CN108920892A - A kind of Urban Rail Transit Stations fragility measurement method - Google Patents
A kind of Urban Rail Transit Stations fragility measurement method Download PDFInfo
- Publication number
- CN108920892A CN108920892A CN201811160182.3A CN201811160182A CN108920892A CN 108920892 A CN108920892 A CN 108920892A CN 201811160182 A CN201811160182 A CN 201811160182A CN 108920892 A CN108920892 A CN 108920892A
- Authority
- CN
- China
- Prior art keywords
- fragility
- adaptability
- index
- fuzzy
- sub
- 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
- 238000000691 measurement method Methods 0.000 title claims abstract description 14
- 238000011156 evaluation Methods 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 17
- 238000005259 measurement Methods 0.000 claims abstract description 12
- 238000003064 k means clustering Methods 0.000 claims abstract description 8
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000010219 correlation analysis Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 abstract description 8
- 238000011160 research Methods 0.000 abstract description 5
- 238000004445 quantitative analysis Methods 0.000 abstract description 4
- 238000004642 transportation engineering Methods 0.000 description 2
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000000875 corresponding Effects 0.000 description 1
- 238000010192 crystallographic characterization Methods 0.000 description 1
- 230000034994 death Effects 0.000 description 1
- 231100000517 death Toxicity 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000002194 synthesizing Effects 0.000 description 1
Abstract
The invention discloses a kind of Urban Rail Transit Stations fragility measurement methods,This method is by establishing Evaluation of vulnerability index system and providing the calculating formula of each index,Fragile degree index is calculated by gray relative analysis method,Fragile degree will be classified further according to calculated result with K-Means clustering procedure,The final measurement for realizing traffic website fragility,The present invention overcomes the shortcomings of in rail traffic contingency management in the prior art for rail traffic website fragility research,A kind of Urban Rail Transit Stations fragility measurement method is provided,It can be used for the fragility of quantitative analysis evaluation Urban Rail Transit Stations,It can learn the weak link in Metro Network,In order to which the department of runing is protected it during contingency management,Convenient for the strain of emergency case,Further increase the flexibility and safety of urban track traffic,It is significant to the stable operation for ensureing urban track traffic.
Description
Fields
The invention belongs to field of track traffic, and in particular to a kind of Urban Rail Transit Stations fragility measurement method.
Background technique
As Rail traffic network pattern is formed increasingly, the sustainable growth of the volume of the flow of passengers and departure interval peak hour
Constantly shorten, brings a series of new safety problems and challenge to organization of driving and operation management.Simultaneously as rail traffic
It is typically in the confined space of underground, in peak period, passenger flow intensity is big, running interval is short, organization of driving's adjustment is difficult, once
Vital emergent event occurs for some website or certain route, can involve adjacent lines even entire gauze rapidly, in this way to entire
The traffic system in city can be brought and seriously affect, and biggish economic loss is caused, and even can generate passenger's injures and deaths when serious,
If can thus measure the fragility of Urban Rail Transit Stations with quantitative analysis, the weak ring in Metro Network is learned
Section can thus be protected it during contingency management in order to operational department's door, this is to guarantee urban track traffic
Stablizing operation has very important meaning.
