Method for organizing special spatio-temporal data of road traffic capacity of inhomogeneous medium
By introducing influencing factors into the road traffic capacity assessment and building a non-homogeneous space, using Voronoi mosaic data structure and Delaunai triangular network for data organization, the problem of failure to effectively consider the impact of non-uniform media in the existing technology is solved, and the accuracy and systematicity of the assessment are improved.
Patent Information
- Application Number
- CN202510146977.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-10
- Publication Date
- 2025-06-13
AI Technical Summary
The existing road traffic capacity assessment technology fails to effectively consider the impact of non-uniform media on road traffic capacity, resulting in inaccurate evaluation results.
By introducing factors affecting road traffic capacity, a non-homogeneous space is constructed, and the Voronoi mosaic data structure and the Delaunette Triangle Network are used for data organization to evaluate road traffic capacity.
It improves the accuracy and systematicity of road traffic capacity assessment, and can more effectively consider the impact of non-uniform media on traffic capacity, providing more accurate analysis results.
Smart Images
Figure CN120144583A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of road traffic capacity evaluation, and specifically to a method for organizing thematic spatio-temporal data of road traffic capacity of non-uniform media. Background Art
[0002] Road traffic capacity refers to the maximum number of vehicles passing through a road or a cross-section of a carriageway per unit time under certain road and traffic conditions. It is also known as road capacity, traffic capacity or simply capacity, with the unit of vehicles per hour (veh / h). The thematic spatial data of road traffic capacity refers to the spatial data related to road traffic capacity, mainly including information such as the geometric shape of the road, the location of intersections, and the driving trajectories of vehicles. These data have spatial location attributes, which can help us better understand the distribution and changes of traffic flow. Currently, existing road traffic capacity evaluation technologies generally believe that the influence of variables of thematic spatial data of road traffic capacity changes uniformly with distance, and regard spatial data as homogeneous spatial data in the form of vector or raster data, and conduct three-dimensional dissection and arrangement based on spatial distance, without considering the non-homogeneous space in solving practical problems such as highway traffic capacity. The influence degree of the non-homogeneous space on the problem does not change uniformly with space. It is affected not only by spatial distance, but also by other factors, and is a non-homogeneous variable. Summary of the Invention
[0003] The purpose of the present invention is to overcome the deficiencies of the prior art and propose a method for organizing thematic spatio-temporal data of road traffic capacity of non-uniform media. By introducing factors affecting road traffic, a non-homogeneous space is constructed, and thematic road data is organized in the non-homogeneous space to evaluate road traffic capacity.
[0004] To achieve the above purpose, the specific technical solution adopted by the present invention is as follows:
[0005] A method for organizing thematic spatio-temporal data of road traffic capacity of non-uniform media includes the following steps:
[0006] S1. Divide the thematic spatial data of road traffic capacity into road thematic elements, terrain elements, and spatial elements affecting highway traffic capacity; among them, the spatial elements affecting highway traffic capacity include surface elements affecting highway traffic capacity and point elements affecting highway traffic capacity;
[0007] S2. In the two-dimensional space, first, weights are assigned to the surface elements affecting highway traffic capacity (urban grade, highway level, land use category), and a traffic background surface under two-dimensional data is generated through algebraic logical operations. Then, the spatial surface TIN and Voronoi tessellation data structure (single layer) of the traffic capacity impact factor point elements (including hospital, school, shopping mall, scenic spot, intersection, etc. that affect traffic capacity) are used. Finally, a composite function operation is performed on the above traffic background surface and the Voronoi minimum unit polygon in the tessellation data structure using a function operation.
[0008] In the three-dimensional space, a TIN and Voronoi tessellation data structure for three-dimensional space zoning and curved surface zoning in a non-uniform state is constructed using the point elements affecting highway traffic capacity, where the organization layer of the TIN tessellation data structure is ≥3.
[0009] In the multi-dimensional space-time, taking time as a discrete variable, differentiating between weekdays and holidays, peak hours in the morning and evening and other time periods, spatial impact point and surface elements at different times are obtained, and a TIN and Voronoi tessellation data structure for curved surface zoning at different time periods is constructed.
