Highway section label generation method, device, equipment and medium

Abstract

The invention relates to the technical field of expressway section label generation, and provides an expressway section label generation method and device, equipment and a medium. The method comprises the steps of performing statistics based on expressway portal passing data to obtain an intra-region traffic proportion, a long-distance traffic proportion, traffic flow in a historical time period, a truck traffic proportion and a vehicle average speed of an expressway section; based on the traffic proportion in the region, the long-distance traffic proportion, the traffic flow, the truck traffic proportion and the average vehicle speed, clustering all the expressway sections to obtain a cluster; calculating a freight function value according to the traffic flow of the highway section in the historical time period and all vehicle types; and carrying out freight label generation according to all the freight function values to obtain a freight label of each expressway section, and carrying out road section label generation according to all the clustering clusters to obtain a road section label of each expressway section. The method can improve the accuracy of highway section label generation.

Description

Technical Field

[0001] The present application relates to the technical field of highway section label generation, and in particular to a method, device, equipment and medium for generating a highway section label. Background Art

[0002] The role of distinguishing labels of different highway sections is mainly reflected in improving road transportation efficiency, ensuring traffic safety and optimizing resource allocation. By dividing the labels of highway sections, more scientific and reasonable transportation plans can be formulated according to the characteristics and conditions of different sections, so as to make full use of the transportation capacity of highways. This division helps transportation companies to choose appropriate sections for cargo transportation, reduce transportation time and cost, and improve transportation efficiency. At the same time, taking corresponding traffic management measures and safety measures for sections with different labels can better ensure the safety of freight vehicles and personnel. In addition, by optimizing resource allocation, the transportation capacity of highways can be more fully utilized, providing more powerful support for economic development. However, in the current analysis of highway sections, single data is usually used for label generation, resulting in low accuracy of label generation for highway sections. Summary of the invention

[0003] The present application provides a method, device, equipment and medium for generating labels for expressway sections, which can solve the problem of low accuracy in generating labels for expressway sections.

[0004] In a first aspect, the present application provides a method for generating a highway section label, the method comprising: Obtaining highway gantry traffic data of a target area; the highway gantry traffic data includes the time when each vehicle passing through the gantry in the target area passes through the gantry and the entrance toll station and exit toll station passed by the vehicle in a historical time period, and the multiple vehicles passing through the gantry in the target area include vehicles of multiple different vehicle types; Based on the highway gantry traffic data, the regional traffic ratio, long-distance traffic ratio, traffic flow in the historical time period, truck traffic ratio, and average vehicle speed of each highway section in the target area are obtained; Based on the regional traffic proportion, long-distance traffic proportion, traffic flow in historical time periods, truck traffic proportion, and average vehicle speed of all highway sections, all highway sections are clustered to obtain multiple clusters; For each expressway section, the freight function value of the expressway section is calculated based on the traffic flow and all vehicle types of the expressway section in the historical time period; the freight function value is used to describe the truck traffic efficiency of the expressway section; Freight labels are generated according to all freight function values ​​to obtain the freight label of each expressway section, and section labels are generated according to all clustering clusters to obtain the section label of each expressway section; freight labels are used to describe the traffic conditions of freight vehicles on expressway sections, and section labels are used to describe the traffic conditions of all vehicle types on expressway sections.

[0005] In a second aspect, the present application provides a device for generating a highway section label, comprising: An acquisition module is used to acquire highway gantry traffic data in a target area; the highway gantry traffic data includes the time when each vehicle passing through the gantry in the target area passes through the gantry and the entrance toll station and exit toll station passed by the vehicle in a historical time period, and the multiple vehicles passing through the gantry in the target area include vehicles of multiple different vehicle types; The statistical module is based on the highway gantry traffic data to obtain the regional traffic ratio, long-distance traffic ratio, traffic flow in the historical time period, truck traffic ratio, and average vehicle speed of each highway section in the target area; The clustering module clusters all highway sections based on their regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed to obtain multiple clusters. The calculation module calculates the freight function value of each expressway section according to the traffic flow and all vehicle types of the expressway section in the historical time period; the freight function value is used to describe the traffic efficiency of trucks on the expressway section; The label generation module generates freight labels according to all freight function values ​​to obtain the freight labels of each expressway section, and generates section labels according to all clustering clusters to obtain the section labels of each expressway section; freight labels are used to describe the traffic conditions of freight vehicles on expressway sections, and section labels are used to describe the traffic conditions of all types of vehicles on expressway sections.

[0006] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the high-speed label generation method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the above-mentioned high-speed label generation method is implemented.

[0008] The above solution of the present application has the following beneficial effects: In an embodiment of the present application, by generating section labels for highway sections based on the regional traffic ratio, long-distance traffic ratio, traffic flow, truck traffic ratio, and average vehicle speed, the information richness and accuracy of label generation can be improved, and freight labels are generated for highway sections to describe the traffic conditions of freight vehicles on the highway sections. Combined with section labels, label generation for highway sections can be achieved from both freight and overall aspects, effectively improving the comprehensiveness and accuracy of label generation for highway sections.

