Traffic flow orderliness evaluation method, device, computer equipment, storage medium and computer program product

Abstract

This application relates to a method, apparatus, computer device, storage medium, and computer program product for assessing traffic flow orderliness. The method includes: determining multiple actual traffic flows corresponding to a target traffic flow type, each actual traffic flow having a starting point and a destination point; determining an actual space fill index based on each actual traffic flow; randomly simulating traffic flow between each starting point and each destination point to obtain multiple simulated traffic flows, and determining a simulated space fill index based on each simulated traffic flow; and determining a traffic flow orderliness assessment index corresponding to the target traffic flow type based on the difference between the actual space fill index and the simulated space fill index. This method enables a quantitative assessment of traffic flow orderliness.

Description

Technical Field

[0001] This application relates to the field of computer information technology, and in particular to a method, apparatus, computer equipment, storage medium and computer program product for assessing traffic flow orderliness. Background Technology

[0002] Urbanization is a major trend in modern times, with more than half of the world's population now living in cities. The acceleration of urbanization and the evolution of urban spatial structures have a profound impact on residents' daily commuting behavior. As a key component of urban dynamics, traffic flow is not only an important consideration in urban planning and traffic management, but also a key indicator of the health and efficiency of urban infrastructure. Therefore, continuous investigation of traffic flow patterns is crucial for advancing urban planning and the well-being of urban residents.

[0003] In the fields of transportation and urban planning, the orderliness of traffic flow is one of the important indicators used to indicate the quality of traffic. Empirically, an orderly distribution of traffic flow indicates a well-designed and compact urban planning system, which can reduce vehicle mileage and alleviate congestion. Conversely, disorderly traffic flow can lead to traffic congestion, increased travel time, and a decrease in the quality of life for urban residents.

[0004] To provide more effective guidance for urban and transportation planning, it is crucial to effectively measure traffic flow orderliness. Therefore, a method for assessing traffic flow orderliness is needed. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, storage medium, and computer program product for assessing traffic flow orderliness in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for assessing traffic flow orderliness. The method includes:

[0007] Identify multiple actual traffic flows corresponding to the target traffic flow type, wherein each actual traffic flow has a starting point and a destination point;

[0008] The actual space fill index is determined based on the actual traffic flow described above;

[0009] Randomly simulate the traffic flow between each of the aforementioned starting points and each of the aforementioned arrival points to obtain multiple simulated traffic flows, and determine the simulated space fill index based on each of the aforementioned simulated traffic flows;

[0010] Based on the difference between the actual space filling index and the simulated space filling index, the traffic flow orderliness assessment index corresponding to the target traffic flow type is determined.

[0011] In one embodiment, determining the multiple actual traffic flows corresponding to the target traffic flow type includes:

[0012] Acquire multiple traffic trajectory information, wherein the traffic trajectory information consists of multiple arrival coordinates;

[0013] For any traffic trajectory information, the starting point and the destination point are determined based on multiple arrival coordinates of the traffic trajectory information, and the trajectory information located between the starting point and the destination point is taken as the actual traffic flow.

[0014] In one embodiment, determining the starting point and destination point based on multiple arrival coordinates of the traffic trajectory information includes:

[0015] Obtain the origin and destination types corresponding to the target traffic flow type;

[0016] If the origin point type and / or the destination point type belong to a personal function type, determine the expected activity period corresponding to the origin point type and / or the destination point type;

[0017] The arrival coordinates corresponding to the expected activity period in the traffic trajectory information are clustered, and the starting point and / or arrival point in the traffic trajectory information are determined based on the clustering results.

[0018] In one embodiment, determining the starting point and arrival point based on multiple arrival coordinates of the traffic trajectory information further includes:

[0019] If the starting point type and / or the arrival point type belong to a common function type, determine multiple target locations corresponding to the starting point type and / or the arrival point type;

[0020] Based on the correspondence between the visited coordinates and the target locations, the starting point and / or arrival point in the traffic trajectory information are determined.

[0021] In one embodiment, determining the actual space fill index based on each of the actual traffic flows includes:

[0022] Determine a first space covering each of the said starting points, and determine a second space covering each of the said arrival points;

[0023] Perform a Cartesian product on the first space and the second space to obtain the target space, and divide the target space into multiple target subspaces of the same size;

[0024] For any of the target subspaces, if the target subspace corresponds to any of the actual traffic flows, the target subspace is determined as a non-empty target subspace;

[0025] Based on the size of the target subspace and the number of non-empty target subspaces, the actual space fill index corresponding to the actual traffic flow is determined.

[0026] In one embodiment, the random simulation of traffic flow between each of the starting points and each of the arrival points yields multiple simulated traffic flows, and a simulated space fill index is determined based on each of the simulated traffic flows, including:

[0027] The starting points and the arrival points are randomly combined to obtain multiple combinations of start and end points. The traffic flow corresponding to each combination of start and end points is simulated to obtain the initial simulated traffic flow. The initial simulated space fill index is determined based on each initial simulated traffic flow.

