Urban road network path utilization rate evaluation method and device

Abstract

The invention provides an urban road network path utilization rate evaluation method and device, and relates to the technical field of near field communication, and the method comprises the steps: obtaining urban road data and population distribution data; determining a first influence value of a first path node pair formed by a first starting point and a first end point based on population distribution data of the first starting point and the first end point corresponding to the network path to be tested, and determining a second influence value in topology with the first path based on a path betweenness centrality model following a shortest time path principle; according to the respective preset weights of the first path node pair and the first path topology, the first influence value and the second influence value, evaluating the utilization rate of the path corresponding to the first path node pair; and marking and displaying all the to-be-tested network paths based on the utilization rate. According to the method, the utilization rate of the target path in the whole network is calculated based on the shortest time path by using the improved path betweenness centrality model.

Description

Technical Field

[0001] The present invention relates to the field of near field communication technology, and in particular to a method and device for evaluating the utilization rate of urban road network paths. Background Art

[0002] Urban road networks are important infrastructure for modern cities and are crucial for maintaining the normal and safe operation of cities. The core function of a road network is to meet the efficient movement of people and objects in space. Road utilization reflects its importance in road network traffic. Traditional studies on the utilization rate of road sections can only reflect the importance of a single road and cannot express the usage of linear paths. Path utilization reflects the importance of a specific path in the road network. Assessing the utilization rate of urban traffic road network paths is of great significance for emergency decision-making in urban traffic accidents, natural disasters, and congestion events. However, there are currently few methods for studying path utilization. Most of them study the utilization rate of a single road section or intersection node, which makes it difficult to accurately display the usage of linear paths. Summary of the Invention

[0003] In view of this, the present invention proposes a method and device for evaluating the utilization rate of urban road network paths.

[0004] The technical solution of the present invention is implemented as follows: In a first aspect, the present invention provides a method for evaluating the utilization rate of urban road network paths, comprising:

[0005] Acquire urban road data and population distribution data; the urban road data includes path nodes and connection relationships between different path nodes, and the population distribution data includes the number of people within the influence range of each path node;

[0006] Determining a first influence value of a first path node pair consisting of the first starting point and the first end point based on population distribution data of a first starting point and a first end point corresponding to the network path to be tested, and determining a second influence value of a first path topology associated with the first path node based on an improved path betweenness centrality model; the improved path betweenness centrality model follows a shortest time path principle;

[0007] Evaluate the usage rate of the path corresponding to the first path node pair according to the preset weights of the first path node pair and the first path topology, the first influence value, and the second influence value;

[0008] All network paths to be tested are marked and displayed based on the usage rate.

[0009] Based on the above technical solution, preferably, the obtaining of urban road data and population distribution data includes:

[0010] The urban road network is divided into units with the path node as the center to determine a plurality of unit areas; the distance from any point in each unit area to the area center is shorter than the distance to the centers of other areas;

[0011] The population of each grid in each unit area is accumulated and summed using the Thiessen polygon plane partitioning method to determine the population within the influence range of each path node.

[0012] On the basis of the above technical solution, preferably, determining the first influence value of the first path node pair consisting of the first starting point and the first end point based on the population distribution data of the first starting point and the first end point corresponding to the network path to be measured includes:

[0013] Determine the sum of the populations of the first starting point and the first end point as a first initial impact value;

[0014] The first initial impact value is mapped to a specified interval using minimum-maximum normalization to obtain a first impact value.

[0015] Based on the above technical solution, preferably, determining the second influence value of the first path topology associated with the first path node based on the improved path betweenness centrality model includes:

[0016] Determining a second initial influence value corresponding to the first path topology based on the improved path betweenness centrality model of the shortest time path;

[0017] The second initial impact value is mapped to a specified interval by using maximum normalization with a fixed maximum value to obtain a second impact value.

