Safety facility system suitable for highway grade crossing with large traffic volume

Abstract

The invention relates to the field of traffic management and control, and discloses a safety facility system suitable for highway grade crossing with large traffic volume, which comprises a data sensing module, a traffic flow modeling module, an optimal control module, a dynamic execution module and a feedback monitoring module, the data sensing module is used for collecting traffic flow speed, density and position information of the intersection in real time; the traffic flow modeling module is used for establishing a traffic flow potential field model and describing a traffic flow density and speed distribution state; the optimal control module generates an optimal control strategy; the dynamic execution module dynamically regulates and controls the duration of the signal lamp and the flicker frequency of the yellow flashing lamp according to the optimal control strategy; and the feedback monitoring module is used for monitoring the traffic flow state and the system performance, and returning feedback data to the data sensing module and the optimal control module. According to the invention, real-time dynamic regulation and control of the traffic flow at the plane intersection are realized, the traffic efficiency is improved, the accident rate of conflict points is reduced, and the safety and smoothness of the road plane intersection are guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of traffic management and control, and in particular to a safety facility system suitable for highway level crossings with heavy traffic. Background Art

[0002] With the rapid development of modern transportation networks, highway intersections, as important transportation hubs, have seen an increasing volume of traffic, and have become one of the key areas of traffic congestion and high-incidence accidents. Especially when traffic volume is high, intersections are prone to low traffic efficiency and potential traffic safety hazards due to the large number of traffic convergence, diversion, and conflict points.

[0003] In the existing technology, the management of flat intersections mainly relies on fixed-cycle signal light control and a simple yellow flashing light warning system. This static control method fails to fully consider the real-time traffic status, resulting in serious lane congestion during peak hours and low resource utilization during off-peak hours, and is unable to adapt to the dynamic changes in traffic. In addition, the flashing frequency of traditional yellow flashing lights is fixed and cannot be dynamically adjusted according to the actual traffic density. The warning effect is gradually weakened, and the driver's safety awareness is difficult to be effectively improved, posing a major traffic safety hazard.

[0004] On the other hand, the existing technology for monitoring and analyzing traffic flow status relies on a single type of sensor equipment, and the accuracy and comprehensiveness of data collection are insufficient, making it difficult to accurately reflect the speed, density and location information of traffic flow in real time. This leads to a lack of scientific basis for subsequent control strategies, and the inability to achieve optimal control of traffic flow at intersections. At the same time, there is a lack of a systematic feedback mechanism, and it is impossible to effectively evaluate and dynamically optimize the actual control effect. The system's adaptive ability is weak, making it difficult to meet the needs of complex traffic environments.

[0005] Therefore, how to provide a highway intersection safety facility system that can collect traffic information in real time, achieve optimal control based on scientific modeling, and have feedback optimization functions has become an important technical direction for solving problems such as low traffic efficiency and many safety hazards in existing technologies. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a safety facility system suitable for highway intersections with heavy traffic. At highway intersections with heavy traffic, the system optimizes the duration of signal lights and the frequency of yellow flashing lights through real-time data collection, traffic flow modeling and dynamic regulation, thereby improving traffic efficiency, reducing the risk of traffic conflicts, and achieving safe and efficient traffic management.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A safety facility system suitable for highway grade crossings with heavy traffic, comprising: Data perception module, used to collect real-time information on the speed, density and location of traffic at intersections; Traffic flow modeling module, which is used to establish a traffic flow potential field model based on traffic flow density and speed to describe the traffic flow distribution state; The optimal control module is used to calculate the green light duration and the flashing frequency of the yellow flashing light of the traffic light according to the traffic flow potential field model, and generate the optimal control strategy; Dynamic execution module, used to dynamically adjust the signal light cycle and the flashing frequency of the yellow flashing light based on the optimal control strategy; The feedback monitoring module is used to monitor the traffic control effect and transmit feedback data to the data perception module to form a closed-loop optimization control.

[0008] Preferably, the data perception module includes a laser radar, a geomagnetic sensor and a visual sensor, which are used to collect vehicle speed, traffic density and location information respectively.

[0009] Preferably, the traffic flow modeling module is used to establish a traffic flow potential field model based on the traffic flow continuity equation and the potential flow control equation, specifically including: The relationship between traffic density and speed is described based on the traffic continuity equation: Among them, ρ is the traffic density, v is the traffic speed; Relating the traffic speed to the potential field gradient, the traffic speed satisfies: Among them, Φ is the traffic potential function, which represents the distribution of traffic potential field; The vehicle flow potential field model is established based on the potential flow control equation, which is: Among them, k is the traffic pressure coefficient, γ is the traffic compression index, C is a constant, is the modulus of the potential field gradient; The vehicle flow potential field model is solved by a numerical method to obtain a dynamic distribution state of vehicle flow density and speed, and the distribution state is output to an optimal control module.

