A dynamic deployment and linkage control method of a road warning sign

Abstract

The application relates to the technical field of traffic control systems, and discloses a dynamic deployment and linkage control method of a road early warning signboard, which comprises the following steps: after comparing and verifying the authenticity of traffic flow parameters through multi-source data, a mobile characteristic boundary representing a congestion state and a propagation speed thereof are recognized; then, a virtual guide field composed of at least two spatially separated guide points is defined on the upstream of the characteristic boundary, and the guide field is dynamically migrated according to real-time position changes of the boundary; the application changes the reference of traffic guidance from a fixed geographical facility to a dynamic boundary that moves in real time with the traffic state, avoids the problem that a traditional fixed early warning mode fails due to the fact that information transmission lags behind the movement of a congestion boundary from the operational mechanism, and realizes intervention on a dynamic process of traffic congestion.

Description

Technical Field

[0001] This invention relates to a method for the dynamic deployment and linkage control of road warning signs, belonging to the field of traffic control system technology. Background Technology

[0002] Currently, traffic flow, as a complex system following fluid dynamics, does not have its internal speed disturbances stationary at the origin. Instead, it propagates upstream at a speed of 15-20 km / h, i.e., in the opposite direction of the traffic flow, in the form of traffic waves. This means that the actual starting boundary of congestion is a dynamic target moving at high speed on the road, rather than a static geographical location. Faced with this dynamically moving congestion boundary, the aforementioned technical approach of deploying information signs based on fixed geographical locations reveals its fundamental limitations in its inherent operating mechanism. Simply increasing the density of signs or improving the frequency of information updates cannot overcome this inherent spatiotemporal mismatch.

[0003] Specifically, existing technologies suffer from the following shortcomings: 1. Spatial mismatch: fixed information markers cannot maintain an effective warning distance with dynamically moving traffic event boundaries, such as congestion fronts or the leading edge of moving maintenance work areas. 2. Temporal lag: the inherent dilemma of existing technologies lies in attempting to manage a dynamically moving traffic state boundary—an abstract congestion boundary moving at high speed in a road coordinate system—using a relatively static physical system and fixed information dissemination method fixed to the Earth's coordinate system. This includes congestion propagation boundaries caused by accidents or slow-moving vehicles, or moving work area boundaries formed by road maintenance work. This mismatch between different coordinate systems and the spatiotemporal mismatch between static management and dynamic targets is the root cause of the current unsatisfactory road congestion warning effect. Therefore, the technical problem this invention aims to solve is how to create an information dissemination mechanism that can dynamically track the movement of traffic state characteristic boundaries and always pre-build effective guidance areas upstream of them, thereby transforming traffic control from a delayed declaration of established facts to intervention in dynamic processes. Summary of the Invention

[0004] This invention provides a dynamic deployment and linkage control method for road warning signs. Its main purpose is to solve the problem that in the prior art, fixedly deployed warning signs cannot adapt to the dynamic characteristics of various mobile traffic disturbances on the road, such as the upstream spread of congestion and mobile road maintenance operations, which leads to the problem of spatial and temporal lag in warning information.

[0005] To achieve the above objectives, this invention provides a dynamic deployment and linkage control method for road warning signs. This method establishes a traffic guidance procedure with the characteristic boundary of the moving speed contour lines as the control target and internal operational status verification as the execution prerequisite. The procedure includes the following steps:

[0006] Step A: Obtain real-time traffic flow parameters covering the target road segment from a first data source and at least one second data source different from the first data source; and before executing subsequent steps, calculate the time change rate or spatial change rate of the real-time traffic flow parameters of the first data source and the second data source respectively. When the difference between the two is less than a set arbitration threshold, the real-time traffic flow parameters are determined to pass the consistency check.

[0007] Step B: When the real-time traffic flow parameters pass the consistency check, based on the real-time traffic flow parameters from the first data source, determine the real-time location of a velocity contour feature boundary and its propagation speed in the opposite direction of traffic.

[0008] Step C: Upstream of the feature boundary, define a virtual guidance field consisting of at least two spatially separated guidance points, and generate corresponding guidance information for each guidance point;

[0009] Step D involves sending guidance information to the warning signs corresponding to the guidance point locations for display, and simultaneously adjusting the positions of each guidance point in the virtual guidance field based on the real-time position changes of the feature boundaries.

[0010] Preferably, the method further includes a performance diagnosis and information mode switching step, the steps of which include: acquiring and calculating the speed dispersion index of traffic flow parameters of one or more sub-segments within the virtual guidance field; when the speed dispersion index is greater than a set performance judgment threshold, determining that the guidance information has failed to guide the behavior of the driver group; and automatically switching the guidance information displayed on the warning sign from the first information mode to a second information mode with a different level of enforcement or warning intensity than the first information mode.

[0011] Preferably, the first in the virtual guidance field Location of each guide point Determined according to the following rules:

[0012] ,in, For the first Real-time location of each guide point

[0013] The real-time location of the feature boundary. These are the basic spacing parameters built into the system. For the guide point number, The propagation speed of the feature boundary, This is the system's built-in speed response coefficient.

