Configuration method of quasi-free flow pre-transaction toll lanes on expressways

Abstract

The present invention discloses a method for configuring quasi-free-flow pre-transaction toll lanes on highways, which belongs to the field of highway technology and includes the following steps: S1: pre-processing and analyzing vehicle traffic big data, and setting a toll standard for each lane of the highway; S2: based on path planning technology, guiding vehicles to travel on lanes with the least traffic volume, automatically identifying vehicles, automatically settling vehicle fees, and allowing vehicles to pass quickly; S3, vehicle traffic situation prediction and early warning: fault prediction and health management of vehicle traffic conditions in each lane on the highway. The present invention solves the existing problem of being unable to intelligently optimize lanes and achieve quasi-free flow of vehicle traffic, reducing road utilization, and causing traffic congestion. The present invention achieves quasi-free flow during vehicle traffic by intelligently optimizing lanes, thereby achieving the purpose of improving road utilization and reducing congestion, and can meet the needs of modern society for efficient and fast transportation.

Description

Technical Field

[0001] The present invention relates to the technical field of expressways, and in particular to a method for configuring quasi-free-flow pre-transaction toll lanes on expressways. Background Art

[0002] During special time periods such as peak hours and holidays, highway toll stations become major bottlenecks, causing large-scale congestion; traditional toll collection methods can no longer meet the modern society's demand for efficient and fast transportation.

[0003] Chinese patent publication number CN114826786A discloses a highway toll auditing system, comprising a head office system, a provincial center system, and a branch office system. This system fully utilizes the head office's system resources and employs advanced information technologies such as the internet and cloud storage to construct a parallel reconciliation system for highway toll revenue based on big data. This system enables in-depth mining and scientific analysis of business data, intelligent integration and decentralized sharing of internal and external information resources, and vertical linkage and horizontal collaboration between the company's functional departments and branches. However, this patent suffers from the following deficiencies:

[0004] When existing vehicles pass through, lanes cannot be intelligently optimized, and quasi-free flow of vehicles cannot be achieved during the passage of vehicles, which reduces road utilization, causes traffic congestion, and cannot meet the needs of modern society for efficient and fast transportation. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for configuring quasi-free-flow pre-transaction toll lanes on highways. By intelligently optimizing the lanes, quasi-free flow of vehicles during passage is achieved, thereby improving road utilization and reducing congestion. This method can meet the needs of modern society for efficient and fast transportation and solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for configuring a quasi-free-flow pre-transaction toll lane on an expressway includes the following steps:

[0008] S1. Vehicle traffic big data collection and preprocessing:

[0009] Real-time monitoring and continuous collection of traffic volume, vehicle types, driving speeds, and driving times for each lane on the highway are performed to determine the vehicle traffic big data for each lane on the highway, pre-process and analyze the vehicle traffic big data, and set toll standards for each lane on the highway;

[0010] S2. Automatic vehicle identification and rapid passage:

[0011] Based on the traffic flow information of each lane during the vehicle's passage, the navigation system uses path planning technology to intelligently guide vehicles to the lane with the least traffic. When vehicles pass through, the vehicle recognition technology is used to automatically identify the vehicle and automatically settle the vehicle fee, allowing the vehicle to pass quickly.

[0012] S3. Vehicle traffic situation prediction and warning:

[0013] Based on machine learning technology, fault prediction and health management are carried out on the vehicle traffic conditions in each lane on the highway, vehicle traffic demand and conditions are predicted, and the quasi-free-flow vehicle traffic results on the highway are determined. In addition, early warning and health management of vehicle traffic conditions are carried out based on the quasi-free-flow vehicle traffic results on the highway to adapt to traffic needs in different scenarios.

[0014] Preferably, in S1, the vehicle traffic big data of each lane on the highway is determined by performing the following operations:

[0015] High-definition cameras installed on highways monitor and continuously collect traffic flow and vehicle type information in each lane of the highway in real time to obtain traffic flow and vehicle type information;

[0016] Based on the on-board sensors installed on the vehicles, the vehicle speed and travel time of each lane on the highway are monitored and continuously collected in real time to obtain vehicle speed and travel time information;

[0017] Among them, based on the traffic volume and vehicle type information, vehicle speed and driving time information, the vehicle traffic big data of each lane on the highway is determined.

[0018] Preferably, in S1, a suitable charging standard is set for each lane of the highway, and the following operations are performed:

[0019] Obtain big data on vehicle traffic in each lane on the highway;

[0020] Clean the big data of vehicle traffic in each lane on the highway;

[0021] Remove inconsistent data, invalid values, and missing values ​​from the vehicle traffic big data of each lane on the expressway that are not useful for the configuration of quasi-free-flow pre-transaction toll lanes on the expressway, and determine the vehicle traffic big data of each lane on the expressway that is useful for the configuration of quasi-free-flow pre-transaction toll lanes on the expressway;

[0022] Data analysis is conducted on the traffic volume and vehicle type information in the vehicle traffic big data of each lane on the highway, and the charging standards are set for each lane of the highway in combination with the vehicle driving speed and driving time information.

