Method and device for estimating missed detection rate of number of times of traveling on road section monitoring

Abstract

The invention belongs to the field of computers, and particularly relates to a method and a device for estimating the omission ratio of the number of times of travel on a road section monitoring trip. The method comprises the following steps: calculating the length distribution of national and provincial arterial highway sections; calculating travel distance distribution of the passenger cars on the national and provincial arterial highways; constructing a simulation road network according to the road section length distribution; a travel sample library is generated according to the characteristics of the travel distance distribution, the travel sample library is loaded to the simulation road network, the effects of travel at different distances are simulated, and the positions, placed in the simulation road network, of travel with different lengths in the travel sample library are generated by random numbers; each road section in the simulation road network is regarded as a basket, the number of trips falling into the basket without touching the edge of the basket is counted to obtain the number of trips, and the ratio of the number of trips to the total number of trips in the trip sample library is the missed detection rate of the number of trips. According to the invention, the difficulty of estimating the omission ratio of the trip times can be reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and device for estimating the missed detection rate of vehicle travel times in highway section monitoring. Background Art

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

[0003] For ordinary roads, there are cases where vehicles do not pass through any detection points. The ratio of the number of trips that have not passed through any detection points to the total number of trips is the missed detection rate of the number of trips (referred to as the missed detection rate). There are 35,000, 110,000, 250,000, and 660,000 intersections between ordinary trunk roads and themselves, county roads, township roads, and village roads, respectively, and the diversion situation is complicated. The higher the density of detection points, the lower the missed detection rate, but the higher the construction and operation and maintenance costs required. Site planning needs to calculate the missed detection rate under different site densities and analyze the cost-effectiveness of various solutions.

[0004] According to the topological structure between the sections 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 investigation, and the OD is loaded into the road network model to simulate the trajectory of each trip; then detection points of different sizes are deployed on each section, and the number of detected trips and the number of missed trips are calculated. However, for the huge national ordinary highway network and intersections, the workload of the above method is extremely huge, and the amount of simulation calculation is extremely huge, which is difficult to implement. Summary of the invention

[0005] The embodiments of the present application provide a method and device for estimating the missed detection rate of vehicle trips in highway section monitoring, which can reduce the difficulty of estimating the missed detection rate of vehicle trips.

[0006] In a first aspect, an embodiment of the present application provides a method for estimating the missed detection rate of vehicle trips in highway section monitoring, comprising:

[0007] Calculate the length distribution of national and provincial trunk highway sections;

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

[0009] constructing a simulated road network according to the road section length distribution;

[0010] Generate a travel sample library according to the characteristics of the travel distance distribution, load it into the simulated road network, and simulate the effects of travels of different distances, wherein the positions of travels of different lengths in the travel sample library placed in the simulated road network are generated by random numbers;

[0011] Each road section in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without hitting the edge of the basket 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 travel sample library is the trip vehicle miss detection rate.

[0012] In a second aspect, an embodiment of the present application provides a device for estimating the missed detection rate of vehicle trips in highway section monitoring, comprising:

[0013] Probability distribution module, used to calculate the length distribution of national and provincial trunk roads; calculate the travel distance distribution of passenger cars on national and provincial trunk roads;

[0014] A simulation module is used to construct a simulated road network according to the distribution of road section lengths; generate a travel sample library according to the characteristics of the travel distance distribution, load it into the simulated road network, and simulate the effects of travels of different distances, wherein the positions where trips of different lengths in the travel sample library are placed in the simulated road network are generated by random numbers; each road section in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without hitting the edge of the basket is counted to obtain the number of trips that fall into the basket, and the ratio of the number of trips that fall into the basket to the total number of trips in the travel sample library is the trip vehicle miss detection rate.

[0015] In a third aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements any of the methods described above when the program is executed by a processor.

