CN106530687B - A kind of transportation network pitch point importance measuring method based on time-space attribute - Google Patents
A kind of transportation network pitch point importance measuring method based on time-space attribute Download PDFInfo
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- CN106530687B CN106530687B CN201610893842.3A CN201610893842A CN106530687B CN 106530687 B CN106530687 B CN 106530687B CN 201610893842 A CN201610893842 A CN 201610893842A CN 106530687 B CN106530687 B CN 106530687B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The present invention provides a kind of transportation network pitch point importance measuring method based on time-space attribute, first using the unit section of road traffic road network as node, to form the detection data makeup time sequence that sensor acquires on component, the correlation based on time series models road network；Then, consider the spatial relationship between node, node importance is calculated, adjacency matrix and transfer matrix including obtaining transportation network；Distance matrix and probability matrix are constructed, and standardizes probability matrix, constructs Google matrix G；With the main feature vector of power method solution matrix G to get arrive each pitch point importance.The present invention considers the time-space attribute of road network when model foundation and different degree are calculated, and can preferably characterize real network, results of measuring also more tallies with the actual situation.
Description
Technical field
The present invention relates to urban road traffic network planning technology field more particularly to a kind of transportation network pitch point importances
Measuring method.
Background technique
The complex network that Traffic Net is made of components such as section, intersections, using Complex Networks Theory to road
Road transportation network is modeled and is analyzed its topological property, and to rising, its efficiency of operation is significant.Transportation network component node
Different degree is for describing effect and location of the component node played in transportation network, to structure in transportation network
Part pitch point importance, which carries out measuring and calculating, to be one and significantly works, and is found out " key point " by pitch point importance measuring and calculating, one
Aspect can dredge the traffic flow of these " key points " by some traffic control measure emphasis to improve entire transportation network
On the other hand reliability can also protect these " weak links " by some traffic measures, to avoid these " weak links "
It is destroyed or blocked, because these " weak links " are once destroyed, the result of entire transportation network paralysis will be caused.
The modeling method of transportation network is only to model on the basis of road network physical topological structure at present, does not consider road network structure
The correlation of part in time, meanwhile, common component node different degree, such as betweenness, the degree of approach, PageRank, all do not have
Consider the influence that space constraint calculates pitch point importance, being applied in real network has certain drawbacks.
Summary of the invention
To solve the above-mentioned problems, the transportation network pitch point importance measuring and calculating based on time-space attribute that the invention proposes a kind of
Method, this method acquire the volume of data (time series) to come up using Traffic flow detecting device, construct based on time series
Traffic network design.On the basis of traffic network design, the space constraint of road network topology structure is considered, to traffic network design
In the different degree of building node be ranked up, find key node, provide foundation to cope with the calculated attack of traffic system.
Technical solution of the invention specifically comprises the following steps:
1) traffic network design based on time series is established
Correlation coefficient r (the S in any two section in road network is calculated firsti,Sj):
Wherein, SiFor the time series on i-th section, SjFor the time series on j-th strip section, yikFor SiThe upper time
K-th of value of sequence,For SiThe mean value of upper time series, yjkFor SjUpper time series k value,For SjUpper time series it is equal
Value, T are the number of days of observation, and D is the space-number observed in one day；
Secondly traffic network design is constructed according to time series:
TNW=(N, E, R)
Wherein, N={ n1,n2,....,nMBe transportation network interior joint finite aggregate, M be nodes number；
E={ eij| i ≠ j, i, j ∈ { 1,2 ..., M } } be connecting node in transportation network side finite aggregate；R={ rij|i
≠ j, i, j ∈ { 1,2 ..., M } } be related coefficient in traffic network finite aggregate；
2) transportation network component node different degree is calculated
21) the adjacency matrix A and shift-matrix A of transportation network are constructed*, the adjacency matrix A and shift-matrix A of transportation network*
It respectively indicates are as follows:
Wherein
22) Distance matrix D and probability matrix K are constructed, and normalized matrix K obtains matrix KN
Distance matrix D indicates are as follows:
Wherein, dijThe length of the shortest path in road network for node i and node j；
Probability matrix K is indicated are as follows:
Wherein,For the Hadamard product of adjacency matrix A and Distance matrix D；F is the M that element is 1
× Metzler matrix；
If probability matrix K=(k1,k2,...,kj,..,kM), wherein kjFor the jth column vector of probability matrix K, K is by such as
Lower formula is standardized, and obtains matrix KN:
23) structural matrix G=(1- α) A*+αKN, α is damping factor；
24) the Principal eigenvalue λ of power method solution matrix G is used1=1 main feature vector, X1:
X1={ x1(1),x1(2),....,x1(N) }, X1It is then the importance value of N number of node in transportation network.