Currently, domestic and foreign scholars have a certain number of researchs for the theory analysis of City Rail Transit System fragility
Achievement, for the difference that different subjects define fragility, the definition for summarizing Urban Rail Transit Stations fragility is：1. fragile
Property be rail traffic website itself build-in attribute, determine station by loss a possibility that and loss severity；②
Urban Rail Transit Stations fragility be in reflection Metro Network part station by a degree of attack and after failing,
The dropping characteristic of network performance；3. fragility is in face of adverse effect, sensibility and adaptibility to response that system is shown；
Although the problem of current domestic and foreign scholars have begun to focus on City Rail Transit System fragility, existing research
Majority is only for the fragility analysis of Influential Factors of Rail traffic network system, fragility quantitative analysis and assessment scheduling theory analysis
Aspect, such as Chang S E【Transportation performance,disaster vulnerability,and
long-term effects of earthquakes[C].In：Second EuroConference on Global Change
And Catastrophe Risk Management.Laxenburg, Austria, 2000.1-12.】Have studied urban transportation fortune
The fragility that defeated system is exposed when facing natural calamity；For example leaf is green【Rail transit network based on Complex Networks Theory
Network vulnerability analysis [J] China Safety Science journal, 2012,22 (2)：122-126】Selected part network characterization parameter quantitative
The fragility for calculating website, identifies the crucial website of Metro Network, provides reference for the optimization and improvement of operation management, but
It is field of track traffic to be still not achieved the requirement of practical application, thus rail traffic website fragility is incorporated into current neck
Domain development, solves the demand of practical application, it has also become the current big technical problem for being badly in need of solving and overcoming.
Summary of the invention
The present invention is precisely in order to overcome in rail traffic contingency management in the prior art for rail traffic website fragility
The deficiency of research provides a kind of Urban Rail Transit Stations fragility measurement method, by establishing Evaluation of vulnerability index system,
Three exposed property, sensibility and adaptability fragility measurement sub-indicator fragile degree indexes are calculated separately, and to node fragile degree
Grade classification is carried out, the weak link in Metro Network can be learned, in order to which the department of runing is right during contingency management
It is protected, and convenient for the strain of emergency case, further increases the flexibility and safety of urban track traffic.
To achieve the goals above, the technical solution adopted by the present invention is that：A kind of Urban Rail Transit Stations fragility survey
Amount method, it is characterised in that：Specifically include following steps：
S1 establishes Evaluation of vulnerability index system：Determine three exposed property, sensibility and adaptability fragility measurement subitems
Index, the step S1 further comprise：
S11, establishes exposed property and sensibility sub-indicator, and the exposed property sub-indicator includes full-time volume of the flow of passengers X1, it is high
Peak coefficient X2And peak passenger flow duration X3；The sensibility sub-indicator includes degree index X4, betweenness index X5And tightness index
X6；
S12, establishes adaptability sub-indicator, and the adaptability sub-indicator includes personnel's adaptability U1, resource adaptability U2
And environmental suitability U3；
S2 calculates fragile degree index：Grey correlation analysis is carried out to exposed property and sensibility measurement sub-indicator respectively, is obtained
To the correlation degree of each sub-indicator and fragility, the fragile degree index of each node is calculated；
S3, fragile degree classification：Using K-Means clustering procedure by node weakness assessment grade.
As an improvement of the present invention, the full-time volume of the flow of passengers X1=xin(i)+xout(i)+xtrans(i), wherein xin(i)For
The full-time volume of the flow of passengers that enters the station of station i, xout(i)For the full-time outbound volume of the flow of passengers of station i, xtrans(i)Transfer station is arrived for full-time transfer
The volume of the flow of passengers of i；
The peaking factor X2That is peak hour volume of the flow of passengers accounting, the X2=QPeak/QIt is flat, wherein QPeakIt enters the station for peak period
Or the outbound volume of the flow of passengers；QIt is flatTo enter the station during flat peak or the outbound volume of the flow of passengers；
The peak passenger flow durationWhereinEnter the moment for peak passenger flow,It is exited for peak passenger flow
Moment；
The degree indexWherein aijFor the element of the adjacency matrix of network, N is the sum of nodes；
The betweenness indexWherein njkIt is node to the number of shortest path between j, k
Amount, njkIt (i) is quantity of the node to shortest path between j, k Jing Guo node i；
The tightness indexWherein DijFor node i to the shortest distance of node j.
As an improvement of the present invention, the step S2 includes：
S21 determines relatively data sequence, is denoted as：
X'i=(x'i(1),x'i(2),…x'i(m))T, i=1,2 ..., n
Wherein, m is the number of index, and n is the quantity of data sequence.