[0018] On the basis of the above technical solution, preferably, the improved path betweenness centrality model based on the shortest time path determines the second initial influence value corresponding to the first path topology, including:

[0019] Obtaining the total number of shortest time paths for a second pair of path nodes using a shortest time path algorithm; the starting point and the end point of the second pair of path nodes are any two different path nodes among all the path nodes;

[0020] Screening the shortest time paths to determine the number of shortest time paths passing through the first path node pair;

[0021] A second initial impact value corresponding to the first path topology is determined based on a ratio of the number of shortest time paths passing through the first path node pair to the total number of the shortest time paths.

[0022] Based on the above technical solution, preferably, the evaluating the usage rate corresponding to the first path node according to the respective preset weights of the first path node pair and the first path topology and the first influence value and the second influence value includes:

[0023] Determine a first influence factor by multiplying a first preset weight corresponding to the first path node pair and the first influence value;

[0024] Determine a second influence factor by multiplying a second preset weight corresponding to the first path topology and the second influence value;

[0025] The sum of the first impact factor and the second impact factor is determined as the usage rate of the first path node for the corresponding path.

[0026] Based on the above technical solution, preferably, marking and displaying all network paths to be tested based on the usage rate includes:

[0027] Classifying the network paths to be tested based on the numerical range of the usage rate to obtain at least a first type of network path and a second type of network path;

[0028] The first type of network paths and the second network paths are marked and integrated to obtain a path usage map.

[0029] More preferably, the second aspect of the present invention provides an urban road network path utilization evaluation device, comprising: a data acquisition module, a first determination module, a second determination module and a path marking module; wherein,

[0030] The data acquisition module is configured to acquire urban road data and population distribution data; the urban road data includes path nodes and connection relationships between different path nodes, and the population distribution data includes the number of people within the influence range of each path node;

[0031] The first determination module is configured to determine a first influence value of a first path node pair consisting of a first starting point and a first end point corresponding to the network path to be tested based on population distribution data of the first starting point and the first end point, and determine a second influence value of a first path topology associated with the first path node based on an improved path betweenness centrality model; the improved path betweenness centrality model follows a shortest time path principle;

[0032] The second determining module is configured to evaluate the usage rate of the path corresponding to the first path node pair according to the preset weight of the first path node pair and the first path topology and the first influence value and the second influence value;

[0033] The marking and displaying module is configured to mark and display all the network paths to be tested based on the usage rate.

[0034] More preferably, the third aspect of the present invention provides an electronic device comprising a processor and a memory; the memory stores a computer program, wherein the computer program, when executed by the processor, implements the urban road network path utilization evaluation method described in the first aspect.

[0035] More preferably, the fourth aspect of the present invention provides a computer storage medium on which a computer program is stored, wherein when the computer program is executed by a processor, the urban road network path utilization evaluation method described in the first aspect is implemented.

[0036] The urban road network path utilization rate evaluation method and device of the present invention have the following beneficial effects compared with the prior art:

[0037] 1. Based on the population distribution data of the first starting point and the first end point of the network path to be tested, the first impact value of the first path node pair is determined, and the second impact value of the first path topology is determined based on the path betweenness centrality model that follows the shortest time path principle. Taking into account the importance of node pairs and path topology, and combining different environmental and traffic conditions, the improved path betweenness centrality model is used to calculate the utilization rate of the target path in the entire network based on the shortest time path, thereby improving the accuracy and reliability of path evaluation.

[0038] 2. Use minimum-maximum normalization to map the first initial influence value of the first path node pair to the specified interval to obtain the first influence value. Use maximum normalization with a fixed maximum value to map the second initial influence value of the first path topology to the specified interval to obtain the second influence value. This avoids the influence value changing with the change of the network structure, facilitates the comparison of the influence strength of different paths, and avoids the excessive influence of outliers on the normalization results.

