Traffic information issuing method and system

Abstract

The invention provides a traffic information release method and system, and belongs to the field of traffic information release, and the method comprises the steps: carrying out the clustering processing of the road condition information of a first region based on a clustering algorithm, and obtaining first clustering data; the first area is an area in which traffic information needs to be published; based on the first clustering data, determining a target publishing strategy for publishing traffic information to a second area, the second area being an area in the first area; and publishing the traffic information to the second area based on the target publishing strategy. According to the traffic information publishing method and system provided by the invention, the accuracy and reliability of traffic information publishing can be improved.

Description

Technical Field

[0001] The present disclosure belongs to the field of traffic information release, and more specifically, to a traffic information release method and system. Background Art

[0002] With the acceleration of urbanization and the rapid development of the transportation industry, traffic problems such as traffic congestion and traffic accidents are becoming increasingly serious, which puts higher requirements on the traffic information release system. Accurate, timely and targeted release of traffic information has become a key measure to alleviate traffic pressure, improve traffic operation efficiency and ensure traffic safety.

[0003] Traditional traffic information release is usually limited to the same release method, and the release method cannot be adjusted according to actual road conditions, resulting in low effectiveness of the released traffic information.

[0004] Therefore, there is an urgent need for an accurate and reliable method for publishing traffic information. Summary of the invention

[0005] The purpose of the present disclosure is to provide a method and system for publishing traffic information to improve the accuracy and reliability of traffic information publishing.

[0006] A first aspect of an embodiment of the present disclosure provides a method for publishing traffic information, including: Clustering the traffic information of the first area based on a clustering algorithm to obtain first clustering data; the first area is an area where traffic information needs to be released; Determining a target release strategy for releasing traffic information to a second area based on the first clustering data, wherein the second area is an area within the first area; Traffic information is released to the second area based on the target release strategy.

[0007] A second aspect of the embodiments of the present disclosure provides a traffic information publishing system, including: A clustering module, used for clustering the traffic information of the first area based on a clustering algorithm to obtain first clustering data; the first area is an area where traffic information needs to be released; A strategy publishing module, used to determine a target publishing strategy for publishing traffic information to a second area based on the first cluster data, wherein the second area is an area within the first area; The publishing module is used to publish traffic information to the second area based on the target publishing strategy.

[0008] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and running on the processor, and when the processor executes the computer program, the steps of the above-mentioned traffic information publishing method are implemented.

[0009] According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for publishing traffic information are implemented.

[0010] The traffic information publishing method and system provided by the embodiments of the present disclosure have the following beneficial effects: The present disclosure can obtain first cluster data by clustering the road condition information of the area where traffic information needs to be released. The clustering algorithm can classify road condition data with similar characteristics into one category, thereby simplifying subsequent analysis and processing. The present disclosure clusters the first area into multiple areas through the clustering algorithm, and then determines the corresponding release strategy for each area, which can make the release strategy more in line with the actual situation of the road conditions in the area, ensure the pertinence and effectiveness of information release, and improve the accuracy and reliability of traffic information release. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A flow chart of a method for publishing traffic information provided by an embodiment of the present disclosure; Figure 2 A structural block diagram of a traffic information publishing system provided by an embodiment of the present disclosure; Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present disclosure. However, it should be clear to those skilled in the art that the present disclosure may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obstructing the description of the present disclosure with unnecessary details.

[0014] In order to make the purpose, technical solutions and advantages of the present disclosure more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0015] Please refer to Figure 1 , Figure 1A flow chart of a method for publishing traffic information provided by an embodiment of the present disclosure, the method comprising: S101: Clustering the traffic information of a first area based on a clustering algorithm to obtain first clustering data; the first area is an area where traffic information needs to be released.

[0016] In this embodiment, the clustering algorithm can be a K-Means algorithm, and the first area is a specific area where traffic information needs to be released. It can be the overall traffic network of a city, a certain urban area, a specific functional area (such as a commercial area, residential area, etc.), or a traffic corridor containing multiple road sections, etc. It is the target geographical scope for the entire traffic information release work.

[0017] The road condition information is used to indicate the characteristics of the road traffic conditions, which may include traffic volume, vehicle speed, accident rate, road grade, road congestion degree, etc. The first area may be clustered based on the above characteristics. For example, intersections with large traffic volume, slow vehicle speed and relatively high accident rate are all classified into one cluster. These intersections may be located in the core area of ​​the commercial district; and sections with small traffic volume, fast vehicle speed and few accidents are classified into another cluster. These sections may be passages connecting commercial districts and residential areas. Through such clustering processing, the traffic conditions around the commercial district can be classified.