S22 determines reference data sequence, is denoted as：
X'o=(x'o(1),x'o(2),…,x'o(m))
S23 carries out nondimensionalization to achievement data, forms following matrix：
S24, asks very poor, and expression formula is：
Δi(k)=| xo(k)-xi(k)|
S25 asks two-stage maximum difference and two-stage lowest difference：
With
S26, determines incidence coefficient, and expression formula is：
Wherein, ξ is resolution ratio, and 0 < ξ < 1, general value ξ=0.5.
S27, calculating correlation, expression formula are：
As another improvement of the invention, adaptability sub-indicator uses fuzzy decision in the step S12
It measures, by personnel's adaptability U1, resource adaptability U2And environmental suitability U3Overall merit obtains.
It is improved as another kind of the invention, the step S12 adaptability sub-indicator measurement further comprises：
S121 determines the set of factors of evaluation object；
S122 determines the evaluation criterion collection V of evaluation object,
V={ V1,V2,V3,V4,V5}={ is good, preferably, generally, poor, poor }；
S123 determines the weight sets of factor of evaluation：If A=(a1,a2,…,am) it is weight distribution fuzzy vector, wherein ai
Indicate theiThe weight of a factor, it is desirable that ai> 0 and ∑ ai=1；
Fuzzy relationship matrix r is established in S124, single factor test fuzzy evaluation：Quantization is evaluated each factor u of objecti(i=
1,2 ..., m), determination is evaluated object to the degree of membership of each grade fuzzy subset from the perspective of single factor test, further obtains
Fuzzy relation matrix：
Wherein rijIt indicates from single factor test uiFrom the point of view of some be evaluated object In Grade fuzzy subset VjDegree of membership.
S125, multiple attribute synthetical evaluation：Fuzzy weight vector A is synthesized with fuzzy relationship matrix r using Fuzzy Arithmetic Operators
Each fuzzy overall evaluation result vector B for being evaluated object is obtained, the model of fuzzy overall evaluation is：
As a further improvement of the present invention, clustering procedure uses the conduct of error sum of squares criterion function in the step S4
Clustering criteria function, is shown below：
In formula, MiIt is class CiThe mean value of middle data object, p are class CiIn spatial point.
As another improvement of the invention, the step S4 node fragile degree grade classification is four.
Compared with prior art, it the invention proposes a kind of Urban Rail Transit Stations fragility measurement method, overcomes
In the prior art for the deficiency of rail traffic website fragility research in rail traffic contingency management, solving can not will be fragile
Property theory analysis research the problem of being merged into field of track traffic practical application request, quantitative analysis evaluation city rail traffic station
Point fragility, can learn the weak link in Metro Network, in order to the department of runing during contingency management to it
It is protected, it is significant to the stable operation for ensureing urban track traffic, further increase the flexible of urban track traffic
Property and safety.
Detailed description of the invention
Fig. 1 is method operating process schematic diagram of the invention；
Fig. 2 is adaptability sub-indicator system figure of the present invention；
Fig. 3 is the cluster process figure of K-Means clustering procedure of the present invention.
Specific embodiment
Below with reference to drawings and examples, the present invention is described in detail, for ease of description, in attached drawing
Only the parts related to the present invention are shown rather than entire infrastructure.