[0039] 3. Based on the numerical range of utilization rates, the network paths to be tested are classified to obtain at least first-class network paths and second-class network paths. The first-class network paths and second-class network paths are then marked and integrated to obtain a path utilization rate map. This can visually and intuitively display the current utilization rate of each network path, which is helpful for making emergency decisions in urban traffic accidents, natural disasters, and congestion incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A schematic diagram of a flow chart of a method for evaluating the utilization rate of a path in an urban road network provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of population division of path nodes based on Thiessen polygons provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of an application scenario based on the shortest time path provided by an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of a target data set for a key road network path provided by an embodiment of the present invention;

[0045] Figure 5 A schematic diagram of the structure of a device for evaluating the utilization rate of a path in an urban road network provided by an embodiment of the present invention;

[0046] Figure 6 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0048] In some embodiments, as Figure 1 As shown, Figure 1 A schematic flow chart of a method for evaluating the utilization rate of a path in an urban road network provided by an embodiment of the present invention; the method for evaluating the utilization rate of a path in an urban road network provided by the present invention comprises:

[0049] S110, obtaining urban road data and population distribution data; the urban road data includes path nodes and connection relationships between different path nodes, and the population distribution data includes the number of people within the influence range of each path node.

[0050] In this example, a topological network is constructed using urban road data. Path nodes are abstracted as vertices in the network, assigned unique IDs and coordinate attributes. Roads are abstracted as edges, connecting adjacent nodes, and assigned weights (such as road length) and direction attributes. For example, the total number of nodes in the urban road network is N, and nodes i and j are any two different nodes.

[0051] In some embodiments, S110, obtaining urban road data and population distribution data, includes:

[0052] Taking the path node as the center, the urban road network is divided into units to determine multiple unit areas; the distance from any point in each unit area to the area center is shorter than the distance to the centers of other areas;

[0053] The population of each grid in each unit area is accumulated and summed using the Thiessen polygon plane partitioning method to determine the population within the influence range of each path node.

[0054] Here, see Figure 2 , Figure 2 A schematic diagram of the population division of path nodes based on Thiessen polygons is provided for an embodiment of the present invention; Thiessen polygons, as a plane division method, can ensure that the distance from any point in each area to the center of the area is minimized. Thiessen polygons divide the plane by drawing perpendicular bisectors, and each generating point corresponds to a polygonal area. The construction process includes calculating the perpendicular bisectors between all generating points and determining the intersection of these bisectors to form the polygon boundary. The Thiessen polygon plane division method is used to accumulate and sum the population of each grid in each unit area, and the calculation is shown in formula (1):

[0055]

[0056] Where: m i is the total population in unit area i, i.e., the demand scale; d k is the population of a single grid within the unit area.

[0057] S120, based on the population distribution data of the first starting point and the first end point corresponding to the network path to be tested, determine the first influence value of the first path node pair composed of the first starting point and the first end point, and determine the second influence value of the first path topology associated with the first path node based on the improved path betweenness centrality model; the improved path betweenness centrality model follows the shortest time path principle.

[0058] In urban road networks, the impact of a node pair is directly related to the population distribution around it. Because traffic demand tends to be concentrated in densely populated areas, larger populations generally generate higher traffic volumes. The importance of a node pair is positively correlated with the population distribution around its origin and destination points. When the population between two nodes is larger, that route typically carries more transportation traffic, and its impact value increases accordingly.

[0059] In some embodiments, determining a first influence value of a first path node pair formed by the first starting point and the first end point based on population distribution data of the first starting point and the first end point corresponding to the network path to be tested includes:

[0060] The sum of the populations of the first starting point and the first end point is determined as the first initial impact value;

[0061] The first initial impact value is mapped to a specified interval using minimum-maximum normalization to obtain a first impact value.

[0062] Here, the calculation of the first initial impact value is shown in formula (2):

[0063] P 0 ij =m i +m j , 1≤i≠j≤N; (2)

[0064] Where: P 0 ij is the first initial influence value corresponding to nodes i and j; m i is the population of path node i; m j is the population of path node j; N is the total number of nodes.