[0018] The first clustering data is the classification result data about the road condition information of the first area obtained after clustering processing. It is presented in the form of clusters. Each cluster contains a group of areas with similar road condition information, and also contains some statistical characteristics of each cluster, such as the traffic volume and speed within the cluster. The above data will serve as the basis for determining the subsequent traffic information release strategy.

[0019] S102: Determine a target release strategy for releasing traffic information to a second area based on the first clustering data, wherein the second area is an area within the first area.

[0020] In this embodiment, the second area is a smaller area further subdivided from the first area. It can be a specific block, street, intersection or a local area composed of several connected road sections in the first area. It is a specific geographical unit that actually receives and needs to publish traffic information in a targeted manner.

[0021] The target release strategy may include the release area and release frequency, as well as the release content and release channel, etc.

[0022] The target release strategy is a specific traffic information release plan formulated for the second area. The relevant information release strategy can be formulated based on the characteristics of the second area, such as traffic volume, vehicle speed, accident rate, and road grade.

[0023] For example, determine the first release frequency; In response to the traffic volume of the second area being greater than the first traffic volume threshold and the vehicle speed of the second area being greater than the first vehicle speed threshold, publishing the traffic information of the second area and the area adjacent to the second area to the second area; And, the first publishing frequency is increased according to the first frequency step to obtain a second publishing frequency, and the traffic information is published to the second area based on the second publishing frequency.

[0024] In this embodiment, the first publishing frequency, the first traffic threshold and the first frequency step can be determined based on experiments, taking into account that when the traffic volume in the area is large, that is, greater than the first traffic threshold, and the vehicle speed in the area is fast, that is, greater than the first speed threshold, the area may be a highway or a city expressway, and the destination of vehicles traveling in these areas may be adjacent areas. Due to the large traffic volume and high speed, traffic information needs to be released to the area in a wider area and at a higher frequency.

[0025] That is, each type of area has a corresponding target release strategy.

[0026] At the same time, different publishing information can be determined for different publishing devices, for example: In response to the publishing device being a mobile device, sending the traffic information to the mobile device according to the first information volume; In response to the publishing device being a fixed device, publishing the traffic information to the fixed device according to the second information volume; Among them, the first amount of information is greater than the second amount of information.

[0027] Mobile devices can be devices such as mobile phones and tablets that can display more detailed information, and fixed devices can be devices such as roadside information boards or signboards that display limited information.

[0028] The first amount of information may include time, location, traffic status, speed limit information, accident-prone areas, estimated travel time, etc. The second amount of information may include location and traffic status, speed limit information, etc.

[0029] S103: Release traffic information to the second area based on the target release strategy.

[0030] In this embodiment, the corresponding traffic information is transmitted to the corresponding second area according to the established target release strategy. The target release strategy covers key elements such as release area, release frequency, release content and release channel. By setting these elements in detail, the traffic information release work is carried out in a planned and targeted manner to meet the traffic information needs of drivers and pedestrians in the second area.

[0031] It can be concluded from the above that the present disclosure can obtain the first cluster data by clustering the road condition information of the area where traffic information needs to be released. The clustering algorithm can classify road condition data with similar characteristics into one category, thereby simplifying subsequent analysis and processing. The present disclosure clusters the first area into multiple areas through the clustering algorithm, and then determines the corresponding release strategy for each area, which can make the release strategy more in line with the actual situation of the road conditions in the area, ensure the pertinence and effectiveness of information release, and improve the accuracy and reliability of traffic information release.

[0032] In one embodiment of the present disclosure, clustering is performed on the traffic information of the first area based on a clustering algorithm to obtain first clustering data, including: Determine multiple cluster quantity values; Clustering the traffic condition information of the first area based on each cluster quantity value to obtain multiple clustering results; Evaluate multiple clustering results to obtain multiple clustering evaluation results; Obtaining a target cluster quantity value based on multiple cluster evaluation results; The cluster data corresponding to the target cluster quantity value is used as the first cluster data.

[0033] In this embodiment, the clustering algorithm can be a K-Means clustering algorithm, and the number of clusters is the K value, which represents the number of clusters into which the data set is to be divided, that is, how many different categories the traffic information data in the first area is expected to be divided into through the clustering algorithm. Different cluster number values ​​will produce different clustering effects.

