and control method based on tourism big data |
| CN106910005A (en) * | 2017-01-17 | 2017-06-30 | 北京万相融通科技股份有限公司 | A kind of trend prediction of station volume of the flow of passengers and statistical analysis technique |
| CN110956089A (en) * | 2019-11-04 | 2020-04-03 | 李苗裔 | Historical block walking performance measuring method based on ICT technology |
| CN110956089B (en) * | 2019-11-04 | 2023-05-23 | 李苗裔 | ICT technology-based historical block walking measurement method |
| CN113033921A (en) * | 2021-04-28 | 2021-06-25 | 北京市交通信息中心 | Bus route passenger flow prediction method based on multivariate stepwise regression analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103413253B (en) | A kind of classification of the annual peak load based on economy, meteorologic factor Forecasting Methodology | |
| CN107370170B (en) | Energy storage system capacity configuration method considering capacity electricity price and load prediction error | |
| CN105260803B (en) | A kind of system power consumption prediction technique | |
| CN102737284A (en) | Application of multiple linear regression method in passenger flow forecast based on SAS (Sequence Retrieval System) | |
| CN104574209A (en) | Modeling method of urban electrical network distribution transform weight overload mid-term forewarning model | |
| CN103632212A (en) | System and method for predicating time-varying user dynamic equilibrium network-evolved passenger flow | |
| CN104992239A (en) | Correlation coefficient-based industry electricity consumption law forecasting method | |
| CN106022536B (en) | Seasonal power consumption prediction method and system | |
| CN110119948B (en) | Power consumer credit evaluation method and system based on time-varying weight dynamic combination | |
| CN111080101A (en) | AHP-based multi-dimensional evaluation method for service efficiency of power supply channel | |
| CN110034561B (en) | Wind power plant energy storage capacity optimization method based on cloud energy storage lease service | |
| CN105184402A (en) | Personalized user short-term load forecasting algorithm based on decision-making tree | |
| CN106600131A (en) | Power grid investment analysis model evaluation method | |
| CN106548285A (en) | The bulk sale power predicating method that meter and small power station exert oneself | |
| CN109165809A (en) | A kind of new electricity changes Electric Power Network Planning project investment Ranking evaluation method under environment | |
| CN108256724B (en) | Power distribution network open capacity planning method based on dynamic industry coefficient | |
| CN106779436A (en) | A kind of Electric Power Network Planning stage construction harmony comprehensive estimation method | |
| CN104866922A (en) | User off-network prediction method and apparatus | |
| CN114169817B (en) | Rural express delivery station site selection method suitable for low-density population | |
| CN116205329A (en) | Holiday passenger flow prediction method | |
| CN104134102A (en) | LEAP-model-based method for predicating medium-and-long-term electricity demand distribution of power grid | |
| CN110648253A (en) | Power distribution network investment allocation method based on historical investment effect | |
| CN114398941A (en) | User payment behavior classification method, device, equipment and readable storage medium | |
| CN104134096A (en) | Intelligent load prediction method based on province and prefecture integration | |
| CN104252647B (en) | Electro-load forecast method based on anti-distance weighting interpolation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20121017 |