Embodiment 1
A kind of Urban Rail Transit Stations fragility measurement method, as shown in Figure 1, specifically including following steps：
S1 establishes Evaluation of vulnerability index system：Determine three exposed property, sensibility and adaptability fragility measurement subitems
Index：
S11, establishes exposed property and sensibility sub-indicator, and the exposed property sub-indicator includes full-time volume of the flow of passengers X1, it is high
Peak coefficient X2And peak passenger flow duration X3；The sensibility sub-indicator includes degree index X4, betweenness index X5And tightness index
X6；
The full-time volume of the flow of passengers X1=xin(i)+xout(i)+xtrans(i), wherein xin(i)For the full-time volume of the flow of passengers that enters the station of station i,
xout(i)For the full-time outbound volume of the flow of passengers of station i, xtrans(i)The volume of the flow of passengers of transfer station i is arrived for full-time transfer；
The peaking factor X2That is peak hour volume of the flow of passengers accounting, the X2=QPeak/QIt is flat, wherein QPeakIt enters the station for peak period
Or the outbound volume of the flow of passengers；QIt is flatTo enter the station during flat peak or the outbound volume of the flow of passengers；
The peak passenger flow durationWhereinEnter the moment for peak passenger flow,It is exited for peak passenger flow
Moment；The sensibility sub-indicator includes degree index X4, betweenness index X5And tightness index X6；
The degree indexWherein aijFor the element of the adjacency matrix of network, N is the sum of nodes；
The betweenness indexWherein njkIt is node to the number of shortest path between j, k
Amount, njkIt (i) is quantity of the node to shortest path between j, k Jing Guo node i；
The tightness indexWherein DijFor node i to the shortest distance of node j.
S12, establishes adaptability sub-indicator, and the adaptability sub-indicator includes personnel's adaptability U1, resource adaptability U2
And environmental suitability U3, adaptability sub-indicator measured using fuzzy decision, further comprises：
S121 determines the set of factors of evaluation object；As shown in Fig. 2, adaptability sub-indicator is by personnel's adaptability U1, resource
Adaptability U2And environmental suitability U3Overall merit obtains, wherein personnel's adaptability U1Including station operation establishment officer, answer first aid
Help staffing situation and manoeuvre training situation, resource adaptability U2Including resources reserve situation and logistics support ability, environment
Adaptability U3Including outer plug into situation, geographical location circumstances and entrance quantity and the passenger flow matching degree of standing；
S122 determines the evaluation criterion collection V of evaluation object,
V={ V1,V2,V3,V4,V5}={ is good, preferably, generally, poor, poor }；
S123 determines the weight sets of factor of evaluation：If A=(a1,a2,…,am) it is weight distribution fuzzy vector, wherein ai
Indicate the weight of i-th of factor, it is desirable that ai> 0 and ∑ ai=1；
Fuzzy relationship matrix r is established in S124, single factor test fuzzy evaluation：Quantization is evaluated each factor u of objecti(i=
1,2 ..., m), determination is evaluated object to the degree of membership of each grade fuzzy subset from the perspective of single factor test, further obtains
Fuzzy relation matrix：
Wherein rijIt indicates from single factor test uiFrom the point of view of some be evaluated object In Grade fuzzy subset VjDegree of membership.
S125, multiple attribute synthetical evaluation：Fuzzy weight vector A is synthesized with fuzzy relationship matrix r using Fuzzy Arithmetic Operators
Each fuzzy overall evaluation result vector B for being evaluated object is obtained, the model of fuzzy overall evaluation is：
S2 calculates fragile degree index：Grey correlation analysis is carried out to exposed property and sensibility measurement sub-indicator respectively, is obtained
To the correlation degree of each sub-indicator and fragility, the fragile degree index of each node is calculated, further comprises：
S21 determines relatively data sequence, is denoted as：
X'i=(x'i(1),x'i(2),…x'i(m))T, i=1,2 ..., n
Wherein, m is the number of index, and n is the quantity of data sequence.
S22 determines reference data sequence, is denoted as：
X'o=(x'o(1),x'o(2),…,x'o(m))
S23 carries out nondimensionalization to achievement data, forms following matrix：
S24, asks very poor, and expression formula is：
Δi(k)=| xo(k)-xi(k)|
S25 asks two-stage maximum difference and two-stage lowest difference：
With
S26, determines incidence coefficient, and expression formula is：
Wherein, ξ is resolution ratio, and 0 < ξ < 1, general value ξ=0.5.