[0034] Multiple cluster quantity values ​​may be determined empirically or according to the size or complexity of the data, where the data is the road condition information, for example: In response to the number of the second regions being greater than the first number, using the first set of cluster number values ​​as a plurality of cluster number values; In response to the number of the second regions being less than or equal to the first number, a second set of cluster number values ​​is used as a plurality of cluster number values.

[0035] The first number, the first set of cluster number values, and the second set of cluster number values ​​may be set based on experience, and the value of the first set of cluster number values ​​is greater than the value of the second set of cluster number values.

[0036] Alternatively, determining an initial cluster quantity value based on the first formula; Taking the first number of values ​​adjacent to the initial cluster quantity value and the initial cluster quantity value as a plurality of cluster quantity values; The first formula can be: ,in, represents the initial number of clusters, represents the total number of data points, Represents the dimension of the data, represents the density variation coefficient of the data, It represents the degree of dispersion of data. and are all weight coefficients, which can be determined through experiments or experience according to specific application scenarios and data characteristics. For example, in data with a large density difference, The value is relatively large.

[0037] In this embodiment, That is the number of second areas. The second areas can be divided according to regions or experience, or the number of second areas can be determined based on the number of areas that monitor traffic flow and speed. For example, some areas do not have the function of monitoring traffic flow and speed. This area can be divided into the nearest area with the above function. This step is set in advance. is the number of feature types, for example, it can be traffic flow and speed. is 2, or it can be traffic volume, speed, accident rate, or road grade. is 4. It can be obtained by comparing the density of the densest area and the sparsest area in the data. For example, the density of the densest area is , the density of the sparsest region is ,but , It can be obtained by calculating statistics such as the variance of each feature of the data. The value range is between 0 and 1, where 0 means that the data is very concentrated and 1 means that the data is highly discrete.

[0038] In this embodiment, the first number may be determined empirically. For example, the first number is 2, and the calculated initial cluster number value is 4. Then, the multiple cluster number values ​​may be 2, 3, 4, 5, and 6.

[0039] After obtaining multiple cluster quantity values, the road condition information of the first area can be clustered based on these cluster quantity values ​​to obtain multiple first cluster data, and the sum of squared errors (SSE) of each first cluster data is calculated respectively, which is the cluster evaluation result, and is plotted as a K-SSE curve with the K value as the horizontal coordinate and the SSE as the vertical coordinate. The K value can be determined based on the elbow rule, or by calculating the secondary derivative of the slope corresponding to two consecutive K values. The K value corresponding to the first time the difference between the secondary derivative and 0 is less than a preset proximity threshold can be used as the target cluster quantity value, or the K value corresponding to the time when the secondary derivative is closest to 0 can be used as the target cluster quantity value.

[0040] From the above, it can be concluded that this embodiment obtains accurate first clustering data by clustering the road condition information of the first area and determining the target cluster quantity value based on multiple clustering evaluation results, thereby improving the accuracy and reliability of the clustering results and providing strong data support for the formulation of subsequent traffic information release strategies. This embodiment predetermines several values ​​that may be the final cluster quantity value based on the complexity of the data, which can reduce the number of iterations, improve the efficiency of traffic information release, and reduce waste of resources.

[0041] In one embodiment of the present disclosure, clustering is performed on the traffic information of the first area based on a clustering algorithm to obtain first clustering data, including: Determine multiple cluster centers; Calculating the distance between the traffic condition information of the second area and the traffic condition information of the plurality of cluster centers based on the first distance measurement method to obtain a plurality of first distances; First clustering data is obtained based on a plurality of first distances.

[0042] In this embodiment, the cluster center is the representative point of each cluster and has a corresponding road condition information value. During the clustering process, other data points (such as road condition information data corresponding to each intersection and road section) will determine the cluster to which they belong based on the distance from the cluster center, and the cluster center will be updated according to the characteristic values ​​of the data points in the cluster during the algorithm iteration process, so that it can better represent the average road condition information of the cluster.

[0043] The density peak point in the data group distribution can be used as the cluster center. The density peak point refers to a point with a high density of data points in its local neighborhood and a certain distance from other high-density areas. The density peak point can be determined by calculating the density of each local area and taking a certain number of points with the largest density as the density peak point.

[0044] Multiple cluster centers are finally determined through continuous iterative calculations. The present disclosure reduces the number of iterations and improves the speed of clustering processing by presetting several cluster centers.

