CN109034578A - A kind of composite communications transport network node different degree appraisal procedure - Google Patents
A kind of composite communications transport network node different degree appraisal procedure Download PDFInfo
- Publication number
- CN109034578A CN109034578A CN201810775641.2A CN201810775641A CN109034578A CN 109034578 A CN109034578 A CN 109034578A CN 201810775641 A CN201810775641 A CN 201810775641A CN 109034578 A CN109034578 A CN 109034578A
- Authority
- CN
- China
- Prior art keywords
- node
- network
- weight
- mileage
- nodes
- 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
- 239000002131 composite material Substances 0.000 title claims abstract description 115
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004891 communication Methods 0.000 title abstract description 4
- 239000011159 matrix material Substances 0.000 claims abstract description 56
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 22
- 238000004364 calculation method Methods 0.000 claims description 17
- 230000007306 turnover Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 3
- 238000002360 preparation method Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 abstract 4
- 238000011156 evaluation Methods 0.000 description 17
- 238000010276 construction Methods 0.000 description 11
- 238000011160 research Methods 0.000 description 7
- 230000002265 prevention Effects 0.000 description 5
- 238000013210 evaluation model Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000010485 coping Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000002787 reinforcement Effects 0.000 description 2
- 206010033799 Paralysis Diseases 0.000 description 1
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 235000013311 vegetables Nutrition 0.000 description 1
- 238000012038 vulnerability analysis Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The embodiment of the present invention proposes a kind of composite communications transport network node different degree appraisal procedure, comprising: obtains the connection relationship as the various modes of transportation between the city and node of topological diagram interior joint；The subtopology figure of each node and each mode of transportation of other nodes is obtained, and determines the mileage and weight of each mode of transportation；For the mileage and weight in each subtopology figure, the corresponding mileage adjacency matrix of the subtopology figure, weight adjacency matrix are generated respectively；And according to the subtopology figure of each node, total topological diagram of entire composite network is generated, and generate the mileage adjacency matrix of total topological diagram, weight adjacency matrix；Determine the weight on each side in the total topological diagram of composite network；Determine the weighting mileage betweenness and weighting node degree of node；The weight of the weighting node degree determined according to expert estimation and the weight for weighting node betweenness, determine the different degree of composite network interior joint.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to a comprehensive transportation network node importance degree evaluation method.
Background
With the development of human society, transportation becomes the most basic requirement of people's life, and a transportation system is inevitably used in both goods transportation and people's trip. In the face of increasing traffic and travel demands, complex and severe natural disasters and social safety situations, once a traffic and transportation network is blocked, public travel, transportation of important materials such as electric coal, grains and vegetables, social and economic operation and the like are seriously influenced. In recent years, serious emergencies (such as ice disaster in the south of 2008 and strong snowfall in the north of 2010) frequently occur in China, so that traffic blocking of regional traffic transportation channels and even traffic paralysis in a larger range are often caused, and the reliability of a traffic transportation network is seriously challenged.
The "integrated transportation network" is generally considered to be a transportation network which is composed of transportation lines, port stations and hubs which cooperate with each other, complement each other and are closely matched with each other, and can carry out direct transportation and combined transportation by taking the transportation lines as connecting lines and the transportation port stations and the hubs as connecting points. As shown in fig. 1, the conventional integrated transportation network has a certain combination structure and a certain level, and is a concrete embodiment of regional combination of transportation productivity. Under the large background of regional transportation integration and the increasingly severe traffic transportation safety situation, how to improve the safety of the integrated traffic transportation network is a problem which needs to be solved at present, and identifying important nodes of the integrated traffic transportation network is a key link therein.
At present, the importance research for single traffic network nodes such as roads, railways, civil aviation and the like at home and abroad is very rich. Most researches are analyzed from the aspect of network vulnerability, but the method only considers the global importance of the nodes and cannot reflect the influence of the nodes on the directly connected local nodes and road sections.
Aiming at the research of the comprehensive transportation network, the research is mainly focused on the aspects of network performance evaluation, composite network characteristic analysis, composite network vulnerability analysis and the like, most research objects are unauthorized high-speed rail-aviation composite networks or composite networks in cities or among cities, and few people research the comprehensive transportation network consisting of roads, railways, civil aviation, waterways and the like and consider weighted composite networks reflecting respective traffic characteristics.
Based on the above, the patent provides an evaluation method for the importance of the nodes of the comprehensive transportation network, which comprises a construction method of a composite network of the comprehensive transportation network, a calculation method for the edge weight of the composite network, an evaluation model for the importance of the nodes of the comprehensive transportation network and the like.
