Automatic highway toll collection management system

Abstract

The invention relates to the technical field of transaction settlement, in particular to an automatic highway toll collection management system, which comprises a traffic state capturing module, a vehicle position recording module, a time sequence updating module, a vehicle speed and direction analyzing module, a driving track determining module, a vehicle following and overtaking behavior determining module and a vehicle toll collection module, and constructing nodes and edges of the graph. According to the invention, through comprehensive collection and real-time analysis of the vehicle driving data, fine capturing and dynamic updating of the traffic state, accurate evaluation of interaction between vehicles, and timely identification of key vehicles influencing the traffic flow are realized, based on this, charging standard dynamic setting is carried out, reasonable distribution of the road use cost is ensured, and the congestion risk is reduced. The method is advantaged in that charging fairness and road use efficiency are improved, charging information is synchronized in real time, vehicle decision-making is made to be more timely, path selection errors caused by information lag are avoided, traffic flow distribution is further optimized, traffic efficiency is improved, and a congestion problem is effectively alleviated.

Description

Technical Field

[0001] The present invention relates to the technical field of transaction settlement, and in particular to an automated highway toll management system. Background Art

[0002] The field of transaction settlement technology involves various systems and methods for processing the transfer, confirmation and recording of funds in transactions. Especially critical in financial and commercial environments, these systems ensure the accuracy, transparency and efficiency of transactions. Technologies in this field include automatic settlement systems, electronic payment portals, blockchain technology and various security mechanisms for processing simple retail transactions to complex cross-border financial transactions. In addition, this field of technology is constantly adapting to new market demands, such as contactless payments and cryptocurrency transactions, all of which are aimed at increasing processing speed and reducing costs.

[0003] Among them, the automated highway toll management system refers to the use of electronic technology to automatically process the fees generated by vehicles when using highways. This system automatically identifies passing vehicles through electronic tags or license plate recognition technology and calculates the corresponding tolls, without the need for vehicles to stop to pay the fees, greatly improving traffic flow and toll collection efficiency. The purpose of the system is mainly to simplify the toll collection process, reduce traffic congestion, and provide a more convenient and efficient road use experience.

[0004] Existing technologies often use fixed standards when dealing with charging standards. They lack flexible adjustments based on the real-time status of vehicles and cannot reflect dynamic changes in traffic, which can easily lead to severe congestion on certain sections of roads during peak hours and uneven traffic distribution. At the same time, they lack the identification of key vehicle behaviors and cannot balance their impact on traffic through charging adjustments, leading to increased congestion. In addition, charging information is updated with a lag, and vehicles cannot obtain accurate rates when choosing routes, affecting traffic efficiency and further exacerbating the imbalance and congestion of traffic flow. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an automated highway toll management system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: an automated highway toll collection management system comprises: The traffic status capture module collects vehicle driving data, records vehicle positions, updates them in time series, analyzes vehicle speed and direction, determines driving trajectories, uses the relative positions between vehicles to determine following and overtaking behaviors, constructs graph nodes and edges, continuously updates the features of graph nodes and edges, and generates real-time traffic graph status; The data flow analysis module quantitatively analyzes node characteristics, extracts driving patterns, calculates the interaction forces between vehicles, analyzes the impact of inter-node connectivity on the network structure, and generates node influence scores based on the real-time traffic map status; The charging logic decision module sets charging standards for vehicle influence values ​​based on the node influence scores, compares the influence differences between adjacent vehicles, incorporates key node vehicles into charging strategy adjustments, determines the importance of driving paths and traffic flows, dynamically sets charging standards for multi-path vehicles, and generates optimized charging strategies; The rate adjustment and publishing module updates the parameters of the toll station based on the optimized charging strategy, sets the rate value of the toll equipment, synchronizes the toll rate information of the vehicle-side system, publishes the vehicle toll fee update notice, records the toll station rate change log, and generates the updated toll standard notice; The traffic flow reorganization module monitors the impact of toll changes on vehicle route selection, records real-time vehicle driving trajectories, analyzes the redistribution process of traffic flow, quantifies changes in vehicle flow density, calculates the route selection ratio after vehicle reorganization, and generates dynamic traffic flow results based on the updated toll standard announcement.

[0007] As a further solution of the present invention, the step of acquiring the driving trajectory is specifically as follows: Based on the global positioning system, the vehicle's geographic location coordinates are collected in real time. The vehicle sends the location coordinate data to the data center every second to obtain the real-time geographic location coordinate data; Performing time serialization processing on the real-time geographic location coordinate data, marking each data point with a timestamp, and organizing the data in chronological order to generate time serialized geographic location coordinate data; The position of each time point is extracted from the time-series geographic location coordinate data, and the displacement between two adjacent time points is calculated using the formula:

[0008] Generate driving trajectory; in, The horizontal coordinate representing the geographical coordinates of the first time point, The ordinate represents the geographic coordinates of the first time point, The horizontal coordinate representing the geographical coordinates of the second time point, The ordinate represents the geographic coordinates of the second point in time, Represents the straight-line distance between two adjacent time points.

