CN111985065A - Road automatic selection technology based on gravitational field theory - Google Patents

Road automatic selection technology based on gravitational field theory Download PDF

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CN111985065A
CN111985065A CN201910427313.8A CN201910427313A CN111985065A CN 111985065 A CN111985065 A CN 111985065A CN 201910427313 A CN201910427313 A CN 201910427313A CN 111985065 A CN111985065 A CN 111985065A
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nodes
importance
road network
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王中辉
韩远
崔洁
胡博伟
余贝贝
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Lanzhou Jiaotong University
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Abstract

The complex network model is widely applied to automatic road selection, but few methods consider the influence of multi-level neighbor nodes in the road network dual graph, and the importance evaluation of the road is lack of accuracy and reliability. Therefore, a road automatic selection technology based on the gravitational field theory is provided, and the basic idea is as follows: firstly, generating a road network dual graph by means of strokes; then, calculating the centrality measurement value of the nodes by adopting a centrality method, regarding the centrality measurement value as mass, regarding the shortest distance between the nodes as distance, and evaluating the importance of stroke by utilizing a Newton gravity formula; and finally, automatically selecting the roads by selecting the strokes with the importance degree ranked in front. The road network of the Lanzhou city gateway area is tested, and the result shows that the road network selected by the invention better keeps the integral structure, coverage, density distribution, topological characteristics and connectivity of the original road network.

Description

Road automatic selection technology based on gravitational field theory
Technical Field
The invention belongs to the technical field of comprehensive drawing, and relates to research on automatic road selection.
Background
A road network is one of typical representatives of a mesh element among map elements, and is an important object of map synthesis. Road selection is a prerequisite for other comprehensive operations, and usually, the importance of roads is measured according to measurement, statistics, topology and thematic information implied by a road network or a single road, and roads with the importance ranking before are selected. Road selection not only simply reduces the detail of the road network, but also maintains the overall structure and local key structure, density distribution, coverage and the like of the road.
The current road automatic selection method mainly adopts graph theory and a complex network model. Compared with a method based on a complex network model, road selection based on graph theory cannot well describe the complexity and the overall shape of a road network structure, and the complex network is a model recognized by a simulated complex system. Therefore, the method for establishing the complex network model of the road network by using the dual topology method is widely applied. The method has the advantages that indexes such as semantics, geometry, node degree, medium neutrality, approach centrality and the like of integrated roads such as Zhangdong and the like are selected by giving weights to various indexes according to drawing experience, and semantic information of the roads is often incomplete and difficult to acquire, so that the method has certain limitation in application; li mu catalpa and the like calculate the accumulated weight number by constructing a hierarchical clustering structure of a road network, so that the importance of the road in the overall structure is measured, but the model has higher calculation complexity; liu Jiang and the like consider the importance of the nodes and the influence of nearby nodes, the road importance is evaluated by combining the node degrees of the dual graph and the mesocentrality, and the selected result can better keep the structural characteristics, topological characteristics and connectivity of the road, but excessive parameters in the method are determined by experience values, and inconsistent selected results can be caused by different parameter settings; and the Caowenw et al considers the length of the stroke, the degree of nodes of the dual graph and the clustering coefficient, determines the weights of different indexes by using a coefficient of variation method and selects the road. However, the road evaluation index of the model can only reflect the local attribute of the road; the method well maintains the hierarchy of the road network and reduces the influence of 'edge effect', but the connectivity of road network layers is damaged after the method is selected.
Disclosure of Invention
At present, the influence of multi-level neighbor nodes is mostly not considered in a method for selecting a road by using a complex network model, so that the importance evaluation of the road lacks accuracy and reliability, and the importance of the nodes in the complex network is not only dependent on the importance of the nearest nodes but also influenced by the multi-level neighbor nodes. Therefore, the road network is mapped into the dual graph by means of strokes, the centrality measurement value of the dual graph nodes and the shortest distance between the nodes are respectively regarded as the quality and the distance in the gravitational field equation, the importance evaluation model of the dual graph nodes is established, and the automatic road selection technology considering the influence of the multi-level neighbor nodes is further provided.