Disclosure of Invention
Aiming at the problem that the use effect is poor due to limited information which can be transmitted by the current static traffic sign, the embodiment of the invention provides a data generation method of a digital traffic sign, which can transmit various data to a terminal of intelligent traffic arranged on a vehicle so as to provide support for the vehicle to realize active accident prevention and intelligent early warning, and can play the roles of comprehensively adjusting traffic flow, dredging traffic, improving road traffic capacity and reducing traffic accidents.
In order to achieve the above object, an embodiment of the present invention provides a data generation method for a digitized traffic sign, including:
a data preparation step, which is used for obtaining cities serving as nodes in the topological graph and connection relations of various traffic modes among the nodes; acquiring a sub-topological graph of each traffic mode of each node and other nodes, and determining the mileage and the weight of each traffic mode; wherein the mode of transportation includes: road, railway, civil aviation, water transport;
a matrix generation step, which is used for respectively generating a mileage adjacent matrix and a weight adjacent matrix corresponding to each sub-topological graph aiming at the mileage and the weight in each sub-topological graph; generating a total topological graph of the whole composite network according to the sub-topological graph of each node, and generating a mileage adjacency matrix and a weight adjacency matrix of the total topological graph;
a weight generation step, which is used for determining the weight of each edge in the total topological graph of the composite network;
a parameter determination step for determining the weighted mileage betweenness b of the nodes according to the mileage adjacency matrix of the total topological graphi(ii) a Determining the weighted node degree D of each node according to the weighted adjacency matrix of the total topological graphi；
an importance degree determination step for determining the weight α of the weighted node degree determined according to the expert score1and weight alpha of weighted node betweenness2By setting the parameter biAnd DiPerforming standardization processing to obtain standardized parametersAndand determining the importance of the node i in the composite network
Further, the city of the nodes in the topological graph has at least one railway station or airport, or at least one road or water channel; and when any one of highways, railways, civil aviation and water transportation is connected between two nodes in the total topological graph of the composite network, the two nodes are connected by edges.
Further, the matrix generating step specifically includes:
step 21, obtaining network topology and corresponding adjacency matrixes of roads, railways, civil aviation and water transportation, wherein the adjacency matrixes of the roads, the railways, the civil aviation and the water transportation are respectively marked as Mh、Mr、Ma、Mw；
Step 22, according to the four adjacent matrixes, constructing a composite network topology M of the comprehensive transportation networkc：
Wherein
In the upper formula, a V-shaped represents a fuzzy operator, and a large operator is taken;
the element representing the ith row and the jth column of the composite network connection matrix,
matrix elements representing the ith row and the jth column of the composite network connection matrix are determined according to road, railway, civil aviation and water transport connections between nodes i and j in the composite network topology;
step 23, generating a mileage adjacency matrix D corresponding to road, railway, civil aviation and water transportation network topology according to mileage of road, railway, civil aviation and water transportation modes among nodesh、Dr、Da、Dw(ii) a And generating the mileage adjacency matrix D of the composite network according to the mileage adjacency matrix Dc：
Dc＝f(Dh,Dr,Da,Dw)
Wherein,
in the above formulaF (-) denotes a median calculation function,matrix elements of ith row and jth column of the composite network mileage adjacency matrix are represented according to the mileage of roads, railways, civil aviation and water transportation between nodes i and j in the composite network topology And (4) determining.
Further, the step of generating the weight of the edge in the composite network specifically includes:
the method comprises the steps that the weight of an edge between two nodes i and j in the composite network is determined, and the weight is the sum of the weights of the edges of each traffic mode sub-network between the two nodes i and j;
wherein,representing an edge between two nodes i, j in a composite networkThe weight of (a) is determined,representing edges of subnetwork mThe weight of (c);
wherein,
in the formula, ωmWeight of the traffic mode m; smAnd ShRespectively representing the traffic mode m and the average running speed of a road; n is a radical ofmAnd NhRespectively representing the traffic mode m and the passenger and goods turnover amount of the road; l ismAnd LhRespectively representing the total scale of the traffic mode m and the road, namely the total infrastructure mileage or the route mileage; f. ofmA correction coefficient that is a weight of the transportation means m;
then
The weight of the composite network edge is calculated by the following formula:
wherein,representing composite network edgesThe weight of (a) is determined,representing edges of subnetwork mThe weight of (c).
