A method and device for evaluating path usage of an urban road network

Abstract

The application provides a kind of urban road network path use rate evaluation method and device, it is related to near field communication technical field, including: obtaining urban road data and population distribution data;Based on the population distribution data of the first start point and the first end point corresponding to the network path to be measured, the first influence value of the first path node pair formed by the first start point and the first end point is determined, and the second influence value of the first path topology is determined based on the path betweenness centrality model following the shortest time path principle;According to the preset weight of each first path node pair and first path topology respectively and first influence value, second influence value, the use rate of the path corresponding to the first path node pair is evaluated;Based on use rate, all network paths to be measured are marked and displayed.The application uses improved path betweenness centrality model to calculate the use rate of target path in the whole network based on the shortest time path.

Description

Technical Field

[0001] This invention relates to the field of near-field communication technology, and in particular to a method and apparatus for evaluating the path utilization rate of urban road networks. Background Technology

[0002] Urban road networks are a crucial infrastructure of modern cities, essential for maintaining their normal and safe operation. The core function of a road network is to facilitate the efficient movement of people and goods within space, and road utilization reflects its importance in the network's traffic flow. Traditional studies of road segment utilization only reflect the importance of individual roads and cannot express the usage of linear paths. Path utilization reflects the importance of a particular path within the road network, and assessing the path utilization of urban traffic networks is of great significance for emergency decision-making regarding urban traffic accidents, natural disasters, and congestion events. However, current methods for studying path utilization are limited, with most focusing on the utilization of individual road segments or intersections, making it difficult to accurately represent the usage of linear paths. Summary of the Invention

[0003] In view of this, the present invention proposes a method and apparatus for evaluating the path utilization rate of urban road networks.

[0004] The technical solution of this invention is implemented as follows: The first aspect of this invention provides a method for evaluating the path utilization rate of urban road networks, comprising:

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

[0006] Based on the population distribution data of the first starting point and the first ending point corresponding to the network path to be tested, a first influence value of the first path node pair formed by the first starting point and the first ending point is determined, and a second influence value of the first path topology associated with the first path node is determined based on the improved path betweenness centrality model; the improved path betweenness centrality model follows the shortest time path principle.

[0007] The utilization rate of the first path node pair corresponding to the first path is evaluated based on 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.

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

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

[0010] The urban road network is divided into units based on the path nodes, and multiple unit areas are determined; the distance from any point within each unit area to the center of the area is less than the distance to the centers of other areas.

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

[0012] Based on the above technical solutions, preferably, the step of determining the first influence value of the first path node pair formed by the first starting point and the first ending point based on the population distribution data corresponding to the first starting point and the first ending point of the network path to be tested includes:

[0013] The sum of the population at the first starting point and the first ending point is determined as the first initial impact value;

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

[0015] Based on the above technical solutions, preferably, the determination of 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] The second initial influence value corresponding to the first path topology is determined based on the path betweenness centrality model improved by the shortest time path.

[0017] The second initial influence value is mapped to a specified interval by normalizing the maximum value of a fixed maximum value to obtain the second influence value.

[0018] Based on the above technical solutions, preferably, the determination of the second initial influence value corresponding to the first path topology by the path betweenness centrality model improved based on the shortest time path includes:

[0019] The total number of shortest time paths for the second path node pair is obtained using the shortest time path algorithm; the start and end points of the second path node pair are any two different path nodes among all the path nodes.

[0020] The shortest time paths are filtered to determine the number of shortest time paths that pass through the first path node pair.

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

[0022] Based on the above technical solutions, preferably, the step of evaluating the utilization rate of the first path node 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, includes:

[0023] The product of the first preset weight corresponding to the first path node pair and the first influence value is determined as the first influence factor;

[0024] The product of the second preset weight corresponding to the first path topology and the second influence value is determined as the second influence factor;

[0025] The sum of the first influence factor and the second influence factor is determined as the utilization rate of the first path node to the corresponding path.

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

[0027] Based on the numerical range of the usage rate, the network paths to be tested are classified to obtain at least a first type of network path and a second type of network path.

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

[0029] More preferably, a second aspect of the present invention provides an urban road network path utilization assessment 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 the connection relationships between different path nodes, and the population distribution data includes the population within the influence range of each path node.

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

[0032] The second determining module is configured to evaluate the utilization rate of the path corresponding to the first path node pair based on the preset weights of the first path topology, the first influence value, and the second influence value.

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

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

[0035] More preferably, a fourth aspect of the present invention provides a computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the urban road network path utilization assessment method described in the first aspect.

