CN110413713A - One kind being based on complexity network analysis and its three-dimensional effect evaluation method - Google Patents
One kind being based on complexity network analysis and its three-dimensional effect evaluation method Download PDFInfo
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Abstract
The present invention relates to one kind to be based on complexity network analysis and its three-dimensional effect evaluation method, comprising: building urban road network, and centrality analysis is carried out, its three-dimensional effect is embodied by evaluating the influence of the centrality index to residential quarters value of leass；Network is overlapped based on the building rail traffic of the actual run time of rail traffic and routine bus system and routine bus system, and carries out centrality analysis, embodies its three-dimensional effect by evaluating its influence of centrality index to residential quarters value of leass；The evaluation result for comparing urban road network and rail traffic and routine bus system overlapping network center's property index, compares urban road network centrality and rail traffic respectively and routine bus system overlaps network center's property influence degree；The influence to residential quarters value of leass carries out comprehensive analysis again, then carries out overall merit to three-dimensional effect produced by it.The accuracy of complexity network analysis and three-dimensional effect evaluation can be improved in the present invention.
Description
Technical field
The present invention relates to traffic network analysis fields, are based on complexity network analysis and its sky more particularly, to one kind
Between Effect Evaluation method.
Background technique
With the development of traffic technique, mode of transportation gradually diversification, compound railway and highway system is gradually formed, traffic study
Networking is turned to from unification；The spatial framework of transportation network, spatial-temporal evolution pattern and it can lead to socio-economic development relationship etc.
Cross transportation network spatial analysis realization.
Complex Networks Theory has parsed the networking composite construction of complication system, and it is special that cyberspace can be explained in geography
Property complexity, inquire into the space relationship feature and service level of transportation system, be mainly used in aviation, track hand over
Logical, urban transportation etc. reflects the tissue and efficiency of network, the interactive relationship of evaluation the interaction stream generated and network structure；
The research of existing traffic complex network comes with some shortcomings: (1) tradition research method is reachable from single-pathway parsing urban transportation
Property, three-dimensional effect etc., it is less that City Traffic Transport System is parsed based on global visual angle；(2) the research majority concern of complex network
Spatial network Euclidean distance, less introducing transit time；(3) spatial effect analysis of traffic complex network do not refine to yet in it is micro-
See scale.
Summary of the invention
The present invention is that overcome complexity network analysis and three-dimensional effect described in the above-mentioned prior art to evaluate not accurate enough
Defect provides a kind of based on complexity network analysis and its three-dimensional effect evaluation method.
The described method includes:
The assay of urban road network: urban road network is constructed by ArcGIS software (GIS software)
Network, and using ArcGIS software and UNA (Urban Network Analysis, urban network analysis) tool to urban road network
Network carries out the analysis of complex network centrality, by evaluation urban road network centrality index to the shadow of residential quarters value of leass
It rings and embodies its three-dimensional effect；
The assay of rail traffic and routine bus system overlapping network: rail traffic and ground are based on by ArcGIS software
The actual run time building rail traffic of public transport and routine bus system overlap network；And utilize ArcGIS and Eclipse software pair
Rail traffic and routine bus system overlapping network carry out the analysis of complex network centrality, folded by evaluation rail traffic and routine bus system
It closes influence of network center's property index to residential quarters value of leass and embodies its three-dimensional effect；Wherein Eclipse software is one
Open source code, expansible development platform based on Java, for making up environment by plug in component.
Overall merit: pass through ArcGIS comparison urban road network centrality index and rail traffic and routine bus system
The evaluation result of network center's property index is overlapped, urban road network centrality and rail traffic is compared respectively and routine bus system is folded
Close neighbouring centrality and Jie's centrality influence degree in network center's property；Using ArcGIS software with Geographical Weighted Regression Model pair
The influence of residential quarters value of leass carries out comprehensive analysis, and it is neighbouring by urban road network to summarize each residential quarters value of leass
Centrality, Jie's centrality, through centrality, rail traffic and routine bus system overlapping network are in centrality, Jie's centrality, degree
The six class centrality influence degree difference such as disposition, then it is produced by SAS (STATISTICALANALYSISSYSTEM) software
Raw three-dimensional effect carries out overall merit, and exports evaluation result.