Further, the parameter determining step specifically includes:
weighted node degree DiA determination sub-step for determining a weighted node degree D of the node iiWeighted node degree DiThe sum of the weights of the edges of the node i which are directly communicated with other n nodes reflects the local importance of the node i; diThe larger the value is, the more important the status of the node i in the network is;
wherein DiRepresenting the weighted node degree of the node i;
a weighted node betweenness determining substep for determining weighted node betweenness b of node iiWeighted node betweenness biThe ratio of the number of shortest routes of mileage passing through the node i in the composite network to the number of shortest routes of mileage among all nodes in the composite network is obtained; the larger the betweenness of the weighted nodes is, the more important the node plays a role in connecting the whole network;
in the formula, i, j, k represents a node identifier; biRepresenting weighted node betweenness of the node i; n isjk(i) The shortest distance path number of the mileage connecting the node j and the node k and passing through the node i; n isjkThe shortest distance path number of the mileage connecting the node j and the node k.
Further, the method further comprises:
a parameter standardization step, which is used for carrying out standardization processing on the weighted node degree and the weighted node betweenness:
in the formula, xsNormalized value, x, representing a parametermax,xminRespectively representing the maximum value and the minimum value of the parameter sequence; x is the number ofiThe node degree or weighted node betweenness is weighted for the node i.
The technical scheme of the invention has the following advantages:
the scheme provides an importance evaluation method of the comprehensive transportation network node, and aims to innovatively provide the importance evaluation method of the comprehensive transportation network node aiming at the requirement problem of 'prior prevention and prior control' in the risk prevention and control of the existing comprehensive transportation system. The embodiment of the invention can effectively identify the key nodes in the comprehensive transportation network, can purposefully perform maintenance and pre-disaster reinforcement in the work of improving the reliability of the network system and preventing and controlling the safety risk, and lays a foundation for improving the capability of the comprehensive transportation network for coping with natural disasters and emergencies and improving the reliability of the transportation system.
Drawings
The technical solutions and effects of the present invention will become more apparent and more easily understood from the following description of a preferred embodiment of the present invention, taken in conjunction with the accompanying drawings. Wherein:
fig. 1 is a schematic view of an integrated transportation network;
FIG. 2 is a flow chart of an embodiment of the present invention.
Detailed Description
A preferred embodiment of the present invention will be described below with reference to the accompanying drawings.
In order to enable the traffic sign to convey digitalized road traffic sign information to digitalized intelligent traffic vehicles and equipment, the embodiment of the invention provides a data generation method of the digitalized traffic sign, which comprises the following steps:
firstly, a composite network topology construction method formed by a comprehensive transportation network is provided, and the method comprises the explanation and the assumption of the composite network topology construction, the construction of the composite network topology and a weight calculation method of a composite network edge; secondly, a node importance evaluation model of the composite network is provided, and the node importance evaluation model comprises a node importance evaluation index determining method and a node importance evaluation method.
The method specifically comprises the following contents: (1) explanation and assumption of composite network topology construction; (2) constructing a composite network; (3) calculating the weight of the composite network edge; (4) determining the index of the composite network node importance evaluation; (5) an important evaluation method for a composite network node.
(1) Description and assumptions for composite network topology construction
And (3) giving extraction descriptions of nodes and edges of the composite network and some assumptions of the characteristics of the composite network, such as constructing a weighted composite network to reflect the difference of the edges of the composite network, and assuming that the composite network is a undirected network to reflect the round-trip connectivity of the traffic between the nodes.
(2) Construction of a composite network
The method comprises the steps of taking a city as a node, adopting an L space method to respectively extract the topology of the network such as a road, a railway, civil aviation, water transportation and the like and the corresponding adjacency matrix, and on the basis, adopting fuzzy logic operation to construct the adjacency matrix of the composite network of the comprehensive transportation network. And extracting the mileage adjacency matrix of each network according to the basic database of each transportation mode, and calculating the mileage of the composite network on the basis. The edges of the composite network have a mileage attribute and are related to the traffic mode of connection between two nodes, and a weight calculation method of the edges of the composite network is given below.
(3) Weight calculation of composite network edges
The weight of the composite network edge is related to the communication mode and the number of paths communicated between the two nodes, and the more the paths between the two nodes are, the higher the weight is. The patent defines the weight of the composite network edge as the sum of the weights of the sub-network edges between the nodes. Different transportation modes are endowed with different weights by considering factors such as network scale, running speed, passenger and cargo turnover amount and the like of each transportation mode.
(4) Index determination for composite network node importance assessment
In a complex network theory, the node degree and the node betweenness respectively reflect the local importance and the global importance of the nodes in the network. Therefore, the weighted node degree and the weighted node betweenness are used as indexes for evaluating the importance of the composite network node.
(5) An important evaluation method for a composite network node.