[0028] Proceed to the step of randomly combining the starting points and the destination points until the preset conditions are met;

[0029] The simulation space fill index is determined based on each of the initial simulation space fill indexes.

[0030] Secondly, this application also provides a traffic flow orderliness assessment device. The device includes:

[0031] The first determining module is used to determine multiple actual traffic flows corresponding to the target traffic flow type, wherein the actual traffic flows have a starting point and an arrival point;

[0032] The second determining module is used to determine the actual space fill index based on each of the actual traffic flows;

[0033] The simulation module is used to randomly simulate the traffic flow between each of the starting points and each of the destination points, obtain multiple simulated traffic flows, and determine the simulated space fill index based on each of the simulated traffic flows;

[0034] The third determining module is used to determine the traffic flow orderliness assessment index corresponding to the target traffic flow type based on the difference between the actual space filling index and the simulated space filling index.

[0035] In one embodiment, the first determining module is further configured to:

[0036] Acquire multiple traffic trajectory information, wherein the traffic trajectory information consists of multiple arrival coordinates;

[0037] For any traffic trajectory information, the starting point and the destination point are determined based on multiple arrival coordinates of the traffic trajectory information, and the trajectory information located between the starting point and the destination point is taken as the actual traffic flow.

[0038] In one embodiment, the first determining module is further configured to:

[0039] Obtain the origin and destination types corresponding to the target traffic flow type;

[0040] If the origin point type and / or the destination point type belong to a personal function type, determine the expected activity period corresponding to the origin point type and / or the destination point type;

[0041] The arrival coordinates corresponding to the expected activity period in the traffic trajectory information are clustered, and the starting point and / or arrival point in the traffic trajectory information are determined based on the clustering results.

[0042] In one embodiment, the first determining module is further configured to:

[0043] If the starting point type and / or the arrival point type belong to a common function type, determine multiple target locations corresponding to the starting point type and / or the arrival point type;

[0044] Based on the correspondence between the visited coordinates and the target locations, the starting point and / or arrival point in the traffic trajectory information are determined.

[0045] In one embodiment, the second determining module is further configured to:

[0046] Determine a first space covering each of the said starting points, and determine a second space covering each of the said arrival points;

[0047] Perform a Cartesian product on the first space and the second space to obtain the target space, and divide the target space into multiple target subspaces of the same size;

[0048] For any of the target subspaces, if the target subspace corresponds to any of the actual traffic flows, the target subspace is determined as a non-empty target subspace;

[0049] Based on the size of the target subspace and the number of non-empty target subspaces, the actual space fill index corresponding to the actual traffic flow is determined.

[0050] In one embodiment, the simulation module is further configured to:

[0051] The starting points and the arrival points are randomly combined to obtain multiple combinations of start and end points. The traffic flow corresponding to each combination of start and end points is simulated to obtain the initial simulated traffic flow. The initial simulated space fill index is determined based on each initial simulated traffic flow.

[0052] Proceed to the step of randomly combining the starting points and the destination points until the preset conditions are met;

[0053] The simulation space fill index is determined based on each of the initial simulation space fill indexes.

[0054] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement any of the methods described above.

[0055] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements any of the above methods.

[0056] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements any of the above methods.

[0057] The aforementioned traffic flow orderliness assessment method, apparatus, computer equipment, storage medium, and computer program product calculate the actual space fill index based on each actual traffic flow corresponding to the target traffic flow type. It then calculates the simulated space fill index by randomly simulating the traffic flow between the starting and ending points of each actual traffic flow. Based on the difference between the actual space fill index and the simulated space fill index, it determines the traffic flow orderliness assessment index corresponding to the target traffic flow type. Since the space fill index reflects the spatial distribution and complexity of traffic flow, and each simulated traffic flow obtained through random simulation represents the most disordered traffic situation corresponding to the target traffic flow type, the traffic flow orderliness assessment index calculated based on the difference between the actual space fill index and the simulated space fill index can represent the degree of optimization of the actual situation compared to the most disordered traffic situation. Therefore, it can be used as a numerical value representing traffic flow orderliness to quantify traffic flow orderliness. Attached Figure Description

[0058] Figure 1 This is a flowchart illustrating a traffic flow orderliness assessment method in one embodiment;

[0059] Figure 2 This is a flowchart illustrating step 102 in one embodiment;

[0060] Figure 3 This is a flowchart illustrating step 204 in one embodiment;

[0061] Figure 4 This is a flowchart illustrating step 104 in one embodiment;

[0062] Figure 5 This is a schematic diagram illustrating the calculation of box dimension in one embodiment;

[0063] Figure 6 This is a flowchart illustrating step 106 in one embodiment;

[0064] Figure 7 This is a structural block diagram of a traffic flow orderliness assessment device in one embodiment;

[0065] Figure 8 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0067] In one embodiment, such as Figure 1 As shown, a method for assessing traffic flow orderliness is provided. This embodiment illustrates the application of this method to a server; however, it is understood that the method can also be applied to a terminal, or to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0068] Step 102: Determine multiple actual traffic flows corresponding to the target traffic flow type. Each actual traffic flow has a starting point and an arrival point.