[0045] The first distance metric can be ,in, represents the distance between the traffic information of the second area and the traffic information of multiple cluster centers. The traffic information vector of the second area is expressed as , the traffic information vector of the cluster center is expressed as , the corresponding weight vector is ,in, Indicates The weight of each road condition information dimension, It is The normalization factor of the road condition information dimension can be calculated based on the statistical information of the feature in the entire data set. For example, if the range of traffic flow is , then the normalization factor of the traffic flow dimension is .

[0046] Through the first distance measurement method, the distance between the road condition information of each second area and the road condition information of multiple cluster centers can be calculated, that is, multiple first distances. The cluster center with the shortest distance is used as the cluster center of the area, that is, the second area is divided into the cluster to which the cluster center with the closest distance belongs, and the distance between each second area and multiple cluster centers is calculated respectively. The final clustering result is the first clustering data.

[0047] It can be concluded from the above that this embodiment uses the density peak point in the data group distribution as the cluster center, and this method can accurately identify the core points representing each cluster. This embodiment weights each feature dimension through a weight vector, thereby more accurately reflecting the impact of different features on the clustering results. The introduction of the normalization factor makes the comparison between different feature dimensions more fair and accurate. This embodiment eliminates the impact of different feature value ranges on distance calculation through normalization processing, thereby improving the reliability of clustering results. This embodiment reduces the number of iterations by pre-setting several cluster centers, thereby improving the speed of clustering processing.

[0048] In one embodiment of the present disclosure, the road condition information includes: vehicle flow and vehicle speed; Calculating the distance between the traffic condition information of the second area and the traffic condition information of the plurality of cluster centers based on the first distance measurement method includes: Determine a first weight corresponding to the vehicle flow and a second weight corresponding to the vehicle speed; The distance between the road condition information of the second area and the road condition information of the plurality of cluster centers is calculated based on the traffic volume, the vehicle speed, the first weight, and the second weight.

[0049] In this embodiment, the road condition information may include vehicle flow and vehicle speed, and the first distance measurement method may be , applied in this embodiment, the weight corresponding to the traffic flow in the first distance measurement method is , which is the first weight, and the weight corresponding to the vehicle speed is , which is the second weight. The normalization factor corresponding to the traffic flow is , the normalization factor corresponding to the vehicle speed is The calculation of the normalization factor of the traffic flow can be obtained by the difference between the maximum and minimum values ​​of the traffic flow, and the calculation of the normalization factor of the vehicle speed can be obtained by the difference between the maximum and minimum values ​​of the vehicle speed.

[0050] The first weight and the second weight can be determined by the correlation between the traffic flow and the vehicle speed and the traffic congestion index. The correlation can be calculated by the Pearson correlation coefficient or the Spearman rank correlation coefficient, etc. The traffic flow and the vehicle speed can be obtained by electromagnetic induction devices buried under the road surface, video detection, etc. The traffic congestion index can be obtained through information collected by the Transportation Bureau, and can also be determined based on the traffic flow and the vehicle speed. For example, when the traffic flow and the vehicle speed are both greater than a certain threshold, the corresponding congestion level is 0. When one and only one of the traffic flow and the vehicle speed is less than a certain threshold, the corresponding congestion level is 1. When the traffic flow and the vehicle speed are both less than a certain threshold, the corresponding congestion level is 2. The above thresholds can be set based on experience.

[0051] For example, the calculated correlation between vehicle flow and traffic congestion index is 0.8, and the correlation between vehicle speed and traffic congestion index is 0.6, then the first weight can be , the second weight can be .

[0052] From the above, it can be concluded that this embodiment can more comprehensively and accurately reflect the traffic conditions of the road by simultaneously considering the two key road condition information, namely, the traffic volume and the vehicle speed. The traffic volume reflects the busyness of the road, while the vehicle speed directly reflects the traffic efficiency of the road. Combining these two factors, the road congestion can be evaluated more accurately. By introducing the first weight and the second weight, this embodiment can make differentiated considerations based on the degree of influence of different road condition information on the traffic congestion index. The allocation of weights is based on the correlation between the traffic volume and the vehicle speed and the traffic congestion index. This data-based weight determination method makes the distance calculation more scientific and reasonable, improves the accuracy of the road condition assessment, and improves the accuracy and reliability of the release of traffic information.

