A method, device and equipment for measuring the complexity of a road network

Abstract

The present application discloses a method, apparatus and device for measuring the complexity of a road network, relating to the field of traffic road networks. The method includes: calculating the degree structure entropy of a target road network; performing line element division on the target road network to obtain a set of line elements of the target road network; the set of line elements includes a plurality of line elements, and each line element includes a plurality of local segments; for any line element of the target road network, determining the geometric shape information amount of the line element according to the length, bottom line width and area of each local segment of the line element; determining the geometric shape information entropy of the target road network according to the geometric shape information amounts of each line element of the target road network; performing weighted summation on the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network. The present application improves the accuracy of measuring the complexity of a road network.

Description

Technical Field

[0001] The present application relates to the field of traffic road networks, and particularly to a method, device, and equipment for measuring the complexity of a road network that takes into account the geometric shape and topological structure characteristics of the road network. Background Art

[0002] The quantitative research on network complexity is an important topic in the field of complex network research. For example, in urban planning and traffic engineering, understanding and quantifying the complexity of road networks is crucial for optimizing traffic flow and improving the response speed of emergency management. A more complex network structure may lead to a greater risk of traffic congestion, and in case of an emergency, a higher network complexity may affect the response time of emergency vehicles. Therefore, it is necessary to accurately measure the complexity of road networks to provide more comprehensive information support for traffic planners and managers, which helps to design and optimize urban road networks more scientifically. Summary of the Invention

[0003] The purpose of the present application is to provide a method, device, and equipment for measuring the complexity of a road network, which can accurately measure the complexity of the road network.

[0004] To achieve the above purpose, the present application provides the following solutions:

[0005] In a first aspect, the present application provides a method for measuring the complexity of a road network, including:

[0006] Calculating the degree structure entropy of the target road network; the target road network includes multiple nodes and the degree of each node;

[0007] Dividing the target road network into line elements to obtain a set of line elements of the target road network; the set of line elements includes multiple line elements, and each line element includes multiple local segments;

[0008] For any line element of the target road network, determining the geometric shape information amount of the line element according to the length of each local segment, the bottom line width of each local segment, and the area of each local segment of the line element; the bottom line width of the local segment is the Euclidean distance between the starting and ending points of the local segment; the area of the local segment is the area of the closed region enclosed by the local segment and its bottom line;

[0009] Determining the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network;

[0010] Performing weighted summation on the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

[0011] Second aspect, the present application provides a device for measuring the complexity of a road network, including:

[0012] Degree structure entropy calculation module, configured to calculate the degree structure entropy of the target road network; the target road network includes multiple nodes and the degree of each node;

[0013] Element division module, configured to perform line element division on the target road network to obtain a set of line elements of the target road network; the set of line elements includes multiple line elements, and each line element includes multiple local segments;

[0014] Geometric shape information amount calculation module, connected to the element division module, and configured to, for any line element of the target road network, determine the geometric shape information amount of the line element according to the length of each local segment, the bottom width of each local segment, and the area of each local segment of the line element; the bottom width of the local segment is the Euclidean distance between the starting and ending points of the local segment; the area of the local segment is the area of the closed region surrounded by the local segment and its bottom line;

[0015] Geometric shape information entropy determination module, connected to the geometric shape information amount calculation module, and configured to determine the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network;

[0016] Complexity determination module, respectively connected to the degree structure entropy calculation module and the geometric shape information entropy determination module, and configured to perform weighted summation on the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

[0017] Third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the computer program to implement the above-mentioned method for measuring the complexity of a road network.

[0018] According to the specific embodiments provided by the present application, the present application has the following technical effects:

[0019] The present application provides a method, device, and equipment for measuring the complexity of a road network, comprehensively considering the topological structure and road geometric morphological characteristics. In addition to using the degree structure entropy to measure the topological characteristics of the road, the road geometric shape information amount is also introduced as a supplement, which can more accurately reflect the differences in the geometric morphology and structure of the road network, thereby improving the accuracy of measuring the complexity of the road network. Description of the Drawings

[0020] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0021] Figure 1 It is an application environment diagram of a method for measuring the complexity of a road network in an embodiment of the present application;

[0022] Figure 2 It is a schematic flowchart of a method for measuring the complexity of a road network provided in an embodiment of the present application;

[0023] Figure 3 It is a schematic flowchart for measuring the complexity of a road network in an embodiment of the present application;

[0024] Figure 4 It is a schematic diagram of the local segment splitting principle in an embodiment of the present application;