[0016] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-described methods when executing the computer program. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram showing a flow chart of a method for estimating the missed detection rate of vehicle trips in highway section monitoring according to an embodiment of the present application;

[0019] Figure 2 A schematic diagram showing the distribution of road section lengths in a method for estimating the missed detection rate of vehicle trips in highway section monitoring according to an embodiment of the present application is shown;

[0020] Figure 3 A histogram of travel distances in a method for estimating the missed detection rate of vehicle trips in highway section monitoring according to an embodiment of the present application is shown;

[0021] Figure 4 A schematic diagram of a simulated road network in a method for estimating the missed detection rate of vehicle trips in highway section monitoring according to an embodiment of the present application is shown;

[0022] Figure 5 A simulation schematic diagram showing a method for estimating the missed detection rate of vehicle trips in highway section monitoring according to an embodiment of the present application is shown;

[0023] Figure 6 A schematic diagram showing the probability distribution of travel distances corresponding to different road sections in a method for estimating the missed detection rate of vehicle trips in highway section monitoring according to an embodiment of the present application is shown;

[0024] Figure 7 , Figure 8 and Fig. 9 Schematic diagrams showing the length distribution of sections formed by ordinary national and provincial roads intersecting with different types of highways in a method for estimating the missed detection rate of trips in highway section monitoring according to an embodiment of the present application;

[0025] Fig.10 A schematic diagram showing the distribution of the travel distance of passenger cars on a trunk road in the method for estimating the missed detection rate of the number of trips monitored in a highway section according to an embodiment of the present application;

[0026] Fig.11 A schematic diagram showing the structure of a device for estimating the missed detection rate of vehicle trips monitored in a highway section according to an embodiment of the present application;

[0027] Fig.12 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0029] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0030] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] See also Figure 1 The present application embodiment provides a method for estimating the missed detection rate of vehicle trips in highway section monitoring, comprising:

[0032] Step 1, calculate the length distribution of national and provincial trunk highway sections;

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

[0034] Step 3: construct a simulated road network according to the road section length distribution;

[0035] Step 4: Generate a travel sample library according to the characteristics of travel distance distribution, load it into the simulated road network, and simulate the effects of travels of different distances, wherein the positions of travels of different lengths in the travel sample library into the simulated road network are generated by random numbers;

[0036] Step 5: Each road section in the simulated road network is regarded as a basket. The number of trips that fall into the basket without hitting the edge of the basket 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 travel sample library is the trip number missed detection rate.

[0037] This application conducts technological innovation according to the basic principles of Monte Carlo statistical simulation, considers the relationship between factors such as road network density, number of trips, and travel distance, establishes a simulation model, analyzes the relationship between the density and layout of detection points and the missed detection rate of the number of trips, and gives the missed detection rate of the number of trips corresponding to the planning scheme of ordinary national and provincial road inspection stations in the target area, such as the whole country, and compares the effects and analyzes the cost-effectiveness of various planning schemes. It solves the key theoretical and technical problems in the planning of highway information infrastructure. This application is the first in the industry and academic fields to propose technological innovation according to the basic principles of Monte Carlo statistical simulation, which can simply and conveniently estimate the missed detection rate of the number of trips, filling the gap in the field. This application is applicable to the problem of estimating the missed detection rate of the number of trips based on various cross-sectional monitoring technologies such as video license plate recognition, ETC, and electronic license plates.

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

[0039] In some embodiments, calculating the distribution of the lengths of national and provincial trunk road sections includes: counting the number of national and provincial trunk road sections, and discretizing the section lengths to obtain a histogram of the number of sections, i.e., the probability density distribution of the discrete lengths of national and provincial trunk road sections.

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

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

[0042] For example, the travel distance of passenger cars on national and provincial trunk roads is discretized into three ranges: 0-5km, 5-10km, and 10-15km. The resulting histogram is as follows: Figure 3 As shown, there are 80,000, 120,000 and 40,000 trips with mainline 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 the number of sections with lengths of 5, 10, and 15 is 2:4:1. The road sections connected end to end are constructed according to this ratio. Figure 4 .

[0044] In some embodiments, a travel sample library is generated according to the travel distance distribution characteristics in step 2, and loaded into the road network model in step 3 to simulate the effects of travel at different distances. Figure 3 The ratio of trips with lengths of 5, 10, and 15 is 2:3:1. According to this ratio, trips of different lengths are placed in the road network of step 3. For details, see Figure 5 The position where the item is placed, 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. Consider the relationship between road network density and travel volume and travel distance at the same time.