[0010] Preferably, the numerical method includes a finite difference method or a finite element method.

[0011] Preferably, the optimal control module is used to calculate the green light duration of the signal light according to the traffic potential field model, specifically including: based on the traffic density ρ of the lane, calculating the green light duration of each lane, so that the green light duration is proportional to the traffic density, satisfying: Among them, t g (i) is the green light duration of the i-th lane, ρ iis the traffic density of the i-th lane, is the sum of the traffic density of all lanes, T is the signal light cycle time; Based on the Hamilton-Jacobi-Bellman equation, the density pressure and vehicle delay at the traffic conflict point are minimized to obtain the optimal green light duration allocation strategy. The optimization objective function is: J = ∫0 T [C1ρ(x,t)+C2v(x,t) 2 ]dt Among them, J is the objective function, C1 is the traffic density weight coefficient, C2 is the speed change weight coefficient, and T is the control cycle time; the calculated optimal green light duration is output to the dynamic execution module for dynamically allocating the green light duration of the traffic light.

[0012] Preferably, the optimal control module is used to dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density, specifically including: Set the traffic density threshold ρ threshold , real-time monitoring of traffic density, when the traffic density exceeds the set threshold, increase the flashing frequency of the yellow flashing light; The flashing frequency of the yellow flashing light satisfies the following relationship: f=f0+k f ·(ρ-ρ threshold ) Among them, f is the dynamic flashing frequency of the yellow flashing light, f0 is the initial flashing frequency of the yellow flashing light, k f is the frequency adjustment coefficient, ρ is the real-time traffic density, ρ threshold is the traffic density threshold; When the traffic density is lower than the threshold, the yellow flashing light returns to the initial flashing frequency f0; The calculated flashing frequency is output to the dynamic execution module for real-time control of the working state of the yellow flashing light.

[0013] Preferably, the dynamic execution module includes: A signal light control unit is used to dynamically allocate the green light duration of the signal light according to the optimal control strategy; The yellow flashing light frequency modulation unit is used to dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density; The Internet of Vehicles communication unit is used to send the optimal travel route and recommended speed to the vehicle to achieve dynamic route guidance.

[0014] Preferably, the Internet of Vehicles communication unit sends a signal to the vehicle through V2I communication to form a green wave band, so that the vehicle passes through the intersection at a uniform speed, reducing the number of emergency stops of the vehicle.

[0015] Preferably, the feedback monitoring module includes: Traffic flow status monitoring unit, used to monitor the speed, density and travel time of controlled traffic flow; System performance analysis unit, used to evaluate traffic flow rate, accident rate at conflict points and vehicle delay time.

[0016] The present invention also provides a safety facility method applicable to a highway grade crossing with heavy traffic, comprising the following steps: Collect traffic speed, density and location information in real time; Based on the collected data, a traffic potential field model is established to describe the traffic density and speed distribution; According to the traffic potential field model, calculate the optimal traffic path and the green light duration and yellow flashing light flashing frequency of the traffic light; According to the optimal control results, dynamically adjust the signal light duration and the frequency of yellow flashing lights; Monitor traffic status and system performance, and perform real-time optimization through feedback data.

[0017] The present invention provides a safety facility system suitable for highway grade crossings with heavy traffic. It has the following beneficial effects: 1. The present invention monitors the traffic flow status in real time through the data perception module, and combines the traffic flow potential field model established by the traffic flow modeling module to dynamically calculate the green light duration of the signal light and the flashing frequency of the yellow flashing light to achieve optimal control of the traffic flow. The green light duration is allocated according to the traffic flow density, so that high-density lanes obtain priority right of way, reducing vehicle waiting time and signal light switching loss, and significantly improving the overall traffic efficiency of the intersection.

[0018] 2. The present invention dynamically adjusts the flashing frequency of the yellow flashing light. When the traffic density exceeds the set threshold, the frequency of the yellow flashing light automatically increases, reminding the driver to slow down in advance, thereby reducing the risk of conflict caused by excessive speed. In addition, the system provides the driver with the optimal passage path and recommended speed through the Internet of Vehicles communication, effectively guiding the vehicle to pass through the intersection smoothly and reducing the occurrence of accidents at the conflict point.

[0019] 3. The present invention provides a feedback monitoring module to monitor the traffic status and system performance in real time, collect key data such as traffic efficiency, traffic density and vehicle delays, and feed back the analysis results to the data perception module and the optimal control module. Through closed-loop feedback control, the system can dynamically optimize the signal light duration and yellow flashing light control parameters according to the actual operation effect, improving the system's adaptability and control accuracy.

[0020] 4. The present invention adopts dynamic modeling and control strategies through the optimal control module, and can quickly respond and adjust the control parameters of signal lights and yellow flashing lights when the traffic flow changes. The system adapts to different traffic flow characteristics and intersection types, and can achieve efficient regulation regardless of high-density traffic during peak hours or sparse traffic during off-peak hours, ensuring that the system can maintain a stable and efficient operating state under various traffic environments.