The composite network node importance degree evaluation model of the comprehensive transportation network mainly comprises the following steps:
5.1, constructing each sub-network topology model based on the L space, and calculating the weight of each sub-network topology graph edge; 5.2, constructing a composite network topology of the comprehensive transportation network, and calculating the weight and the mileage of the composite network; 5.3, calculating the weighted node degree and the weighted node betweenness, and carrying out standardization processing; and 5.4, calculating the importance of the composite network node.
The embodiment of the invention aims at solving the problem of 'prior prevention and prior control' in the risk prevention and control of the existing comprehensive transportation system, and innovatively provides a comprehensive transportation network node importance degree evaluation method. The embodiment of the invention can effectively identify the key nodes in the comprehensive transportation network, can purposefully perform maintenance and pre-disaster reinforcement in the work of improving the reliability of the network system and preventing and controlling the safety risk, and lays a foundation for improving the capability of the comprehensive transportation network for coping with natural disasters and emergencies and improving the reliability of the transportation system.
The embodiment of the invention provides an evaluation method for the importance of a comprehensive transportation network node. Firstly, a composite network topological structure extraction method facing the comprehensive transportation network is provided by combining the characteristics of the comprehensive transportation network and the adaptability of different network extraction methods; secondly, taking the network scale, the running speed, the passenger and cargo turnover amount and other factors of each transportation mode into consideration, and providing a weight calculation formula of each sub-network extension edge; and thirdly, selecting the weighted node degree and the weighted node betweenness as evaluation indexes, and providing an evaluation method for the node importance degree of the comprehensive transportation network. The specific flow chart is shown in fig. 2. The specific implementation mode is as follows:
(1) description and assumptions for composite network topology construction
1) Taking a city as a node: when a city has more than 1 railway station or airport or more than 1 road passes by, abstracting the city into a node;
2) edge in the composite network topology: in the construction of sub-network topologies of roads, railways, civil aviation and the like, the number of edges between nodes is consistent with the number of actually communicated roads, railways and routes. But the composite network topology of the embodiment of the invention is constructed without repeated connection; that is, although there is a highway accessible between city a and city B and a railway or civil aviation accessible, in the composite network topology, only one edge is connected between node a and node B.
3) A weighting network: the differences in the aspects of infrastructure scale, passenger and freight turnover, transportation speed and the like of traffic transportation modes such as roads, railways, civil aviation, waterways and the like are large; therefore, in the embodiment of the invention, when the composite network topology is extracted, the different traffic modes connected between two nodes represent different functional attributes, reliability and the like of edges. Therefore, when the composite network is constructed, the weighting network is selected to reflect the difference of the composite network edges.
4) A unidirectional network. Usually, if the city a and the city B are connected, the connection between the node a and the node B is undirected; therefore, in the composite network constructed in the embodiment of the present invention, a undirected network is assumed between any two nodes.
(2) Construction of a composite network
And constructing a composite network by using the city as a node and adopting an L space method. The method specifically comprises the following steps: firstly, network topology and corresponding adjacency matrixes of roads, railways, civil aviation and water transportation are obtained, wherein the adjacency matrixes of the roads, the railways, the civil aviation and the water transportation are respectively marked as Mh、 Mr、Ma、Mw(ii) a Then, a composite network topology of the comprehensive transportation network is constructed according to the four adjacent matrixes, and an adjacent matrix M of the composite network is obtainedc(ii) a Wherein M iscThe following formula is used for calculation:
wherein
In the upper formula, a V-shaped represents a fuzzy operator, and a large operator is taken;the element representing the ith row and the jth column of the composite network connection matrix,matrix elements representing the ith row and the jth column of the composite network connection matrix are determined according to road, railway, civil aviation and water transport connections between nodes i and j in the composite network topology. As can be seen from the formula (1), only one traffic way topological adjacency matrix element exists between the nodes i and jIf not, the nodes i and j in the composite network topology are connected by edges.
Extracting the mileage adjacency matrix of each network according to the basic database of each transportation mode, and respectively recording the mileage adjacency matrix as Dh，Dr，DaAnd Dw. Recording the mileage adjacency matrix of the composite network as DcThen D iscThe calculation formula of (a) is as follows:
Dc＝f(Dh,Dr,Da,Dw)
wherein,
in the above formula, f (. cndot.) represents a median valueThe function is calculated as a function of the time,matrix elements of ith row and jth column of the composite network mileage adjacency matrix are represented according to the mileage of roads, railways, civil aviation and water transportation between nodes i and j in the composite network topologyAnd (4) determining.
