Methods and devices for estimating the missed detection rate of vehicle traffic during highway cross-section monitoring

Abstract

This application belongs to the field of computer science, specifically relating to a method and apparatus for estimating the missed detection rate of vehicle trips in highway cross-section monitoring. The method includes: calculating the length distribution of national and provincial trunk highway segments; calculating the travel distance distribution of passenger cars on national and provincial trunk highways; constructing a simulated road network based on the segment length distribution; generating a travel sample library based on the characteristics of the travel distance distribution, loading it into the simulated road network, and simulating the effects of travel at different distances. The positions of trips of different lengths in the travel sample library within the simulated road network are generated by random numbers. Each segment in the simulated road network is considered a basket, and the number of trips that fall into the basket without touching its edge is counted to obtain the "fall-in" count. The ratio of the "fall-in" count to the total number of trips in the travel sample library is the missed detection rate of vehicle trips. This application can reduce the difficulty of estimating the missed detection rate of vehicle trips.

Description

Technical Field

[0001] This application relates to the field of computer technology, specifically to a method and apparatus for estimating the missed detection rate of vehicle traffic during highway cross-section monitoring. Background Technology

[0002] Installing vehicle identification devices such as license plate recognition and ETC on highways to detect vehicles passing through sections and track their trajectories can provide a basic basis for highway traffic statistics, route planning, highway maintenance, and economic operation analysis.

[0003] For ordinary highways, there are instances where vehicles do not pass any checkpoints. The ratio of the number of trips without passing any checkpoints to the total number of trips is the trip miss rate (or simply trip miss rate). Nationwide, there are 35,000, 110,000, 250,000, and 660,000 intersections between ordinary trunk highways and highways of different grades (county roads and above, township roads and above, village roads and above), respectively, resulting in complex traffic flow. Higher checkpoint density leads to a lower miss rate, but also higher construction and maintenance costs. Checkpoint planning requires calculating the miss rate under different checkpoint densities and analyzing the cost-effectiveness of various solutions.

[0004] Based on the topology of each segment of the real road network, a simulation model of the entire road network is built. The origin and destination (OD) of each trip is obtained through surveys, and the OD is loaded onto the road network model to simulate the trajectory of each trip. Then, detection points of different sizes are deployed on each road segment to calculate the number of detected trips and the number of missed trips. However, for the vast national highway network and intersections, the above method involves an extremely large workload and a huge amount of computation in the simulation, making it very difficult to implement. Summary of the Invention

[0005] This application provides a method and apparatus for estimating the missed detection rate of vehicle trips in highway cross-section monitoring, which can reduce the difficulty of estimating the missed detection rate of vehicle trips.

[0006] In a first aspect, embodiments of this application provide a method for estimating the missed detection rate of vehicle traffic during highway cross-section monitoring, including:

[0007] Calculate the length distribution of national and provincial trunk highways, where detection points are set up on the highway sections;

[0008] Calculate the travel distance distribution of passenger cars on national and provincial highways;

[0009] Based on the road segment length distribution, a simulated road network is constructed;

[0010] A travel sample library is generated based on the characteristics of the travel distance distribution and loaded into the simulated road network to simulate the effects of travel of different lengths. The positions of the travel samples of different lengths in the simulated road network are generated by random numbers.

[0011] In the simulated road network, each road segment is regarded as a basket. The number of trips that fall into the basket without touching the edge is counted to obtain the number of trips that fall into the basket. The ratio of the number of trips that fall into the basket to the total number of trips in the trip sample library is the trip miss rate.

[0012] Secondly, embodiments of this application provide a device for estimating the missed detection rate of vehicle traffic during highway cross-section monitoring, comprising:

[0013] The probability distribution module is used to calculate the length distribution of national and provincial trunk highways and the travel distance distribution of passenger cars on national and provincial trunk highways, where detection points are set up on the highway sections.

[0014] The simulation module is used to construct a simulated road network based on the road segment length distribution; generate a travel sample library based on the characteristics of the travel distance distribution, load it into the simulated road network, and simulate the effect of travel of different lengths. The positions of travel of different lengths in the travel sample library placed in the simulated road network are generated by random numbers. Each road segment in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without touching the edge is counted to obtain the fall-in count. The ratio of the fall-in count to the total number of trips in the travel sample library is the trip miss rate.

[0015] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any of the above-mentioned embodiments.

[0016] Fourthly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any of the above-mentioned embodiments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the method for estimating the missed detection rate of vehicle traffic during highway section monitoring according to an embodiment of this application is shown.