[0021] 5. The present invention uses the vehicle networking communication unit to send the optimal route and recommended speed to vehicles approaching the intersection, guiding the vehicles to pass along the "green wave belt". Through the precise guidance of vehicle speed and path, unnecessary emergency stops and waiting are reduced, the driving experience is improved, and the traffic capacity and operating efficiency of the intersection are further improved.

[0022] 6. By reasonably allocating the duration of the green light of the signal light and dynamically adjusting the frequency of the yellow flashing light, the present invention effectively reduces the waiting time of vehicles at the intersection, reduces the number of frequent stops and emergency brakes, improves the smoothness of vehicle traffic, helps reduce fuel consumption and exhaust emissions, and has a significant energy-saving and environmental protection effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the system architecture of the present invention; Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] Please refer to the attached Figure 1 The present invention provides a safety facility system suitable for highway level crossings with large traffic volume. The system realizes dynamic control and optimization of traffic flow based on traffic potential theory and optimal control theory, improves the traffic efficiency of intersections, and reduces the accident rate at conflict points. The various modules of the system of the present invention are described in detail below.

[0026] The present invention is applicable to a safety facility system for highway level intersections with heavy traffic volume and may include a data perception module, a vehicle flow modeling module, an optimal control module, a dynamic execution module, and a feedback monitoring module.

[0027] In this embodiment, the data perception module is used to collect the traffic speed, traffic density and vehicle position information of the highway intersection in real time, provide data input for the subsequent traffic modeling module, and ensure the accurate modeling of the traffic potential field and the reliability of dynamic control.

[0028] Specifically, the data perception module includes lidar, geomagnetic sensors and visual sensors. These devices are deployed in key areas of intersections to monitor vehicle speed, traffic density and location information, and achieve high-precision collection of traffic information through data fusion technology.

[0029] As an option, LiDAR can be deployed in all directions of the intersection, mainly used to collect vehicle speed and position information. LiDAR calculates the vehicle's speed and spatial position by emitting a laser beam and receiving the reflected signal, measuring the time difference of laser propagation. In one possible implementation, the data output format of the LiDAR includes the vehicle speed v(x, t) and the vehicle's real-time position x(t), where x represents the vehicle's spatial coordinates and t represents time.

[0030] For example, the geomagnetic sensor is installed under the road surface of each lane, specifically buried at a certain distance from the intersection. The geomagnetic sensor monitors the disturbance of the geomagnetic field by the vehicle, determines the passing of the vehicle, and thus calculates the number of vehicles passing the sensor per unit time, and then derives the density of the traffic flow. The calculation formula for traffic density is: Wherein, ρ is the traffic density, N is the number of vehicles passing through the sensor area, L is the length of the lane, and W is the width of the lane. It should be noted that the geomagnetic sensors can be distributed in each lane in series or in parallel to improve the comprehensiveness and accuracy of traffic density monitoring.

[0031] In one possible implementation, the visual sensor is placed at a high point at the intersection, such as on a signal light pole or monitoring bracket, to obtain dynamic images of the entire intersection. Specifically, the visual sensor combines image recognition technology with edge detection, target recognition, and region segmentation algorithms to analyze the number, speed, and location of vehicles, and compares and verifies the data with the laser radar and geomagnetic sensor data, thereby improving the accuracy of data collection.

[0032] As an option, visual sensors can be used to identify and track vehicles based on deep learning algorithms. For example, object detection models such as YOLO and Faster R-CNN can be used to achieve real-time identification of vehicles at intersections and output the speed and position coordinate data of the vehicles. It should be noted that the data from visual sensors can be combined with the real-time speed data from lidar to correct possible errors and ensure the accuracy of traffic speed information.

[0033] It is understandable that the various types of data collected by the data perception module need to be processed through data fusion technology to uniformly output the vehicle speed, density and location information. In one possible implementation, data fusion can use weighted average method or Kalman filter algorithm to fuse the data of lidar, geomagnetic sensor and visual sensor. For example, the Kalman filter algorithm is used to predict and correct the vehicle speed data of different sensors: v fused =K1v laser +K2v visual Among them, v fused is the fused vehicle speed data, v laser is the vehicle speed collected by the laser radar, v visual is the vehicle speed detected by the visual sensor, K1 and K2 are weighting coefficients.

[0034] In some embodiments, various sensors of the data perception module can transmit data in real time to the control center of the system through the wireless communication module to facilitate subsequent vehicle flow modeling and optimization control. In specific implementation, the wireless communication module can use communication technologies such as 5G network, Zigbee or LoRa to ensure low latency and high reliability of data transmission.

[0035] It should be noted that the sensor layout of the data perception module needs to be optimized according to the specific layout of the intersection. For example, the lidar should be set in the main direction of vehicle traffic, while the geomagnetic sensor can be placed in the center of the lane of the key section.