Automatic road selection technology
1.1 Stroke-unit-based dual topology expression of road network
In the research of road automatic selection, there are mainly two road expression modes: one method is to establish generalized road network topology by taking road units as edges and intersections as nodes; and the other method is to map the road network into a dual graph by using a dual topology method and taking road units as nodes and road intersections as edges.
Can use simple unauthorized drawing
Figure 269879DEST_PATH_IMAGE001
To define a dual graph in which,
Figure 187019DEST_PATH_IMAGE002
representing the number of nodes in the dual graph,
Figure 29073DEST_PATH_IMAGE003
Indicating the number of connected edges in the dual graph. Two ways of expressing the road network are shown in figure 1. Compared with the generalized road network topology, the dual topology method can clearly express the connection relation among roads, the importance degree of the roads in the network and the local and global efficiency of the road network, so that the dual graph is beneficial to further analyzing the structural characteristics and the overall form of the roads. stroke refers to a combination of road segments with good continuity in direction, which is superior to the road segments in terms of keeping the longitudinal gradation and geometrical characteristics of the road.
Therefore, the method carries out dual topological expression on the road network by taking strokes as units.
1.2 evaluation of Stroke importance
1.2.1 centrality measure analysis
The centrality index is based on the importance of the nodes in the network structure evaluation network, and can be mathematically summarized into centrality, intermediary centrality and approximate centrality. The three indexes reflect local and global topological properties of nodes in the network. In addition, in order to take the geometric characteristics of roads into consideration, the stroke length is also introduced into the centrality measure analysis.
(1) Center of gravity
Centrality refers to the number of edges connected to a node. The method can determine the local influence of the node most effectively, and has the defect that the influence of the first-level neighbor node is only utilized, so that the importance of the node in the network is often required to be evaluated by combining other indexes. Node point
Figure 424282DEST_PATH_IMAGE004
Value of (A)
Figure 238655DEST_PATH_IMAGE005
Can be expressed by equation (1):
Figure 61117DEST_PATH_IMAGE006
(1)
wherein the content of the first and second substances,
Figure 328150DEST_PATH_IMAGE007
representing the number of nodes in the network. If node
Figure 340100DEST_PATH_IMAGE004
And
Figure 8979DEST_PATH_IMAGE008
the two parts are directly connected with each other,
Figure 2342DEST_PATH_IMAGE009
is 1; otherwise
Figure 756672DEST_PATH_IMAGE009
Is 0.
(2) Center of medium
The intermediary centrality reflects the importance of the node when performing path selection, in other words, represents the significance of the "intermediary" influence of the node in the network, and the theoretical formula is represented as:
Figure 493684DEST_PATH_IMAGE010
(2)
wherein the content of the first and second substances,
Figure 79386DEST_PATH_IMAGE011
is the total number of nodes of the network,
Figure 509230DEST_PATH_IMAGE012
and
Figure 750856DEST_PATH_IMAGE013
on behalf of any two nodes in the network,
Figure 25979DEST_PATH_IMAGE014
representative node
Figure 669450DEST_PATH_IMAGE012
And
Figure 4616DEST_PATH_IMAGE013
the number of shortest paths between the two,
Figure 65630DEST_PATH_IMAGE015
representative node
Figure 144445DEST_PATH_IMAGE012
And
Figure 642422DEST_PATH_IMAGE013
all shortest paths of
Figure 414069DEST_PATH_IMAGE004
The number of the cells.
Figure 364708DEST_PATH_IMAGE016
The larger the value, the node
Figure 247213DEST_PATH_IMAGE004
The more pronounced the "mediator" function, the more important it is.