SAS (STATISTICAL ANALYSIS SYSTEM) software is statistical analysis software.
Preferably, the urban road network assay the following steps are included:
S1.1: inputting each grade standard path desin speed in ArcGIS software, and calculates each section of transit time of road
As impedance, using urban road intersection point as node, using urban road as side building can operation urban road vector topological network；
S1.2: UNA tool is utilized, using the road speed of service as weight, with complexity network analysis method calculate node
Centrality (centrality)；It include: neighbouring centrality (closeness), Jie's centrality (betweenness) and through center
Property (straightness)；(three centrality indexs of urban road network evaluate respectively urban road network opposite accessibility,
Middle rotating function and the convenience degree directly reached.)
S1.3: all road circuit nodes within the scope of 1000 meters of search residence cell buffer zone domain calculate separately in buffer area
All nodes are adjacent to centrality average value, Jie's centrality average value and through centrality average value, for indicating residential quarters city
City's road network node center evaluation result, by residential quarters road traffic attribute quantification；
Wherein, centrality average value is higher, and centrality is higher；
Buffer area is a kind of coverage or service range of Geography spatial object, refers specifically to the week in point, line, surface entity
Enclose, the one fixed width established automatically it is polygon.
S1.4: each residential quarters urban road network centrality is exported, and city is calculated according to Geographical Weighted Regression Model
Road network centrality influences residential quarters value of leass.
Preferably, rail traffic and routine bus system overlapping network assay the following steps are included:
S2.1: all rail line transit times are inputted in ArcGIS software；Then rail line is chosen
Routine bus system route between identical starting point calculates routine bus system route transit time；
S2.2: rail line transit time and routine bus system route transit time are based on；Wherein, rail line
Transit time and routine bus system route transit time are obtained using the real-time current data of each map software, include the transfer time,
With the weight of routine bus system transit time for 1, each route of rail traffic is according to practical transit time by itself and corresponding ground public transport
Transit time ratio be set as weight；
S2.3: search track traffic website is plugged into the routine bus system website in region, and by rail traffic website with plug into
Routine bus system website in region merges to form a new operation node, the new operation node reflection rail traffic and routine bus system
The connection of transfer；Rail traffic and routine bus system network are overlapped by the new operation node, and with routine bus system website,
Rail traffic website, new operation node are network node, and public transport operation route, rail traffic working line are side, construct track
Traffic and routine bus system overlap network；
S2.4: Eclipse software is utilized, with complexity network analysis method calculate node centrality
(centrality)；Degree of including: centrality, neighbouring centrality, Jie's centrality；
S2.5: all sections of the range inner orbit traffic of 500 meters of search residence cell buffer zone and routine bus system overlapping network
Point, and the degree centrality average value of each node, neighbouring centrality average value and Jie's centrality average value are calculated separately, for indicating
Using residential quarters as the degree centrality of arithmetic element, neighbouring centrality and Jie center in rail traffic and routine bus system overlapping network
The evaluation result of property, average value is higher, and centrality level is higher, thus by residential quarters public transport attribute quantification；
S2.6: public transport complex network is introduced in the Geographical Weighted Regression Model of rail traffic and routine bus system overlapping network
Centrality index, i.e., using actual run time as weight to the side of each rail traffic working line in public transport complex network into
Row assigns power, and overlaps all kinds of centrality of network based on this weight calculation rail traffic and routine bus system, analyzes it to value of leass
Spacial influence.
Practical operation situation is not accounted in the traffic complex network algorithm of traditional cities, only with " 0-1 " building based on practical
The transportation network of utility lines and node.In urban transportation, the setting of each grade road speeds to network operation influence degree compared with
Greatly, large error will be generated by being such as ignored；Rail traffic and ground public transport belong to urban public transport scope, but the two by
In speed and the difference of network organization, actual operating efficiency gap is larger, therefore considers actual track traffic and routine bus system
Speed of service difference is of great significance.
The plugging into of rail traffic and routine bus system, transfer ability is to be connected to the network the important embodiment of efficiency.In existing research
Majority is less to comprehensively consider rail traffic and routine bus system using rail traffic as research object.The present invention will consider rail traffic
Plugging into, changing to progress network struction between routine bus system, by a certain range of bus station of rail traffic website and enters the orbit
It is calculated in road traffic website.
Preferably, rail traffic website plugs into area as the region within the scope of 200 meters of rail traffic website periphery.
Preferably, the calculating of node center are as follows:
(1) centrality is spent
The size of degree centrality node degree is measured, and can intuitively reflect that other nodes occur in the node and network
A possibility that directly contacting size；The degree centrality value of node is bigger, then with other node contacts a possibility that it is bigger；It calculates public
Formula are as follows:
Wherein, CdTo spend centrality, i is network node, and n is the quantity of network node, kiIt is directly contacted to have with node i
Node total number.
(2) neighbouring centrality
Neighbouring centrality is measured with the size reciprocal of the shortest distance sum of given node to all nodes, reflects the node
Opposite accessibility size in a network；Calculation formula are as follows:
Wherein, j is other nodes in addition to node i, dijIt is the shortest distance of the node i to node j, CcFor neighbouring center
Property, i.e., inverse of any node to every other node average distance in network；
(3) Jie's centrality
Jie's centrality, by given degree of node and measurement, reflects node in net with the shortest path between all nodes pair
Transfer and converged functionality in network；Calculation formula are as follows:
δijIndicate node i to node j shortest path sum,It is that these shortest paths need quantity by node k；
CbFor Jie's centrality, indicates the middle rotating function of a node in a network, that is, need the route quantity by the node；
(4) through centrality
Through centrality measures the departure degree of shortest path and straight line path between two nodes, and departure degree is smaller, directly
Better up to centrality, traffic efficiency is higher；If certain node can reach any node in network with shortest straight line path, that
Node centrality of going directly is best, traffic efficiency also highest；Calculation formula is as follows:
For node i to the Euclidean distance between node j, CsFor the through centrality of node, transportation network effect is measured
A possibility that rate, i.e., a certain node can directly be reached from other nodes.
Preferably, Geographical Weighted Regression Model (GWR) formula are as follows:
Wherein, p is each residential quarters node in Geographical Weighted Regression Model, and m is all types of in Geographical Weighted Regression Model
Explanatory variable；ypIndicate the domestic tenancy price of p, xpmIt is the explanatory variable of p, β0(up,vp) be p parameter, βk(up,vp) it is solution
Variable m is released in the local regression coefficient of p, (up,vp) be p position, ξpIt is the accidental error of p；
The calculating matrix of regression coefficient are as follows:
X is the explanatory variable of matrix, and y is the vector of unitary variant, β (up,vp) be n+1 local regression coefficient vector, W
(up,vp) be geographical weighted oversavtion judgment matrix；
Wherein, v is other residential quarters in addition to the node p of residential quarters that model is included in Geographical Weighted Regression Model
Node；WpvIt is weight of the v in p observation scope；dpvIt is the shortest distance of the node p to node v；B is bandwidth.
Conurbation road network of the present invention and rail traffic and two factor analyses of network of routine bus system overlapping its to living
The spacial influence of residence cell value of leass, then comprehensive analysis urban road network three-dimensional effect and rail traffic and ground are public again
Overlapping to close cyberspace effect, the influence to residential quarters value of leass carries out comprehensive analysis.
In terms of urban road network, it is first depending on the standard speed of category of roads setting urban road network, and is calculated
The transit time of each section of road, then urban road network is constructed by network impedance of the transit time of road, it is finally multiple to weight
The centrality of miscellaneous network evaluates its three-dimensional effect.
Network facet is overlapped in rail traffic and routine bus system, firstly, obtaining each route actual run time of rail traffic
It is then public with rail traffic and ground with the actual run time of the route upper ground surface public transport of starting point (OD) identical as rail traffic
Hand over the ratio of actual run time for the weight of rail traffic website, input track traffic website weight constructs rail traffic and ground
Face public transport overlaps network, evaluates its three-dimensional effect with the centrality of Weighted Complex Networks.
Urban road and public transport are the important components of Urban Transportation network.Urban road system is traffic
Facility network, the layout of route and planning have an impact city space；Public transportation system is tissue network, passes through route
Organization optimization influence spatial framework.The present invention compares means of transportation network and organizes the spatial diversity of network complexity, and right
Its three-dimensional effect generated is evaluated.
Network is constructed only in accordance with node and route relative to traditional complexity network analysis, the power without considering route
Weight；The weight of route is introduced into the central operation of complex network by the present invention, i.e., the travel time factor in transportation network；Together
When, rail network and routine bus system network are superimposed as single network analysis, when considering the transfer of rail traffic to a certain degree
Between, transfer time of rail traffic and routine bus system, refine result more, accuracy.
Relative to more single for considering for traffic indicators in the factor analysis of traditional residential price, majority use and city
Public bus network quantity etc. in downtown distance and periphery subway station distance, buffer area, only from single evaluating models residential quarters
Traffic attribute；The present invention is based on the overall networks of urban transportation, consider two kinds of main traffic mode carriers of daily trip, i.e. city
City's road and urban public transport, and traffic status and category with globalization, networking visual angle evaluation residential quarters in city
Property, in this, as one of the factor of evaluation of value of leass.
Compared with prior art, the beneficial effect of technical solution of the present invention is:
The travel time is introduced in urban transportation Complex Networks Analysis, can more accurately reflect the spy of traffic complex network
Property.Less its network characteristic of consideration in the analysis of existing Traffic Space effect lacks multiple to facility network and tissue network
The cognition of polygamy spatial diversity.Therefore the present invention uses complexity network analysis centrality index, assesses each of node and website
Class function center degree, including direct reachability, network center's degree and hinge degree etc., and parse disparate networks function and rent
The Space Coupling degree for price of renting.
Detailed description of the invention
Fig. 1 is a kind of flow chart based on complexity network analysis and its three-dimensional effect evaluation method.
Fig. 2 is the assay method flow schematic diagram of urban road network.
Fig. 3 is the assay method flow schematic diagram that rail traffic and routine bus system overlap network.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to better illustrate this embodiment, the certain components of attached drawing have omission, zoom in or out, and do not represent actual product
Size；
To those skilled in the art, it is to be understood that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
The present embodiment provides one kind to be based on complexity network analysis and its three-dimensional effect evaluation method, as shown in Figure 1, described
Method includes:
The assay of urban road network: by ArcGIS software building urban road network, and it is soft using ArcGIS
Part and UNA tool carry out the analysis of complex network centrality to urban road network, are referred to by evaluating urban road network centrality
It marks the influence to residential quarters value of leass and embodies its three-dimensional effect；
The assay of rail traffic and routine bus system overlapping network: rail traffic and ground are based on by ArcGIS software
The actual run time building rail traffic of public transport and routine bus system overlap network, and utilize ArcGIS and Eclipse software pair
Rail traffic and routine bus system overlapping network carry out the analysis of complex network centrality, folded by evaluation rail traffic and routine bus system
It closes influence of network center's property index to residential quarters value of leass and embodies its three-dimensional effect；
Overall merit: pass through ArcGIS comparison urban road network centrality index and rail traffic and routine bus system
The evaluation result of network center's property index is overlapped, urban road network centrality and rail traffic is compared respectively and routine bus system is folded
Close neighbouring centrality and Jie's centrality influence degree in network center's property；Using ArcGIS software with Geographical Weighted Regression Model pair
The influence of residential quarters value of leass carries out comprehensive analysis, and it is neighbouring by urban road network to summarize each residential quarters value of leass
Centrality, Jie's centrality, through centrality, rail traffic and routine bus system overlapping network are in centrality, Jie's centrality, degree
The six class centrality influence degree difference such as disposition, then overall merit is carried out to three-dimensional effect produced by it by SAS software.
As shown in Fig. 2, the assay of the urban road network the following steps are included:
S1.1: inputting each grade standard path desin speed in ArcGIS software, and calculates each section of transit time of road
As impedance, using urban road intersection point as node, using urban road as side building can operation urban road vector topological network；
S1.2: UNA tool is utilized, using the road speed of service as weight, with complexity network analysis method calculate node
Centrality (centrality)；It include: neighbouring centrality (closeness), Jie's centrality (betweenness) and through center
Property (straightness)；
Three centrality indexs of urban road network evaluate respectively the opposite accessibility of urban road network, middle rotating function and
The convenience degree directly reached.
S1.3: all road circuit nodes within the scope of 1000 meters of search residence cell buffer zone domain calculate separately in buffer area
All nodes are adjacent to centrality average value, Jie's centrality average value and the central average value that goes directly, for indicating residential quarters
Urban road network node center evaluation result, by residential quarters road traffic attribute quantification；
Wherein, centrality average value is higher, and centrality is higher；
S1.4: each residential quarters urban road network centrality is exported, and city is calculated according to Geographical Weighted Regression Model
Road network centrality influences residential quarters value of leass.
As shown in figure 3, rail traffic and routine bus system overlapping network assay the following steps are included:
S2.1: all rail line transit times are inputted in ArcGIS software；Then rail line is chosen
Routine bus system route between identical starting point calculates routine bus system route transit time；
S2.2: rail line transit time and routine bus system route transit time are based on；Wherein, rail line
Transit time and routine bus system route transit time are obtained using the real-time current data of each map software, include the transfer time；
With the weight of routine bus system transit time for 1, each route of rail traffic is according to practical transit time by itself and corresponding ground public transport
Transit time ratio be set as weight；
S2.3: search track traffic website is plugged into the routine bus system website in region, and by rail traffic website with plug into
Routine bus system website in region merges to form a new operation node, the new operation node reflection rail traffic and routine bus system
The connection of transfer；Rail traffic and routine bus system network are overlapped by the new operation node, and with routine bus system website,
Rail traffic website, new operation node are network node, and public transport operation route, rail traffic working line are side, construct track
Traffic and routine bus system overlap network；
S2.4: complexity network analysis method calculate node centrality (centrality) is used using Eclipse software；
Degree of including: centrality, neighbouring centrality, Jie's centrality；
S2.5: all sections of the range inner orbit traffic of 500 meters of search residence cell buffer zone and routine bus system overlapping network
Point, and the degree centrality average value of each node, neighbouring centrality average value and Jie's centrality average value are calculated separately, for indicating
Using residential quarters as the degree centrality of arithmetic element, neighbouring centrality and Jie center in rail traffic and routine bus system overlapping network
The evaluation result of property, average value is higher, and centrality level is higher, thus by residential quarters public transport attribute quantification；
S2.6: public transport complex network is introduced in the Geographical Weighted Regression Model of rail traffic and routine bus system overlapping network
Centrality index, i.e., using actual run time as weight to the side of each rail traffic working line in public transport complex network into
Row assigns power, and overlaps all kinds of centrality of network based on this weight calculation rail traffic and routine bus system, analyzes it to value of leass
Spacial influence.
Rail traffic website plugs into area as the region within the scope of 200 meters of rail traffic website periphery.
The calculating of node center are as follows:
(1) centrality is spent
The size of degree centrality node degree is measured, and can intuitively reflect that other nodes occur in the node and network
A possibility that directly contacting size；The degree centrality value of node is bigger, then with other node contacts a possibility that it is bigger；It calculates public
Formula are as follows:
Wherein, CdTo spend centrality, i is network node, and n is the quantity of network node, kiIt is directly contacted to have with node i
Node total number.
(2) neighbouring centrality
Neighbouring centrality is measured with the size reciprocal of the shortest distance sum of given node to all nodes, reflects the node
Opposite accessibility size in a network；Calculation formula are as follows:
Wherein, j is other nodes in addition to node i, dijIt is the shortest distance of the node i to node j, CcFor neighbouring center
Property, i.e., inverse of any node to every other node average distance in network；
(3) Jie's centrality
Jie's centrality, by given degree of node and measurement, reflects node in net with the shortest path between all nodes pair
Transfer and converged functionality in network；Calculation formula are as follows:
δijIndicate node i to node j shortest path sum,It is that these shortest paths need number by node k
Amount；CbFor Jie's centrality, indicates the middle rotating function of a node in a network, that is, need the route quantity by the node；
(4) through centrality
Through centrality measures the departure degree of shortest path and straight line path between two nodes, and departure degree is smaller, directly
Better up to centrality, traffic efficiency is higher；If certain node can reach any node in network with shortest straight line path, that
Node centrality of going directly is best, traffic efficiency also highest；Calculation formula are as follows:
For node i to the Euclidean distance between node j, CsFor the through centrality of node, transportation network effect is measured
A possibility that rate, i.e., a certain node can directly be reached from other nodes.
Geographical Weighted Regression Model (GWR) formula are as follows:
Wherein, p is each residential quarters node in Geographical Weighted Regression Model, and m is all types of in Geographical Weighted Regression Model
Explanatory variable；ypIndicate the domestic tenancy price of p, xpmIt is the explanatory variable of p, β0(up,vp) be p parameter, βk(up,vp) it is solution
Variable m is released in the local regression coefficient of p, (up,vp) be p position, ξpIt is the accidental error of p；
The calculating matrix of regression coefficient are as follows:
X is the explanatory variable of matrix, and y is the vector of unitary variant, β (up,vp) be n+1 local regression coefficient vector, W
(up,vp) be geographical weighted oversavtion judgment matrix；
Wherein, v is other residential quarters in addition to the node p of residential quarters that model is included in Geographical Weighted Regression Model
Node；WpvIt is weight of the v in p observation scope；dpvIt is the shortest distance of the node p to node v；B is bandwidth.
The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent；
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (6)
1. one kind is based on complexity network analysis and its three-dimensional effect evaluation method, which is characterized in that the described method includes:
The assay of urban road network: by ArcGIS software building urban road network, and using ArcGIS software and
UNA tool carries out the analysis of complex network centrality to urban road network, by evaluating urban road network centrality index pair
The influence of residential quarters value of leass embodies its three-dimensional effect；
The assay of rail traffic and routine bus system overlapping network: rail traffic and routine bus system are based on by ArcGIS software
Actual run time building rail traffic and routine bus system overlap network, and using ArcGIS and Eclipse software to track
Traffic and routine bus system overlapping network carry out the analysis of complex network centrality, overlap net by evaluation rail traffic and routine bus system
Influence of the network centrality index to residential quarters value of leass embodies its three-dimensional effect；
Overall merit: it is overlapped by ArcGIS comparison urban road network centrality index and rail traffic and routine bus system
The evaluation result of network center's property index, compares urban road network centrality and rail traffic respectively and routine bus system overlaps net
Neighbouring centrality and Jie's centrality influence degree in network centrality；Using ArcGIS software with Geographical Weighted Regression Model to house
The influence of cell value of leass carries out comprehensive analysis, summarizes each residential quarters value of leass by urban road network adjacent to center
Property, Jie's centrality, through centrality, rail traffic and routine bus system overlapping network are adjacent to centrality, Jie's centrality, degree centrality
Influence degree difference, then overall merit is carried out to three-dimensional effect produced by it by SAS software, and exports evaluation result.
2. according to claim 1 be based on complexity network analysis and its three-dimensional effect evaluation method, which is characterized in that institute
State the assay of urban road network the following steps are included:
S1.1: inputting each grade standard path desin speed in ArcGIS software, and calculates each section of transit time conduct of road
Impedance, using urban road intersection point as node, using urban road as side building can operation urban road network；
S1.2: UNA tool is utilized, using the road speed of service as weight, with complexity network analysis method calculate node center
Property；It include: neighbouring centrality, Jie's centrality and through centrality；
S1.3: all road circuit nodes within the scope of 1000 meters of search residence cell buffer zone domain calculate separately in buffer area and own
Node is adjacent to centrality average value, Jie's centrality average value and through centrality average value, for indicating residential quarters city road
Road network node center evaluation result, by residential quarters road traffic attribute quantification；
Wherein, centrality average value is higher, and centrality is higher；
S1.4: each residential quarters urban road network centrality is exported, and urban road is calculated according to Geographical Weighted Regression Model
Network center's property influences residential quarters value of leass.
3. according to claim 2 be based on complexity network analysis and its three-dimensional effect evaluation method, which is characterized in that rail
Road traffic and routine bus system overlapping network assay the following steps are included:
S2.1: all rail line transit times are inputted in ArcGIS software；Then it is identical to choose rail line
Routine bus system route between starting point calculates routine bus system route transit time；
S2.2: being based on rail line transit time and routine bus system route transit time, and wherein transit time includes transfer
Time；With the weight of routine bus system transit time for 1, each route of rail traffic is according to practical transit time by itself and corresponding ground
The transit time ratio of public transport is set as weight；
S2.3: search track traffic website is plugged into the routine bus system website in region, and by rail traffic website and region of plugging into
Interior routine bus system website merges to form a new operation node, and the new operation node reflection rail traffic and routine bus system are changed to
Connection；Rail traffic and routine bus system network are overlapped by the new operation node, and with routine bus system website, track
Traffic website, new operation node are network node, and public transport operation route, rail traffic working line are side, construct rail traffic
Network is overlapped with routine bus system；
S2.4: Eclipse software is utilized, with complexity network analysis method calculate node centrality；Degree of including: centrality,
Neighbouring centrality, Jie's centrality；
S2.5: all nodes of the range inner orbit traffic of 500 meters of search residence cell buffer zone and routine bus system overlapping network, and
Degree centrality average value, neighbouring centrality average value and Jie's centrality average value of each node are calculated separately, for indicating track
Traffic as the degree centrality of arithmetic element, neighbouring centrality and is situated between central with routine bus system overlapping network using residential quarters
Evaluation result, average value is higher, and centrality level is higher, thus by residential quarters public transport attribute quantification；
S2.6: public transport complex network center is introduced in the Geographical Weighted Regression Model of rail traffic and routine bus system overlapping network
Property index, i.e., assign by side of the weight to each rail traffic working line in public transport complex network of actual run time
Power, and all kinds of centrality of network are overlapped based on this weight calculation rail traffic and routine bus system, analyze its sky to value of leass
Between influence.
4. according to claim 3 be based on complexity network analysis and its three-dimensional effect evaluation method, which is characterized in that rail
Road traffic website plugs into area as the region within the scope of 200 meters of rail traffic website periphery.
5. according to claim 3 be based on complexity network analysis and its three-dimensional effect evaluation method, which is characterized in that section
The calculating of dot center's property are as follows:
(1) central calculation formula is spent are as follows:
Wherein, CdTo spend centrality, i is network node, and n is the quantity of network node, kiTo there is the section directly contacted with node i
Point sum.
(2) neighbouring central calculation formula are as follows:
Wherein, j is other nodes in addition to node i, dijIt is the shortest distance of the node i to node j, CcFor neighbouring centrality；
(3) be situated between central calculation formula are as follows:
δijIndicate node i to node j shortest path sum,It is that these shortest paths need quantity by node k；CbFor
Jie's centrality；
(4) central calculation formula of going directly is as follows:
For node i to the Euclidean distance between node j, CsFor the through centrality of node.
6. according to claim 3 be based on complexity network analysis and its three-dimensional effect evaluation method, which is characterized in that ground
Manage Weight Regression Model are as follows:
Wherein, p is each residential quarters node in Geographical Weighted Regression Model, and m is all types of explanations in Geographical Weighted Regression Model
Variable；ypIndicate the domestic tenancy price of p, xpmIt is the explanatory variable of p, β0(up, vp) be p parameter, βk(up, vp) it is to explain to become
Measure local regression coefficient of the m in p, (up, vp) be p position, ξpIt is the accidental error of p；
The calculating matrix of regression coefficient are as follows:
X is the explanatory variable of matrix, and y is the vector of unitary variant, β (up, vp) be n+1 local regression coefficient vector, W (up,
vp) be geographical weighted oversavtion judgment matrix；
Wherein, v is other residential quarters section in addition to the node p of residential quarters that model is included in Geographical Weighted Regression Model
Point；WpvIt is weight of the v in p observation scope；dpuIt is the shortest distance of the node p to node v；B is bandwidth.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111640294A (en) * | 2020-04-27 | 2020-09-08 | 河海大学 | Method for predicting passenger flow change of urban bus line under influence of newly-built subway line |
| CN113657688A (en) * | 2021-08-31 | 2021-11-16 | 广州市城市规划勘测设计研究院 | Simulation measurement method for community life circle |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111640294A (en) * | 2020-04-27 | 2020-09-08 | 河海大学 | Method for predicting passenger flow change of urban bus line under influence of newly-built subway line |
| CN111640294B (en) * | 2020-04-27 | 2022-02-11 | 河海大学 | Method for predicting passenger flow change of urban bus line under influence of newly-built subway line |
| CN113657688A (en) * | 2021-08-31 | 2021-11-16 | 广州市城市规划勘测设计研究院 | Simulation measurement method for community life circle |
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