[0036] As an extension, the data perception module can also integrate weather detection devices and light sensors to obtain environmental data at intersections, such as slippery roads, low visibility and other special conditions. These environmental data can be transmitted to the traffic flow modeling module together with the traffic flow data to improve the robustness and adaptability of the traffic flow model.

[0037] In practical applications, it is understandable that the real-time data collection capability of the data perception module is crucial to the overall operation of the system. Through multi-sensor fusion and data verification mechanisms, the accuracy and reliability of data input can be ensured, providing basic data support for subsequent traffic modeling and dynamic control.

[0038] In this embodiment, the traffic modeling module is used to construct a traffic potential field model based on traffic speed, traffic density and location data, describe the dynamic distribution state of traffic in the highway intersection area, and provide a data basis for the signal light duration allocation and yellow flashing light frequency adjustment of the optimal control module.

[0039] Specifically, the traffic flow modeling module combines the traffic flow continuity equation and the potential flow control equation to establish a traffic flow potential field model. The traffic flow potential field model can accurately reflect the distribution characteristics of traffic flow in the plane intersection area, including the spatiotemporal changes of traffic flow density, velocity field distribution, and the state of potential field gradient.

[0040] As an option, the traffic flow modeling module describes the relationship between traffic density and speed based on the traffic flow continuity equation, which is expressed as: Among them, ρ is the traffic density, v is the traffic speed, is the divergence operator, represents the time rate of change of traffic density, Represents the spatial change rate of traffic flow.

[0041] In a possible implementation, the vehicle flow speed is associated with the vehicle flow potential field gradient, and the vehicle flow speed satisfies the following relationship: Among them, Φ is the traffic flow potential function, which represents the potential field distribution state in the traffic flow field. is the gradient of the traffic potential function, Indicates the speed of traffic.

[0042] It should be noted that the traffic flow potential function Φ is solved by the potential flow control equation, which is: Among them, k is the traffic pressure coefficient, γ is the traffic compression index, is the square of the gradient of the traffic potential function, and C is the constant energy value of the potential field.

[0043] As an option, in the potential flow governing equation, the traffic pressure term It can be used to characterize the impact of changes in traffic density on the potential field. When the traffic density is high, the traffic pressure increases, thereby changing the speed distribution and potential field distribution of the traffic.

[0044] In one possible implementation, the traffic modeling module numerically solves the potential flow control equations by using the finite difference method (FDM). Specifically, the continuous potential flow equations are discretized, and the time and space are segmented for iterative calculation to solve the traffic density, traffic speed and potential field distribution.

[0045] For example, suppose the intersection area is divided into several grid cells, and the vehicle flow potential field Φ in each grid cell satisfies the discretized control equation: Among them, Φi,j represents the potential field value in the i,jth grid cell, ρ i,j is the traffic density within the grid cell.

[0046] As an option, the traffic modeling module can interpolate and fit the calculation results of each grid cell to generate a traffic potential field distribution map for the entire plane intersection area, and finally output the traffic density distribution ρ(x, t), speed distribution v(x, t) and potential field distribution Φ(x, t).

[0047] In some embodiments, the traffic modeling module can also optimize the traffic potential field based on boundary conditions. Specifically, the traffic speed and density at the entrance of the intersection are used as boundary inputs, and the potential field gradient at the exit is set to zero, indicating that there is no external resistance when the vehicle passes through the exit.

[0048] For example, the boundary condition can be expressed as: Inlet boundary: ρ(x,t)=ρ0,v(x,t)=v0 Export Boundary: It should be noted that the traffic flow modeling module can be further adapted to different types of intersections, including cross intersections, T-intersections, and multi-lane complex intersections. In a possible implementation, for complex intersections, the traffic flow modeling module divides the entire intersection area into several sub-areas through the method of regional decomposition, solves the traffic flow potential field separately, and finally splices and merges the results of each sub-area.

[0049] It can be understood that the traffic potential field model generated by the traffic modeling module provides the optimal control module with dynamic distribution data of traffic density and speed, thereby supporting the optimal regulation of traffic light duration allocation and yellow flashing light frequency.

[0050] In this embodiment, the optimal control module is used to calculate the green light duration and flashing frequency of the yellow flashing light of the traffic lights in each lane of the flat intersection based on the traffic potential field model provided by the traffic modeling module, and generate an optimal control strategy, thereby realizing dynamic regulation of traffic flow, improving the traffic efficiency of the intersection and reducing the risk of traffic conflict.

[0051] Specifically, the optimal control module analyzes the traffic density, speed and potential field distribution, and uses the optimal control theory to dynamically adjust the signal control parameters of each lane, including the green light duration and the operating frequency of the yellow flashing light.

