CN111145540A - A discovery method and discovery system for topological connection edges of urban road network - Google Patents
A discovery method and discovery system for topological connection edges of urban road network Download PDFInfo
- Publication number
- CN111145540A CN111145540A CN201911307758.9A CN201911307758A CN111145540A CN 111145540 A CN111145540 A CN 111145540A CN 201911307758 A CN201911307758 A CN 201911307758A CN 111145540 A CN111145540 A CN 111145540A
- Authority
- CN
- China
- Prior art keywords
- track data
- key nodes
- connecting edge
- road network
- urban road
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
Landscapes
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
The discovery method comprises the steps of obtaining key nodes for constructing the urban road network topological graph from a to-be-discovered area and correlation values among the key nodes, comparing each correlation value with a preset correlation value threshold value α, establishing a connection edge between two key nodes with correlation values larger than α, comparing the connection edge with original track data, removing the connection edge without the track data, and fitting the connection edge with the track data into a shape which is the same as or similar to the original track data.
Description
Technical Field
The application belongs to the field of data processing, and particularly relates to a method and a system for discovering topological connecting edges of an urban road network.
Background
At present, along with the development of cities, the number of people and motor vehicles also increases rapidly, the increased people and vehicles increase the pressure on traffic traveling, and the prior art has no technical scheme for effectively solving the traffic traveling pressure. Big data analysis is the research direction that rises up now, can know people's the custom of going out, the flow of people of every street through big data analysis. However, big data analysis is not yet effectively applied to solving the problems of travel.
Disclosure of Invention
The embodiment of the invention mainly aims to provide a method for discovering topological connecting edges of an urban road network.
In a first aspect, a method for discovering a topological connecting edge of an urban road network is provided, which includes:
obtaining key nodes for constructing a topological graph of an urban road network and correlation values among the key nodes from a region to be discovered;
comparing each correlation value with a preset correlation value threshold value α, and establishing a connecting edge between two key nodes of which the correlation values are greater than α;
and comparing the connecting edges with the original track data, removing the connecting edges without the track data, and fitting the connecting edges with the track data into a shape which is the same as or similar to the original track data.
In another possible implementation, the trajectory data includes: sampling point position, sampling time and sampling speed.
In yet another possible implementation, the moving object of the trajectory data includes: human, automotive, non-automotive.
In a second aspect, a system for discovering a topological connecting edge of an urban road network is provided, which includes:
the system comprises an acquisition module, a searching module and a searching module, wherein the acquisition module is used for acquiring key nodes for constructing a topological graph of the urban road network from a region to be discovered and correlation values among the key nodes;
a connecting edge constructing module, configured to compare each correlation value with a preset correlation value threshold α, and establish a connecting edge between two key nodes whose correlation values are greater than α;
and the fitting module is used for comparing the connecting edge with the original track data, removing the connecting edge without the track data, and fitting the connecting edge with the track data into a shape which is the same as or similar to the original track data.
In yet another possible implementation, the trajectory data includes: sampling point position, sampling time and sampling speed.
In yet another possible implementation, the moving object of the trajectory data includes: human, automotive, non-automotive.
The beneficial effect that technical scheme that this application provided brought is: the connecting edge of the topological graph of the urban trajectory data can be conveniently and quickly obtained, and then the topological graph of the urban trajectory data can be quickly drawn.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a flowchart of a method for discovering a topological connecting edge of an urban road network according to an embodiment of the present invention;
fig. 2 is a structural diagram of a system for discovering a topological connecting edge of an urban road network according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar modules or modules having the same or similar functionality throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, modules, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, modules, components, and/or groups thereof. It will be understood that when a module is referred to as being "connected" or "coupled" to another module, it can be directly connected or coupled to the other module or intervening modules may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The technical solutions of the present application and the technical solutions of the present application, for example, to solve the above technical problems, will be described in detail with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Example one
Fig. 1 is a flowchart of a method for discovering a topological connecting edge of an urban road network according to an embodiment of the present invention, which includes:
step S101, obtaining key nodes for constructing the urban road network topological graph and correlation values among the key nodes from the area to be discovered.
In the embodiment of the invention, in the construction process of the urban road network topological graph, the key nodes represent intersections of roads, and the association values represent the association degrees among the key nodes.
Step S102, comparing each correlation value with a preset correlation value threshold α, and establishing a connection edge between two key nodes whose correlation values are greater than α.