[0036] The method and apparatus for evaluating urban road network path utilization of the present invention have the following advantages over the prior art:

[0037] 1. Based on the population distribution data of the first starting point and the first ending point of the network path to be tested, the first influence value of the first path node pair is determined, and the second influence 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 the node pair and the importance of the path topology, and combined with 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. The first initial influence value of the first path node pair is mapped to the specified interval using minimum-maximum normalization to obtain the first influence value. The second initial influence value of the first path topology is mapped to the specified interval using maximum-maximum normalization to obtain the second influence value. This avoids the influence value from changing with the network structure, facilitates the comparison of the influence intensity of different paths, and avoids the excessive influence of outliers on the normalization result.

[0039] 3. Based on the numerical range of the usage rate, the network paths to be tested are classified to obtain at least a first-class network path and a second-class network path. The first-class network path and the second-class network path are then marked and integrated to obtain a path usage rate map, which can vividly and intuitively display the current usage rate of each network path, and is helpful for the formulation of emergency decisions for urban traffic accidents, natural disasters, and congestion events. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 A flowchart illustrating a method for evaluating the path utilization rate of an urban road network provided in an embodiment of the present invention;

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

[0043] Figure 3 This is a schematic diagram of an application scenario based on the shortest time path provided in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram of the target data set for the road critical network path provided in an embodiment of the present invention;

[0045] Figure 5 This is a schematic diagram of the structure of an urban road network path utilization assessment device provided in an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0047] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0048] In some embodiments, such as Figure 1 As shown, Figure 1 This is a flowchart illustrating a method for assessing the path utilization rate of an urban road network according to an embodiment of the present invention; the method for assessing the path utilization rate of an urban road network according to the present invention includes:

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

[0050] In this embodiment, a topological network is constructed using urban road data. Path nodes are abstracted as vertices in the network, assigned unique IDs and coordinate attributes, while 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 node i and node j are any two distinct nodes.

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

[0052] The urban road network is divided into units based on path nodes, and multiple unit areas are determined; the distance from any point within each unit area to the center of the area is less than the distance to the centers of other areas.

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

[0054] Please see here. Figure 2 , Figure 2 This is a schematic diagram of path node population division based on Thiessen polygons provided in an embodiment of the present invention. Thiessen polygons, as a planar division method, can ensure that the distance from any point in each region to the center of that region is minimized. Thiessen polygons divide the plane by drawing perpendicular bisectors, with each generated point corresponding to a polygonal region. The construction process includes calculating the perpendicular bisectors between all generated points and determining the intersection points of these bisectors, thereby forming the polygonal boundary. The population of each grid cell in each unit region is summed using the Thiessen polygon planar division method, as shown in equation (1):

[0055]

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

[0057] S120, based on the population distribution data of the first starting point and the first ending point corresponding to the network path to be tested, determine the first influence value of the first path node pair consisting of the first starting point and the first ending 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 network traffic, the impact of a node pair is directly related to the population distribution around it. Because traffic demand is often concentrated in densely populated areas, a larger population typically results in higher traffic volume. The importance of a node pair is positively correlated with the population distribution around its origin and destination. When the surrounding population between two nodes is large, the path usually undertakes more transportation tasks, and its impact value increases accordingly.

[0059] In some embodiments, based on population distribution data of the first starting point and the first ending point corresponding to the network path under test, a first influence value of the first path node pair consisting of the first starting point and the first ending point is determined, including:

[0060] The sum of the population at the first starting point and the first ending point is determined as the first initial impact value;

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

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

[0063] P 0 ij =m i +m j , 1≤i≠j≤N;(2)

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

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

[0066]

[0067] In the formula: P ij P represents the first influence value corresponding to nodes i and j under a unified dimension. 0 ij Let P be the first initial influence value corresponding to path nodes i and j; min(P 0 ) represents the minimum value in the set of influence values ​​of path node pairs; max(P) 0 ) represents the maximum value in the set of influence values ​​of the path node pair; N represents the total number of path nodes.

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

[0069] The second initial influence value corresponding to the topology of the first path is determined based on the path betweenness centrality model improved by the shortest time path.

[0070] The second initial influence value is mapped to a specified interval by normalizing the maximum value of the fixed maximum value, thus obtaining the second influence value.

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

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

[0073] Filter the shortest time paths to determine the number of shortest time paths that pass through the first path node pair;

[0074] The second initial influence value corresponding to the first path topology is determined based on the 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, please refer to Figure 3 , Figure 3 This is a schematic diagram of an application scenario based on the shortest time path provided in an embodiment of the present invention; the path, as an ordered combination of road segments, can be regarded as a whole and used to quantify the selection frequency 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 the starting point i and the ending point j, which can be expressed as formula (4):

[0076]

[0077] In the formula, p i，j Let Line be the shortest time path between starting point i and ending point j, where a path between starting point i and ending point j consists of n segments, and the length of the k-th segment is Line. k The passage 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] In the formula, EB 0 i，j Let t be the betweenness centrality of the shortest time path from start point i to end point j, i.e., the second initial influence value. o，d (pi，j ) represents the number of shortest time paths passing through the first path node pair, t o，d The total number of shortest time paths between path node O and path node d is represented by N, where path node O and path node d are any two distinct path nodes from all path nodes, and N is the number of nodes, where i ≠ j.