[0025] Figure 5 It is a schematic diagram of a road network in an embodiment of the present application;

[0026] Figure 6 It is a schematic diagram of the functional modules of a device for measuring the complexity of a road network provided in an embodiment of the present application;

[0027] Figure 7 It is a schematic diagram of the first type of simple road;

[0028] Figure 8 It is a schematic diagram of the second type of simple road;

[0029] Figure 9 It is a schematic diagram of the third type of simple road;

[0030] Figure 10 It is a schematic diagram of the fourth type of simple road;

[0031] Figure 11 It is a schematic diagram of a road network with the first morphological structure;

[0032] Figure 12 It is a schematic diagram of a road network with the second morphological structure. Detailed implementation manners

[0033] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0034] Regarding the measurement problem of network complexity, many scholars have proposed various methods such as using connectivity, graph diameter, clustering coefficient, information entropy, etc. to measure the complexity of the network. Network density is an important indicator for measuring network complexity. The higher the density, the more connections there are between the nodes of the network, the easier the information flow between the networks, and the higher the complexity. However, using only network density to describe the complexity of the network has great limitations. It is not applicable to measuring the complexity of large-scale but low-density networks in the real world, such as the Internet. Secondly, it cannot reflect the differences in node heterogeneity, which also leads to its inability to describe the complexity differences between networks of different scales.

[0035] Entropy is generally understood as a measure of chaos, disorder, or uncertainty in a system. The definitions of entropy in different fields are different, but they are all based on similar concepts, that is, measuring the probability of the system state or the uncertainty of information. The methods of information entropy mainly include degree structure entropy, betweenness structure entropy, etc. Degree structure entropy and betweenness structure entropy respectively describe the degree distribution characteristics of nodes and the complexity and uncertainty of information flow between nodes. These two methods quantify the topological structure characteristics of nodes between networks through the method of information entropy to describe the complexity of the network. Among them, the traditional degree structure entropy method measures the complexity of the network by using the ratio of the degree of each node in the network to the sum of the degrees of all nodes in the network as the parameter of Shannon entropy. And betweenness measures the complexity of the network by measuring the intermediary role of nodes in the network. Therefore, scholars have improved Tsallis entropy by combining degree and betweenness to obtain a new structure entropy method that combines the advantages of degree and betweenness. Based on this, information entropy has become one of the most widely used methods for measuring network complexity. However, the existing entropy methods often describe the complexity of the network globally, which leads to their inability to comprehensively consider the local characteristics of the network. Secondly, different topological structures (such as small-world networks, scale-free networks, and random networks) may have the same information entropy value, but their structures and properties are completely different.

[0036] This application believes that when measuring complexity, especially in the field of real-world road networks, it is necessary to consider not only the topological properties of nodes themselves but also the impact of the unique morphological characteristics of roads on network complexity. To more accurately describe the complexity characteristics of road networks, it is necessary to describe them from both geometric and topological perspectives. Therefore, this application proposes a method for measuring the complexity of road networks considering road geometric morphology and intersection node topological structure characteristics.

[0037] To make the above objects, features, and advantages of this application more obvious and understandable, the following further details this application in conjunction with the accompanying drawings and specific embodiments.

[0038] The method for measuring the complexity of road networks provided by the embodiments of this application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, placed in the cloud or on other servers. The terminal 102 can send the target road network to the server 104. After receiving the target road network, the server 104 calculates its degree structure entropy and geometric shape information entropy, performs a weighted sum of the degree structure entropy and the geometric shape information entropy, and obtains the comprehensive complexity of the target road network. The server 104 can feedback the comprehensive complexity of the target road network to the terminal 102. In addition, in some embodiments, the method for measuring the complexity of road networks can also be implemented separately by the server 104 or the terminal 102.

[0039] Among them, the terminal 102 can be, but is not limited to, various desktop computers, laptop computers, smartphones, tablets, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers, and can also be a cloud server.

[0040] In an exemplary embodiment, as Figure 2 and Figure 3 shown, a method for measuring the complexity of road networks is provided. This method is executed by a computer device, and can be specifically executed separately by a computer device such as a terminal or a server, or jointly executed by a terminal and a server. In the embodiments of this application, taking this method applied to Figure 1 the server 104 in as an example for illustration, it includes the following steps 201 to step 205. Among them:

[0041] Step 201, calculate the degree structure entropy of the target road network. The target road network includes multiple nodes and the degree of each node. Specifically, first import the target road network data, then extract the nodes in the target road network, then calculate the degree of the nodes in the network according to the road network node data, and then calculate the degree structure entropy of the target road network.