[0009] As a further solution of the present invention, the step of acquiring the real-time traffic map status is specifically as follows: Through sensors and communication technology, the vehicle collects the geographic location and speed data of itself and surrounding vehicles, determines the relative motion status between vehicles, including following or overtaking, and generates relative position and behavior data between vehicles; Using the relative position and behavior data between the vehicles, constructing nodes and edges of a graph, each vehicle as a node, and its relative motion behavior defines the direction and type of the edge, thereby generating the basic structure of the graph; Based on the basic structure of the graph, the properties of nodes and edges are updated in real time, including vehicle speed and vehicle spacing, using the formula:

[0010] Generate real-time traffic map status; in, Represents the updated node or edge features, represents the features before updating, Represents the monitored feature changes, Used to control the degree of fusion of previous data and new data.

[0011] As a further solution of the present invention, the step of obtaining the node influence score is specifically as follows: Based on the real-time traffic map state, feature data of multiple nodes are collected, including vehicle speed, driving direction and position, and a node feature matrix is ​​generated by analyzing the features, and the feature matrix is ​​called to extract node features to generate node feature data; Based on the real-time traffic map state, feature data of multiple nodes are collected, including vehicle speed, driving direction and position, and a node feature matrix is ​​generated by analyzing the features, and the feature matrix is ​​called to extract node features to generate node feature data; Using the node feature data, the driving mode is extracted, and the feature fit is calculated using the formula:

[0012] Generate node driving mode scores; in, Representative Node Driving mode rating, Is a node With Node The characteristic fit of Is a node The characteristic dimension of is the time factor, To avoid constants with zero denominators; According to the node driving mode scores, the interaction force between vehicles is calculated using the formula:

[0013] Generate vehicle-to-vehicle interaction force data; in, Represents the impact factor, indicating the node With Node The interaction between Representative Node Driving mode rating, Representative Node With Node The distance between them reflects the physical distance between the two nodes. is the proportionality coefficient of the interaction force, which is used to adjust the strength of the interaction force; The vehicle-to-vehicle interaction force data is used to analyze the impact of node connectivity on the network structure, and a node influence score is generated by calculating the connectivity score.

[0014] As a further solution of the present invention, the step of obtaining the optimized charging strategy is specifically as follows: Based on the node influence score, analyze the impact of vehicles on traffic flow, identify key node vehicles, set charging benchmarks for vehicles, and generate preliminary charging standard data; Using the preliminary charging standard data, the influence score difference between adjacent vehicles is calculated using the formula:

[0015] Generate impact differential data; in, and Represent the influence scores of adjacent vehicles, which are used to measure the influence of differentiated vehicles in the traffic network. is a small positive number used to avoid the denominator being zero when calculating the influence difference. Represents the difference in influence between adjacent vehicles and is used to evaluate the difference in influence between two vehicles; According to the influence difference data and the criticality of the vehicle's driving path, the charging standard is dynamically adjusted using the formula:

[0016] Generate an adjusted charging strategy; in, It is the basic charging standard, reflecting the general charging standard without reference to the target influence of individual vehicles. is an adjustment parameter used to adjust the charging standards according to the influence differences between vehicles. Represents the current fee schedule; In combination with the adjusted charging strategy, an optimized charging strategy is generated with reference to the criticality of traffic flow of multi-path selection.

[0017] As a further solution of the present invention, the steps for obtaining the updated charging standard notice are specifically as follows: Based on the optimized charging strategy, update the toll station parameters, collect the rate values ​​of the current toll equipment, compare the difference between the current rate and the current adjustment standard, and generate the current toll equipment rate value result; Using the current charging equipment rate numerical result, calculate the updated rate using the formula:

[0018] Generate updated rate results; in, represents the updated rate, Represents the base rate, Represents the current rate, represents the adjustment factor, represents the coefficient of additional factors; In combination with the updated rate result, the toll rate information of the vehicle-side system is set, information synchronization and verification operations are performed, and the toll rate information result of the vehicle-side system is generated; According to the toll rate information results of the vehicle-side system, a vehicle toll fee update notice is issued, a toll station rate change log is recorded, and an updated toll standard notice is generated.

[0019] As a further solution of the present invention, the steps of obtaining the traffic flow dynamic results are specifically as follows: Based on the updated toll standard notice, monitor the real-time impact of toll changes on vehicle route selection, record changes in route selection, and generate vehicle route selection data; Using the vehicle routing data, record the real-time driving trajectory of the vehicle, analyze the data and monitor the traffic flow on key sections to generate real-time driving trajectory results; Extract data from the real-time driving trajectory results and analyze the redistribution process of traffic flow using the formula:

[0020] Calculate and generate traffic flow redistribution indicators; in, Indicates The traffic volume of a path indicates the number of vehicles passing through the path. Representative The distance of a path indicates the length of the path. Represents the total number of vehicles on all paths during the monitoring period, used to normalize path data. represents the redistribution index, which is used to measure the utilization and mobility of multiple paths in the overall traffic flow; Combined with the traffic flow redistribution index, the change of vehicle flow density is quantified and the selection ratio of multiple paths is calculated. The dynamic analysis method is used to evaluate the changing trend of path selection and generate traffic flow dynamic results.

