A safety system for high-volume highway flat intersections

Abstract

The application relates to the field of traffic management and control, and discloses a safety facility system suitable for a highway flat intersection 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 the traffic flow speed, density and position information of the intersection in real time; the traffic flow modeling module establishes a traffic flow potential field model to describe the 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 signal lamp duration and the yellow flashing lamp flicker frequency according to the optimal control strategy; and the feedback monitoring module is used for monitoring the traffic flow state and system performance and feeding back the feedback data to the data sensing module and the optimal control module. The application realizes real-time dynamic regulation and control of the traffic flow of the flat intersection, improves the passing efficiency, reduces the conflict point accident rate, and guarantees the safety and smoothness of the highway flat intersection.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of traffic management and control, in particular to a safety facility system suitable for highway flat intersection with large traffic volume. BACKGROUND

[0002] With the rapid development of modern transportation network, highway flat intersection as an important transportation hub, its traffic volume is increasing, has become one of the key areas of traffic congestion and accident-prone. Especially in the case of large traffic volume, flat intersection due to the flow of traffic, shunt and conflict point is more, easy to lead to low traffic efficiency and traffic safety hazards.

[0003] In the prior art, the management of flat intersection mainly depends on the fixed cycle signal light control and simple yellow flashing light warning system. This static control method does not fully consider the real-time traffic state, leading to serious lane congestion during peak hours, while the resource utilization rate is low during off-peak hours, which cannot adapt to the dynamic changes of traffic flow. In addition, the flashing frequency of traditional yellow flashing light is fixed, which cannot be dynamically adjusted according to the actual traffic density, and the warning effect is gradually weakened, and the safety awareness of drivers is difficult to improve effectively, which has great traffic safety hazards.

[0004] On the other hand, the existing technology relies on a single type of sensor device for traffic state monitoring and analysis, and the data collection accuracy and comprehensiveness are insufficient, which is difficult to reflect the traffic speed, density and position information in real time and accurately. This leads to the lack of scientific basis for the subsequent control strategy, and the optimal regulation and control of intersection traffic cannot be realized. At the same time, lack of systematic feedback mechanism, unable to effectively evaluate and dynamically optimize the actual control effect, the self-adaptive ability of the system is weak, it is difficult to meet the needs of complex traffic environment.

[0005] Therefore, how to provide a highway flat intersection safety facility system which can collect traffic information in real time, realize optimal regulation and control based on scientific modeling, and has feedback optimization function, has become an important technical direction to solve the problems of low traffic efficiency and many safety hazards in the prior art. SUMMARY

[0006] In view of the shortcomings of the prior art, the present application provides a safety facility system suitable for highway flat intersection with large traffic volume, which optimizes the signal light duration and yellow flashing light frequency, improves the traffic efficiency, reduces the traffic conflict risk, and realizes the safe and efficient traffic management through real-time data collection, traffic modeling and dynamic regulation at the highway flat intersection with large traffic volume.

[0007] To achieve the above purpose, the present application is realized by the following technical scheme: a safety facility system suitable for highway flat intersection with large traffic volume, comprising:

[0008] a data perception module for collecting intersection traffic flow speed, density and position information in real time;

[0009] a traffic flow modeling module for establishing a traffic flow potential field model based on traffic flow density and speed to describe traffic flow distribution state;

[0010] an optimal control module for calculating signal light green light duration and yellow flashing light flicker frequency according to the traffic flow potential field model to generate an optimal control strategy;

[0011] a dynamic execution module for dynamically regulating signal light cycle and yellow flashing light flicker frequency based on the optimal control strategy;

[0012] a feedback monitoring module for monitoring traffic flow control effect and transmitting feedback data to the data perception module to form a closed-loop optimal control.

[0013] Preferably, the data perception module includes a laser radar, a geomagnetic sensor and a visual sensor for collecting vehicle speed, traffic flow density and position information, respectively.

