CN110490787A - A kind of method for building up of the multiple agent model of residential location choice - Google Patents
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Abstract
A kind of method for building up of the multiple agent model of residential location choice, include the following steps: adaptive behavior model foundation of home intelligent body during house addressing, the setting of home intelligent body: each home intelligent body has the property that age, marital status, member, deposit, income and the vehicles；Each home intelligent body has seven life cycle phases: birth, get married, child-rearing, children leave home, retirement, single old man and death；Home intelligent body determines the satisfaction in current address；For each home intelligent body, when family's resettlement, it is necessary to according to the necessity in new life stage；Compare the different facilities of residential quarter, determines that the selection of resident's inhabitation addressing, the model can promote the residential building moving process under policy implication with simcity center house with the place input urban information and family information, adjusting parameter that adapt to new.
Description
[technical field]
The present invention relates to a kind of method for building up of the multiple agent model of residential location choice.
[background technique]
Most resident's inhabitation addressing simulation at present is all simulation land use, traffic and house selection interactive process
Integrated framework, stronger to the analog capability of steric course but relatively weak between the concern to interact adjacent body, Wu Fayu
Government policy forms stronger interaction.
[summary of the invention]
Technical problem to be solved by the present invention lies in a kind of foundation for the multiple agent model for providing residential location choice
Method, the model can promote the residential building moving process under policy implication with simcity center house.
The present invention is implemented as follows:
A kind of method for building up of the multiple agent model of residential location choice, includes the following steps:
Step 1: adaptive behavior model foundation of home intelligent body during house addressing specifically includes:
Step 1.1: the setting of home intelligent body:
Each home intelligent body has the property that age, marital status, member, deposit, income and the vehicles；
Each home intelligent body have seven life cycle phases: birth, get married, child-rearing, children leave home, retirement,
Single old man and death；
Step 1.2: home intelligent body determines the satisfaction in current address:
Wherein n=1 ..., 18；U=1 ..., 6 (1)
0<Sthreshold<4 (2)
Si>Sthreshlold (3)
Si≤Sthreshlold (4)
Wherein SiIt is satisfaction of the family i to Current Housing area, bugijIt is the regression coefficient vector of variable j, u indicates income
The life cycle phase of family i in group g；
xijsIt is the satisfaction of family；There are four ranks in the position S that variable j is generated: 1) it is very dissatisfied, 2) and it is discontented
Meaning, 3) it is satisfied, 4) it is very satisfied；
If one family intelligent body is lower than S to the satisfaction of current locationthreshold, then the intelligent body will consider to move；
Step 1.3: city divides into three different region CCA, UPA, UPA；Wherein CCA is downtown area, and UPA is city
Promotion area, city, UPA are city control zone；For each home intelligent body, it is assumed that the public thing of house that different cities region provides
Industry is different three regions, and public utilities are homogeneities in a region, and random range, which follows, represents personal preference
Normal distribution；When family's resettlement, it is necessary to according to the necessity in new life stage, compare the different facilities of residential quarter, to adapt to
New place；
Step 2: the policy responses in house moving process between home intelligent body specifically include:
Step 2.1: the setting of virtual city space:
According to urban planning information, city space is divided into grid cell, each cell size is 500m × 500 meter,
Each unit in the space of virtual city will have predefined space attribute, the space attribute, including land use zoning
Type, family's density, planning region classification, room rate, multiple space characteristics；
Step 2.2: city space provides effectiveness for home intelligent body:
Uis=Vis+Viinter+εis (5)
Wherein, n=1,2,3 ... 18 (6)
xijs=(1+ai)xjsOr xijs=dijs (7)
bugij=bugj(1+βi) (8)
Viinter=GVi+NVi (9)
GVi=Gmove/GTotal NVi=Nmove/Nnomove (10)
εis=μ-β ln (- ln γ) (β>0), (0<γ<1) (11)
VisIt is the effectiveness of the family i provided by position s, without unobservable random element and home intelligent body Viinter