[0021] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through comprehensive collection and real-time analysis of vehicle driving data, the fine capture and dynamic update of traffic status are achieved, the interaction between vehicles is accurately evaluated, and key vehicles that affect traffic flow are timely identified. Based on this, the charging standards are dynamically set to ensure the reasonable allocation of road use costs, reduce congestion risks, and improve charging fairness and road use efficiency. Real-time synchronization of charging information makes vehicle decisions more timely, avoids path selection errors caused by information lag, further optimizes traffic flow distribution, improves traffic efficiency and effectively alleviates congestion problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart of the steps for obtaining the driving trajectory of the present invention; Figure 3 A flowchart of the steps for obtaining the real-time traffic map status of the present invention; Figure 4 A flowchart of the steps for obtaining the node influence score of the present invention; Figure 5 A flowchart of the steps for obtaining the charging strategy optimized by the present invention; Figure 6 A flowchart of the steps for obtaining the updated charging standard notice of the present invention; Figure 7 This is a flow chart of the steps for obtaining traffic flow dynamic results of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0024] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0025] See also Figure 1 , an automated highway toll collection management system comprising: The traffic status capture module collects vehicle driving data, records vehicle positions, updates them in time series, analyzes vehicle speed and direction, determines driving trajectories, uses the relative positions between vehicles to determine following and overtaking behaviors, constructs graph nodes and edges, continuously updates the features of graph nodes and edges, and generates real-time traffic graph status; The data flow analysis module quantitatively analyzes node characteristics based on the real-time traffic map status, extracts driving patterns, calculates the interaction force between vehicles, analyzes the impact of node connectivity on the network structure, and generates node influence scores; The charging logic decision module sets charging standards based on the node influence score, compares the influence differences between adjacent vehicles, incorporates key node vehicles into the charging strategy adjustment, determines the importance of driving paths and traffic flows, dynamically sets charging standards for multi-path vehicles, and generates optimized charging strategies; The rate adjustment and release module is based on optimizing the charging strategy, updating the parameters of the toll station, setting the rate values ​​of the toll equipment, synchronizing the toll rate information of the vehicle-side system, publishing the vehicle toll fee update notice, recording the toll station rate change log, and generating the updated toll standard notice; The traffic flow reorganization module is based on the updated toll standard announcement, monitors the impact of toll changes on vehicle path selection, records real-time vehicle driving trajectories, analyzes the redistribution process of traffic flow, quantifies changes in traffic density, calculates the path selection ratio after vehicle reorganization, and generates dynamic traffic flow results.

[0026] The real-time traffic map status specifically includes node characteristics and edge characteristics. The node influence score includes node feature quantification, driving mode extraction, vehicle-to-vehicle force calculation, and node connectivity analysis. The optimized charging strategy specifically includes charging differentiation, key node pricing, and multi-path pricing. The updated charging standard notification includes charging rate setting, toll rate synchronization, and rate change records. The dynamic results of traffic flow include changes in traffic density and vehicle path selection ratio.

[0027] See also Figure 2 , the specific steps for obtaining the driving trajectory are: The vehicle's geographic location coordinates are collected in real time based on the global positioning system. The vehicle sends the location coordinate data to the data center every second to obtain the real-time geographic location coordinate data; Based on the geographic location coordinates collected in real time by the global positioning system, the vehicle uploads its current location coordinates to the data center every second. The real-time data can be used to track the location and movement path of the vehicle. By marking the timestamp of each data point, the data points can be organized into a time series. This organization method not only ensures the continuity of the data, but also helps with subsequent location data processing and analysis. When performing route optimization and traffic flow analysis, time-series data can provide more information and insights. For example, by analyzing the vehicle's movement speed and dwell time, the scheduling of traffic signals can be optimized and traffic congestion can be reduced. The real-time geographic location coordinate data obtained provides a basis for subsequent analysis.

[0028] Perform time serialization processing on real-time geographic location coordinate data, mark each data point with a timestamp, and organize the data in chronological order to generate time serialized geographic location coordinate data; After the real-time geographic location coordinate data is time-serialized, each data point is marked with a timestamp and the data is arranged in chronological order. This method of organizing data can track the vehicle's driving path. By connecting the data points, a vehicle driving trajectory map is formed. This map not only helps to monitor the vehicle's current location, but also analyze its driving efficiency and route selection. In addition, time-serialized data can also be used to predict traffic patterns and optimize routes. By comparing data from different time periods, peak hours and traffic bottlenecks can be identified, and then the operation of traffic lights can be adjusted or the scheduling of public transportation can be optimized. The generated time-serialized geographic location coordinate data provides an estimate for building a more effective traffic management system.

[0029] Extract the position of each time point from the time-series geographic location coordinate data and calculate the displacement between two adjacent time points using the formula:

[0030] Generate driving trajectory; in, The horizontal coordinate representing the geographical coordinates of the first time point, The ordinate represents the geographic coordinates of the first time point, The horizontal coordinate representing the geographical coordinates of the second time point, The ordinate represents the geographic coordinates of the second point in time, Represents the straight-line distance between two adjacent time points.

[0031] formula:

[0032] The benefit of the formula is that by calculating the distance between two time points, the vehicle's moving speed and driving status can be monitored in real time, helping the traffic management center respond to predicted traffic accidents or congestion.