[0019] Figure 2 This illustration shows a schematic diagram of road segment length distribution in a method for estimating the missed detection rate of traffic frequency in highway cross-section monitoring according to an embodiment of this application.

[0020] Figure 3This invention illustrates a travel distance histogram in a method for estimating the missed detection rate of vehicle trips during highway cross-section monitoring, according to an embodiment of this application.

[0021] Figure 4 This illustration shows a simulated road network diagram in a method for estimating the missed detection rate of vehicle trips during highway cross-section monitoring according to an embodiment of this application.

[0022] Figure 5 A simulation diagram illustrating a method for estimating the missed detection rate of vehicle traffic during highway cross-section monitoring according to an embodiment of this application is shown.

[0023] Figure 6 This diagram illustrates the probability distribution of travel distances for different road segments in a method for estimating the missed detection rate of vehicle trips during highway cross-section monitoring according to an embodiment of this application.

[0024] Figure 7 , Figure 8 and Figure 9 The diagrams show the length distribution of road segments formed after ordinary national and provincial highways intersect with different types of highways in the highway cross-section monitoring missed detection rate estimation method of one embodiment of this application.

[0025] Figure 10 This diagram illustrates the distribution of passenger car travel distances on trunk highways in the method for estimating the missed detection rate of vehicle trips during highway section monitoring, as described in this embodiment of the application.

[0026] Figure 11 This is a schematic diagram of the structure of the highway section monitoring vehicle count missed rate estimation device according to an embodiment of this application;

[0027] Figure 12 This diagram illustrates the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0029] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0030] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0031] See Figure 1 This application provides a method for estimating the missed detection rate of vehicle traffic during highway cross-section monitoring, including:

[0032] Step 1: Calculate the length distribution of national and provincial trunk highways;

[0033] Step 2: Calculate the travel distance distribution of passenger cars on national and provincial highways;

[0034] Step 3: Construct a simulated road network based on the road segment length distribution;

[0035] Step 4: Generate a travel sample library based on the characteristics of travel distance distribution, load it into the simulated road network, and simulate the effect of travel of different lengths. The positions of travel of different lengths in the travel sample library in the simulated road network are generated by random numbers.

[0036] Step 5: In the simulated road network, each road segment is regarded as a basket. The number of trips that fall into the basket without touching the edge is counted to obtain the number of trips that fall into the basket. The ratio of the number of trips that fall into the basket to the total number of trips in the trip sample library is the trip miss rate.

[0037] This application innovates upon the fundamental principles of Monte Carlo statistical simulation, considering the relationships between factors such as road network density, trip frequency, and travel distance. It establishes a simulation model to analyze the relationship between the density and layout of detection points and the missed detection rate of trip frequency. It provides the missed detection rate of trip frequency for different planning schemes for ordinary national and provincial highways within a target area, such as nationwide. The application also compares the effectiveness and cost-effectiveness of various planning schemes, solving key theoretical and technical problems in highway information infrastructure planning. This application is the first in the industry and academia to propose an innovative approach based on the fundamental principles of Monte Carlo statistical simulation, enabling simple and convenient estimation of the missed detection rate of trip frequency, filling a gap in the field. This application is applicable to the problem of estimating the missed detection rate of trip frequency in various cross-section monitoring technologies, such as video license plate recognition, ETC, and electronic license plates.

[0038] In this embodiment of the application, it is assumed that a sign recognition station is deployed on each road segment.

[0039] In some embodiments, calculating the distribution of road segment lengths of national and provincial trunk highways includes: counting the number of road segments of national and provincial trunk highways, and discretizing the road segment lengths to obtain a histogram of the number of road segments, which is the probability density distribution of the discrete road segment lengths of national and provincial trunk highways.

[0040] For example, the road segment length is discretized into three ranges: 0-5km, 5-10km, and 10-15km, and the resulting histogram is as follows. Figure 2 As shown, there are 4,000 road sections with lengths of 0-5km, 8,000 and 2,000 sections with lengths of 5-10km and 10-15km, respectively.

[0041] In some embodiments, calculating the travel distance distribution of passenger cars on national and provincial trunk highways includes: calculating a histogram of travel distances based on the travel distance of each passenger car trip on national and provincial trunk highways.

[0042] For example, the travel distance of a passenger car on national and provincial highways is discretized into three ranges: 0-5km, 5-10km, and 10-15km. The resulting histogram is as follows: Figure 3 As shown, there were 80,000, 120,000, and 40,000 trips on main lines with travel distances of 0-5km, 5-10km, and 10-15km, respectively.