(3) Weight calculation of composite network edges
The weight of the composite network edge is related to the communication mode and the path number of the nodes i and j in the composite network topology; the more paths between two nodes, the higher its weight. The weight of a composite network edge is defined herein as the sum of the weights of each sub-network edge between nodes. Different transportation modes are endowed with different weights by considering factors such as network scale, running speed, passenger and cargo turnover amount and the like of each transportation mode. Taking the road as a reference, converting other transportation modes into multiples relative to the road, and giving a weight calculation method of the other transportation modes by considering the factors, wherein the calculation formula is as follows:
in the formula, ωmFor the weight of the traffic mode m, for the same traffic mode, the weights of roads in different levels can be calculated according to the turnover amount of passengers and cargoes born by the traffic mode m and the construction scale; smAnd ShRespectively representing the traffic mode m and the average running speed of a road; n is a radical ofmAnd NhRespectively representing the traffic mode m and the passenger and goods turnover amount of the road; l ismAnd LhRespectively representing the total scale of the traffic mode m and the road, namely the total infrastructure mileage or the route mileage; f. ofmThe value range of the correction coefficient of the weight of the traffic mode m is between 0 and 2.
The weight of the composite network edge is calculated by the following formula:
wherein,representing composite network edgesThe weight of (a) is determined,representing edges of subnetwork mThe weight of (c).
Because part of railway lines are separated from passengers and cargoes, the timeliness of freight transportation is lower than that of passenger transportation under the event condition, and therefore, only the passenger railway is selected as an analysis object in the node importance degree evaluation oriented to the safety risk analysis of the comprehensive transportation network system. Furthermore, in the calculation of the weight, only the passenger transportation turnover number of each transportation mode is selected as a calculation basis.
By the method, according to the development statistics bulletin of the transportation industry in 2017, the weight of the network side of various transportation modes is calculated by combining the road mileage and the passenger transportation turnover data according to the formula (3) as follows:
ωhighway with a light-emitting diode＝1.0,ωGeneral railway＝1.03，ωHigh speed railway＝19.75，ωWater route＝0.01，ωCivil aviation＝0.21。
An example of the weight calculation of a composite network edge of two nodes is as follows:
assuming that two railways are arranged between the nodes 1 and 2 and one highway is connected, the composite network edge e is obtained by the formula (4) according to the result12Right of (1)Heavy omega12Is composed of
ω12＝1.03+19.75+1.0＝21.78。
(4) Index determination for composite network node importance assessment
In a complex network theory, the node degree and the node betweenness respectively reflect the local importance and the global importance of the nodes in the network. Therefore, based on the above-mentioned research, the weighted node degree and the weighted node betweenness are used as the index for evaluating the importance of the composite network node.
1) Weighted node degree
Weighted node degree D of node iiThe weight sum of the edges of the node i which are directly communicated with other n nodes reflects the local importance of the node i; diThe larger the value, the more important it means that node i is in the network. The calculation formula is as follows,
Direpresenting the weighted node degree of node i.
2) Weighted node betweenness
The weighted node betweenness is the ratio of the number of shortest routes of mileage passing through the node i in the network to the number of shortest routes of mileage among all nodes in the network. The larger the weighted node betweenness is, the more important the node plays a role in connecting the whole network. Is calculated by the formula
In the formula, i, j, k represents a node identifier; biRepresenting weighted node betweenness of the node i; n isjk(i) The shortest distance path number of the mileage connecting the node j and the node k and passing through the node i; n isjkThe shortest distance path number of the mileage connecting the node j and the node k.
(5) And (3) important evaluation of the composite network nodes:
the method comprises the following specific steps of evaluating the importance of the composite network node of the comprehensive transportation network:
step 1: a data preparation stage: respectively inputting road data, railway data, civil aviation route data and the like, and extracting topological graphs of sub-networks by using a city as a node by using an L space method; and determining the mileage and the weight of each topological graph edge.
Step 2: generating a sub-network adjacency matrix based on the sub-network topology map: and (4) generating a weight adjacent matrix and a mileage adjacent matrix of each sub-network based on the connection relation obtained in the step (1) and the mileage and the weight of each sub-network side.
And step 3: constructing a composite network topological structure model: and establishing a composite network topological structure model on the basis of each sub-network topological model. On the basis, a mileage adjacency matrix and a weight adjacency matrix of the composite network are generated.
And 4, step 4: calculating the weighted node degree and weighted node betweenness of the composite network: calculating the weighted node degree of each node through the weighted adjacent matrix; and calculating the weighted mileage betweenness of each node by adopting a Dijkstra algorithm through the mileage adjacency matrix.