[0033] Detailed explanation of the formula and the process of formula calculation and derivation: Set the vehicle's geographic location at time t1 , t2 time point , calculate the distance between these two time points:

[0034] The results show that the vehicle moved about 42.43 meters between the two time points. If the time interval is 1 second, the vehicle's speed can be calculated to monitor whether its driving condition is normal. The calculation results provide basic data for subsequent traffic behavior analysis and traffic flow prediction.

[0035] See also Figure 3 ,The specific steps for obtaining the real-time traffic map status are: Through sensors and communication technology, the vehicle collects the geographic location and speed data of itself and surrounding vehicles, determines the relative motion status between vehicles, including following or overtaking, and generates relative position and behavior data between vehicles; Through the sensors and communication technology installed on the vehicle, the position and speed information of the vehicle and its surrounding vehicles are collected in real time. Based on this data, data processing and analysis methods are used to determine the relative motion state between vehicles, including following and overtaking behaviors. This process includes obtaining raw data from sensors, identifying vehicle behavior patterns through algorithms, and classifying and storing the behavior data to provide input data for subsequent traffic map construction and generate relative position and behavior data between vehicles.

[0036] Use the relative position and behavior data between vehicles to construct the nodes and edges of the graph. Each vehicle is a node, and its relative motion behavior defines the direction and type of the edge, generating the basic structure of the graph. The nodes and edges of the traffic flow graph are constructed by using the vehicle behavior data obtained from the sensors and the real-time location information of the vehicles. Each vehicle represents a node, and its relative behavior with other vehicles, including following or overtaking, defines the direction and type of the edge. This process includes not only the collection and analysis of data, but also the implementation of a graph construction algorithm based on vehicle behavior patterns, so as to dynamically describe the traffic conditions on the road and generate the basic structure of the traffic graph.

[0037] Based on the basic structure of the graph, the properties of nodes and edges are updated in real time, including vehicle speed and vehicle spacing, using the formula:

[0038] Generate real-time traffic map status; in, Represents the updated node or edge features, represents the features before updating, Represents the monitored feature changes, Used to control the degree of fusion of previous data and new data.

[0039] formula:

[0040] The benefit of the formula is that it provides a method to dynamically update the node and edge features of the traffic graph, making the traffic status more real-time and accurate. , the influence of previous data and new observation data can be balanced to optimize the prediction and management of traffic flow.

[0041] Detailed explanation of the formula and the process of formula calculation and derivation: Set the original speed characteristic value of a vehicle in a data update cycle The observed speed change is 30 km / h. is 5km / h, smoothing factor Set to 0.2, representing the weight of the new data. According to the formula, the current speed characteristic value The calculation is as follows:

[0042] The results show that referring to the combination of previous data and new observation data, the vehicle update speed is 25km / h, which helps to adjust the status of vehicles in the traffic map in real time, thereby providing more accurate traffic flow information.

[0043] See also Figure 4 , the specific steps for obtaining the node influence score are: Based on the real-time traffic map status, the feature data of multiple nodes are collected, including vehicle speed, driving direction and position. By analyzing the features, a node feature matrix is ​​generated, and the feature matrix is ​​called to extract node features and generate node feature data; Based on the real-time traffic map status, the collection of node feature data involves real-time monitoring of vehicle speed, driving direction and position. The data is continuously captured by the sensors of the traffic monitoring system and transmitted to the data processing center in real time through the wireless network. The data processing center uses high-performance servers to analyze and process the collected data, and uses data mining technology to identify driving patterns and behavioral anomalies. The feature matrix of each node is composed of speed vector, direction angle and position coordinates. The features are merged through a data fusion algorithm to ensure the integrity and accuracy of the information. The system dynamically adjusts and optimizes the traffic flow prediction model by comparing previous data with real-time data. The generated node feature data is used for traffic management and control decision support.

[0044] Use node feature data to extract driving modes and calculate feature fit using the formula:

[0045] Generate node driving mode scores; in, Representative Node Driving mode rating, Is a node With Node The characteristic fit of Is a node The characteristic dimension of is the time factor, To avoid constants with zero denominators; formula:

[0046] Detailed explanation of the formula and the process of formula calculation and derivation: This formula is used to evaluate the vehicle Driving mode rating , based on other vehicles and vehicles The degree of fit and time factor , normalized by the feature dimension and avoid divide by zero errors .

[0047] :vehicle With vehicle The characteristic fit of is obtained by calculating the cosine fit of the characteristic vectors of the two vehicles. The speed of vehicle is 60 km / h and the direction is north, while the speed of vehicle j is 62 km / h and the direction is also north. Close to 1, indicating high homogeneity.

[0048] : Time factor, reflecting the vehicle The closeness of the data time point to the score calculation time is calculated by the inverse of the time difference. If the data is 1 hour away from now, is 1, if the data is 2 hours away from now, is 0.5.

[0049] :vehicle The characteristic dimensions of the vehicle include speed, direction and behavior. There are three characteristics, .

[0050] : A small constant of 0.01 used to ensure that the denominator is not zero.

[0051] Set up with two cars =1 and =2 and vehicle By comparison, the following parameter values ​​are obtained: (Same height as vehicle 1) (Moderately similar to vehicle 2) (Data from 1 hour ago) (Data from 2 hours ago) (vehicle There are three characteristic dimensions) (Small constant to avoid division by zero) Formula calculation process:

[0052] The results show that the vehicle The driving pattern score of the vehicle is 0.424, which reflects the weighted fit of other vehicles with the most similar characteristics in time and behavior. This score helps determine the vehicle Behavioral patterns in traffic networks are the basis for iterative analysis of vehicle interactions and mobility.