[0010] S3. In the two-dimensional space, a two-dimensional road model is constructed based on road thematic elements, using an indexed data structure.
[0011] In the three-dimensional space, based on the expression of three-dimensional road point cloud data, an octree data structure is used.
[0012] In the multi-dimensional space-time, since time has no impact on the state of the road entity model, three-dimensional road point cloud data is used, and the three-dimensional road model uses an octree data structure for indexing.
[0013] S4. Under the two-dimensional space-time benchmark, the single-layer TIN and Voronoi tessellation data structure are organized using an indexed data structure to obtain a two-dimensional space traffic capacity evaluation model.
[0014] Under the three-dimensional space-time benchmark, the TIN and Voronoi tessellation data structure for three-dimensional space zoning and curved surface zoning and the octree data structure of the road three-dimensional model are organized to obtain a three-dimensional space traffic capacity evaluation model.
[0015] Under the multi-dimensional space-time benchmark, the TIN and Voronoi tessellation data structure for curved surface zoning at different time periods and the octree data structure of the road three-dimensional model are organized to obtain a multi-dimensional space traffic capacity evaluation model.
[0016] S5. At different discrete times (such as weekdays and holidays), based on the two-dimensional space traffic capacity evaluation model, three-dimensional space traffic capacity evaluation model, and multi-dimensional space traffic capacity evaluation model, the traffic capacity of the two- and three-dimensional space data of the road is analyzed.
[0017] Further, in the step S1, the main factors affecting the construction of the regional background space mainly include urban levels (municipalities directly under the Central Government, provincial capitals, prefecture-level cities, counties, townships and below), highway levels (expressways, national highways, expressways, arterial roads, secondary arterial roads, internal roads), rough classification of land use types (construction land, agricultural land, unused land), road organization (intersections, traffic lights, road widths, etc.). The main factors affecting the construction of the mosaic data structure mainly include intersections, traffic lights, road widths, schools, hospitals, shopping malls, scenic spots, etc.
[0018] Further, the road thematic elements include road vector data, road raster data, and road three-dimensional point cloud data.
[0019] Further, in the three-dimensional space, in the step S2, the Delaunay triangulation network TIN is first constructed, and then the intersection points of the bisectors of the vertical cutting planes (normal vectors) are taken to construct the Voronoi polygon, and the three-dimensional space is represented by the mosaic data structure.
[0020] Further, the mosaic data structure includes Voronoi data structure and TIN data structure; the Voronoi data structure includes sample point data, Voronoi cell adjacency relation table, Voronoi vertex information table, and Voronoi cell composition table; the TIN data structure includes point file structure and triangle topology file structure.
[0021] Further, in the three-dimensional space, in the step S4, the three-dimensional space data organization and indexing method is constructed with the mosaic data structure, and the road three-dimensional model is loaded to realize the analysis of road traffic capacity.
[0022] The present invention has the following characteristics and beneficial effects:
[0023] When organizing data, the present invention takes into account that in specific applications, the influence of the physical three-dimensional space is actually non-uniform, so the mosaic data structure is used for three-dimensional data organization; at the same time, a discrete time variable is introduced in the time dimension to further refine the traffic capacity evaluation model. Taking the influencing factors in the local space as element points, the Delaunay triangulation network is constructed, and on this basis, the Voronoi polygon is established for space segmentation, and a Voronoi polygon area after segmentation is regarded as the smallest homogeneous unit. Such a design will provide a systematic solution and data model for traffic capacity analysis;
[0024] The present invention organizes the traffic capacity influencing factors and the highway model distribution as two sub-spaces; among them, the traffic capacity is constructed with the logical model of the Voronoi mosaic data structure, and the three-dimensional highway model is constructed with the octree logical model, which improves the storage and retrieval efficiency in subsequent applications. BRIEF DESCRIPTION OF THE DRAWINGS
[0025] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read in conjunction with the accompanying drawings:
[0026] Figure 1 It is a flowchart of a spatial data organization method for analyzing the traffic capacity of a planar highway in an embodiment of the present invention;
[0027] Figure 2 It is a flowchart of a spatial data organization method for analyzing the traffic capacity of a three-dimensional highway in an embodiment of the present invention;
[0028] Figure 3 It is a flowchart of a spatial data organization method for analyzing the traffic capacity of a spatio-temporal highway in an embodiment of the present invention.