[0014] Preferably, the traffic flow modeling module is used to establish a traffic flow potential field model based on a traffic flow continuity equation and a potential flow control equation, specifically including:

[0015] the traffic flow continuity equation is used to describe the relationship between traffic flow density and speed:

[0016]

[0017] wherein ρ is traffic flow density and v is traffic flow speed;

[0018] the traffic flow speed is associated with the potential field gradient, and the traffic flow speed satisfies:

[0019]

[0020] wherein Φ is a traffic flow potential function representing traffic flow potential field distribution;

[0021] the potential flow control equation is used to establish the traffic flow potential field model, and the potential flow control equation is:

[0022]

[0023] wherein k is a traffic flow pressure coefficient, γ is a traffic flow compression index, C is a constant, is a modulus value of the potential field gradient;

[0024] the traffic flow potential field model is solved by a numerical method to obtain a dynamic distribution state of traffic flow density and speed, and the distribution state is output to the optimal control module.

[0025] Preferably, the numerical method includes a finite difference method or a finite element method.

[0026] Preferably, the optimal control module is configured to calculate the green light duration of the signal light according to a traffic flow potential field model, and specifically comprises: calculating the green light duration of each lane based on the traffic density ρ of the lane, and distributing the green light duration in proportion to the traffic density, so as to satisfy:

[0027]

[0028] wherein t g (i) is the green light duration of the i-th lane, ρ i is the traffic density of the i-th lane, and is the sum of the traffic densities of all lanes, and T is the signal light cycle time.

[0029] Based on the Hamilton-Jacobi-Bellman equation, the density pressure of the traffic conflict point and the vehicle passing delay are minimized to obtain an optimal green light duration distribution strategy, and the optimization objective function is:

[0030] J = ∫0 T [C1ρ(x, t) + C2v(x, t) 2 ]dt

[0031] wherein J is the objective function, C1 is a traffic density weight coefficient, C2 is a vehicle speed variation weight coefficient, and T is a control cycle time; and the calculated optimal green light duration is output to a dynamic execution module for dynamically distributing the green light duration of the signal light.

[0032] Preferably, the optimal control module is configured to dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density, and specifically comprises:

[0033] setting a traffic density threshold ρ threshold , monitoring the traffic density in real time, and increasing the flashing frequency of the yellow flashing light when the traffic density exceeds the set threshold;

[0034] The flashing frequency of the yellow flashing light satisfies the following relationship:

[0035] f = f0+ k f ·(ρ-ρ threshold )

[0036] wherein 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 a frequency adjustment coefficient, ρ is the real-time traffic density, and ρ threshold is the traffic density threshold.

[0037] When the traffic density is lower than the threshold, the yellow flashing light returns to the initial flashing frequency f0.

[0038] The calculated flicker frequency is output to a dynamic execution module for real-time regulation of the working state of the yellow flashing light.

[0039] Preferably, the dynamic execution module comprises:

[0040] A signal light control unit for dynamically allocating the green light duration of the signal light according to the optimal control strategy.

[0041] A yellow flashing light frequency adjustment unit for dynamically adjusting the flicker frequency of the yellow flashing light according to the traffic density.

[0042] A vehicle networking communication unit for sending the optimal passing path and the recommended speed to the vehicle to realize dynamic path guidance.

[0043] Preferably, the vehicle networking communication unit sends signals to the vehicle through V2I communication to form a green wave band, so that the vehicle passes through the intersection at a uniform speed, and the number of vehicle sudden stops is reduced.

[0044] Preferably, the feedback monitoring module comprises:

[0045] A traffic state monitoring unit for monitoring the speed, density and passing time of the controlled traffic flow.

[0046] A system performance analysis unit for evaluating the traffic passing rate, conflict point accident rate and vehicle delay time.

[0047] The application also provides a safety facility method suitable for a highway flat intersection with large traffic volume, comprising the following steps:

[0048] Real-time acquisition of traffic speed, density and position information;

[0049] Establishment of a traffic potential field model based on the acquired data to describe the traffic density and speed distribution state.

[0050] Calculation of the optimal traffic path and the green light duration of the signal light and the flicker frequency of the yellow flashing light according to the traffic potential field model.

[0051] Dynamic regulation of the signal light duration and the yellow flashing light frequency according to the optimal control result.

[0052] Monitoring of the traffic state and the system performance for real-time optimization through feedback data.