And 5: standardizing parameters;
carrying out standardization processing on the weighted node degree and the weighted node betweenness index, wherein the calculation formula is as follows:
in the formula, xsNormalized value, x, representing a parametermax,xminRespectively representing the maximum and minimum values of the parameter sequence.
Step 6: determining evaluation index weight: determining weighted nodes by expert scoringweight alpha of degree and weighted node betweenness1and alpha2。
And 7: and calculating the importance of the comprehensive transportation network node.
Wherein, IiRepresenting importance, parameters, of node iAndrespectively representing weighted node degrees biAnd weighted node betweenness DiNormalized values.
The inventive concept can be implemented in different ways as the technology advances, as will be clear to a person skilled in the art. The embodiments of the invention are not limited to the above-described embodiments but may vary within the scope of the claims.
Claims (6)
1. A method for evaluating the importance of a comprehensive transportation network node is characterized by comprising the following steps:
a data preparation step, which is used for obtaining cities serving as nodes in the topological graph and connection relations of various traffic modes among the nodes; acquiring a sub-topological graph of each traffic mode of each node and other nodes, and determining the mileage and the weight of each traffic mode; wherein the mode of transportation includes: road, railway, civil aviation, water transport;
a matrix generation step, which is used for respectively generating a mileage adjacent matrix and a weight adjacent matrix corresponding to each sub-topological graph aiming at the mileage and the weight in each sub-topological graph; generating a total topological graph of the whole composite network according to the sub-topological graph of each node, and generating a mileage adjacency matrix and a weight adjacency matrix of the total topological graph;
a weight generation step, which is used for determining the weight of each edge in the total topological graph of the composite network;
a parameter determination step for determining the weighted mileage betweenness b of the nodes according to the mileage adjacency matrix of the total topological graphi(ii) a Determining the weighted node degree D of each node according to the weighted adjacency matrix of the total topological graphi；
an importance degree determination step for determining the weight α of the weighted node degree determined according to the expert score1and weight alpha of weighted node betweenness2Determining the importance of node i in the composite network
2. The method for evaluating the importance of nodes in an integrated transportation network according to claim 1, wherein the city of the nodes in the topological graph has at least one railway station or airport, or at least one road or water channel; and when any one of highways, railways, civil aviation and water transportation is connected between two nodes in the total topological graph of the composite network, the two nodes are connected by edges.
3. The method for evaluating the importance of nodes of the integrated transportation network according to claim 1, wherein the matrix generating step specifically comprises:
step 21, obtaining network topology and corresponding adjacency matrixes of roads, railways, civil aviation and water transportation, wherein the adjacency matrixes of the roads, the railways, the civil aviation and the water transportation are respectively marked as Mh、Mr、Ma、Mw；
Step 22, according to the obtained four adjacency matrixes,and a composite network topology M of the comprehensive transportation network is constructed by the methodc：
Wherein
In the upper formula, a V-shaped represents a fuzzy operator, and a large operator is taken;
the element representing the ith row and the jth column of the composite network connection matrix,
matrix elements representing the ith row and the jth column of the composite network connection matrix are determined according to road, railway, civil aviation and water transport connections between nodes i and j in the composite network topology;
step 23, generating a mileage adjacency matrix D corresponding to road, railway, civil aviation and water transportation network topology according to mileage of road, railway, civil aviation and water transportation modes among nodesh、Dr、Da、Dw(ii) a And generating the mileage adjacency matrix D of the composite network according to the mileage adjacency matrix Dc：
Dc＝f(Dh,Dr,Da,Dw)
Wherein,
in the above formula, f (-) represents a median calculation function,matrix elements representing ith row and jth column of composite network mileage adjacency matrix according to composite network topologyMileage of highway, railway, civil aviation and water transportation between nodes i and j And (4) determining.
4. The method for evaluating the importance of the integrated transportation network node according to claim 1, wherein the step of generating the weight of the edge in the composite network specifically comprises:
the method comprises the steps that the weight of an edge between two nodes i and j in the composite network is determined, and the weight is the sum of the weights of the edges of each traffic mode sub-network between the two nodes i and j;
wherein,representing an edge between two nodes i, j in a composite networkThe weight of (a) is determined,representing edges of subnetwork mThe weight of (c);
wherein,
in the formula, ωmWeight of the traffic mode m; smAnd ShRespectively representing the traffic mode m and the average running speed of a road; n is a radical ofmAnd NhRespectively representing the traffic mode m and the passenger and goods turnover amount of the road; l ismAnd LhRespectively representing the total scale of the traffic mode m and the road, namely the total infrastructure mileage or the route mileage; f. ofmA correction coefficient that is a weight of the transportation means m;
then
The weight of the composite network edge is calculated by the following formula:
wherein,representing composite network edgesThe weight of (a) is determined,representing edges of subnetwork mThe weight of (c).