[0069] In this embodiment, the target traffic flow type is the type of traffic flow to be evaluated, and the actual traffic flow is the traffic flow that actually occurs in reality and belongs to the target traffic flow type. Each actual traffic flow has a starting point and a destination point.

[0070] To illustrate with a practical example, when the target traffic flow type is commuting flow from residence to workplace, if resident 1 commutes between residence A1 and workplace B1, and resident 2 commutes between residence A2 and workplace B2, then the actual traffic flow can be represented as (A1, B1) and (A2, B2). The starting point of (A1, B1) is A1 and the destination point is B1, and the starting point of (A2, B2) is A2 and the destination point is B2.

[0071] Actual traffic flow can be actively uploaded to the server by residents; alternatively, the server can collect residents' traffic trajectory information and identify actual traffic flow from this information. In one embodiment, such as... Figure 2 As shown, when identifying actual traffic flow using traffic trajectory information, step 102 includes:

[0072] Step 202: Obtain multiple traffic trajectory information, which consists of multiple arrival coordinates;

[0073] Step 204: For any traffic trajectory information, determine the starting point and destination point based on multiple arrival coordinates of the traffic trajectory information, and take the trajectory information located between the starting point and destination point as the actual traffic flow.

[0074] In this embodiment of the application, a traffic trajectory information consists of the coordinates of all locations visited by a resident within a day (hereinafter referred to as the visit coordinates). Based on multiple visit coordinates of the traffic trajectory information, it can be determined whether there are start points and destination points in the traffic trajectory information that conform to the target traffic flow type, and then the trajectory information between the start points and destination points can be used as the actual traffic flow.

[0075] For example, clustering can be performed on the various visit coordinates, and the clustering results represent the locations where residents stayed during the day. This can further determine whether each location contains the starting and ending points corresponding to the target traffic flow type. For instance, if the target traffic flow type is commuting from residence to workplace, and the locations are respectively residential buildings, shopping malls, stations, and office buildings, it can be determined that residential buildings are highly likely to be the starting points corresponding to the target traffic flow type, and office buildings are highly likely to be the ending points.

[0076] If the starting point or destination cannot be identified from a traffic trajectory using the above method, it indicates that the traffic trajectory may not contain actual traffic flow belonging to the target traffic flow type. In subsequent steps, this type of traffic trajectory can be disregarded. Alternatively, if all the timestamps of the arrival coordinates corresponding to the starting point are later than the timestamps of the arrival coordinates corresponding to the destination, it indicates that the traffic trajectory does not contain traffic flow from the starting point to the destination, and this type of traffic trajectory can also be disregarded.

[0077] For traffic trajectory information from which the origin and destination points can be identified, the trajectory information located between the origin and destination points can be considered as the actual traffic flow. "Trajectory information located between the origin and destination points" refers to all visiting coordinates from the last visiting coordinate located at the origin point to the first visiting coordinate located at the destination point.

[0078] In one embodiment, the origin and destination points can also be identified based on the origin and destination types corresponding to the target traffic flow type. In this case, step 204 includes:

[0079] Step 302: Obtain the origin and destination types corresponding to the target traffic flow type;

[0080] Step 304: If the origin type and / or destination type belong to the personal function type, determine the expected activity period corresponding to the origin type and / or destination type;

[0081] Step 306: Cluster the arrival coordinates corresponding to the expected activity period in the traffic trajectory information, and determine the starting point and / or arrival point in the traffic trajectory information based on the clustering results.

[0082] In this embodiment, the target traffic flow type corresponds to an origin point type and an arrival point type. For any actual traffic flow belonging to the target traffic flow type, the origin point of the actual traffic flow belongs to the origin point type, and the arrival point belongs to the arrival point type. For example, if the origin point type corresponding to the target traffic flow type is a residence and the arrival point type is a school, the origin point of the actual traffic flow belonging to the target traffic flow type is any residence of the resident, and the arrival point is any school attended by the resident.

[0083] After obtaining the origin and destination types, it can be determined whether they belong to public function types or personal function types, that is, whether the origin and destination types represent the public or personal functions of the location. Public function refers to the function of the location as a public place, while personal function refers to the function of the location for individual residents. For example, for a school, its function as a public place is "school," while its functions for individual residents might include "workplace," "residence," "dining place," etc.

[0084] Taking the example of the aforementioned target traffic flow type, where the starting point type is a residence and the destination type is a school, the starting point type "residence" represents the personal function of the location, so this starting point type belongs to the personal function type. The destination type "school" represents the public function of the location, so this destination type belongs to the public function type.