[0042] In a road network, a node usually refers to the intersection or end point between roads. The principle of obtaining nodes is to break the line elements in the road network at the intersection points, and then extract the end points of all line elements and delete the same end points to obtain the node data of the road network.

[0043] Degree structure entropy is used to calculate the order and randomness of a network based on the probability distribution of the degrees of nodes. At the same time, it can also be used to measure the complexity of the network. It is a method for measuring the complexity of a network derived from traditional Shannon entropy:

[0044]

[0045] where E Shannon is the Shannon entropy, N is the number of nodes in the target road network, and p i is the degree structure entropy of the i-th node.

[0046] The traditional degree structure entropy method uses the degree of nodes to measure the complexity of the network. The higher the entropy value, the more complex the network. Conversely, the lower the degree structure entropy, the simpler and more ordered the network. When it is applied to Shannon entropy, where degree(i) is the degree of the i-th node and degree(j) is the degree of the j-th node, is used to traverse the degrees of each node in the network and sum them in the loop calculation. Therefore, the present application uses the following formula to calculate the degree structure entropy of the target road network:

[0047]

[0048] where E D is the degree structure entropy of the target road network.

[0049] Degree structure entropy can capture the global characteristics of the network more comprehensively, reflect the complexity, heterogeneity and dispersion of the network. At the same time, as a topological attribute of network nodes, degree can well reflect the complexity of the network from a topological perspective.

[0050] Step 202, divide the line elements of the target road network to obtain a set of line elements of the target road network. The set of line elements includes multiple line elements, and each line element includes multiple local segments.

[0051] The measurement of line features in terms of geometric shape information entropy in this application is based on the geometric shape information content, which focuses on the local attributes of the network, that is, individual line features. Therefore, to calculate the geometric shape quantity, it is necessary to divide the line features.

[0052] In an exemplary embodiment, step 202 includes the following steps 301 to 303.

[0053] Step 301, perform road division on the target road network to obtain a plurality of line features. Each line feature represents a road.

[0054] Step 302, determine all the bending points in each road.

[0055] Step 303, determine the local segments in each line feature according to the bending points in each road. Wherein, between two adjacent bending points is a local segment.

[0056] This application uses the bending point as a method for dividing individual line features to obtain the local line feature sequence obtained by dividing a single line feature. As Figure 4 shown, point A and point D are respectively the starting and ending nodes of the arc segment, point B and point C are respectively the vertices of the two bends. Define the bending point as the point where the twisting direction of the line feature changes, and use the midpoint of the local line segment in the line feature where the point is located to represent, that is, Figure 4 point a1 in is the bending point. Among them, the method of calculating the included angle of vectors is used to judge whether the direction between two vectors twists through the cross product.

[0057] In a specific example, as Figure 5 shown, there are 7 roads in this road network, which are represented by different colors respectively. To divide the local segments of the roads in the road network, it is necessary to determine all the bending points in each road according to the above-mentioned segmentation algorithm for each road in the road network, and then divide the processed road according to the bending points, and the set of local segments divided by each road can be obtained. By calculating the local segments in the set, the geometric shape information content of each road can be obtained, and then the geometric shape information entropy of the entire road network can be obtained.

[0058] Step 203, for any line feature of the target road network, determine the geometric shape information content of the line feature according to the length of each local segment, the bottom line width of each local segment, and the area of each local segment of the line feature.

[0059] Wherein, the bottom line width of the local segment is the Euclidean distance between the starting and ending points of the local segment. The area of the local segment is the area of the closed region enclosed by the local segment and its bottom line.

[0060] It should be noted that all of the above parameters can be obtained by extracting each independent road from the road network, then segmenting local road sections, and calculating using existing common relevant distance calculation algorithms.

[0061] The basic idea of the geometric shape information quantity is to use the method of information theory to locally quantify the complexity of the geometric shape of a single link in the network. The complexity of the shape can be measured by the concept of information entropy. The larger the entropy, the higher the complexity of the shape and the more information is required; the smaller the entropy, the simpler the shape and the less information is required. In this embodiment, the method of information theory is used to describe the degree of bending of the line element, and then the geometric information quantity of the line element is calculated.

[0062] In an exemplary embodiment, step 203 includes the following steps 401 to 403.