Between interaction；
XijsIt is the vector of an observable explanatory variable j, for describing the attribute of the family i in the s of position, XijsWith two kinds
Formal definition, one form of them are the assessments of the family i of space attribute xj in the s of position, and another form is the family in the s of position
The distance between front yard i and the communal facility j of proximal most position；
In order to reflect the X in the s of position between all home intelligent body iijsDifference, generate random number ai, wherein following just
State distribution, average value 0, standard deviation 0.1；
Since different life stage household resident effectiveness preference has differences, so setting bugij, formula (8) are
The component j observed in u life cycle receives the enrolled stage to the coefficient g of family i；β is that average value is 0 and standard deviation is
0.1 random perturbation is generated with normal distribution to indicate individual preference；The decision rule of formula (5) come determine resident live
Different effectiveness preferences in site selection model between home intelligent body；
In formula (9), ViinterThe interaction for representing other home intelligent bodies in family i and city space, is divided into neighbours' shadow
Ring NViWith the global implication GV of home intelligent body ii；
In formula (10), NViTo use house subsidy policy to be repositioned onto urban district NmoveNeighbours ratio divided by not making
With the neighbours N of reorientation strategynomove；GViIt is defined as the family sum G using resettlement policymoveDivided by family sum GTotal's
Ratio；
In formula (11), component εisIt reflects to effectiveness VisAnd ViinterUnobservable random contribution, the random element
Plain εisIt follows Gumble to be distributed and can generate with formula (11), wherein γ is followed is uniformly distributed at random, and constant μ and β points
- 4.5 and 2 are not set as it, by εisRange be fixed between -10 and 10, in addition, QisIt is the probability of family selection position s,
Shown in form such as formula (12)；
Step 3: model running:
Input urban information and family information, adjusting parameter determine the selection of resident's inhabitation addressing, export resident
Inhabitation addressing result under policy promulgation；
According to resident's inhabitation addressing as a result, the inhabitation addressing variation diagram of resident in seclected time period can be drawn, instead
Reflect influence of the policy execution to resident's moving process.
Further, in step 2.1, the space characteristics, comprising: construction area, the safety of earthquake typhoon, fire safety evaluating
Property, the shabby depreciation of building, the elderly's accessible facilities, Environmental security equipment, road surface walking safety, crime rate, air or make an uproar
Sound pollution, convenience, purchasing convenience, Yi Zi institute, community convenience, cultural facility, children's activity field from work or school
Institute, vert space, wide open space.
The present invention has the advantages that be directed to government's policy for promotion promulgation, the present invention in family's multiple agent at random into
Change to adapt to city space, response life cycle phase and house and promote policy, is on the one hand relatively accurately to predict
On the other hand the implementation effect of government policy is that more can intuitively observe different elements to the influence knot of resident's inhabitation addressing
Fruit.The present invention can promote the residential building moving process under policy implication with simcity center house.It can not only simulate
Resident carries out the adaptive behavior of house resettlement in predetermined city space, can also pass through tissue resident's intelligent body and political affairs
Interaction between plan reflects influence of the policy execution to resident's moving process.By house resettlement selection and policy attitude, carry out mould
Quasi- adaptive behavior and reciprocation of the resident in the different life stage can be intuitive to see the validity that policy is promulgated
And influence of the different elements to policy is as a result, help government's preferably decision.
[Detailed description of the invention]
The invention will be further described in conjunction with the embodiments with reference to the accompanying drawings.
Fig. 1 is inhabitation resident's addressing flow diagram in the present invention.
Fig. 2 is the partial correlation coefficient table that influence factor moves effectiveness and satisfaction to resident in the present invention.