[0029] Figure 4 It is the spatial abstraction level of the traffic capacity data model of the highway in an embodiment of the present invention;
[0030] Figure 5 It is the spatial traffic capacity data organization of the two-dimensional planar tessellation data structure in an embodiment of the present invention:
[0031] In the figure: (a) The 1-layer tessellation data structure (triangulation network and polygon as the surface); (b) The function operation is performed on the spatial expression result of the tessellation data structure and the result of the algebraic logic operation.
[0032] Figure 6 It is the spatial traffic capacity data organization of the three-dimensional tessellation data structure in an embodiment of the present invention;
[0033] In the figure: (a) The 3-layer triangulation network is divided into codes; (b) The 3-layer tessellation data structure organization.
[0034] Figure 7 It is the organization of the vector road index data structure and the organization of the raster road quadtree data in an embodiment of the present invention;
[0035] In the figure: (a) The vector road index data structure; (b) The m0rt0n code of the raster image linear quadtree data structure.
[0036] Figure 8 It is the octree data structure and index of the three-dimensional road point cloud in an embodiment of the present invention. Specific Embodiments
[0037] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.
[0038] Embodiment 1
[0039] As Figure 1 shown, a method for organizing spatio-temporal data of the road traffic capacity of a non-uniform medium in an embodiment of the present invention includes the following steps:
[0040] S1. Divide the two-dimensional thematic data of road traffic capacity into road thematic elements (road vector data, road raster data) and spatial elements affecting highway traffic capacity;
[0041] S2. In a two-dimensional space, use the spatial elements affecting highway traffic capacity to allocate weights, generate a traffic background surface under two-dimensional data through algebraic logic operations, and construct a Voronoi tessellation data structure; in this embodiment, the Voronoi tessellation data structure in a two-dimensional space is a layer of TIN triangular network, as Figure 5 shown by the single-layer triangular network. The spatial elements affecting highway traffic capacity mainly include urban levels (municipalities directly under the Central Government, provincial capitals, prefecture-level cities, counties, townships and below), highway levels (expressways, national highways, expressways, main roads, secondary roads, internal roads), rough classification of land use types (construction land, agricultural land, unused land), road organization (intersections, traffic lights, road widths, etc.). Among them, urban levels use point data.shp and polygon data.shp, and land use types use point data.shp and polygon data.shp; road organization uses point data.shp, line data.shp and polygon data.shp. Specifically, convert the vector data of the polygon data of urban levels (municipalities directly under the Central Government, provincial capitals, prefecture-level cities, counties, townships and below) and highway levels into rasters to generate urban level polygon raster data and highway level polygon raster data, and then perform overlay analysis on the urban level polygon and highway level polygon raster data, and perform algebraic logic addition operation. Urban levels of municipalities directly under the Central Government 1, provincial capitals 2, prefecture-level cities 3, counties 4, townships and below 5 correspond to low traffic capacity, relatively low traffic capacity, average traffic capacity, good traffic capacity, and excellent traffic capacity respectively; highway levels (internal roads 1, secondary roads 2, main roads 3, expressways 4, national highways 5, expressways 6) correspond to low traffic capacity, relatively low traffic capacity, average traffic capacity, good traffic capacity, and excellent traffic capacity respectively; perform algebraic logic addition operation on highway levels and urban levels, and take the lower level as the traffic capacity after overlay. The larger the number, the stronger the traffic capacity; perform algebraic logic multiplication operation on the obtained result and land use type (construction land 1, agricultural land 2, unused land 3), such as the internal road of county-level construction land (4 + 1) * 1 = 5.