[0009] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0011] Figure 1 A flowchart of a method for generating a highway section label according to an embodiment of the present application; Figure 2 A statistical bar chart of the number of times a gantry is passed provided in an embodiment of the present application; Figure 3 A statistical bar chart of the number of times a vehicle enters a highway provided in an embodiment of the present application; Figure 4 A schematic diagram of a highway section provided in an embodiment of the present application; Figure 5 A schematic diagram of the door frame distribution provided in an embodiment of the present application; Figure 6 A schematic diagram of a vehicle trajectory provided in an embodiment of the present application; Figure 7 A contour coefficient curve diagram provided in an embodiment of the present application; Figure 8 A traffic flow statistics diagram provided in an embodiment of the present application; Fig. 9 A statistical chart of the proportion of intra-provincial traffic provided in an embodiment of the present application; Fig.10 A statistical chart of traffic proportions over 500 kilometers provided in an embodiment of the present application; Fig.11 A truck traffic ratio statistical chart provided for an embodiment of the present application; Fig.12 A statistical graph of average vehicle speed provided in an embodiment of the present application; Fig.13A schematic diagram of the clustering results of expressway sections provided in an embodiment of the present application; Fig.14 A schematic diagram of a shipping label provided in accordance with an embodiment of the present application; Fig.15 A schematic diagram of the structure of a device for generating a highway section label according to an embodiment of the present application; Fig.16 A schematic diagram of the structure of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also 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 prevent unnecessary details from obstructing the description of the present application.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0014] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0015] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0016] In response to the problem of low accuracy in the existing label generation of expressway sections, an embodiment of the present application provides a method for generating labels for expressway sections. By generating section labels for expressway sections based on the regional traffic ratio, long-distance traffic ratio, traffic flow, truck traffic ratio, and average vehicle speed, the information richness and accuracy of label generation can be improved. Freight labels are generated for expressway sections, and the traffic conditions of freight vehicles on the expressway sections can be described. Combined with section labels, label generation for expressway sections can be achieved from both freight and overall aspects, effectively improving the comprehensiveness and accuracy of label generation for expressway sections.

[0017] Next, the method for generating highway section labels provided in this application is exemplified.

[0018] like Figure 1 As shown, the method for generating a highway section label provided by the present application includes the following steps: Step 11, obtaining highway gantry traffic data in the target area.

[0019] The above-mentioned highway gantry traffic data includes the time when each vehicle passing through the gantry in the target area passes through the gantry during the historical time period, as well as the entry toll station and exit toll station that the vehicle passes through (the information of the entry toll station and exit toll station that the vehicle passes through is collected by the information collection equipment on the gantry installed at the entry toll station and exit toll station when the vehicle passes through the entry toll station and exit toll station. Therefore, in the highway management gantry traffic data, the time when the vehicle passes through the gantry is recorded. If the gantry corresponds to the entry toll station or exit toll station passed by the vehicle, the gantry is marked as the entry toll station or exit toll station of the vehicle. The gantry of the entry toll station and the gantry of the exit toll station may not be the gantry in the target area). The multiple vehicles passing through the gantries in the target area include vehicles of multiple different vehicle types, and the multiple vehicle types include multiple trucks (small trucks, large trucks, etc.) and other types (small passenger cars, large passenger cars, engineering vehicles, ambulances, etc.). The target area is the area where highway labels need to be generated, such as a province. The gantry is a door frame type frame installed on the highway for road traffic. The gantry is equipped with intelligent traffic cameras, speed meters and other equipment to collect vehicle driving information.

[0020] For example, during the historical time period, a total of 10 vehicles passed through the gantries in the target area, of which the first vehicle only passed through three gantries, and the first gantry was the entrance toll station, and the third gantry was the exit toll station. The highway gantry traffic data includes the time when the first vehicle passed through the three gantries, and the information that the first gantry was the entrance toll station and the third gantry was the exit toll station, and also includes the number of gantries passed by the other 9 vehicles and the corresponding time, entrance toll station and exit toll station information.

[0021] In some embodiments of the present application, highway gantry traffic data for a historical period of time can be obtained by accessing the gantry system of the target area, the vehicle positioning system, etc.

[0022] It should be noted that the highway gantry traffic data also includes the vehicle license plate number, vehicle type, gantry information, etc. For the sake of data integrity, the highway gantry traffic data can be obtained by obtaining the gantry data and the vehicle traffic data, and then integrating the gantry data and the traffic data. After obtaining the gantry data, it needs to be preprocessed. Specifically, first, the gantry redundant data is removed. There are some redundant data in the original gantry data. The "traffic identification ID", "gantry number", "gantry HEX", "driving direction", "passing time", "billing vehicle license plate number", "billing vehicle model code", "billing vehicle type code", "entrance station HEX", "entrance name", "entrance time", "previous gantry HEX", "time passing the previous gantry", "billing mileage", "longitude", "latitude", "pile number", "number of repetitions of the billing vehicle license plate number" fields are extracted from the gantry data. The extracted partial gantry data is shown in Table 1.

[0023] Table 1

[0024] NULL indicates missing data, and gantry HEX is the gantry number. Next, remove the redundant data of the traffic data. There are some redundant data in the original traffic data. Extract the "traffic identification ID", "charge vehicle license plate number", "entry HEX", "entry time", "exit HEX", "exit time", and "accumulated charging mileage" fields in the traffic data. The extracted part of the traffic data is shown in Table 2.

[0025] Table 2

[0026] With the gantry data and traffic data, theoretically, the complete trajectory of the vehicle from the entrance toll station to all the gantries it passes through to the exit toll station can be traced. The extracted traffic data and the extracted gantry data are filtered out according to the same traffic identification ID and merged. These trajectory data theoretically have data from the entrance toll station to the collected gantry during the collection period and then to the exit toll station. The gantry traffic data of some highways obtained by combining the gantry data with the traffic data is shown in Table 3.

[0027] Table 3

[0028] For example, for a certain target area, the number of vehicles during the survey period was 1,136,951, accounting for 6.9% of the gantry data, that is, on average, each vehicle collected data from 14 gantry. By counting the number of repetitions of each vehicle, that is, the number of gantries each vehicle passed, it was found that 41.2% of the vehicles passed through the gantries 6 times or less, of which 10 times or less accounted for 41.2%. Figure 2 As shown in the figure, the horizontal axis represents the number of vehicles passing through the gantries, and the vertical axis represents the vehicle proportion. It can be found that as the number of gantries increases, the number of corresponding vehicles shows an overall downward trend, among which 8.6% of vehicles have only one gantry data record, and the number of vehicles passing through the gantries 10 times is less than 3.4%.