[0050] In some embodiments, when the relationship between road network density, travel volume and travel distance is not considered in solution a, Figure 5 In the simulated road network shown, the travel starting point positions are uniformly distributed between the road starting point and the end point; the travel distances are also uniformly randomly distributed.

[0051] In some embodiments, scheme b, considering the relationship between road network density and travel volume, without considering the relationship with travel distance, the road network density is positively correlated with population density (travel volume). The denser the population, the shorter the average road section length, and the travel volume within the road section does not increase significantly with the increase in road section length. Figure 5 Each road section in the simulated road network shown, regardless of its length, is allocated the same amount of travel samples.

[0052] In some embodiments, scheme c, considering the relationship between road network density and travel distance, but not considering the relationship with travel volume, the travel scale in areas with high road network density is smaller, and the travel scale in areas with low road network density is larger, and the probability that the starting point of a short trip falls on a short road segment is greater than the probability that a long trip falls on a short road segment.

[0053] According to the length of the road section, the random distribution parameters are calculated and a random distribution P is assigned to each road section. i , use P i The travel length of the starting point on the i-th road section is randomly generated to simulate the rule that the travel distance in long sections is longer and the travel distance in short sections is shorter.

[0054] In some embodiments, scheme d, when considering the relationship between road network density, travel volume and travel distance at the same time, the travel location is generated according to scheme b, and the travel samples in the road section are assigned distances according to scheme c.

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

[0056] c1. Number the road sections from shortest to longest according to the distance, 1, 2, L, N, where N is the number of road sections; number the samples in the travel sample library from shortest to longest according to the distance, 1, 2, L, M, where M is the number of samples;

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

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

[0059] c4. Normalize the length of the road section to a value in the interval [0,1] and calculate the normalized value θ of each road section i , see formula (1):

[0060]

[0061] Among them l i is the length of the i-th road segment;

[0062] c5, for the i-th road segment, the mean value is θ within the range [0,1]. i The probability distribution P i , according to P i Generate a random variable a i ;

[0063] In specific implementation, the random.normal() function in the NumPy library can be used to generate it. The code is as follows:

[0064]

[0065] For example, there are 10 road sections, numbered 1 to 10 from short to long, and the longest one is l 10 = 10 km, the lengths of the first, second and third sections are l1 = 2, l2 = 3.5, l3 = 6.5 respectively. Then according to formula (1), θ1 = 0.25, θ2 = 0.35, θ3 = 0.65, corresponding to probability distributions P1, P2, P3 respectively, as Figure 6 As shown. Then, according to P1, P2, P3, random variables a1, a2, a3 corresponding to the 1st, 2nd, and 3rd road sections are generated respectively.

[0066] c6. Match the random number interval [0,1] in step c5 with the travel number library [1,2,L,M] and use denormalization to calculate the random variable a i Corresponding travel sample number See formula (2):

[0067]

[0068] Where int represents the integer part, then the number The sample of is a travel sample of the i-th road segment;

[0069] c7. The number obtained in step c6 is The samples are put into the road network according to the departure location obtained in step c2 to realize the travel simulation in the i-th road section. Then, the q-th road section is deleted from the travel sample library. ai road section;

[0070] c8 Repeat steps c2 to c7.

[0071] In some embodiments, the method of the embodiments of the present application also includes: cyclically increasing the number of travel samples and loading them into a simulated road network, calculating the missed detection rate of the number of vehicle trips each time, until the error of the estimated value of the missed detection rate for multiple consecutive times is less than a set threshold, and the estimated result of the missed detection rate of the number of vehicle trips converges.

[0072] Application Example 1

[0073] S1. Statistical results of road sections

[0074] The number of road sections formed by the intersection of highways of different grades and above with ordinary national and provincial roads is shown in Table 1.

[0075] Table 1 Number of sections formed by intersections between different types of highways (unit: number)

[0076]

[0077]

[0078] S2. Section length distribution

[0079] Corresponding to Schemes 1 to 3 in Table 1, the length distribution of the sections formed by the intersection of ordinary national and provincial roads with different types of highways is shown in Figures 7 to 9 .