[0023] Preferably, the data quality test is performed on the vehicle traffic big data of each lane on the expressway useful for the configuration of the quasi-free flow pre-transaction toll lane on the expressway, including:

[0024] Extracting and classifying vehicle traffic big data for each lane on the highway that is useful for configuring quasi-free-flow pre-transaction toll lanes on the highway, and obtaining a data set corresponding to each data type, wherein the data types include vehicle volume, vehicle type, driving speed, and driving time;

[0025] Extract the number of data contained in the data set corresponding to the traffic flow data;

[0026] Comparing the number of data contained in the data set corresponding to the vehicle flow data with a preset first data number threshold;

[0027] When the number of data contained in the data set corresponding to the vehicle flow data is lower than a preset first data number threshold, retrieving the missing value position corresponding to the vehicle flow data;

[0028] For each missing value position corresponding to the traffic flow data, extract the two valid data with the most recent data collection time as a reference valid data group;

[0029] Comparing the data average value corresponding to the reference valid data group with the median value of the data set corresponding to the traffic flow data, and taking the difference data between the data average value corresponding to the reference valid data group and the median value of the data set corresponding to the traffic flow data as the first difference data;

[0030] The first difference data corresponding to each missing value in the traffic flow data is combined with the valid data in the data set corresponding to the traffic flow data to obtain the filling value corresponding to each missing value contained in the traffic flow data, wherein the filling value corresponding to each missing value in the traffic flow data is obtained by the following formula:

[0031]

[0032] Among them, R t Indicates the filling value corresponding to each missing value of the traffic flow data; X 01 and X 02 Respectively represent the two valid data values ​​contained in the reference valid data group; X c01 represents the first difference data; n represents the total number of valid data in the data set corresponding to the traffic flow data; X i Indicates the data value of the i-th valid data in the data set corresponding to the traffic flow data;

[0033] The missing value position is filled with data using the filling value corresponding to each missing value in the vehicle flow data.

[0034] Preferably, the data quality detection of the vehicle traffic big data of each lane on the expressway where the quasi-free flow pre-transaction toll lane is configured is performed, further comprising:

[0035] Extract the number of data contained in the data set corresponding to the driving speed data;

[0036] Comparing the number of data contained in the data set corresponding to the driving speed data with a preset second data number threshold;

[0037] When the number of data contained in the data set corresponding to the driving speed data is lower than a preset second data number threshold, retrieving the missing value position corresponding to the driving speed data;

[0038] For each missing value position corresponding to the driving speed data, extract the two valid data with the most recent data collection time as the observation valid data group;

[0039] performing weighted average of the data average value corresponding to the observed valid data group, the median value of the data set corresponding to the driving speed data, and the data maximum value of the data set corresponding to the driving speed data to obtain a driving data reference value for comparison, and taking the difference data between the data average value corresponding to the observed valid data group and the driving data reference value as the second difference data;

[0040] The second difference data corresponding to each missing value of the driving speed data is combined with the valid data in the data set corresponding to the driving speed data to obtain the filling value corresponding to each missing value contained in the driving speed data, wherein the filling value corresponding to each missing value of the driving speed data is obtained by the following formula:

[0041]

[0042] Among them, K t Indicates the fill value corresponding to each missing value of the driving speed data; Y 01 and Y 02 Respectively represent the two valid data values ​​contained in the reference valid data group; Y c02 represents the second difference data; m represents the total number of valid data in the data set corresponding to the driving speed data; Y i Indicates the data value of the i-th valid data in the data set corresponding to the driving speed data; Y z Indicates the median value of the valid data in the data set corresponding to the driving speed data; Y p Indicates the average value of valid data in the data set corresponding to the driving speed data;

[0043] The missing value position is filled with data using the filling value corresponding to each missing value of the driving speed data.

[0044] Preferably, in S2, the vehicle automatically identifies and quickly passes by performing the following operations:

[0045] Carry out real-time monitoring of the traffic flow of each lane of the quasi-free flow pre-transaction toll collection system on the expressway, and determine the traffic flow information of each lane during the vehicle passage process;

[0046] Based on the traffic flow information of each lane during vehicle passage, the system selects the quasi-free-flow pre-trading toll lane on the expressway with the least traffic flow. Based on path planning technology, the navigation system intelligently guides vehicles to the quasi-free-flow pre-trading toll lane on the expressway with the least traffic flow.

[0047] When a vehicle passes, it is automatically identified based on vehicle recognition technology, and the vehicle fees are automatically settled, allowing the vehicle to pass quickly.

[0048] Preferably, in S2, the navigation system intelligently guides the vehicle to travel on a lane with the least traffic volume, and performs the following operations:

[0049] Get the lane information of the vehicle;

[0050] Monitor the traffic flow of the lane where the vehicle is located and other lanes in real time to determine the traffic flow information of all lanes;

[0051] Analyze the traffic flow information of all lanes and determine the lane with the least traffic flow based on the lane information of the vehicles during the passage process;

[0052] Path planning is performed for the lane with the least traffic flow, and the navigation system intelligently guides vehicles to the lane with the least traffic flow, allowing vehicles to pass quickly.