Preferably, significance test is carried out to the related coefficient of time series on two nodes, judges the side of two nodes
It whether there is:
Z statistic is constructed first with Fisher transformation:
Z statistic approximation obeys standardized normal distribution: Z~N (0,1)
Then, using bilateral inspection, probability P (| Z | >=Z (r)) is calculated,
Wherein, r=r (Si,Sj), β is significance, eijWhen=1, the side between node i and node j exists；eij=0
When, the side between node i and node j is not present.
The present invention, which has the following technical effect that, initially sets up the traffic network design based on time series, with conventional traffic
Network is using crossing as node, section Bian Butong, and the present invention is using section as node, using correlation among nodes as side, considers section
On time series construct entire traffic network design, then construct distance matrix, probability matrix, Google matrix, use power method
The main feature vector for solving Google matrix obtains each pitch point importance, considers road network when model foundation and different degree are calculated
Time-space attribute, can preferably characterize real network, more tally with the actual situation than other measuring methods.
Detailed description of the invention
The Beijing Fig. 1 urban road structure chart
Fig. 2 time series chart
Fig. 3 algorithm flow chart
Fig. 4 transportation network constructs effect picture
Specific embodiment
Be standardized to time series on section: according to the sensor on section, each section can generate one
Time seriesWherein SiFor in transportation network i-th section generate time series,
For the traffic flow parameter (including flow, speed, occupation rate etc.) of d days t moments, T is the number of days of observation, and D is to observe in one day
Time interval number.Each time series is standardized using following formula:
In above-mentioned formula,For the mean value in the i-th section,It is i-th
The standard deviation in section.
It calculates the relative coefficient in any two sections in road network: calculating the Pearson phase in any two section in road network
Relationship number r (Si,Sj):
In above-mentioned formula, SiFor the time series on i-th section, SjFor the time series on j-th strip section, yikFor Si
K-th of value of upper time series,For SiThe mean value of upper time series, yjkFor SjUpper time series k value,For SjUpper time sequence
The mean value of column.R ∈ [- 1,1], when r takes -1, Si, SjTwo fairly linear negative correlation of time series；When r takes 1, Si, SjAt two
Between the fairly linear positive correlation of sequence；When r takes 0, Si, SjTwo time serieses do not have linear relationship.
Transportation network is constructed based on time series: on the basis of normalized temporal sequence, constructing traffic network design, it can
To be described by a triple:
TNW=(N, E, R)
In above-mentioned formula, N and E are used to characterize the component set in transportation network, N={ n1,n2,....,nMIt is the network of communication lines
The finite aggregate of network interior joint；M is the number of nodes；E={ eij| i ≠ j, i, j ∈ 1,2 ..., M } it is the network of communication lines
The finite aggregate on the side in network；eijWhen=1, the side between node i and node j exists；eijWhen=0, between node i and node j
Side be not present；R={ rij| i ≠ j, i, j ∈ { 1,2 ..., M } } be Pearson correlation coefficient in traffic network finite aggregate
It closes.
Component in transportation network refers to node and side in transportation network.Node refers to the unit section in road network, single
There is a Traffic flow detecting device on first section；While referring to the correlation of time series on two nodes in road network, when two nodes
When the Pearson correlation coefficient of upper time series passes through significance test, the side of two nodes exists.
The side of any two point is with the presence or absence of determination method in transportation network, utilizes time series on two nodes
Pearson correlation coefficient significance test judges that the side of two nodes whether there is.
Z statistic is constructed first with Fisher transformation:
Z statistic approximation obeys standardized normal distribution: Z~N (0,1)
Then, using bilateral inspection, probability P (| Z | >=Z (r)) is calculated, if P (| Z | >=Z (r))≤2 β, then it is assumed that two
The side of node exists, i.e.,
In above-mentioned formula, r=r (Si,Sj), β is significance.
During sorting to component node, it is contemplated that the space constraint of road network topology structure, the closer node of distance
It influences each other bigger, otherwise influences smaller, specific steps are as follows:
Step 1: obtaining the adjacency matrix A and shift-matrix A of transportation network*；
Step 2: construction Distance matrix D and probability matrix K, and normalized matrix K obtains matrix KN；
Step 3: construction Google matrix G=(1- α) A*+αKN, α is damping factor；
Step 4: with the Principal eigenvalue λ of power method solution matrix G1=1 main feature vector, X1={ x1(1),x1(2),....,
x1(N) }, X1For the importance value of node N number of in transportation network.
Adjacency matrix A and shift-matrix A described in step 1*It can respectively indicate are as follows:
Wherein
Distance matrix D described in step 2 may be expressed as:
In above-mentioned formula, dijThe length of the shortest path in road network, unit km for node i and node j.
Probability matrix K described in step 2 may be expressed as:
In above-mentioned formula,For local probability matrix,Indicate adjacency matrix A and Distance matrix D
Hadamard product；ε F is a global probability matrix,F is M × Metzler matrix that element is 1.