[0014] Preferably, the guidance information is a suggested driving speed, and along the driving direction, the suggested driving speed at different guidance points in the virtual guidance field decreases in a stepwise manner.

[0015] Preferably, the dynamic deployment step further includes: adjusting the guidance information based on the actual changes in traffic flow parameters upstream of the virtual guidance field under the influence of the guidance information.

[0016] Preferably, the state diagnosis step further includes: determining whether there is a dissipation boundary characterizing the dissipation of congestion, and determining the propagation of the dissipation boundary along the driving direction; and the method further includes: when it is determined that a dissipation boundary exists, downstream of the dissipation boundary, deploying coordinated guidance information aimed at guiding vehicles to accelerate smoothly through one or more warning signs.

[0017] Preferably, the method further includes: determining a free-flow speed baseline under standard operating conditions for the target road segment based on historical traffic flow parameters; comparing the real-time traffic flow parameters of the target road segment under non-congested conditions with the free-flow speed baseline, and determining that the current condition is non-standard when the deviation between the two meets the set operating condition switching conditions; and when the condition is determined to be non-standard, adaptively adjusting the judgment criteria of the feature boundary in the state diagnosis step and the guidance information in the guidance field generation step according to the real-time traffic flow parameters.

[0018] Preferably, the real-time traffic flow parameters include at least the average vehicle speed of each sub-segment, and the feature boundary is defined as the contour line at the location point where the average vehicle speed is equal to 60 km / h.

[0019] Preferably, the second information mode includes one or a combination of mandatory speed limit information, accident warning information, and instructions to activate hazard warning lights and maintain a safe distance.

[0020] Preferably, the arbitration threshold in step A, the velocity contour lines in step B, and the performance judgment threshold are all parameters set and stored during the system initialization phase.

[0021] Compared with the prior art, the beneficial effects of the present invention are:

[0022] 1. This invention continuously acquires real-time traffic flow parameters from multiple sub-segments along the driving direction to determine whether a characteristic boundary representing changes in traffic conditions exists and to determine the propagation speed of this boundary. Furthermore, upstream of this characteristic boundary, a virtual guidance field consisting of at least two spatially separated guidance points is defined. Based on the real-time position changes of this boundary, the positions of each guidance point in the guidance field and the corresponding guidance information are dynamically adjusted. Thus, the benchmark for traffic guidance is no longer a fixed geographical facility, but a dynamic boundary that moves in real time with the traffic conditions. This allows the early warning information to continuously lead the upstream propagation of congestion in space, avoiding the risk of information transmission lagging behind congestion in traditional fixed early warning methods from the operational mechanism.

[0023] 2. While deploying the virtual guidance field, the dispersion index of traffic flow parameters at different locations within the guidance field is also acquired and compared. When the dispersion index meets the preset low efficiency condition, the currently issued guidance information is determined to be invalid, and the guidance information displayed on the warning sign is automatically switched from the first information mode to a second information mode with a different level of enforcement or warning intensity. This method enables the control system to diagnose whether the guidance information it issues is effectively received and executed by the driver group, forming a control process with feedback adjustment and redundancy protection at the information interaction level. This avoids the technical limitation of the system unilaterally issuing information and failing to confirm the actual control effect under special conditions such as decreased driver perception.

[0024] 3. While diagnosing traffic congestion, the invention further determines whether there is a dissipation boundary indicating that the congestion is dissipating and its propagation along the direction of travel. When the dissipation boundary is determined to exist, downstream of the dissipation boundary, one or more warning signs are deployed to provide coordinated guidance information aimed at guiding vehicles to accelerate smoothly. By providing symmetrical full-cycle guidance and adjustment for the deceleration process during the congestion formation phase and the acceleration process during the congestion dissipation phase, this invention suppresses the secondary speed oscillations that may be caused by the disorderly acceleration of vehicles after congestion is relieved, enabling the entire traffic system to recover to a smooth state more orderly and stably after experiencing disturbances. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the logic flow of the traffic congestion dynamic intervention method of the present invention;

[0026] Figure 2 This is a schematic diagram illustrating the dynamic migration of the guide point position with the congestion boundary according to the present invention;