[0053] Preferably, in said S3, the vehicle traffic condition prediction and warning comprises the following operations:

[0054] Collect big data on vehicle traffic in each lane of the highway;

[0055] Based on machine learning technology, the vehicle traffic big data of each lane on the highway is divided and trained to build the optimal highway quasi-free flow vehicle traffic prediction model;

[0056] The optimal highway quasi-free-flow vehicle traffic prediction model is applied to the actual highway quasi-free-flow environment. According to the optimal highway quasi-free-flow vehicle traffic prediction model, the vehicle traffic conditions of each lane on the highway are predicted to have faults and health management, the vehicle traffic demand and conditions are predicted, and the highway quasi-free-flow vehicle traffic results are determined. According to the highway quasi-free-flow vehicle traffic results, early warning and health management of the vehicle traffic conditions are carried out.

[0057] Preferably, in S3, an optimal highway quasi-free flow vehicle traffic prediction model is constructed by performing the following operations:

[0058] Obtain big data on vehicle traffic in each lane on the highway;

[0059] Classify the vehicle traffic big data of each lane on the highway and divide the vehicle traffic big data of each lane on the highway into training set and test set;

[0060] Based on the training set, the machine learning model is trained and iteratively optimized to build a quasi-free-flow vehicle traffic prediction model for highways;

[0061] Based on the test set, the performance of the highway quasi-free-flow vehicle traffic prediction model is tested to determine whether the highway quasi-free-flow vehicle traffic prediction model achieves the expected effect and to determine the optimal highway quasi-free-flow vehicle traffic prediction model.

[0062] Preferably, in S3, early warning and health management of vehicle traffic conditions are performed based on the results of quasi-free flow vehicles on the expressway, and the following operations are performed:

[0063] When it is predicted that a lane will have a large traffic volume and will be congested, timely warnings will be issued and charging strategies and services will be adjusted. Congestion can be alleviated by adjusting charging standards or guiding vehicles to choose other lanes, and dynamic adjustments can be made to vehicle tolls and routes to adapt to traffic needs in different scenarios.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] 1. The present invention determines the big data of vehicle traffic in each lane of the expressway by monitoring and continuously collecting the traffic volume, vehicle type, driving speed and driving time in real time. The present invention then analyzes the traffic volume and vehicle type information in the big data of vehicle traffic in each lane of the expressway, and sets a toll standard for each lane of the expressway in combination with the vehicle driving speed and driving time information.

[0066] 2. The present invention uses path planning technology and intelligent navigation system to guide vehicles to lanes with the least traffic, based on traffic flow information for each lane during vehicle passage. Vehicle identification technology is used to automatically identify vehicles and automatically settle vehicle fees during passage, allowing vehicles to pass quickly. This is used in a quasi-free-flow charging mode. By intelligently optimizing lanes, quasi-free flow is achieved during vehicle passage, thereby improving road utilization and reducing congestion, meeting the modern society's demand for efficient and fast transportation.

[0067] 3. Based on machine learning technology, the present invention conducts fault prediction and health management of vehicle traffic conditions in each lane on the highway, predicts vehicle traffic demand and conditions, determines the results of quasi-free-flow vehicle traffic on the highway, and conducts early warning and health management of vehicle traffic conditions based on the results of quasi-free-flow vehicle traffic on the highway. Congestion can be alleviated by adjusting toll standards or guiding vehicles to choose other lanes, realizing dynamic adjustment of vehicle tolls and routes to meet traffic needs in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 The present invention is a flow chart of the method for configuring quasi-free-flow pre-transaction toll lanes on expressways. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] In order to solve the problem that the existing vehicles cannot optimize the lanes intelligently, cannot achieve quasi-free flow during the vehicle passage process, reduce road utilization, cause traffic congestion, and cannot meet the needs of modern society for efficient and fast transportation, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0071] The method for configuring a quasi-free-flow pre-transaction toll lane on an expressway includes the following steps:

[0072] S1. Vehicle traffic big data collection and preprocessing:

[0073] Real-time monitoring and continuous collection of traffic volume, vehicle types, driving speeds, and driving times for each lane on the highway are performed to determine the vehicle traffic big data for each lane on the highway, pre-process and analyze the vehicle traffic big data, and set toll standards for each lane on the highway;

[0074] In this embodiment, as a preferred technical solution of the present invention, the vehicle traffic big data of each lane on the highway is determined by performing the following operations:

[0075] High-definition cameras installed on highways monitor and continuously collect traffic flow and vehicle type information in each lane of the highway in real time to obtain traffic flow and vehicle type information;

[0076] Based on the on-board sensors installed on the vehicles, the vehicle speed and travel time of each lane on the highway are monitored and continuously collected in real time to obtain vehicle speed and travel time information;

[0077] Among them, based on the traffic volume and vehicle type information, vehicle speed and driving time information, the vehicle traffic big data of each lane on the highway is determined.