[0085] After determining the target traffic flow type, technicians can manually set the corresponding origin and destination types for the target traffic flow, and specify whether the origin and destination types belong to personal or public function types.

[0086] After acquiring multiple traffic trajectory information, the server extracts the origin and destination points from the traffic trajectory information using different methods, depending on whether the origin and destination types belong to personal or public function types. For origin and / or destination types belonging to personal function types, the server can determine the expected activity time period corresponding to the origin and / or destination types. The expected activity time period is the estimated time during which residents need a certain location to fulfill the personal function represented by the first type. The expected activity time period can be determined by technical personnel based on the area setting for assessing traffic flow orderliness, through resident surveys, etc. For example, if the destination type is a dining location, the expected activity time period for this destination type can be set as 11:00-13:00 and 17:00-20:00 for an eastern city; and as 13:00-15:00 and 20:00-22:00 for a western city.

[0087] After obtaining the expected activity period, for each traffic trajectory information, based on the timestamps of each arrival coordinate, the arrival coordinates within the expected activity period are extracted from the traffic trajectory information, and these arrival coordinates are then clustered. If the expected activity period consists of multiple time periods, the arrival coordinates for each time period need to be clustered separately. After clustering, the location corresponding to the largest cluster can be used as the starting point or destination point included in the traffic trajectory information. Since the largest cluster represents the location where residents spend the most time during the expected activity period, and locations where residents spend the most time during the expected activity period are more likely to fulfill their personal functional needs, using the largest cluster as the starting point or destination point helps improve the accuracy of starting point and destination point identification.

[0088] In one embodiment, for origin and / or destination types belonging to a public function type, the server may use the following process to identify origin and / or destination: if the origin and / or destination types belong to a public function type, determine multiple target locations corresponding to the origin and / or destination types; and determine the origin and / or destination in the traffic trajectory information based on the correspondence between each arrival coordinate and each target location.

[0089] In this embodiment, the server can determine multiple target locations belonging to the origin and / or destination types. For example, when the destination type is a school, the server can determine multiple locations that conform to the "school" category from the area where the traffic flow orderliness needs to be assessed, and use these locations as target locations. The server can then obtain the location coordinate range corresponding to each target location, and then determine whether each arrival coordinate contained in the traffic trajectory information is located within any location coordinate range. If there is a location coordinate range that covers more than a preset number of arrival coordinates, it can be determined that the resident has stayed at the target location corresponding to that location coordinate range for a sufficient period of time, and that target location can be used as the origin and / or destination in the traffic trajectory information.

[0090] The following example illustrates the entire process of identifying the origin / destination point based on traffic trajectory information. Assume that traffic trajectory information 1 contains arrival coordinates a1, b1, c1, and traffic trajectory information 2 contains arrival coordinates a2, b2, c2, d2. The origin point type corresponding to the target traffic flow type is residential, and the destination point type is school. Therefore, the origin point type belongs to the personal function type, and the destination point type belongs to the public function type.

[0091] For the starting point type "Residence," first determine the expected activity time period corresponding to this starting point type, for example: 0:00 to 6:00. Cluster the arrival coordinates (let's say b1) corresponding to the timestamps in traffic trajectory information 1 for this expected activity time period, obtaining the clustering results. Since only one clustering result can be obtained from traffic trajectory information 1, the location corresponding to this clustering result (i.e., the location corresponding to b1) can be used as the starting point in traffic trajectory information 1. Cluster the arrival coordinates (let's say a2, b2, c2) corresponding to the timestamps in traffic trajectory information 2 for this expected activity time period, obtaining the clustering results (let's say a2 and b2 correspond to one cluster, and c2 corresponds to another). The location corresponding to the largest clustering result (i.e., the location corresponding to a2 and b2) can be used as the starting point in traffic trajectory information 2.

[0092] For the arrival point type "School," we can determine which schools exist in the area where traffic flow orderliness is being assessed. Assume there are School A, School B, and School C. For each arrival coordinate in traffic trajectory information 1, we determine which arrival coordinates fall within the coordinate range of School A, School B, and School C. For example, if c1 falls within the coordinate range of School B, then School B can be considered the arrival point in traffic trajectory information 1. Similarly, we perform the same operation for traffic trajectory information 2. Assuming d2 falls within the coordinate range of School A, then School A can be considered the arrival point in traffic trajectory information 2.

[0093] Then, the trajectory information (b1, c1) between the starting point and the destination in traffic trajectory information 1 is taken as the actual traffic flow, and the trajectory information (b2, c2, d2) between the starting point and the destination in traffic trajectory information 2 is taken as the actual traffic flow.

[0094] Step 104: Determine the actual space fill index corresponding to the actual traffic flow.

[0095] In this embodiment, the actual space fill index is used to characterize the spatial distribution complexity and regularity of actual traffic flow. This index can be calculated based on any mathematical concept that can describe the complexity of spatial distribution, such as fractal dimension, information entropy, topological entropy, etc.