[0027] Figure 3 This is a schematic diagram of the system implementation architecture of the linkage control method of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] This invention provides a dynamic deployment and linkage control method for road warning signs. This method establishes a traffic guidance procedure with the characteristic boundary of moving speed contour lines as the control target and internal operational status verification as the execution prerequisite. Its overall operation mainly includes data acquisition and verification steps, status diagnosis steps, virtual guidance field generation steps, and dynamic deployment and linkage control steps. These four steps are logically connected sequentially, forming a closed-loop control system for proactively intervening in the dynamic process of traffic congestion. In a specific application scenario, such as a highway with a design speed of 120 km / h, warning signs with remote information update capabilities are deployed at regular intervals along the route. The execution of this method begins with data acquisition. The verification step, designed to provide reliable input for subsequent diagnostics, faces the technical challenge that single-source real-time traffic flow data may exhibit false speed fluctuations that do not represent the overall traffic conditions of the road segment due to sampling bias or data contamination. If the system makes decisions based on such data, it may generate warnings that disrupt normal traffic order, thereby affecting the long-term effectiveness of the system. To address this issue, step A of the present invention is configured to acquire real-time traffic flow parameters covering the target road segment from a first data source and at least one second data source different from the first data source. For example, the first data source may be the average speed data of the road segment provided by a navigation map service provider, while the second data source may be data from another map service provider or... Anonymized regional pedestrian movement speed data from mobile communication operators; before proceeding to subsequent steps, the system performs a consistency check through a pattern comparison logic. Specifically, the system calculates the spatial rate of change of real-time traffic flow parameters from the first data source and the second data source. Only when the difference between the two is less than a set arbitration threshold is the real-time traffic flow parameter deemed to have passed the consistency check. The arbitration threshold is a parameter determined during the system initialization phase by analyzing the maximum normal deviation between different data sources when real traffic events occur, calibrated in historical data. For example, it can be set to 10 (km / h) / km. To illustrate with a numerical example, if at a certain moment, the system detects a target road segment between K105 and K106... The first data source shows that the average vehicle speed decreased from 100 km / h to 85 km / h, with a spatial gradient of -15 (km / h) / km. The second data source shows that the average vehicle speed decreased from 98 km / h to 88 km / h on the same road segment, with a spatial gradient of -10 (km / h) / km. The difference between the two is |-15-(-10)|=5 (km / h) / km. This value is less than the preset arbitration threshold of 10 (km / h) / km, so the system determines that the currently observed speed decrease pattern has high confidence. Through this multi-source data cross-validation procedure, this method establishes a data validity verification mechanism for the decision-making link of the entire control system, ensuring that the input signal can accurately reflect the actual traffic conditions.

[0030] After the real-time traffic flow parameters pass the consistency check, the system enters the state diagnosis step, i.e., step B. The core task of this step is to identify the moving characteristic boundary representing the congestion state and its propagation speed. Given that traffic congestion manifests as the upstream propagation of traffic waves, the system needs an objective and quantifiable indicator to capture this dynamic process. To this end, based on the real-time traffic flow parameters from the first data source, the system determines the real-time location of a speed contour feature boundary and its propagation speed in the opposite direction of traffic. The feature boundary is preferably identified as the location point where the average vehicle speed is equal to 60 km / h. The basis for setting this threshold is that 60 km / h is usually a critical speed range for the transition from free flow to congested flow on highways. Within a specific execution cycle, for example, at a certain time point... The system determines the precise geographical location of a speed of 60 km / h by scanning the average vehicle speed data of each sub-segment covering the target road segment and using a spatial interpolation algorithm. This location is denoted as... Accordingly, in the next execution cycle, i.e., at time point The system then calculates the position of the feature boundary again. Furthermore, the propagation speed of this feature boundary can be It is determined that since congestion waves propagate in the opposite direction of traffic, this velocity value is usually negative. By transforming the evolution of traffic conditions into a characteristic boundary with a clear physical location and propagation velocity, this step provides a precise control target for the subsequent dynamic deployment of the guidance field. After capturing the characteristic boundary, the system immediately enters the guidance field generation step, i.e., step C. In this step, upstream of the characteristic boundary, a virtual guidance field consisting of at least two spatially separated guidance points is defined, and corresponding guidance information is generated for each guidance point. The purpose of this is to transform a sharp decrease in velocity downstream into a gradual and expected deceleration process upstream, thereby actively regulating the behavior of upstream traffic flow. The virtual guidance field... Location of each guide point Determined according to the following rules:

[0031] ,in, For the first Real-time location of each guide point The real-time location of the feature boundary. These are the basic spacing parameters built into the system. The guide point number (incrementing in the opposite direction of traffic, such as 1, 2, ...). The propagation speed of the feature boundary, and This refers to the system's built-in speed response coefficient; the absolute value of the speed is used here. This ensures that when traffic waves propagate faster, the location of the guidance point can be extended further upstream, providing a longer warning distance; based on the principle of dimensional consistency, and Its dimension is length. Its dimension is also length. For a dimensionless pure number to make the equation true, It must be dimensionless, therefore the velocity response coefficient The units are time / length, such as seconds / meter; these built-in system parameters are all obtained through offline simulation and historical data calibration, for example, The distance can be set to 500 meters based on the road design level and the driver's typical reaction time. The speed is set to 0.05 s / m. Meanwhile, the recommended driving speed generated for each guide point is preferably the suggested driving speed. Along the driving direction, the suggested driving speed for different guide points in the virtual guide field decreases in a step-like manner. For example, the recommended speed for the upstream guide point is 80 km / h, and the recommended speed for the next guide point is 60 km / h.