[0078] It should be noted that high-definition cameras refer to cameras with HD 1080P, HD 960P or HD 720P, 1080P being full HD, and high-definition cameras being the standard achieved by the combination of camera and clarity; high-definition cameras can be used to monitor and continuously collect traffic flow and vehicle type conditions in each lane on the highway in real time, thereby obtaining information on traffic flow and vehicle types.

[0079] It should be noted that, in this embodiment, the on-board sensors include a speed sensor and a driving recorder. The speed sensor can monitor and continuously collect the vehicle speeds in each lane on the highway in real time to obtain vehicle speed information; the driving recorder can monitor and continuously collect the vehicle driving time in each lane on the highway in real time to obtain vehicle driving time information.

[0080] In this embodiment, as a preferred technical solution of the present invention, a suitable toll rate is set for each lane of the expressway by performing the following operations:

[0081] Obtain big data on vehicle traffic in each lane on the highway;

[0082] Clean the big data of vehicle traffic in each lane on the highway;

[0083] Remove inconsistent data, invalid values, and missing values ​​from the vehicle traffic big data of each lane on the expressway that are not useful for the configuration of quasi-free-flow pre-transaction toll lanes on the expressway, and determine the vehicle traffic big data of each lane on the expressway that is useful for the configuration of quasi-free-flow pre-transaction toll lanes on the expressway;

[0084] It should be noted that data cleaning is one of the important links in data analysis, data mining and application, aiming to improve data quality and the accuracy of analysis results.

[0085] Among them, the vehicle traffic big data of each lane on the highway is cleaned, including:

[0086] Conduct consistency checks on the big data of vehicle traffic in each lane on the highway;

[0087] According to the data consistency requirements, the vehicle traffic big data of each lane on the expressway is checked to see if there is any inconsistent data that is useless for the configuration of quasi-free flow pre-transaction toll lanes on the expressway, and the inconsistent data in the vehicle traffic big data of each lane on the expressway is removed;

[0088] Check invalid and missing values ​​for the vehicle traffic big data of each lane on the highway;

[0089] According to the requirements of data validity and integrity, check whether there are invalid values ​​and missing values ​​in the vehicle traffic big data of each lane on the expressway that are useless for the configuration of quasi-free flow pre-transaction toll lanes on the expressway, and remove the invalid values ​​and missing values ​​in the vehicle traffic big data of each lane on the expressway;

[0090] Then, the vehicle traffic big data of each lane on the highway that is useful for the configuration of quasi-free flow pre-transaction toll lanes on the highway is determined.

[0091] It should be noted that by cleaning the vehicle traffic big data of each lane on the highway, inconsistent data, invalid values ​​and missing values ​​that are useless for the configuration of quasi-free-flow pre-transaction toll lanes on the highway can be removed from the vehicle traffic big data of each lane on the highway, and the vehicle traffic big data of each lane on the highway that is useful for the configuration of quasi-free-flow pre-transaction toll lanes on the highway can be determined, which can improve the subsequent processing accuracy and analysis efficiency of the vehicle traffic big data of each lane on the highway.

[0092] Data analysis is conducted on the traffic volume and vehicle type information in the vehicle traffic big data of each lane on the highway, and the charging standards are set for each lane of the highway in combination with the vehicle driving speed and driving time information.

[0093] S2. Automatic vehicle identification and rapid passage:

[0094] Based on the traffic flow information of each lane during the vehicle's passage, the navigation system uses path planning technology to intelligently guide vehicles to the lane with the least traffic. When vehicles pass through, the vehicle recognition technology is used to automatically identify the vehicle and automatically settle the vehicle fee, allowing the vehicle to pass quickly.

[0095] Specifically, the data quality test is performed on the vehicle traffic big data of each lane on the expressway that is useful for the configuration of quasi-free flow pre-transaction toll lanes on the expressway, including:

[0096] Extracting and classifying vehicle traffic big data for each lane on the highway that is useful for configuring quasi-free-flow pre-transaction toll lanes on the highway, and obtaining a data set corresponding to each data type, wherein the data types include vehicle volume, vehicle type, driving speed, and driving time;

[0097] Extract the number of data contained in the data set corresponding to the traffic flow data;

[0098] Comparing the number of data contained in the data set corresponding to the vehicle flow data with a preset first data number threshold;

[0099] When the number of data contained in the data set corresponding to the vehicle flow data is lower than a preset first data number threshold, retrieving the missing value position corresponding to the vehicle flow data;

[0100] For each missing value position corresponding to the traffic flow data, extract the two valid data with the most recent data collection time as a reference valid data group;

[0101] Comparing the data average value corresponding to the reference valid data group with the median value of the data set corresponding to the traffic flow data, and taking the difference data between the data average value corresponding to the reference valid data group and the median value of the data set corresponding to the traffic flow data as the first difference data;