[0042] S3. Under the two-dimensional space reference, use spatial point data such as shopping malls, schools, hospitals, scenic spots, and residential communities to construct a TIN triangular network, and take the intersection of the perpendicular bisectors of the Delaunay triangular network to construct Voronoi polygons. The plane TIN and Voronoi tessellation data structures are shown in the following table:
[0043] Table 1 Point File Structure
[0044]
[0045] Table 2 Triangle Topology File Structure
[0046]
[0047] The described Voronoi data structure includes sample point data, Voronoi cell adjacency relation table, Voronoi vertex information table, and Voronoi cell composition table. Among them, the sample point data (shopping malls, schools, hospitals, scenic spots, road intersections, traffic lights) is shown in Table 3, the Voronoi cell adjacency relation table is shown in Table 4, the Voronoi vertex information table is shown in Table 5, and the Voronoi cell vertex composition table is shown in Table 6.
[0048] Table 3 Sample Point Data
[0049]
[0050] Table 4 Voronoi Cell Adjacency Relation Table
[0051]
[0052] Table 5 Voronoi Vertex Information Table
[0053]
[0054]
[0055] Table 6 Voronoi Cell Vertex Composition Table
[0056]
[0057] S4. Under the two-dimensional space reference, the traffic capacity background E in S2 1 , and the traffic capacity E represented by the minimum unit voronoi polygon constructed by the S3 mosaic data structure 2 are used for function operation to construct the traffic capacity E of the two-dimensional plane area. Among them, E = F(E 1 , E 2 ); E 2 The closer the road is to the TIN triangle point, the lower the traffic capacity; the closer to the polygon edge, the higher the traffic capacity. Specifically, under two-dimensional conditions as Figure 5 shown, the traffic capacity E is composed of E 1 , E 2 . The closer E 2 is to the TIN intersection point (Voronoi polygon center point), the lower the traffic capacity level; the closer to the polygon edge, the weaker the congestion.
[0058] In the two-dimensional space, construct a road two-dimensional space data model for road vector data and road raster data, as Figure 7 shown. The road vector line / surface data uses an index data structure to establish an index file between polygons and lines, and between lines and nodes, as shown in Table 7-9; the road raster data is represented by a quadtree data structure, and the road position and depth pointer indexes are constructed using the morton code.
[0059] Table 7 Road point coordinate file
[0060]
[0061] Table 8 Edge file
[0062]
[0063] Table 9 Polygon file
[0064]
[0065] The area E where the road is located = F(E 1 , E 2 ) is used to represent its traffic capacity in a heterogeneous space.
[0066] The organization method of the two-dimensional space data in this embodiment generally conducts traffic capacity assessment in case of emergency without considering elevation changes.
[0067] Embodiment 2
[0068] As Figure 2 shown, a method for organizing thematic spatio-temporal data of road traffic capacity of non-uniform media includes the following steps:
[0069] S1. Divide the three-dimensional thematic data of road traffic capacity into road three-dimensional point cloud data and spatial elements affecting highway traffic capacity;
[0070] S2. In the three-dimensional space, use Voronoi and TIN to spatially partition the spatial elements affecting highway traffic capacity and construct a Voronoi tessellation data structure; in this embodiment, the number of layers of the Voronoi tessellation data structure in the three-dimensional space ≥ 3, as Figure 6as shown in the multi-layer structure; specifically, based on the location data of construction sites such as shopping malls, schools, hospitals, scenic spots, etc., and the data of road intersections and signal traffic organization points, the Delauay triangulation network TIN is constructed, and the intersection points of the perpendicular bisectors are taken to construct the Voronoi polygon, and the three-dimensional space is represented by the tessellation data structure. In this step, Voronoi and TIN are used for spatial partitioning, and the entire heterogeneous region is divided into n Voronoi polygons and m triangulation networks. Each Voronoi polygon is regarded as the smallest unit of a homogeneous region surface, and a two-layer three-dimensional data organization space is constructed;
[0071] S3. In the three-dimensional space, the three-dimensional road data is expressed by three-dimensional point cloud data, and the three-dimensional road data is indexed by the octree data structure; as Figure 8 shown: The smallest enclosing cube of the three-dimensional road point cloud data is regarded as the root node of the octree. The root node is divided into 8 sub-nodes, and the sub-nodes are further divided into eight equal parts layer by layer until the condition of the given threshold M is met, and n layers of leaf nodes are obtained. The cube is encoded using the Morton code, and the relationship between the Morton code and the spatial object is established. The Morton code stores information such as the value, position, and depth of the leaf node.
