CN114757004A - Urban rail transit network planning method, system and storage medium - Google Patents
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Abstract
The application relates to a method, a system and a storage medium for planning an urban rail transit network, which relate to the technical field of traffic planning, and the method for planning the urban rail transit network comprises the following steps: acquiring an analysis unit and trip distribution information corresponding to the analysis unit; matching the trip distribution information corresponding to the analysis unit and the analysis unit to the same coordinate system; constructing a network topology model with travel degrees as weights among the analysis units; dividing the network topology model into a plurality of clustering groups through a non-overlapping community discovery algorithm; calculating a cluster center of the cluster; connecting the cluster centers of the clustering clusters to obtain a track network path set; and selecting the optimal planned path from the track network path set. The method and the device have the effect of planning the urban rail transit network.
Description
Technical Field
The present application relates to the field of traffic planning technologies, and in particular, to a method, a system, and a storage medium for planning an urban rail transit network.
Background
Urban rail transit is a vehicle transportation system which adopts a rail structure for bearing and guiding, and is a public transportation mode of conveying passenger flow of a considerable scale in a train or single vehicle mode by arranging a fully-closed or partially-closed special rail line according to the requirements of the overall planning of urban traffic.
At present, the research on rail transit network planning methods at home and abroad is usually based on passenger flow simulation, and the method can be further subdivided into a mathematical analysis method and a traffic model method. The difference is that the traffic model method generally refers to calculating the rail passenger flow by using a four-stage method, and the analytic rule adopts other forms of mathematical models to solve the rail passenger flow.
The above prior art solutions have the following drawbacks: the analytic method is complex in model implementation, so that the analytic method does not obtain an evidence opportunity with guiding significance, and the traffic model method is also required to perform certain qualitative aggregation on channels when in use, so that the analytic method is often used for scheme evaluation, and a method for helping to evolve urban rail transit network planning is urgently needed to be designed.
Disclosure of Invention
The method, the system and the storage medium for planning the urban rail transit network have the characteristic of being capable of planning the urban rail transit network.
The above object of the present application is achieved by the following technical solutions:
a method for planning an urban rail transit network comprises the following steps:
acquiring an analysis unit and trip distribution information corresponding to the analysis unit;
matching the trip distribution information corresponding to the analysis unit and the analysis unit to the same coordinate system;
constructing a network topology model with travel degrees as weights among the analysis units;
dividing the network topology model into a plurality of clustering groups through a non-overlapping community discovery algorithm;
calculating a cluster center of the cluster;
connecting the cluster centers of the clustering clusters to obtain a track network path set;
and selecting the optimal planned path from the track network path set.
By adopting the technical scheme, the network topology model is constructed according to the analysis unit and the trip distribution information corresponding to the analysis unit, the network topology model is divided into a plurality of cluster groups by using a non-overlapping community discovery algorithm, each cluster group consists of more than one associated analysis unit, the group center of the cluster group is calculated, the group centers of the cluster groups are connected to form a track network path set, and then the optimal planning path is selected from the track network path set, so that the planning of the urban track traffic network is realized.
Optionally, the non-overlapping community discovery algorithm is a Louvain algorithm.
Optionally, the calculating a cluster group in the network topology model through a non-overlapping community discovery algorithm includes:
s1, taking each analysis unit of the network topology model as a first community;
s2, repartitioning the analysis units according to a relative gain maximum principle to obtain a plurality of second communities, wherein the second communities comprise more than one analysis unit;
s3, regarding each second community as a node, and subdividing the second communities into third communities according to the principle of maximum relative gain;
and S4, repeating the step S3 until the communities to which all the analysis units belong do not change any more, namely when the modularity does not change any more, stopping iteration, and taking the final community as a cluster group.
By adopting the technical scheme, all the analysis units are divided into a plurality of communities, each community is composed of a plurality of mutually-related and adjacent analysis units, and automatic classification and division of the analysis units are realized.
Optionally, the calculating the cluster center of the cluster group includes:
calculating the travel degree of the analysis units in the clustering group according to the network topology model;
calculating the accumulated travel degree of the analysis unit according to the travel degree of the analysis unit;
and taking the analysis unit corresponding to the maximum accumulated travel degree in the clustering group as the group center of the clustering group.