[0096] like Figure 4 As shown, taking the calculation of the actual space filling index based on the box dimension in the fractal dimension as an example, step 104 includes the following steps:

[0097] Step 402: Determine the first space covering each starting point and the second space covering each arrival point;

[0098] Step 404: Perform a Cartesian product on the first space and the second space to obtain the target space, and divide the target space into multiple target subspaces of the same size;

[0099] Step 406: For any target subspace, if the target subspace corresponds to any actual traffic flow, the target subspace is determined as a non-empty target subspace.

[0100] Step 408: Based on the size of the target subspace and the number of non-empty target subspaces, determine the actual space fill index corresponding to the actual traffic flow.

[0101] In this embodiment, the dimensions of the first space and the second space can be determined by a technician based on the actual needs of studying traffic flow. For example, when it is necessary to study the planar distribution orderliness of traffic flow, the first space and the second space can be two-dimensional spaces. The first space covering each starting point refers to a two-dimensional space that can encompass the coordinates (x, y) of each starting point on a real two-dimensional plane, and the second space refers to a two-dimensional space that can encompass the coordinates (x, y) of each arrival point on a real two-dimensional plane. If the technician has a need to study the orderliness of traffic flow in three-dimensional space, then the first space and the second space can be three-dimensional spaces. The first space and the second space can be the smallest of these spaces, or spaces of any size; this embodiment does not specifically limit this.

[0102] Taking the Cartesian product of the first and second spaces yields a new target space. This target space can then be divided according to the concept of box-dimensionality, resulting in multiple target subspaces of equal size. These target subspaces are essentially the "mesh boxes" used in box-dimensionality; therefore, the dimensions of the target subspaces are the same as those of the target space, and each target subspace is of the same size. Furthermore, "same size" means that the projection length in each dimension is the same.

[0103] Then, for each target subspace, it can be determined whether the target subspace is a non-empty target subspace, that is, whether the target subspace corresponds to at least one actual traffic flow. The definition of "target subspace corresponding to actual traffic flow" can be that the target subspace corresponds to the actual traffic flow if any point in the actual traffic flow is located within the target subspace. Alternatively, it can be that the target subspace corresponds to the actual traffic flow only if the starting point or destination point of the actual traffic flow is located within the target subspace. The former definition yields a more accurate box dimension, while the latter is more computationally efficient in practical applications. Those skilled in the art can choose either definition method according to their actual needs.

[0104] Furthermore, the box dimension can be calculated based on the size of the target subspace and the number of non-empty target subspaces, and the box dimension can be used as an indicator of the actual space fill rate corresponding to the actual traffic flow.

[0105] Reference Figure 5 The above process is illustrated with a practical example of calculating the box dimension. Figure 5 (a) shows the traffic flow distribution on a two-dimensional plane, where green dots represent starting or ending points, and arrows connecting two green dots represent traffic flows. The first space corresponding to each starting point is determined separately. Figure 5 (b) the two-dimensional O-plane and the second space corresponding to each arrival point ( Figure 5 (c) After the two-dimensional D-plane, a Cartesian product is performed on these two planes to obtain the four-dimensional flow space (target space). Then, the four-dimensional flow space is divided into multiple target subspaces, such as... Figure 5 As shown in (d). Figure 5 In (d), each square represents a four-dimensional space. U11 represents a four-dimensional space whose projection on the O plane is O1, and whose projection on the D plane is D1, and so on. The criterion for determining whether a target subspace is non-empty is that the starting or ending point of the actual traffic flow is located within the target subspace. The resulting non-empty subspaces are then... Figure 5 The blue squares in (d) are then used to calculate the box dimension based on the number of blue squares and their side lengths.

[0106] It should be noted that, since the number of dimensions of the target subspace in this embodiment is not necessarily two-dimensional (as commonly used in box-counting), existing methods for calculating box-counting may not be applicable to this embodiment. When the number of dimensions of the target subspace is not two-dimensional, the actual space fill index can be calculated using the following method:

[0107] Determine the length feature value corresponding to the target subspace;

[0108] The box dimension is determined based on the length feature value and the number of non-empty target subspaces, and the box dimension is used as an indicator of the actual space fill degree corresponding to the actual traffic flow.

[0109] The length feature value corresponding to the target subspace can be any one-dimensional value that can characterize the size of the target subspace, such as the length of the target subspace in any dimension, the distance between any two dimensions of the target subspace, etc. This application does not limit this, as long as the method of calculating the length feature value is the same for each target subspace.

[0110] After obtaining the length feature value, the box dimension can be determined by referring to the existing box dimension calculation method, see formula (I):

[0111] Formula (1)

[0112] in, Let the box dimension be , Is the number of non-empty target subspaces? It is a length feature value. In practical applications, this can be achieved by gradually reducing the size of the target subspace and... and By performing linear regression and obtaining the linear regression coefficients, we can quickly obtain an approximate value for the box dimension mentioned above.