S27, calculating correlation, expression formula are：
The classification of S3 fragile degree：Using K-Means clustering procedure by node weakness assessment grade level Four, K-Means clustering procedure
Cluster process it is as shown in Fig. 3, K-Means clustering procedure is a kind of clustering method based on division, and this method is existing thick by sample
Then rough classification is modified according to certain principle, guidance classification relatively rationally until.K-Means algorithm is with Euclidean distance
As similarity measure, it is randomly chosen K object first in cluster process, is divided at each object and represent a cluster
Average value or center.To remaining each object according to it at a distance from each cluster center, it is assigned to nearest cluster.Then it weighs
Newly calculate the average value of each cluster.This process constantly repeats, and until criterion function is restrained, wherein clustering procedure uses square-error
With criterion function as clustering criteria function, it is shown below：
In formula, MiIt is class CiThe mean value of middle data object, p are class CiIn spatial point；To finally learn rail line
Weak link in net, in order to which the department of runing is protected it during contingency management.
In the present embodiment, using cut-off in November, 2017 Urban Rail Transit data, end in November, 2017, it should
Urban Rail Transit includes 7 routes, 128 seat coach stations altogether.The Urban Rail Transit is mapped as network, it is corresponding
128 nodes in undirected graph G=(E, V), 132 sides.
Firstly, S1, establishes Evaluation of vulnerability index system：Determine that three exposed property, sensibility and adaptability fragility are surveyed
Sub-indicator is measured, the exposed property sub-indicator includes full-time volume of the flow of passengers X1, peaking factor X2And peak passenger flow duration X3；It is described
Sensibility sub-indicator includes degree index X4, betweenness index X5And tightness index X6；The adaptability sub-indicator includes personnel
Adaptability U1, resource adaptability U2And environmental suitability U3；
Then, S2 calculates fragile degree index：Grey correlation point is carried out respectively to exposed property and sensibility measurement sub-indicator
Analysis, obtains the correlation degree of each sub-indicator and fragility, calculates the fragile degree index of each node；
Grey correlation analysis is taken, the synthesis fragile degree index of each website of the Urban Rail Transit can be calculated,
The following table 1 is the comprehensive fragile degree index calculated result of the rail traffic website：
Finally, S3, fragile degree classification：By the calculated result of above-mentioned S2 website fragile degree index, using K-Means clustering procedure
By four grades of node weakness assessment, the classification results for obtaining the comprehensive fragile degree of each website are as shown in table 2, and table 2 is website
Comprehensive fragile degree hierarchical table：
By upper table 2 it is found that the highest node of fragile degree grade is the biggish transfer website of the full-time volume of the flow of passengers, to avoid this
The adverse effect caused by entire subway line net of the catastrophic failure of class website should carry out conscious prevention & protection and emergency to it
Management.Meanwhile analysis, the result shows that the fragility and node degree of node are inconsistent, the node V2 if degree is 4 is (relatively slight crisp
It is weak), fragile degree grade is less than the node V5 that degree is 3 (moderate is fragile).Therefore, it is high that fragility is coped with during operation
Key node and do not have to reinforce protection to whole high degree hinges, improve efficiency.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel only illustrate the present invention it should be appreciated that the present invention is not limited by examples detailed above described in examples detailed above and specification
Principle, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these variation and
Improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended claims and its is equal
Object defines.
Claims (7)
1. a kind of Urban Rail Transit Stations fragility measurement method, it is characterised in that：Specifically include following steps：
S1 establishes Evaluation of vulnerability index system：Determine that three exposed property, sensibility and adaptability fragility measurement subitems refer to
Mark, the step S1 further comprise：
S11, establishes exposed property and sensibility sub-indicator, and the exposed property sub-indicator includes full-time volume of the flow of passengers X1, peaking factor
X2And peak passenger flow duration X3；The sensibility sub-indicator includes degree index X4, betweenness index X5And tightness index X6；
S12, establishes adaptability sub-indicator, and the adaptability sub-indicator includes personnel's adaptability U1, resource adaptability U2And ring
Border adaptability U3；
S2 calculates fragile degree index：Grey correlation analysis is carried out to exposed property and sensibility measurement sub-indicator respectively, is obtained each
The correlation degree of sub-indicator and fragility calculates the fragile degree index of each node；
S3, fragile degree classification：Using K-Means clustering procedure by node weakness assessment grade.