[0063] Step 401, for any local segment in the line element, determine the bending degree index of the local segment according to the length of the local segment and the bottom width of the local segment.

[0064] Specifically, the following formula is used to determine the bending degree index of the m-th local segment:

[0065]

[0066] Where, is the bending degree index of the m-th local segment, is the length of the m-th local segment, is the bottom width of the m-th local segment, 1 ≤ m ≤ M.

[0067] Step 402, determine the bending area ratio index of each local segment according to the area of each local segment in the line element.

[0068] Specifically, the following formula is used to determine the bending degree index of the m-th local segment:

[0069]

[0070] Where, is the bending degree index of the m-th local segment, is the area of the m-th local segment, is the average area of all local segments in the line element to which the m-th local segment belongs, 1 ≤ m ≤ M.

[0071] Step 403, determine the geometric shape information quantity of the line element according to the bending degree index and the bending area ratio index of each local segment in the line element.

[0072] Specifically, the following formula is used to determine the geometric shape information amount of the \(l\)-th line element:

[0073]

[0074] where \(I\) GS (l) is the geometric shape information amount of the \(l\)-th line element, \(M\) is the number of local segments in the \(l\)-th line element, is the curvature index of the \(m\)-th local segment, is the curvature index of the \(m\)-th local segment. When the base of the logarithm is 2, the information unit is bit.

[0075] Step 204: Determine the geometric shape information entropy of the target road network according to the geometric shape information amounts of each line element of the target road network.

[0076] The geometric shape information entropy is a holistic measure for the road network based on the geometric shape information amounts of line elements. It combines the analysis method of information theory and is used to quantitatively evaluate the unique geometric morphological attributes of the road network from a global perspective. Specifically, the geometric shape information entropy introduces the idea of entropy on the basis of the geometric shape information amounts by examining the geometric shape complexity and spatial distribution patterns of line elements (such as roads, rivers, etc.), combines the geometric shape information amounts considered for individual line elements, and thus reveals the unique information redundancy and complexity characteristics of the road network structure affected by road morphological features. When it is applied to the traditional Shannon entropy that is, the following formula is used to determine the geometric shape information entropy of the target road network:

[0077]

[0078] where \(E\) G is the geometric shape information entropy of the target road network, \(L\) is the number of line elements in the target road network, \(g\) l is the geometric shape information entropy of the \(l\)-th line element, \(I\) GS (l) is the geometric shape information amount of the \(l\)-th line element.

[0079] Step 205: Perform a weighted sum of the degree structure entropy and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

[0080] This application uses a weighted method to combine these two different measures, namely the degree structure entropy and the geometric shape information entropy, to more comprehensively reflect the complexity of the urban road network. By weighting the two measures, the topological structure and geometric morphological information can be combined to obtain a more comprehensive complexity measure.

[0081] Specifically, the comprehensive complexity of the target road network is as follows:

[0082]

[0083] Among them, E C is the comprehensive complexity of the target road network, ω is the weight coefficient. Since the dimensions of the two measures in this application are the same, the weight coefficient is set to 0.5, that is, the degree structure entropy and the geometric shape information entropy are respectively given a weight of 50%. This weighted method ensures that the two measures have equal importance in the comprehensive result.

[0084] Furthermore, the comprehensive complexity of the target road grid can be applied to the comprehensive processing of the road network map, and the road network can be classified according to the road complexity to achieve thinning and simplification operations.

[0085] Traditional methods for measuring the complexity of road networks often ignore the structural differences brought about by changes in road geometric forms, resulting in difficulty in fully reflecting the differences between networks when faced with road networks of similar scale but different structures. The method for measuring the complexity of road networks provided in this application comprehensively considers topological structures and road geometric form characteristics. In addition to using the degree structure entropy of traditional nodes to measure the topological characteristics of roads, it also introduces the amount of road geometric form information as a supplement, including but not limited to changes in the morphological curvature of roads, the ratio of curved area, etc. These characteristics can more precisely reflect the complexity of roads in geometric forms and help quantify and analyze the complexity of road networks under different network structure layouts. Through this comprehensive method, the differences in the geometric forms and structures of road networks can be more accurately reflected, providing more detailed and accurate support for complexity measurement. Therefore, considering geometric form characteristics as part of the complexity measure can provide more comprehensive information support for traffic planners and managers, helping to more scientifically design and optimize urban road networks.