[0053] According to the node driving mode score, the interaction force between vehicles is calculated using the formula:

[0054] Generate vehicle-to-vehicle interaction force data; in, Represents the impact factor, indicating the node With Node The interaction between Representative Node Driving mode rating, Representative Node With Node The distance between them reflects the physical distance between the two nodes. is the proportionality coefficient of the interaction force, which is used to adjust the strength of the interaction force; formula:

[0055] Detailed explanation of the formula and the process of formula calculation and derivation: This formula is used to calculate the vehicle With vehicle The interaction force between , which is a key indicator that reflects the degree of mutual influence between the behaviors of two vehicles. The parameters in the formula are defined as follows: and :vehicle and vehicles The driving mode scores reflect the behavior characteristics of the vehicle. The above steps have calculated ,set up is 0.5.

[0056] :Influence factor, characterizing vehicle and The strength of the interaction between the two vehicles. If the two vehicles often travel on the same road, the value is higher and is set to 0.8.

[0057] :vehicle and The actual distance between the two vehicles in meters. Set the distance between the two vehicles to 100 meters.

[0058] : Proportional coefficient, determined according to vehicle density and traffic flow, is set to 0.05. This value is obtained based on real-time traffic conditions and data analysis.

[0059] The formula calculation process assumes the following numbers: , , , , (rice) Calculation process:

[0060] The results show that the vehicle With vehicle The interaction force between them is 0.000848, which is a small value, indicating that although the two vehicles are similar and relatively close, the interaction force is not large because of the high traffic mobility or similar vehicle speeds.

[0061] The interaction force data between vehicles is used to analyze the impact of node connectivity on the network structure, and the node influence score is generated by calculating the connectivity score.

[0062] The vehicle-to-vehicle interaction force data is used to analyze the connectivity between nodes. This process involves comparing the actual monitored vehicle behavior with the predictions in the traffic model, and calculating the influence score of each node in the traffic network. This score refers to the vehicle's geographical location, speed, and the interaction force with surrounding vehicles. By establishing a mathematical model to calculate the connectivity of multiple nodes, it iteratively analyzes how these connectivity affects the fluidity and stability of the entire network. The model will output the influence score of each node. These scores assist the traffic management system to identify key sections and traffic congestion points, optimize the allocation of traffic signals and route planning, and the results are directly used to adjust traffic lights and issue traffic warnings to improve road use efficiency and reduce congestion.

[0063] See also Figure 5 , the specific steps for obtaining the optimized charging strategy are: Based on the node influence score, analyze the impact of vehicles on traffic flow, identify key node vehicles, set charging benchmarks for vehicles, and generate preliminary charging standard data; By analyzing the vehicle influence scores, we can determine which vehicles belong to key nodes. Vehicles at key nodes tend to have greater influence, and their impact on traffic flow is particularly significant. Therefore, it is crucial to refer to this factor when setting charging standards. By collecting influence data, higher charging standards can be set for vehicles with greater influence. This can more fairly distribute road use costs, while encouraging drivers to choose more reasonable driving times and routes, thereby optimizing traffic fluidity and reducing congestion.

[0064] Using the preliminary charging standard data, the difference in influence scores between adjacent vehicles is calculated using the formula:

[0065] Generate impact differential data; in, and Represent the influence scores of adjacent vehicles, which are used to measure the influence of differentiated vehicles in the traffic network. is a small positive number used to avoid the denominator being zero when calculating the influence difference. Represents the difference in influence between adjacent vehicles and is used to evaluate the difference in influence between two vehicles; formula:

[0066] The benefit of the formula is that it quantifies the difference in influence between the two vehicles and provides an actual numerical difference, which facilitates the adjustment of charging strategies, which is crucial when referring to the fairness of charging strategies and the effectiveness of traffic management.

[0067] Detailed explanation of the formula and the process of formula calculation and derivation: set up , , which represents the influence score of the two cars, where This ensures that the calculation can be performed stably even when the difference in influence scores is very small. The calculation process is as follows:

[0068]

[0069]

[0070]

[0071] The results show that the difference in influence scores between the two vehicles is 10.05, and this difference will be used to adjust subsequent charging standards to ensure that vehicles with greater influence bear more traffic costs, thereby incentivizing drivers to avoid peak hours or choose alternative routes, thereby helping to ease traffic pressure on key roads.

[0072] The charging standards are adjusted dynamically based on the influence difference data and the criticality of the vehicle's driving path, using the formula:

[0073] Generate an adjusted charging strategy; in, It is the basic charging standard, reflecting the general charging standard without reference to the target influence of individual vehicles. is an adjustment parameter used to adjust the charging standards according to the influence differences between vehicles. Represents the current fee schedule; formula:

[0074] The benefit of the formula is that by charging a basic Add dependency on influence difference Adjustment items can be used to flexibly adjust the charging strategy to match the actual influence of differentiated vehicles, so as to more fairly distribute road use costs, and encourage drivers to optimize their driving routes and times through economic means to alleviate traffic congestion.