[0065] Since population data is static data, it can be normalized using conventional methods. Here, minimum-maximum normalization is used, and the calculation is shown in formula (3):

[0066]

[0067] Where: P ij is the first impact value corresponding to nodes i and j under the unified dimension; P 0 ij is the first initial impact value corresponding to path nodes i and j; min(P 0 ) is the minimum value in the set of path node influence values; max(P 0 ) is the maximum value in the set of path node influence values; N is the total number of path nodes.

[0068] In some embodiments, determining a second influence value of a first path topology associated with a first path node based on the improved path betweenness centrality model includes:

[0069] Determine the second initial influence value corresponding to the first path topology based on the improved path betweenness centrality model of the shortest time path;

[0070] The second initial influence value is mapped to a specified interval by using maximum normalization with a fixed maximum value to obtain a second influence value.

[0071] In some embodiments, determining a second initial influence value corresponding to the first path topology based on the improved path betweenness centrality model of the shortest time path includes:

[0072] The total number of shortest time paths of the second path node pair is obtained by using the shortest time path algorithm; the starting point and the end point of the second path node pair are any two different path nodes among all the path nodes;

[0073] Screening the shortest time paths to determine the number of shortest time paths passing through the first path node pair;

[0074] A second initial influence value corresponding to the first path topology is determined based on a ratio of the number of shortest time paths passing through the first path node pair to the total number of shortest time paths.

[0075] In this embodiment, here, see Figure 3 , Figure 3 Schematic diagram of an application scenario based on the shortest time path provided by an embodiment of the present invention; a path, as an ordered combination of road segments, can be considered as a whole and used to quantify the frequency of selection of the shortest path, reflecting the structural differences in transmission volume between each node pair. The shortest time path is the path with the shortest travel time among all paths between starting point i and end point j, which can be expressed as formula (4):

[0076]

[0077] Where p i，j is the shortest time path between starting point i and end point j, where a path between starting point i and end point j consists of n segments, and the length of the kth segment is Line k , the passing speed is V k .

[0078] Based on the shortest time path, the improved path betweenness centrality model corresponds to the calculation formula (5):

[0079]

[0080] Where EB 0 i，j is the betweenness centrality of the shortest time path from starting point i to end point j, that is, the second initial influence value, t o，d (pi，j ) is the number of shortest time paths passing through the first path node pair, t o，d Represents the total number of shortest time paths between path node O and path node d, where path node O and path node d are any two different path nodes among all path nodes, N is the number of nodes, and i≠j.

[0081] The connotation of path betweenness centrality is the number of times a path appears in all shortest paths. It changes with the changes in network structure. As a dynamic indicator, normalization will cause it to become a relative value, which cannot be compared in different network structures. Therefore, the maximum value normalization with a fixed maximum value is used to make the data fall into the [0,1] interval as much as possible. This does not modify the connotation of path betweenness centrality, but also makes the impact close to the importance of node pairs. Its calculation formula is shown in (6).

[0082]

[0083] Where: EB i，j is the betweenness centrality of the shortest time path from starting point i to end point j under unified dimension, that is, the second influence value; EB 0 ，i，j is the betweenness centrality of the shortest time path from starting point i to end point j, that is, the second initial influence value; max(EB 0 ) is the maximum value in the betweenness centrality set of the path; N is the total number of path nodes.

[0084] S130 : Evaluate the usage rate of the path corresponding to the first path node pair according to the preset weights of the first path node pair and the first path topology, as well as the first influence value and the second influence value.

[0085] In some embodiments, S130 determines the usage rate of the path corresponding to the first path node pair according to the preset weights of the first path node pair and the first path topology, as well as the first influence value and the second influence value, including:

[0086] Determine the product of the first preset weight corresponding to the first path node pair and the first influence value as the first influence factor;

[0087] Determine the product of the second preset weight corresponding to the first path topology and the second influence value as the second influence factor;

[0088] The sum of the first impact factor and the second impact factor is determined as the usage rate of the first path node on the corresponding path.