2. a kind of Urban Rail Transit Stations fragility measurement method as described in claim 1, it is characterised in that：
The full-time volume of the flow of passengers X1=xin(i)+xout(i)+xtrans(i), wherein xin(i)For the full-time volume of the flow of passengers that enters the station of station i, xout(i)
For the full-time outbound volume of the flow of passengers of station i, xtrans(i)The volume of the flow of passengers of transfer station i is arrived for full-time transfer；
The peaking factor X2That is peak hour volume of the flow of passengers accounting, the X2=QPeak/QIt is flat, wherein QPeakIt enters the station or goes out for peak period
Standee's flow；QIt is flatTo enter the station during flat peak or the outbound volume of the flow of passengers；
The peak passenger flow durationWhereinEnter the moment for peak passenger flow,The moment is exited for peak passenger flow；
The degree indexWherein aijFor the element of the adjacency matrix of network, N is the sum of nodes；
The betweenness indexWherein njkIt is node to the quantity of shortest path between j, k, njk
It (i) is quantity of the node to shortest path between j, k Jing Guo node i；
The tightness indexWherein DijFor node i to the shortest distance of node j.
3. a kind of Urban Rail Transit Stations fragility measurement method as claimed in claim 2, it is characterised in that the step
S2 includes：
S21 determines relatively data sequence, is denoted as：
X′i=(x 'i(1),x′i(2),…x′i(m))T, i=1,2,…,n
Wherein, m is the number of index, and n is the quantity of data sequence.
S22 determines reference data sequence, is denoted as：
X'o=(x'o(1),x'o(2),…,x'o(m))
S23 carries out nondimensionalization to achievement data, forms following matrix：
S24, asks very poor, and expression formula is：
Δi(k)=| xo(k)-xi(k)|
S25 asks two-stage maximum difference and two-stage lowest difference：
With
S26, determines incidence coefficient, and expression formula is：
Wherein, ξ is resolution ratio, and 0 < ξ < 1, general value ξ=0.5.
S27, calculating correlation, expression formula are：
4. a kind of Urban Rail Transit Stations fragility measurement method as described in claim 1, it is characterised in that：The step
Adaptability sub-indicator is measured using fuzzy decision in S12, by personnel's adaptability U1, resource adaptability U2And environment
Adaptability U3Overall merit obtains.
5. a kind of Urban Rail Transit Stations fragility measurement method as claimed in claim 4, it is characterised in that the step
S12 adaptability sub-indicator measurement further comprises：
S121 determines the set of factors of evaluation object；
S122 determines the evaluation criterion collection V of evaluation object,
V={ V1,V2,V3,V4,V5}={ is good, preferably, generally, poor, poor }；
S123 determines the weight sets of factor of evaluation：If A=(a1,a2,…,am) it is weight distribution fuzzy vector, wherein aiIndicate the
The weight of i factor, it is desirable that ai> 0 and ∑ ai=1；
Fuzzy relationship matrix r is established in S124, single factor test fuzzy evaluation：Quantization is evaluated each factor u of objecti(i=1,
2 ..., m), determination is evaluated object to the degree of membership of each grade fuzzy subset from the perspective of single factor test, further obtains mould
Paste relational matrix：
Wherein rijIt indicates from single factor test uiFrom the point of view of some be evaluated object In Grade fuzzy subset VjDegree of membership.