[0081] The meaning of path betweenness centrality is the number of times a path appears in all shortest paths. It changes with the 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 adopted to make the data fall into the [0,1] interval as much as possible. This does not modify the meaning of path betweenness centrality, but also makes the influence similar to the importance of node pairs. Its calculation formula is shown in (6).

[0082]

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

[0084] S130, evaluate the utilization rate of the corresponding path of the first path node pair based on 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 utilization rate of the path corresponding to the first path node pair based on 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] The product of the first preset weight and the first influence value corresponding to the first path node is determined as the first influence factor;

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

[0088] The sum of the first impact factor and the second impact factor is determined as the utilization rate of the first path node to 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] In the formula, W i，j Let P be the utilization rate of the path from node i to node j, N be the total number of nodes in the road traffic network, and P be the utilization rate of the path from node i to node j. i，j To determine the first influence value corresponding to node i to node j under a unified dimension, EB i，j Let β be the second influence value of the first path topology formed by node i to node j under a unified dimension, and let α be the first preset weight and α be the second preset weight, where α+β=1.

[0092] S140 marks and displays all network paths under test based on usage.

[0093] In some embodiments, S140 marks and displays all network paths under test based on usage, including:

[0094] Based on the numerical range of the usage rate, the network paths to be tested are classified to obtain at least the first type of network path and the second type of network path.

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

[0096] In this embodiment, different types of paths are marked and integrated, such as assigning colors to different types of paths (e.g., red represents high impact value, green represents low impact value), see details. Figure 4 , Figure 4 This is a schematic diagram of the target data set for critical network paths provided in this embodiment of the invention, or symbols (such as asterisks or arrows) can be 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 prioritized to improve the overall efficiency of the network. Furthermore, machine learning models can be built 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 charges) on path usage rates.

[0097] In some embodiments, please refer to Figure 5 , Figure 5 This is a schematic diagram of the structure of an urban road network path utilization assessment device provided in an embodiment of the present invention. The present invention provides an urban road network path utilization assessment 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] The 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, and the population distribution data includes the population within the influence range of each path node.

[0099] The first determining module 520 is configured to determine the first influence value of the first path node pair consisting of the first starting point and the first ending point based on the population distribution data of the first starting point and the first ending point corresponding to the network path to be tested, and to 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;

[0100] The second determining module 530 is configured to evaluate the utilization rate of the corresponding path of the first path node pair based on 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.

[0101] The tagging and display module 540 is configured to tag and display all network paths under test based on usage.

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

[0103] The urban road network is divided into units based on path nodes, and multiple unit areas are determined; the distance from any point within each unit area to the center of the area is less than the distance to the centers of other areas.

[0104] The population of each grid cell within each unit area is summed using the Thiessen polygon planar 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 as follows:

[0106] The sum of the population at the first starting point and the first ending point is determined as the first initial impact value;

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

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

[0109] The second initial influence value corresponding to the topology of the first path is determined based on the path betweenness centrality model improved by the shortest time path.

[0110] The second initial influence value is mapped to a specified interval by normalizing the maximum value of the fixed maximum value, thus obtaining the second influence value.

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

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

[0113] Filter the shortest time paths to determine the number of shortest time paths that pass through the first path node pair;

[0114] The second initial influence value corresponding to the first path topology is determined based on the 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 as follows:

[0116] The product of the first preset weight and the first influence value corresponding to the first path node is determined as the first influence factor;

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

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

[0119] In some embodiments, the marker display module 540 is specifically configured as follows:

[0120] Based on the numerical range of the usage rate, the network paths to be tested are classified to obtain at least the first type of network path and the second type of network path.

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

[0122] It should be noted that the urban road network path utilization rate assessment device provided in this application embodiment and the urban road network path utilization rate assessment method provided in this application embodiment 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 rate assessment method, and the repeated parts will not be described again.

[0123] In some embodiments, please refer to Figure 6 , Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 600 provided in this 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 aforementioned urban road network path utilization evaluation method.

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

[0125] Memory 620 may be any medium capable of containing, storing, transmitting, propagating, or transmitting instructions. For example, memory 620 may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, instruments, 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 optical discs (CD-ROMs); and may also be random access memory (RAM) or flash memory; and / or wired / wireless communication links.

[0126] This application also provides a computer-readable medium storing a computer program that, when executed by a processor, implements the aforementioned urban road network path utilization assessment method. This computer-readable medium may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into that device / apparatus / system. The aforementioned computer-readable medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0127] According to embodiments of this 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, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this 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, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, optical fiber, radio frequency signals, etc., 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 this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments and / or claims of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application. Therefore, the scope of this application should not be limited to the above embodiments, but should be defined not only by the appended claims, but also by their equivalents. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the protection scope of this invention.