[0080] S3. Distribution of driving distance on trunk lines

[0081] The distribution of travel distances between counties and between urban and rural areas on trunk roads is shown in Fig.10 ,The travel distance is obtained from travel navigation data of Amap, Baidu, etc.

[0082] S4. Missed detection rate estimation results

[0083] The estimated results of missed detection rates corresponding to the three card recognition station layout schemes are shown in Table 2.

[0084] Table 2 Estimation results of missed detection rate of trip times for various schemes

[0085]

[0086] As shown in Table 2, the results of Scheme d, which combines the above four simulation schemes and considers the relationship between road network density (number of road sections) and travel volume and travel distance, are closer to the actual results.

[0087] An embodiment of the present application provides a device for estimating the missed detection rate of vehicle trips in highway section monitoring. The device of the embodiment of the present application can implement the method of the above-mentioned embodiment. The above-mentioned method embodiment can be used to understand the device of the embodiment of the present application, and the description part of the embodiment of the following device can also be used to understand the method of the above-mentioned embodiment.

[0088] See also Fig.11The apparatus for estimating the missed detection rate of the number of vehicle trips monitored in a highway section according to an embodiment of the present application comprises a probability distribution module and a simulation module, wherein the probability distribution module is used to calculate the length distribution of sections of national and provincial trunk roads; calculate the travel distance distribution of passenger cars on national and provincial trunk roads; the simulation module is used to construct a simulated road network according to the section length distribution; a travel sample library is generated according to the characteristics of the travel distance distribution, loaded into the simulated road network, and the effects of travels of different distances are simulated, wherein the positions of trips of different lengths in the travel sample library into the simulated road network are generated by random numbers; each section in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without hitting the edge of the basket is counted to obtain the number of trips that fall into the basket, and the ratio of the number of trips that fall into the basket to the total number of trips in the travel sample library is the missed detection rate of the number of vehicle trips.

[0089] An embodiment of the present 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 implements any of the above methods when executing the computer program.

[0090] See also Fig.12 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Fig.12 As shown, the terminal 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 .

[0091] The communication bus 602 is used to realize the connection and communication between these components.

[0092] The user interface 603 may include a display screen (Display) and a camera (Camera), and the optional user interface 603 may also include a standard wired interface and a wireless interface.

[0093] The network interface 604 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0094] Among them, the processor 601 may include one or more processing cores. The processor 601 uses various interfaces and lines to connect various parts in the entire terminal 600, and executes various functions and processes data of the terminal 600 by running or executing instructions, programs, code sets or instruction sets stored in the memory 605, and calling data stored in the memory 605. Optionally, the processor 601 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 601 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 601, and it can be implemented separately through a chip.

[0095] Among them, the memory 605 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 605 includes a non-transitory computer-readable storage medium. The memory 605 can be used to store instructions, programs, codes, 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 605 may optionally be at least one storage device located away from the aforementioned processor 601. As Figure 3 As shown, the memory 605 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program.

[0096] exist Fig.12In the electronic device 600 shown, the user interface 603 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 601 can be used to call the application stored in the memory 605 and specifically perform the operations of any of the above method embodiments.

[0097] The present application also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0098] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any one of the methods recorded in the above method embodiments.

[0099] Those skilled in the art can clearly understand that the technical solution of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0100] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0101] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0102] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0105] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk, etc., and other media that can store program codes.

[0106] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0107] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for estimating the missed detection rate of vehicle trips in highway section monitoring, characterized in that: include: Calculate the length distribution of national and provincial trunk highway sections; Calculate the travel distance distribution of passenger cars on national and provincial trunk roads; constructing a simulated road network according to the road section length distribution; Generate a travel sample library according to the characteristics of the travel distance distribution, load it into the simulated road network, and simulate the effects of travels of different distances, wherein the positions of travels of different lengths in the travel sample library placed in the simulated road network are generated by random numbers; Each road section in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without hitting the edge of the basket 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 travel sample library is the trip vehicle miss detection 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, and the missed detection rate of the number of vehicle trips is calculated each time until the error of the estimated value of the missed detection rate for multiple consecutive times is less than the set threshold value, and the estimated result of the missed detection rate of the number of vehicle trips converges.