[0032] Finally, the system executes the dynamic deployment step, namely step D, which sends the guidance information to the warning signs corresponding to the guidance point locations for display, and synchronously adjusts the positions of each guidance point in the virtual guidance field based on the real-time position changes of the feature boundaries. It should be noted that the guidance point here is a logical concept; the system will determine its position based on the calculated... The real-time geographic coordinates are used to select the physical sign closest to the given coordinates from a pre-set database of roadside warning signs using a location matching algorithm; as the feature boundary... As the system continues to move upstream, it recalculates all guide points at a high frequency, for example, every 10 seconds. The location of the congestion boundary is determined and the corresponding signage is dynamically updated, enabling the guidance field to track the movement of the congestion boundary in real time. A numerical example illustrates this process, assuming that in... Time, Feature Boundary Located at kilometer 105, its propagation speed With a speed of -18 km / h (i.e., -5 m / s), using the above parameters, the first guiding point... The system indicates that a sign near K105+625m shows slow traffic ahead and suggests a speed of 60 km / h; the second guide point... The system then instructed a sign near K106+250m to indicate heavy traffic 2 kilometers ahead and suggested a speed of 80 km / h; after 10 minutes, at... At any given moment, when the feature boundary moves to K102, the system will recalculate and relocate the entire guidance field. The signage information originally located at K105+625m may no longer be effective, while the signage located further upstream between K103 and K104 will be activated and begin displaying new guidance information. By changing the benchmark for traffic guidance from fixed geographical facilities to a dynamic boundary that moves with traffic conditions, this method avoids the problem of traditional fixed early warnings failing due to information transmission lagging behind the movement of congestion boundaries.

[0033] To form a control process with feedback adjustment, this method also includes a performance diagnosis and information mode switching step. This step aims to address the problem that under certain special conditions, such as at night or in severe weather, the responsiveness of drivers to conventional text-based suggestions may decrease, leading to a weakening of the guidance effect. To address this situation, this method, while deploying a virtual guidance field, also acquires and calculates the speed dispersion index of traffic flow parameters, such as the speed standard deviation, for one or more sub-segments within the virtual guidance field. An effective guidance field should allow traffic flow to smoothly transition from a state of large upstream speed differences to a state of convergent speeds within the field. Therefore, when the system determines that the speed dispersion index is greater than a set effectiveness judgment threshold, for example, if the speed standard deviation in the core area of ​​the site is still higher than 15 km / h, it determines that the guidance information has failed to effectively guide the driver group. At this time, the system will automatically switch the guidance information displayed on the warning sign from the first information mode (such as suggested speed) to a second information mode with a different level of enforcement or warning intensity than the first information mode. The second information mode may include one or a combination of mandatory speed limit information, accident warning information, and instructions to turn on hazard warning lights and maintain a safe distance, thereby ensuring driving safety by increasing the warning intensity.

[0034] Furthermore, to control both the formation and dissipation phases of traffic congestion, the state diagnosis step of this method further determines whether a dissipation boundary characterizing the dissipation of congestion exists, and determines the propagation of this dissipation boundary along the driving direction. This dissipation boundary can be identified by monitoring whether a sustained speed increase begins at the head of the queue in the original congestion core area, such as an area with an average vehicle speed below 20 km / h. When a dissipation boundary is determined to exist, this method also includes deploying coordinated guidance information downstream of the dissipation boundary through one or more warning signs, aiming to guide vehicles to accelerate smoothly, such as "Congestion ahead is about to ease, please accelerate steadily and maintain a safe distance." This aims to suppress secondary speed oscillations that may be caused by disorderly acceleration of vehicles after congestion eases, promoting the orderly and stable recovery of the entire traffic system to a smooth state. To improve the method's adaptability to changes in the external environment, this method also includes an environmental adaptive calibration. The mechanism first determines the free-flow speed baseline under standard operating conditions for the target road segment based on historical traffic flow parameters. Then, the system compares the real-time traffic flow parameters of the target road segment under non-congested conditions with the free-flow speed baseline. When the actual average speed of a large area of ​​non-congested road segments is consistently and significantly lower than its historical baseline (e.g., the deviation meets the set operating condition switching conditions, such as a general reduction of more than 20%), the system determines that the current condition is non-standard, usually caused by environmental factors such as severe weather. When a non-standard condition is determined, the system will, based on real-time traffic flow parameters, dynamically adjust the speed contour lines (e.g., dynamically adjusting the 60 km / h speed contour to a certain percentage of the current new free-flow speed) and the guidance information (e.g., proportionally lowering the suggested speed gradient) in the guidance field generation step, adaptively adjusting the control strategy to ensure its rationality under different traffic conditions.

[0035] Example 1: On a long, straight section of highway at night, the road surface is dry and there are no obstructions to visibility. Vehicles are traveling at 110 km / h. At this time, several kilometers downstream on an uphill section, a slow-moving convoy of multiple heavy trucks reduces traffic capacity, causing a traffic congestion wave to form and propagate in the opposite direction of travel at 15 km / h. For drivers approaching at high speed upstream, this moving congestion boundary is far beyond their line of sight, creating a rear-end collision risk scenario. Under this condition, the method of the present invention continues to operate, and its loop... The adaptive calibration mechanism has identified the current time period as nighttime based on historical data and adjusted the free-flow speed baseline from 120km / h to 115km / h, enabling subsequent state diagnosis to be based on the traffic characteristics of the current time period. The system's data acquisition and verification steps obtain traffic flow parameters from two different navigation map service providers. When the congestion traffic wave begins to propagate, both data sources detect a slow-moving low-speed zone upstream. The difference in the spatial change rate is lower than the preset arbitration threshold. Based on this, the system determines that the traffic state change has a high degree of confidence and triggers the state diagnosis step.