[0053] In one embodiment of the present disclosure, the road condition information includes: vehicle flow, vehicle speed, accident rate and road grade; The distance between the traffic condition information of the second area and the traffic condition information of the plurality of cluster centers is calculated based on the first distance measurement method to obtain a plurality of first distances, including: In response to the traffic information feature satisfying the first condition, respectively determining weights corresponding to the traffic volume, the vehicle speed, the accident rate and the road grade; A plurality of first distances are obtained by performing calculations based on the traffic volume, vehicle speed, accident rate, road grade and corresponding weights.

[0054] In this embodiment, the first distance measurement method can be , applied in this embodiment, the weight corresponding to the traffic flow in the first distance measurement method is , which is the first weight, and the weight corresponding to the vehicle speed is , which is the second weight, and the weight corresponding to the accident rate is , which is the third weight, and the weight corresponding to the road grade is , the normalization factor corresponding to the traffic volume is , the normalization factor corresponding to the vehicle speed is , the normalization factor corresponding to the accident rate is , the normalization factor corresponding to the road grade is .

[0055] The first condition may be that a traffic accident occurs in the first area, and / or an average traffic volume in the first area is greater than a second traffic volume threshold, and / or an average vehicle speed in the first area is greater than a second vehicle speed threshold.

[0056] Considering that when an accident occurs in the first area, or the average traffic volume in the first area is greater than the second traffic volume threshold, or the average vehicle speed in the first area is greater than the second vehicle speed threshold, it means that the traffic information in the first area is relatively complex. At this time, the first area can be clustered into more accurate clusters by considering more road condition information. For example, the accident rate and road grade can be considered in the clustering process. The second traffic volume threshold and the second vehicle speed threshold can be determined based on experiments.

[0057] The weights corresponding to the traffic volume, vehicle speed, accident rate and road grade can be determined based on experience, or based on the correlation between the traffic volume, vehicle speed, accident rate and road grade and the traffic congestion index.

[0058] It can be concluded from the above that this embodiment can more comprehensively and accurately reflect the traffic conditions of the road by simultaneously considering the four key road condition information, namely, the traffic volume, vehicle speed, accident rate and road grade. Not only the traffic capacity and efficiency of the road are taken into account, but also the safety condition and road grade of the road, thereby improving the accuracy and comprehensiveness of the road condition assessment. This embodiment dynamically adjusts the weights of each road condition information so that the road condition assessment can more flexibly adapt to different traffic conditions, thereby improving the accuracy of the assessment. This embodiment considers more road condition information and dynamically adjusts the weights under specific conditions, so that this embodiment can more accurately cluster the first area into multiple clusters. This accurate clustering helps to better understand and analyze traffic conditions, provide strong support for traffic management and planning, and improve the accuracy and reliability of traffic information release.

[0059] In one embodiment of the present disclosure, a method for publishing traffic information further includes: In response to the environmental characteristics of the first area satisfying the first environmental condition, weights corresponding to the traffic volume, vehicle speed, accident rate and road grade are adjusted based on the target step size.

[0060] In this embodiment, the first environmental condition may be bad weather, such as strong winds, heavy rain, heavy snow, foggy weather, etc. At this time, due to the bad environment, the impact of vehicle speed and accident rate on traffic information release should be considered.

[0061] The target step length may include: a first flow rate step length, a first vehicle speed step length, a first accident step length, and a first level step length.

[0062] Based on the target step size, the weight corresponding to the traffic flow, the weight corresponding to the vehicle speed, the weight corresponding to the accident rate, and the weight corresponding to the road grade are adjusted, including: Reduce the weight corresponding to the vehicle flow based on the first flow step; and, increasing the weight corresponding to the vehicle speed based on the first vehicle speed step; and, increasing the weight corresponding to the accident rate based on the first accident step; And, the weight corresponding to the road level is reduced based on the first level step.

[0063] In this embodiment, the first flow step, the first vehicle speed step, the first accident step and the first level step can all be set empirically. It should be noted that the sum of the first flow step and the first level step should be equal to the sum of the first vehicle speed step and the first accident step, that is, to ensure that the adjusted sum of the weights is 1.

[0064] In this embodiment, considering that the severity of the weather will also affect the vehicle speed and the accident rate, it is possible to: In response to the severity of the weather being greater than a first severity threshold and less than a second severity threshold, adjusting the first flow rate step length, the first vehicle speed step length, the first accident step length, and the first level step length based on the first severity step length; In response to the severity of the weather being greater than a second severity threshold, the first flow rate step size, the first vehicle speed step size, the first accident step size, and the first level step size are adjusted based on the second severity step size.

