Method for estimating flow of passenger cars in festivals and holidays on expressway and related equipment

Abstract

The invention discloses an expressway passenger car flow estimation method and related equipment in holidays and festivals. According to the method, expressway portal license plate recognition data and charging flow data are fused, and time-space conflicts of multi-source heterogeneous data are solved through data cleaning, timestamp alignment and normalization; secondly, a lightweight estimation model is constructed based on a support vector regression algorithm, grid search is adopted to optimize parameters, road section license plate recognition flow, vehicle type classification flow and cyclic coding time characteristics serve as input, the total traffic flow is estimated, and the passenger car flow is indirectly deduced; and finally, through real-time data updating and online calculation, hour-level flow dynamic is provided. According to the method, the calculation resource consumption is remarkably reduced while the precision is ensured, a complete highway passenger car flow estimation process and method during free release of festivals and holidays are constructed, and the accuracy of traffic flow statistics is improved.

Description

Technical Field

[0001] The present invention relates to the technical fields of machine learning methods and highway traffic flow estimation, and in particular to a method for estimating the traffic flow of passenger cars on highways during holidays and related equipment. Background Art

[0002] Expressways, as the main arteries of economic development, are a powerful guarantee for economic growth. With the continuous advancement of science and technology, intelligent highway construction has become a trend. Improving traffic conditions on expressway networks has gradually shifted from simply increasing network density to improving operational management. Accurately measuring expressway traffic flow is a key prerequisite for scientific management and decision-making, and is also a fundamental condition for road network guidance and traffic control.

[0003] Estimating holiday traffic volume on highways is crucial for traffic management, road network scheduling, and public travel services. During certain holidays, due to the implementation of free passage for passenger cars, toll barriers are raised at toll booths, making it impossible to obtain toll data. Consequently, accurate statistics on passenger car traffic on highways during these free passage periods are difficult to obtain. Furthermore, during free passage holidays, highways experience increased traffic volume, uneven temporal and spatial distribution, and increased congestion compared to weekdays. Accurate statistics on highway traffic flow can provide a scientific basis for traffic management departments to formulate control measures (such as preventive measures and post-event evacuation strategies), enabling them to rationally and effectively guide vehicle traffic flow, thereby achieving balanced traffic distribution across the highway network during holidays, maximizing road capacity, and alleviating the severe congestion experienced during free passage holidays.

[0004] Existing traffic estimation methods often rely on a single data source, such as toll booth traffic records or fixed-point sensor data. Due to the single data dimension, these methods struggle to capture the correlation between holiday traffic volume and multiple external factors. This is especially true during periods of severe weather or major events, where estimated results can deviate significantly from actual traffic flow. Existing technologies have significant shortcomings in spatiotemporal alignment of multi-source data, feature-level fusion, and noise filtering, limiting the robustness of traffic estimation.

[0005] In terms of machine learning and deep learning methods, algorithms such as Transformers, long short-term memory networks, random forests, and convolutional neural networks are relatively mature and have achieved promising results in traffic volume forecasting. However, in the field of real-time traffic flow estimation, under limited computing conditions, these complex models have many parameters, are prone to overfitting, consume large amounts of computing resources, and take a long time to predict, which affects prediction accuracy. Support vector regression algorithms, on the other hand, perform better under these conditions. Summary of the Invention

[0006] In order to at least solve one of the technical problems existing in the prior art to a certain extent, the purpose of the present invention is to provide a method for estimating holiday passenger car traffic on highways by integrating multi-source data and related equipment.

[0007] The first technical solution adopted by the present invention is:

[0008] A method for estimating passenger car traffic on highways during holidays comprises the following steps:

[0009] S1. Based on the historical highway license plate recognition data collected by the highway gantry, the basic characteristic parameter data of the road section traffic flow is obtained at a certain time interval, including license plate recognition flow and vehicle travel time;

[0010] S2. Counting traffic volume data at regular time intervals from historical toll collection data on expressways, and classifying traffic volume by vehicle type, to obtain the total traffic volume on the road section, the traffic volume of large buses, large trucks, small buses, small trucks, and their temporal characteristics;

[0011] S3: Integrate the traffic volume obtained by highway license plate recognition data and toll flow data, perform data normalization, and divide it into training data and test data;

[0012] S4. Using the fused basic characteristic parameter data of the road section traffic flow as input, the support vector regression algorithm is used to train the data set, the support vector regression model parameters are determined using the grid search method, and the model performance evaluation index is calculated based on the model output results;

[0013] S5. Calculate traffic flow data from real-time highway gantry license plate recognition data and toll flow data at certain time intervals. Take the license plate recognition traffic flow, large passenger bus flow, large truck flow, small truck flow, and time characteristics of the road section as input. Relying on the output of the model trained in step S4, the estimated total traffic flow of the road section within the time period is obtained, thereby estimating the small passenger car flow of the road section within the time period.