By adopting the technical scheme, the group center of the clustering group is calculated, so that the urban rail path can be conveniently planned.
Optionally, the obtaining an optimal planned path from the track network path set includes:
calculating the comprehensive benefit of each path in the track network path set:
L(i,j)＝a×Lw(i,j)+b×Lw(i,j)/l(i,j): i, j belongs to N and i is not equal to j, wherein L(i,j)For the sum total of the benefits of the paths, Lw(i，j)Is the accumulated travel degree, l, of any path between the cluster centers i and j(i，j)The length of any path between group centers i and j is defined, N is the number of all analysis units between i and j, and a and b are weight coefficients;
and selecting to obtain an optimal planning path according to the comprehensive benefits of each path in the track network path set.
By adopting the technical scheme, the proper path is selected from the track network path set to serve as the optimal planning path.
Optionally, the path corresponding to the maximum comprehensive benefit is selected as the optimal planning path.
By adopting the technical scheme, the path corresponding to the maximum comprehensive benefit is used as the optimal planning path, and the travel requirement of the personnel can be met to the greatest extent.
Optionally, before calculating the comprehensive benefit of each path in the track network path set, the method further includes selecting a width of a search range of paths between centers of the group.
By adopting the technical scheme, the calculation complexity of the path comprehensive benefits is reduced, and the calculation efficiency of the path comprehensive benefits is improved.
The second purpose of the application is to provide an urban rail transit network planning system which has the characteristic of being capable of planning an urban rail transit network.
The second application object of the present application is achieved by the following technical scheme:
an urban rail transit network planning system comprises,
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring an analysis unit and trip distribution information corresponding to the analysis unit;
the matching module is used for matching the trip distribution information corresponding to the analysis unit and the analysis unit to the same coordinate system;
the model construction module is used for constructing a network topology model with travel degrees as weights among the analysis units;
the cluster grouping module is used for dividing the network topology model into a plurality of cluster groups through a non-overlapping community discovery algorithm;
the group center calculating module is used for calculating the group center of the clustering group;
the group center connecting module is used for connecting the group centers of the clustering groups to obtain a track network path set; and the path selection module is used for selecting the optimal planning path from the track network path set.
By adopting the technical scheme, the network topology model is constructed according to the analysis unit and the trip distribution information corresponding to the analysis unit, the network topology model is divided into a plurality of cluster groups by using a non-overlapping community discovery algorithm, each cluster group consists of more than one associated analysis unit, the group center of the cluster group is calculated, the group centers of the cluster groups are connected to form a track network path set, and then the optimal planning path is selected from the track network path set, so that the planning of the urban track traffic network is realized.
The third purpose of the present application is to provide a computer storage medium, which can store corresponding programs and has the characteristics of being convenient for realizing the method for planning the urban rail transit network.
The third application purpose of the present application is achieved through the following technical scheme:
a computer readable storage medium storing a computer program that can be loaded by a processor and executed to perform a method as any one of the above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the method comprises the steps of constructing a network topology model according to an analysis unit and trip distribution information corresponding to the analysis unit, dividing the network topology model into a plurality of cluster groups by using a non-overlapping community discovery algorithm, wherein each cluster group consists of more than one associated analysis unit, calculating group center of the cluster groups, connecting the group centers of the cluster groups to form a track network path set, and selecting an optimal planning path from the track network path set to realize the planning of the urban track traffic network;
2. the group center of the clustering group is calculated, and the urban rail path can be conveniently planned.
Drawings
Fig. 1 is a flowchart of a method for planning an urban rail transit network according to an embodiment of the present application.
Fig. 2 is a flowchart of calculating an accumulated travel degree of an analysis unit according to an embodiment of the present application.
Fig. 3a is a relationship diagram of the group accumulated travel degree in the service range of 800 meters of the track and the unremoved extremum of the passenger volume on the track in the group according to the embodiment of the present application.
Fig. 3b is a relationship diagram of the group cumulative travel degree in the service range of 800 meters of the track and the extreme value of the passenger capacity removal on the track in the group according to the embodiment of the present application.
Fig. 4 is a structural framework diagram of an urban rail transit network planning system according to an embodiment of the present application.