[0113] Step 106: Randomly simulate the traffic flow between each starting point and each destination point to obtain the simulated traffic flow, and determine the simulated space filling index corresponding to the simulated traffic flow.

[0114] In this embodiment of the application, randomly simulating traffic flow between each starting point and each destination point refers to randomly selecting a starting point from among the various starting points and a destination point from among the various destination points, and then randomly constructing the traffic flow between the selected starting points and the selected destination points. All randomly constructed traffic flows are collectively referred to as simulated traffic flows. Simulated traffic flows can reflect the most disordered traffic flow situation corresponding to the target traffic flow type.

[0115] The space fill index is also calculated for simulated traffic flow, resulting in a simulated space fill index. The method for calculating the space fill index for simulated traffic flow is similar to the method for calculating the space fill index for actual traffic flow, and will not be repeated in the embodiments of this application.

[0116] In one embodiment, random errors can be avoided by performing traffic flow simulations multiple times, such as... Figure 6 As shown, step 106 includes:

[0117] Step 602: Randomly combine each starting point and each destination point to obtain multiple combinations of starting and ending points. Simulate the traffic flow corresponding to each combination of starting and ending points to obtain the initial simulated traffic flow, and determine the initial simulated space filling index based on each initial simulated traffic flow.

[0118] Proceed to the step of randomly combining the starting points and the destination points until the preset conditions are met;

[0119] Step 604: Determine the simulation space fill index based on each initial simulation space fill index.

[0120] In this embodiment, after randomly combining the starting points and arrival points, each combination of start and end points should cover all starting points and all arrival points. Random combination can be performed as follows: randomly select one starting point from all existing starting points, randomly select one arrival point from all existing arrival points, combine the starting and arrival points, and then delete the starting and arrival points from their respective existing starting and arrival points. Repeat this process until no starting or arrival point remains.

[0121] It should be noted that, in the embodiments of this application, starting points corresponding to the same location in different actual traffic flows are considered different starting points. Similarly, arrival points corresponding to the same location in different actual traffic flows are also considered different arrival points.

[0122] A traffic flow simulation cycle is considered complete after obtaining all combinations of start and end points covering all starting and ending points, and simulating the initial traffic flow for each combination. Then, the initial simulated space fill index corresponding to this cycle of traffic flow simulation can be calculated.

[0123] The traffic flow simulation is repeated multiple times until preset conditions are met (e.g., the number of simulation rounds reaches a preset number (e.g., 100 times), or the combinations of start and end points in each round have covered all possible combinations of start and end points, etc.). Then, the simulated space fill index can be calculated using the initial simulated space fill index from each round. For example, the simulated space fill index can be obtained by averaging the initial simulated space fill indices, or by weighting the initial simulated space fill indices according to their deviations from the average value, and then averaging them again, etc. This application does not specifically limit how to synthesize the various initial simulated space fill indices to obtain the simulated space fill index.

[0124] Step 108: Based on the difference between the actual space filling index and the simulated space filling index, determine the traffic flow orderliness assessment index corresponding to the target traffic flow type.

[0125] In this embodiment, since the actual space filling index represents the spatial distribution complexity of the target traffic flow type under actual conditions, while the simulated space filling index represents the spatial distribution complexity of the target traffic flow type under the most disordered traffic conditions, the traffic flow orderliness assessment index calculated based on the difference between the actual and simulated space filling indices can represent the degree of optimization of the actual situation compared to the most disordered traffic conditions. The greater the difference, the stronger the traffic flow orderliness characterized by the traffic flow orderliness assessment index.

[0126] This application does not limit how to calculate the traffic flow orderliness assessment index based on the difference between these two indicators. For example, the difference between these two indicators can be directly used as the traffic flow orderliness assessment index. Alternatively, to ensure the comparability of traffic flow orderliness assessment indices calculated for different areas requiring traffic flow orderliness assessment, the idea of ​​relative difference can also be used to calculate the traffic flow orderliness assessment index, specifically including the following steps:

[0127] Determine the difference between the simulated space fill index and the actual space fill index;

[0128] The ratio of the difference to the simulated space filling index is used as the traffic flow orderliness assessment index corresponding to the target traffic flow type.

[0129] In this embodiment, after subtracting the simulated space filling index from the actual space filling index, the difference can be normalized by taking the ratio of the difference to the simulated space filling index, thereby obtaining the traffic flow orderliness assessment index. As shown in Formula (II):

[0130] Formula (II)

[0131] in, This is the fractal dimension deviation (FDD), which is also an indicator for assessing the orderliness of traffic flow. To simulate the space filling index, This is an indicator of actual space fill rate. The larger the FDD, the higher the orderliness of traffic flow.