[0086] Based on the same inventive concept, the embodiments of this application also provide a device for measuring the complexity of a road network for implementing the method for measuring the complexity of a road network involved above. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the device for measuring the complexity of a road network provided below can refer to the limitations on the method for measuring the complexity of a road network above, and will not be elaborated here.

[0087] In an exemplary embodiment, as Figure 6 shown, a device for measuring the complexity of a road network is provided, including: a degree structure entropy calculation module 601, an element division module 602, a geometric shape information amount calculation module 603, a geometric shape information entropy determination module 604, and a complexity determination module 605.

[0088] The degree structure entropy calculation module 601 is used to calculate the degree structure entropy of the target road network. The target road network includes a plurality of nodes and the degree of each node.

[0089] The element division module 602 is used to divide the line elements of the target road network to obtain a set of line elements of the target road network. The set of line elements includes a plurality of line elements, and each line element includes a plurality of local segments.

[0090] The geometric shape information amount calculation module 603 is connected to the element division module 602. The geometric shape information amount calculation module 603 is used to determine the geometric shape information amount of any line element of the target road network according to the length of each local segment, the bottom width of each local segment, and the area of each local segment of the line element. Wherein, the bottom width of the local segment is the Euclidean distance between the starting point and the ending point of the local segment. The area of the local segment is the area of the closed region enclosed by the local segment and its bottom line.

[0091] The geometric shape information entropy determination module 604 is connected to the geometric shape information amount calculation module 603. The geometric shape information entropy determination module 604 is used to determine the geometric shape information entropy of the target road network according to the geometric shape information amount of each line element of the target road network.

[0092] The complexity determination module 605 is respectively connected to the degree structure entropy calculation module 601 and the geometric shape information entropy determination module 604. The complexity determination module 605 is used to perform weighted summation on the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

[0093] This application uses the traditional degree structure entropy method to consider the topological structure characteristics of nodes in the road network, and considers the geometric shape characteristics of the road, that is, the comprehensive consideration of the road shape complexity is reflected by using the concept of curvature. Finally, combined with the global measurement of the road network complexity by the traditional degree structure entropy method, compared with the traditional degree structure entropy complexity measurement method, this application has higher sensitivity to road networks with different morphological structures, can measure the complexity of road networks with different morphological structures more accurately, realizes the refined measurement of the road network complexity, and uses comparative experiments of multiple road networks to verify the effectiveness of the method of this application. The results are shown in Table 1, Table 2 and Figures 7 to 12 as shown.

[0094] As can be seen from Table 1, when only considering the topological structure characteristics of road network nodes, the results reflected by measuring the complexity of a single road with fewer nodes are the same and single, and for a circular road with only one node, the complexity result is even 0. However, by using geometric shape information, the complexity of the local characteristics of the road network (i.e., a single road) can be measured, making the present application more sensitive to the complexity of the road network.

[0095] Table 1 Complexity of Four Common Road Forms

[0096]

[0097] From the data in Table 2, it can be seen that compared with the traditional method, by considering the geometric morphological structure of the road network, the geometric shape information entropy makes the results of the two networks more significantly different. The result of the geometric shape information entropy is 0.0124 higher than that of the traditional degree structure entropy, and the difference in the final comprehensive information entropy increases by 0.0109. When evaluating road networks of similar scale, the present application can more acutely reflect the differences between road network structures compared with the traditional method, and more accurately reflect the differences generated by similar-scale road networks under the influence of different structures. The introduction of the geometric structure makes the evaluation results more scientific and accurate, and improves the reliability of the results.

[0098] Table 2 Complexity of Different Road Network Structures

[0099]

[0100] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0101] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0102] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0103] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0104] In this application, all actions of obtaining signals, information, or data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located and obtaining the authorization given by the owner of the corresponding device.

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the various embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random-access memory (ReRAM), magnetoresistive random-access memory (MRAM), ferroelectric random-access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0106] The databases involved in the various embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the various embodiments provided in this application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logics, data processing logics based on quantum computing, etc., without limitation.

[0107] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

[0108] In this article, specific examples are used to illustrate the principles and implementation manners of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application. At the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manners and application scopes. To sum up, the content of this specification should not be construed as a limitation to the present application.