5. The method for evaluating the importance of the integrated transportation network node according to claim 4, wherein the parameter determining step specifically comprises:
weighted node degree DiA determination sub-step for determining a weighted node degree D of the node iiWeighted node degree DiThe sum of the weights of the edges of the node i which are directly communicated with other n nodes reflects the local importance of the node i; diThe larger the value is, the more important the status of the node i in the network is;
wherein DiTo representWeighted node degree of the node i;
a weighted node betweenness determining substep for determining weighted node betweenness b of node iiWeighted node betweenness biThe ratio of the number of shortest routes of mileage passing through the node i in the composite network to the number of shortest routes of mileage among all nodes in the composite network is obtained; the larger the betweenness of the weighted nodes is, the more important the node plays a role in connecting the whole network;
in the formula, i, j, k represents a node identifier; biRepresenting weighted node betweenness of the node i; n isjk(i) The shortest distance path number of the mileage connecting the node j and the node k and passing through the node i; n isjkThe shortest distance path number of the mileage connecting the node j and the node k.
6. The integrated transportation network node importance assessment method of claim 5, further comprising:
a parameter standardization step, which is used for carrying out standardization processing on the weighted node degree and the weighted node betweenness:
in the formula, xsNormalized value, x, representing a parametermax,xminRespectively representing the maximum value and the minimum value of the parameter sequence; x is the number ofiThe node degree or weighted node betweenness is weighted for the node i.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810775641.2A CN109034578A (en) | 2018-07-13 | 2018-07-13 | A kind of composite communications transport network node different degree appraisal procedure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810775641.2A CN109034578A (en) | 2018-07-13 | 2018-07-13 | A kind of composite communications transport network node different degree appraisal procedure |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109034578A true CN109034578A (en) | 2018-12-18 |
Family
ID=64642556
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810775641.2A Pending CN109034578A (en) | 2018-07-13 | 2018-07-13 | A kind of composite communications transport network node different degree appraisal procedure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109034578A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111260924A (en) * | 2020-02-10 | 2020-06-09 | 北京中交国通智能交通系统技术有限公司 | Traffic intelligent control and service release strategy method adapting to edge calculation |
| CN111882241A (en) * | 2020-08-05 | 2020-11-03 | 北京交通大学 | Method for evaluating toughness of urban rail transit network |
| CN113128892A (en) * | 2021-04-28 | 2021-07-16 | 中国水利水电科学研究院 | Chained disaster risk assessment method and device based on complex network topological relation |
| CN113450558A (en) * | 2020-03-27 | 2021-09-28 | 上海仙豆智能机器人有限公司 | Method, system and storage medium for identifying network key node |
| CN113837582A (en) * | 2021-09-17 | 2021-12-24 | 武汉理工大学 | Urban road traffic safety key risk identification method and device and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102270388A (en) * | 2011-06-03 | 2011-12-07 | 王正武 | Method for measuring and calculating importance of traffic network nodes with consideration of cascading failure |
| CN103326899A (en) * | 2013-06-16 | 2013-09-25 | 周健 | Weighting network node importance assessment method based on network heterogeneity |
| CN104394202A (en) * | 2014-11-13 | 2015-03-04 | 西安交通大学 | A node vitality quantifying method in a mobile social network |
| CN104992266A (en) * | 2015-06-15 | 2015-10-21 | 广东电网有限责任公司电力调度控制中心 | Method of determining power grid node importance degree and system thereof |
| CN106096798A (en) * | 2016-07-05 | 2016-11-09 | 南京航空航天大学 | A kind of city road network optimization method under accessibility optimal conditions |
| CN106506231A (en) * | 2016-11-30 | 2017-03-15 | 大连大学 | The charge network key node recognition methodss in structure-oriented hole |
| CN107423897A (en) * | 2017-07-20 | 2017-12-01 | 青岛大学 | A kind of method that public traffic network is evaluated using public transport archives |
-
2018
- 2018-07-13 CN CN201810775641.2A patent/CN109034578A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102270388A (en) * | 2011-06-03 | 2011-12-07 | 王正武 | Method for measuring and calculating importance of traffic network nodes with consideration of cascading failure |
| CN103326899A (en) * | 2013-06-16 | 2013-09-25 | 周健 | Weighting network node importance assessment method based on network heterogeneity |