Detailed Description
The present application is described in further detail below with reference to the attached drawings.
The present embodiment is only for explaining the present application, and it is not limited to the present application, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present application.
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter associated objects are in an "or" relationship, unless otherwise specified.
The embodiments of the present application will be described in further detail with reference to the drawings attached hereto.
The embodiment of the application provides an urban rail transit network planning method, and the main flow of the method is described as follows.
As shown in fig. 1:
step 110: and acquiring an analysis unit and trip distribution information corresponding to the analysis unit.
The analysis unit refers to a minimum aggregation unit for analyzing the spatial travel characteristics, and may be a square grid, a traffic cell, a Thiessen polygon, or the like. The analysis unit determined by using the square grid method means that the selected range area is divided into a plurality of squares by using an a × a square grid, where a denotes a length, which may be 500m, and a minimum square is an analysis unit. The traffic district is used as an abstract space unit for analyzing travel and flow in the traffic model, and is the basis for establishing the traffic model. The Thiessen polygons are a group of continuous polygons formed by perpendicular bisectors connecting two adjacent point line segments, and the distance from any point in one Thiessen polygon to the control point forming the polygon is less than the distance from any point in the Thiessen polygon to the control points of other polygons. The travel distribution information refers to a set of all travel personnel in a certain area from one place to another place, and the travel distribution information is obtained by survey statistics and then is stored in a database. Travel distribution information corresponding to an analysis unit refers to a set of all traveling persons in the area of the analysis unit from one location to another.
Step 120: and matching the trip distribution information corresponding to the analysis unit and the analysis unit to the same coordinate system.
Step 130: and constructing a network topology model with travel degrees as weights among the analysis units.
Whether the analysis units are related or not is judged according to the trip distribution information corresponding to the analysis units, for example, if a trip person goes to the analysis unit 2 in the area of the analysis unit 1, that is, trip exchange exists between the analysis units 1 and 2, the analysis units 1 and 2 are related together, that is, the center of the analysis unit 1 and the center of the analysis unit 2 are connected together through a line, and finally, a network topology model among all the analysis units is obtained, as shown in fig. 2, the weight of the edge of the network topology model is the trip exchange amount among the analysis units. The circle in the analysis unit in fig. 2 represents the center of the analysis unit, where the center of the analysis unit may be the centroid or a location where the concentration of people within the analysis unit is high.
Step 140: and dividing the network topology model into a plurality of clustering groups through a non-overlapping community discovery algorithm.
The non-overlapping community discovery algorithm may be a Louvain algorithm or a GN algorithm.
Specifically, step 140 includes:
step 141, regarding each analysis unit of the network topology model as a first community;
step 142, repartitioning the analysis units according to a principle of maximum relative gain to obtain a plurality of second communities, where the second communities include more than one analysis unit, as shown in the following formula:
wherein k isi,inRepresents the sum of the edge weights between all the nodes in the clique into which nodes i and i are to be moved, m represents the number of nodes in the clique, i.e. the number of analysis centers in the clique, ∑totRepresents the sum of the weights of all nodes moved into the group of cliques, kiRepresenting the total weight of the move-in node i.
And 143, regarding each second community as a node, and subdividing the second communities into third communities according to the principle of maximum relative gain.
Step 144, stopping iteration until the communities of all the analysis units do not change any more, that is, the modularity does not change any more, and using the final communities as cluster groups, as follows:
wherein A isijRepresents the edge weight, k, between node i and node jiRepresenting the sum of all the edge weights connected to node i, ciGroup number, σ (c), representing node ii,cj) Indicating that if the node i and the node j are in the same group graph, 1 is returned, otherwise 0 is returned.
Step 150: and calculating the cluster center of the cluster.
Specifically, step 150 includes:
and 151, calculating the travel degree of the analysis units in the clustering group according to the network topology model.
The travel degree reflects information of both the external exchange quantity of the analysis unit and the radiation range of the external connection. In fig. 2, the weighted degree of a point refers to the sum of weights of edges connected to the point, the degree of a point refers to the sum of numbers of edges connected to the point, and the travel degree refers to the weighted sum of the degree calculated based on the travel distribution and the weighted degree thereof, as follows:
tgi＝α*dgi+β*wdgi；
tgirepresents the degree of travel, dg, of node iiDegree of node i, wdgiAnd representing the weighting degree of the node i, wherein alpha and beta are both hyper-parameters and take values of 0-1, the alpha and the beta can be calibrated by using a grid search method, and the alpha is 0.65 and the beta is 0.35.