(3) Near centrality
The proximity centrality reflects the proximity of a node to all other nodes in the network, and means the reciprocal of the average shortest distance between any node and all other nodes in the network. The greater the proximity centrality value of a node is, the closer it is to other nodes, the more the node is located in the center of the network, the higher its importance is, and it is recorded as:
Figure 662014DEST_PATH_IMAGE017
(3)
wherein the content of the first and second substances,
Figure 604562DEST_PATH_IMAGE007
indicating the number of nodes in the network,
Figure 42497DEST_PATH_IMAGE008
which represents any one of the nodes in the network,
Figure 463114DEST_PATH_IMAGE018
representing nodes
Figure 935683DEST_PATH_IMAGE004
And
Figure 862182DEST_PATH_IMAGE008
the shortest distance therebetween.
(4) Length of stroke
Length of stroke: (
Figure 787413DEST_PATH_IMAGE019
) The index describes the range of influence of a stroke as the sum of the lengths of the road segments that make up the stroke. The longer the length of stroke, the greater the range of influence and the higher the importance.
1.2.2 index integration
Because the dimensions and the magnitude of the indexes are different,in order to not highlight the influence of a certain index with a high value, each index needs to be subjected to dispersion standardization so that the value range is [0,1 ]]In the meantime. In addition, the CRITIC weighting method is selected to give weight to each index. The CRITIC weighting method uses standard deviation to measure the contrast intensity in the indexes and uses the correlation coefficient to measure the conflict intensity between the indexes, so as to make the information amount reach the maximum. First, the
Figure 11721DEST_PATH_IMAGE004
Information amount of individual index
Figure 73218DEST_PATH_IMAGE020
Can be expressed by equation (4):
Figure 419885DEST_PATH_IMAGE021
(4)
wherein the content of the first and second substances,
Figure 832412DEST_PATH_IMAGE022
indicating index
Figure 860411DEST_PATH_IMAGE004
The standard deviation of (a) is determined,
Figure 41994DEST_PATH_IMAGE023
the number of the indexes is represented,
Figure 543251DEST_PATH_IMAGE024
indicating index
Figure 443074DEST_PATH_IMAGE004
And
Figure 9184DEST_PATH_IMAGE008
the correlation coefficient of (2). From this, an index can be obtained
Figure 310853DEST_PATH_IMAGE004
The higher the information amount of (2), the higher the importance of the index to other indexes, so that the index
Figure 671427DEST_PATH_IMAGE004
Weight of (2)
Figure 58546DEST_PATH_IMAGE025
Can be expressed as:
Figure 490664DEST_PATH_IMAGE026
(5)
based on the above introduction, a click centrality measurement value can be obtained
Figure 381260DEST_PATH_IMAGE027
：
Figure 178315DEST_PATH_IMAGE028
(6)
Wherein
Figure 787150DEST_PATH_IMAGE029
、
Figure 960643DEST_PATH_IMAGE030
、
Figure 971324DEST_PATH_IMAGE031
、
Figure 752329DEST_PATH_IMAGE032
Respectively are the weights of four indexes,
Figure 848461DEST_PATH_IMAGE033
、
Figure 560066DEST_PATH_IMAGE016
、
Figure 425253DEST_PATH_IMAGE034
、
Figure 564111DEST_PATH_IMAGE035
is a node after dispersion standardization
Figure 209856DEST_PATH_IMAGE004
Centrality, mesocentrality, recenterness, and stroke length values.