If probability matrix K=(k1,k2,...,kj,..,kM), wherein kjFor the jth column vector of probability matrix K, K can pass through
Following formula is standardized, and obtains matrix KN:
With reference to the accompanying drawing, it elaborates to embodiment.It is emphasized that following the description is only exemplary,
The range and its application being not intended to be limiting of the invention.The present embodiment constructs the network of communication lines by taking the trunk road network of Beijing as an example
Network model.Beijing's road network is made of 1661 unit sections, i.e. a sensor is arranged on every unit section.According to this
To the definition of component in invention, unit section is a node, then the distribution situation of road network interior joint is as shown in Figure 1.Unit road
The data of upload in sensor every 2 minutes in section, data include flow, speed, occupation rate, and the ordered series of numbers of sensor acquisition is constituted
Time series.The time series that this example selects data on flows to constitute carries out transportation network modeling.Two in the road Tu2Wei+net
The schematic diagram of time series on unit section, the ID number and section title in this two unit sections are as shown in the table.
1 unit road section information of table
According to the logic flow of Fig. 3, the transportation network based on time series is carried out to the trunk road network of Beijing first and is built
Then mould carries out pitch point importance measuring and calculating.Fig. 4 is to big northern kiln bridge section using eastern second overline bridge of the international trade bridge on Jianguo Lu as structure
Part node, using time series correlation determine the sides of other component nodes in the component node and road network there are the case where；With
Afterwards, importance measuring and calculating is carried out to the building node in network, the different degree of each component node is obtained, to choose crucial section
Point, 10 building nodes are as shown in table 2 before importance.
The importance value of node is constructed in 2 preceding 10 Beijing road networks of table
Inventive algorithm is compared with common network node importance measuring method Pagerank algorithm, PageRank
Algorithm calculates the important building node come and is all distributed on the interconnection of 6 loops, 5 loops to 6 rings.Considering space attribute
The important node found out of network node importance measuring method be mainly distributed on the trunk roads in two rings, tricyclic and Second Ring Road
On, more tally with the actual situation.
Claims (1)
1. a kind of transportation network pitch point importance measuring method based on time-space attribute, wherein with unit section for a node,
It is characterized by comprising the following steps:
1) traffic network design based on time series is established
Correlation coefficient r (the S in any two section in road network is calculated firsti,Sj):
Wherein, SiFor the time series on i-th section, SjFor the time series on j-th strip section, yikFor SiUpper time series
K-th of value,For SiThe mean value of upper time series, yjkFor SjUpper time series k value,For SjThe mean value of upper time series, T
For the number of days of observation, D is the space-number observed in one day；
Secondly traffic network design is constructed according to time series:
TNW=(N, E, R)
Wherein, N={ n1,n2,....,nMBe transportation network interior joint finite aggregate, M be nodes number；E=
{eij| i ≠ j, i, j ∈ { 1,2 ..., M } } be connecting node in transportation network side finite aggregate；R={ rij|i≠j,
I, j ∈ { 1,2 ..., M } } be related coefficient in traffic network finite aggregate；
2) transportation network component node different degree is calculated
21) the adjacency matrix A and shift-matrix A of transportation network are constructed*, the adjacency matrix A and shift-matrix A of transportation network*Respectively
It indicates are as follows:
Wherein
22) Distance matrix D and probability matrix K are constructed, and normalized matrix K obtains matrix KN；
Distance matrix D indicates are as follows:
Wherein, dijThe length of the shortest path in road network for node i and node j；
Probability matrix K is indicated are as follows:
Wherein,For the Hadamard product of adjacency matrix A and Distance matrix D；F is M × M square that element is 1
Battle array；
If probability matrix K=(k1,k2,...,kj,..,kM), wherein kjFor the jth column vector of probability matrix K, K passes through following public
Formula is standardized, and obtains matrix KN:
23) structural matrix G=(1- α) A*+αKN, α is damping factor；
24) the Principal eigenvalue λ of power method solution matrix G is used1=1 main feature vector, X1:
X1={ x1(1),x1(2),....,x1(N) }, X1It is then the importance value of N number of node in transportation network.
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| CN108257382A (en) * | 2018-01-11 | 2018-07-06 | 上海应用技术大学 | Intersection congestion key point finding method and system based on correlation analysis |
| CN109887280B (en) * | 2019-02-28 | 2020-04-24 | 北京航空航天大学 | Traffic network node criticality assessment method |
| CN110021163B (en) * | 2019-03-02 | 2020-10-13 | 合肥学院 | Network appointment road network occupancy analysis method based on travel mileage data |
| CN110070713B (en) * | 2019-04-15 | 2021-01-01 | 浙江工业大学 | Traffic flow prediction method based on bidirectional nested LSTM neural network |
| CN110807931B (en) * | 2019-11-08 | 2021-09-17 | 许凌 | Traffic network directed graph path model construction and solving method based on steering relation |
| CN113450558B (en) * | 2020-03-27 | 2022-11-08 | 上海仙豆智能机器人有限公司 | Method, system and storage medium for identifying network key node |
| CN111798663B (en) * | 2020-08-07 | 2021-12-14 | 交通运输部公路科学研究所 | Road network key road section identification method |
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