[0072] S4. Under the three-dimensional spatio-temporal reference, the data organization of the three-dimensional space partition, the TIN and Voronoi tessellation data structures of the surface partition, and the octree data structure of the road three-dimensional model are carried out to realize the traffic capacity evaluation. Specifically, a three-dimensional space data organization and indexing method is constructed with the tessellation data structure. The road three-dimensional model is loaded to analyze the road traffic capacity, and the three-dimensional index of the tessellation data structure is constructed using the hierarchical Morton code. Each smallest polygon surface in the heterogeneous space is regarded as the smallest unit for quantifying the traffic capacity. The closer the road is to the TIN triangle point, the lower the traffic capacity, and the closer it is to the polygon edge, the higher the traffic capacity; to realize the qualitative evaluation of the spatio-temporal highway traffic capacity. As Figure 6 shown in the multi-layer structure. In the figure, each triangle point is composed of point elements such as shopping malls, schools, hospitals, scenic spots, road intersections, and traffic lights. The closer the traffic capacity E is to the center point of the Voronoi surface, the stronger the congestion, and the closer it is to the surface edge line, the weaker the congestion.
[0073] In this embodiment, as Figure 2 shown, the said tessellation data structure includes the Voronoi data structure and the TIN data structure; the said TIN data structure includes a point file structure and a triangle topology file structure. Among them, the point file is the location data of construction sites such as shopping malls, schools, hospitals, scenic spots, etc., and the data of road intersections and signal traffic organization points; the point file structure (shopping malls, schools, hospitals, scenic spots, road intersections, traffic lights) is shown in Table 10; the triangle topology file structure is shown in Table 11.
[0074] Table 10-point file structure
[0075]
[0076] Table 11 triangular topology file structure
[0077]
[0078] The described Voronoi data structure includes sample point data, Voronoi cell adjacency relation table, Voronoi vertex information table, and Voronoi cell composition table. Among them, the sample point data (shopping malls, schools, hospitals, scenic spots, road intersections, traffic lights) is shown in Table 12, the Voronoi cell adjacency relation table is shown in Table 13, the Voronoi vertex information table is shown in Table 14, and the Voronoi cell vertex composition table is shown in Table 15.
[0079] Table 12 Sample point data
[0080]
[0081] Table 13 Voronoi cell adjacency relation table
[0082]
[0083] Table 14 Voronoi vertex information table
[0084]
[0085] Table 15 Voronoi cell vertex composition table
[0086]
[0087] The organization method of the three-dimensional space data in this embodiment is generally used for traffic capacity assessment under the conditions of major impact projects, sensitive elevation changes, and high precision requirements.
[0088] Embodiment 3
[0089] As Figure 3 shown, applying the organization method of the spatio-temporal data of the road traffic capacity of a non-uniform medium in the embodiment of the present invention to the spatio-temporal highway traffic capacity analysis includes the following steps:
[0090] S1. Discretize time into segments, distinguish weekdays from holidays, and morning and evening peak periods from other periods; then divide the three-dimensional thematic data of road traffic capacity into road thematic elements, terrain elements, and geographical elements of point and surface traffic capacity influencing factors at different times;
[0091] S2. Based on the geographical elements of the influencing factors of point and surface traffic capacity at different times, use Voronoi and TIN to perform curved surface space partitioning, and construct TIN and Voronoi mosaic data structures;
[0092] In multi-dimensional space-time, taking time as a discrete variable, distinguishing weekdays from holidays, peak hours in the morning and evening from other time periods, and performing variable analysis on the spatial influencing point and surface elements; adding a time variable to construct a highway traffic capacity curved surface mosaic data structure; the data organization within a single time point is as Figure 6 shown, and multiple discrete points form a dynamic mosaic data structure.
[0093] S3. Construct a 3D road model based on road thematic elements and terrain elements, and the 3D road model uses an octree data structure for indexing;
[0094] S4. Under the multi-dimensional space-time benchmark, organize the TIN and Voronoi mosaic data structures of the curved surface partitions and the octree data structure of the 3D road model at different time periods. The closer to the TIN triangle point, the lower the road traffic capacity, and the closer to the polygon edge, the higher the traffic capacity; to achieve a qualitative evaluation of the space-time highway traffic capacity.