[0052] As an option, the green light duration is allocated in proportion to the traffic density, and the specific calculation formula is: in: t g (i) represents the green light duration of the i-th lane; ρ i represents the traffic density of the i-th lane; is the sum of the traffic density of all lanes; T represents the entire signal light cycle time.

[0053] It can be understood that this allocation method ensures that lanes with higher traffic density receive longer green light time, giving priority to directing high-density traffic and avoiding vehicle backlogs, thereby achieving balanced control between lanes.

[0054] In one possible implementation, the optimal control module optimizes the traffic density and vehicle speed by establishing an objective function to minimize the traffic conflict pressure and travel time delay at the intersection. The specific objective function is: J = ∫0 T [C1ρ(x,t)+C2v(x,t) 2 ]dt in: J is the objective function of optimal control; C1 is the weight coefficient of traffic density, which is used to indicate the impact of traffic density on the system; C2 is the weight coefficient of the traffic speed change, which is used to measure the stability of vehicle speed; ρ(x,t) is the real-time traffic density distribution; v(x,t) is the traffic speed distribution; T is the optimization control period.

[0055] As an option, the optimal control module solves the Hamilton-Jacobi-Bellman (HJB) equation to obtain the optimal control strategy for the system. The HJB equation is expressed as: in: V represents the optimal value function; L(x,u,t) is the instantaneous loss function, which includes the influence of traffic density and speed; f(x,u,t) is the evolution equation of the system state; u is the control variable, including the control parameters of the signal light duration and the frequency of the yellow flashing light.

[0056] In one possible implementation, the optimal control module uses an iterative method to numerically solve the HJB equation, determine the optimal signal control parameters for each lane, and transmit the calculation results to the dynamic execution module for execution.

[0057] For example, the optimal control module can also dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density to improve the warning effect of the intersection and reduce the risk of vehicle conflict. The specific frequency adjustment relationship is: f=f0+k f ·(ρ-ρ threshold ) in: f is the dynamic flashing frequency of the yellow flashing light; f0 is the initial flashing frequency of the yellow flashing light; k f is the frequency adjustment coefficient; ρ is the current traffic density; ρ threshold is the threshold of traffic density.

[0058] Specifically, when the traffic density exceeds the set threshold, the flashing frequency of the yellow flashing light increases to remind the driver to slow down; when the traffic density is lower than the threshold, the yellow flashing light returns to the initial frequency to avoid waste of resources caused by frequency changes.

[0059] It should be noted that the control period T and the traffic density threshold ρ in the optimal control module threshold It can be set according to the traffic characteristics of the actual intersection. For example, for intersections with large traffic fluctuations and obvious peak hours, the control cycle T can be appropriately shortened to improve the response speed of dynamic control.

[0060] In some embodiments, the optimal control module can also combine historical traffic flow data with real-time traffic flow data to predict future traffic flow status through a prediction model, thereby generating an optimal control strategy in advance. Exemplarily, the prediction model can use a time series analysis method, such as an ARIMA model or an LSTM neural network model, to improve the foresight and accuracy of the optimal control.

[0061] It can be understood that the optimal control module effectively guides traffic and reduces pressure at conflict points by dynamically adjusting the duration of green lights and the frequency of yellow flashing lights, and provides clear control instructions to the dynamic execution module to ensure the orderly operation of the entire system.

[0062] In this embodiment, the dynamic execution module is used to receive the control strategy generated by the optimal control module, dynamically adjust the signal light cycle and the flashing frequency of the yellow flashing light at the highway intersection, and at the same time provide the vehicle with the travel path and recommended speed through the vehicle network communication unit to achieve dynamic and orderly traffic diversion.

[0063] Specifically, the dynamic execution module includes a traffic light control unit, a yellow flashing light frequency modulation unit and a vehicle network communication unit. Each unit has a clear division of labor and operates in a coordinated manner to ensure real-time control of traffic flow at the intersection.

[0064] As an option, the signal light control unit is used to dynamically adjust the green light duration of each lane based on the green light duration t calculated by the optimal control module. g (i) Perform dynamic signal control.

[0065] It should be noted that the execution cycle T of the signal light control unit can be adjusted according to the actual traffic flow status of the intersection. During peak traffic hours, the value of T can be appropriately shortened to improve the control response speed; during sparse traffic hours, the value of T can be appropriately extended to improve the resource utilization of the signal light.

[0066] In a possible implementation, the yellow flashing light frequency modulation unit is used to dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density. For specific relationships, refer to the adjustment of the flashing frequency of the yellow flashing light by the optimal control module.

[0067] Specifically, when the real-time traffic density ρ exceeds the set threshold ρ threshold When the traffic density drops below the threshold, the flashing frequency of the yellow flashing light increases accordingly, thereby reminding the driver to slow down in advance and reduce the risk of traffic conflict in the intersection area. When the traffic density drops below the threshold, the yellow flashing light automatically returns to the initial frequency f0 to reduce interference to the driver.