[0014] The method for estimating holiday passenger car traffic on highways according to claim 1 is characterized in that in step S1, counting traffic flow data at certain time intervals specifically refers to counting traffic flow data at one-hour time intervals.

[0015] Furthermore, in step S1, the time points of the historical data specifically include: New Year's Day holiday dates, dates during the Spring Festival travel period when free travel for passenger cars on highways is excluded, Dragon Boat Festival holiday dates, and dates during the Mid-Autumn Festival when free travel for passenger cars on highways is not permitted.

[0016] Furthermore, the step S3 specifically includes:

[0017] Preprocess the two data types to be fused, including data cleaning, format unification, timestamp alignment, and road segment matching, to establish a mapping relationship between highway toll flow data and gantry license plate recognition data.

[0018] The data normalization adopts min-max standardization, and the formula is as follows:

[0019]

[0020] Among them, x ′ is the normalized traffic flow data, where max is the maximum value in the sample data and min is the minimum value in the sample data;

[0021] The division into training data and test data is specifically as follows: the fused historical traffic flow data is grouped according to different road sections and different hourly time periods, and a random sampling method is used in the same group of data to divide the extracted data into a training set at a ratio of 70% and a test set at a ratio of 30%.

[0022] Furthermore, the step S4 specifically includes:

[0023] The basic characteristic parameter data of the traffic flow of the road section obtained by fusion is used as input, specifically the license plate recognition flow, large passenger bus flow, large truck flow, small truck flow, and time characteristics of the road section are used as input data. The support vector regression model generates an estimated value of the total traffic flow of the road section based on the input data and model parameters, and compares it with the actual total traffic flow of the toll flow to obtain the model evaluation index.

[0024] Furthermore, the time feature is 24 hours of a day divided according to 1-hour time intervals, and cyclic encoding is performed on it. The formula used is as follows:

[0025]

[0026] Among them, T is the original time feature, sin T is the first feature obtained after cyclic encoding, cos T is the second feature obtained after cyclic encoding.

[0027] Furthermore, in step S4, the model performance evaluation indicators specifically refer to mean square error and root mean square error, and the formula is as follows:

[0028] 1) Mean Square Error:

[0029]

[0030] 2) Root mean square error:

[0031]

[0032] Among them, n is the sample size of the road section test set, Represents the model estimation data, y i Represents actual data.

[0033] Furthermore, the step S5 specifically includes:

[0034] The estimated value of the passenger car flow on the road section is calculated from the output value of the support vector regression model, and the formula is as follows:

[0035] V scar =VV bcar -V btruc k -V struc k

[0036] Among them, V scar To estimate the flow of passenger cars, V is the model output value, V bcar is the bus flow in the input data, V btruck is the truck traffic in the input data, V struck is the volume of small trucks in the input data.

[0037] The second technical solution adopted by the present invention is:

[0038] An electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, at least one program, a code set, or an instruction set is loaded and executed by the processor to implement a method for estimating holiday passenger car traffic on highways as described above.

[0039] The third technical solution adopted by the present invention is:

[0040] A computer-readable storage medium stores at least one instruction, at least one program, a code set, or an instruction set. The at least one instruction, at least one program, the code set, or the instruction set is loaded and executed by a processor to implement a method for estimating holiday passenger car traffic on highways as described above.

[0041] The fourth technical solution adopted by the present invention is:

[0042] A computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method for estimating holiday passenger vehicle traffic on highways.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] This invention integrates highway gantry license plate recognition data with toll flow data to create a complementary data source. During holiday periods when passenger cars can travel for free, traditional methods fail to calculate traffic flow due to a lack of toll data. This invention leverages the real-time nature of license plate recognition data and the vehicle type classification information in toll data to indirectly estimate passenger car traffic using a support vector regression model, effectively filling this data gap.