[0029] Count the number of vehicles entering the highway and the corresponding vehicle share. Figure 3 As shown in the figure, the horizontal axis represents the number of times vehicles enter the highway, and the vertical axis represents the proportion of vehicles. It can be found that within the data collection range, 32.0% of vehicles entered the highway only once; 16.7% of vehicles entered the highway twice; and only a very small number of vehicles entered the highway more than five times.

[0030] Step 12, based on the highway gantry traffic data, statistics are performed to obtain the regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed of each highway section in the target area.

[0031] The above-mentioned regional traffic ratio is used to describe the proportion of regional vehicles (vehicles whose corresponding entrance toll station and exit toll station are both located in the target area) among all vehicles passing through the expressway section. The long-distance traffic ratio is used to describe the proportion of long-distance vehicles among all vehicles passing through the expressway section. The traffic flow in the historical time period is used to describe the number of vehicles passing through the expressway section in the historical time period. The truck traffic ratio is used to describe the proportion of trucks among all vehicles passing through the expressway section. The average vehicle speed is used to describe the average speed of vehicles passing through the expressway section.

[0032] In some embodiments of the present application, the above-mentioned step of performing statistics based on highway gantry traffic data to obtain the regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed of each highway section in the target area includes: In the first step, for each vehicle in all highway gantry traffic data, all the gantries passed by the vehicle, the time of passing the gantries, the exit toll stations passed and the entrance toll stations passed are obtained from all highway gantry traffic data, and all the gantries passed by the vehicle, the time of passing the gantries, the exit toll stations passed and the entrance toll stations passed are integrated to obtain the vehicle trajectory. If the entrance toll station and the exit toll station passed by the vehicle belong to the gantries within the target area, the vehicle is regarded as a vehicle in the area. If the trajectory length of the vehicle is greater than the length threshold, the vehicle is regarded as a long-distance vehicle.

[0033] For example, when integrating all the gantries that the vehicle passes through, the time of passing through the gantries, the exit toll stations and the entrance toll stations passed through, the position of the entrance toll station is used as the starting point, and the positions of all the gantries passed through are sorted from early to late according to the time of passing through the gantries, and finally the position of the exit toll station is used as the end point. If one of the entrance toll station and the exit toll station passed by the vehicle does not belong to the gantries in the target area, the vehicle will not be processed; if the trajectory length of the vehicle is less than or equal to the length threshold, the vehicle will not be processed.

[0034] In the second step, based on the trajectories of all vehicles and all vehicles in the area, the intra-area traffic ratio of each highway section in the target area is calculated.

[0035] Specifically, for each expressway section, the total number of vehicles passing through the expressway section is counted based on the trajectories of all vehicles, the number of vehicles in the area passing through the expressway section is counted based on the trajectories of vehicles in all areas, and the ratio between the total number of vehicles passing through the expressway section and the number of vehicles in the area passing through the expressway section is calculated to obtain the traffic ratio within the area.

[0036] In the third step, based on the trajectories of all vehicles and all long-distance vehicles, the proportion of long-distance traffic on each highway section in the target area is calculated.

[0037] Specifically, for each expressway section, the number of long-distance vehicles passing through the expressway section is counted based on the trajectories of all long-distance vehicles, and the ratio between the total number of vehicles passing through the expressway section and the number of long-distance vehicles passing through the expressway section is calculated to obtain the long-distance traffic ratio of the expressway section.

[0038] The fourth step is to calculate the traffic flow of each highway section in the target area during the historical time period based on the trajectories of all vehicles.

[0039] Specifically, for each expressway section, the total number of vehicles passing through the expressway section is used as the traffic flow of the expressway section in the historical time period.

[0040] In the fifth step, based on the trajectories of all vehicles and all vehicle types, the proportion of truck traffic on each highway section in the target area is calculated.

[0041] Specifically, for each expressway section, the number of trucks passing through the expressway section is counted according to the trajectories of all vehicles whose vehicle type is trucks, and the ratio between the number of trucks passing through the expressway section and the total number of vehicles passing through the expressway section is calculated to obtain the truck traffic ratio.

[0042] In the sixth step, the average vehicle speed of each highway section in the target area is calculated based on the trajectories of all vehicles.

[0043] Specifically, for each expressway section, perform the following steps: The vehicle passing through the highway section in each trajectory is regarded as the target vehicle; For each target vehicle, the time it takes for the target vehicle to pass through adjacent gantries in the expressway section is calculated, and the ratio between the length of adjacent gantries in the expressway section and the time is calculated to obtain the speed of the target vehicle at the adjacent gantries in the expressway section; if the speed of the target vehicle at the adjacent gantries in the expressway section is within a preset speed threshold range, the journey of the vehicle between adjacent gantries in the expressway section is regarded as a normal journey without stopping, and the average driving speed of all target vehicles whose journey between adjacent gantries in the corresponding expressway section is a normal journey is calculated, thereby obtaining the average vehicle speed of the expressway section.

[0044] For example, the average speed calculation formula can be used to calculate the time difference and distance between the starting point and the end point of the vehicle passing through the adjacent gantries to obtain the average speed of the vehicle between the adjacent gantries. In the above text, the adjacent gantries of the highway are any two adjacent gantries in the highway section.

[0045] The above-mentioned expressway section is exemplarily described below with reference to a specific example.

[0046] The expressway section of a province is as follows Figure 4 As shown, Figure 4 The numbers in the middle are the numbers of the expressway sections, and the solid lines represent the expressways in the province. The distribution of the gantries on the expressway sections in the local area of ​​the province is as follows: Figure 5 As shown, Figure 5 The solid line represents the expressway, the dot represents the gantries, the numbers and letter-number combinations (such as 481A07) represent the gantries or toll booth numbers, and the triangles represent the entrance and exit toll booths. Figure 5 The partial vehicle trajectory from the entrance toll station to the exit toll station is shown as Figure 6 As shown in the figure, the solid line represents one vehicle trajectory (path A), and the dotted line represents another vehicle trajectory (path B).

[0047] Step 13, clustering all expressway sections based on the intra-regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed of all expressway sections to obtain multiple clusters.

[0048] Specifically, the regional traffic proportion, long-distance traffic proportion, traffic flow in historical time periods, truck traffic proportion, and average vehicle speed of each highway section are combined into one data point, and then a clustering algorithm is used to cluster all data points to obtain multiple clusters.

[0049] For example, the above clustering algorithm can be an algorithm such as K-means clustering. In K-means clustering, the selection of k is very critical, which determines the number of final clusters. In practical applications, some methods are usually used to select a suitable k value, such as the elbow method, the silhouette coefficient method, etc. By testing the silhouette coefficient corresponding to k values ​​(number of classifications) from 2 to 10, the silhouette coefficients corresponding to different k values ​​are as follows: Figure 7 As shown in the figure, the horizontal axis represents the value of the number of clusters k, and the vertical axis represents the silhouette coefficient. It can be found that 9 is the best k value, and it is most appropriate to divide the data into 9 categories. Therefore, all highway sections can be clustered to obtain 9 clusters.

[0050] Step 14, for each expressway section, respectively, calculate the freight function value of the expressway section according to the traffic flow and all vehicle types of the expressway section in the historical time period.

[0051] The freight function value is used to describe the truck traffic efficiency of the highway section.

[0052] Specifically, through the formula: ; Calculate freight function value .

[0053] in, Indicates the number of vehicles passing through the expressway. The proportion of trucks Indicates The total number of axles of the truck. Indicates the number of truck types. Indicates the traffic flow of the expressway section in the historical time period, Indicates the average vehicle speed on the expressway section.

[0054] For example, different types of trucks have different axle numbers and weights. The transportation effects of different types of trucks on a road section are also different. Therefore, different types of trucks need to be distinguished. The definitions of different truck models are shown in Table 4.

[0055] Table 4

[0056] It is worth mentioning that the proportion of different types of trucks, their total number of axles (reflecting the vehicle's heavy load capacity and potential impact on the road surface), the traffic flow of the section (reflecting the busyness and capacity of the section), and the average hourly speed of the section (reflecting the traffic flow and efficiency) are comprehensively considered. By calculating the proportion of each type of vehicle in the traffic flow of the section, the contribution of each type of truck to the freight function value can be accurately measured. At the same time, combined with the total number of axles of each type of vehicle, its impact on the transportation capacity of the section and road wear can be further evaluated. The traffic flow and average speed of the section are directly related to the actual capacity and traffic efficiency of the section. The comprehensive use of these indicators provides strong data support for scientifically evaluating the freight function of expressway sections, optimizing the selection of freight vehicles, and improving the operational efficiency of expressways.

[0057] Step 15, generating freight labels according to all freight function values ​​to obtain freight labels for each expressway section, and generating section labels according to all clustering clusters to obtain section labels for each expressway section.

[0058] The above-mentioned freight labels are used to describe the traffic conditions of freight vehicles on the expressway section (for example, the freight labels can describe the freight grade of the expressway section), and the section labels are used to describe the traffic conditions of all types of vehicles on the expressway section.

[0059] Specifically, the freight labels of the expressway sections can be obtained by classifying the expressway sections according to the freight function values. For example, the expressway sections with freight function values ​​less than the first preset value are classified as the first-level freight grade, the expressway sections with freight function values ​​greater than or equal to the first preset value and less than the second preset value are classified as the second-level freight grade, and the expressway sections with freight function values ​​greater than or equal to the second preset value are classified as the third-level freight sections. The larger the freight function value, the higher the freight grade, and the higher the freight vehicle capacity and traffic efficiency of the expressway section. The label of each cluster can be obtained by classifying the data such as the regional traffic proportion, long-distance traffic proportion, traffic flow in historical time periods, truck traffic proportion, and average vehicle speed of the expressway sections in each cluster.

[0060] For example, classification models such as convolutional neural networks can be used to classify labels to obtain cluster labels, and the labels are preset according to the actual road segment analysis requirements. For example, the label "high-traffic provincial-dominated road segment" indicates that the proportion of provincial traffic on the expressway section in the cluster is extremely high, and it may be the main expressway connecting large cities or important economic regions in the province, with a large traffic volume and mainly vehicles from within the province.

[0061] It should be noted that after obtaining the freight label and the road section label, corresponding traffic management measures and safety measures can be adopted for the expressway section according to the freight label and the road section label. For example, for expressway sections with high freight grades, there are many freight vehicles on this expressway section, and the consequences of traffic accidents are serious, so safety facilities should be added to this expressway section.

[0062] It is worth mentioning that generating section labels for highway sections based on regional traffic proportion, long-distance traffic proportion, traffic flow, truck traffic proportion and average vehicle speed can improve the information richness and accuracy of label generation. Generating freight labels for highway sections can describe the traffic conditions of freight vehicles on highway sections. Combined with section labels, it is possible to generate labels for highway sections from both freight and overall aspects, effectively improving the comprehensiveness and accuracy of label generation for highway sections.

[0063] The above steps are illustrated below with reference to a specific example.

[0064] All expressway sections in a province are clustered into 9 clusters. The traffic flow statistics of the expressway sections in the 9 clusters in a certain historical period are as follows: Figure 8 As shown, Figure 8 The horizontal axis represents the traffic volume, which is the number of vehicles per hour (veh / h), and the vertical axis represents the frequency. Figure 8 a represents the traffic flow statistics of the first cluster, Figure 8 b represents the traffic flow statistics of the second cluster. Figure 8 c represents the traffic statistics of the third cluster, Figure 8 d represents the traffic statistics of the fourth cluster. Figure 8 e represents the traffic statistics of the fifth cluster, Figure 8 f represents the traffic statistics of the sixth cluster, Figure 8 g represents the traffic flow statistics of the 7th cluster, Figure 8 h represents the traffic statistics of the 8th cluster, Figure 8 i represents the traffic flow statistics of the 9th cluster.

[0065] It can be found that from the perspective of traffic flow differences, the 9 clusters can be divided into several different traffic flow intervals. Clusters 5 and 8 have the highest traffic flow, which may represent busy urban centers or transportation hubs. Clusters 1 and 7, as well as clusters 3, 4, and 6 are at medium-high and medium levels, respectively, and may represent urban or suburban traffic of different densities and busyness. Clusters 2 and 9 have relatively low traffic flows, which may represent areas with lighter or less developed traffic.

[0066] The proportion of intra-provincial traffic (i.e., intra-regional traffic) on the expressway sections in the 9 clusters is shown in the following table: Fig. 9 As shown, Fig. 9 The horizontal axis represents the value of the intra-provincial traffic ratio, and the vertical axis represents the frequency. Fig. 9 a represents the intra-provincial traffic proportion statistics of the first cluster, Fig. 9 b represents the intra-provincial traffic proportion statistics of the second cluster. Fig. 9 c represents the intra-provincial traffic proportion statistics of the third cluster, Fig. 9 d represents the intra-provincial traffic proportion statistics of the fourth cluster. Fig. 9 e represents the intra-provincial traffic proportion statistics of the fifth cluster, Fig. 9 f represents the intra-provincial traffic proportion statistics of the sixth cluster, Fig. 9 g represents the intra-provincial traffic proportion statistics of the 7th cluster, Fig. 9 h represents the intra-provincial traffic proportion statistics of the 8th cluster, Fig. 9 i represents the intra-provincial traffic proportion statistics of the 9th cluster.

[0067] It can be found that the proportion of intra-provincial traffic in clusters 1, 4, 5, and 7 is relatively high. The high proportion of intra-provincial traffic may indicate that the traffic flow in these cluster areas is mainly composed of vehicles from the province, reflecting strong intra-provincial traffic connections and mobility. These areas may be transportation hubs, economic centers, or densely populated areas within the province. The proportion of intra-provincial traffic in clusters 2 and 8 is at a medium level, indicating that vehicles from within and outside the province each account for a certain proportion of the traffic flow in these areas. There may be both strong intra-provincial traffic connections and certain inter-provincial traffic flows. These areas may be major traffic arteries connecting the province and the outside or have certain economic appeal. The proportion of intra-provincial traffic in clusters 3, 6, and 9 is relatively low. The low proportion of intra-provincial traffic may mean that vehicles from outside the province account for a large proportion of the traffic flow in these areas, reflecting strong inter-provincial traffic connections. In particular, Cluster 3 and Cluster 6 have a much lower proportion of intra-provincial traffic than other clusters, which may indicate that these areas are the main channels or destinations for vehicles from other provinces to enter the province and have strong inter-provincial traffic mobility. Although Cluster 9 also has a lower proportion of intra-provincial traffic, it still has a certain proportion of intra-provincial traffic compared to Cluster 3 and Cluster 6.

[0068] From the perspective of differences in intra-provincial traffic proportions, the nine clusters can be divided into three different proportion ranges: high, medium, and low. High-proportion clusters may represent transportation hubs, economic centers, or densely populated areas within the province; medium-proportion clusters may have both strong intra-provincial traffic connections and certain inter-provincial traffic flows; low-proportion clusters may have strong inter-provincial traffic connections and are the main channels or destinations for vehicles from other provinces to enter the province.

[0069] The proportion of traffic (long-distance traffic) of more than 500 kilometers on the expressway sections in the 9 clusters is as follows: Fig.10 As shown, Fig.10 The horizontal axis represents the value of the proportion of traffic over 500 kilometers, and the vertical axis represents the frequency. Fig.10 a represents the traffic statistics of the first cluster over 500 kilometers. Fig.10 b represents the traffic statistics of the second cluster over 500 kilometers. Fig.10 c represents the traffic statistics of the third cluster with a distance of more than 500 kilometers. Fig.10 d represents the traffic statistics of the fourth cluster over 500 kilometers. Fig.10 e represents the traffic statistics of the fifth cluster over 500 kilometers. Fig.10 f represents the traffic statistics of the sixth cluster over 500 kilometers. Fig.10 g represents the traffic statistics of the 7th cluster with a distance of more than 500 kilometers. Fig.10 h represents the traffic statistics of the 8th cluster with a distance of more than 500 kilometers. Fig.10 i represents the traffic statistics of the 9th cluster over 500 kilometers.

[0070] It can be found that clusters 3 and 6 have the highest proportion of traffic over 500 kilometers. The high proportion of long-distance traffic may indicate that a large proportion of the traffic flow in these cluster areas is from long-distance or highway vehicles, which may reflect the role of these areas as transportation hubs or long-distance travel destinations, or the close transportation connection with surrounding areas. The proportion of traffic over 500 kilometers in cluster 8 is at a medium-to-high level, which indicates that the area also has a certain amount of long-distance traffic flow and may be a transportation node connecting different regions. The proportion of traffic over 500 kilometers in clusters 2, 4, 5, 7, and 9 is relatively low. The low proportion of long-distance traffic may mean that there is less long-distance traffic flow in these areas, and the traffic is mainly concentrated in local or short-distance travel. These areas may be within or around the city, and the traffic demand is mainly daily commuting and local activities. The proportion of traffic over 500 kilometers in cluster 1 is relatively low, which indicates that there is little long-distance traffic flow in the area, and the traffic is mainly concentrated in local or short-distance travel. This may be related to the specific geographical location, transportation network layout, or transportation policy of the area.

[0071] From the perspective of differences in the proportion of long-distance traffic, the nine clusters can be divided into three different proportion intervals: high, medium, and low. High-proportion clusters may represent transportation hubs or long-distance travel destinations, medium-proportion clusters may be transportation nodes connecting different regions, and low-proportion clusters may be mainly local or short-distance travel. This difference in the proportion of long-distance traffic is of great reference value for understanding traffic mobility in different regions and formulating traffic planning strategies (such as highway construction, transportation hub layout, etc.). At the same time, it should also be noted that the proportion of long-distance traffic may be affected by many factors, including geographical location, transportation network layout, economic development level, etc.

[0072] The statistics of truck traffic proportions on highway sections in the 9 clusters are as follows: Fig.11 As shown, Fig.11 The horizontal axis represents the value of truck traffic proportion, and the vertical axis represents the frequency. Fig.11 a represents the truck traffic proportion statistics of the first cluster, Fig.11 b represents the truck traffic proportion statistics of the second cluster. Fig.11 c represents the truck traffic proportion statistics of the third cluster, Fig.11 d represents the truck traffic proportion statistics of the fourth cluster. Fig.11 e represents the truck traffic proportion statistics of the fifth cluster, Fig.11 f represents the truck traffic proportion statistics of the sixth cluster, Fig.11 g represents the truck traffic proportion statistics of the 7th cluster, Fig.11 h represents the truck traffic proportion statistics of the 8th cluster, Fig.11 i represents the truck traffic proportion statistics of the 9th cluster.

[0073] It can be found that cluster 3 has the highest proportion of truck traffic. The high proportion of truck traffic may indicate that the cluster area is an important node for cargo transportation, with a large number of trucks entering and leaving, which may involve logistics, warehousing, manufacturing and other industries. The average traffic proportion of cluster 9 is also relatively high. Although it is slightly lower than cluster 3, it still indicates that the area has a certain demand for cargo transportation and may also be one of the distribution centers for cargo transportation. The proportion of truck traffic in clusters 6 and 8 is at a medium level, which indicates that these areas also have certain cargo transportation activities, but the proportion of truck traffic is lower than that of clusters 3 and 9. The proportion of truck traffic in clusters 1, 2, 4, 5 and 7 is relatively low. The low proportion of truck traffic may mean that there is less demand for cargo transportation in these areas, or that truck traffic is covered by other types of traffic (such as private cars). From the perspective of differences in truck traffic proportions, the nine clusters can be divided into three different proportion intervals: high, medium and low. High-proportion clusters (such as clusters 3 and 9) may be important nodes for cargo transportation, involving multiple industries related to logistics, warehousing, manufacturing, etc. Medium-proportion clusters (such as clusters 6 and 8) also have some cargo transportation activities, but relatively few. Low-proportion clusters (such as clusters 1, 2, 4, 5, and 7) have less demand for cargo transportation, or truck traffic is not the main type of transportation in the area.

[0074] The average vehicle speed statistics of the high-speed sections in the 9 clusters are as follows: Fig.12 As shown, Fig.12 The horizontal axis represents the average speed in kilometers per hour (km / h), and the vertical axis represents the frequency. Fig.12a represents the average speed statistics of the first cluster, Fig.12 b represents the average speed statistics of the second cluster. Fig.12 c represents the average speed statistics of the third cluster, Fig.12 d represents the average speed statistics of the fourth cluster, Fig.12 e represents the average speed statistics of the fifth cluster, Fig.12 f represents the average speed statistics of the sixth cluster, Fig.12 g represents the average speed statistics of the 7th cluster, Fig.12 h represents the average speed statistics of the 8th cluster, Fig.12 i represents the average speed statistics of the 9th cluster.

[0075] It can be found that from the perspective of speed differences, the 9 clusters can be divided into several different speed ranges. Cluster 2 and Cluster 7 have the highest average speeds, indicating that the traffic conditions in these two clusters are the smoothest. Cluster 1 has the lowest average speed, which may be affected by traffic congestion or poor road conditions. The other clusters are between these two extremes.

[0076] The means of the five data items of the nine clusters are shown in Table 5.

[0077] Table 5

[0078] It can be found that the road segment labels corresponding to each cluster category are as follows: Category 1 is a high-volume intra-provincial dominant road section, with a high average traffic volume (932.0880), indicating that this category of road section has a large traffic volume. The average intra-provincial traffic ratio is extremely high (0.8914161), indicating that it is mainly vehicles from within the province. The average speed is moderate (66.48984 km / h), which may be affected by the large traffic volume. This category of road section may be a major expressway connecting large cities or important economic regions in the province, with a large traffic volume and mainly vehicles from within the province.

[0079] Category 2 is a medium-flow mixed section, with a medium average traffic volume (552.2524), a medium average intra-provincial traffic ratio (0.4012433), but a high truck ratio (0.2899051), and a high average speed (97.28498 km / h), indicating good road conditions and smooth traffic. This category of road may be a mixed-use road connecting different regions or cities, with both intra-provincial vehicles and a certain proportion of out-of-province vehicles and trucks.

[0080] Category 3 is a high truck ratio road section, with a medium average traffic volume (611.7678), a low average intra-provincial traffic ratio (0.2056401), but a very high truck ratio (0.6294887), and a medium average speed (81.85080 km / h). This category of road section may be the main channel for cargo transportation, with a high truck ratio, and may connect industrial areas, logistics centers or ports.

[0081] Category 4 is a medium-volume intra-provincial road section with a medium average traffic volume (624.5211), a high average intra-provincial traffic ratio (0.7357545), and a medium to high average speed (88.65332 km / h). It is similar to category 1, but with slightly lower traffic volume and speed. It may be a road connecting medium-sized cities or economic regions.

[0082] Category 5 is the extremely high-traffic intra-provincial dominant road section, with an extremely high average traffic volume (3112.2228), much higher than other categories, and a high average intra-provincial traffic ratio (0.8105511), and a medium average speed (88.01008 km / h), which may be affected by extremely high traffic volume. This category of road section may be the main highway connecting the largest city or economic center in a province, with extremely high traffic volume.

[0083] Category 6 is a mixed section of high-freight trucks and long-distance buses, with a medium average traffic volume (614.2532), a low average intra-provincial traffic ratio (0.2316328), but a high proportion of trucks (0.3238583), and a possible high proportion of long-distance buses. The average speed is medium to high (89.12245 km / h). This category of road may be a mixed-use road connecting different provinces or long-distance areas within a province, with both trucks and long-distance buses.

[0084] Category 7 is a high-volume highway section, with a high average traffic volume (982.0054), a very high average intra-provincial traffic ratio (0.8302548), and a high average speed (101.08568 km / h), which is the fastest of all categories. This category of section may be a highway connecting large cities or economic regions, with a large traffic volume and high speed, and excellent road conditions.

[0085] Category 8 is a mixed road section with medium to high traffic volume, with a medium to high average traffic volume (1903.0337), a medium average intra-provincial traffic ratio (0.4254804), a high truck ratio (0.3504164), and a medium to high average speed (91.97421km / h). This category of road section may be a mixed-use road connecting different cities or regions, with both intra-provincial vehicles and a certain proportion of out-of-province vehicles and trucks, and the road conditions are good.

[0086] Category 9 is a low-volume mixed road section, with low average traffic volume (381.0570), medium average intra-provincial traffic ratio (0.5207122), high truck ratio (0.4974762), and medium average speed (83.34337 km / h). This category of road section may be a road connecting smaller cities or rural areas, with low traffic volume but high truck ratio, and may be used for freight transportation or local transportation.

[0087] The clustering results of highway sections from late night to early morning are as follows: Fig.13 As shown in the figure, the numbers represent the cluster numbers, and different types of lines represent highway sections belonging to different clusters. There are 9 clusters in total.

[0088] A freight label is generated for the province, and the preset freight levels are shown in Table 6.

[0089] Table 6

[0090] The province's shipping label is Fig.14 As shown in the figure, different types of lines represent expressway sections with different freight levels, with a total of five levels.

[0091] It can be seen that the method of the present application can intuitively display the freight labels and section labels of the highway section, effectively improving the intuitiveness and comprehensiveness of the highway section labels.

[0092] The following is an exemplary description of the high-speed road section label generation device provided in this application.

[0093] like Fig.15 As shown, the embodiment of the present application provides a device for generating a highway section label, and the device for generating a highway section label 1500 includes: The acquisition module 1501 acquires the highway gantry traffic data of the target area; the highway gantry traffic data includes the time when each vehicle passing through the gantry in the target area passes through the gantry and the entrance toll station and exit toll station passed by the vehicle in the historical time period, and the multiple vehicles passing through the gantry in the target area include vehicles of multiple different types of vehicles; The statistical module 1502 performs statistics based on the highway gantry traffic data to obtain the regional traffic ratio, long-distance traffic ratio, traffic flow in the historical time period, truck traffic ratio, and average vehicle speed of each highway section in the target area; The clustering module 1503 clusters all highway sections based on the intra-regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed of all highway sections to obtain multiple clusters; The calculation module 1504 calculates the freight function value of each expressway section according to the traffic flow and all vehicle types of the expressway section in the historical time period; the freight function value is used to describe the traffic efficiency of trucks on the expressway section; The label generation module 1505 generates freight labels according to all freight function values ​​to obtain the freight labels of each expressway section, and generates section labels according to all clustering clusters to obtain the section labels of each expressway section; the freight labels are used to describe the traffic conditions of freight vehicles on the expressway section, and the section labels are used to describe the traffic conditions of all types of vehicles on the expressway section.

[0094] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0095] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0096] like Fig.16 As shown, an embodiment of the present application provides a terminal device. The terminal device D10 of this embodiment includes: at least one processor D100 ( Fig.16 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the steps of any of the above-mentioned method embodiments when executing the computer program D102.

[0097] Specifically, when the processor D100 executes the computer program D102, by generating section labels for the highway sections based on the regional traffic ratio, long-distance traffic ratio, traffic flow, truck traffic ratio, and average vehicle speed, it is possible to improve the information richness and accuracy of label generation, generate freight labels for the highway sections, and describe the traffic conditions of freight vehicles on the highway sections. Combined with the section labels, it is possible to generate labels for the highway sections from both freight and overall aspects, effectively improving the comprehensiveness and accuracy of label generation for the highway sections.

[0098] The processor D100 may be a central processing unit (CPU), or 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 any conventional processor, etc.

[0099] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC, SmartMedia Card), a secure digital (SD, Secure Digital) card, a flash card (Flash Card), etc. equipped on the terminal device D10. Further, the memory D101 may also include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0100] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0101] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0102] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps 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. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the high-speed road section label generation method device / terminal device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0103] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for generating a label for a highway section, characterized in that: include: Obtaining highway gantry traffic data of a target area; the highway gantry traffic data includes the time when each vehicle passing through the gantry in the target area passes through the gantry and the entrance toll station and exit toll station passed by the vehicle in a historical time period, and the multiple vehicles passing through the gantry in the target area include vehicles of multiple different vehicle types; Based on the highway gantry traffic data, statistics are performed to obtain the regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed of each highway section in the target area; Based on the regional traffic proportion, long-distance traffic proportion, traffic flow in historical time periods, truck traffic proportion, and average vehicle speed of all highway sections, all highway sections are clustered to obtain multiple clusters; For each of the expressway sections, respectively, the freight function value of the expressway section is calculated according to the traffic flow and all vehicle types of the expressway section in a historical time period; the freight function value is used to describe the traffic efficiency of trucks on the expressway section; A freight label is generated according to all freight function values ​​to obtain a freight label for each of the expressway sections, and a section label is generated according to all clustering clusters to obtain a section label for each of the expressway sections; the freight label is used to describe the traffic conditions of freight vehicles on the expressway section, and the section label is used to describe the traffic conditions of all types of vehicles on the expressway section.

2. The method for generating a high-speed road section label according to claim 1, characterized in that: The statistics based on the highway gantry traffic data are used to obtain the regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed of each highway section in the target area, including: For each vehicle in all highway gantry traffic data, all gantries passed by the vehicle, the time of passing the gantries, the exit toll stations and the entrance toll stations passed by the vehicle are obtained from all highway gantry traffic data, and all gantries passed by the vehicle, the time of passing the gantries, the exit toll stations and the entrance toll stations passed by the vehicle are integrated to obtain the trajectory of the vehicle. If the entrance toll station and the exit toll station passed by the vehicle belong to the gantries within the target area, the vehicle is regarded as a vehicle within the area. If the trajectory length of the vehicle is greater than the length threshold, the vehicle is regarded as a long-distance vehicle. Calculate the regional traffic ratio of each highway section in the target area based on the trajectories of all vehicles and all vehicles in the area; Calculate the proportion of long-distance traffic in each highway section of the target area based on the trajectories of all vehicles and all long-distance vehicles; Calculate the traffic flow of each highway section in the target area in a historical time period based on the trajectories of all vehicles; Calculate the truck traffic ratio of each highway section in the target area based on all vehicle trajectories and all vehicle types; The average speed of vehicles in each highway section of the target area is calculated based on the trajectories of all vehicles.

3. The method for generating a high-speed road section label according to claim 2, characterized in that: The calculating of the regional traffic ratio of each highway section in the target area based on the trajectories of all vehicles and all vehicles in the area includes: For each of the expressway sections, the total number of vehicles passing through the expressway section is counted based on the trajectories of all vehicles, the number of vehicles in the area passing through the expressway section is counted based on the trajectories of all vehicles in the area, and the ratio between the total number of vehicles passing through the expressway section and the number of vehicles in the area passing through the expressway section is calculated to obtain the traffic ratio in the area; The calculating of the long-distance traffic ratio of each highway section in the target area based on the trajectories of all vehicles and all long-distance vehicles includes: For each of the expressway sections, the number of long-distance vehicles passing through the expressway section is counted based on the trajectories of all long-distance vehicles, and the ratio between the total number of vehicles passing through the expressway section and the number of long-distance vehicles passing through the expressway section is calculated to obtain the long-distance traffic ratio of the expressway section.

4. The method for generating a high-speed road section label according to claim 3, characterized in that: The calculating of the traffic flow of each highway section in the target area in the historical time period based on the trajectories of all vehicles includes: For each of the expressway sections, the total number of vehicles passing through the expressway section is used as the traffic flow of the expressway section in the historical time period.

5. The method for generating a high-speed road section label according to claim 4, characterized in that: Said vehicle types include a variety of trucks; The calculating, based on the trajectories of all vehicles and all vehicle types, the truck traffic ratio of each highway section in the target area comprises: For each expressway section, the number of trucks passing through the expressway section is counted according to the trajectories of all vehicles whose vehicle type is trucks, and the ratio between the number of trucks passing through the expressway section and the total number of vehicles passing through the expressway section is calculated to obtain the truck traffic ratio; The calculating the average vehicle speed of each highway section in the target area based on the trajectories of all vehicles includes: For each of the expressway sections, perform the following steps: Taking each vehicle passing through the expressway section as a target vehicle; For each of the target vehicles, respectively, calculate the time length of the target vehicle passing through the adjacent gantries in the highway section, and calculate the ratio between the length of the adjacent gantries in the highway section and the time length to obtain the speed of the target vehicle at the adjacent gantries in the highway section; If the speed of the target vehicle between adjacent gantries on the expressway section is within a preset speed threshold range, the vehicle's journey between adjacent gantries on the expressway section is regarded as a normal journey without stopping, and the average driving speed of all target vehicles whose journey between adjacent gantries on the corresponding expressway section is a normal journey is calculated to obtain the average vehicle speed of the expressway section.

6. The method for generating a high-speed road section label according to claim 1, characterized in that: Based on the regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed of all highway sections, all highway sections are clustered to obtain multiple clusters, including: Combining the regional traffic ratio, long-distance traffic ratio, traffic flow in the historical time period, truck traffic ratio, and average vehicle speed of each of the expressway sections into one data point; All data points are clustered using a clustering algorithm to obtain multiple clusters.

7. The method for generating a high-speed road section label according to claim 5, characterized in that: The calculating the freight function value of the expressway section according to the traffic flow and all vehicle types of the expressway section in the historical time period includes: By formula: ； Calculate freight function value ; in, Indicates the number of vehicles passing through the expressway section. The proportion of trucks Indicates the The total number of axles of the truck. Indicates the number of truck types. represents the traffic flow of the expressway section in the historical time period, Indicates the average vehicle speed on the expressway section.

8. A device for generating a label for a highway section, characterized in that: include: Acquisition module, to obtain highway gantry traffic data in the target area; The highway gantry traffic data includes the time when each vehicle passing through the gantry in the target area passes through the gantry and the entrance toll station and exit toll station passed by the vehicle in the historical time period, and the multiple vehicles passing through the gantry in the target area include vehicles of multiple different vehicle types; A statistical module, based on the highway gantry traffic data, performs statistics to obtain the regional traffic ratio, long-distance traffic ratio, traffic flow in a historical time period, truck traffic ratio, and average vehicle speed of each highway section in the target area; The clustering module clusters all highway sections based on their regional traffic ratio, long-distance traffic ratio, traffic flow in historical time periods, truck traffic ratio, and average vehicle speed to obtain multiple clusters. A calculation module, for each of the expressway sections, calculates the freight function value of the expressway section according to the traffic flow and all vehicle types of the expressway section in a historical time period; the freight function value is used to describe the traffic efficiency of trucks on the expressway section; The label generation module generates freight labels according to all freight function values ​​to obtain freight labels for each of the expressway sections, and generates section labels according to all clustering clusters to obtain section labels for each of the expressway sections; the freight labels are used to describe the traffic conditions of freight vehicles on the expressway sections, and the section labels are used to describe the traffic conditions of all types of vehicles on the expressway sections.

9. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for generating a highway section label according to any one of claims 1 to 7 is implemented.

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

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