| CN104394202A (en) * | 2014-11-13 | 2015-03-04 | 西安交通大学 | A node vitality quantifying method in a mobile social network |
| CN104992266A (en) * | 2015-06-15 | 2015-10-21 | 广东电网有限责任公司电力调度控制中心 | Method of determining power grid node importance degree and system thereof |
| CN106096798A (en) * | 2016-07-05 | 2016-11-09 | 南京航空航天大学 | A kind of city road network optimization method under accessibility optimal conditions |
| CN106506231A (en) * | 2016-11-30 | 2017-03-15 | 大连大学 | The charge network key node recognition methodss in structure-oriented hole |
| CN107423897A (en) * | 2017-07-20 | 2017-12-01 | 青岛大学 | A kind of method that public traffic network is evaluated using public transport archives |
Non-Patent Citations (2)
| Title |
|---|
| 张玉荣等: ""一种基于城市交通加权网络模型的节点重要性评价方法"", 《中国科技论文在线》 * |
| 欧雯: ""恐怖袭击下城市轨道交通网络抗毁性分析"", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111260924A (en) * | 2020-02-10 | 2020-06-09 | 北京中交国通智能交通系统技术有限公司 | Traffic intelligent control and service release strategy method adapting to edge calculation |
| CN111260924B (en) * | 2020-02-10 | 2021-01-26 | 北京中交国通智能交通系统技术有限公司 | Traffic intelligent control and service release strategy method adapting to edge calculation |
| CN113450558A (en) * | 2020-03-27 | 2021-09-28 | 上海仙豆智能机器人有限公司 | Method, system and storage medium for identifying network key node |
| 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 |
| CN111882241A (en) * | 2020-08-05 | 2020-11-03 | 北京交通大学 | Method for evaluating toughness of urban rail transit network |
| CN111882241B (en) * | 2020-08-05 | 2023-11-07 | 北京交通大学 | Urban rail transit network toughness assessment method |
| CN113128892A (en) * | 2021-04-28 | 2021-07-16 | 中国水利水电科学研究院 | Chained disaster risk assessment method and device based on complex network topological relation |
| CN113128892B (en) * | 2021-04-28 | 2022-04-22 | 中国水利水电科学研究院 | Chained disaster risk assessment method and device based on complex network topological relation |
| CN113837582A (en) * | 2021-09-17 | 2021-12-24 | 武汉理工大学 | Urban road traffic safety key risk identification method and device and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109034578A (en) | A kind of composite communications transport network node different degree appraisal procedure | |
| Kim et al. | Integrating travel demand modeling and flood hazard risk analysis for evacuation and sheltering | |
| CN109657843B (en) | Integrated planning decision support system of urban plug-in bus system | |
| Sang et al. | A novel method for restoring the trajectory of the inland waterway ship by using AIS data | |
| Saghapour et al. | Public transport accessibility in metropolitan areas: A new approach incorporating population density | |
| Iida | Basic concepts and future directions of road network reliability analysis | |
| CN107274667A (en) | Urban transportation intelligence managing and control system networking joint control framework and implementation | |
| CN108520353B (en) | Method for identifying safety risk points of integrated transportation network | |
| CN110111574B (en) | Urban traffic imbalance evaluation method based on flow tree analysis | |
| CN110779529B (en) | Forest disaster emergency commanding and scheduling system based on big data and cloud computing | |
| Kåresdotter et al. | First mile/last mile problems in smart and sustainable cities: A case study in Stockholm County | |
| CN106448165B (en) | A kind of road network Travel Time Reliability evaluation method for data of about being hired a car based on network | |
| CN106897791B (en) | Method for constructing public transport subway composite network model | |
| CN101706993A (en) | Calculation method and system of road emergency evacuation capability index system | |
| CN106875675A (en) | A kind of quick identification slag-soil truck deviates the detecting system and method for traveling | |
| CN109727173A (en) | Service area big data intelligence s ervice platform | |
| CN115169795A (en) | Method and device for evaluating importance of site | |
| CN113362599B (en) | Method for calculating reachable index of road traffic facility | |
| Ramkumar et al. | Real time path planning using intelligent transportation system for hybrid VANET | |
| Sert et al. | Determination of transportation networks base on the optimal public transportation policy using spatial and network analysis methods: a case of the Konya, Turkey | |
| CN114971085B (en) | Method and system for predicting accessibility of bus station and storage medium | |
| Yang et al. | Arterial link travel time estimation considering traffic signal delays using cellular handoff data | |
| CN116823039A (en) | Microscopic plot scale facility accessibility measurement method based on multi-source data | |
| CN113723859B (en) | Method for evaluating stability of multi-layer urban group comprehensive passenger transport network | |
| TWM613926U (en) | System for tracking living footprint |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181218 |
|
| RJ01 | Rejection of invention patent application after publication |