S125, multiple attribute synthetical evaluation：Fuzzy weight vector A is synthesized to obtain with fuzzy relationship matrix r using Fuzzy Arithmetic Operators
Each fuzzy overall evaluation result vector B for being evaluated object, the model of fuzzy overall evaluation are：
6. a kind of Urban Rail Transit Stations fragility measurement method as described in claim 1, it is characterised in that the step
Clustering procedure is shown below using error sum of squares criterion function as clustering criteria function in S4：
In formula, MiIt is class CiThe mean value of middle data object, p are class CiIn spatial point.
7. a kind of Urban Rail Transit Stations fragility measurement method as described in above-mentioned any claim, it is characterised in that：
The step S4 node fragile degree grade classification is four.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811160182.3A CN108920892A (en) | 2018-09-30 | 2018-09-30 | A kind of Urban Rail Transit Stations fragility measurement method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811160182.3A CN108920892A (en) | 2018-09-30 | 2018-09-30 | A kind of Urban Rail Transit Stations fragility measurement method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108920892A true CN108920892A (en) | 2018-11-30 |
Family
ID=64409069
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811160182.3A Pending CN108920892A (en) | 2018-09-30 | 2018-09-30 | A kind of Urban Rail Transit Stations fragility measurement method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108920892A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110033048A (en) * | 2019-04-18 | 2019-07-19 | 西南交通大学 | A kind of rail traffic key node and key road segment recognition methods |
| CN110162912A (en) * | 2019-05-30 | 2019-08-23 | 北京航空航天大学 | Consider the network resource optimization method of resource contention coupling |
| CN112270462A (en) * | 2020-10-10 | 2021-01-26 | 中南大学 | Subway network fragile line identification method based on Rich curvature |
| CN113255979A (en) * | 2021-05-17 | 2021-08-13 | 上海旷途科技有限公司 | Method and device for identifying vulnerability of urban public transport network, computer equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101826200A (en) * | 2010-04-02 | 2010-09-08 | 北京交通大学 | Method for evaluating operating effect of urban track traffic hub |
| CN104765944A (en) * | 2014-03-26 | 2015-07-08 | 中国科学院地理科学与资源研究所 | Comprehensive measuring technological method for urban vulnerability |
| CN105388496A (en) * | 2015-10-30 | 2016-03-09 | 上海交通大学 | Traffic application vulnerability detection system based on GPS (Global Positioning System) and method thereof |
| CN105912835A (en) * | 2016-03-15 | 2016-08-31 | 华侨大学 | Power line vulnerability identification model building method taking wind farm access into consideration |
| CN107247275A (en) * | 2017-05-27 | 2017-10-13 | 上海交通大学 | City GNSS fragility monitoring system and its method based on bus |
| CN108197747A (en) * | 2018-01-04 | 2018-06-22 | 东南大学 | Urban track traffic emergency management and rescue station site selecting method and device |
| US20180241721A1 (en) * | 2017-02-17 | 2018-08-23 | Royal Bank Of Canada | Web application firewall |
-
2018
- 2018-09-30 CN CN201811160182.3A patent/CN108920892A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101826200A (en) * | 2010-04-02 | 2010-09-08 | 北京交通大学 | Method for evaluating operating effect of urban track traffic hub |
| CN104765944A (en) * | 2014-03-26 | 2015-07-08 | 中国科学院地理科学与资源研究所 | Comprehensive measuring technological method for urban vulnerability |
| CN105388496A (en) * | 2015-10-30 | 2016-03-09 | 上海交通大学 | Traffic application vulnerability detection system based on GPS (Global Positioning System) and method thereof |
| CN105912835A (en) * | 2016-03-15 | 2016-08-31 | 华侨大学 | Power line vulnerability identification model building method taking wind farm access into consideration |
| US20180241721A1 (en) * | 2017-02-17 | 2018-08-23 | Royal Bank Of Canada | Web application firewall |
| CN107247275A (en) * | 2017-05-27 | 2017-10-13 | 上海交通大学 | City GNSS fragility monitoring system and its method based on bus |