[0068] For example, the yellow flash light frequency modulation unit can achieve precise frequency control through PWM (pulse width modulation) technology to ensure the stability and real-time performance of frequency adjustment. In specific implementation, the PWM controller receives the frequency adjustment instruction from the optimal control module and adjusts the power supply pulse period of the yellow flash light, thereby achieving dynamic frequency control.

[0069] As an option, the vehicle networking communication unit is used to send the optimal route and recommended speed to vehicles approaching the intersection, forming a "green wave belt" to guide vehicles through the intersection with the optimal route, reducing unnecessary emergency stops and waiting. Specifically, the vehicle networking communication unit realizes real-time data transmission through V2I (vehicle to infrastructure) communication between the on-board unit (OBU) and the roadside unit (RSU).

[0070] In a possible implementation, the vehicle network communication unit calculates the optimal travel speed v in real time. opt Send to vehicle terminal: in: v opt is the recommended speed of travel; L is the distance from the intersection to the signal light area; t g (i) is the remaining green light time for the current lane.

[0071] It should be noted that the recommended speed v optThrough dynamic calculation and adjustment, vehicles can pass through intersections smoothly during the green light period, minimizing the number of stops and improving traffic efficiency.

[0072] It can be understood that the signal light control unit, yellow flashing light frequency modulation unit and vehicle network communication unit in the dynamic execution module cooperate with each other to jointly execute the optimal control strategy to achieve dynamic guidance and control of traffic at the intersection.

[0073] In some embodiments, the dynamic execution module can also interact with the feedback monitoring module in real time, receive feedback information on traffic flow status, and dynamically update the control parameters of the signal light and the yellow flashing light. For example, when an abnormal increase in traffic density in a lane is detected, the dynamic execution module can temporarily adjust the green light duration of the lane to prioritize traffic flow and reduce lane congestion.

[0074] In this embodiment, the feedback monitoring module is used to monitor the effect of dynamic control of the highway intersection, and feed back the monitoring data to the data perception module and the optimal control module to form a closed-loop optimization control to achieve real-time adjustment and optimization of the system.

[0075] Specifically, the feedback monitoring module includes a traffic status monitoring unit and a system performance analysis unit, which are used to collect and evaluate the traffic operation status in real time, and transmit the feedback data to the data perception module for loop optimization.

[0076] As an option, the traffic status monitoring unit evaluates the dynamic control effect of traffic lights and yellow flashing lights by monitoring key parameters such as traffic speed, density, and travel time in real time.

[0077] The vehicle flow speed v(x, t) is obtained jointly by the lidar and visual sensor, and the vehicle flow density ρ(x, t) is collected in real time by the geomagnetic sensor.

[0078] For example, the traffic time T c It can be calculated by the time difference between entry and exit. The specific relationship is as follows: T c =t exit -t entry Among them, t exit is the time it takes for the vehicle to pass through the exit, t entry The time when the vehicle enters the intersection.

[0079] In a possible implementation, the feedback monitoring module can also monitor lane congestion in the intersection area based on the above data. For example, if the traffic density ρ of a lane exceeds the threshold ρ threshold , it is considered that congestion has occurred, and the traffic status monitoring unit will mark the congested lane in real time and output a congestion warning signal.

[0080] As an option, the system performance analysis unit is used to quantitatively evaluate the effect of dynamic control of intersections, mainly including the calculation and analysis of indicators such as traffic flow rate, vehicle delay time and accident rate at conflict points.

[0081] Specifically, the traffic flow rate Q represents the total number of vehicles passing through the intersection per unit time, and the calculation formula is: in: Q is the traffic flow rate; N is the monitoring period T monitor The number of vehicles passing through the intersection; T monitor The monitoring cycle time.

[0082] Vehicle delay time T d It represents the average waiting time of vehicles in the intersection area, which is calculated as follows: T d =T c -T f in: T d Delay time for vehicles; T c The actual travel time of the vehicle; T f is the ideal travel time (i.e. the time required to pass the intersection without waiting).

[0083] For example, the conflict point accident rate R can be calculated by counting the ratio of the number of conflict events in the intersection area to the total traffic volume: in: R is the accident rate; C is the number of conflict point events that occurred during the monitoring period; N is the total number of vehicles passing through the intersection.

[0084] It should be noted that the analysis results of the system performance analysis unit will be fed back to the optimal control module to update the traffic light duration distribution and yellow flashing light frequency control parameters, thereby achieving dynamic optimization.

[0085] In one possible implementation, the feedback monitoring module can combine historical monitoring data and real-time monitoring data to perform trend analysis on the traffic flow status and predict future traffic flow changes at the intersection. For example, the feedback monitoring module can use time series prediction methods, such as ARIMA models or deep learning models based on LSTM (Long Short-Term Memory Network) to achieve short-term prediction of traffic flow status.

[0086] As an option, the feedback monitoring module can also interact with the dynamic execution module to update the traffic status information in real time. For example, when the traffic status monitoring unit detects that a lane is congested, the system performance analysis unit marks the lane status as "high priority", and the dynamic execution module can temporarily extend the green light duration of the lane to give priority to diverting traffic.

[0087] It can be understood that the feedback monitoring module forms a closed-loop feedback control through real-time monitoring of traffic status and system performance analysis, ensuring that the system can be dynamically adjusted according to actual operating results to improve traffic efficiency and safety at intersections.

[0088] In general, the system of the present invention collects traffic speed, density and position information in real time through the data perception module. The traffic modeling module constructs a traffic potential field model based on the traffic continuity equation and the potential flow control equation to describe the dynamic distribution state of the traffic flow; the optimal control module calculates the green light duration of the traffic light and the flashing frequency of the yellow flashing light according to the traffic potential field model to generate the optimal control strategy; the dynamic execution module dynamically adjusts the traffic light cycle and the frequency of the yellow flashing light and provides the vehicle with the travel path and recommended speed through the vehicle network; the feedback monitoring module monitors the system operation effect in real time, and transmits the traffic status data back to the data perception module and the optimal control module to form a closed-loop optimization control.

[0089] Please refer to the attached Figure 2 The present invention also provides a safety facility method suitable for highway level intersections with heavy traffic. The specific implementation methods of each step are described below in conjunction with the workflow of the system of the present invention.

[0090] S1, real-time collection of traffic speed, density and location information; In this step, the data perception module uses laser radar, geomagnetic sensor and visual sensor to obtain the speed, density and position information of the traffic flow in the plane intersection area. The laser radar is used to measure the real-time speed and spatial position of the vehicle, the geomagnetic sensor is used to monitor the traffic density per unit time, and the visual sensor uses image recognition technology to assist in identifying the dynamic position of the vehicle and the lane occupancy. After the collected data is fused and processed, it provides basic input for the traffic modeling module.

[0091] S2. Establish a traffic potential field model based on the collected data to describe the traffic density and speed distribution; In this step, the traffic flow modeling module establishes a traffic flow potential field model based on the traffic flow speed, density and location information provided by the data perception module. The traffic flow potential field model reflects the dynamic distribution of traffic flow in the plane intersection area, including the spatial distribution of traffic flow density, the change trend of the speed field and the distribution of the potential field gradient. The generated traffic flow potential field model provides data support for subsequent optimal control.

[0092] S3. Calculate the optimal traffic path, green light duration of traffic lights, and flashing frequency of yellow flashing lights according to the traffic potential field model; In this step, the optimal control module calculates the optimal traffic path, green light duration of traffic lights, and flashing frequency of yellow flashing lights for each lane according to the traffic potential field model. The green light duration is allocated based on the real-time traffic density of each lane, giving priority to diverting high-density traffic. The frequency of yellow flashing lights is dynamically adjusted according to the traffic density. When the density is high, the flashing frequency is increased to remind drivers to slow down and reduce the risk of conflict.

[0093] S4. Dynamically adjust the duration of the signal light and the frequency of the yellow flashing light according to the optimal control result; In this step, the dynamic execution module receives the control results generated by the optimal control module and adjusts the signal light cycle and yellow flashing light frequency of each lane of the intersection in real time. The signal light control unit dynamically allocates the green light duration of each lane to ensure the orderly flow of traffic. The yellow flashing light frequency modulation unit adjusts the flashing frequency according to the change of traffic density to provide early warning of traffic. At the same time, the Internet of Vehicles communication unit provides the optimal passage path and recommended passage speed to vehicles approaching the intersection, guiding the vehicles to pass through the intersection smoothly.

[0094] S5, monitor traffic status and system performance, and perform real-time optimization through feedback data; In this step, the feedback monitoring module monitors the control effect of the system in real time and collects traffic status data, including traffic speed, traffic density, travel time and system performance indicators (such as vehicle delay time, traffic flow rate, etc.). The system performance analysis unit quantitatively analyzes the collected data and transmits the feedback results to the data perception module and the optimal control module to form a closed-loop optimization control. By continuously iteratively updating the signal light duration and the frequency of the yellow flashing light, the system's ability to dynamically control traffic is improved.

[0095] Through the above steps, the method of the present invention realizes dynamic monitoring, modeling, optimal control, execution and feedback optimization of traffic flow at the flat intersection, improves the traffic efficiency of the intersection, reduces the risk of traffic conflict, and ensures the safety and smoothness of the highway flat intersection.