[specific embodiment]
A kind of method for building up of the multiple agent model of residential location choice, includes the following steps:
Step 1: adaptive behavior model foundation of home intelligent body during house addressing specifically includes:
Step 1.1: the setting of home intelligent body:
Each home intelligent body has the property that age, marital status, member, deposit, income and the vehicles；
Each home intelligent body have seven life cycle phases: birth, get married, child-rearing, children leave home, retirement,
Single old man and death；
Step 1.2: home intelligent body determines the satisfaction in current address:
Wherein n=1 ..., 18；U=1 ..., 6 (1)
0<Sthreshold<4 (2)
Si>Sthreshlold (3)
Si≤Sthreshlold (4)
Wherein SiIt is satisfaction of the family i to Current Housing area, bugijIt is the regression coefficient vector of variable j, u indicates income
The life cycle phase of family i in group g；
xijsIt is the satisfaction of family；There are four ranks in the position S that variable j is generated: 1) it is very dissatisfied, 2) and it is discontented
Meaning, 3) it is satisfied, 4) it is very satisfied；
If one family intelligent body is lower than S to the satisfaction of current locationthreshold, then the intelligent body will consider to move；
Step 1.3: city divides into three different region CCA, UPA, UPA；Wherein CCA is downtown area, and UPA is city
Promotion area, city, UPA are city control zone；For each home intelligent body, it is assumed that the public thing of house that different cities region provides
Industry is different three regions, and public utilities are homogeneities in a region, and random range, which follows, represents personal preference
Normal distribution；When family's resettlement, it is necessary to according to the necessity in new life stage, compare the different facilities of residential quarter, to adapt to
New place, as shown in Figure 1；
Step 2: the policy responses in house moving process between home intelligent body specifically include:
Step 2.1: the setting of virtual city space:
According to urban planning information, city space is divided into grid cell, each cell size is 500m × 500 meter,
Each unit in the space of virtual city will have predefined space attribute, the space attribute, including land use zoning
Type, family's density, planning region classification, room rate, 18 space characteristics (please refer to influence factor shown in Fig. 2 to move resident
The partial correlation coefficient of effectiveness and satisfaction)；
Step 2.2: city space provides effectiveness for home intelligent body:
Uis=Vis+Viinter+εis (5)
Wherein, n=1,2,3 ... 18 (6)
xijs=(1+ai)xjsOr xijs=dijs (7)
bugij=bugj(1+βi) (8)
Viinter=GVi+NVi (9)
GVi=Gmove/GTotal NVi=Nmove/Nnomove (10)
εis=μ-β ln (- ln γ) (β>0), (0<γ<1) (11)
VisIt is the effectiveness of the family i provided by position s, without unobservable random element and home intelligent body Viinter
Between interaction；
XijsIt is the vector of an observable explanatory variable j, for describing the attribute of the family i in the s of position, XijsWith two kinds
Formal definition, one form of them are the assessments of the family i of space attribute xj in the s of position, and another form is the family in the s of position
The distance between front yard i and the communal facility j of proximal most position, for example, school, shop etc.；
In order to reflect the X in the s of position between all home intelligent body iijsDifference, generate random number ai, wherein following just
State distribution, average value 0, standard deviation 0.1；
Since different life stage household resident effectiveness preference has differences, so setting bugij, formula (8) are
The component j observed in u life cycle receives the enrolled stage to the coefficient g of family i；β is that average value is 0 and standard deviation is
0.1 random perturbation is generated with normal distribution to indicate individual preference；The decision rule of formula (5) come determine resident live
Different effectiveness preferences in site selection model between home intelligent body；
In formula (9), ViinterThe interaction for representing other home intelligent bodies in family i and city space, is divided into neighbours' shadow
Ring NViWith the global implication GV of home intelligent body ii；
In formula (10), NViTo use house subsidy policy to be repositioned onto urban district NmoveNeighbours ratio divided by not making
With the neighbours N of reorientation strategynomove；GViIt is defined as the family sum G using resettlement policymoveDivided by family sum GTotal's
Ratio；
In formula (11), component εisIt reflects to effectiveness VisAnd ViinterUnobservable random contribution, the random element
Plain εisIt follows Gumble to be distributed and can generate with formula (11), wherein γ is followed is uniformly distributed at random, and constant μ and β points
- 4.5 and 2 are not set as it, by εisRange be fixed between -10 and 10, in addition, QisIt is the probability of family selection position s,
Shown in form such as formula (12)；
Step 3: model running:
Input urban information and family information, adjusting parameter determine the selection of resident's inhabitation addressing, export resident
Inhabitation addressing result under policy promulgation；
According to resident's inhabitation addressing as a result, the inhabitation addressing variation diagram of resident in seclected time period can be drawn, instead
Reflect influence of the policy execution to resident's moving process.
Further, in step 2.1, the space characteristics, comprising: construction area, the safety of earthquake typhoon, fire safety evaluating
Property, the shabby depreciation of building, the elderly's accessible facilities, Environmental security equipment, road surface walking safety, crime rate, air or make an uproar
Sound pollution, convenience, purchasing convenience, Yi Zi institute, community convenience, cultural facility, children's activity field from work or school
Institute, vert space, wide open space.