[0036] The state diagnosis step is then initiated. Based on the traffic flow parameters that have passed the consistency check, the system identifies a velocity contour feature boundary with an average vehicle speed of 60 km / h, determines its initial location at K250 km, and calculates its propagation speed in the opposite direction of traffic. The speed is -15 km / h; the identification of this characteristic boundary and the calculation of its propagation speed provide a spatiotemporal reference for the generation of the virtual guidance field, making the intervention target of the control system no longer a fixed geographical location, but a moving dynamic phenomenon; this method allows the spatiotemporal reference for the release of early warning information to move synchronously with the risk source, thereby avoiding the problem of premature or delayed release caused by a fixed warning location; subsequently, the guidance field generation step and the dynamic deployment step are activated, and the system is based on the real-time location of the characteristic boundary. meters and propagation speed km / h, via formula The locations of the two levels of guidance points were calculated, and the corresponding warning signs were instructed to display tiered guidance information. The sign at K251.5 km began to indicate slow traffic ahead, suggesting a speed of 80 km / h, while the sign at K250.8 km indicated congestion 1 km ahead, suggesting a speed of 60 km / h. This did not directly announce a distant congestion event unrelated to the driver's current perception, but rather constructed a dynamic speed transition zone. As the feature boundary moved upstream in the following minutes, the entire virtual guidance field also migrated synchronously, always remaining ahead of the congestion front, forming a moving forward deceleration buffer. The efficiency diagnosis and information mode switching steps operated in parallel during this process. By calculating the speed dispersion index of the road segments within the virtual guidance field, the system confirmed that the vehicle speed standard deviation remained at a low level below 10 km / h, indicating that the driver group responded well to the suggested information and there was no need to switch to the second information mode. Finally, the traffic flow in this segment formed a stable synchronous flow upstream of the feature boundary, and vehicles passed smoothly through the original congestion initiation area at a safe speed.

[0037] Example 2: To objectively verify the technical effectiveness of the method of the present invention in suppressing traffic wave propagation and improving traffic flow stability and safety, a comparative experiment based on microscopic traffic simulation was conducted. The purpose of the experiment was to quantitatively analyze the changes in vehicle speed deceleration and speed dispersion when encountering sudden congestion after introducing the dynamic deployment and linkage control method of the present invention. The test platform used traffic flow simulation software based on the Intelligent Driver Model (IDM), which can simulate the following and lane-changing behavior of individual vehicles, thereby reproducing the formation and propagation of traffic waves. The test scenario was set as a 10km long highway section with 3 lanes, a design speed of 120km / h, and a background traffic volume of 1800 vehicles / hour / lane, which is at a critical state where traffic congestion is likely to occur. At the 300th second after the start of the simulation, a bottleneck event lasting 600 seconds was set 8km downstream of the road section, that is, the 300th second of the simulation was... A slow vehicle traveling at 40 km / h appears in the two lanes, generating a reproducible upstream congestion wave. To collect data, a virtual loop detector is placed every 500 meters along the simulated road segment, with a data update cycle set to 10 seconds. This cycle was determined after balancing the real-time nature of data acquisition with the system's processing load. The experiment includes a control group and an experimental group. Except for the control strategy, all other simulation parameters and traffic flow settings are kept consistent between the two groups. In the control group, a fixed-location warning method is used, with a permanently displayed warning sign placed 3 km upstream of the bottleneck event (at the 5 km marker), displaying the message "Congestion ahead, drive with caution." In the experimental group, no fixed warning sign is used; instead, the dynamic deployment and linkage control method of this invention is fully deployed. The speed contour lines used to identify characteristic boundaries are set to 60 km / h, and the basic spacing parameters of the virtual guidance field are... Set at 500 meters, speed response coefficient Set to 0.05 s / m.

[0038] After the experiment began, in the control group, a congestion traffic wave rapidly formed and propagated upstream at approximately 16 km / h after the bottleneck event occurred. Vehicles located upstream of the leading edge of the traffic wave, unable to predict the exact location of the congestion boundary, generally took emergency braking measures upon approach. In the experimental group, the method of this invention confirmed the existence of the traffic wave in its early stages and immediately generated and dynamically deployed a virtual guidance field upstream of it. This guidance field moved synchronously upstream with the traffic wave. By comparing and analyzing the data collected by virtual detectors at different locations upstream of the bottleneck event, significant differences between the two control methods could be observed. At a distance of 2 km from the bottleneck, i.e., at the 6 km marker, the average speed of vehicles in the control group had dropped to 41.5 km / h, with a maximum deceleration reaching [missing value]. The speed standard deviation was as high as 18.2 km / h, while at the same location, the test group's vehicles, benefiting from forward guidance information, maintained an average speed of 58.1 km / h, with a maximum deceleration of only... The speed standard deviation was controlled at 5.6 km / h; as the traffic wave continued to propagate upstream, at a distance of 3 km from the bottleneck, i.e., at the fixed warning sign location of the 5 km kilometer marker, the traffic conditions in the control group further deteriorated, with the average speed dropping to 35.2 km / h, and the maximum deceleration of vehicles due to frequent braking increasing to The speed standard deviation also increased to 23.4 km / h, indicating that the traffic flow had entered a highly unstable state. In the test group, since the virtual guidance field had moved upstream of this area with the traffic wave, the vehicles here had already completed speed adjustments, with an average speed of 56.2 km / h, and the maximum deceleration and speed standard deviation were respectively... At a speed of 6.8 km / h, all indicators remained stable. This data comparison shows that the maximum deceleration value of the experimental group was reduced by about 65% to 75% compared with the control group, and the speed standard deviation was reduced by more than 70%. The reduction in maximum deceleration directly corresponds to a reduction in vehicle emergency braking behavior, thereby reducing the risk of rear-end collisions caused by congestion. The reduction in speed standard deviation indicates that the internal stability of traffic flow has been enhanced, avoiding drastic stop-and-go phenomena. The experimental results confirm that the method of the present invention can effectively smooth the speed gradient of traffic flow and improve the safety and efficiency of road operation by dynamically tracking and guiding the traffic congestion boundary.

[0039] Example 3: This example combines Figures 1 to 3 This document describes a method for the dynamic deployment and coordinated control of road warning signs, such as... Figure 1As shown, the process begins with data acquisition and verification from multi-source real-time traffic parameters to ensure the authenticity of the input data. The verified data is then input into the congestion status diagnosis module. The core function of this module is to accurately identify the characteristic boundaries representing congestion status and their propagation speed. Simultaneously, a parallel environmental adaptive calibration mechanism dynamically adjusts the diagnostic criteria and guidance information based on historical data and real-time operating conditions. After determining the characteristic boundaries, the virtual guidance field generation module constructs a guidance field composed of spatially separated guidance points upstream of the boundary. Then, the dynamic deployment and linkage control module sends the generated guidance information to the warning signs for display and dynamically migrates this guidance field as the boundary position changes. During this deployment process, an effectiveness diagnosis link runs continuously, which judges whether the guidance information is invalid based on indicators such as speed dispersion. If it is judged to be valid, the process continuously updates the information on the warning signs dynamically. If it is judged to be invalid, it triggers an information mode switch, switching the guidance information to a second information mode with a higher level of mandatory level or warning intensity. This constitutes a closed-loop control system with data verification, status diagnosis, dynamic deployment, and feedback adjustment.

[0040] like Figure 2 As shown in the figure, the horizontal axis represents time, and the vertical axis represents mileage location, with the unit being kilometers (km). The dashed line marked as the feature boundary position represents a congestion front that is spreading at a constant speed upstream in the direction of decreasing mileage. It moves from a location at mileage 250km to around mileage 247.5km within 10 minutes. The solid line marked as guide point P1 and the dotted line marked as guide point P2 represent the first and second guide points in the virtual guide field, respectively. According to the location determination rules of this invention, these two guide points are always located upstream of the feature boundary and maintain a relatively stable safe distance from it, dynamically adjusted according to the boundary propagation speed. Thus, in space, they constitute a forward warning and guide zone that moves synchronously with the congestion risk source. This figure intuitively demonstrates the core mechanism of the traffic guidance benchmark transforming from a fixed geographical facility to a moving dynamic boundary.

[0041] like Figure 3 As shown, multi-source data providers, such as navigation map services and mobile communication data services, are responsible for providing real-time traffic flow parameters to the system. The data center or cloud platform serves as the core of the system, integrating a multi-source data verification module, a traffic condition diagnosis module, a virtual guidance field generation module, a dynamic deployment and linkage control module, an efficiency diagnosis and mode switching module, and a historical traffic flow database to support the environmental adaptive calibration module. The roadside facility layer consists of physical devices such as roadside control units and variable message signs. This layer is responsible for receiving guidance instructions from the cloud platform and updating its display content in real time, thereby applying the guidance information generated by the virtual guidance field to the actual traffic flow, ultimately forming a complete technical link from data perception to information dissemination to efficiency feedback.

[0042] Example 4: Before deploying the method of the present invention on a specific highway section, its core control parameters need to be calibrated to adapt to the road geometry and traffic flow characteristics of this section. The purpose of this work is to determine the key parameters in the virtual guidance field, specifically the speed response coefficient. The system provides a systematic procedure for determining the speed dispersion threshold in the performance diagnosis step to optimize the application effect of the method. The initial state of the calibration procedure is defined as follows: the object of the procedure is traffic flow data collected by virtual loop detectors deployed every 500 meters along the target road segment over the past 12 months. The time resolution of the data is 1 minute, which includes the average speed and flow information of each section. The enabling environment is a data processing server capable of running micro-traffic simulation and executing optimization algorithms. The first step of the calibration process is data preprocessing and feature event screening. The system automatically scans all historical data to identify traffic wave events that meet the preset conditions as analysis samples. The screening rule is that at any location in the road segment, the average speed at three consecutive time points is less than 60 km / h, and the boundary of the low-speed area steadily propagates upstream at a speed of 10 km / h to 25 km / h in the following 30 minutes. Through this procedure, the system selects 50 representative congestion propagation events from the historical data.

