A method and system for identifying and managing the impact of vehicles on a bridge deck

Abstract

The application provides a bridge vehicle influence identification and management method and system, which comprises the following steps: monitoring the full-section information of the bridge deck, performing target relay tracking on the vehicles entering the bridge deck, and assigning a unique identification (UID) for the whole journey; based on the real-time traffic flow data of the bridge deck, a dynamic traffic flow multi-dimensional baseline reflecting the current traffic state is dynamically generated; based on the deviation of the vehicle behavior from the multi-dimensional baseline, a bridge vehicle traffic safety disturbance influence domain is identified, and the disturbance source is marked; within the bridge vehicle traffic safety disturbance influence domain, a safety influence disturbance conduction chain is identified based on a preset causal rule, and a critical blocking node on the safety influence disturbance conduction chain is identified based on node conduction sensitivity; and hierarchical management is performed on the identified critical blocking node.

Description

Technical Field

[0001] This invention relates to the field of intelligent traffic management, specifically to a method and system for identifying and controlling the impact of vehicles on bridge surfaces. Background Technology

[0002] As a key node in the road traffic network, the safety of bridge traffic is of paramount importance. Current technologies for identifying and controlling the impact of vehicles entering bridges primarily rely on weighing systems to identify overloaded vehicles, video surveillance equipment to identify vehicle types and traffic violations, or monitoring equipment to assess operational safety based on weather conditions.

[0003] The existing technical solutions have the following main shortcomings: (1) Existing technical solutions mostly capture and punish illegal acts after they have occurred, but do not classify the dynamic impact of traffic accidents and other events before the entire process of a vehicle entering the bridge, making it difficult to achieve advance intervention of risks. (2) The existing technical solutions adopt a one-size-fits-all, crude control method such as speed limits and road closures, which is very likely to cause secondary congestion and secondary accidents.

[0004] Therefore, how to achieve dynamic and accurate identification of the impact of vehicles on the bridge and take efficient and low-interference control measures is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] In view of this, this application provides a method and system for identifying and controlling the impact of vehicles on bridge surfaces, so as to at least solve the above-mentioned technical problems.

[0006] To achieve the above objectives, the technical solution of this application is implemented in the following manner.

[0007] According to one aspect of this application, a method for identifying and controlling the impact of vehicles on a bridge deck is provided, comprising the following steps: monitoring the full cross-sectional information of the bridge deck, performing target relay tracking of vehicles entering the bridge deck, and assigning a unique identifier (UID) throughout the process; dynamically generating a multi-dimensional baseline for dynamic traffic flow on the bridge deck reflecting the current traffic state based on real-time traffic flow data on the bridge deck; identifying the impact domain of safety disturbances on bridge deck traffic based on the degree of deviation between vehicle behavior and the multi-dimensional baseline, and marking the disturbance source; within the impact domain of safety disturbances on bridge deck traffic, identifying the transmission chain of safety impact disturbances based on preset causal rules, and identifying critical blocking nodes on the transmission chain of safety impact disturbances based on node transmission sensitivity; and performing hierarchical control on the identified critical blocking nodes.

[0008] In the above scheme, the step of monitoring the full cross-sectional information of the bridge deck further includes: deploying multiple sets of sensing units along the longitudinal direction of the bridge, with overlap between the longitudinal coverage areas of adjacent sensing units; assigning temporary identifiers to entering vehicles within a single sensing unit and recording their vehicle and driving status information; and when a vehicle enters the overlapping coverage area of ​​adjacent sensing units, assigning the unique identifier UID if the vehicle license plate matches; if the vehicle license plate cannot be identified, matching is performed based on the target matching degree, and assigning the unique identifier UID to the same vehicle when the matching degree exceeds a threshold.

[0009] In the above scheme, the target matching degree is: , Where, Δ x The longitudinal coordinate deviation of the target vehicle monitored by two adjacent sensing units at the same time; Δ v The speed deviation of the target vehicle monitored by two adjacent sensing units at the same time; Δ S The deviation of the target vehicle body contour area monitored by two adjacent sensing units at the same time. λ 1. λ 2. λ 3 represents the weighting coefficients, and their sum is 1; x max The longitudinal length of the overlapping area; v l This is the speed limit for the bridge surface; S r The product of the length and width of the target vehicle's body outline.

[0010] In the above scheme, the multi-dimensional baseline for dynamic traffic flow on the bridge includes a speed baseline, a following baseline, a driving stability baseline, and a lane coupling baseline. The step of dynamically generating the multi-dimensional baseline for dynamic traffic flow on the bridge reflecting the current traffic state further includes: dynamically adjusting the length of the sliding time window based on the degree of vehicle fluctuation in the bridge traffic flow; using an improved interquartile range method to remove abnormal data within the sliding time window; and generating the multi-dimensional baseline for dynamic traffic flow on the bridge and its confidence interval based on the stable data after removing outliers.

[0011] In the above scheme, the step of identifying the impact domain of traffic safety disturbance on the bridge surface further includes: calculating the initial disturbance intensity based on the vehicle's measured speed, acceleration, maximum speed difference with vehicles in adjacent lanes, and lateral offset; marking the current vehicle as the disturbance source when the initial disturbance intensity exceeds a preset threshold; dynamically calculating the longitudinal and lateral impact domains of the traffic safety disturbance based on the current speed baseline and following baseline, wherein the backward coverage length of the longitudinal impact domain is calculated based on the driver's standard reaction time and the bridge surface adhesion coefficient; and initiating high-frequency tracking and binding a unique identifier (UID) to all vehicles within the disturbance impact domain.

[0012] In the above scheme, the step of identifying the safety impact disturbance transmission chain further includes: determining the impact transmission relationship based on causal rules, including vehicles that simultaneously satisfy all rules into the disturbance transmission chain, and marking them as transmission response nodes; drawing the longitudinal transmission chain and lateral diffusion chain of the disturbance in real time; and for all transmission response nodes on the disturbance transmission chain, judging their control capability over the entire subsequent transmission chain based on the node transmission sensitivity, and including nodes with a node transmission sensitivity greater than a preset threshold into the blocking node set. The causal rules include: behavioral response rules: the speed baseline, following baseline, driving stability baseline, and lane coupling baseline of the responding vehicle exceed the normal disturbance range; temporal causal rules: the behavioral change of the responding vehicle occurs after the behavioral change of the disturbance source; spatial association rules: the responding vehicle is located within the disturbance influence domain of the disturbance source.

[0013] In the above scheme, the step of identifying the critical blocking node on the security impact disturbance transmission chain based on node conduction sensitivity further includes: calculating the node conduction sensitivity of each response node on the disturbance transmission chain. S n : , in, For the first n The total number of nodes transmitting the response behind each node; This represents the total number of response nodes in the propagation chain; For the first n The measured headway between each node and the vehicle in front; , For actual measurement of vehicle speed and acceleration; This is the midpoint of the acceleration / deceleration interval; The maximum speed difference between the average speed of the lane and that of the adjacent lane; This represents the average speed difference between adjacent lanes. This represents the lateral offset of the vehicle. Lane width; The standard deviation of speed for each lane; The standard deviation of the speed difference between adjacent lanes; The standard deviation of lane acceleration; Transmitting node sensitivity S n Nodes exceeding a preset threshold are identified as critical blocking nodes.

[0014] In the above scheme, the step of implementing hierarchical control over the identified critical blocking nodes further includes: statistically analyzing the distribution of historical node transmission sensitivity, setting a first constant and a second constant, and classifying critical blocking nodes based on the distribution of historical node transmission sensitivity; implementing the highest level of control for vehicles with the most serious impact on traffic safety, including forcibly guiding vehicles with the highest transmission sensitivity of such blocking nodes to decelerate smoothly, temporarily closing entrance channels in the upstream entrance section of the disturbance transmission chain, or issuing unified deceleration warnings across the entire bridge surface; implementing secondary control for vehicles with the second most serious impact on traffic safety, including sending voice and text warnings to the disturbance source and response nodes on the transmission chain, and sending individual early deceleration warnings to vehicles with the highest transmission sensitivity of such blocking nodes; and implementing the lowest level of control for vehicles with less serious impact on traffic safety, including issuing precise driving prompts through variable message signs.

[0015] In the above scheme, the rules for determining the lateral influence zone include: for bridge decks with central physical barriers, the zone covers one adjacent lane in the same direction; for bridge decks with four or fewer lanes in both directions without central physical barriers, the zone covers two adjacent lanes on the left and right; for bridge decks with six or more lanes in both directions without central physical barriers, the zone covers two adjacent lanes on the left and right and two lanes on the left and right next to each other.

[0016] According to another aspect of this application, a system for identifying and controlling the impact of vehicles on a bridge deck is provided, comprising: a bridge deck information monitoring module for monitoring the full cross-sectional information of the bridge deck, performing target relay tracking of vehicles entering the bridge deck, and assigning a unique identifier (UID) throughout the process; a multi-dimensional baseline construction module for dynamically generating a multi-dimensional baseline for dynamic traffic flow on the bridge deck based on real-time traffic flow data; a safety disturbance identification module for identifying the safety disturbance impact domain of bridge deck vehicles and marking the disturbance source based on the degree of deviation of vehicle behavior from the multi-dimensional baseline; a blocking node identification module for identifying the safety impact disturbance transmission chain within the disturbance impact domain based on a preset causal rule, and identifying critical blocking nodes on the transmission chain based on node transmission sensitivity; and a hierarchical control module for performing hierarchical control on the identified critical blocking nodes.

[0017] The method and system for identifying and controlling the impact of vehicles on the bridge deck proposed in this application can prevent the transmission of risks in advance by intervening in the control from the initial generation stage of disturbance; by performing precise control on only the minimum range of critical nodes, it does not interfere with normal traffic vehicles, thereby improving the safety control effect while reducing the impact on traffic efficiency.

[0018] Specifically, compared with the prior art, the present invention: (1) It solves the technical problem that existing technologies only focus on the static behavior assessment of a single vehicle, ignoring the core accident causes of the one-way chain transmission and step-by-step amplification of traffic disturbances, making it difficult to achieve the prevention and control of chain accidents in advance; (2) It solves the technical problem that the existing technology adopts extensive control methods such as speed limits and road closures, which cannot accurately locate the core nodes of risk blocking, resulting in low control efficiency and easy to cause secondary congestion and secondary accidents. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the method for identifying and controlling the impact of vehicles on the bridge deck according to an embodiment of this application. Figure 2 This is a schematic diagram of the structural composition of the bridge surface vehicle impact identification and control system according to an embodiment of this application; Figure 3 This is a schematic diagram of the structural composition of the bridge surface vehicle impact identification and control equipment according to an embodiment of this application. Detailed Implementation

[0020] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] The specific technical features described in the various embodiments in the detailed implementation can be combined in various ways without contradiction. For example, different implementation methods can be formed by combining different specific technical features. In order to avoid unnecessary repetition, the various possible combinations of the specific technical features in this application will not be described separately.

[0022] The core application scenario of the bridge deck vehicle impact identification and control method and system in this application is linear, continuously flowing bridge sections, specifically including urban expressway viaducts, long-span highway bridges crossing rivers / lines, and expressway interchange mainline bridges. Vehicles entering the bridge deck are identified and classified based on their safety impact, and tiered control is implemented to improve traffic safety.

[0023] Example 1 This embodiment provides a method for identifying and controlling the impact of vehicles on bridge decks, such as... Figure 1 As shown, the method includes the following steps.

[0024] Step S1: Monitor the full cross-sectional information of the bridge deck, perform target relay tracking on vehicles entering the bridge deck, and assign a unique identifier (UID) throughout the process.

[0025] This step utilizes the linear continuity and non-branching characteristics of bridges to achieve unique identification and relay tracking monitoring of vehicles entering the bridge deck, thereby enabling full-process tracking of vehicles. This step specifically includes the following steps S1.1 to S1.3.

[0026] Step S1.1: Deploy monitoring equipment with optimal overlap and redundancy. Specifically, deploy a group of sensing units consisting of millimeter-wave radar and AI video monitoring equipment at predetermined intervals, such as 60 meters, along the longitudinal direction of the bridge. Each group of sensing units laterally covers all lanes on the entire bridge surface, and the longitudinal coverage areas of adjacent sensing units overlap to achieve optimal redundancy and seamless relay coverage. It should be noted that the deployment can be based on the maximum size of vehicles that have passed through historically, and the overlap length should be kept as small as possible without being less than the length of a vehicle to reduce redundancy.

[0027] Step S1.2: Identify vehicles within a single sensing unit. Specifically, a two-dimensional absolute coordinate system for the bridge surface is established with the center line of the driving lane at the bridge entrance as the origin, the driving direction as the X-axis, and the vertical direction as the Y-axis. For each vehicle entering the capture range of each single sensing unit, a temporary identifier ID is assigned, and its real-time coordinates (x, y), speed v, acceleration a, vehicle outline dimensions, and other vehicle and driving status information are recorded.

[0028] Step S1.3: Cross-sensor unit relay matching and target tracking. Specifically, when a vehicle enters the overlapping coverage area of ​​adjacent sensor units, if the license plates detected by the two adjacent sensor units are the same, a unique identifier (UID) is assigned; if the license plate cannot be detected due to vehicle obstruction, the target matching degree (M) is calculated based on the following formula:

[0029] Where, Δ x The longitudinal coordinate deviation of the target vehicle monitored by two adjacent sensing units at the same time; Δ v The speed deviation of the target vehicle monitored by two adjacent sensing units at the same time; Δ S The deviation of the target vehicle body contour area monitored by two adjacent sensing units at the same time. λ 1. λ 2. λ 3 represents the weighting coefficients, and their sum is 1; x max The longitudinal length of the overlapping area; v l This is the speed limit for the bridge surface; S ris the product of the length and width of the target vehicle's body contour. As a specific example, it may be set that when the target matching degree M ≥ 95%, a unique identifier UID is assigned to the same vehicle. Further, the unique identifier UID remains unique and unchanged from the time the vehicle drives onto the bridge deck until it drives off the bridge deck, and all behavior data of the vehicle is bound to the UID.

[0030] Step S2: Construct a multi-dimensional baseline for dynamic traffic flow on the bridge deck.

[0031] In this step, a normal traffic baseline reflecting the current traffic state is dynamically generated based on the real-time traffic flow data on the bridge deck, which provides a basis for subsequent judgment of vehicle traffic safety disturbances. This step specifically includes the following steps S2.1 to S2.3.

[0032] Step S2.1: Adjust the length of the sliding time window based on the fluctuation degree of the traffic flow on the bridge deck. A sliding time window is set for collecting stable traffic flow data on the bridge deck within a period of time. Specifically, the traffic flow stability Cv is calculated according to the following formula to quantify the fluctuation degree of the current traffic flow on the bridge deck:

[0033] where, σ v is the standard deviation of the traffic flow speed on the bridge deck within the sliding time window; is the average speed of the traffic flow on the bridge deck within the sliding time window; the smaller the traffic flow stability Cv, the more stable the traffic flow.

[0034] In a specific example, based on the traffic flow stability Cv, the adjustment rule for adjusting the length T of the sliding time window may be as follows: (1) When Cv ≤ 0.1, it is a stable flow, and T=5min is set to ensure the stability of the baseline; (2) When 0.1<Cv≤0.2, it is a fluctuating flow, and T=3min is set to balance stability and timeliness; (3) When Cv>0.2, it is a strongly fluctuating flow, and T=2min is set to ensure that the baseline can quickly adapt to sudden changes in traffic flow.

[0035] Step S2.2: Eliminate abnormal data in the sliding time window. For the vehicle data monitored in the sliding time window, an improved interquartile range method is used to eliminate abnormal values, so as to avoid abnormal behaviors interfering with baseline construction. In a specific example, the following method may be adopted.

[0036] For the vehicle speed, headway, and acceleration / deceleration data monitored in the sliding time window, calculate the lower quartile Q1, upper quartile Q3 and interquartile range IQR=Q3-Q1 respectively, and eliminate the abnormal values outside the range of [Q1-3*IQR, Q3+3*IQR].

[0037] Preferably, when the amount of valid data after elimination is less than 80% of the total amount of data within the sliding time window, the length of the sliding time window is shortened to ensure the validity of the baseline construction samples.

[0038] Step S2.3: Based on the stable data after removing outliers, dynamically generate the current bridge deck's traffic baseline and confidence interval. Preferably, based on the stable data after removing outliers, generate the current bridge deck's four-dimensional dynamic traffic baseline and calculate the 95% confidence interval as the normal fluctuation range. The specific generation rules are as follows: (1) Speed ​​baseline: The speed baseline is the average speed of traffic flow in each lane. The normal fluctuation range, the range value is ,in, The standard deviation of speed for each lane; , This is a constant, with a preferred value of 1.96, which can be adjusted according to actual needs; (2) Car-following baseline: The car-following baseline is the average headway of vehicles in each lane. The normal fluctuation range, the range value is ,in, Standard deviation of headway for lane-specific vehicles; , This is a constant, with a preferred value of 1.96, which can be adjusted according to actual needs; (3) Driving stability baseline: The driving stability baseline is the normal acceleration and deceleration fluctuation range of the traffic flow, and the range value is... ,in, , These are the deceleration and acceleration values ​​at the 95th percentile within the window, respectively. (4) Lane coupling baseline: The lane coupling baseline is the normal fluctuation range of the average speed difference between adjacent lanes, and the range value is... ,in, This represents the average speed difference between adjacent lanes. The standard deviation of the speed difference between adjacent lanes; w It is a constant, with a preferred value of 1.96, which can be adjusted according to actual needs.

[0039] Preferably, the sliding time window is updated every 10 seconds. Step S3: Based on the degree of deviation between vehicle behavior and the multidimensional baseline, identify the influence domain of bridge surface vehicle traffic safety disturbance. This step specifically includes the following steps S3.1 to S3.3.

[0040] Step S3.1: Identify the initial disturbance intensity of vehicle traffic. Calculate the initial disturbance intensity after a vehicle enters the bridge surface based on the following formula, quantifying the degree of deviation of vehicle behavior from the normal traffic flow. P 0: , In the formula, v , a For actual measurement of vehicle speed and acceleration; This is the midpoint of the acceleration / deceleration interval; The maximum speed difference between the average speed of the lane and that of the adjacent lane; This represents the lateral offset of the vehicle. This refers to the lane width.

[0041] when P 0> w 0 means that when a vehicle's behavior exceeds the normal fluctuation range below a certain baseline threshold, the vehicle is marked as a disturbance source, and high-frequency tracking of the vehicle is initiated. The threshold is... w 0 is a constant that can be adjusted according to actual needs, for example, when performing statistics according to a normal distribution. w A value of 0 = 1.96 indicates that the vehicle behavior exceeds the normal fluctuation range of the baseline 95% confidence interval.

[0042] Step 3.2: Identify the impact domain of vehicle traffic safety disturbances. Specifically, based on the current speed baseline and following baseline of the bridge deck, dynamically calculate the maximum possible impact range of the traffic safety disturbance, dividing it into longitudinal and lateral impact domains: (1) Longitudinal influence domain: Starting from the initial location where the disturbance source is discovered, a fixed length is covered forward, and the backward coverage length is calculated according to the following formula. L r As the current longitudinal influence range for vehicle traffic safety: , In the formula, t r This is the standard reaction time for drivers; This represents the current bridge deck adhesion coefficient.

[0043] (2) Lateral influence domain: Centered on the lane where the disturbance source is located, covering adjacent lanes, and the rules are determined as follows: 1) Bridge decks with central physical barriers cover one adjacent lane in the same direction; 2) For two-way bridges with four lanes or less without a central physical barrier, the two adjacent lanes on the left and right must be covered; 3) Two-way bridges with six or more lanes without a central physical barrier, covering the two adjacent lanes on the left and right and the two next adjacent lanes on the left and right.

[0044] Step 3.3: Synchronously track associated vehicles. Specifically, high-frequency tracking is initiated simultaneously for all vehicles within the influence domain, and each vehicle is bound to a unique UID. The driving status of all vehicles and their spatiotemporal relative relationship with the disturbance source are collected in real time.

[0045] Step S4: Within the disturbance influence domain of the bridge deck vehicle traffic safety disturbance, identify the critical blocking node affected by the disturbance chain. This step specifically includes the following steps S4.1 to S4.3.

[0046] Step 4.1: Identify the safety impact disturbance transmission chain. Specifically, the impact transmission relationship is determined based on the following three causal rules (1)-(3). Vehicles that simultaneously satisfy all three rules are included in the disturbance transmission chain and marked as transmission response nodes. The longitudinal transmission chain and lateral diffusion chain of the disturbance are drawn in real time, and the length of the transmission chain and the number of response nodes are counted simultaneously. Here, the so-called longitudinal transmission chain is a continuous sequence of response nodes in the same lane; the so-called lateral diffusion chain is a sequence of response nodes in adjacent lanes.

[0047] (1) Behavioral response rules: The response vehicle’s speed baseline, following baseline, driving stability baseline, or lane coupling baseline exceeds the normal disturbance range.

[0048] (2) Temporal causal rule: The change in the behavior of the responding vehicle must occur after the change in the behavior of the disturbance source.

[0049] (3) Spatial association rule: The responding vehicle must be located within the disturbance influence domain of the disturbance source.

[0050] Step 4.2: Identify safety impact blocking nodes. Specifically, for all response nodes (affected vehicles) in the safety impact transmission chain, based on node transmission sensitivity... S n Assess its control over the entire subsequent conduction chain. Node conduction sensitivity. S n Calculated based on the following formula: , in, For the first n The total number of nodes transmitting the response behind each node; This represents the total number of response nodes in the propagation chain; For the first n The measured headway between each node and the vehicle in front; , For actual measurement of vehicle speed and acceleration; This is the midpoint of the acceleration / deceleration interval; The maximum speed difference between the average speed of the lane and that of the adjacent lane; This represents the average speed difference between adjacent lanes. This represents the lateral offset of the vehicle. Lane width; The standard deviation of speed for each lane; The standard deviation of the speed difference between adjacent lanes; The standard deviation of lane acceleration is given.

[0051] Node conduction sensitivity S n The larger the node, the greater the probability of subsequent disturbance propagation failure if it is blocked. Therefore, the node's propagation sensitivity... S n Nodes exceeding a certain threshold are included in the blocking node set.

[0052] Step S5: Implement tiered control over critical blocking nodes in the transmission chain affecting vehicle traffic safety. This step specifically includes the following steps S5.1 to S5.3.

[0053] Step S5.1: Classify critical blocking nodes. Specifically, statistically analyze the node conduction sensitivity of vehicles passing through the current bridge surface in a recent period (e.g., within the past year). S n The size distribution is based on the vehicle's node conduction sensitivity. S n The size distribution is used to classify critical blocking nodes according to the following rules: (1) When At that time, the current vehicle is determined to be a Class I vehicle, which is the vehicle with the most serious impact on traffic safety; (2) When At that time, the vehicle was determined to be a Class II vehicle, the second most serious vehicle in terms of impact on traffic safety; (3) When At that time, the current vehicle is determined to be a Class III vehicle, which is a vehicle whose traffic safety impact is not serious.

[0054] In the above formula, , It is a constant and can be adjusted according to actual needs.

[0055] Step 5.2: Implement tiered control for critical blocking nodes. Specifically, implement different levels of control for vehicles with different safety impact categories, as follows: (1) Implement Level III control for Class III vehicles: Issue precise driving prompts corresponding to the disturbance behavior through the nearest variable message sign in front of the disturbance source, such as please keep your lane, do not speed, please drive smoothly, etc.

[0056] (2) Implement Level II control for Class II vehicles: ① Based on the vehicle UID and real-time coordinates, send voice and text warnings to the disturbance source and response nodes on the transmission chain to guide the vehicle back to normal driving status; ② For the vehicle at the largest blocking node identified, send an early deceleration warning separately to force it to increase the safe distance and block the disturbance from being transmitted backward.

[0057] (3) Implement Level I control for Class I vehicles: ① For the largest blocking node of all transmission chains, send precise control instructions through the corresponding information boards and other equipment to force vehicles at the largest blocking node to decelerate smoothly to a safe speed and increase the safe distance between vehicles; ② In the upstream entrance section of the disturbance transmission chain, temporarily close the entrance channel to restrict vehicles from entering and avoid further increase in traffic density and aggravate the transmission; ③ Issue unified deceleration prompts on the entire bridge deck to guide vehicles in non-affected areas to decelerate in advance and avoid entering the disturbance area and causing secondary risks.

[0058] The bridge vehicle impact identification and control method according to the embodiments of this application involves locking a unique identifier for the entire vehicle segment when it enters the bridge, identifying the initial disturbance source, tracking the longitudinal transmission and lateral diffusion paths of the disturbance in real time, quantifying the node transmission sensitivity and locating blocking nodes, classifying the impact on vehicle traffic safety, matching graded control actions, and implementing spatiotemporally precise node vehicle control through equipment along the route, ultimately achieving proactive prevention of traffic risks and avoiding accidents. Specifically, the technical effects of this embodiment are as follows: (1) Achieving root cause prevention and control of chain accidents on bridge decks, significantly improving safety management effectiveness. This invention intervenes in management from the initial generation stage of disturbance, and cuts off the chain transmission path of disturbance through causal disturbance transmission tracking technology and node blocking method, which can significantly reduce the incidence of major chain accidents on closed bridges.

[0059] (2) Full-scene adaptive adaptation with strong engineering feasibility. All traffic baselines and control logic proposed in this invention are dynamically generated based on real-time traffic flow and environmental data on the bridge deck, and can automatically adapt to closed bridges with different spans, different numbers of lanes, and different traffic flow characteristics.

[0060] (3) Improved control efficiency and traffic efficiency, effectively avoiding secondary risks. This invention applies node-based precise control logic, which only performs control actions on the smallest range of nodes that block the transmission of disturbances. It has little interference with vehicles passing normally on the bridge. While improving the safety control effect, the bridge has high traffic efficiency, avoiding secondary congestion and secondary accidents caused by rough control.

[0061] (4) Solving the problem of difficult vehicle tracking on long linear road sections. The bridge vehicle spatiotemporal unique identifier and relay tracking method proposed in this invention is based on the calculation of the bridge absolute coordinate system and spatiotemporal matching degree, realizing the whole-bridge tracking of vehicles without interruption, loss and duplicate identification. It solves the problem of difficult tracking of targets across sensing units on long linear road sections of closed bridges, and can provide full and accurate data support for control and management.

[0062] Example 2 Figure 2 This is a schematic diagram illustrating the structural composition of the bridge surface vehicle impact identification and control system according to an embodiment of this application. Figure 2As shown, the system 200 includes: The bridge deck information monitoring module 210 is used to monitor the full cross-sectional information of the bridge deck, perform target relay tracking of vehicles entering the bridge deck, and assign a unique identifier (UID) throughout the process. Multidimensional baseline construction module 220 is used to dynamically generate multidimensional baselines for dynamic traffic flow on the bridge based on real-time traffic flow data on the bridge. The safety disturbance identification module 230 is used to identify the influence domain of bridge vehicle traffic safety disturbances and mark the disturbance sources based on the degree of deviation between vehicle behavior and the multidimensional baseline; The blocking node identification module 240 is used to identify the security impact disturbance transmission chain within the disturbance influence domain based on a preset causal rule, and to identify critical blocking nodes on the transmission chain based on node transmission sensitivity; and The hierarchical control module 250 is used to perform hierarchical control on the identified critical blocking nodes.

[0063] The system described in this application embodiment is similar to the one described above. Figure 1 The methods provided belong to the same concept, and the specific implementation process can be referred to the above method implementation examples, which will not be repeated here.

[0064] Figure 3 This is a schematic diagram of the structural composition of the bridge vehicle impact identification and control equipment according to an embodiment of this application, as shown below. Figure 3 As shown, the bridge vehicle impact identification and control device 300 includes at least one processor 301 and a memory 302 for storing a computer program that can run on the processor 301. When the processor 301 runs the computer program, it executes the bridge vehicle impact identification and control method described in the above embodiments of this application. The bridge vehicle impact identification and control device 300 also includes at least one network interface 304 and a user interface 303. The various components in the bridge vehicle impact identification and control device 300 are coupled together through a bus system 305. It is understood that the bus system 305 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 305 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in Figure 3 The general designated all buses as Bus System 305.

[0065] The user interface 303 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0066] It is understood that memory 302 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 302 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0067] In this embodiment, the memory 302 is used to store various types of data to support the operation of the bridge vehicle impact identification and control device 300. Examples of this data include any computer program used to operate on the bridge vehicle impact identification and control device 300, such as the operating system 3021, application program 3022, and bridge vehicle impact identification and control system 3023. The operating system 3021 includes various system programs, such as a framework layer, core library layer, and driver layer, used to implement various basic services and handle hardware-based tasks. The application program 3022 may include various applications, such as a media player and browser, used to implement various application services. The bridge vehicle impact identification and control system 3023 is the one described in this application. Figure 2 The system shown is used to identify and control the impact of vehicles on the bridge deck. The program for implementing the method of the embodiments of this application can be included in the application program 3022 or in the system 3023 for identifying and controlling the impact of vehicles on the bridge deck.

[0068] Processor 301 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 301 or by instructions in software form. Processor 301 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 301 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, specifically memory 302. Processor 301 reads information from memory 302 and, in conjunction with its hardware, completes the steps of the aforementioned method.

[0069] In an exemplary embodiment, the bridge vehicle impact identification and control device 300 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0070] In an exemplary embodiment, this application also provides a computer-readable storage medium, such as a memory 302 including a computer program, which can be executed by the processor 301 of the bridge vehicle impact identification and control device 300 to complete the steps described in the aforementioned method. The computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM; it can also be various devices including one or any combination of the above-mentioned memories, such as computers, tablet devices, personal digital assistants, etc.

[0071] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the method for identifying and controlling the impact of vehicles on the bridge surface as described in the above embodiments of this application.

[0072] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. Furthermore, the features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0073] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for identifying and controlling the impact of vehicles on bridge decks, characterized in that, Includes the following steps: Monitor the entire cross-section information of the bridge deck, perform target relay tracking of vehicles entering the bridge deck, and assign a unique identifier (UID) throughout the process; Based on real-time traffic flow data on the bridge deck, a multi-dimensional baseline for dynamic traffic flow on the bridge deck is dynamically generated to reflect the current traffic conditions. Based on the degree of deviation of vehicle behavior from the multidimensional baseline, the influence domain of disturbance to bridge vehicle traffic safety is identified, and the disturbance source is marked; Within the safety disturbance influence domain of the bridge deck, the safety disturbance transmission chain is identified based on a preset causal rule, and the critical blocking node on the safety disturbance transmission chain is identified based on the node transmission sensitivity. as well as Hierarchical control is implemented for the identified critical blocking nodes.

2. The method according to claim 1, characterized in that, The step of monitoring the full cross-sectional information of the bridge deck further includes: Multiple sets of sensing units are deployed along the longitudinal direction of the bridge, and there is overlap between the longitudinal coverage areas of adjacent sensing units; Within a single sensing unit, a temporary identifier is assigned to each entering vehicle, and its vehicle and driving status information is recorded; and When a vehicle enters the overlapping coverage area of ​​adjacent sensing units, if the vehicle license plate matches, the unique identifier UID is assigned; if the vehicle license plate cannot be identified, matching is performed based on the target matching degree, and when the matching degree exceeds the threshold, the unique identifier UID is assigned to the same vehicle.

3. The method according to claim 2, characterized in that, The target matching degree is: ， Where, Δ x The longitudinal coordinate deviation of the target vehicle monitored by two adjacent sensing units at the same time; Δ v The speed deviation of the target vehicle monitored by two adjacent sensing units at the same time; Δ S The deviation of the target vehicle body contour area monitored by two adjacent sensing units at the same time. λ 1. λ 2. λ 3 represents the weighting coefficients, and their sum is 1; x max The longitudinal length of the overlapping area; v l This is the speed limit for the bridge surface; S r The product of the length and width of the target vehicle's body outline.

4. The method according to claim 1, characterized in that, The bridge deck traffic dynamic multidimensional baseline includes speed baseline, following baseline, driving stability baseline, and lane coupling baseline. The step of dynamically generating a multi-dimensional baseline for bridge traffic flow that reflects the current traffic conditions further includes: The length of the sliding time window is dynamically adjusted based on the degree of vehicle fluctuation in the bridge traffic flow. An improved interquartile range method is used to remove outlier data within the sliding time window; and Based on stable data after removing outliers, a multidimensional baseline and its confidence interval for the current bridge deck traffic dynamics are generated.

5. The method according to claim 1, characterized in that, The step of identifying the impact zone of vehicle traffic safety disturbance on the bridge deck further includes: Based on the vehicle's measured speed, acceleration, maximum speed difference with vehicles in adjacent lanes, and lateral offset, the initial disturbance intensity is calculated. When the initial disturbance intensity exceeds a preset threshold, the current vehicle is marked as the disturbance source. Based on the current speed baseline and following baseline, the longitudinal and lateral influence domains of traffic safety disturbances are dynamically calculated, wherein the rearward coverage length of the longitudinal influence domain is calculated based on the driver's standard reaction time and the bridge surface adhesion coefficient; and High-frequency tracking is initiated for all vehicles within the disturbance influence area, and a unique identifier (UID) is assigned.

6. The method according to claim 1, characterized in that, The step of identifying the security impact disturbance transmission chain further includes: Based on the causal rules, the influence transmission relationship is determined, and vehicles that simultaneously satisfy all the rules are included in the disturbance transmission chain and marked as transmission response nodes; Real-time plotting of the longitudinal conduction chain and lateral diffusion chain of the disturbance; and For all the conduction response nodes in the disturbance conduction chain, their control over the entire subsequent conduction chain is determined based on the node's conduction sensitivity, and nodes with conduction sensitivity greater than a preset threshold are included in the blocking node set. The causal rules include: Behavioral response rules: The vehicle's speed baseline, following baseline, driving stability baseline, or lane coupling baseline exceeds the normal disturbance range. Temporal causality rule: The change in the behavior of the responding vehicle occurs after the change in the behavior of the disturbance source; Spatial association rule: The responding vehicle is located within the disturbance influence domain of the disturbance source.

7. The method according to claim 1 or 6, characterized in that, The step of identifying the critical blocking node on the security impact disturbance conduction chain based on node conduction sensitivity further includes: Calculate the nodal propagation sensitivity of each response node in the disturbance propagation chain. S n : ， in, For the first n The total number of nodes transmitting the response behind each node; This represents the total number of response nodes in the propagation chain; For the first n The measured headway between each node and the vehicle in front; , For actual measurement of vehicle speed and acceleration; This is the midpoint of the acceleration / deceleration interval; The maximum speed difference between the average speed of the lane and that of the adjacent lane; This represents the average speed difference between adjacent lanes. This represents the lateral offset of the vehicle. Lane width; The standard deviation of speed for each lane; The standard deviation of the speed difference between adjacent lanes; The standard deviation of lane acceleration; Transmitting node sensitivity S n Nodes exceeding a preset threshold are identified as critical blocking nodes.

8. The method according to claim 1, characterized in that, The step of performing hierarchical control on the identified critical blocking nodes further includes: The distribution of historical node conduction sensitivity is statistically analyzed, and a first constant and a second constant are set. Based on the distribution of historical node conduction sensitivity, critical blocking nodes are classified. The highest level of control will be implemented for vehicles that have the most serious impact on traffic safety, including forcibly guiding vehicles at the blockage nodes with the highest sensitivity to the transmission of disturbances to decelerate smoothly, temporarily closing entrance channels in the upstream entrance section of the disturbance transmission chain, or issuing unified deceleration warnings on the entire bridge surface. For vehicles with the second most serious impact on traffic safety, secondary control measures will be implemented, including sending voice and text warnings to the source of disturbance and response nodes in the transmission chain, and sending early deceleration warnings separately to vehicles at the blocking nodes with the highest sensitivity to transmission in this category. Vehicles with minimal impact on traffic safety will be subject to the lowest level of control, including providing precise driving guidance via variable message signs.

9. The method according to claim 5, characterized in that, The rules for determining the lateral influence domain include: The bridge deck has a central physical barrier that covers one adjacent lane in the same direction; For two-way bridges with four lanes or less without a central physical barrier, the two adjacent lanes on the left and right must be covered. A two-way bridge with six or more lanes without a central physical barrier covers the two adjacent lanes on the left and right and the two next adjacent lanes on the left and right.

10. A system for identifying and controlling the impact of vehicles on a bridge deck, characterized in that, include: The bridge deck information monitoring module is used to monitor the full cross-sectional information of the bridge deck, perform target relay tracking of vehicles entering the bridge deck, and assign a unique identifier (UID) throughout the process; The multidimensional baseline construction module is used to dynamically generate multidimensional baselines for dynamic traffic flow on the bridge based on real-time traffic flow data on the bridge surface. The safety disturbance identification module is used to identify the influence domain of bridge vehicle traffic safety disturbances and mark the disturbance sources based on the degree of deviation of vehicle behavior from the multidimensional baseline; The blocking node identification module is used to identify the safety impact disturbance transmission chain within the disturbance influence domain based on a preset causal rule, and to identify the critical blocking node on the transmission chain based on the node transmission sensitivity. as well as The hierarchical control module is used to perform hierarchical control on the identified critical blocking nodes.

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