From this, it can be seen that the trip degrees of the nodes (analysis units) in fig. 2 are as follows:
w1＝w12+w19w1 indicates the travel degree of analysis unit 1;
w2＝w12+w25+w26w2 indicates the travel degree of analysis unit 2;
w5＝w25+w56w5 indicates the travel degree of the analysis unit 5;
w6＝w26+w56+w69w6 indicates the travel degree of analysis unit 6;
w9＝w19+w69and w9 denotes the travel degree of the analysis unit 9.
And 152, calculating the accumulated travel degree of the analysis unit according to the travel degree of the analysis unit.
The accumulated trip degree of the analysis unit is the sum of trip degrees of all analysis units covered by a circle drawn by taking the center of the analysis unit as an origin and taking R as a radius, wherein R can be taken according to specific situations, and in the embodiment, R is introduced by taking 800 meters as an example. Referring to fig. 2, taking the accumulated trip degree of the analysis unit 5 as an example for description, the accumulated trip degree of the analysis unit 5 is:
cw5＝w2+w5+w6；
in the formula, cw5 is the cumulative travel degree of analysis section 5, w2 is the travel degree of analysis section 2, w5 is the travel degree of analysis section 5, and w6 is the travel degree of analysis section 6.
The method is based on the fact that the passenger flow of the tracks in the cluster group is obviously related to the accumulated travel degree of the track path, the phenomenon is verified in Ningbo city, and the correlation coefficient of the passenger flow and the accumulated travel degree reaches 0.76 as shown in figure 3. The travel degree can effectively reflect the source of the rail passenger flow in the group, and the travel degree can reflect two types of passenger flow information served by a rail network path, namely direct passenger flow of a single line and transfer passenger flow between multiple lines.
Step 153, using the analysis unit corresponding to the largest accumulated travel degree in the cluster group as the group center of the cluster group.
And comparing the accumulated travel degrees of all the analysis units in the clustering group, and taking the analysis unit with the largest accumulated travel degree as the group center of the clustering group so as to determine the group centers of all the clustering groups.
Step 160: and connecting the group centers of the clustering groups to obtain a track network path set.
Wherein, the cluster centers of all the clustering clusters are connected to obtain a set of a plurality of orbit networks.
Step 170: and selecting the optimal planned path from the track network path set.
Step 170 comprises:
step 171: calculating the comprehensive benefit of each path in the track network path set,
L(i,j)＝a×Lw(i,j)+b×Lw(i,j)/l(i,j): i, j ∈ N and i ≠ j,
wherein L is(i,j)For the sum total of the benefits of the paths, Lw(i，j)Is the accumulated travel degree, l, of any path between the cluster centers i and j(i，j)The length of any path between the cluster centers i and j is shown, N is the number of all analysis units between i and j, a and b are both weight coefficients, a and b can be selected according to actual conditions, and a and b in the embodiment are both 0.5.
Step 172: and selecting to obtain an optimal planning path according to the comprehensive benefits of all paths in the track network path set.
The path corresponding to the maximum comprehensive benefit can be selected as the optimal planning path, the path corresponding to the moderate comprehensive benefit can also be selected as the optimal planning path, and the path corresponding to the maximum comprehensive benefit can be selected as the optimal planning path according to specific situations.
In addition, before the calculating the comprehensive benefit of each path in the track network path set, the method further includes selecting a width of a path search range between group centers, where the width of the path search range between the group centers selected in this embodiment is 2km, that is, before calculating the comprehensive benefit of each path in the track network path set between the group centers i and j, first connecting the group centers i and j, then shifting the connecting line between the group centers i and j to both sides by 2km to form a target calculation area, removing track network paths outside the target calculation area, and only calculating track network paths in the target calculation area.
Referring to fig. 4, a structural framework diagram of an urban rail transit network planning system according to an embodiment of the present application is shown, where the urban rail transit network planning system includes an obtaining module, a matching module, a model building module, a cluster grouping dividing module, a cluster center calculating module, a cluster center connecting module, and a path selecting module.