[0043] Next, the procedure enters the parameter optimization stage to determine the velocity response coefficients. The value; for each selected traffic wave event, the system extracts the traffic flow state 10 minutes before its occurrence as the initial condition for simulation, and performs a series of retrospective simulations in a simulation model consistent with the actual road segment topology; in each round of simulation, the virtual guidance field intervention method of this invention is applied, but different speed response coefficients are used. value, The value range is set to 0.01 s / m to 0.20 s / m, with a step size of 0.01 s / m; this is for evaluating different... To assess the intervention effect at different values, a comprehensive performance index function was defined, calculated as the weighted sum of the average maximum deceleration of all affected vehicles and the average travel delay time. This was achieved by analyzing 20 different events per sample from all 50 events. After simulation of the values, the system calculates each The average value of the comprehensive performance index corresponding to the value is determined, and the value that minimizes this index is identified. The set values ​​for this road segment; through this process, the speed response coefficient of the target road segment can be determined. The optimal value is 0.047 s / m, and the engineering value of 0.05 s / m is adopted in actual deployment. Simultaneously, this procedure is also used to determine the speed dispersion threshold in the performance diagnosis step. The system filters synchronous flow traffic state samples from the historical database, where the traffic flow is greater than 1500 vehicles / hour / lane and the average vehicle speed is stable between 40 km / h and 60 km / h. The system calculates the speed standard deviation of all these sample road segments and performs statistical distribution analysis. Finally, the 95th percentile of this statistical distribution is set as the performance judgment threshold. The threshold for this road segment is calculated to be 14.5 km / h, and is set to 15 km / h in engineering applications. Ultimately, this calibration procedure outputs a set of optimized core control parameters with clear engineering basis for the target road segment, providing initial conditions for the actual deployment of the method of this invention.

[0044] Example 5: In the initialization phase, the method of this invention quantifies and calibrates key logical judgment rules to adapt to different data source environments and traffic conditions. Regarding the arbitration threshold in multi-source data consistency verification, the calibration procedure is as follows: First, obtain the synchronous historical traffic flow parameters of the target road segment from the first and second data sources over the past 6 months, and select multiple time periods with stable traffic flow and no congestion events as the benchmark dataset. Then, calculate the difference in the spatial rate of change of the real-time traffic flow parameters reported by the two data sources at each time section within this benchmark dataset, and perform statistical analysis on all differences to calculate their mean. with standard deviation Ultimately, the arbitration threshold was set at... This setting is based on the standard deviation statistical method and is used to identify signals that exceed the normal fluctuation range.

[0045] The determination of congestion dissipation boundaries is performed through an algorithmic rule. This rule is set so that when the system continuously monitors the head of the queue in the original congestion core area, the average vehicle speed... continuous All data update cycles meet the conditions. When this occurs, it is determined that a dissipation boundary has been formed; among which, the velocity threshold... The speed was set to 40 km / h, which is higher than the typical congested speed but lower than the free-flowing speed, and is used to characterize the change in state; the duration N was set to 3, corresponding to a duration of 30 seconds. This setting is used to filter out short-term speed fluctuations caused by individual vehicle acceleration, thereby identifying stable changes in the overall traffic flow state.

[0046] Example 6: To ensure the reliable operation of the environmental adaptive calibration mechanism in this invention, a dynamic free-flow speed baseline model needs to be constructed to characterize the standard operating characteristics of the target road segment at different time periods. This example illustrates the construction procedure of this baseline model and the method for determining the corresponding operating condition switching conditions. The procedure first acquires all historical traffic flow data of the target road segment over the past 12 months with a time resolution of 15 minutes, preprocesses the data, and filters out data points with traffic flow below 800 vehicles / hour / lane and average vehicle speed higher than 90% of the road segment's design speed. This set of data points is defined as the free-flow sample set under standard operating conditions. Next, the procedure aggregates and analyzes the data in the free-flow sample set according to the time dimension to construct a baseline model; the week is divided into two types of days: weekdays and weekends, and each 24 hours is divided into 96 15-minute time slices; for each combination of day type and time slice, the average velocity value of all free-flow sample sets falling within the interval of that combination is calculated, and this average value is used as the free-flow velocity baseline for this specific combination; by traversing and calculating all combinations, a baseline model in the form of a two-dimensional matrix is ​​finally generated. When the system is running in real time, it can obtain the expected free-flow velocity under standard operating conditions by querying the day type and time slice corresponding to the current moment.