3. The method according to claim 1, characterized in that Calculate the length distribution of national and provincial trunk highway sections, including: The number of national and provincial trunk highway sections is counted, and the section lengths are discretized to obtain a section number histogram, that is, the probability density distribution of the discrete section lengths of 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 trunk roads, including: Based on the travel distance of each passenger car trip on national and provincial highways, a histogram of travel distance is calculated.

5. The method according to claim 1, characterized in that The random number is generated by 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. Consider the relationship between road network density and travel volume and travel distance at the same time.

6. The method according to claim 5, characterized in that in a. When the relationship between road network density, travel volume and travel distance is not considered, the travel starting point is uniformly distributed between the road starting point and the end point; the travel distance is also uniformly randomly distributed; b. Considering the relationship between road network density and travel volume, and not considering the relationship with travel distance, road network density is positively correlated with population density. The denser the population, the shorter the average road section length. The increase in travel volume within the road section with the length of the road section is not obvious. Each road section in the simulated road network, regardless of its length, is allocated the same travel sample volume; c. When the relationship between road network density and trip distance is considered but the relationship with trip volume is not considered, the trip scale is smaller in areas with high road network density and larger in areas with low road network density, and the probability that the starting point of a short trip falls on a short road segment is greater than the probability that a long trip falls on a long road segment; According to the length of the road section, the random distribution parameters are calculated and a random distribution P is assigned to each road section. i , use P i Randomly generate the travel length of the starting point on the i-th road segment to simulate the rule that the travel distance in long sections is longer and the travel distance in short sections is shorter; d. When considering the relationship between road network density, travel volume and travel distance at the same time, the travel locations are generated according to plan b, and the distances are assigned to the travel samples within the road section according to plan c.

7. The method according to claim 6, characterized in that The steps for generating random numbers in scheme c are as follows: c1. Number the road sections from shortest to longest according to the distance, 1, 2, L, N, where N is the number of road sections; number the samples in the travel sample library from shortest to longest according to the distance, 1, 2, L, M, where M is the number of samples; c2. Generate the starting point of the trip in a uniform distribution in the simulated road network; c3. Determine the road section where the starting point is located and its length l i ; c4. Normalize the length of the road section to a value in the interval [0,1] and calculate the normalized value θ of each road section i , see formula (1): Among them l i is the length of the i-th road segment; c5, for the i-th road segment, the mean value is θ within the range [0,1]. i The probability distribution P i , according to P i Generate a random variable a i ; c6. Match the random number interval [0,1] in step c5 with the travel number library [1,2,L,M] and use denormalization to calculate the random variable a i Corresponding travel sample number See formula (2): Where int represents the integer part, then the number The sample of is a travel sample of the i-th road segment; c7. The number obtained in step c6 is The samples are put into the road network according to the departure location obtained in step c2 to realize the travel simulation in the i-th road section. Then, the i-th road section is deleted from the travel sample library. road section; c8. Repeat steps c2 to c7.

8. A device for estimating the missed detection rate of vehicle trips in highway section monitoring, characterized in that: include: Probability distribution module, used to calculate the length distribution of national and provincial trunk highway sections; Calculate the travel distance distribution of passenger cars on national and provincial trunk roads; A simulation module is used to construct a simulated road network according to the distribution of road section lengths; generate a travel sample library according to the characteristics of the travel distance distribution, load it into the simulated road network, and simulate the effects of travels of different distances, wherein the positions where trips of different lengths in the travel sample library are placed in the simulated road network are generated by random numbers; each road section in the simulated road network is regarded as a basket, and the number of trips that fall into the basket without hitting the edge of the basket is counted to obtain the number of trips that fall into the basket, and the ratio of the number of trips that fall into the basket to the total number of trips in the travel sample library is the trip vehicle miss detection rate.

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

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, the method according to any one of claims 1 to 7 is implemented.

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