In the embodiment of the present invention, the higher the association value is, the more likely the topology structure is formed between the two corresponding key nodes, but it is impossible that all the nodes form the topology, so an association value threshold α is set, the key nodes with the association value greater than α are selected from all the key nodes, and a connecting edge is constructed between the key nodes.
Step S103, comparing the connecting sides with the original track data, removing the connecting sides without the track data, and fitting the connecting sides with the track data into a shape which is the same as or similar to the original track data.
In the embodiment of the invention, the connecting edge is constructed by the relationship of the relevance and has uncertainty, so that the connecting edge is compared with the original track data containing real data, the connecting edge without the track data is removed by comparison, meanwhile, the connecting edge with the track data is compared with the original track data, the connecting edge in a straight line shape is fitted into the shape which is the same as or similar to the track data, and the real connecting edge which can be used for a topological graph is obtained.
And comparing and fitting all the connecting edges in sequence until all the connecting edges are fitted.
It should be noted that the moving objects in the trajectory data include, but are not limited to: human, automotive, non-automotive.
According to the embodiment of the invention, key nodes for constructing the topological graph of the urban road network and the correlation values among the key nodes are obtained from the area to be discovered, each correlation value is compared with a preset correlation value threshold value α, a connecting edge is established between two key nodes with correlation values larger than α, the connecting edge is compared with original track data, the connecting edge without track data is removed, and the connecting edge with track data is fitted into a shape which is the same as or similar to the original track data.
Example two
Fig. 2 is a structural diagram of a system for discovering a topological connecting edge of an urban road network according to an embodiment of the present invention, where the system for discovering includes:
an obtaining module 201, configured to obtain, from a region to be discovered, key nodes for constructing a topological graph of an urban road network and association values between the key nodes.
In the embodiment of the invention, in the construction process of the urban road network topological graph, the key nodes represent intersections of roads, and the association values represent the association degrees among the key nodes.
A connecting edge constructing module 202, configured to compare each correlation value with a preset correlation value threshold α, and establish a connecting edge between two key nodes whose correlation values are greater than α.
In the embodiment of the present invention, the higher the association value is, the more likely the topology structure is formed between the two corresponding key nodes, but it is impossible that all the nodes form the topology, so an association value threshold α is set, the key nodes with the association value greater than α are selected from all the key nodes, and a connecting edge is constructed between the key nodes.
And the fitting module 203 is configured to compare the connection edge with the original trajectory data, remove the connection edge without trajectory data, and fit the connection edge with trajectory data into a shape the same as or similar to the original trajectory data.
In the embodiment of the invention, the connecting edge is constructed by the relationship of the relevance and has uncertainty, so that the connecting edge is compared with the original track data containing real data, the connecting edge without the track data is removed by comparison, meanwhile, the connecting edge with the track data is compared with the original track data, the connecting edge in a straight line shape is fitted into the shape which is the same as or similar to the track data, and the real connecting edge which can be used for a topological graph is obtained.
And comparing and fitting all the connecting edges in sequence until all the connecting edges are fitted.
It should be noted that the moving objects in the trajectory data include, but are not limited to: human, automotive, non-automotive.
According to the embodiment of the invention, key nodes for constructing the topological graph of the urban road network and the correlation values among the key nodes are obtained from the area to be discovered, each correlation value is compared with a preset correlation value threshold value α, a connecting edge is established between two key nodes with correlation values larger than α, the connecting edge is compared with original track data, the connecting edge without track data is removed, and the connecting edge with track data is fitted into a shape which is the same as or similar to the original track data.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A method for discovering topological connecting edges of an urban road network is characterized by comprising the following steps:
obtaining key nodes for constructing a topological graph of an urban road network and correlation values among the key nodes from a region to be discovered;
comparing each correlation value with a preset correlation value threshold value α, and establishing a connecting edge between two key nodes of which the correlation values are greater than α;
and comparing the connecting edges with the original track data, removing the connecting edges without the track data, and fitting the connecting edges with the track data into a shape which is the same as or similar to the original track data.
2. The discovery method of claim 1 wherein said trajectory data comprises: sampling point position, sampling time and sampling speed.
3. The discovery method of claim 1 wherein said moving objects of said trajectory data comprise: human, automotive, non-automotive.
4. A system for discovering topological connecting edges of an urban road network is characterized by comprising:
the system comprises an acquisition module, a searching module and a searching module, wherein the acquisition module is used for acquiring key nodes for constructing a topological graph of the urban road network from a region to be discovered and correlation values among the key nodes;
a connecting edge constructing module, configured to compare each correlation value with a preset correlation value threshold α, and establish a connecting edge between two key nodes whose correlation values are greater than α;
and the fitting module is used for comparing the connecting edge with the original track data, removing the connecting edge without the track data, and fitting the connecting edge with the track data into a shape which is the same as or similar to the original track data.