For the promulgation of government's policy for promotion, family's multiple agent stochastic evolution in the present invention with adapt to city space,
It responds life cycle phase and house promotes policy, be on the one hand the implementation effect that can relatively accurately predict government policy
On the other hand fruit is that more can intuitively observe different elements to the influence result of resident's inhabitation addressing.The present invention can be with mould
Quasi- downtown area house promotes the residential building moving process under policy implication.Resident can not only be simulated predetermined
The adaptive behavior of house resettlement is carried out in city space, it can also be by interacting between tissue resident's intelligent body and policy, instead
Reflect influence of the policy execution to resident's moving process.By house resettlement selection and policy attitude, to simulate resident in different lifes
The adaptive behavior and reciprocation for ordering the phase of the cycles can be intuitive to see validity and different elements pair that policy is promulgated
The influence of policy is as a result, help government's preferably decision.
The foregoing is merely preferable implementation use-cases of the invention, are not intended to limit the scope of the present invention.It is all
Within the spirit and principles in the present invention, made any modification, equivalent replacement and improvement etc., should be included in of the invention
Within protection scope.
Claims (2)
1. a kind of method for building up of the multiple agent model of residential location choice, characterized by the following steps:
Step 1: adaptive behavior model foundation of home intelligent body during house addressing specifically includes:
Step 1.1: the setting of home intelligent body:
Each home intelligent body has the property that age, marital status, member, deposit, income and the vehicles；
Each home intelligent body has seven life cycle phases: birth, marriage, child-rearing, children leave home, are retired, single
Old man and death；
Step 1.2: home intelligent body determines the satisfaction in current address:
Wherein n=1 ..., 18；U=1 ..., 6 (1)
0<Sthreshold<4 (2)
Si>Sthreshlold (3)
Si≤Sthreshlold (4)
Wherein SiIt is satisfaction of the family i to Current Housing area, bugijIt is the regression coefficient vector of variable j, u is indicated in income group g
The life cycle phase of family i；
xijsIt is the satisfaction of family；There are four ranks in the position S that variable j is generated: 1) it is very dissatisfied, 2) and it is dissatisfied, 3)
It is satisfied, 4) it is very satisfied；
If one family intelligent body is lower than S to the satisfaction of current locationthreshold, then the intelligent body will consider to move；
Step 1.3: city divides into three different region CCA, UPA, UPA；Wherein CCA is downtown area, and UPA is city rush
Time zone, UPA are city control zone；For each home intelligent body, it is assumed that different cities region provide homes utility be
Three different regions, and public utilities are homogeneities in a region, random range follows the normal state for representing personal preference
Distribution；When family's resettlement, it is necessary to according to the necessity in new life stage, compare the different facilities of residential quarter, it is new to adapt to
Place；
Step 2: the policy responses in house moving process between home intelligent body specifically include:
Step 2.1: the setting of virtual city space:
According to urban planning information, city space is divided into grid cell, each cell size is 500m × 500 meter, virtually
Each unit in city space will have a predefined space attribute, the space attribute, including land use zoning type,
Family's density, planning region classification, room rate, multiple space characteristics；
Step 2.2: city space provides effectiveness for home intelligent body:
Uis=Vis+Viinter+εis (5)
Wherein, n=1,2,3 ... 18 (6)
xijs=(1+ai)xjsOr xijs=dijs (7)
bugij=bugj(1+βi) (8)
Viinter=GVi+NVi (9)
GVi=Gmove/GTotal NVi=Nmove/Nnomove (10)
εis=μ-β ln (- ln γ) (β>0), (0<γ<1) (11)
VisIt is the effectiveness of the family i provided by position s, without unobservable random element and home intelligent body ViinterBetween
Interaction；
XijsIt is the vector of an observable explanatory variable j, for describing the attribute of the family i in the s of position, XijsIn two forms
Definition, one form of them is the assessment of the family i of space attribute xj in the s of position, and another form is the family i in the s of position
The distance between communal facility j of proximal most position；