[0102] The first difference data corresponding to each missing value in the traffic flow data is combined with the valid data in the data set corresponding to the traffic flow data to obtain the filling value corresponding to each missing value contained in the traffic flow data, wherein the filling value corresponding to each missing value in the traffic flow data is obtained by the following formula:

[0103]

[0104] Among them, R t Indicates the filling value corresponding to each missing value of the traffic flow data; X 01 and X 02 Respectively represent the two valid data values ​​contained in the reference valid data group; X c01 represents the first difference data; n represents the total number of valid data in the data set corresponding to the traffic flow data; X i Indicates the data value of the i-th valid data in the data set corresponding to the traffic flow data;

[0105] The missing value position is filled with data using the filling value corresponding to each missing value in the vehicle flow data.

[0106] The technical effect of the above-mentioned technical solution is that by classifying and extracting vehicle traffic big data from each lane on a highway, specifically targeting key data types such as traffic volume, vehicle type, driving speed, and travel time, the comprehensiveness and relevance of the data are ensured. By checking the integrity of the data count in the traffic flow dataset and comparing it with a preset first data count threshold, missing data issues can be promptly detected, triggering the subsequent data filling process. When missing traffic flow data are detected, these missing values ​​are not simply ignored or deleted, but are filled with valid data near the missing value, ensuring data continuity and consistency. By calculating the difference between the data mean of the reference valid data group and the median value of the dataset (the first difference data), and combining it with the valid data in the dataset, a complex formula is used to estimate the fill value for the missing value. This method considers the overall distribution and local characteristics of the data, making the fill value closer to the actual value, thereby improving data accuracy and reliability. By performing quality inspection and data filling on vehicle traffic big data, the data required for toll lane configuration can be ensured to be complete and accurate, thereby supporting more accurate traffic flow prediction, vehicle type classification, and toll collection strategy formulation. Data accuracy and completeness are crucial for implementing quasi-free-flow pre-transaction toll collection on highways, as it relies on real-time, accurate information on vehicle volume and vehicle type to optimize toll collection processes and improve traffic efficiency. Automated data quality inspection and data population processes can reduce manual intervention and errors, improving data processing efficiency and accuracy. Accurate data can support smarter traffic management and resource allocation decisions, thereby optimizing highway operation and maintenance costs.

[0107] In summary, this technical solution not only improves the integrity and accuracy of highway vehicle traffic big data through refined data quality detection and data filling strategies, but also provides strong support for improving the operating efficiency of highway toll lanes, optimizing resource allocation, and reducing operation and maintenance costs.

[0108] Specifically, the data quality test is performed on the vehicle traffic big data of each lane on the expressway that is useful for the configuration of quasi-free flow pre-transaction toll lanes on the expressway, which also includes:

[0109] Extract the number of data contained in the data set corresponding to the driving speed data;

[0110] Comparing the number of data contained in the data set corresponding to the driving speed data with a preset second data number threshold;

[0111] When the number of data contained in the data set corresponding to the driving speed data is lower than a preset second data number threshold, retrieving the missing value position corresponding to the driving speed data;

[0112] For each missing value position corresponding to the driving speed data, extract the two valid data with the most recent data collection time as the observation valid data group;

[0113] performing weighted average of the data average value corresponding to the observed valid data group, the median value of the data set corresponding to the driving speed data, and the data maximum value of the data set corresponding to the driving speed data to obtain a driving data reference value for comparison, and taking the difference data between the data average value corresponding to the observed valid data group and the driving data reference value as the second difference data;

[0114] The second difference data corresponding to each missing value of the driving speed data is combined with the valid data in the data set corresponding to the driving speed data to obtain the filling value corresponding to each missing value contained in the driving speed data, wherein the filling value corresponding to each missing value of the driving speed data is obtained by the following formula:

[0115]

[0116] Among them, K t Indicates the fill value corresponding to each missing value of the driving speed data; Y 01 and Y 02 Respectively represent the two valid data values ​​contained in the reference valid data group; Y c02 represents the second difference data; m represents the total number of valid data in the data set corresponding to the driving speed data; Y i Indicates the data value of the i-th valid data in the data set corresponding to the driving speed data; Y z Indicates the median value of the valid data in the data set corresponding to the driving speed data; Y p Indicates the average value of valid data in the data set corresponding to the driving speed data;

[0117] The missing value position is filled with data using the filling value corresponding to each missing value of the driving speed data.