[0132] The traffic flow orderliness assessment method provided in this application calculates the actual space fill factor index based on each actual traffic flow corresponding to the target traffic flow type, and calculates the simulated space fill factor index by randomly simulating the traffic flow between the starting and ending points of each actual traffic flow. Then, based on the difference between the actual and simulated space fill factor indices, a traffic flow orderliness assessment index corresponding to the target traffic flow type is determined. Since the space fill factor index reflects the distribution and complexity of traffic flow in space, and each simulated traffic flow obtained through random simulation represents the most disordered traffic situation corresponding to the target traffic flow type, the traffic flow orderliness assessment index calculated based on the difference between the actual and simulated space fill factor indices can represent the degree of optimization of the actual situation compared to the most disordered traffic situation. Therefore, it can be used as a numerical value representing traffic flow orderliness to quantify traffic flow orderliness.

[0133] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0134] Based on the same inventive concept, this application also provides a traffic flow orderliness assessment device for implementing the traffic flow orderliness assessment method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the traffic flow orderliness assessment device provided below can be found in the limitations of the traffic flow orderliness assessment method described above, and will not be repeated here.

[0135] In one embodiment, such as Figure 7 As shown, a traffic flow orderliness assessment device 700 is provided, comprising: a first determining module 702, a second determining module 704, a simulation module 706, and a third determining module 708, wherein:

[0136] The first determining module 702 is used to determine multiple actual traffic flows corresponding to the target traffic flow type, wherein the actual traffic flows have a starting point and an arrival point;

[0137] The second determining module 704 is used to determine the actual space filling index based on each of the actual traffic flows;

[0138] The simulation module 706 is used to randomly simulate the traffic flow between each of the starting points and each of the destination points to obtain multiple simulated traffic flows, and to determine the simulated space filling index based on each of the simulated traffic flows.

[0139] The third determining module 708 is used to determine the traffic flow orderliness assessment index corresponding to the target traffic flow type based on the difference between the actual space filling index and the simulated space filling index.

[0140] The traffic flow orderliness assessment device provided in this application calculates the actual space filling index based on each actual traffic flow corresponding to the target traffic flow type, and calculates the simulated space filling index by randomly simulating the traffic flow between the starting and ending points of each actual traffic flow. Then, based on the difference between the actual space filling index and the simulated space filling index, it determines the traffic flow orderliness assessment index corresponding to the target traffic flow type. Since the space filling index reflects the distribution and complexity of traffic flow in space, and each simulated traffic flow obtained through random simulation represents the most disordered traffic situation corresponding to the target traffic flow type, the traffic flow orderliness assessment index calculated based on the difference between the actual space filling index and the simulated space filling index can represent the degree of optimization of the actual situation compared to the most disordered traffic situation. Therefore, it can be used as a numerical value representing traffic flow orderliness to quantify traffic flow orderliness.

[0141] In one embodiment, the first determining module 702 is further configured to:

[0142] Acquire multiple traffic trajectory information, wherein the traffic trajectory information consists of multiple arrival coordinates;

[0143] For any traffic trajectory information, the starting point and the destination point are determined based on multiple arrival coordinates of the traffic trajectory information, and the trajectory information located between the starting point and the destination point is taken as the actual traffic flow.

[0144] In one embodiment, the first determining module 702 is further configured to:

[0145] Obtain the origin and destination types corresponding to the target traffic flow type;

[0146] If the origin point type and / or the destination point type belong to a personal function type, determine the expected activity period corresponding to the origin point type and / or the destination point type;

[0147] The arrival coordinates corresponding to the expected activity period in the traffic trajectory information are clustered, and the starting point and / or arrival point in the traffic trajectory information are determined based on the clustering results.

[0148] In one embodiment, the first determining module 702 is further configured to:

[0149] If the starting point type and / or the arrival point type belong to a common function type, determine multiple target locations corresponding to the starting point type and / or the arrival point type;

[0150] Based on the correspondence between the visited coordinates and the target locations, the starting point and / or arrival point in the traffic trajectory information are determined.

[0151] In one embodiment, the second determining module 704 is further configured to:

[0152] Determine a first space covering each of the said starting points, and determine a second space covering each of the said arrival points;

[0153] Perform a Cartesian product on the first space and the second space to obtain the target space, and divide the target space into multiple target subspaces of the same size;

[0154] For any of the target subspaces, if the target subspace corresponds to any of the actual traffic flows, the target subspace is determined as a non-empty target subspace;

[0155] Based on the size of the target subspace and the number of non-empty target subspaces, the actual space fill index corresponding to the actual traffic flow is determined.

[0156] In one embodiment, the simulation module 706 is further configured to:

[0157] The starting points and the arrival points are randomly combined to obtain multiple combinations of start and end points. The traffic flow corresponding to each combination of start and end points is simulated to obtain the initial simulated traffic flow. The initial simulated space fill index is determined based on each initial simulated traffic flow.