[0065] The severity of the weather can be calculated by the second formula, which can be: ,in Due to the severity of the weather, represents the wind intensity factor, represents the rainfall intensity factor, represents the snowfall intensity factor, represents the visibility factor, and They are the weights of the corresponding factors, which can be determined according to actual conditions.

[0066] The strong wind intensity factor can be obtained through the preset mapping table of wind speed and strong wind intensity factor, and the strong wind intensity factor can be determined by monitoring the wind speed. The rainfall intensity factor can be obtained through the preset mapping table of rainfall amount and rainfall intensity factor, and the rainfall intensity factor can be determined by monitoring the rainfall. The snowfall intensity factor can be obtained through the preset mapping table of snowfall amount and snowfall intensity factor, and the snowfall intensity factor can be determined by monitoring the snowfall. The visibility factor can be obtained through the preset mapping table of visibility and visibility factor, and the visibility factor can be determined by monitoring the visibility.

[0067] The first harsh threshold, the second harsh threshold, the first harsh step size, and the second harsh step size may be determined empirically.

[0068] In this embodiment, the first flow step can be reduced according to the first severe step, the first vehicle speed step can be increased according to the first severe step, the first accident step can be increased according to the first severe step, and the first level step can be reduced according to the first severe step.

[0069] Alternatively, the first flow step length is reduced according to the second worst step length, the first vehicle speed step length is increased according to the second worst step length, the first accident step length is increased according to the second worst step length, and the first level step length is reduced according to the second worst step length. Further adjusting the step length according to the severity of the weather can achieve better clustering results.

[0070] From the above, it can be concluded that the present embodiment can respond to the environmental characteristics of the first area, especially severe weather conditions, and adjust the weights of road condition information such as traffic volume, vehicle speed, accident rate, road grade, etc. according to the severity of the environment, so that the traffic information release can more accurately reflect the actual traffic conditions, improve the practicality and reliability of the information, and improve the accuracy and reliability of the traffic information release.

[0071] In one embodiment of the present disclosure, the second region is multiple; Target release strategy, including: release regions and release frequency; The first clustering data includes a plurality of target clusters; Determining a target release strategy for releasing traffic information to the second area based on the first clustering data includes: Determine a target release strategy from a plurality of standard release strategies based on traffic information of a center point of the target cluster; The target release policy is used as the target release policy for all second regions in the target cluster.

[0072] In this embodiment, the target release strategy includes: a release area and a release frequency. The release area can be the second area and an area that is a certain distance away from the second area geographically, or the release area can be a key area in the second area, such as an economic center, a commercial street, an overpass, etc. The release frequency can be updated once every hour or once every 30 minutes, etc.

[0073] The first clustering data includes multiple clusters, each cluster represents a category, for example, it can be a cluster with heavy traffic and high speed, a cluster with heavy traffic and low speed, a cluster with light traffic and high speed, and a cluster with light traffic and low speed.

[0074] The release strategy corresponding to each cluster is different. For example, for a cluster with heavy traffic and high speed, the areas therein (that is, multiple second areas) may be highways, urban expressways, etc. The traffic information that vehicles traveling on such roads should pay attention to is not limited to this area, but should also pay attention to traffic information in other areas, such as traffic information in adjacent areas or traffic information in an adjacent area of ​​10 km. It should be noted that the above-mentioned adjacent areas refer to adjacent areas in terms of geographical location, not adjacent areas of the second area in the first clustering data.

[0075] Regions with various characteristics correspond to different target release strategies. The target release strategy can be determined based on actual conditions. The target cluster is each cluster in the first clustering data. The center point of the target cluster can represent the traffic information of the cluster to a certain extent, so the release strategy of the cluster can be determined based on the center point of the cluster.

[0076] The standard release strategy is the standard release strategy corresponding to each road condition information, which is set in advance, that is, each standard release strategy corresponds to one or more standard road condition information. The similarity between the road condition information at the center point of the target cluster and the standard road condition information can be calculated, and the similarity can be calculated by methods such as the Pearson correlation coefficient method. The standard release strategy corresponding to the standard road condition information with the highest similarity is used as the release strategy for all points in the cluster (that is, all second areas).