| CN108197747A (en) * | 2018-01-04 | 2018-06-22 | 东南大学 | Urban track traffic emergency management and rescue station site selecting method and device |
Non-Patent Citations (5)
| Title |
|---|
| 曲迎春等: "城市轨道交通网络脆弱性分析", 《铁道科学与工程学报》 * |
| 王林等: "复杂网络的中心化", 《复杂系统与复杂性科学》 * |
| 白亚飞: "大客流条件下地铁车站的脆弱性研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 邓勇亮: "城市地铁网络系统物理脆弱性的评价及控制研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 魏令峰: "K-means聚类算法的改进与应用", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110033048A (en) * | 2019-04-18 | 2019-07-19 | 西南交通大学 | A kind of rail traffic key node and key road segment recognition methods |
| CN110162912A (en) * | 2019-05-30 | 2019-08-23 | 北京航空航天大学 | Consider the network resource optimization method of resource contention coupling |
| CN110162912B (en) * | 2019-05-30 | 2020-09-01 | 北京航空航天大学 | Network resource optimization method considering resource competition coupling |
| CN112270462A (en) * | 2020-10-10 | 2021-01-26 | 中南大学 | Subway network fragile line identification method based on Rich curvature |
| CN112270462B (en) * | 2020-10-10 | 2021-06-29 | 中南大学 | Subway network fragile line identification method based on Rich curvature |
| CN113255979A (en) * | 2021-05-17 | 2021-08-13 | 上海旷途科技有限公司 | Method and device for identifying vulnerability of urban public transport network, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108920892A (en) | A kind of Urban Rail Transit Stations fragility measurement method | |
| Taylor et al. | Application of accessibility based methods for vulnerability analysis of strategic road networks | |
| He et al. | Customer preference and station network in the London bike-share system | |
| Xia et al. | Ranking station importance with human mobility patterns using subway network datasets | |
| CN107516417B (en) | A kind of real-time highway flow estimation method for excavating spatial and temporal association | |
| Liu et al. | Incorporating household gathering and mode decisions in large‐scale no‐notice evacuation modeling | |
| Feng et al. | Identification of critical roads in urban transportation network based on GPS trajectory data | |
| CN107527293A (en) | Group of cities space relationship Strength Analysis System based on statistical model | |
| Oh et al. | Demand calibration of multimodal microscopic traffic simulation using weighted discrete SPSA | |
| Wang et al. | A network-based model of passenger transfer flow between bus and metro: An application to the public transport system of Beijing | |
| Tu et al. | Research on intelligent calculation method of intelligent traffic flow index based on big data mining | |
| Yao et al. | Understanding travel behavior adjustment under COVID-19 | |
| Cascetta et al. | Acceptance and equity in advanced path-related road pricing schemes | |
| Karabay et al. | Mathematical model and stochastic multi-criteria acceptability analysis for facility location problem | |
| CN110189020A (en) | A kind of colleges and universities' Intelligent campus construction level evaluation method | |
| Han et al. | A new type-2 fuzzy multi-criteria hybrid method for rail transit operation safety assessment | |
| Carteni | A new look in designing sustainable city logistics road pricing schemes | |
| CN113344408A (en) | Processing method for multi-scale situation perception process of civil aviation traffic control operation | |
| Hwang et al. | Travel time prediction by weighted fusion of probing vehicles and vehicle detectors data sources | |
| Zhou et al. | Refined taxi demand prediction with ST-Vec | |
| Zhang et al. | Discovering station patterns of urban transit network with multisource data: empirical evidence in Jinan, China | |
| Wasserman et al. | Evaluating OpenStreetMap’s performance potential for level of traffic stress analysis | |
| CN108229751B (en) | Method for predicting delivery behavior based on parent delivery space mode | |
| Li et al. | Route restoration method for sparse taxi GPS trajectory based on Bayesian network | |
| Liu et al. | Assessing accessibility of dockless sharing-bike networks by the social network analysis method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |