Method, device, computer equipment and storage medium for identifying speeding on highways

Abstract

The present invention discloses a method, device, computer equipment and storage medium for identifying speeding on a highway. The method comprises: obtaining a plurality of initial gantry data of a vehicle, performing data preprocessing on each initial gantry data, and obtaining a plurality of optimized gantry data; calculating the vehicle interval speed according to the optimized gantry data, constructing a utility function according to the optimized gantry data, and solving the maximum point of the utility function, and taking the maximum point as the road speed limit value; identifying whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value. The present application utilizes gantry data to automatically and batch-identify road speed limit values, effectively solving the problem of speeding identification by vehicle type in the short-term variable speed limit scenario in highway management, and providing basic support and basis for highway transportation management.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method, device, computer equipment and storage medium for identifying speeding on a highway. Background Art

[0002] Speeding is an important cause of highway traffic accidents. Accurately identifying speeding vehicles is conducive to improving highway traffic safety and traffic efficiency, and plays an important role in road traffic management and policy formulation. At present, there are mainly the following methods for identifying speeding on highways: one is to use continuous speed measuring poles to obtain the instantaneous speed of the vehicle passing through the speed measuring poles, and determine whether it is speeding by comparing the measured speed with the road speed limit; the other is to use speed measuring instruments and speeding camera capture systems installed on the roadside or on the gantry, obtain the instantaneous speed of the vehicle through image processing, and determine whether it is speeding by comparing the measured speed with the road speed limit.

[0003] However, both of the above methods use a single threshold judgment method to identify speeding violations and rely on database query thresholds. It is difficult to determine speeding by vehicle type and is unable to cope with situations when short-term traffic conditions change (such as abnormal weather, construction sections, etc.). The instantaneous speed obtained is difficult to represent the driving status of the vehicle in the travel interval. The recognition results lack scientificity and accuracy, which is not conducive to traffic management on highways. Summary of the invention

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and to provide a method, device, computer equipment and storage medium for identifying speeding on a highway.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present disclosure provides a method for identifying speeding on a highway, the method comprising:

[0007] Acquire multiple initial gantry data of the vehicle, perform data preprocessing on each of the initial gantry data, and obtain multiple optimized gantry data;

[0008] Calculating the vehicle interval speed according to the optimized gantry data, constructing a utility function according to the optimized gantry data, and solving the maximum point of the utility function, and using the maximum point as the road speed limit value;

[0009] It is determined whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value.

[0010] Furthermore, the data preprocessing is performed on each of the initial gantry data to obtain a plurality of optimized gantry data, including:

[0011] Eliminate missing data, abnormal data, and duplicate data in key fields of each of the initial gantry data to obtain multiple intermediate gantry data;

[0012] Mapping the gantry coordinates in each intermediate gantry data to the preset road network through a map matching algorithm, and displaying the coordinate offset of each gantry coordinate;

[0013] The intermediate gantry data corresponding to the gantry coordinates whose coordinate drift is greater than a preset drift threshold are eliminated to obtain a plurality of the optimized gantry data.

[0014] Further, the calculating the vehicle interval speed according to the optimized gantry data includes:

[0015] Extracting the time data of the vehicle passing through each gantry from the optimized gantry data, and calculating the passing time data of the vehicle passing between two consecutive gantries according to the time data of the vehicle passing through each gantry;

[0016] Determine whether the stake number data of the gantry is missing, and if not, calculate the running distance data of the vehicle passing through two consecutive gantries according to the stake number data of the gantry;

[0017] The vehicle interval speed of the vehicle is calculated based on the travel time data and the running distance data of the vehicle passing through two consecutive gantries.

[0018] Further, after determining whether the pile number data of the gantry is missing, the method further includes:

[0019] If missing, the gantry coordinates are imported into a preset geographic information software, and a network dataset based on a preset road network is created with the help of the preset geographic information software;

[0020] The running distance data of the vehicle passing through two consecutive gantries is calculated using the network data set.

[0021] Further, constructing a utility function according to the optimized gantry data includes:

[0022] Classifying the vehicles into a first type of vehicle, a second type of vehicle, and a third type of vehicle;

[0023] Calculating a first sub-utility function of the first vehicle model according to the optimized gantry data, calculating a second sub-utility function of the second vehicle model according to the optimized gantry data, and calculating a third sub-utility function of the third vehicle model according to the optimized gantry data;

[0024] The utility function is constructed according to the first sub-utility function, the second sub-utility function and the third sub-utility function.

[0025] Furthermore, the utility function is:

[0026]

[0027] in:

[0028]

[0029] Wherein, U(v) is the group utility value, v is the preset road speed limit value, v1 is the first preset speed limit value of the first vehicle model, v2 is the second preset speed limit value of the second vehicle model and the third vehicle model, Δv is the difference between the first preset speed limit value and the second preset speed limit value; f1(v1), f2(v2), f3(v3) are the first sub-utility function, the second sub-utility function and the third sub-utility function respectively, ω1, ω2, ω3, k1, k2, k3 are preset weight coefficients, is the ratio of the number of vehicles within the first preset speed limit value interval to the total number of samples, is the ratio of the number of vehicles within the second preset speed limit value interval to the total number of samples, is the reciprocal of the standard deviation of the range before and after the first preset speed limit value, is the inverse of the standard deviation of the second preset speed limit value, m is the preset minimum threshold range, t is a preset integer, r is a preset threshold real number, softplus(v1) is the speeding penalty term of the first preset speed limit value, softplus(v2) is the speeding penalty term of the second preset speed limit value, v0 is the preset initial speed limit value, k is the preset slope parameter, V s is the preset percentile speed value, V max The preset maximum speed limit standard.

[0030] Further, the identifying whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value includes:

[0031] If the vehicle interval speed is greater than the road speed limit, identifying that the vehicle is speeding;

[0032] If the vehicle interval speed is less than or equal to the road speed limit, it is identified that the vehicle is not speeding.

[0033] In a second aspect, an embodiment of the present disclosure provides a device for identifying speeding on a highway, the device comprising:

[0034] A preprocessing module is used to obtain a plurality of initial gantry data of the vehicle, and perform data preprocessing on each of the initial gantry data to obtain a plurality of optimized gantry data;

[0035] A construction module, used to calculate the vehicle interval speed according to the optimized gantry data, construct a utility function according to the optimized gantry data, and solve the maximum value point of the utility function, and use the maximum value point as the road speed limit value;

[0036] The identification module is used to identify whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value.

[0037] In a third aspect, a computer device is provided in an embodiment of the present disclosure, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps of the highway speeding identification method described in the first aspect when executing the computer program.

[0038] In a fourth aspect, a computer-readable storage medium is provided in an embodiment of the present disclosure, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for identifying speeding on highways described in the first aspect are implemented.

[0039] Beneficial effects of this application:

[0040] The method for identifying speeding on a highway provided in an embodiment of the present application includes: obtaining multiple initial gantry data of a vehicle, performing data preprocessing on each of the initial gantry data to obtain multiple optimized gantry data; calculating the vehicle interval speed according to the optimized gantry data, constructing a utility function according to the optimized gantry data, and solving the maximum point of the utility function, using the maximum point as the road speed limit value; identifying whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value. The present application uses gantry data to automatically and batch-identify road speed limit values, effectively solving the problem of speeding identification by vehicle type in short-term variable speed limit scenarios in highway management, and at the same time providing basic support and basis for highway transportation management.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and understandable, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work. In each of the drawings, similar components are numbered similarly.

[0043] Figure 1A flowchart of a method for identifying speeding on a highway provided by an embodiment of the present application is shown;

[0044] Figure 2 A schematic diagram of a Softplus function provided in an embodiment of the present application is shown;

[0045] Figure 3 A schematic diagram of the structure of a highway speeding identification device provided in an embodiment of the present application is shown;

[0046] Figure 4 A schematic diagram of the structure of a computer device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0047] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0048] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. In contrast, when an element is referred to as being "directly on" another element, there is no intermediate element. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of the template are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0052] Example 1

[0053] like Figure 1 FIG. 1 is a flowchart of a method for identifying speeding on a highway in an embodiment of the present application. The method for identifying speeding on a highway provided by the embodiment of the present application includes the following steps:

[0054] Step S110, acquiring a plurality of initial gantry data of the vehicle, performing data preprocessing on each of the initial gantry data, and obtaining a plurality of optimized gantry data.

[0055] It is understandable that data quality is the key to accurately calculate the vehicle interval speed. In the process of obtaining information and data, there may be missing data, duplicate data, abnormal data, etc. Therefore, in this embodiment, after obtaining multiple initial gantry data, each initial gantry data is only pre-processed from the data.

[0056] Firstly, the missing data, abnormal data and duplicate data in the key fields of each initial gantry data are eliminated to obtain multiple intermediate gantry data, and the timestamps are synchronized to ensure the accuracy of the time data in the intermediate gantry data.

[0057] Secondly, check whether there is data drift problem in the gantry coordinates in each intermediate gantry data, and map each gantry coordinate to the preset road network through the map matching algorithm to display the coordinate offset of each gantry coordinate.

[0058] Finally, the projection method is used to project the coordinate values ​​of the gantry coordinates with smaller coordinate drift to the nearest road, and the intermediate gantry data corresponding to the gantry coordinates with coordinate drift greater than the preset drift threshold are eliminated, and finally multiple optimized gantry data after data preprocessing are obtained.

[0059] The above method optimizes the preprocessing of big data, effectively improves the data quality, and thus improves the accuracy of subsequent related indicators.

[0060] Step S120, calculating the vehicle interval speed according to the optimized gantry data, constructing a utility function according to the optimized gantry data, and solving the maximum point of the utility function, and taking the maximum point as the road speed limit value.

[0061] Optionally, the time data of vehicles passing through each gantry in the optimized gantry data is extracted, and the time data of vehicles passing through two consecutive gantries can be obtained by subtracting the time data of vehicles passing through each gantry.

[0062] It is understandable that the actual running distance of the vehicle is the key variable in calculating the vehicle interval speed. The optimization gantry data contains the gantry coordinates. The conventional algorithm can only obtain the straight-line distance between the two points of the gantry. However, the line shape of the actual highway is more complicated, and the straight-line distance is not necessarily equal to the actual driving distance of the vehicle.

[0063] Therefore, in order to obtain the actual distance between adjacent gantries, the present application needs to first determine whether the gantry's pile number data is missing; if it is not missing, the running distance data of the vehicle passing between two consecutive gantries is directly calculated based on the gantry's pile number data in the gantry dictionary data set; if it is missing, the gantry coordinates are imported into the preset geographic information software (such as ArcGIS software), and a network dataset based on the preset road network is created with the help of the network analysis (Network Analyst) extension module in the preset geographic information software. The running distance data of the vehicle passing between two consecutive gantries can also be calculated through the network dataset.

[0064] Next, the quotient of the running distance data and the passing time data of the vehicle passing through two consecutive gantries is calculated, which is the vehicle interval speed of the vehicle. For example, the running distance data of the vehicle passing through two consecutive gantries is 200 kilometers, and the passing time data of the vehicle passing through two consecutive gantries is 2 hours, then the vehicle interval speed of the vehicle is 200÷2=100 kilometers / hour.

[0065] The above method uses optimized gantry data and combines gantry coordinate information to solve the actual distance and speed of vehicle travel, thereby improving the calculation accuracy of vehicle interval speed in practice.

[0066] It should be noted that, in an optional implementation, a situation may occur in reality, that is, a vehicle enters the service area between the gantries and stays for a period of time. At this time, due to the increase in time, the calculated vehicle interval speed will be lower than the actual vehicle interval speed, and affect the overall data distribution. In order to solve the above problem, the present application can identify the speed dividing point between vehicles entering the service area (constituting set A) and vehicles not entering the service area (constituting set B), and use the kernel density estimation method to fit the probability density function of the vehicles within the range of set B, so as to correct the interval speed of vehicles entering the service area (A), while the interval speed of vehicles not entering the service area can be directly calculated.

[0067] Next, divide the entire vehicle interval speed into bins with multiples of 10, and the width of each bin can be 10 km / h. Count the number of samples in each bin. For each bin, count the number of samples whose vehicle interval speed falls within the range and divide it by the total number of samples to obtain the probability distribution of the vehicle interval speed in each bin.

[0068] There are different standards for speed limits on vehicle types on existing highways. This application adopts a conventional classification method to divide vehicles into three categories: first-type vehicles, second-type vehicles and third-type vehicles. The first-type vehicles are small passenger cars, the second-type vehicles are large passenger cars, and the third-type vehicles are trucks. The subsequent utility function will also discuss road speed limits according to the above three types of vehicles.

[0069] Furthermore, this application uses statistical methods and group decision theory to construct a utility function based on the optimized gantry data, and then infers the road speed limit value. The decision-making basis of the group decision theory is: assuming that individual vehicles are rational, the choice of their driving speed in the speed limit scenario is a decision-making problem after weighing multiple factors; on the other hand, the statistical basis of the statistical method is: the statistical data reflects the balanced distribution of vehicle speeds under the current speed limit, which can be regarded as the optimal solution at the group level. Therefore, the problem of solving the road speed limit value is transformed into a problem of inferring the conditions of the known group decision results.

[0070] It is understood that the choice of vehicle speed involves a balance between efficiency, safety and compliance. The following are explanations of the three, which can be understood:

[0071] (1) Safety: One of the factors affecting the accident rate on highways is the speed difference between vehicles. The greater the speed difference, the higher the accident rate. However, highways are spaces where vehicles of different models coexist, and the speed of vehicles is restricted by the model. In order to reduce the safety hazards caused by the speed difference of vehicles of different models, it is usually recommended that vehicles of different models run in different lanes, and different speed limits correspond to different vehicle models. Therefore, the road speed limit of the vehicle can be calculated by model, and the constraint relationship between the road speed limits of vehicles of different models needs to be considered. The specific manifestation of this application is: the speed difference of the road speed limit of vehicles of different models is set not to exceed the preset speed difference (for example, 40Km / h). For example, the speed of a small passenger car>the speed of a large passenger car≈the speed of a truck. The above relationship is reflected in the utility function as follows: ① The road speed limit of the vehicle is discussed by model, and the difference in the road speed limit of vehicles of different models does not exceed the preset speed difference; ② The standard deviation of the speed within the range before and after the road speed limit of vehicles of the same model is the smallest.

[0072] (2) Efficiency: The flow rate (efficiency) of highways is mainly determined by the speed of vehicles. The higher the speed, the higher the efficiency. Therefore, the interval speed distribution characteristic is that vehicles account for the largest proportion within the speed limit threshold interval. On the other hand, when the speed limit is formulated using the travel speed method, a speed cumulative frequency curve is usually drawn, and the speed value of the preset percentile (for example, the 80th percentile or the 85th percentile) is taken as the preset percentile speed value V. s .

[0073] (3) Compliance: Speeding is a traffic violation, which can easily lead to traffic accidents and have a negative impact on the overall traffic order. However, the existence of a small amount of speeding data in the statistical data is reasonable for the following reasons: ① The "New Traffic Regulations" stipulate that "no points will be deducted if the speed is less than 20% on highways and urban expressways"; ② There may also be factors such as equipment errors and transmission errors. Therefore, this application introduces a speeding penalty item. When the vehicle interval speed exceeds the speed limit, the penalty value and the speed increase gradually increase to restrain excessive speeding behavior.

[0074] According to the above description, this application uses the preset speed limit value of each type of vehicle in the optimized gantry data as an independent variable to construct a utility function. The utility function comprehensively considers individual benefits and group performance. When the speed takes the preset speed limit value of each type of vehicle, the value of the utility function is maximized. The utility function is the weighted sum of the sub-utility functions f1(v1), f2(v2), and f3(v3) of the three types of vehicles, where the sub-utility function of each type of vehicle consists of three parts: efficiency, safety, and compliance. Safety is represented by the reciprocal of the standard deviation of the preset speed limit value of the same type of vehicles. The smaller the reciprocal of the standard deviation, the higher its safety; efficiency is represented by the ratio of the number of vehicles in the preset speed limit range to the total number of samples. The larger the ratio, the higher the overall efficiency of the traffic flow; compliance adds a speeding penalty term, and the corresponding sub-utility function is preceded by a negative sign, and is characterized by the Softplus function. The Softplus function is a smooth growth curve, and its values ​​are all positive numbers (curve such as Figure 2 As shown in the figure, when the actual interval speed value approaches the preset speed limit value, its utility is small. When the actual interval speed value exceeds and gradually deviates from the preset speed limit value, its utility value decreases and the speed gradually increases, which is used to restrain excessive speeding behavior.

[0075] The following is the specific utility function:

[0076]

[0077] in:

[0078]

[0079] Wherein, U(v) is the group utility value, v is the preset road speed limit value, v1 is the first preset speed limit value of the first vehicle model, v2 is the second preset speed limit value of the second vehicle model and the third vehicle model, Δv is the difference between the first preset speed limit value and the second preset speed limit value. According to the provisions of high-speed speed limit, in most cases, the speed of other motor vehicles is 20 km / h lower than that of small passenger cars. When applied, it can be adjusted according to the actual situation, with the speed of small passenger cars as the main factor. In this application, Δv∈[20,40; f1(v1), f2(v2), f3(v3) are the first sub-utility function, the second sub-utility function and the third sub-utility function respectively, ω1, ω2, ω3, k1, k2, k3 are preset weight coefficients, is the ratio of the number of vehicles within the first preset speed limit value interval to the total number of samples, is the ratio of the number of vehicles within the second preset speed limit value interval to the total number of samples, is the reciprocal of the standard deviation of the range before and after the first preset speed limit value, is the inverse of the standard deviation of the range before and after the second preset speed limit value, m is the preset minimum threshold range, and in this application, m=10 / h can be taken, t is a preset integer, and r is a preset threshold real number for controlling the threshold range, is the speeding penalty item of the first preset speed limit value, is the speeding penalty term of the second preset speed limit value, v0 is the preset initial speed limit value, k is the preset slope parameter for controlling the slope, V s is the preset percentile speed value, V max To preset the maximum speed limit standard, this application can take my country's maximum speed limit standard of 120Km / h.

[0080] Furthermore, as an optional implementation, the present application may select a genetic algorithm as an optimization algorithm to find the maximum point of the utility function, that is, the road speed limit value corresponding to each vehicle model that is optimal.

[0081] It should be noted that existing technologies mostly rely on database queries, but speed limit data are mostly in the hands of specific road operating agencies. Therefore, widely collecting speed limit data from various cities and operating agencies is not only cumbersome, but also requires a lot of manpower and material resources. It is also very easy to introduce errors due to human factors, thereby reducing the accuracy of the data. From the perspective of actual operations, when encountering the situation of newly built or rebuilt roads, the existing database query method often cannot obtain the latest speed limit information in a timely manner. This lag may affect the safety and traffic efficiency of the road.

[0082] Therefore, this application adopts the above-mentioned method of calculating the speed limit value and uses group decision-making theory to transform the speed limit solution problem into a utility maximization problem, which can realize large-scale data batch calculation, which is conducive to improving the level of intelligent highway management and enhancing the application value of data.

[0083] Step S130: identifying whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value.

[0084] Finally, after obtaining the vehicle interval speed and road speed limit of each vehicle model, the two are compared. If the vehicle interval speed is greater than the road speed limit, the vehicle is identified as speeding; if the vehicle interval speed is less than or equal to the road speed limit, the vehicle is identified as not speeding.

[0085] The above method uses an analysis method based on big data, and calculates the corresponding road speed limit value by vehicle type, reducing the reliance on querying a single speed limit value. By comparing the operation of spatial and temporal traffic flow, it solves the problem that speeding cannot be identified under special road conditions, and can adapt to temporary speed limit regulations under various road conditions and weather conditions, thereby automatically realizing speeding identification of vehicles of different vehicle types. Compared with the existing technology, this application saves manpower and material resources, is easy to promote, and has good application prospects.