[0045] The present invention takes massive highway gantry data and toll collection data as research objects. The accuracy and reliability of the data lay a solid foundation for the implementation of the present invention.

[0046] A lightweight estimation model based on the support vector regression algorithm is constructed to replace complex deep learning models such as long short-term memory networks and Transformers. This reduces model complexity, overfitting, and computational complexity. Grid search is used to optimize model parameters, achieving high-precision estimation with limited computing resources.

[0047] Designing a multi-source data preprocessing process, including timestamp alignment, road segment matching, and normalization, to resolve fusion conflicts caused by data heterogeneity. By cyclically encoding temporal features to capture the cyclical changes in holiday traffic volume, combined with vehicle classification features, the model's estimation stability is improved in scenarios such as sudden weather or traffic accidents.

[0048] To address the issues of misidentification and missed recognition caused by occlusion in highway gantry license plate recognition, we introduced features such as bus and truck traffic that are strongly correlated with occlusion to improve the accuracy of license plate recognition data.

[0049] To address the surge in holiday traffic and its uneven spatial and temporal distribution, we screened key time points from historical data, such as the Spring Festival travel rush and the Dragon Boat Festival, for model training. We used highway traffic flow data from non-free travel holidays to simulate free travel holiday patterns, enhancing the model's ability to identify free travel holiday traffic patterns.

[0050] Through hourly data updates and online estimation, it provides traffic management departments with real-time passenger car flow dynamics, supporting congestion warnings, lane scheduling, and emergency response decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0052] Figure 1 This is a flow chart of a method for estimating holiday passenger vehicle traffic on highways by integrating multi-source data in an embodiment of the present invention;

[0053] Figure 2 is the estimated mean square error in different data of the four models in the embodiment of the present invention;

[0054] Figure 3 is the estimated root mean square error in different data of the four models in the embodiment of the present invention. DETAILED DESCRIPTION

[0055] The embodiments of the present application are described in detail below, and examples of the embodiments 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 application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0056] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the embodiments of the present application. The singular forms of "a", "said", and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. In addition, unless otherwise clearly defined, words such as setting, installing, and connecting should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above words in the present invention in combination with the specific content of the technical solution.

[0057] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0058] In the description of this application, "several" means one or more, "many" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly specifying the number or order of the technical features indicated.

[0059] In the description of this application, "and / or" describes the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0060] In response to existing technical problems, the present invention provides a highway holiday passenger car flow estimation solution that integrates multi-source data. The solution integrates highway gantry license plate recognition data and toll flow data, trains the dataset based on a support vector regression model, and quantitatively evaluates the estimation effect of the model using mean square error and root mean square error. By inputting highway traffic flow parameters, the estimated value of passenger car flow during the holiday free release period can be obtained.

[0061] Example 1

[0062] like Figure 1 As shown, an embodiment of the present invention provides a method for estimating holiday passenger vehicle traffic on highways by integrating multi-source data, including the following steps:

[0063] S1. The Shenzhen-Zhongshan Link was selected as the research section, covering the following periods: New Year's Day 2024 (December 29, 2023 to January 1, 2024), the non-free travel dates during the 2024 Spring Festival travel rush (January 25 to February 8, 2024, and February 18 to March 3, 2024), the 2024 Mid-Autumn Festival (September 14 to September 18, 2024), and New Year's Day 2025 (December 31, 2024 to January 2, 2025). Based on the historical license plate recognition data collected by the gantries of this section, the basic characteristic parameter data of the traffic flow on the section were obtained at one-hour intervals, including license plate recognition flow and vehicle travel time.

[0064] S2. Traffic volume data is collected from the historical toll flow data of the Shenzhen-Zhongshan Link at one-hour intervals, and divided by vehicle type to obtain the total traffic flow of the section, large passenger car flow, large truck flow, small passenger car flow, small truck flow and time characteristics.

[0065] S3. Integrate the license plate recognition data and toll flow data of the Shenzhen-Zhongshan Link to obtain the traffic volume, normalize the data, and divide it into training data and test data.