In order to reflect the X in the s of position between all home intelligent body iijsDifference, generate random number ai, wherein followed normal distribution is divided
Cloth, average value 0, standard deviation 0.1；
Since different life stage household resident effectiveness preference has differences, so setting bugij, formula (8) is raw in u
The component j observed in the life period receives the enrolled stage to the coefficient g of family i；β is that average value is 0 and standard deviation is 0.1
Random perturbation is generated with normal distribution to indicate individual preference；The decision rule of formula (5) determines resident's inhabitation addressing mould
Different effectiveness preferences in type between home intelligent body；
In formula (9), ViinterThe interaction for representing other home intelligent bodies in family i and city space, being divided into neighbours influences NVi
With the global implication GV of home intelligent body ii；
In formula (10), NViTo use house subsidy policy to be repositioned onto urban district NmoveNeighbours ratio divided by without using weight
The neighbours N of positioning strategynomove；GViIt is defined as the family sum G using resettlement policymoveDivided by family sum GTotalRatio
Rate；
In formula (11), component εisIt reflects to effectiveness VisAnd ViinterUnobservable random contribution, random element εis
It follows Gumble to be distributed and can generate with formula (11), wherein γ is followed is uniformly distributed at random, and constant μ and β are set respectively
It is set to -4.5 and 2, by εisRange be fixed between -10 and 10, in addition, QisIt is the probability of family selection position s, form
As shown in formula (12)；
Step 3: model running:
Input urban information and family information, adjusting parameter determine the selection of resident's inhabitation addressing, export resident in political affairs
Inhabitation addressing result under plan promulgation；
According to resident's inhabitation addressing as a result, the inhabitation addressing variation diagram of resident in seclected time period, Lai Fanying political affairs can be drawn
Plan implements the influence to resident's moving process.
2. a kind of method for building up of the multiple agent model of residential location choice as described in claim 1, it is characterised in that: step
In rapid 2.1, the space characteristics, comprising: construction area, fire safety, builds shabby depreciation, is old at the safety of earthquake typhoon
Year people accessible facilities, Environmental security equipment, road surface walking safety, crime rate, air or noise pollution, from work or school
Convenience, purchasing convenience, Yi Zi institute, community convenience, cultural facility, Children's recreational sites, vert space, wide open space.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110991740A (en) * | 2019-12-03 | 2020-04-10 | 海南电网有限责任公司 | Power grid planning method and system based on operation simulation and intelligent agent technology |
| CN115049159A (en) * | 2022-08-12 | 2022-09-13 | 北京大学 | Population distribution prediction method and device, storage medium and electronic equipment |
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| CN102750411A (en) * | 2012-06-19 | 2012-10-24 | 中国地质大学（武汉） | Urban dynamic micro-simulation method based on multi-agent discrete choice model |
| US20140372173A1 (en) * | 2012-06-13 | 2014-12-18 | Rajasekhar Koganti | Home investment report card |
| CN109472522A (en) * | 2019-01-13 | 2019-03-15 | 大连理工大学 | Environmental passenger-cargo roll-on berth system multiple agent microscopic simulation modeling method |
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| CN101419623A (en) * | 2008-12-09 | 2009-04-29 | 中山大学 | Geographical simulation optimizing system |
| US20140372173A1 (en) * | 2012-06-13 | 2014-12-18 | Rajasekhar Koganti | Home investment report card |
| CN102750411A (en) * | 2012-06-19 | 2012-10-24 | 中国地质大学（武汉） | Urban dynamic micro-simulation method based on multi-agent discrete choice model |
| CN109472522A (en) * | 2019-01-13 | 2019-03-15 | 大连理工大学 | Environmental passenger-cargo roll-on berth system multiple agent microscopic simulation modeling method |
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|---|---|---|---|---|
| CN110991740A (en) * | 2019-12-03 | 2020-04-10 | 海南电网有限责任公司 | Power grid planning method and system based on operation simulation and intelligent agent technology |
| CN110991740B (en) * | 2019-12-03 | 2023-12-15 | 海南电网有限责任公司 | Power grid planning method and system based on operation simulation and intelligent agent technology |
| CN115049159A (en) * | 2022-08-12 | 2022-09-13 | 北京大学 | Population distribution prediction method and device, storage medium and electronic equipment |
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