[0096] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A safety facility system suitable for high-traffic highway intersections, characterized in that: include: Data perception module, used to collect real-time information on the speed, density and location of traffic at intersections; Traffic flow modeling module, which is used to establish a traffic flow potential field model based on traffic flow density and speed to describe the traffic flow distribution state; The optimal control module is used to calculate the green light duration and the flashing frequency of the yellow flashing light of the traffic light according to the traffic flow potential field model, and generate the optimal control strategy; Dynamic execution module, used to dynamically adjust the signal light cycle and the flashing frequency of the yellow flashing light based on the optimal control strategy; The feedback monitoring module is used to monitor the traffic control effect and transmit feedback data to the data perception module to form a closed-loop optimization control.

2. A safety facility system suitable for high-traffic highway intersections according to claim 1, characterized in that: The data perception module includes a laser radar, a geomagnetic sensor and a visual sensor, which are used to collect vehicle speed, traffic density and location information respectively.

3. A safety facility system suitable for high-traffic highway intersections according to claim 1, characterized in that: The vehicle flow modeling module is used to establish a vehicle flow potential field model based on the vehicle flow continuity equation and the potential flow control equation, and specifically includes: The relationship between traffic density and speed is described based on the traffic continuity equation: Among them, ρ is the traffic density, v is the traffic speed; Relating the traffic speed to the potential field gradient, the traffic speed satisfies: Among them, Φ is the traffic potential function, which represents the distribution of traffic potential field; The vehicle flow potential field model is established based on the potential flow control equation, which is: Among them, k is the traffic pressure coefficient, γ is the traffic compression index, C is a constant, is the modulus of the potential field gradient; The vehicle flow potential field model is solved by a numerical method to obtain a dynamic distribution state of vehicle flow density and speed, and the distribution state is output to an optimal control module.

4. A safety facility system suitable for high-traffic highway grade crossings according to claim 3, characterized in that: The numerical method includes a finite difference method or a finite element method.

5. The safety facility system suitable for high-traffic highway intersections according to claim 1, characterized in that: The optimal control module is used to calculate the green light duration of the signal light according to the traffic potential field model, specifically including: based on the traffic density ρ of the lane, calculating the green light duration of each lane, so that the green light duration is proportional to the traffic density, satisfying: Among them, t g (i) is the green light duration of the i-th lane, ρ i is the traffic density of the i-th lane, is the sum of the traffic density of all lanes, T is the signal light cycle time; Based on the Hamilton-Jacobi-Bellman equation, the density pressure and vehicle delay at the traffic conflict point are minimized to obtain the optimal green light duration allocation strategy. The optimization objective function is: Among them, J is the objective function, C1 is the traffic density weight coefficient, C2 is the speed change weight coefficient, and T is the control cycle time; the calculated optimal green light duration is output to the dynamic execution module for dynamically allocating the green light duration of the traffic light.

6. A safety facility system suitable for high-traffic highway grade crossings according to claim 1, characterized in that: The optimal control module is used to dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density, specifically including: Set the traffic density threshold ρ threshold , real-time monitoring of traffic density, when the traffic density exceeds the set threshold, increase the flashing frequency of the yellow flashing light; The flashing frequency of the yellow flashing light satisfies the following relationship: f=f0+k f ·(ρ-ρ threshold ) Among them, f is the dynamic flashing frequency of the yellow flashing light, f0 is the initial flashing frequency of the yellow flashing light, k f is the frequency adjustment coefficient, ρ is the real-time traffic density, ρ threshold is the traffic density threshold; When the traffic density is lower than the threshold, the yellow flashing light returns to the initial flashing frequency f0; The calculated flashing frequency is output to the dynamic execution module for real-time control of the working state of the yellow flashing light.

7. A safety facility system suitable for high-traffic highway grade crossings according to claim 1, characterized in that: The dynamic execution module includes: A signal light control unit is used to dynamically allocate the green light duration of the signal light according to the optimal control strategy; The yellow flashing light frequency modulation unit is used to dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density; The Internet of Vehicles communication unit is used to send the optimal travel route and recommended speed to the vehicle to achieve dynamic route guidance.

8. A safety facility system suitable for high-traffic highway grade crossings according to claim 7, characterized in that: The vehicle networking communication unit sends signals to the vehicle through V2I communication, forming a green wave band, so that the vehicle passes through the intersection at a uniform speed, reducing the number of emergency stops of the vehicle.

9. The safety facility system for high-traffic highway intersections according to claim 1, characterized in that: The feedback monitoring module comprises: Traffic flow status monitoring unit, used to monitor the speed, density and travel time of controlled traffic flow; System performance analysis unit, used to evaluate traffic flow rate, accident rate at conflict points and vehicle delay time.

10. A safety facility method suitable for high-traffic highway grade crossings, applied to the system as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: Collect traffic speed, density and location information in real time; Based on the collected data, a traffic potential field model is established to describe the traffic density and speed distribution; According to the traffic potential field model, calculate the optimal traffic path and the green light duration and yellow flashing light flashing frequency of the traffic light; According to the optimal control results, dynamically adjust the signal light duration and the frequency of yellow flashing lights; Monitor traffic status and system performance, and perform real-time optimization through feedback data.

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