[0086] The highway speeding identification method provided in the embodiment of the present application obtains multiple initial gantry data of the vehicle, performs data preprocessing on each of the initial gantry data, and obtains multiple optimized gantry data; calculates the vehicle interval speed according to the optimized gantry data, constructs a utility function according to the optimized gantry data, and solves the maximum point of the utility function, and uses the maximum point as the road speed limit value; identifies whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value. The present application uses gantry data to automatically and batch-identify road speed limit values, effectively solving the problem of speeding identification by vehicle type in the short-term variable speed limit scenario in highway management, and at the same time provides basic support and basis for highway transportation management.

[0087] Example 2

[0088] like Figure 3 FIG. 1 is a schematic diagram of a highway speeding identification device 300 in an embodiment of the present application, wherein the device comprises:

[0089] A preprocessing module 310 is used to obtain a plurality of initial gantry data of the vehicle, and perform data preprocessing on each of the initial gantry data to obtain a plurality of optimized gantry data;

[0090] A construction module 320 is used to calculate the vehicle interval speed according to the optimized gantry data, construct a utility function according to the optimized gantry data, and solve the maximum value point of the utility function, and use the maximum value point as the road speed limit value;

[0091] The identification module 330 is used to identify whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value.

[0092] Optionally, the above-mentioned highway speeding identification device 300 further includes:

[0093] A first elimination module is used to eliminate missing data, abnormal data, and duplicate data in key fields of each of the initial gantry data to obtain a plurality of intermediate gantry data;

[0094] A display module is used to map the gantry coordinates in each intermediate gantry data to a preset road network through a map matching algorithm, and display the coordinate offset of each gantry coordinate;

[0095] The second elimination module is used to eliminate the intermediate gantry data corresponding to the gantry coordinates whose coordinate drift is greater than a preset drift threshold, so as to obtain a plurality of the optimized gantry data.

[0096] Optionally, the above-mentioned highway speeding identification device 300 further includes:

[0097] A first calculation module is used to extract the time data of the vehicle passing through each gantry in the optimized gantry data, and calculate the passage time data of the vehicle passing between two consecutive gantries according to the time data of the vehicle passing through each gantry;

[0098] A second calculation module is used to determine whether the stake number data of the gantry is missing, and if not, to calculate the running distance data of the vehicle passing through two consecutive gantries according to the stake number data of the gantry;

[0099] The third calculation module is used to calculate the vehicle interval speed of the vehicle according to the travel time data and the running distance data of the vehicle passing through two consecutive gantries.

[0100] Optionally, the above-mentioned highway speeding identification device 300 further includes:

[0101] A creation module, used for importing the gantry coordinates into a preset geographic information software if missing, and creating a network dataset based on a preset road network with the help of the preset geographic information software;

[0102] The fourth calculation module is used to calculate the running distance data of the vehicle passing through two consecutive gantries through the network data set.

[0103] Optionally, the above-mentioned highway speeding identification device 300 further includes:

[0104] A classification module, used for classifying the vehicle into a first type of vehicle, a second type of vehicle and a third type of vehicle;

[0105] a fifth calculation module, configured to calculate a first sub-utility function of the first vehicle model according to the optimized gantry data, calculate a second sub-utility function of the second vehicle model according to the optimized gantry data, and calculate a third sub-utility function of the third vehicle model according to the optimized gantry data;

[0106] A construction submodule is used to construct the utility function according to the first sub-utility function, the second sub-utility function and the third sub-utility function.

[0107] Optionally, the above-mentioned highway speeding identification device 300 further includes:

[0108] A first identification submodule, configured to identify that the vehicle is speeding if the vehicle interval speed is greater than the road speed limit;

[0109] The second identification submodule is used to identify that the vehicle is not speeding if the vehicle interval speed is less than or equal to the road speed limit.

[0110] The highway speeding identification device provided in the embodiment of the present application uses gantry data to automatically and batch-identify road speed limit values, effectively solving the problem of speeding identification for different vehicle types under short-term variable speed limit scenarios in highway management, and at the same time providing basic support and basis for highway transportation management.

[0111] Example 3

[0112] The present application also provides a computer device. Figure 4 , Figure 4 This is a basic structural block diagram of the computer device in this embodiment.