[0077] It can be concluded from the above that the present disclosure determines the release strategy through the target cluster in the first clustering data, and the method can more accurately match the road condition information with the release strategy. Each cluster represents a type of similar road condition information, making the formulation of the release strategy more scientific and reasonable. This embodiment simplifies the calculation process and ensures the accuracy and pertinence of the release strategy by using the center point of the target cluster to represent the road condition information of the cluster, and selects the most appropriate strategy from the standard release strategy based on these features. This embodiment determines the most appropriate release strategy by calculating the similarity between the road condition information at the center point of the target cluster and the standard road condition information, ensuring the close connection between the release strategy and the current road condition information, improving the practicality and effectiveness of traffic information release, and improving the accuracy and reliability of traffic information release.

[0078] Corresponding to a traffic information publishing method in the above embodiment, Figure 2 This is a structural block diagram of a traffic information publishing system provided by an embodiment of the present disclosure. For ease of explanation, only the parts related to the embodiment of the present disclosure are shown. Figure 2 The traffic information publishing system 20 includes: a clustering module 21, a strategy publishing module 22 and a publishing module 23.

[0079] The clustering module 21 is used to perform clustering processing on the traffic information of the first area based on a clustering algorithm to obtain first clustering data; the first area is the area where traffic information needs to be released; A strategy publishing module 22, for determining a target publishing strategy for publishing traffic information to a second area based on the first cluster data, wherein the second area is an area within the first area; The publishing module 23 is used to publish the traffic information to the second area based on the target publishing strategy.

[0080] In one embodiment of the present disclosure, the clustering module 21 is specifically configured to determine a plurality of cluster quantity values; Clustering the traffic condition information of the first area based on each cluster quantity value to obtain multiple clustering results; Evaluate multiple clustering results to obtain multiple clustering evaluation results; Obtaining a target cluster quantity value based on multiple cluster evaluation results; The cluster data corresponding to the target cluster quantity value is used as the first cluster data.

[0081] In one embodiment of the present disclosure, the clustering module 21 is further specifically configured to determine a plurality of cluster centers; Calculating the distance between the traffic condition information of the second area and the traffic condition information of the plurality of cluster centers based on the first distance measurement method to obtain a plurality of first distances; First clustering data is obtained based on a plurality of first distances.

[0082] In one embodiment of the present disclosure, the road condition information includes: vehicle flow and vehicle speed; The clustering module 21 is further configured to determine a first weight corresponding to the vehicle flow and a second weight corresponding to the vehicle speed; The distance between the road condition information of the second area and the road condition information of the plurality of cluster centers is calculated based on the traffic volume, the vehicle speed, the first weight, and the second weight.

[0083] In one embodiment of the present disclosure, the road condition information includes: vehicle flow, vehicle speed, accident rate and road grade; The clustering module 21 is further configured to determine weights corresponding to the traffic volume, the vehicle speed, the accident rate and the road grade in response to the traffic information feature satisfying the first condition; A plurality of first distances are obtained by performing calculations based on the traffic volume, vehicle speed, accident rate, road grade and corresponding weights.

[0084] In one embodiment of the present disclosure, a traffic information release system 20 further includes: an environmental factor adjustment module; The environmental factor adjustment module is used to adjust the weight corresponding to the traffic flow, the weight corresponding to the vehicle speed, the weight corresponding to the accident rate and the weight corresponding to the road grade based on the target step length in response to the environmental characteristics of the first area satisfying the first environmental condition.

[0085] In one embodiment of the present disclosure, the second region is multiple; Target release strategy, including: release regions and release frequency; A strategy release module 22, specifically configured to determine a target release strategy from a plurality of standard release strategies based on the traffic information of the center point of the target cluster; The target release policy is used as the target release policy for all second regions in the target cluster.

[0086] See also Figure 3 , Figure 3 A schematic block diagram of an electronic device provided by an embodiment of the present disclosure. Figure 3 The electronic device 300 in the embodiment shown may include: one or more processors 301, one or more input devices 302, one or more output devices 303 and one or more memories 304. The processors 301, input devices 302, output devices 303 and memories 304 communicate with each other via a communication bus 305. The memory 304 is used to store computer programs, which include program instructions. The processor 301 is used to execute the program instructions stored in the memory 304. The processor 301 is configured to call the program instructions to execute the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 The functions of modules 21 to 23 are shown.

[0087] It should be understood that in the embodiment of the present disclosure, the processor 301 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0088] The input device 302 may include a touch panel, a fingerprint collection sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.

[0089] The memory 304 may include a read-only memory and a random access memory, and provide instructions and data to the processor 301. A portion of the memory 304 may also include a non-volatile random access memory. For example, the memory 304 may also store information about the device type.