[0075] Detailed explanation of the formula and the process of formula calculation and derivation: set up The unit is the basic charging standard, obtained from the above results , set the adjustment factor , the calculation process is as follows:

[0076]

[0077]

[0078]

[0079]

[0080] The results show that after referring to the influence differences between adjacent vehicles, the current charging standard should be adjusted to 50.52 units, which reflects the principle of fair charging based on the actual influence of vehicles, and through this differentiated charging, drivers are encouraged to choose driving times and routes that have less impact on traffic flow, with the aim of optimizing traffic flow and reducing congestion.

[0081] Combined with the adjusted charging strategy, the optimized charging strategy is generated with reference to the criticality of traffic flow in multi-path selection.

[0082] Based on the criticality of traffic flow of multiple driving paths, combined with the adjusted charging strategy, the overall charging strategy is iteratively optimized to ensure that the charging standards can dynamically reflect various driving conditions and traffic loads. This optimization refers to multiple variables, including the vehicle's driving path, traffic density and time period. These factors are integrated through the algorithm model to generate an optimized charging strategy that aims to regulate traffic flow through economic means to make it smoother and reduce traffic congestion during peak hours. The optimization strategy is supported by real-time traffic data to achieve the best charging effect under differentiated traffic conditions and improve the efficiency of road resource utilization. The implementation of this strategy can significantly improve the adaptability and predictability of traffic management, making the transportation system more efficient and sustainable.

[0083] See also Figure 6 The specific steps for obtaining the updated fee standard notice are as follows: Based on the optimized charging strategy, update the toll station parameters, collect the rate values ​​of the current toll equipment, compare the difference between the current rate and the current adjustment standard, and generate the current toll equipment rate value results; When updating the toll station parameters, the current equipment rate values ​​are collected first. The data collection work is completed by accessing the management system of each toll station. The system records the settings and adjustment records of multiple devices. By comparing the data with the current policy-oriented standards, it is determined which equipment needs to adjust the rate values ​​to comply with the current toll policy. This comparison process involves data extraction, analysis and comparison, which ensures the accuracy and timeliness of rate adjustments and generates the current toll equipment rate value results.

[0084] Using the current charging equipment rate numerical results, calculate the updated rate using the formula:

[0085] Generate updated rate results; in, represents the updated rate, Represents the base rate, Represents the current rate, represents the adjustment factor, represents the coefficient of additional factors; formula:

[0086] The formula is useful in that it introduces an adjustment factor and the additional factor coefficient , dynamically adjust rates based on market and policy changes, and improve the adaptability and flexibility of the charging model.

[0087] Detailed explanation of the formula and the process of formula calculation and derivation: assumed (The basic rate is set at 1 yuan per kilometer), (Current rate has been increased by 20%) (Adjustment coefficient, indicating the adjustment range is 50% of the difference between the base rate and the current rate), (Additional factor coefficient, indicating an additional increase of 5%).

[0088] The calculation process is as follows: 1. Calculate the difference adjustment:

[0089] 2. Apply the adjustment factor:

[0090] 3. Calculate the new base rate:

[0091] 4. Apply additional factor coefficients:

[0092] Updated rates Yuan.

[0093] The results show that, based on current market conditions and policy requirements, the current toll rate has been adjusted to RMB 1.155 per kilometer, ensuring the fairness of the rate and consistency of the policy.

[0094] Combined with the updated rate results, the toll rate information of the vehicle-side system is set, information synchronization and verification operations are performed, and the toll rate information results of the vehicle-side system are generated; When setting the toll rate information of the vehicle-side system, the operator needs to extract the relevant values ​​from the updated rate data, input the values ​​through vehicle management, and perform format verification and logic checks on these data to avoid data errors or inconsistencies, and ensure that the current rate can be accurately synchronized to each passing vehicle, including data uploading, processing and distribution. The operator is required to monitor the data transmission process, handle any synchronization errors in a timely manner, and generate the toll rate information results of the vehicle-side system. The data will be used to calculate the actual toll fees of the vehicle.

[0095] According to the toll rate information results of the vehicle-side system, a vehicle toll fee update notice is issued, a toll station rate change log is recorded, and an updated toll standard notice is generated.

[0096] The work of issuing vehicle toll update notices and recording toll rate change logs at toll booths involves extracting the current rates from the vehicle-side system toll rate information and publishing the change information to the public and related departments through the webcast system. At the same time, the operator needs to update the log in the internal management system to record the details of each rate change, including the time of change, the equipment involved, the rates before and after the change, and the reasons for the change and the expected impact. These records are crucial for subsequent data review, policy evaluation and compliance checks, ensuring the transparency and traceability of the charging policy and generating updated charging standard notices.

[0097] See also Figure 7 ,The specific steps for obtaining traffic flow dynamic results are: Based on the updated toll notices, monitor the real-time impact of toll changes on vehicle route selection, record changes in route selection, and generate vehicle route selection data; Based on the updated toll standard announcement, the changes in vehicle choices on key sections are recorded, data is captured through the real-time traffic monitoring system, and GPS tracking technology is used to determine the actual driving path of the vehicle. It is compared with the data before the announcement is issued, and the differences in vehicle route preferences before and after the toll changes are analyzed. The sensitivity of vehicles to differentiated toll sections is displayed in the form of charts and percentages. This real-time monitoring not only assists urban traffic management departments in evaluating the actual impact of toll strategies, but also provides data support for adjusting future toll policies.

[0098] Using vehicle routing data, record the real-time driving trajectory of vehicles, analyze data and monitor the traffic flow on key sections to generate real-time driving trajectory results; Based on vehicle path selection data, a trajectory recording program is implemented, and cameras and sensors are deployed at multiple key traffic nodes to collect vehicle traffic data. Big data analysis technology is used to process the collected information, and high-frequency paths and traffic congestion points are identified through algorithm models. The analysis model will predict traffic flow changes based on time series, thereby providing a scientific basis for traffic scheduling and effectively predicting and managing urban traffic flows.

[0099] Extract data from the real-time driving trajectory results and analyze the redistribution process of traffic flow using the formula:

[0100] Calculate and generate traffic flow redistribution indicators; in, Indicates The traffic volume of a path indicates the number of vehicles passing through the path. Representative The distance of a path indicates the length of the path. Represents the total number of vehicles on all paths during the monitoring period, used to normalize path data. represents the redistribution index, which is used to measure the utilization and mobility of multiple paths in the overall traffic flow; formula:

[0101] The benefit of the formula is that it provides an indicator to quantify the redistribution of traffic flow and can calculate the impact of changes in traffic flow on route selection. Detailed explanation of the formula and the process of formula calculation and derivation: There are three path branches on a key road, and the traffic flow is , , , and the corresponding path distances are , , , total traffic volume , the calculation process is as follows:

[0102] The results show that on average each vehicle travels nearly 2 kilometers, which reflects that after the toll change, vehicles tend to choose shorter routes to reduce costs.

[0103] Combined with the traffic flow redistribution index, the changes in vehicle flow density are quantified and the selection ratio of multiple paths is calculated. The dynamic analysis method is used to evaluate the changing trend of path selection and generate traffic flow dynamic results.

[0104] A dynamic analysis method is used to evaluate the changing trend of route selection, record the choice of each vehicle and the corresponding time point, and input the data into a computer simulation program for processing. By setting differentiated traffic volume and time parameters, the traffic distribution under differentiated scenarios is simulated, and the routes that vehicles prefer under differentiated charging standards are calculated. A report is generated, which includes the impact of various charging changes on traffic density and route selection ratio, providing data for urban transportation planning and charging policy adjustments.

[0105] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An automated highway toll collection management system, characterized in that: The system comprises: The traffic status capture module collects vehicle driving data, records vehicle positions, updates them in time series, analyzes vehicle speed and direction, determines driving trajectories, uses the relative positions between vehicles to determine following and overtaking behaviors, constructs graph nodes and edges, continuously updates the features of graph nodes and edges, and generates real-time traffic graph status; The data flow analysis module quantitatively analyzes node characteristics, extracts driving patterns, calculates the interaction forces between vehicles, analyzes the impact of inter-node connectivity on the network structure, and generates node influence scores based on the real-time traffic map status; The charging logic decision module sets charging standards for vehicle influence values ​​based on the node influence scores, compares the influence differences between adjacent vehicles, incorporates key node vehicles into charging strategy adjustments, determines the importance of driving paths and traffic flows, dynamically sets charging standards for multi-path vehicles, and generates optimized charging strategies; The rate adjustment and publishing module updates the parameters of the toll station based on the optimized charging strategy, sets the rate value of the toll equipment, synchronizes the toll rate information of the vehicle-side system, publishes the vehicle toll fee update notice, records the toll station rate change log, and generates the updated toll standard notice; The traffic flow reorganization module monitors the impact of toll changes on vehicle route selection, records real-time vehicle driving trajectories, analyzes the redistribution process of traffic flow, quantifies changes in vehicle flow density, calculates the route selection ratio after vehicle reorganization, and generates dynamic traffic flow results based on the updated toll standard announcement.

2. The automated highway toll collection management system according to claim 1, characterized in that: The real-time traffic map status specifically includes node features and edge features. The node influence score includes node feature quantification, driving mode extraction, vehicle-to-vehicle force calculation, and node connectivity analysis. The optimized charging strategy specifically includes charging differentiation, key node pricing, and multi-path pricing. The updated charging standard notification includes charging rate setting, toll rate synchronization, and rate change records. The traffic flow dynamic results include changes in traffic density and vehicle path selection ratio.

3. The automated highway toll collection management system according to claim 2, characterized in that: The steps for obtaining the driving trajectory are specifically as follows: The vehicle's geographic location coordinates are collected in real time based on the global positioning system. The vehicle sends the location coordinate data to the data center every second to obtain the real-time geographic location coordinate data; Performing time serialization processing on the real-time geographic location coordinate data, marking each data point with a timestamp, and organizing the data in chronological order to generate time serialized geographic location coordinate data; The position of each time point is extracted from the time-series geographic location coordinate data, and the displacement between two adjacent time points is calculated using the formula: ； Generate driving trajectory; in, The horizontal coordinate representing the geographical coordinates of the first time point, The ordinate represents the geographic coordinates of the first point in time, The horizontal coordinate representing the geographical coordinates of the second time point, The ordinate represents the geographic coordinates of the second point in time, Represents the straight-line distance between two adjacent time points.

4. The automated highway toll collection management system according to claim 3, characterized in that: The steps for obtaining the real-time traffic map status are specifically as follows: Through sensors and communication technology, the vehicle collects the geographic location and speed data of itself and surrounding vehicles, determines the relative motion status between vehicles, including following or overtaking, and generates relative position and behavior data between vehicles; Using the relative position and behavior data between the vehicles, constructing nodes and edges of a graph, each vehicle as a node, and its relative motion behavior defines the direction and type of the edge, thereby generating the basic structure of the graph; Based on the basic structure of the graph, the properties of nodes and edges are updated in real time, including vehicle speed and vehicle spacing, using the formula: ； Generate real-time traffic map status; in, Represents the updated node or edge features, represents the features before updating, Represents the monitored feature changes, Used to control the degree of fusion of previous data and new data.

5. The automated highway toll collection management system according to claim 4, characterized in that: The steps for obtaining the node influence score are specifically as follows: Based on the real-time traffic map state, feature data of multiple nodes are collected, including vehicle speed, driving direction and position, and a node feature matrix is ​​generated by analyzing the features, and the feature matrix is ​​called to extract node features to generate node feature data; Using the node feature data, the driving mode is extracted, and the feature fit is calculated using the formula: ； Generate node driving mode scores; in, Representative Node Driving mode rating, Is a node With Node The characteristic fit of Is a node The characteristic dimension of is the time factor, To avoid constants with zero denominators; According to the node driving mode scores, the interaction force between vehicles is calculated using the formula: ； Generate vehicle-to-vehicle interaction force data; in, Represents the impact factor, indicating the node With Node The interaction between Representative Node Driving mode rating, Representative Node With Node The distance between them reflects the physical distance between the two nodes. is the proportionality coefficient of the interaction force, which is used to adjust the strength of the interaction force; The vehicle-to-vehicle interaction force data is used to analyze the impact of node-to-node connectivity on the network structure, and a node influence score is generated by calculating the connectivity score.

6. The automated highway toll collection management system according to claim 5, characterized in that: The steps for obtaining the optimized charging strategy are specifically as follows: Based on the node influence score, analyze the impact of vehicles on traffic flow, identify key node vehicles, set charging benchmarks for vehicles, and generate preliminary charging standard data; Using the preliminary charging standard data, the influence score difference between adjacent vehicles is calculated using the formula: ； Generate impact differential data; in, and Represent the influence scores of adjacent vehicles, which are used to measure the influence of differentiated vehicles in the traffic network. is a small positive number used to avoid the denominator being zero when calculating the influence difference. Represents the difference in influence between adjacent vehicles and is used to evaluate the difference in influence between two vehicles; According to the influence difference data and the criticality of the vehicle's driving path, the charging standard is dynamically adjusted using the formula: ； Generate an adjusted charging strategy; in, It is the basic charging standard, reflecting the general charging standard without reference to the target impact of individual vehicles. is an adjustment parameter used to adjust the charging standards according to the influence differences between vehicles. Represents the current fee schedule; In combination with the adjusted charging strategy, an optimized charging strategy is generated with reference to the criticality of traffic flow of multi-path selection.

7. The automated highway toll collection management system according to claim 6, characterized in that: The specific steps for obtaining the updated charging standard notice are as follows: Based on the optimized charging strategy, update the toll station parameters, collect the rate values ​​of the current toll equipment, compare the difference between the current rate and the current adjustment standard, and generate the current toll equipment rate value result; Using the current charging equipment rate numerical result, calculate the updated rate using the formula: ； Generate updated rate results; in, represents the updated rate, Represents the base rate, Represents the current rate, represents the adjustment factor, represents the coefficient of additional factors; In combination with the updated rate result, the toll rate information of the vehicle-side system is set, information synchronization and verification operations are performed, and the toll rate information result of the vehicle-side system is generated; According to the toll rate information results of the vehicle-side system, a vehicle toll fee update notice is issued, a toll station rate change log is recorded, and an updated toll standard notice is generated.

8. The automated highway toll collection management system according to claim 7, characterized in that: The steps for obtaining the traffic flow dynamic results are specifically as follows: Based on the updated toll standard notice, monitor the real-time impact of toll changes on vehicle route selection, record changes in route selection, and generate vehicle route selection data; Using the vehicle routing data, record the real-time driving trajectory of the vehicle, analyze the data and monitor the traffic flow on key sections to generate real-time driving trajectory results; Extract data from the real-time driving trajectory results and analyze the redistribution process of traffic flow using the formula: ； Calculate and generate traffic flow redistribution indicators; in, Indicates The traffic volume of a path indicates the number of vehicles passing through the path. Representative The distance of a path indicates the length of the path. Represents the total number of vehicles on all paths during the monitoring period, used to normalize path data. represents the redistribution index, which is used to measure the utilization and mobility of multiple paths in the overall traffic flow; Combined with the traffic flow redistribution index, the change of vehicle flow density is quantified and the selection ratio of multiple paths is calculated. The dynamic analysis method is used to evaluate the changing trend of path selection and generate traffic flow dynamic results.

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