1.3 Stroke importance evaluation method combining gravitational field theory
The importance of nodes in a network is typically affected by multiple levels of neighboring nodes, with the effect between nodes becoming weaker as the shortest distance between them increases. On the basis of the obtained node centrality measurement value, the following click importance evaluation method is proposed:
Figure 459571DEST_PATH_IMAGE036
(7)
wherein the content of the first and second substances,
Figure 444845DEST_PATH_IMAGE037
representing nodes
Figure 754604DEST_PATH_IMAGE004
And
Figure 139842DEST_PATH_IMAGE008
the shortest distance between the two elements,
Figure 193249DEST_PATH_IMAGE027
and
Figure 33029DEST_PATH_IMAGE038
is a node
Figure 513688DEST_PATH_IMAGE004
And
Figure 806130DEST_PATH_IMAGE008
a centrality measure of;
Figure 725544DEST_PATH_IMAGE039
is a node
Figure 419831DEST_PATH_IMAGE004
A set of neighboring nodes of, a node to node in the set
Figure 71392DEST_PATH_IMAGE004
Does not exceed a given range
Figure 116708DEST_PATH_IMAGE040
. Because the time complexity of the shortest path algorithm such as Dijkstra or bellman-ford is very high, the following algorithm is adopted to obtain a multi-stage neighbor node set:
node point
Figure 511917DEST_PATH_IMAGE004
The distance between the node and the neighbor node is 1, and the node
Figure 60710DEST_PATH_IMAGE004
Neighbor node and node of neighbor node
Figure 961801DEST_PATH_IMAGE004
Is 2, and so on. In this way, the nodes are acquired step by step
Figure 228835DEST_PATH_IMAGE004
And each node therein is endowed with the node
Figure 427735DEST_PATH_IMAGE004
The shortest distance value of. Node point
Figure 893351DEST_PATH_IMAGE004
A schematic diagram of a multi-level neighbor node of (2) is shown.
The method integrates the length of the strokes and various centrality indexes and combines a Newton gravity formula to calculate the importance of the nodes of the dual graph, thereby considering the geometrical and topological properties of the strokes, the shortest distance between the nodes and the influence of multi-level neighbor nodes, and being capable of evaluating the importance of the strokes more accurately and comprehensively.
1.4 automatic road selection process
Mapping the road network into a dual graph by taking strokes as a unit; obtaining the importance sequence of strokes by using the method provided by 1.3, selecting the roads with the front rank according to the selection proportion to form a road network; and finally, maintaining the global connectivity through a connectivity maintaining algorithm. The specific process of road selection is as follows:
(1) and generating a stroke network by using the original road network.
(2) Mapping the stroke network into a dual graph defined as
Figure 152294DEST_PATH_IMAGE041
，
Figure 906624DEST_PATH_IMAGE002
The number of nodes in the network is represented,
Figure 643636DEST_PATH_IMAGE003
representing the number of edges in the network.
(3) The centrality measure values for all nodes in the dual graph are calculated from the 1.2.2 integrated polynomial indices.
(4) For any node in the network
Figure 167021DEST_PATH_IMAGE004
Obtaining a node
Figure 596865DEST_PATH_IMAGE004
And it
Figure 884496DEST_PATH_IMAGE042
The level neighbor nodes regard the centrality measurement values thereof as quality; simultaneous acquisition node
Figure 425199DEST_PATH_IMAGE004
And it
Figure 130986DEST_PATH_IMAGE042
The shortest path of each node in the level neighbor node set is regarded as a distance; solving for nodes according to 1.3
Figure 466153DEST_PATH_IMAGE004
The importance of (c). And repeating the execution until the importance of all the nodes is solved.
(5) According to the selection proportion, the number of strokes to be selected is calculated
Figure 195074DEST_PATH_IMAGE043
。
(6) Selecting from big to small according to the importance of the nodes of the dual graph
Figure 273889DEST_PATH_IMAGE043
A node and its connecting edge
Figure 584916DEST_PATH_IMAGE044
Forming a network
Figure 90983DEST_PATH_IMAGE045
。
(7) According to the method for maintaining connectivity of 1.5, maintaining
Figure 307201DEST_PATH_IMAGE046
All-around communication.
(8) Will be provided with
Figure 189706DEST_PATH_IMAGE047
Mapping to a road network.