[0113] The computer device 4 includes a memory 41, a processor 42, and a network interface 43 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 4 with a memory 41, a processor 42, and a network interface 43, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0114] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0115] The memory 41 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or D slot compatibility test memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 41 can be an internal storage unit of the computer device 4, such as a hard disk or memory of the computer device 4. In other embodiments, the memory 41 can also be an external storage device of the computer device 4, such as a plug-in hard disk equipped on the computer device 4, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 41 can also include both the internal storage unit of the computer device 4 and its external storage device. In this embodiment, the memory 41 is generally used to store the operating system and various application software installed on the computer device 4, such as computer-readable instructions of the slot compatibility test method, etc. In addition, the memory 41 can also be used to temporarily store various types of data that have been output or are to be output.

[0116] The processor 42 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other highway speeding identification chips in some embodiments. The processor 42 is generally used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is used to run computer-readable instructions or process data stored in the memory 41, such as computer-readable instructions for running the slot compatibility test method.

[0117] The network interface 43 may include a wireless network interface or a wired network interface. The network interface 43 is generally used to establish a communication connection between the computer device 4 and other electronic devices.

[0118] The computer device provided in this embodiment can execute the above-mentioned highway speeding identification method. Here, the highway speeding identification method can be the highway speeding identification method of each of the above-mentioned embodiments.

[0119] Example 4

[0120] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the highway speeding identification method in the embodiment are implemented.

[0121] In this embodiment, the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the computer-readable storage medium can be an internal storage unit of a computer device, such as a hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium can also be an external storage device of a computer device, such as a plug-in hard disk equipped on the computer device, a smart memory card (Smart Media Card, referred to as SMC), a secure digital (SecureDigital, referred to as SD) card, a flash card, etc. Of course, the computer-readable storage medium can also include both the internal storage unit of the computer device and its external storage device. In this embodiment, the computer-readable storage medium is generally used to store an operating system and various application software installed on the computer device. In addition, the computer-readable storage medium can also be used to temporarily store various types of data that have been output or are to be output.

[0122] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in an alternative implementation, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the structure diagram and / or the flow chart, and the combination of boxes in the structure diagram and / or the flow chart, can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] In addition, the functional modules or units in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0124] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or partly contributed to the prior art or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and other media that can store program codes.

[0125] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for identifying speeding on a highway, characterized in that: The method comprises: Acquire multiple initial gantry data of the vehicle, perform data preprocessing on each of the initial gantry data, and obtain multiple optimized gantry data; Calculating the vehicle interval speed according to the optimized gantry data, constructing a utility function according to the optimized gantry data, and solving the maximum point of the utility function, and using the maximum point as the road speed limit value; Identify whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value; Wherein, constructing a utility function according to the optimized gantry data comprises: Classifying the vehicles into a first type of vehicle, a second type of vehicle, and a third type of vehicle; Calculating a first sub-utility function of the first vehicle model according to the optimized gantry data, calculating a second sub-utility function of the second vehicle model according to the optimized gantry data, and calculating a third sub-utility function of the third vehicle model according to the optimized gantry data; constructing the utility function according to the first sub-utility function, the second sub-utility function and the third sub-utility function; Wherein, the utility function is: in: Wherein, U(v) is the group utility value, v is the preset road speed limit value, v1 is the first preset speed limit value of the first vehicle model, v2 is the second preset speed limit value of the second vehicle model and the third vehicle model, Δv is the difference between the first preset speed limit value and the second preset speed limit value; f1(v1), f2(v2), f3(v2) are the first sub-utility function, the second sub-utility function and the third sub-utility function respectively, ω1, ω2, ω3, k1, k2, k3 are preset weight coefficients, is the ratio of the number of vehicles within the first preset speed limit value interval to the total number of samples, is the ratio of the number of vehicles within the second preset speed limit value interval to the total number of samples, is the reciprocal of the standard deviation of the range before and after the first preset speed limit value, is the inverse of the standard deviation of the second preset speed limit value, m is the preset minimum threshold range, t is a preset integer, r is a preset threshold real number, is the speeding penalty item of the first preset speed limit value, is the speeding penalty term of the second preset speed limit value, v0 is the preset initial speed limit value, k is the preset slope parameter, V s is the preset percentile speed value, V max The preset maximum speed limit standard.