Claims

1. A method for evaluating the path utilization rate of urban road networks, characterized in that, include: Acquire urban road data and population distribution data; the urban road data includes path nodes and the connection relationships between different path nodes, and the population distribution data includes the population within the influence range of each path node; Based on the population distribution data of the first starting point and the first ending point corresponding to the network path to be tested, the first influence value of the first path node pair formed by the first starting point and the first ending point is determined, and the second influence value of the first path topology associated with the first path node is determined based on the improved path betweenness centrality model. The improved path betweenness centrality model follows the shortest time path principle; the determination of the first influence value of the first path node pair formed by the first starting point and the first ending point based on the population distribution data of the first starting point and the first ending point corresponding to the path to be tested includes: determining the sum of the population of the first starting point and the first ending point as the first initial influence value; mapping the first initial influence value to a specified interval using minimum-maximum normalization to obtain the first influence value; the determination of the second influence value of the first path topology associated with the first path node based on the improved path betweenness centrality model includes: determining the second initial influence value corresponding to the first path topology based on the path betweenness centrality model improved by the shortest time path; mapping the second initial influence value to a specified interval using maximum normalization of a fixed maximum value to obtain the second influence value; The utilization rate of the first path node pair corresponding to the first path is evaluated based on 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. All network paths to be tested are marked and displayed based on the usage rate.

2. The method for evaluating the path utilization rate of urban road networks as described in claim 1, characterized in that, The acquisition of urban road data and population distribution data includes: The urban road network is divided into units based on the path nodes, and multiple unit areas are determined; the distance from any point within each unit area to the center of the area is less than the distance to the centers of other areas. The population of each grid cell within each unit area is summed using the Thiessen polygon planar partitioning method to determine the population within the influence range of each path node.

3. The method for evaluating the path utilization rate of urban road networks as described in claim 1, characterized in that, The path betweenness centrality model improved based on the shortest time path determines the second initial influence value corresponding to the first path topology, including: The total number of shortest time paths for the second path node pair is obtained using the shortest time path algorithm; the start and end points of the second path node pair are any two different path nodes among all the path nodes. The shortest time paths are filtered to determine the number of shortest time paths that pass through the first path node pair. The second initial influence value corresponding to the first path topology is determined based on the ratio of the number of shortest time paths passing through the first path node pair to the total number of shortest time paths.

4. The method for evaluating the path utilization rate of urban road networks as described in claim 1, characterized in that, The step of evaluating the utilization rate of the first path node pair corresponding to the path based on 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, includes: The product of the first preset weight corresponding to the first path node pair and the first influence value is determined as the first influence factor; The product of the second preset weight corresponding to the first path topology and the second influence value is determined as the second influence factor; The sum of the first influence factor and the second influence factor is determined as the utilization rate of the first path node to the corresponding path.

5. The method for evaluating the path utilization rate of urban road networks as described in claim 1, characterized in that, The process of marking and displaying all network paths under test based on the usage rate includes: Based on the numerical range of the usage rate, the network paths to be tested are classified to obtain at least a first type of network path and a second type of network path. The first type of network path and the second type of network path are marked and integrated to obtain a path utilization map.

6. A device for evaluating the path utilization rate of an urban road network, characterized in that, include: The data acquisition module comprises a first determination module, a second determination module, and a marker display module; among which, The data acquisition module 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, and the population distribution data includes the population within the influence range of each path node; The first determining module is configured to determine a first influence value of a first path node pair consisting of the first starting point and the first ending point based on population distribution data corresponding to the first starting point and the first ending point of the network path to be tested. This includes: determining the sum of the population of the first starting point and the first ending point as a first initial influence value; mapping the first initial influence value to a specified interval using minimum-maximum normalization to obtain a first influence value; 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. This includes: determining a second initial influence value corresponding to the first path topology based on the path betweenness centrality model improved by the shortest time path; mapping the second initial influence value to a specified interval using maximum normalization of a fixed maximum value to obtain a second influence value; wherein the improved path betweenness centrality model follows the shortest time path principle. The second determining module is configured to evaluate the utilization rate of the path corresponding to the first path node pair based on the preset weights of the first path topology, the first influence value, and the second influence value. The tagging and display module is configured to tag and display all network paths to be tested based on the usage rate.

7. An electronic device, comprising a processor and a memory; said memory having storage for computer programs, wherein, When executed by the processor, the computer program implements the urban road network path utilization assessment method according to any one of claims 1 to 5.

8. A computer storage medium, characterized in that, It stores a computer program, wherein the computer program, when executed by a processor, implements the urban road network path utilization assessment method as described in any one of claims 1 to 5.

Images