[0043] In some embodiments, a simulated road network is constructed based on the road segment length distribution obtained in step 1. For example, Figure 2 The ratio of road segments with lengths of 5, 10, and 15 is 2:4:1. Road sections with consecutive ends are constructed according to this ratio. See details... Figure 4 .

[0044] In some embodiments, a travel sample library is generated based on the travel distance distribution characteristics in step 2, and loaded into the road network model in step 3 to simulate the effects of travel of different lengths. For example, Figure 3 The ratio of trips with lengths of 5, 10, and 15 is 2:3:1. Based on this ratio, trips of different lengths are placed into the road network in step 3. See details below. Figure 5 The placement location, i.e., the starting point of the trip, is generated by a random number.

[0045] In some embodiments, the random number is generated by one of the following schemes:

[0046] a. The relationship between road network density and travel volume and travel distance is not considered;

[0047] b. Consider the relationship between road network density and travel volume, but not the relationship with travel distance;

[0048] c. Consider the relationship between road network density and travel distance, but not the relationship with travel volume;

[0049] d. Simultaneously consider the relationship between road network density, travel volume, and travel distance.

[0050] In some embodiments, scheme a, which does not consider the relationship between road network density and travel volume and travel distance, Figure 5 In the simulated road network shown, the starting point of each trip follows a uniform distribution between the starting and ending points of the road; the trip distance is also uniformly random.

[0051] In some embodiments, scheme b considers the relationship between road network density and travel volume, but does not consider the relationship with travel distance. Road network density is positively correlated with population density (travel volume). The denser the population, the shorter the average road segment length. Therefore, the travel volume within a road segment does not increase significantly with the road segment length. Figure 5 In the simulated road network shown, each road segment, regardless of its length, is allocated the same number of trips.

[0052] In some embodiments, scheme c, which considers the relationship between road network density and travel distance but does not consider the relationship with travel volume, has a smaller travel scale in areas with high road network density and a larger travel scale in areas with low road network density. The probability of a short trip originating in a short road segment is greater than the probability of a long trip originating in a long road segment.

[0053] Calculate the random distribution parameters based on the length of the road segment, and assign a random distribution to each road segment. ,use Randomly generated starting point at the 1st The travel length of each road segment simulates the pattern that travel distances are longer in long road segments and shorter in short road segments.

[0054] In some embodiments, when considering the relationship between road network density, travel volume, and travel distance, scheme d generates the location of travel according to scheme b, and assigns distance to travel samples within the road segment according to scheme c.

[0055] In some embodiments, the steps for generating random numbers in scheme c are as follows:

[0056] c1. Number the road segments from shortest to longest distance. , This represents the number of road segments; samples in the travel sample database are numbered from shortest to longest distance. , The number of samples;

[0057] c2. Generate the starting point of the trip according to the uniform distribution in the simulated road network;

[0058] c3. Determine the road segment where the starting point is located and its length. ;

[0059] c4. Normalize the road segment lengths to values ​​within the range [0,1], and calculate the normalized value for each road segment. See equation (1):

[0060] (1)

[0061] in For the first The length of the road segment;

[0062] c5, regarding the first The average value is within the range [0,1] of the generated road segment. probability distribution ,according to Generate random variables ;

[0063] In practice, the random.normal() function from the NumPy library can be used to generate it, as shown in the following code:

[0064] import numpy as np

[0065] mu = 0.2

[0066] sigma = 1

[0067] lower_bound = 0

[0068] upper_bound = 1

[0069] random_number = np.random.normal(mu, sigma)

[0070] while random_number<lower_bound or random_number> upper_bound:

[0071] random_number = np.random.normal(mu, sigma)

[0072] print(random_number)

[0073] For example, there are 10 road segments in total, numbered 1 to 10 from shortest to longest, with the longest one being... kilometers, the lengths of the first, second, and third road sections are respectively , , Then, according to equation (1), we get... , , , respectively corresponding to probability distributions , , ,like Figure 6 As shown. Then, according to... , , Generate random variables corresponding to the 1st, 2nd, and 3rd road segments respectively. , , .

[0074] c6. Combine the random number interval [0,1] from step c5 with the travel number database [...]. Correspondingly, inverse normalization is used to calculate random variables. Corresponding travel sample number See equation (2):

[0075] (2)

[0076] in If the integer part is taken, then the number is... The sample is the first A travel sample from this road segment;

[0077] c7. The number obtained in step c6 is... The samples are placed into the road network according to the starting location obtained in step c2, to achieve the first The travel simulation was performed on that road segment, and then the first segment was deleted from the travel sample database. This section of road;

[0078] c8 Repeat steps c2 through c7.