[0158] Proceed to the step of randomly combining the starting points and the destination points until the preset conditions are met;

[0159] The simulation space fill index is determined based on each of the initial simulation space fill indexes.

[0160] Each module in the above-mentioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0161] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a traffic flow orderliness assessment method.

[0162] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0163] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0164] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0165] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0166] 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 used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0167] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. 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), magnetic 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 take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0168] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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, they should be considered to be within the scope of this specification.

[0169] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for assessing traffic flow orderliness, characterized in that, The method includes: Identify multiple actual traffic flows corresponding to the target traffic flow type, wherein each actual traffic flow has a starting point and a destination point; Determine a first space covering each of the said starting points, and determine a second space covering each of the said arrival points; Perform a Cartesian product on the first space and the second space to obtain the target space, and divide the target space into multiple target subspaces of the same size; For any of the target subspaces, if the target subspace corresponds to any of the actual traffic flows, the target subspace is determined as a non-empty target subspace; Based on the size of the target subspace and the number of non-empty target subspaces, the actual space fill index corresponding to the actual traffic flow is determined; The starting points and the arrival points are randomly combined to obtain multiple combinations of start and end points. The traffic flow corresponding to each combination of start and end points is simulated to obtain the initial simulated traffic flow. The initial simulated space fill index is determined based on each initial simulated traffic flow. Proceed to the step of randomly combining the starting points and the destination points until the preset conditions are met; The simulation space fill index is determined based on each of the initial simulation space fill indexes; Based on the difference between the actual space filling index and the simulated space filling index, the traffic flow orderliness assessment index corresponding to the target traffic flow type is determined.

2. The method according to claim 1, characterized in that, The determination of the multiple actual traffic flows corresponding to the target traffic flow type includes: Acquire multiple traffic trajectory information, wherein the traffic trajectory information consists of multiple arrival coordinates; For any traffic trajectory information, the starting point and the destination point are determined based on multiple arrival coordinates of the traffic trajectory information, and the trajectory information located between the starting point and the destination point is taken as the actual traffic flow.

3. The method according to claim 2, characterized in that, The determination of the starting point and destination point based on multiple arrival coordinates of the traffic trajectory information includes: Obtain the origin and destination types corresponding to the target traffic flow type; If the origin point type and / or the destination point type belong to a personal function type, determine the expected activity period corresponding to the origin point type and / or the destination point type; The arrival coordinates corresponding to the expected activity period in the traffic trajectory information are clustered, and the starting point and / or arrival point in the traffic trajectory information are determined based on the clustering results.

4. The method according to claim 3, characterized in that The method of determining the starting point and destination point based on multiple arrival coordinates of the traffic trajectory information also includes: If the starting point type and / or the arrival point type belong to a common function type, determine multiple target locations corresponding to the starting point type and / or the arrival point type; Based on the correspondence between the visited coordinates and the target locations, the starting point and / or arrival point in the traffic trajectory information are determined.

5. A traffic flow orderliness assessment device, characterized in that, The device includes: The first determining module is used to determine multiple actual traffic flows corresponding to the target traffic flow type, wherein the actual traffic flows have a starting point and an arrival point; The second determining module is used to determine a first space covering each of the starting points and a second space covering each of the arrival points; to perform a Cartesian product on the first space and the second space to obtain a target space, and to divide the target space into multiple target subspaces of equal size; for any target subspace, if the target subspace corresponds to any of the actual traffic flows, the target subspace is determined as a non-empty target subspace; based on the size of the target subspace and the number of non-empty target subspaces, the actual space fill index corresponding to the actual traffic flow is determined; The simulation module is used to randomly combine the starting points and the arrival points to obtain multiple start-end point combinations, simulate the traffic flow corresponding to each start-end point combination to obtain an initial simulated traffic flow, and determine an initial simulated space fill index based on each initial simulated traffic flow; jump to the step of randomly combining the starting points and the arrival points until a preset condition is met; and determine a simulated space fill index based on each initial simulated space fill index. The third determining module is used to determine the traffic flow orderliness assessment index corresponding to the target traffic flow type based on the difference between the actual space filling index and the simulated space filling index.

6. The apparatus according to claim 5, characterized in that, The first determining module is further configured to: Acquire multiple traffic trajectory information, wherein the traffic trajectory information consists of multiple arrival coordinates; For any traffic trajectory information, the starting point and the destination point are determined based on multiple arrival coordinates of the traffic trajectory information, and the trajectory information located between the starting point and the destination point is taken as the actual traffic flow.

7. The apparatus according to claim 6, characterized in that, The first determining module is further configured to: Obtain the origin and destination types corresponding to the target traffic flow type; If the origin point type and / or the destination point type belong to a personal function type, determine the expected activity period corresponding to the origin point type and / or the destination point type; The arrival coordinates corresponding to the expected activity period in the traffic trajectory information are clustered, and the starting point and / or arrival point in the traffic trajectory information are determined based on the clustering results.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

Images