5. The discovery system of claim 4 wherein said trajectory data comprises: sampling point position, sampling time and sampling speed.
6. The discovery system of claim 4 wherein the moving objects of said trajectory data comprise: human, automotive, non-automotive.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911307758.9A CN111145540B (en) | 2019-12-18 | 2019-12-18 | Method and system for discovering topological connecting edges of urban road network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911307758.9A CN111145540B (en) | 2019-12-18 | 2019-12-18 | Method and system for discovering topological connecting edges of urban road network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111145540A true CN111145540A (en) | 2020-05-12 |
| CN111145540B CN111145540B (en) | 2021-09-03 |
Family
ID=70518865
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911307758.9A Active CN111145540B (en) | 2019-12-18 | 2019-12-18 | Method and system for discovering topological connecting edges of urban road network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111145540B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114399124A (en) * | 2022-03-24 | 2022-04-26 | 腾讯科技（深圳）有限公司 | Path data processing method, path planning method, path data processing device, path planning device and computer equipment |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE69800039D1 (en) * | 1998-03-04 | 1999-12-16 | Hewlett Packard Co | Illustration of a network topology |
| US7003559B1 (en) * | 2000-10-23 | 2006-02-21 | Hewlett-Packard Development Company, L.P. | System and method for determining probable network paths between nodes in a network topology |
| KR100839568B1 (en) * | 2007-10-08 | 2008-06-20 | 대한민국 | Standard manufacturing method and system of numerical clinical |
| CN101639850A (en) * | 2009-06-01 | 2010-02-03 | 北京四维图新科技股份有限公司 | Merging method of road network data and merging device |
| CN106092109A (en) * | 2016-05-30 | 2016-11-09 | 清华大学 | The road of the efficient pathfinding of a kind of electronic chart connects logically method and application |
| CN106323301A (en) * | 2015-06-29 | 2017-01-11 | 北京四维图新科技股份有限公司 | Road information obtaining method and device |
| CN106709126A (en) * | 2016-10-18 | 2017-05-24 | 电子科技大学 | Road network construction model based on urban roads |
| US20180066957A1 (en) * | 2016-09-08 | 2018-03-08 | Here Global B.V. | Method and apparatus for providing trajectory bundles for map data analysis |
| US20180121032A1 (en) * | 2016-10-31 | 2018-05-03 | Appdynamics Llc | Displaying a complex service topology for monitoring |
| CN108763687A (en) * | 2018-05-17 | 2018-11-06 | 重庆大学 | The analysis method of public traffic network topological attribute and space attribute |
| CN109583626A (en) * | 2018-10-30 | 2019-04-05 | 厦门大学 | Road network topology method for reconstructing, medium and system |
| CN110110019A (en) * | 2018-01-31 | 2019-08-09 | 北京四维图新科技股份有限公司 | A kind of method and device that road network topological data updates |
| CN110543728A (en) * | 2019-09-05 | 2019-12-06 | 大连理工大学 | A method for finding key intersections in urban traffic network |
| CN110554688A (en) * | 2018-05-30 | 2019-12-10 | 北京京东尚科信息技术有限公司 | Method and device for generating topological map |
-
2019
- 2019-12-18 CN CN201911307758.9A patent/CN111145540B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE69800039D1 (en) * | 1998-03-04 | 1999-12-16 | Hewlett Packard Co | Illustration of a network topology |
| US7003559B1 (en) * | 2000-10-23 | 2006-02-21 | Hewlett-Packard Development Company, L.P. | System and method for determining probable network paths between nodes in a network topology |
| KR100839568B1 (en) * | 2007-10-08 | 2008-06-20 | 대한민국 | Standard manufacturing method and system of numerical clinical |
| CN101639850A (en) * | 2009-06-01 | 2010-02-03 | 北京四维图新科技股份有限公司 | Merging method of road network data and merging device |
| CN106323301A (en) * | 2015-06-29 | 2017-01-11 | 北京四维图新科技股份有限公司 | Road information obtaining method and device |
| CN106092109A (en) * | 2016-05-30 | 2016-11-09 | 清华大学 | The road of the efficient pathfinding of a kind of electronic chart connects logically method and application |
| US20180066957A1 (en) * | 2016-09-08 | 2018-03-08 | Here Global B.V. | Method and apparatus for providing trajectory bundles for map data analysis |
| CN106709126A (en) * | 2016-10-18 | 2017-05-24 | 电子科技大学 | Road network construction model based on urban roads |
| US20180121032A1 (en) * | 2016-10-31 | 2018-05-03 | Appdynamics Llc | Displaying a complex service topology for monitoring |