1.5 connectivity maintenance
The strokes selected according to the importance ranking may form several parts that are not connected to each other, and thus the global connectivity of the network cannot be maintained. Some lower-ranked roads connecting these disconnected parts should also be reserved. The following conditions should be followed in connection with connectivity maintenance:
Figure 542190DEST_PATH_IMAGE048
adding as few new nodes as possible.
Figure 547055DEST_PATH_IMAGE049
And preferentially adding the nodes with higher importance. Thus, first a road network dual graph is defined
Figure 984990DEST_PATH_IMAGE050
Middle edge
Figure 405607DEST_PATH_IMAGE003
Then, the edge weight and the maximum spanning tree are obtained for maintaining the connectivity.
1.5.1 redefinition of dual graph edge weights
The importance of each edge in an unweighted complex network is different and is influenced by the importance of the two nodes that make up it. So if the importance of two nodes is higher in the dual graph, the importance of the edge they compose is also higher. Therefore, the edge weights are defined as follows:
Figure 878177DEST_PATH_IMAGE051
　 　(8)
wherein the content of the first and second substances,
Figure 726047DEST_PATH_IMAGE052
representing nodes
Figure 959932DEST_PATH_IMAGE004
And
Figure 184240DEST_PATH_IMAGE008
the weight of the constituent edges is such that,
Figure 511316DEST_PATH_IMAGE053
and
Figure 795667DEST_PATH_IMAGE054
respectively represent the nodes obtained in 3.3
Figure 208194DEST_PATH_IMAGE004
And
Figure 32931DEST_PATH_IMAGE008
the importance of (c). Thus, the dual graph is constructed as a weighted graph.
1.5.2 connectivity maintenance Algorithm
Taking fig. 3 as an example, fig. 3 (a) shows a dual graph of an original road network, black nodes show selected strokes with higher importance, white nodes show other strokes in the original road network, and it can be seen from the graph that the network formed by the selected strokes does not maintain connectivity. The method orders the edges of the network from big to small according to the weight, and then calculates the edge weight and the maximum spanning tree. The specific steps of the connectivity maintaining method are as follows:
(1) Calculating original road network dual graph according to 1.5.1
Figure 214513DEST_PATH_IMAGE050
Edge of (1)
Figure 669765DEST_PATH_IMAGE003
The weight of (c).
(2) Building spanning trees
Figure 569588DEST_PATH_IMAGE055
At the beginning of
Figure 135699DEST_PATH_IMAGE056
，
Figure 171788DEST_PATH_IMAGE057
And = 0. Will be provided with
Figure 610991DEST_PATH_IMAGE058
Each node in (a) is considered as an independent tree.
(3) According to the weight value from large to small
Figure 998110DEST_PATH_IMAGE003
In the selection edge is added to
Figure 102332DEST_PATH_IMAGE057
The two vertices of the selected edge should belong to different trees and the two trees are merged into one tree.
(4) Repeating the step (3) until all nodes are in the same tree, and obtaining the result as shown in fig. 3 (b).
(5) Judgment of
Figure 258507DEST_PATH_IMAGE058
Whether the leaf node in (1) belongs to the selected node set
Figure 55561DEST_PATH_IMAGE043
And if not, deleting the data. This step is performed iteratively until
Figure 726714DEST_PATH_IMAGE058
There are no leaf nodes in (fig. 3 (c)).
（6）
Figure 900207DEST_PATH_IMAGE058
The remaining nodes in the list are strokes selected after the connectivity is maintained (fig. 3 (d)).
2 experiments and analysis
The experimental data is a generalized road network topological graph of a Lanzhou city customs area, as shown in FIG. 4. The reason for selecting the data is that roads in the area are closely connected, the road network is complex, and relatively obvious relative density distribution difference exists, so that the selection effect can be effectively reflected. The deflection angle threshold value was set to 45。And generating the strokes, and then mapping the strokes network into a dual graph. The generated dual graph contains 420 nodes, 1941 edges, a network diameter of 11, and an average shortest path of 4.601. Weights of four indexes
Figure 910888DEST_PATH_IMAGE029
、
Figure 878844DEST_PATH_IMAGE030
、
Figure 974976DEST_PATH_IMAGE031
、
Figure 421001DEST_PATH_IMAGE032
The calculation results of (a) were 0.196, 0.150, 0.447, 0.207.