[0053] The application provides a safety facility system suitable for a highway flat intersection with large traffic volume. The system has the following beneficial effects:

[0054] 1.The present application monitors the traffic flow state in real time through the data perception module, and dynamically calculates the green light duration and yellow flashing light frequency by combining the traffic flow potential field model established by the traffic flow modeling module, to achieve optimal regulation and control of the traffic flow. According to the traffic density, the green light duration is allocated to give priority to high-density lanes, reduce vehicle waiting time and signal switching loss, and significantly improve the overall traffic efficiency of the intersection.

[0055] 2.The present application dynamically regulates the flashing frequency of the yellow flashing light. When the traffic density exceeds the set threshold, the yellow flashing light frequency is automatically increased to remind the driver to slow down in advance, reducing the risk of conflicts caused by excessive speed. In addition, the system provides the optimal traffic path and recommended speed to the driver through vehicle networking communication, effectively guiding the vehicle to pass through the intersection smoothly and reducing the occurrence of conflict point accidents.

[0056] 3.The present application provides a feedback monitoring module to monitor the traffic state and system performance in real time, collects key data such as traffic efficiency, traffic density, and vehicle delay, and feeds back the analysis results to the data perception module and 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 adaptability and control accuracy of the system.

[0057] 4.The present application uses dynamic modeling and control strategies through the optimal control module. When the traffic flow changes, the control parameters of the signal light and yellow flashing light can be quickly responded and adjusted. The system adapts to different traffic characteristics and intersection types, whether it is high-density traffic during peak hours or sparse traffic during off-peak hours, efficient regulation and control can be achieved, ensuring that the system can maintain stable and efficient operation in various traffic environments.

[0058] 5.The present application sends the optimal traffic path and recommended speed to the vehicle approaching the intersection through the vehicle networking communication unit, guiding the vehicle to pass through the "green wave band". Through precise guidance of vehicle speed and path, unnecessary sudden stops and waiting are reduced, driving experience is improved, and the traffic capacity and operating efficiency of the intersection are further improved.

[0059] 6.By reasonably allocating the signal light green light duration and dynamically adjusting the yellow flashing light frequency, the present application effectively reduces the waiting time of vehicles at the intersection, reduces the number of frequent stops and sudden brakes. The smoothness of vehicle traffic is improved, which helps to reduce fuel consumption and exhaust emission, and has significant energy-saving and environmental protection effect. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is a schematic diagram of the system architecture of the present application;

[0061] Figure 2 is a schematic diagram of the method flow of the present application. DETAILED DESCRIPTION

[0062] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the specification of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0063] Please refer to the drawings in the specification of the present application Figure 1 The present application provides a safety facility system suitable for a highway flat intersection with large traffic volume. The system realizes dynamic regulation and optimization of traffic flow based on traffic flow potential theory and optimal control theory, improves the traffic efficiency of the intersection, and reduces the accident rate at conflict points. The various modules of the system of the present application will be described in detail below.

[0064] The safety facility system suitable for a highway flat intersection with large traffic volume can include a data perception module, a traffic flow modeling module, an optimal control module, a dynamic execution module, and a feedback monitoring module.

[0065] In this embodiment, the data perception module is used to collect the traffic flow speed, traffic flow density and vehicle position information of the highway flat intersection in real time, to provide data input for the subsequent traffic flow modeling module, and to ensure the accuracy of the traffic potential field modeling and the reliability of the dynamic control.

[0066] Specifically, the data perception module includes a laser radar, a geomagnetic sensor and a visual sensor. These devices are arranged in the key areas of the intersection and are respectively used to monitor the speed, traffic density and position information of the vehicles, and to realize high-precision collection of traffic information through data fusion technology.

[0067] As an option, the laser radar can be arranged in each direction of the intersection and is mainly used to collect the speed and position information of the vehicles. The laser radar measures the time difference of laser propagation by emitting a laser beam and receiving a reflected signal to calculate the speed and spatial position of the vehicles. In a possible implementation, the data output format of the laser radar includes the vehicle speed v(x,t) and the real-time position x(t) of the vehicle, where x represents the spatial coordinates of the vehicle and t represents the time.