[0089] In this embodiment, the calculation is shown in formula (7):

[0090] W i，j =αEB i，j +βP i，j, 1≤i≠j≤N; (7)

[0091] Where W i，j is the usage rate of the path from node i to node j, N is the total number of nodes in the road traffic network, P i，j EB is the first impact value corresponding to node i to node j under unified dimension, i，j is the second influence value of the first path topology formed by node i to node j under the unified dimension, β is the first preset weight, α is the second preset weight, and α+β=1.

[0092] S140: Mark and display all network paths to be tested based on usage.

[0093] In some embodiments, S140 marks and displays all network paths to be tested based on usage, including:

[0094] Classifying the network paths to be tested based on the numerical range of the usage rates to obtain at least a first type of network path and a second type of network path;

[0095] The first type of network paths and the second type of network paths are marked and integrated to obtain a path usage rate map; the path usage rate is positively correlated with the utilization rate.

[0096] In this embodiment, different types of paths are marked and integrated, such as assigning colors to different types of paths (such as red represents high impact value and green represents low impact value), see Figure 4 , Figure 4 A schematic diagram of the target data set of the key network paths of the roads provided in the embodiment of the present invention, or symbols (such as asterisks, arrows) are added next to the paths to indicate priority or direction. High-impact paths are frequently used due to their convenience, resulting in high usage rates. After identifying high-impact paths, high-impact but non-congested paths can be recommended to users to balance efficiency and comfort. Maintenance resources (such as road maintenance and traffic light optimization) can also be allocated on a priority basis to improve the overall efficiency of the network. Furthermore, a machine learning model can be constructed to predict future path usage rates, and public transportation schedules or traffic control measures can be adjusted based on the prediction results to quantify the impact of policies (such as traffic restrictions and congestion fees) on path usage rates.

[0097] In some embodiments, see Figure 5 , Figure 5 This is a schematic diagram of the structure of an urban road network path utilization evaluation device provided by an embodiment of the present invention. The present invention provides an urban road network path utilization evaluation device 500, comprising: a data acquisition module 510, a first determination module 520, a second determination module 530 and a path marking module 540; wherein,

[0098] Data acquisition module 510 is configured to acquire urban road data and population distribution data; the urban road data includes path nodes and the connection relationships between different path nodes; the population distribution data includes the number of people within the influence range of each path node;

[0099] A first determination module 520 is configured to determine a first influence value of a first path node pair consisting of the first starting point and the first end point based on population distribution data of the first starting point and the first end point corresponding to the network path to be tested, and to determine a second influence value of a first path topology associated with the first path node based on an improved path betweenness centrality model; the improved path betweenness centrality model follows a shortest time path principle;

[0100] The second determining module 530 is configured to evaluate the usage rate of the path corresponding to the first path node pair according to the preset weight of the first path node pair and the first path topology and the first influence value and the second influence value;

[0101] The marking and displaying module 540 is configured to mark and display all network paths to be tested based on usage.

[0102] In some embodiments, the data acquisition module 510 is specifically configured to:

[0103] Taking the path node as the center, the urban road network is divided into units to determine multiple unit areas; the distance from any point in each unit area to the area center is shorter than the distance to the centers of other areas;

[0104] The population of each grid in each unit area is accumulated and summed using the Thiessen polygon plane partitioning method to determine the population within the influence range of each path node.

[0105] In some embodiments, the first determining module 520 is specifically configured to:

[0106] The sum of the populations of the first starting point and the first end point is determined as the first initial impact value;

[0107] The first initial impact value is mapped to a specified interval using minimum-maximum normalization to obtain a first impact value.