(1) According to the present technique, the importance of each node is from 0 to
Figure 863352DEST_PATH_IMAGE042
Level neighbor node, if
Figure 2210DEST_PATH_IMAGE042
Too large a value will affect the calculation efficiency of the method, and quick selection cannot be realized. If it is not
Figure 585638DEST_PATH_IMAGE042
The accuracy of an evaluation result can be influenced by excessively small values. Therefore, the text tests the series of different neighbor nodes
Figure 835353DEST_PATH_IMAGE042
Impact on click importance ranking. Due to the fact that
Figure 820627DEST_PATH_IMAGE042
The value of (A) is valid only within the range of the network diameter, so that only the value of (B) is valid
Figure 192702DEST_PATH_IMAGE059
Tests were carried out. Five strokes of different grades were selected as test subjects, and the results are shown in fig. 5. It can be seen that when
Figure 263427DEST_PATH_IMAGE042
When the size is small, the importance ordering of strokes greatly fluctuates; when in use
Figure 316833DEST_PATH_IMAGE042
Greater than average shortest path
Figure 156613DEST_PATH_IMAGE060
4.601, strokes are no longer ordered in their importance
Figure 637273DEST_PATH_IMAGE042
Change, so in this experiment
Figure 929714DEST_PATH_IMAGE061
5. This reveals an important kinetic phenomenon: when in use
Figure 599861DEST_PATH_IMAGE042
Greater than the average path length
Figure 294148DEST_PATH_IMAGE019
At the same time, the relative importance between strokes does not change, which means that consistent selection results can be obtained by the present technique.
(2) In order to test the effectiveness and the applicability of the technology, the roads networks are constructed by selecting strokes with different proportions according to the proportion of the total number of the strokes, and visual comparison is carried out, wherein the selection effect of 10%, 20%, 30% and 40% of the selection proportions is shown in fig. 6. It can be seen that:
Figure 945709DEST_PATH_IMAGE048
On the premise of not using semantic information, the coverage range and the whole structure of the original road network are maintained under different selection proportions;
Figure 991025DEST_PATH_IMAGE049
along with the increase of the selection proportion, the detail degree of the road network is increased step by step, and certain hierarchy is embodied;
Figure 386235DEST_PATH_IMAGE062
the road networks under different selection proportions all reflect the density distribution characteristics of the original road networks.
(3) Further taking 30% as an example, inviting professional cartographers to select according to the traditional road selection rule, namely keeping the characteristics of the road network plane graph, keeping the connection between the road and the residential area and ensuring the selection of important roads. The result pairs of manual and automatic selection are shown in fig. 7. It can be seen that: the results of the two selection methods are integrally consistent; due to the effect of the "edge effect", the centrality values of the high-ranked roads located at the edge of the road network are low, so that they are not preferentially retained.
The maximum similarity and its related parameters are used to quantitatively analyze the two selection modes. The comparative statistical results are shown in table 1, and it can be seen that the selection effect of the technology has higher consistency with manual selection.
TABLE 1 maximum similarity and associated parameter statistics
Figure 935028DEST_PATH_IMAGE064
Drawings
FIG. 1: two expression modes of road network
FIG. 2: node point
Figure 85386DEST_PATH_IMAGE065
Multi-level neighbor node of
FIG. 3: selected strokes connectivity preservation
FIG. 4: lanzhou city gate area generalized road network
FIG. 5: stroke ordering (rank) for different range neighbor node sets (range)
FIG. 6: selection effect graph based on stroke quantity percentage
FIG. 7: and comparing the automatic selection result with the manual selection result.