[0068] For example, the geomagnetic sensor is installed below the road surface of each lane and is specifically buried at a position a certain distance away from the intersection. The geomagnetic sensor judges the passing of vehicles by monitoring the disturbance of the geomagnetic field caused by the vehicles, calculates the number of vehicles passing through the sensor per unit time, and further deduces the density of the traffic flow. The calculation formula of the traffic flow density is:

[0069]

[0070] Wherein, p is the traffic density, N represents 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 sensor can be distributed in each lane in series or in parallel to improve the comprehensiveness and accuracy of traffic density monitoring.

[0071] In a possible implementation, the visual sensor is arranged at a high position of the intersection, such as a signal lamp pole or a monitoring support, to obtain the vehicle flow dynamic image of the entire intersection. Specifically, the visual sensor combines image recognition technology to analyze the number, speed, and position of vehicles through edge detection, target recognition, and region segmentation algorithms, and compares and verifies the data with the data of the laser radar and the geomagnetic sensor, thereby improving the accuracy of data acquisition.

[0072] As an option, the visual sensor can perform vehicle recognition and tracking based on a deep learning algorithm. For example, a target detection model such as YOLO or Faster R-CNN is used to realize real-time recognition of vehicles at the intersection, and output the speed and position coordinate data of the vehicles. It should be noted that the data of the visual sensor can be combined with the real-time speed data of the laser radar to correct possible errors and ensure the accuracy of the vehicle flow speed information.

[0073] It can be understood 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 flow speed, density, and position information. In a possible implementation, data fusion can use a weighted average method or a Kalman filter algorithm to fuse and process the data of the laser radar, the geomagnetic sensor, and the visual sensor. For example, the Kalman filter algorithm is used to predict and correct the vehicle speed data of different sensors:

[0074] v fused = K1v laser + K2v visual

[0075] Wherein, 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, and K1 and K2 are weighting coefficients.

[0076] In some embodiments, the various types of sensors of the data perception module can transmit data in real time to the control center of the system through a wireless communication module to facilitate subsequent vehicle flow modeling and optimized 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.

[0077] It should be noted that the sensor layout position of the data perception module needs to be optimized according to the specific layout of the intersection. For example, the laser radar should be arranged in the main direction of vehicle passing, and the geomagnetic sensor can be arranged in the central lane of the key road section.

[0078] As an extension, the data perception module can also integrate weather detection devices and light sensors to obtain environmental data of the intersection, such as slippery road surface, low visibility, etc. 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.

[0079] In practical application, it can be understood that the real-time acquisition capability of the data perception module is crucial to the overall operation effect of the system, and through multi-sensor fusion and data verification mechanism, the accuracy and reliability of data input can be ensured, providing basic data support for subsequent traffic flow modeling and dynamic control.

[0080] In this embodiment, the traffic flow modeling module is used to construct a traffic potential field model based on traffic speed, traffic density and position data, to describe the dynamic distribution state of traffic in the highway flat intersection area, and to provide data basis for signal light duration allocation and yellow flashing light frequency adjustment of the optimal control module.

[0081] Specifically, the traffic flow modeling module establishes a traffic potential field model by combining the traffic continuity equation and the potential flow control equation. The traffic potential field model can accurately reflect the distribution characteristics of traffic in the flat intersection area, including the temporal and spatial variation of traffic density, the distribution of velocity field, and the state of potential field gradient.

[0082] As an option, the traffic flow modeling module describes the relationship between traffic density and speed based on the traffic continuity equation, which is expressed as:

[0083]

[0084] wherein 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 rate of change of traffic.

[0085] In one possible implementation, the traffic speed is associated with the traffic potential field gradient, and the traffic speed satisfies the following relationship:

[0086]

[0087] wherein Φ is the traffic potential function, representing the potential field distribution state in the traffic field, is the gradient of the traffic potential function, represents the magnitude of the traffic flow velocity.

[0088] It is noted that the traffic potential function Φ is solved by the potential flow governing equation, which is:

[0089]

[0090] where k is the traffic pressure coefficient, γ is the traffic compressibility index, is the square of the traffic potential function gradient, and C is the constant energy value of the potential field.