The system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring an analysis unit and trip distribution information corresponding to the analysis unit;
the matching module is used for matching the trip distribution information corresponding to the analysis unit and the analysis unit to the same coordinate system;
the model construction module is used for constructing a network topology model with travel degrees as weights among the analysis units;
the cluster grouping module is used for dividing the network topology model into a plurality of cluster groupings through a non-overlapping community discovery algorithm, and each cluster grouping comprises at least one analysis unit;
the cluster center calculating module is used for calculating the cluster center of the clustering cluster;
the cluster center connecting module is used for connecting the cluster centers of the clustering clusters to obtain a track network path set; and the path selection module is used for selecting the optimal planning path from the track network path set.
In one embodiment, the cluster grouping module comprises:
the initialization submodule is used for taking each analysis unit of the network topology model as a first community;
the first dividing module is used for dividing the analysis units again according to the principle of maximum relative gain to obtain a second community, and the second community comprises more than one analysis unit;
the second division submodule is used for taking the second community as a node and obtaining a third community by re-division; and
and the output sub-module is used for outputting the final community as the cluster group when the communities of all the analysis units do not change any more.
In one embodiment, the cluster center calculation module includes:
the travel degree calculation operator module is used for calculating the travel degree of the analysis units in the clustering group according to the network topology model;
the accumulated travel degree calculation operator module is used for calculating the accumulated travel degree of the analysis unit according to the travel degree of the analysis unit; and
and the cluster center output sub-module is used for taking the analysis unit corresponding to the maximum accumulated travel degree in the cluster as the cluster center of the cluster.
In one embodiment, the path selection module comprises:
the comprehensive benefit calculation submodule is used for calculating the comprehensive benefit of each path in the track network path set; and
and the path selection submodule is used for selecting and obtaining an optimal planning path according to the comprehensive benefits of all paths in the track network path set, and the path corresponding to the maximum comprehensive benefit is selected as the optimal planning path in the embodiment.
The embodiment of the application also discloses a computer readable storage medium, which stores a computer program capable of being loaded by a processor and executing the method.
It can be clearly understood by those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the foregoing function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above-described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: u disk, removable hard disk, read only memory, random access memory, magnetic disk or optical disk, etc. for storing program codes.
The above embodiments are only used to describe the technical solutions of the present application in detail, but the above embodiments are only used to help understanding the method and the core idea of the present invention, and should not be construed as limiting the present invention. Those skilled in the art should also appreciate that various modifications and substitutions can be easily made without departing from the scope of the present invention.
Claims (9)
1. A method for planning an urban rail transit network is characterized by comprising the following steps:
acquiring an analysis unit and trip distribution information corresponding to the analysis unit;
matching the trip distribution information corresponding to the analysis unit and the analysis unit to the same coordinate system;
constructing a network topology model with travel degrees as weights among the analysis units;
dividing the network topology model into a plurality of clustering groups through a non-overlapping community discovery algorithm;
calculating a cluster center of the cluster;
connecting the cluster centers of the clustering clusters to obtain a track network path set;
and selecting the optimal planned path from the track network path set.
2. The method of claim 1, wherein the non-overlapping community discovery algorithm is a Louvain algorithm.
3. The method of claim 2, wherein computing cluster groups in the network topology model by a non-overlapping community discovery algorithm comprises:
s1, taking each analysis unit of the network topology model as a first community;
s2, repartitioning the analysis units according to a relative gain maximum principle to obtain a plurality of second communities, wherein the second communities comprise more than one analysis unit;
s3, regarding each second community as a node, and subdividing the second communities into third communities according to the principle of maximum relative gain;
and S4, repeating the step S3 until the communities to which all the analysis units belong do not change any more, namely when the modularity does not change any more, stopping iteration, and taking the final community as a cluster group.
4. The method of claim 1, wherein said calculating a cluster center for the clustered cluster comprises:
calculating the travel degree of the analysis units in the clustering group according to the network topology model;
calculating the accumulated travel degree of the analysis unit according to the travel degree of the analysis unit;
and taking the analysis unit corresponding to the maximum accumulated travel degree in the clustering group as the group center of the clustering group.