[0047] The determination of the operating condition switching conditions is based on statistical analysis of normal speed fluctuations. The procedure calculates the percentage deviation between the measured speed value of each free-flow sample and its corresponding baseline value in the baseline model, thus obtaining a deviation dataset describing the range of normal speed fluctuations. After fitting the probability density distribution of this deviation dataset, the 5th percentile of its negative distribution is selected as the trigger threshold for operating condition switching. If this value is calculated to be -18%, the operating condition switching condition is set as follows: when the average vehicle speed of a large area of ​​non-congested road sections monitored in real time is lower than 18% of its corresponding baseline value in the baseline model, the system determines that the current operating condition is non-standard. The process ultimately generates a dynamic free-flow velocity baseline model that reflects the multi-dimensional operational characteristics of the target road segment, and equips it with statistically based operating condition switching conditions, thus providing a reference basis for the operation of the environmental adaptive calibration mechanism. After a congestion event occurs and lasts for a period of time, the bottleneck event 8km downstream is cleared at the 900th second, and the vehicles at the head of the congestion queue begin to accelerate. A dissipation boundary representing the dissipation of the congestion state is formed and propagates along the driving direction at a speed of approximately 30km / h. At this time, the state diagnosis step of the method of this invention monitors the average vehicle speed of the head of the original congestion core area for multiple consecutive cycles to ensure that... The system determines that a dissipation boundary has been formed based on the given speed conditions. Downstream of this boundary, a coordinated guidance information field is deployed via warning signs along the route. For example, a sign 1 km ahead of the dissipation boundary indicates that congestion is about to ease and advises a steady increase in speed; a sign 2 km ahead indicates that traffic flow is recovering and suggests a maximum speed of 80 km / h. Simultaneously, the system continuously monitors the actual changes in downstream traffic flow parameters under the influence of this coordinated guidance information. If it detects excessive vehicle acceleration leading to an increase in the speed standard deviation, it dynamically adjusts the suggested maximum speed from 80 km / h to 70 km / h to suppress disorderly acceleration. In this way, traffic flow in the previously congested area smoothly and orderly returns to a free-flow state, avoiding secondary speed oscillations that could be caused by uneven acceleration.

[0048] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A dynamic deployment and linkage control method of a road warning signboard, characterized in that, The method establishes a traffic guidance procedure with a moving isovelocity characteristic boundary as a control target and an internal operating state check as an execution prerequisite, and the procedure comprises the following steps: Step A, obtaining real-time traffic flow parameters covering the target road section from a first data source and at least one second data source different from the first data source; and calculating the time or spatial variation rate of the real-time traffic flow parameters of the first data source and the second data source before executing the subsequent steps, and when the difference between the two is less than a set arbitration threshold, determining that the real-time traffic flow parameters pass the consistency check; Step B, when the real-time traffic flow parameters pass the consistency check, determining the real-time position of a speed isovelocity characteristic boundary and its propagation speed in the opposite direction of the driving direction based on the real-time traffic flow parameters of the first data source; Step C, defining a virtual guidance field composed of at least two spatially separated guidance points upstream of the characteristic boundary, and generating corresponding guidance information for each guidance point; Step D, displaying the guidance information on the warning sign corresponding to the position of the guidance point, and synchronously adjusting the positions of the guidance points in the virtual guidance field according to the real-time position change of the characteristic boundary; And, the first in the virtual guidance field Location of each guide point Determined according to the following rules: ,in, For the first Real-time location of each guide point The real-time location of the feature boundary. These are the basic spacing parameters built into the system. For the guide point number, The propagation speed of the feature boundary, This is the system's built-in speed response coefficient.

2. The dynamic deployment and linkage control method of a road warning signboard according to claim 1, characterized in that, The method further comprises an efficiency diagnosis and information mode switching step, which comprises: obtaining and calculating the speed dispersion index of the traffic flow parameters of one or more sub-road sections in the virtual guidance field; when the speed dispersion index is greater than a set efficiency determination threshold, determining that the guidance information is ineffective for the guidance behavior of the driver group; and automatically switching the guidance information displayed on the warning sign from the first information mode to the second information mode with a different mandatory level or warning intensity from the first information mode. 3.The dynamic deployment and linkage control method of a road warning signboard according to claim 1, wherein, The guidance information is the recommended driving speed, and the recommended driving speeds of different guidance points in the virtual guidance field decrease in steps in the driving direction.

4. The dynamic deployment and linkage control method of a road warning signboard according to claim 1, characterized in that, The dynamic deployment step further comprises: feedback adjustment of the guidance information based on the actual change of the traffic flow parameters upstream of the virtual guidance field under the action of the guidance information.

5. The dynamic deployment and linkage control method of a road warning signboard according to claim 1, characterized in that, The state diagnosis step further comprises: judging whether there is a dissipation boundary representing that the congestion state is dissipating, and determining the propagation of the dissipation boundary in the driving direction; and the method further comprises: when it is judged that there is a dissipation boundary, deploying, through one or more warning signs downstream of the dissipation boundary, coordinated guidance information aimed at guiding vehicles to accelerate smoothly.

6. The dynamic deployment and linkage control method of a road warning signboard according to claim 1, characterized in that, The method further comprises: determining the free flow speed baseline of the target road section under standard working conditions based on historical traffic flow parameters; comparing the real-time traffic flow parameters of the target road section in the non-congestion state with the free flow speed baseline, and when the deviation between the two meets the set working condition switching condition, determining that it is a non-standard working condition; and when it is determined to be a non-standard working condition, adaptively adjusting the judgment basis of the characteristic boundary in the state diagnosis step and the guidance information in the guidance field generation step according to the real-time traffic flow parameters.

7. The dynamic deployment and linkage control method of a road warning signboard according to claim 1, characterized in that, The real-time traffic flow parameters at least include the average vehicle speed of each sub-road section, and the characteristic boundary is to identify the position point with an average vehicle speed of 60 km / h as an isovelocity line.

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