[0091] As an option, in the potential flow governing equation, the traffic pressure term can be used to represent the influence of the traffic density change on the potential field. When the traffic density is high, the traffic pressure increases accordingly, thereby changing the traffic velocity distribution and the potential field distribution.

[0092] In a possible implementation, the traffic modeling module solves the potential flow governing equation by the finite difference method (FDM). Specifically, the continuous potential flow equation is discretized, and the time and space are segmented for iterative calculation to solve the traffic density, traffic velocity, and potential field distribution.

[0093] For example, assume that the planar intersection region is divided into a plurality of grid cells, and the traffic potential field Φ in each grid cell satisfies the discretized governing equation:

[0094]

[0095] where Φ i,j represents the potential field value in the i,jth grid cell, and ρ i,j is the traffic density in the grid cell.

[0096] As an option, the traffic modeling module can perform interpolation fitting on the calculation results of each grid cell to generate a traffic potential field distribution map of the entire planar intersection region, and finally output the traffic density distribution ρ(x,t), the velocity distribution v(x,t), and the potential field distribution Φ(x,t).

[0097] In some embodiments, the traffic modeling module can also optimize the traffic potential field based on boundary conditions. Specifically, the traffic velocity and density at the entrance of the intersection are input as boundary conditions, 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.

[0098] For example, the boundary conditions can be represented as:

[0099] Entrance boundary: ρ(x,t) = ρ0, v(x,t) = v0

[0100] Exit boundary:

[0101] It should be noted that the traffic flow modeling module can be further adapted to different types of intersections, including crosses, T-intersections, and complex multi-lane intersections. In one possible implementation, for complex intersections, the traffic flow modeling module uses a regional decomposition method to divide the entire intersection area into several sub-regions, solves the traffic flow potential field for each sub-region, and finally merges the results of each sub-region.

[0102] 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 distribution and yellow flashing light frequency.

[0103] In this embodiment, the optimal control module is used to calculate the green light duration and flashing frequency of the yellow flashing lights 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 conflicts.

[0104] Specifically, the optimal control module analyzes traffic density, speed, and potential field distribution, and uses 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.

[0105] As an option, the green light duration is allocated in proportion to the traffic density, and the specific calculation formula is:

[0106]

[0107] in:

[0108] t g (i) represents the green light duration of lane i;

[0109] ρ i represents the traffic density of lane i;

[0110] is the sum of traffic density of all lanes;

[0111] T represents the entire signal light cycle time.

[0112] It can be understood that this allocation method ensures that lanes with higher traffic density receive longer green light times, giving priority to diverting high-density traffic and avoiding vehicle backlogs, thereby achieving balanced control between lanes.

[0113] In one possible implementation, the optimal control module establishes an objective function to comprehensively optimize traffic density and vehicle speed to minimize traffic conflict pressure and travel time delay at the intersection. The specific objective function is:

[0114] J = ∫0 T [C1p(x, t) + C2v(x, t) 2 ]dt

[0115] wherein:

[0116] J is the objective function of the optimal control;

[0117] C1is the weight coefficient of the traffic density, used to represent the influence of the traffic density on the system;

[0118] C2is the weight coefficient of the traffic speed variation, used to measure the smoothness of the vehicle speed;

[0119] p(x, t) is the real-time traffic density distribution;

[0120] v(x, t) is the traffic speed distribution;

[0121] T is the optimization control period.

[0122] As an option, the optimal control module is solved based on the Hamilton-Jacobi-Bellman (HJB) equation to obtain the optimal control strategy of the system. The HJB equation is expressed as:

[0123]

[0124] wherein:

[0125] V represents the optimal value function;

[0126] L(x, u, t) is the instantaneous loss function, including the influence of the traffic density and speed;

[0127] f(x, u, t) is the evolution equation of the system state;

[0128] u is the control variable, including the control parameters of the signal light duration and the yellow flashing light frequency.

[0129] In a possible implementation manner, the optimal control module adopts an iterative method to numerically solve the HJB equation, determines the optimal signal control parameters of each lane, and transmits the calculation results to the dynamic execution module for execution.