Claims (1)

1. Road automatic selection technology based on gravitational field theory
A road automatic selection technology for considering influence of multi-level neighbor nodes of a road network dual graph by utilizing a gravitational field theory is characterized in that: firstly, generating a road network dual graph by means of strokes; then, calculating the centrality measurement value of the nodes by adopting a centrality method, regarding the centrality measurement value as mass, regarding the shortest distance between the nodes as distance, evaluating the importance of strokes by utilizing a Newton gravity formula, and constructing a road network by selecting the strokes with the importance ranking in the front; and finally, connectivity maintenance is carried out by defining the weight of the edge in the dual graph of the selected road network, so that automatic road selection is realized.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113096396A (en) * 2021-03-31 2021-07-09 电子科技大学 Path selection method based on gravitational field theory
CN113408089A (en) * 2021-05-31 2021-09-17 上海师范大学 Cluster influence modeling method based on gravitational field idea and storage medium

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004101447A (en) * 2002-09-11 2004-04-02 Ncm:Kk Optimum route searching method and apparatus
WO2012157343A1 (en) * 2011-05-13 2012-11-22 財団法人電力中央研究所 Position analysis program, position analysis device, and position analysis method
CN109711425A (en) * 2018-11-15 2019-05-03 南京图申图信息科技有限公司 Urban road skeleton hierarchical division method and system
CN109740289A (en) * 2019-01-28 2019-05-10 首都师范大学 A kind of road network choosing method of integrated line, surface model

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004101447A (en) * 2002-09-11 2004-04-02 Ncm:Kk Optimum route searching method and apparatus
WO2012157343A1 (en) * 2011-05-13 2012-11-22 財団法人電力中央研究所 Position analysis program, position analysis device, and position analysis method
CN109711425A (en) * 2018-11-15 2019-05-03 南京图申图信息科技有限公司 Urban road skeleton hierarchical division method and system
CN109740289A (en) * 2019-01-28 2019-05-10 首都师范大学 A kind of road network choosing method of integrated line, surface model

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
NAMTIRTHA, A: "Weighted kshell degree neighborhood method: an approach independent of completeness of global network structure for identifying the influential spreaders", 《INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS AND NETWORKS (COMSNETS)》, pages 81 - 88 *
RESTREPO, JG: "Characterizing the dynamical importance of network nodes and links", 《PHYSICAL REVIEW LETTERS》, vol. 97, no. 9 *
廖薇薇;何家律;李秋萍;喻言;谭敏;吴仪;: "城市道路结构等级与基于手机定位数据的交通流量匹配度分析", 地理与地理信息科学, no. 02 *
张喜平;李永树;刘刚;王蕾;: "城市复杂交通网络道路重要性评估方法", 复杂系统与复杂性科学, no. 03, pages 7 - 13 *
徐智邦;王中辉;闫浩文;武芳;段晓旗;孙立;: "结合POI数据的道路自动选取方法", 地球信息科学学报, no. 02 *
王庆国;: "城市道路重要性评价方法的综合分析", 测绘通报, no. 08 *
韩远等: "结合引力场理论的道路自动选取方法_韩远", 《测绘科学》, vol. 46, no. 1, pages 189 - 195 *
马超;孙群;陈换新;徐青;温伯威;: "加权网页排序算法在道路网自动选取中的应用", 武汉大学学报(信息科学版), no. 08 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113096396A (en) * 2021-03-31 2021-07-09 电子科技大学 Path selection method based on gravitational field theory
CN113408089A (en) * 2021-05-31 2021-09-17 上海师范大学 Cluster influence modeling method based on gravitational field idea and storage medium
CN113408089B (en) * 2021-05-31 2023-09-26 上海师范大学 Inter-cluster influence modeling method based on gravitational field idea and storage medium

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