[0079] In some embodiments, the method of this application further includes: cyclically increasing the number of travel samples and loading them into the simulated road network, calculating the missed detection rate of the number of travel trips each time, until the error of the estimated value of the missed detection rate of multiple consecutive trips is less than a set threshold, and the estimated result of the missed detection rate of the number of travel trips converges.

[0080] Application Example 1

[0081] S1, Road Segment Statistical Results

[0082] Table 1 shows the number of road segments formed when highways of different grades intersect with ordinary national and provincial highways.

[0083] Table 1. Number of road segments formed by intersections between different types of highways (unit: segments)

[0084]

[0085] S2, Road segment length distribution

[0086] Corresponding to schemes 1 to 3 in Table 1, the length distribution of road segments formed after ordinary national and provincial highways intersect with different types of highways is shown below. Figures 7 to 9 .

[0087] S3, Mainline Driving Distance Distribution

[0088] The distribution of travel distances between counties and between urban and rural areas on trunk highways is shown in the figure. Figure 10 The travel distance is obtained from travel navigation data from Gaode, Baidu, and other sources.

[0089] S4. Result of False Detection Rate Estimation

[0090] The estimated missed detection rates for the three sign recognition station deployment schemes are shown in Table 2.

[0091] Table 2. Estimated Missed Detection Rates for Various Trip Strategies

[0092]

[0093] Referring to Table 2, considering the above four simulation schemes, the results of scheme d, which takes into account the relationship between road network density (number of road segments), travel volume, and travel distance, are closer to the actual results.

[0094] This application provides a device for estimating the missed detection rate of vehicle trips during highway cross-section monitoring. The device of this application can implement the method of the above embodiments. The above method embodiments can be used to understand the device of this application, and the description of the device embodiments below can also be used to understand the method of the above embodiments.

[0095] See Figure 11The highway cross-section monitoring vehicle count miss rate estimation device of this application includes a probability distribution module and a simulation module. The probability distribution module is used to calculate the length distribution of national and provincial trunk highway segments and the travel distance distribution of passenger cars on national and provincial trunk highways. The simulation module is used to construct a simulated road network based on the segment length distribution and to generate a travel sample library based on the characteristics of the travel distance distribution. This library is then loaded into the simulated road network to simulate the effect of travel of different lengths. The positions of different lengths of travel in the travel sample library placed in the simulated road network are generated by random numbers. Each segment in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without touching the edge is counted to obtain the fall-in count. The ratio of the fall-in count to the total number of trips in the travel sample library is the vehicle count miss rate.

[0096] This application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0097] Please see Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 12 As shown, the electronic device 600 may include: at least one processor 601, at least one network interface 604, a user interface 603, a memory 605, and at least one communication bus 602.

[0098] The communication bus 602 is used to enable communication between these components.

[0099] The user interface 603 may include a display screen and a camera. Optionally, the user interface 603 may also include a standard wired interface and a wireless interface.

[0100] The network interface 604 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0101] The processor 601 may include one or more processing cores. The processor 601 connects to various parts within the electronic device 600 using various interfaces and lines, and performs various functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 605, and by calling data stored in the memory 605. Optionally, the processor 601 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 601 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 601 and may be implemented as a separate chip.

[0102] The memory 605 may include random access memory (RAM) or read-only memory. Optionally, the memory 605 may include a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 605 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 605 may also be at least one storage device located remotely from the aforementioned processor 601. Figure 3 As shown, the memory 605, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and application programs.

[0103] exist Figure 12In the electronic device 600 shown, the user interface 603 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 601 can be used to call the application stored in the memory 605 and specifically execute the operations of any of the above method embodiments.

[0104] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0105] This application also provides a computer program product including a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0106] Those skilled in the art will clearly understand that the technical solutions of this application can be implemented using software and / or hardware. In this specification, "unit" and "module" refer to software and / or hardware capable of independently or in conjunction with other components to perform a specific function. Hardware may include, for example, a Field-Programmable Gate Array (FPGA), an Integrated Circuit (IC), etc.

[0107] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0108] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0112] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0113] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0114] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for estimating the missed detection rate of vehicle traffic frequency in highway cross-section monitoring, characterized in that, include: Calculate the length distribution of national and provincial trunk highways, where detection points are set up on the highway sections; Calculate the travel distance distribution of passenger cars on national and provincial highways; Based on the road segment length distribution, a simulated road network is constructed; A travel sample library is generated based on the characteristics of the travel distance distribution and loaded into the simulated road network to simulate the effects of travel of different lengths. The positions of the travel samples of different lengths in the simulated road network are generated by random numbers. In the simulated road network, each road segment is regarded as a basket. The number of trips that fall into the basket without touching the edge is counted to obtain the number of trips that fall into the basket. The ratio of the number of trips that fall into the basket to the total number of trips in the trip sample library is the trip miss rate.