[0118] The technical effect of the above-mentioned technical solution is that by classifying and extracting vehicle traffic big data from each lane on a highway, specifically targeting key data types such as traffic volume, vehicle type, driving speed, and travel time, the comprehensiveness and relevance of the data are ensured. By checking the integrity of the data count in the traffic flow dataset and comparing it with a preset first data count threshold, missing data issues can be promptly detected, triggering the subsequent data filling process. When missing traffic flow data are detected, these missing values ​​are not simply ignored or deleted, but are filled with valid data near the missing value, ensuring data continuity and consistency. By calculating the difference between the data mean of the reference valid data group and the median value of the dataset (the first difference data), and combining it with the valid data in the dataset, a complex formula is used to estimate the fill value for the missing value. This method considers the overall distribution and local characteristics of the data, making the fill value closer to the actual value, thereby improving data accuracy and reliability. By performing quality inspection and data filling on vehicle traffic big data, the data required for toll lane configuration can be ensured to be complete and accurate, thereby supporting more accurate traffic flow prediction, vehicle type classification, and toll collection strategy formulation. Data accuracy and completeness are crucial for implementing quasi-free-flow pre-transaction toll collection on highways, as it relies on real-time, accurate information on vehicle volume and vehicle type to optimize toll collection processes and improve traffic efficiency. Automated data quality inspection and data population processes can reduce manual intervention and errors, improving data processing efficiency and accuracy. Accurate data can support smarter traffic management and resource allocation decisions, thereby optimizing highway operation and maintenance costs.

[0119] In summary, this technical solution not only improves the integrity and accuracy of highway vehicle traffic big data through refined data quality detection and data filling strategies, but also provides strong support for improving the operating efficiency of highway toll lanes, optimizing resource allocation, and reducing operation and maintenance costs.

[0120] In this embodiment, as a preferred technical solution of the present invention, the automatic identification and rapid passage of vehicles are performed by performing the following operations:

[0121] Carry out real-time monitoring of the traffic flow of each lane of the quasi-free flow pre-transaction toll collection system on the expressway, and determine the traffic flow information of each lane during the vehicle passage process;

[0122] Based on the traffic flow information of each lane during vehicle passage, the system selects the quasi-free-flow pre-trading toll lane on the expressway with the least traffic flow. Based on path planning technology, the navigation system intelligently guides vehicles to the quasi-free-flow pre-trading toll lane on the expressway with the least traffic flow.

[0123] When a vehicle passes, it is automatically identified based on vehicle recognition technology, and the vehicle fees are automatically settled, allowing the vehicle to pass quickly.

[0124] In this embodiment, as a preferred technical solution of the present invention, the navigation system intelligently guides the vehicle to the lane with the least traffic flow, and performs the following operations:

[0125] Get the lane information of the vehicle;

[0126] Monitor the traffic flow of the lane where the vehicle is located and other lanes in real time to determine the traffic flow information of all lanes;

[0127] Analyze the traffic flow information of all lanes and determine the lane with the least traffic flow based on the lane information of the vehicles during the passage process;

[0128] Path planning is performed for the lane with the least traffic flow, and the navigation system intelligently guides vehicles to the lane with the least traffic flow, allowing vehicles to pass quickly.

[0129] S3. Vehicle traffic situation prediction and warning:

[0130] Based on machine learning technology, fault prediction and health management are carried out on the vehicle traffic conditions in each lane on the highway, vehicle traffic demand and conditions are predicted, and the quasi-free-flow vehicle traffic results on the highway are determined. In addition, early warning and health management of vehicle traffic conditions are carried out based on the quasi-free-flow vehicle traffic results on the highway to adapt to traffic needs in different scenarios.

[0131] In this embodiment, as a preferred technical solution of the present invention, vehicle traffic condition prediction and warning are performed by performing the following operations:

[0132] Collect big data on vehicle traffic in each lane of the highway;

[0133] Based on machine learning technology, the vehicle traffic big data of each lane on the highway is divided and trained to build the optimal highway quasi-free flow vehicle traffic prediction model;

[0134] The optimal highway quasi-free-flow vehicle traffic prediction model is applied to the actual highway quasi-free-flow environment. According to the optimal highway quasi-free-flow vehicle traffic prediction model, the vehicle traffic conditions of each lane on the highway are predicted to have faults and health management, the vehicle traffic demand and conditions are predicted, and the highway quasi-free-flow vehicle traffic results are determined. According to the highway quasi-free-flow vehicle traffic results, early warning and health management of the vehicle traffic conditions are carried out.

[0135] In this embodiment, as a preferred technical solution of the present invention, an optimal highway quasi-free flow vehicle traffic prediction model is constructed, and the following operations are performed:

[0136] Obtain big data on vehicle traffic in each lane on the highway;

[0137] Classify the vehicle traffic big data of each lane on the highway and divide the vehicle traffic big data of each lane on the highway into training set and test set;

[0138] Based on the training set, the machine learning model is trained and iteratively optimized to build a quasi-free-flow vehicle traffic prediction model for highways;

[0139] Based on the test set, the performance of the highway quasi-free-flow vehicle traffic prediction model is tested to determine whether the highway quasi-free-flow vehicle traffic prediction model achieves the expected effect and to determine the optimal highway quasi-free-flow vehicle traffic prediction model.