[0090] In a specific implementation, the processor 301, input device 302, and output device 303 described in the embodiments of the present disclosure can execute the implementation methods described in the first and second embodiments of a traffic information publishing method provided in the embodiments of the present disclosure, and can also execute the implementation methods of the electronic device described in the embodiments of the present disclosure, which will not be repeated here.

[0091] In another embodiment of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by the processor, all or part of the processes in the above-mentioned embodiment method are implemented, and the computer program can also be completed by instructing the relevant hardware through the computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0092] The computer-readable storage medium may be an internal storage unit of the electronic device of any of the aforementioned embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the computer-readable storage medium may also include both an internal storage unit of the electronic device and an external storage device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0093] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this disclosure.

[0094] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the electronic devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0095] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces or units, or it can be an electrical, mechanical or other form of connection.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present disclosure.

[0097] In addition, each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0098] The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these modifications or replacements should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for publishing traffic information, characterized in that: include: Performing clustering processing on the traffic condition information of the first area based on a clustering algorithm to obtain first clustering data; The first area is an area where traffic information needs to be released; Determining a target release strategy for releasing traffic information to a second area based on the first clustering data, wherein the second area is an area within the first area; Traffic information is released to the second area based on the target release strategy.

2. A method for publishing traffic information according to claim 1, characterized in that: The clustering process is performed on the traffic condition information of the first area based on the clustering algorithm to obtain the first clustering data, including: Determine multiple cluster quantity values; Clustering the traffic condition information of the first area based on each cluster quantity value to obtain multiple clustering results; Evaluating the multiple clustering results to obtain multiple clustering evaluation results; Obtaining a target cluster quantity value based on the multiple cluster evaluation results; The cluster data corresponding to the target cluster quantity value is used as the first cluster data.

3. A method for issuing traffic information according to claim 1, characterized in that: The clustering process is performed on the traffic condition information of the first area based on the clustering algorithm to obtain the first clustering data, including: Determine multiple cluster centers; Calculating the distance between the traffic condition information of the second area and the traffic condition information of the plurality of cluster centers based on a first distance measurement method to obtain a plurality of first distances; First clustering data is obtained based on the multiple first distances.

4. A method for issuing traffic information as claimed in claim 3, characterized in that: The road condition information includes: traffic volume and vehicle speed; The calculating the distance between the traffic condition information of the second area and the traffic condition information of the plurality of cluster centers based on the first distance measurement method includes: Determine a first weight corresponding to the vehicle flow and a second weight corresponding to the vehicle speed; The distance between the road condition information of the second area and the road condition information of the plurality of cluster centers is calculated based on the vehicle flow, the vehicle speed, the first weight, and the second weight.

5. A method for issuing traffic information as claimed in claim 3, characterized in that: The road condition information includes: traffic volume, vehicle speed, accident rate and road grade; The calculating the distance between the traffic condition information of the second area and the traffic condition information of the plurality of cluster centers based on the first distance measurement method to obtain a plurality of first distances includes: In response to the traffic information feature satisfying the first condition, respectively determining weights corresponding to the traffic volume, the vehicle speed, the accident rate and the road grade; A plurality of first distances are obtained by performing calculation based on the traffic volume, the vehicle speed, the accident rate, the road grade and the corresponding weights.

6. A method for issuing traffic information as claimed in claim 5, characterized in that: Also includes: In response to the environmental characteristics of the first area satisfying a first environmental condition, weights corresponding to the vehicle flow, the vehicle speed, the accident rate, and the road grade are adjusted based on a target step size.

7. A method for issuing traffic information according to claim 1, characterized in that: The second area is multiple; The target release strategy includes: release area and release frequency; The first clustering data includes a plurality of target clusters; The step of determining a target release strategy for releasing traffic information to a second area based on the first clustering data includes: Determining a target release strategy from a plurality of standard release strategies based on the traffic information of the center point of the target cluster; The target release policy is used as the target release policy for all second areas in the target cluster.

8. A traffic information publishing system, characterized in that: include: A clustering module, used for clustering the traffic information of the first area based on a clustering algorithm to obtain first clustering data; The first area is an area where traffic information needs to be released; A strategy publishing module, used to determine a target publishing strategy for publishing traffic information to a second area based on the first clustering data, wherein the second area is an area within the first area; A publishing module is used to publish traffic information to the second area based on the target publishing strategy.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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