[0066] In one embodiment, a partial data sample set obtained after fusion is shown in Table 1;

[0067] Table 1 Part of the sample data set (Shenzhen-Zhongshan Channel)

[0068]

[0069] S4. Use the fused basic characteristic parameter data of the road section traffic flow as input, use the support vector regression algorithm to train the data set, use the grid search method to determine the support vector regression model parameters, and calculate the model performance evaluation index based on the model output results. In one embodiment, the determined model parameters include: kernel = 'rbf', degree = 3, gamma = 'scale', coef0 = 0.0, tol = 0.001, C = 0.5, epsilon = 0.1, shrinking = True, cache_size = 200, verbose = False, max_iter = -1.

[0070] When calculating the model performance evaluation index in the test set, the random forest algorithm (RF), K-nearest neighbor algorithm (KNN), and long short-term memory network algorithm (LSTM) were selected as controls and compared with the support vector regression algorithm (SVR) of the present invention.

[0071] Figure 2 are the mean square error and root mean square error obtained by training the four models on the same dataset. The comparison of the estimated performance of the four models is shown in Table 2.

[0072] Table 2 shows the comparison of the estimation performance of the four models

[0073]

[0074] S5. Count the traffic flow data at one-hour intervals from the real-time license plate recognition data of the Shenzhen-Zhongshan Channel gantry and the toll flow data. Take the license plate recognition traffic flow, passenger bus flow, large truck flow, small truck flow, and time characteristics of the road section as input. Relying on the output of the model trained in step S4, the estimated total traffic flow of the road section within the time period is obtained, thereby estimating the passenger car flow of the road section within the time period.

[0075] In one embodiment, the real-time statistical verification data is the non-free release date of the 2025 Spring Festival (January 14 to January 26, 2025). The output results of the four models are compared with the actual total traffic volume of the road section to obtain the average accuracy as follows: Figure 3 As shown, the average accuracy is calculated as follows:

[0076]

[0077] Among them, Acc is the average accuracy, The estimated value of the model, y i is the actual total flow.

[0078] In summary, the present invention proposes a method for estimating the passenger car traffic flow on highways during holidays by integrating multi-source data, which can achieve better traffic flow statistical performance. Based on the support vector regression model, it reduces the complexity of the model, reduces overfitting, and reduces the amount of calculation. At the same time, it improves the estimation accuracy of the passenger car traffic flow on highways during holidays in real time. Figure 3 It can be seen that the overall estimation accuracy of the present invention is as high as 97.84%. The method proposed by the present invention has the lowest error in comparison with other models, which proves that the method has good applicability.

[0079] Example 2

[0080] An embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the following Figure 1 A method for estimating passenger car traffic on highways during holidays is shown.

[0081] It is understood that the memory may include random access memory (RAM) or read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory 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, instructions for implementing the various method embodiments described above, etc.; the data storage area may store data created based on the use of the server, etc.

[0082] The processor may include one or more processing cores. The processor utilizes various interfaces and circuits to connect various components within the server. It executes various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory, as well as accessing data stored in memory. Optionally, the processor 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 may integrate one or a combination of a central processing unit (CPU) and a modem. The CPU primarily processes the operating system and application programs, while the modem handles wireless communications. It is understood that the modem may not be integrated into the processor and may be implemented separately via a single chip.

[0083] Since the electronic device is an electronic device corresponding to a method for estimating holiday passenger car traffic on highways in an embodiment of the present invention, and the principle of solving the problem by the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0084] Example 3

[0085] An embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the following Figure 1 A method for estimating passenger car traffic on highways during holidays is shown.

[0086] Those skilled in the art will appreciate 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. The program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0087] Since the storage medium is the storage medium corresponding to a method for estimating holiday passenger car traffic on highways in an embodiment of the present invention, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0088] Example 4

[0089] In some possible implementations, various aspects of the methods of the embodiments of the present invention may also be implemented in the form of a program product, which includes program code. When the program product is executed on a computer device, the program code is used to cause the computer device to perform the steps of the method for estimating holiday passenger vehicle traffic on highways according to various exemplary embodiments of the present application as described above in this specification. The executable computer program code or "code" used to perform the various embodiments may be written in a high-level programming language such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0090] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0091] In the description of this specification, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0092] The above embodiments are intended only to illustrate the technical concepts and features of the present invention. Their purpose is to enable those skilled in the art to understand the contents of the present invention and implement them accordingly. They are not intended to limit the scope of protection of the present invention. Any equivalent changes or modifications made based on the essence of the present invention are intended to be covered by the scope of protection of the present invention.