| CN110110019A (en) * | 2018-01-31 | 2019-08-09 | 北京四维图新科技股份有限公司 | A kind of method and device that road network topological data updates |
| CN108763687A (en) * | 2018-05-17 | 2018-11-06 | 重庆大学 | The analysis method of public traffic network topological attribute and space attribute |
| CN110554688A (en) * | 2018-05-30 | 2019-12-10 | 北京京东尚科信息技术有限公司 | Method and device for generating topological map |
| CN109583626A (en) * | 2018-10-30 | 2019-04-05 | 厦门大学 | Road network topology method for reconstructing, medium and system |
| CN110543728A (en) * | 2019-09-05 | 2019-12-06 | 大连理工大学 | A method for finding key intersections in urban traffic network |
Non-Patent Citations (3)
| Title |
|---|
| YUAN-KOHUANG，LIEN-FALIN: "Efficient processing of continuous min–max distance bounded query with updates in road networks", 《INFORMATION SCIENCES》 * |
| 刘文文: "高速路网拓扑结构分析与实例验证", 《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》 * |
| 蒋新华等: "基于浮动车数据的道路单向限行状态动态识别", 《计算机应用》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114399124A (en) * | 2022-03-24 | 2022-04-26 | 腾讯科技（深圳）有限公司 | Path data processing method, path planning method, path data processing device, path planning device and computer equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111145540B (en) | 2021-09-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6561199B2 (en) | Urban road recognition method, apparatus, storage medium and equipment based on laser point cloud | |
| CN105258704B (en) | Multiple dimensioned space-time hotspot path detection method based on through street net modeling | |
| CN108549378A (en) | A kind of mixed path method and system for planning based on grating map | |
| Davies et al. | Scalable, distributed, real-time map generation | |
| CN112325892B (en) | A 3D-like Path Planning Method Based on Improved A* Algorithm | |
| CN107798079B (en) | Method and system for road segment splicing based on vehicle trajectory data | |
| CN102136192B (en) | Method for identifying trip mode based on mobile phone signal data | |
| CN102779410B (en) | Parallel implementation method of multi-source heterogeneous traffic data fusion | |
| CN103413437A (en) | A road intersection steering recognition method and system based on vehicle data collection | |
| CN104657424A (en) | Clustering method for interest point tracks under multiple temporal and spatial characteristic fusion | |
| CN111028511B (en) | Traffic jam early warning method and device | |
| CN102024325A (en) | Traffic jam point identification method based on floating car technology | |
| CN110444015B (en) | Speed decision-making method of intelligent networked vehicles based on zoning of unsignaled intersections | |
| CN102521973A (en) | Road matching method for mobile phone switching positioning | |
| CN106228819A (en) | The traffic signal optimization control method of a kind of multi-intersection and device | |
| CN102663890B (en) | Method for determining left-turning forbiddance traffic limitation intersection by using floating car data | |
| CN110992676A (en) | Road traffic capacity and internet automatic driving vehicle equivalent coefficient estimation method | |
| Li et al. | A vehicular collision warning algorithm based on the time-to-collision estimation under connected environment | |
| CN110502596A (en) | A Trajectory Online Sliding Window Compression Method Based on Pedestrian Trajectory Features | |
| CN103079270A (en) | Method for sensing aggregation of mobile nodes on basis of wireless positioning | |
| CN113744541A (en) | Road network discharge loss space-time distribution reconstruction method and system for confrontation graph convolution network | |
| CN111145540B (en) | Method and system for discovering topological connecting edges of urban road network | |
| CN104680775B (en) | The parallel extraction method of the real-time optimal path of road network based on huge traffic data | |
| CN110060470A (en) | A kind of RSU coverage area division methods based on section connected probability | |
| CN115578860B (en) | Vehicle travel path identification method in urban dense road network environment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: No. 33, Xuefu South Road, New University District, Shangjie Town, Minhou County, Fuzhou City, Fujian Province, 350118 Patentee after: Fujian University of Science and Technology Country or region after: China Address before: No. 33, Xuefu South Road, New University District, Shangjie Town, Minhou County, Fuzhou City, Fujian Province, 350118 Patentee before: FUJIAN University OF TECHNOLOGY Country or region before: China |