[0108] In some embodiments, the first determining module 520 is specifically configured to:

[0109] Determine the second initial influence value corresponding to the first path topology based on the improved path betweenness centrality model of the shortest time path;

[0110] The second initial influence value is mapped to a specified interval by using maximum normalization with a fixed maximum value to obtain a second influence value.

[0111] In some embodiments, the first determining module 520 is specifically configured to:

[0112] The total number of shortest time paths of the second path node pair is obtained by using the shortest time path algorithm; the starting point and the end point of the second path node pair are any two different path nodes among all the path nodes;

[0113] Screening the shortest time paths to determine the number of shortest time paths passing through the first path node pair;

[0114] A second initial influence value corresponding to the first path topology is determined based on a ratio of the number of shortest time paths passing through the first path node pair to the total number of shortest time paths.

[0115] In some embodiments, the second determining module 530 is specifically configured to:

[0116] Determine the product of the first preset weight corresponding to the first path node pair and the first influence value as the first influence factor;

[0117] Determine the product of the second preset weight corresponding to the first path topology and the second influence value as the second influence factor;

[0118] The sum of the first impact factor and the second impact factor is determined as the usage rate of the first path node on the corresponding path.

[0119] In some embodiments, the tag display module 540 is specifically configured to:

[0120] Classifying the network paths to be tested based on the numerical range of the usage rates to obtain at least a first type of network path and a second type of network path;

[0121] The first type of network paths and the second type of network paths are marked and integrated to obtain a path usage rate map; the path usage rate is positively correlated with the utilization rate.

[0122] It should be noted that the urban road network path utilization evaluation device provided in the embodiment of the present application and the urban road network path utilization evaluation method provided in the embodiment of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned urban road network path utilization evaluation method, and the repeated parts will not be repeated.

[0123] In some embodiments, see Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. An electronic device 600 provided in an embodiment of the present application includes a processor 610 and a memory 620; the memory 620 stores a computer program, wherein the computer program, when executed by the processor, implements the above-mentioned urban road network path utilization evaluation method.

[0124] Specifically, the processor 610 may include, for example, a general-purpose microprocessor, an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 610 may also include onboard memory for caching purposes. The processor 610 may be a single processing unit or multiple processing units for executing different actions of the method flow according to the embodiments of the present application.

[0125] Memory 620 can be, for example, any medium capable of containing, storing, conveying, disseminating, or transmitting instructions. For example, memory 620 can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, components, or propagation media. Specific examples of memory 620 include: magnetic storage devices, such as magnetic tape or hard disk drives (HDDs); optical storage devices, such as compact discs (CD-ROMs); random access memory (RAM) or flash memory; and / or wired or wireless communication links.

[0126] The present application also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned urban road network path utilization evaluation method. The computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not be assembled into the device / apparatus / system. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed, the method according to the embodiment of the present application is implemented.

[0127] According to an embodiment of the present application, a computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical cable, radio frequency signals, or any suitable combination thereof.

[0128] Those skilled in the art will understand that the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present application. In particular, without departing from the spirit and teachings of the present application, the features described in the various embodiments and / or claims of the present application may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present application. Therefore, the scope of the present application should not be limited to the above-mentioned embodiments, but should be determined not only by the attached claims, but also by the equivalents of the attached claims. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the utilization rate of urban road network paths, characterized in that: include: Acquire urban road data and population distribution data; the urban road data includes path nodes and connection relationships between different path nodes, and the population distribution data includes the number of people within the influence range of each path node; Determining, based on population distribution data of a first starting point and a first end point corresponding to the network path to be tested, a first influence value of a first path node pair consisting of the first starting point and the first end point, and determining, based on an improved path betweenness centrality model, a second influence value of a first path topology associated with the first path node; The improved path betweenness centrality model follows the shortest time path principle; Evaluate the usage rate of the path corresponding to the first path node pair according to the preset weights of the first path node pair and the first path topology, the first influence value, and the second influence value; All network paths to be tested are marked and displayed based on the usage rate.