Claims

1. A method for measuring the complexity of a road network, characterized in that, The method for measuring the complexity of the road network includes: Use the formula to calculate the degree structure entropy of the target road network; where E D is the degree structure entropy of the target road network, N is the number of nodes in the target road network, p i is the degree structure entropy of the i-th node, degree(i) is the degree of the i-th node, and degree(j) is the degree of the j-th node; the target road network includes multiple nodes and the degree of each node; Performing line element division on the target road network to obtain a set of line elements of the target road network, specifically including: dividing the target road network into roads to obtain a plurality of line elements; determining all the bending points in each road; determining the local segments in each line element according to the bending points in each road; the set of line elements includes a plurality of line elements, each line element includes a plurality of local segments; each line element represents a road; a local segment is between two adjacent bending points; For any line element of the target road network, determining the geometric shape information quantity of the line element according to the length, bottom width and area of each local segment of the line element, specifically including: for any local segment in the line element, determining the bending degree index of the local segment according to the length and bottom width of the local segment; determining the bending area ratio index of each local segment according to the area of each local segment in the line element; determining the geometric shape information quantity of the line element according to the bending degree index and bending area ratio index of each local segment in the line element; the bottom width of the local segment is the Euclidean distance between the start and end points of the local segment; the area of the local segment is the area of the closed region formed by the local segment and its bottom line; According to the geometric information amount of each line element of the target road network, the formula is used to determine the geometric information entropy of the target road network; where E G is the geometric information entropy of the target road network, L is the number of line elements in the target road network, and g l is the geometric information entropy of the l-th line element, and I GS (l) is the geometric information amount of the l-th line element; Performing weighted summation on the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

2. The method for measuring the complexity of a road network according to claim 1, wherein The bending degree index of the m-th local segment is determined by the following formula: Among them, is the bending degree index of the m-th local fold segment, is the length of the m-th local fold segment, is the bottom line width of the m-th local fold segment.

3. The method for measuring the complexity of a road network according to claim 1, wherein The bending degree index of the m-th local segment is determined by the following formula: Among them, is the curvature index of the m-th local fold segment, is the area of the m-th local fold segment, is the average area of all local fold segments in the line element to which the m-th local fold segment belongs.

4. The method for measuring the complexity of a road network according to claim 1, wherein The geometric shape information quantity of the l-th line element is determined by the following formula: Among them, I GS (l) is the geometric shape information amount of the l-th line element, M is the number of local segments in the l-th line element, is the curvature index of the m-th local segment, is the curvature index of the m-th local segment.

5. A road network complexity measurement device, applied to the road network complexity measurement method according to any one of claims 1-4, characterized in that, The device for measuring the complexity of the road network includes: Degree structure entropy calculation module, which is used to adopt the formula to calculate the degree structure entropy of the target road network; where E D is the degree structure entropy of the target road network, N is the number of nodes in the target road network, p i is the degree structure entropy of the i-th node, degree(i) is the degree of the i-th node, and degree(j) is the degree of the j-th node; the target road network includes multiple nodes and the degree of each node; An element division module, configured to perform line element division on the target road network to obtain a set of line elements of the target road network, specifically including: dividing the target road network into roads to obtain a plurality of line elements; determining all the bending points in each road; determining the local segments in each line element according to the bending points in each road; the set of line elements includes a plurality of line elements, each line element includes a plurality of local segments; each line element represents a road; a local segment is between two adjacent bending points; A geometric shape information quantity calculation module, connected to the element division module, is configured to determine the geometric shape information quantity of any line element of the target road network according to the length, bottom width, and area of each local segment of the line element. Specifically, it includes: for any local segment in the line element, determining the curvature index of the local segment according to the length of the local segment and the bottom width of the local segment; determining the bending area ratio index of each local segment according to the area of each local segment in the line element; determining the geometric shape information quantity of the line element according to the curvature index and bending area ratio index of each local segment in the line element; the bottom width of the local segment is the Euclidean distance between the starting and ending points of the local segment; the area of the local segment is the area of the closed region enclosed by the local segment and its bottom line; The geometric shape information entropy determination module, connected to the geometric shape information quantity calculation module, is used to determine the geometric shape information entropy of the target road network according to the geometric shape information quantity of each line element of the target road network, using the formula to determine the geometric shape information entropy of the target road network; where E G is the geometric shape information entropy of the target road network, L is the number of line elements in the target road network, g l is the geometric shape information entropy of the l-th line element, and L GS (l) is the geometric shape information quantity of the l-th line element; A complexity determination module, respectively connected to the degree structure entropy calculation module and the geometric shape information entropy determination module, is configured to perform weighted summation on the degree structure entropy of the target road network and the geometric shape information entropy of the target road network to obtain the comprehensive complexity of the target road network.

6. A computer device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the road network complexity measurement method according to any one of claims 1-4.

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