2. The method for identifying speeding on a highway according to claim 1, characterized in that: The data preprocessing is performed on each of the initial gantry data to obtain a plurality of optimized gantry data, including: Eliminate missing data, abnormal data, and duplicate data in key fields of each of the initial gantry data to obtain multiple intermediate gantry data; Mapping the gantry coordinates in each intermediate gantry data to the preset road network through a map matching algorithm, and displaying the coordinate offset of each gantry coordinate; The intermediate gantry data corresponding to the gantry coordinates whose coordinate drift is greater than a preset drift threshold are eliminated to obtain a plurality of the optimized gantry data.

3. The method for identifying speeding on a highway according to claim 2, characterized in that: The calculating the vehicle interval speed according to the optimized gantry data includes: Extracting the time data of the vehicle passing through each gantry from the optimized gantry data, and calculating the passing time data of the vehicle passing between two consecutive gantries according to the time data of the vehicle passing through each gantry; Determine whether the stake number data of the gantry is missing, and if not, calculate the running distance data of the vehicle passing through two consecutive gantries according to the stake number data of the gantry; The vehicle interval speed of the vehicle is calculated based on the travel time data and the running distance data of the vehicle passing through two consecutive gantries.

4. The method for identifying speeding on a highway according to claim 3, characterized in that: After determining whether the pile number data of the gantry is missing, the method further includes: If missing, the gantry coordinates are imported into a preset geographic information software, and a network dataset based on a preset road network is created with the help of the preset geographic information software; The running distance data of the vehicle passing through two consecutive gantries is calculated using the network data set.

5. The method for identifying speeding on a highway according to claim 1, characterized in that: The identifying whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value includes: If the vehicle interval speed is greater than the road speed limit, identifying that the vehicle is speeding; If the vehicle interval speed is less than or equal to the road speed limit, it is identified that the vehicle is not speeding.

6. A highway speeding identification device, characterized in that: The device comprises: A preprocessing module is used to obtain a plurality of initial gantry data of the vehicle, and perform data preprocessing on each of the initial gantry data to obtain a plurality of optimized gantry data; A construction module, used to calculate the vehicle interval speed according to the optimized gantry data, construct a utility function according to the optimized gantry data, and solve the maximum value point of the utility function, and use the maximum value point as the road speed limit value; An identification module, used for identifying whether the vehicle is speeding according to the vehicle interval speed and the road speed limit value; The building blocks further include: A classification module, used for classifying the vehicle into a first type of vehicle, a second type of vehicle and a third type of vehicle; a fifth calculation module, configured to calculate a first sub-utility function of the first vehicle model according to the optimized gantry data, calculate a second sub-utility function of the second vehicle model according to the optimized gantry data, and calculate a third sub-utility function of the third vehicle model according to the optimized gantry data; A construction submodule, configured to construct the utility function according to the first sub-utility function, the second sub-utility function and the third sub-utility function; Wherein, the utility function is: in: Wherein, U(v) is the group utility value, v is the preset road speed limit value, v1 is the first preset speed limit value of the first vehicle model, v2 is the second preset speed limit value of the second vehicle model and the third vehicle model, Δv is the difference between the first preset speed limit value and the second preset speed limit value; f1(v1), f2(v2), f3(v2) are the first sub-utility function, the second sub-utility function and the third sub-utility function respectively, ω1, ω2, ω3, k1, k2, k3 are preset weight coefficients, is the ratio of the number of vehicles within the first preset speed limit value interval to the total number of samples, is the ratio of the number of vehicles within the second preset speed limit value interval to the total number of samples, is the reciprocal of the standard deviation of the range before and after the first preset speed limit value, is the inverse of the standard deviation of the second preset speed limit value, m is the preset minimum threshold range, t is a preset integer, r is a preset threshold real number, is the speeding penalty item of the first preset speed limit value, is the speeding penalty term of the second preset speed limit value, v0 is the preset initial speed limit value, k is the preset slope parameter, V s is the preset percentile speed value, V max The preset maximum speed limit standard.

7. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the highway speeding identification method according to any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the highway speeding identification method according to any one of claims 1 to 5 are implemented.

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