Claims

1. A method for estimating passenger car traffic on highways during holidays, characterized in that: The following steps are involved: S1. Based on the historical highway license plate recognition data collected by the highway gantry, the basic characteristic parameter data of the road section traffic flow is obtained at a certain time interval, including license plate recognition flow and vehicle travel time; S2. Counting traffic volume data at regular time intervals from historical toll collection data on expressways, and classifying traffic volume by vehicle type, to obtain the total traffic volume on the road section, the traffic volume of large buses, large trucks, small buses, small trucks, and their temporal characteristics; S3: Integrate the traffic volume obtained by highway license plate recognition data and toll flow data, perform data normalization, and divide it into training data and test data; S4. Using the fused basic characteristic parameter data of the road section traffic flow as input, the support vector regression algorithm is used to train the data set, the support vector regression model parameters are determined using the grid search method, and the model performance evaluation index is calculated based on the model output results; S5. Calculate traffic flow data from real-time highway gantry license plate recognition data and toll flow data at certain time intervals. Take the license plate recognition traffic flow, large passenger bus flow, large truck flow, small truck flow, and time characteristics of the road section as input. Relying on the output of the model trained in step S4, the estimated total traffic flow of the road section within the time period is obtained, thereby estimating the small passenger car flow of the road section within the time period.

2. A method for estimating holiday passenger car traffic on highways according to claim 1, characterized in that: In the step S1, counting the traffic flow data at a certain time interval specifically refers to counting the traffic flow data at a one-hour time interval.

3. The method for estimating holiday passenger car traffic on highways according to claim 1, characterized in that: In step S1, the time points of the historical data specifically include: New Year's Day holiday dates, dates during the Spring Festival travel period when free travel for passenger cars on highways is excluded, Dragon Boat Festival holiday dates, and dates during the Mid-Autumn Festival when free travel for passenger cars on highways is not permitted.

4. The method for estimating passenger car traffic volume on highways during holidays according to claim 1, characterized in that: The step S3 specifically includes: Pre-process the two types of data that need to be fused and establish a mapping relationship between highway toll flow data and gantry license plate recognition data; The data normalization adopts min-max standardization, and the formula is as follows: Among them, x ′ is the normalized traffic flow data, where max is the maximum value in the sample data and min is the minimum value in the sample data; The division into training data and test data is specifically as follows: the fused historical traffic flow data is grouped according to different road sections and different hourly time periods, and a random sampling method is used in the same group of data to divide the extracted data into a training set at a ratio of 70% and a test set at a ratio of 30%.

5. The method for estimating holiday passenger car traffic on highways according to claim 1, characterized in that: The step S4 specifically includes: The basic characteristic parameter data of the traffic flow of the road section obtained by fusion is used as input, specifically the license plate recognition flow, large passenger bus flow, large truck flow, small truck flow, and time characteristics of the road section are used as input data. The support vector regression model generates an estimated value of the total traffic flow of the road section based on the input data and model parameters, and compares it with the actual total traffic flow of the toll flow to obtain the model evaluation index.

6. The method for estimating passenger car traffic volume on highways during holidays according to claim 1, characterized in that: The time feature is 24 hours a day divided into 1-hour time intervals, which are cyclically encoded using the following formula: Among them, T is the original time feature, sin T is the first feature obtained after cyclic encoding, cos T is the second feature obtained after cyclic encoding.

7. The method for estimating passenger car traffic volume on highways during holidays according to claim 1, characterized in that: In step S4, the model performance evaluation indicators specifically refer to the mean square error and the root mean square error, and the formula is as follows: 1) Mean Square Error: 2) Root mean square error: Among them, n is the sample size of the road section test set, Represents the model estimation data, y i Represents actual data.

8. The method for estimating passenger car traffic volume on highways during holidays according to claim 1, characterized in that: The step S5 specifically includes: The estimated value of the passenger car flow on the road section is calculated from the output value of the support vector regression model, and the formula is as follows: V scar =VV bcar -V btruc k -V struc k Among them, V scar To estimate the flow of passenger cars, V is the model output value, V bcar is the bus flow in the input data, V btruck is the truck traffic in the input data, V struck is the volume of pickup trucks in the input data.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set, or an instruction set, and the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

Images