[0095] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for organizing thematic spatiotemporal data of road capacity in heterogeneous media, characterized in that: The steps include: S1. The road capacity thematic spatial data is divided into road thematic elements, terrain elements and spatial elements affecting highway capacity; S2. In two-dimensional space, weights are allocated to surface elements that affect highway traffic capacity, and a background surface for traffic under two-dimensional data is generated through algebraic logic operations. Then, single-layer TlN and Voronoi mosaic data structures are constructed using point elements that affect traffic capacity. In three-dimensional space, point elements that affect highway traffic capacity are used to construct TIN and Voronoi mosaic data structures for three-dimensional space partitions and surface partitions under non-homogeneous conditions, where the organizational layering of the TIN mosaic data structure is ≥ 3; In multidimensional space-time, time is used as a discrete variable to distinguish between working days and holidays, morning and evening peak periods and other time periods, obtain spatial influencing points and surface elements at different times, and construct TIN and Voronoi mosaic data structures of surface partitions at different time periods; S3. In two-dimensional space, a two-dimensional road model is constructed based on road vector data and road raster data, using an indexed data structure; In the three-dimensional space, a three-dimensional road model is constructed based on the three-dimensional point cloud data of the road, and the three-dimensional road model adopts an octree data structure; In multi-dimensional space-time, a three-dimensional road model is constructed based on road thematic elements and terrain elements. The three-dimensional road model adopts an octree data structure. S4, under the two-dimensional space-time benchmark, the single-layer TIN and Voronoi mosaic data structure are organized using the index data structure to obtain a two-dimensional space capacity assessment model; Under the three-dimensional space-time benchmark, the three-dimensional space partition, the surface partition TIN, the Voronoi mosaic data structure and the road three-dimensional model octree data structure are organized to obtain a three-dimensional space capacity assessment model; Under the multi-dimensional space-time benchmark, the TIN, Voronoi mosaic data structure and octree data structure of the surface partitions in different time periods and the road three-dimensional model are organized to obtain a multi-dimensional space capacity assessment model; S5. Analyze the two-dimensional and three-dimensional spatial data capacity of roads at different discrete times based on the two-dimensional spatial capacity assessment model, the three-dimensional spatial capacity assessment model, and the multi-dimensional spatial capacity assessment model.
2. The method for organizing thematic spatiotemporal data of road capacity in a non-uniform medium as claimed in claim 1, characterized in that: In the step S1, the spatial factors affecting the highway traffic capacity include city level, highway level, land use type, road organization, and traffic characteristics under different time conditions.
3. The method for organizing thematic spatiotemporal data of road capacity in a non-uniform medium as claimed in claim 1, characterized in that: The road thematic elements include road intersection point vectors, road line vector data, road raster data, and road three-dimensional point cloud data.
4. The method for organizing thematic spatiotemporal data of road capacity in a non-uniform medium as claimed in claim 1, characterized in that: In the three-dimensional space, the step S2 first constructs a triangulated network TIN, and then takes the intersection of the perpendicular tangent plane bisectors to construct the Voronoi polygons, and uses a mosaic data structure to represent the three-dimensional space.
5. The method for organizing thematic spatiotemporal data of road capacity in a non-uniform medium as claimed in claim 1, characterized in that: The mosaic data structure includes a Voronoi data structure and a TIN data structure; the Voronoi data structure includes sample point data, a Voronoi unit adjacency relationship table, a Voronoi vertex information table and a Voronoi unit composition table; the TIN data structure includes a point file structure and a triangle topology file structure.
6. The method for organizing thematic spatiotemporal data of road capacity in a non-uniform medium as claimed in claim 1, characterized in that: In the multidimensional space-time, in the step S4, different time periods are discrete variables, and the mosaic data structure is used to construct the data organization and index of the three-dimensional space influencing factors, and the three-dimensional road model is loaded to realize the analysis of road capacity.