2. The urban road network path utilization rate evaluation method according to claim 1, characterized in that: The obtaining of urban road data and population distribution data includes: The urban road network is divided into units with the path node as the center to determine a plurality of unit areas; the distance from any point in each unit area to the area center is shorter than the distance to the centers of other areas; The population of each grid in each unit area is accumulated and summed using the Thiessen polygon plane partitioning method to determine the population within the influence range of each path node.

3. The urban road network path utilization rate evaluation method according to claim 1, characterized in that: The determining, based on population distribution data of a first starting point and a first end point corresponding to the network path to be measured, a first influence value of a first path node pair formed by the first starting point and the first end point includes: Determine the sum of the populations of the first starting point and the first end point as a first initial impact value; The first initial impact value is mapped to a specified interval using minimum-maximum normalization to obtain a first impact value.

4. The urban road network path utilization rate evaluation method according to claim 1, characterized in that: The determining, based on the improved path betweenness centrality model, a second influence value of the first path topology associated with the first path node includes: Determining a second initial influence value corresponding to the first path topology based on the improved path betweenness centrality model of the shortest time path; The second initial impact value is mapped to a specified interval by using maximum normalization with a fixed maximum value to obtain a second impact value.

5. The urban road network path utilization rate evaluation method according to claim 4, characterized in that: The determining of the second initial influence value corresponding to the first path topology based on the improved path betweenness centrality model based on the shortest time path includes: Obtaining the total number of shortest time paths for a second pair of path nodes using a shortest time path algorithm; the starting point and the end point of the second pair of path nodes are any two different path nodes among all the path nodes; Screening the shortest time paths to determine the number of shortest time paths passing through the first path node pair; A second initial impact value corresponding to the first path topology is determined based on a ratio of the number of shortest time paths passing through the first path node pair to the total number of the shortest time paths.

6. The urban road network path utilization rate evaluation method according to claim 1, characterized in that: The evaluating the usage rate of the path corresponding to the first path node pair according to the preset weights of the first path node pair and the first path topology, the first influence value, and the second influence value includes: Determine a first influence factor by multiplying a first preset weight corresponding to the first path node pair and the first influence value; Determine a second impact factor by multiplying a second preset weight corresponding to the first path topology and the second impact value; The sum of the first impact factor and the second impact factor is determined as the usage rate of the first path node for the corresponding path.

7. The urban road network path utilization rate evaluation method according to claim 1, characterized in that: The marking and displaying of all network paths to be tested based on the usage rate includes: Classifying the network paths to be tested based on the numerical range of the usage rate to obtain at least a first type of network path and a second type of network path; The first type of network paths and the second network paths are marked and integrated to obtain a path usage map.

8. A device for evaluating the utilization rate of urban road network paths, characterized in that: include: Data acquisition module, first determination module, second determination module and mark display module; wherein, The data acquisition module is configured to acquire urban road data and population distribution data; the urban road data includes path nodes and connection relationships between different path nodes, and the population distribution data includes the number of people within the influence range of each path node; The first determination module is configured to determine a first influence value of a first path node pair consisting of a first starting point and a first end point corresponding to the network path to be tested based on population distribution data of the first starting point and the first end point, and determine a second influence value of a first path topology associated with the first path node based on an improved path betweenness centrality model; the improved path betweenness centrality model follows a shortest time path principle; The second determining module is configured to evaluate the usage rate of the path corresponding to the first path node pair according to the preset weight of the first path node pair and the first path topology and the first influence value and the second influence value; The marking and displaying module is configured to mark and display all the network paths to be tested based on the usage rate.

9. An electronic device comprising a processor and a memory; the memory stores a computer program, wherein: When executed by the processor, the computer program implements the urban road network path usage evaluation method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored thereon, wherein when the computer program is executed by a processor, the urban road network path utilization evaluation method as described in any one of claims 1 to 7 is implemented.

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