[0140] In this embodiment, as a preferred technical solution of the present invention, early warning and health management of vehicle traffic conditions are performed based on the results of quasi-free flow vehicles on the expressway, and the following operations are performed:

[0141] When it is predicted that a lane will have a large traffic volume and will be congested, timely warnings will be issued and charging strategies and services will be adjusted. Congestion can be alleviated by adjusting charging standards or guiding vehicles to choose other lanes, and dynamic adjustments can be made to vehicle tolls and routes to adapt to traffic needs in different scenarios.

[0142] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0143] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for configuring quasi-free flow pre-transaction toll lanes on expressways, characterized in that: The steps include: S1. Vehicle traffic big data collection and preprocessing: Real-time monitoring and continuous collection of traffic volume, vehicle types, driving speeds, and driving times for each lane on the highway are performed to determine the vehicle traffic big data for each lane on the highway, pre-process and analyze the vehicle traffic big data, and set toll standards for each lane on the highway; Clean the big data of vehicle traffic in each lane of the highway and determine the big data of vehicle traffic in each lane of the highway that is useful for configuring quasi-free flow pre-transaction toll lanes on the highway; Data quality testing is performed on the vehicle traffic big data of each lane on the expressway that is useful for the configuration of quasi-free flow pre-transaction toll lanes on the expressway, including: Extracting and classifying vehicle traffic big data for each lane on the highway that is useful for configuring quasi-free-flow pre-transaction toll lanes on the highway, and obtaining a data set corresponding to each data type, wherein the data types include vehicle volume, vehicle type, driving speed, and driving time; Extract the number of data contained in the data set corresponding to the traffic flow data; Comparing the number of data contained in the data set corresponding to the vehicle flow data with a preset first data number threshold; When the number of data contained in the data set corresponding to the vehicle flow data is lower than a preset first data number threshold, retrieving the missing value position corresponding to the vehicle flow data; For each missing value position corresponding to the traffic flow data, extract the two valid data with the most recent data collection time as a reference valid data group; Comparing the data average value corresponding to the reference valid data group with the median value of the data set corresponding to the traffic flow data, and taking the difference data between the data average value corresponding to the reference valid data group and the median value of the data set corresponding to the traffic flow data as the first difference data; The first difference data corresponding to each missing value in the traffic flow data is combined with the valid data in the data set corresponding to the traffic flow data to obtain the filling value corresponding to each missing value contained in the traffic flow data, wherein the filling value corresponding to each missing value in the traffic flow data is obtained by the following formula: Among them, R t Indicates the filling value corresponding to each missing value of the traffic flow data; X 01 and X 02 Respectively represent the two valid data values ​​contained in the reference valid data group; X c01 represents the first difference data; n represents the total number of valid data in the data set corresponding to the traffic flow data; X i Indicates the data value of the i-th valid data in the data set corresponding to the traffic flow data; Filling the missing value position with data using the filling value corresponding to each missing value in the vehicle flow data; S2. Automatic vehicle identification and rapid passage: Based on the traffic flow information of each lane during the vehicle's passage, the navigation system uses path planning technology to intelligently guide vehicles to the lane with the least traffic. When vehicles pass through, the vehicle recognition technology is used to automatically identify the vehicle and automatically settle the vehicle fee, allowing the vehicle to pass quickly. S3. Vehicle traffic situation prediction and warning: Based on machine learning technology, fault prediction and health management are carried out on the vehicle traffic conditions in each lane on the highway, vehicle traffic demand and conditions are predicted, and the quasi-free-flow vehicle traffic results on the highway are determined. In addition, early warning and health management of vehicle traffic conditions are carried out based on the quasi-free-flow vehicle traffic results on the highway to adapt to traffic needs in different scenarios.

2. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 1, characterized in that: In S1, the vehicle traffic big data of each lane on the highway is determined, and the following operations are performed: High-definition cameras installed on highways monitor and continuously collect traffic flow and vehicle type information in each lane of the highway in real time to obtain traffic flow and vehicle type information; Based on the on-board sensors installed on the vehicles, the vehicle speed and travel time of each lane on the highway are monitored and continuously collected in real time to obtain vehicle speed and travel time information; Among them, based on the traffic volume and vehicle type information, vehicle speed and driving time information, the vehicle traffic big data of each lane on the highway is determined.

3. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 2, characterized in that: In S1, a suitable charging standard is set for each lane of the expressway by performing the following operations: Obtain big data on vehicle traffic in each lane on the highway; Clean the big data of vehicle traffic in each lane on the highway; Remove inconsistent data, invalid values, and missing values ​​from the vehicle traffic big data of each lane on the expressway that are not useful for the configuration of quasi-free-flow pre-transaction toll lanes on the expressway, and determine the vehicle traffic big data of each lane on the expressway that is useful for the configuration of quasi-free-flow pre-transaction toll lanes on the expressway; Data analysis is conducted on the traffic volume and vehicle type information in the vehicle traffic big data of each lane on the highway, and the charging standards are set for each lane of the highway in combination with the vehicle driving speed and driving time information.

4. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 3, characterized in that: Data quality testing is performed on the vehicle traffic big data of each lane on the expressway that is useful for the configuration of quasi-free flow pre-transaction toll lanes on the expressway, including: Extract the number of data contained in the data set corresponding to the driving speed data; Comparing the number of data contained in the data set corresponding to the driving speed data with a preset second data number threshold; When the number of data contained in the data set corresponding to the driving speed data is lower than a preset second data number threshold, retrieving the missing value position corresponding to the driving speed data; For each missing value position corresponding to the driving speed data, extract the two valid data with the most recent data collection time as the observation valid data group; performing weighted average of the data average value corresponding to the observed valid data group, the median value of the data set corresponding to the driving speed data, and the data maximum value of the data set corresponding to the driving speed data to obtain a driving data reference value for comparison, and taking the difference data between the data average value corresponding to the observed valid data group and the driving data reference value as the second difference data; The second difference data corresponding to each missing value of the driving speed data is combined with the valid data in the data set corresponding to the driving speed data to obtain the filling value corresponding to each missing value contained in the driving speed data, wherein the filling value corresponding to each missing value of the driving speed data is obtained by the following formula: Among them, K t Indicates the fill value corresponding to each missing value of the driving speed data; Y 01 and Y 02 Respectively represent the two valid data values ​​contained in the reference valid data group; Y c02 represents the second difference data; m represents the total number of valid data in the data set corresponding to the driving speed data; Y i Indicates the data value of the i-th valid data in the data set corresponding to the driving speed data; Y z Indicates the median value of the valid data in the data set corresponding to the driving speed data; Y p Indicates the average value of valid data in the data set corresponding to the driving speed data; The missing value position is filled with data using the filling value corresponding to each missing value of the driving speed data.

5. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 3, characterized in that: In S2, the vehicle is automatically identified and passes quickly, and the following operations are performed: Carry out real-time monitoring of the traffic flow of each lane of the quasi-free flow pre-transaction toll collection system on the expressway, and determine the traffic flow information of each lane during the vehicle passage process; Based on the traffic flow information of each lane during vehicle passage, the system selects the quasi-free-flow pre-trading toll lane on the expressway with the least traffic flow. Based on path planning technology, the navigation system intelligently guides vehicles to the quasi-free-flow pre-trading toll lane on the expressway with the least traffic flow. When a vehicle passes, it is automatically identified based on vehicle recognition technology, and the vehicle fees are automatically settled, allowing the vehicle to pass quickly.

6. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 5, characterized in that: In S2, the navigation system intelligently guides the vehicle to drive on the lane with the least traffic volume, and performs the following operations: Get the lane information of the vehicle; Monitor the traffic flow of the lane where the vehicle is located and other lanes in real time to determine the traffic flow information of all lanes; Analyze the traffic flow information of all lanes and determine the lane with the least traffic flow based on the lane information of the vehicles during the passage process; Path planning is performed for the lane with the least traffic flow, and the navigation system intelligently guides vehicles to the lane with the least traffic flow, allowing vehicles to pass quickly.

7. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 6, characterized in that: In S3, the vehicle traffic situation prediction and warning are performed by performing the following operations: Collect big data on vehicle traffic in each lane of the highway; Based on machine learning technology, the vehicle traffic big data of each lane on the highway is divided and trained to build the optimal highway quasi-free flow vehicle traffic prediction model; The optimal highway quasi-free-flow vehicle traffic prediction model is applied to the actual highway quasi-free-flow environment. According to the optimal highway quasi-free-flow vehicle traffic prediction model, the vehicle traffic conditions of each lane on the highway are predicted to have faults and health management, the vehicle traffic demand and conditions are predicted, and the highway quasi-free-flow vehicle traffic results are determined. According to the highway quasi-free-flow vehicle traffic results, early warning and health management of the vehicle traffic conditions are carried out.

8. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 7, characterized in that: In S3, an optimal free-flow vehicle traffic prediction model for highways is constructed, and the following operations are performed: Obtain big data on vehicle traffic in each lane on the highway; Classify the vehicle traffic big data of each lane on the highway and divide the vehicle traffic big data of each lane on the highway into training set and test set; Based on the training set, the machine learning model is trained and iteratively optimized to build a quasi-free-flow vehicle traffic prediction model for highways; Based on the test set, the performance of the highway quasi-free-flow vehicle traffic prediction model is tested to determine whether the highway quasi-free-flow vehicle traffic prediction model achieves the expected effect and to determine the optimal highway quasi-free-flow vehicle traffic prediction model.

9. The method for configuring quasi-free flow pre-transaction toll lanes on expressways according to claim 8, characterized in that: In S3, based on the results of the quasi-free flow vehicles on the expressway, early warning and health management of vehicle traffic conditions are performed, and the following operations are performed: When it is predicted that a lane will have a large traffic volume and will be congested, timely warnings will be issued and charging strategies and services will be adjusted. Congestion can be alleviated by adjusting charging standards or guiding vehicles to choose other lanes, and dynamic adjustments can be made to vehicle tolls and routes to adapt to traffic needs in different scenarios.

Images