2. The method according to claim 1, characterized in that, Also includes: The number of travel samples is increased cyclically and loaded into the simulated road network. The missed detection rate of the number of travel trips is calculated each time until the error of the estimated value of the missed detection rate of multiple consecutive trips is less than the set threshold, and the estimated result of the missed detection rate of the number of travel trips converges.

3. The method according to claim 1, characterized in that, Calculate the distribution of road segment lengths for national and provincial trunk highways, including: The number of road segments on national and provincial trunk highways is counted, and the length of each road segment is discretized to obtain a histogram of the number of road segments, which is the probability density distribution of the discrete length of road segments on national and provincial trunk highways.

4. The method according to claim 1, characterized in that, Calculate the travel distance distribution of passenger cars on national and provincial highways, including: A histogram of travel distances is calculated based on the distance traveled by each passenger car on national and provincial highways.

5. The method according to claim 1, characterized in that, The random number is generated using one of the following schemes: a. The relationship between road network density and travel volume and travel distance is not considered; b. Consider the relationship between road network density and travel volume, but not the relationship with travel distance; c. Consider the relationship between road network density and travel distance, but not the relationship with travel volume; d. Simultaneously consider the relationship between road network density, travel volume, and travel distance.

6. The method according to claim 5, characterized in that, in a. Without considering the relationship between road network density, travel volume, and travel distance, the starting point of a trip follows a uniform distribution between the starting and ending points of the road; the travel distance is also uniformly and randomly distributed. b. Considering the relationship between road network density and travel volume, but not the relationship with travel distance, road network density is positively correlated with population density. The denser the population, the shorter the average road segment length. Therefore, the travel volume within a road segment does not increase significantly with the length of the road segment. In the simulated road network, each road segment, regardless of its length, is allocated the same amount of travel samples. c. Considering the relationship between road network density and travel distance, but not the relationship with travel volume, areas with high road network density have smaller travel scales, while areas with low road network density have larger travel scales. The probability of a short trip originating in a short road segment is greater than the probability of a long trip originating in a long road segment. Calculate the random distribution parameters based on the length of the road segment, and assign a random distribution to each road segment. ,use Randomly generated starting point at the 1st The travel length of each road segment simulates the pattern that travel distances are longer in long road segments and shorter in short road segments. d. When considering the relationship between road network density, travel volume, and travel distance, the location of travel is generated according to scheme b, and the distance is assigned to the travel samples within the road segment according to scheme c.

7. The method according to claim 6, characterized in that, The steps for generating random numbers using scheme c are as follows: c1. Number the road segments from shortest to longest distance. , Number of road segments; The samples in the travel sample database are numbered from shortest to longest distance. , The number of samples; c2. Generate the starting point of the trip according to the uniform distribution in the simulated road network; c3. Determine the road segment where the starting point is located and its length. ; c4. Normalize the road segment lengths to values ​​within the range [0,1], and calculate the normalized value for each road segment. See equation (1): （1） in For the first The length of the road segment; c5, regarding the first The average value of the generated road segments is within the range [0,1]. probability distribution ,according to Generate random variables ; c6. Combine the random number interval [0,1] from step c5 with the travel number database [...]. Correspondingly, inverse normalization is used to calculate random variables. Corresponding travel sample number See equation (2): （2） in If the integer part is taken, then the number is... The sample is the first A travel sample from this road segment; c7. The number obtained in step c6 is... The samples are placed into the road network according to the starting location obtained in step c2, to achieve the first The travel simulation was performed on that road segment, and then the first segment was deleted from the travel sample database. This section of road; c8. Repeat steps c2 to c7.

8. A device for estimating the missed detection rate of vehicle traffic during highway cross-section monitoring, characterized in that, include: The probability distribution module is used to calculate the length distribution of national and provincial trunk highways. Calculate the travel distance distribution of passenger cars on national and provincial trunk highways, where detection points are set up on the highway sections; The simulation module is used to construct a simulated road network based on the road segment length distribution; generate a travel sample library based on the characteristics of the travel distance distribution, load it into the simulated road network, and simulate the effect of travel of different lengths. The positions of travel of different lengths in the travel sample library placed in the simulated road network are generated by random numbers. Each road segment in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without touching the edge is counted to obtain the fall-in count. The ratio of the fall-in count to the total number of trips in the travel sample library is the trip miss rate.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method of any one of claims 1-7.

10. An electronic 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, it implements the method of any one of claims 1-7.

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