5. The method of claim 4, wherein the obtaining an optimal planned path from the set of orbital network paths comprises:
calculating the comprehensive benefit of each path in the track network path set:
L(i,j)＝a×Lw(i,j)+b×Lw(i,j)/l(i,j): i, j belongs to N and i is not equal to j, wherein L(i,j)For the sum total of the benefits of the paths, Lw(i，j)Cumulative travel degree, l, for any path between cluster centers i and j(i，j)The length of any path between group centers i and j is shown, N is the number of all analysis units between i and j, and a and b are weight coefficients;
and selecting to obtain an optimal planning path according to the comprehensive benefits of all paths in the track network path set.
6. The method of claim 5, wherein the path corresponding to the maximum overall benefit is selected as the optimal planned path.
7. The method according to claim 5 or 6, wherein before calculating the combined benefit of each path in the track network path set, further comprising selecting a width of a search range of paths between groups.
8. An urban rail transit network planning system is characterized by comprising,
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring an analysis unit and trip distribution information corresponding to the analysis unit;
the matching module is used for matching the trip distribution information corresponding to the analysis unit and the analysis unit to the same coordinate system;
the model construction module is used for constructing a network topology model with travel degrees as weights among the analysis units;
the cluster grouping module is used for dividing the network topology model into a plurality of cluster groups through a non-overlapping community discovery algorithm;
the cluster center calculating module is used for calculating the cluster center of the clustering cluster;
the cluster center connecting module is used for connecting the cluster centers of the clustering clusters to obtain a track network path set; and the number of the first and second groups,
and the path selection module is used for selecting the optimal planning path from the track network path set.
9. A computer-readable storage medium, in which a computer program is stored which can be loaded by a processor and which executes the method of any one of claims 1 to 7.
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| CN202210344302.5A CN114757004A (en) | 2022-04-02 | 2022-04-02 | Urban rail transit network planning method, system and storage medium |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107025518A (en) * | 2017-03-20 | 2017-08-08 | 中国电力科学研究院 | A kind of electric automobile charging station method and device for planning |
| CN109005108A (en) * | 2018-06-29 | 2018-12-14 | 全球能源互联网研究院有限公司 | A kind of spatial network route planning method and system |
| CN113516277A (en) * | 2021-04-13 | 2021-10-19 | 南京大学 | Network connection intelligent traffic path planning method based on dynamic pricing of road network |
| CN113849942A (en) * | 2021-09-11 | 2021-12-28 | 宁波市规划设计研究院 | Public transport network analysis method, system, storage medium and SP-Space model based on complex network |
| CN113987103A (en) * | 2021-12-27 | 2022-01-28 | 广州市交通规划研究院 | Combined travel path searching method and system for urban comprehensive transportation network |
| CN113987729A (en) * | 2021-12-28 | 2022-01-28 | 广州市交通规划研究院 | Large-scale urban rail network automatic generation method based on land utilization |
-
2022
- 2022-04-02 CN CN202210344302.5A patent/CN114757004A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107025518A (en) * | 2017-03-20 | 2017-08-08 | 中国电力科学研究院 | A kind of electric automobile charging station method and device for planning |
| CN109005108A (en) * | 2018-06-29 | 2018-12-14 | 全球能源互联网研究院有限公司 | A kind of spatial network route planning method and system |
| CN113516277A (en) * | 2021-04-13 | 2021-10-19 | 南京大学 | Network connection intelligent traffic path planning method based on dynamic pricing of road network |
| CN113849942A (en) * | 2021-09-11 | 2021-12-28 | 宁波市规划设计研究院 | Public transport network analysis method, system, storage medium and SP-Space model based on complex network |
| CN113987103A (en) * | 2021-12-27 | 2022-01-28 | 广州市交通规划研究院 | Combined travel path searching method and system for urban comprehensive transportation network |
| CN113987729A (en) * | 2021-12-28 | 2022-01-28 | 广州市交通规划研究院 | Large-scale urban rail network automatic generation method based on land utilization |
Non-Patent Citations (1)
| Title |
|---|
| 王欢: "基于网络社团结构的轨道交通线网生成研究", 《中国优秀硕士学位论文库 工程科技II辑》, pages 26 - 27 * |
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