[0130] For example, the optimal control module can also dynamically adjust the flashing frequency of the yellow flashing light according to the traffic density, so as to improve the warning effect of the intersection and reduce the risk of vehicle conflict. The specific frequency adjustment relationship is:

[0131] f = f0+ k f · (p - p threshold )

[0132] wherein:

[0133] f is a dynamic flashing frequency of the yellow flashing light;

[0134] f0is an initial flashing frequency of the yellow flashing light;

[0135] k f is a frequency adjustment coefficient;

[0136] p is a current traffic density;

[0137] p threshold is a threshold value of the traffic density.

[0138] Specifically, when the traffic density exceeds the set threshold value, the flashing frequency of the yellow flashing light is increased to remind the driver to slow down; when the traffic density is below the threshold value, the yellow flashing light returns to the initial frequency to avoid resource waste caused by frequency changes.

[0139] It should be noted that the control period T and the traffic density threshold p threshold in the optimal control module can be set according to the traffic characteristics of the actual intersection. For example, for intersections with large traffic fluctuations and obvious peak periods, the control period T can be shortened appropriately to improve the response speed of dynamic regulation.

[0140] In some embodiments, the optimal control module can also combine historical traffic data with real-time traffic data to predict future traffic states through a prediction model, thereby generating an optimized control strategy in advance. For example, the prediction model can use time series analysis methods such as ARIMA models or LSTM neural network models to improve the forward-looking and accuracy of optimal control.

[0141] It can be understood that the optimal control module effectively relieves traffic and reduces conflict point pressure through dynamic regulation of green light duration and yellow flashing light frequency, and provides clear control instructions for the dynamic execution module to ensure the orderly operation of the entire system.

[0142] In this embodiment, the dynamic execution module is used to receive the control strategy generated by the optimal control module, dynamically regulate the signal light period and the flashing frequency of the yellow flashing light of the highway flat intersection, and provide the vehicle with a passing path and a recommended speed through the vehicle networking communication unit, thereby achieving dynamic and orderly traffic relief.

[0143] Specifically, the dynamic execution module includes a signal light control unit, a yellow flashing light frequency adjustment unit, and a vehicle networking communication unit, each unit has clear division of labor and cooperative operation to ensure real-time regulation of intersection traffic.

[0144] 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 g (i) performing dynamic signal control.

[0145] It should be noted that the execution period T of the signal lamp control unit can be adjusted according to the actual traffic state of the intersection. During the peak traffic period, the value of T can be appropriately shortened to improve the control response speed; during the sparse traffic period, the value of T can be appropriately lengthened to improve the resource utilization rate of the signal lamp.

[0146] In a possible implementation, the yellow flashing lamp frequency adjusting unit is configured to dynamically adjust the flashing frequency of the yellow flashing lamp according to the traffic density, and the specific relationship refers to the adjustment of the flashing frequency of the yellow flashing lamp by the optimal control module.

[0147] Specifically, when the real-time traffic density p exceeds the set threshold p threshold , the flashing frequency of the yellow flashing lamp increases accordingly, thereby reminding the driver to slow down in advance and reducing the risk of traffic conflict in the intersection area. When the traffic density falls below the threshold, the yellow flashing lamp automatically restores the initial frequency f0 to reduce the interference to the driver.

[0148] For example, the yellow flashing lamp frequency adjusting unit can realize precise control of the frequency through the PWM (pulse width modulation) technology to ensure the smoothness and real-time performance of the frequency adjustment. In specific implementation, the PWM controller receives the frequency adjustment instruction of the optimal control module, adjusts the power supply pulse period of the yellow flashing lamp, and thus realizes dynamic frequency regulation.

[0149] As an alternative, the vehicle networking communication unit is configured to send the optimal passing path and the recommended speed to the vehicles approaching the intersection to form a "green wave belt", guiding the vehicles to pass through the intersection along the optimal path and reducing unnecessary sudden stops and waiting. Specifically, the vehicle networking communication unit realizes real-time transmission of data through V2I (vehicle-to-infrastructure) communication between the on-board unit (OBU) and the road side unit (RSU).

[0150] In a possible implementation, the vehicle networking communication unit sends the real-time calculated optimal passing speed v opt to the vehicle terminal:

[0151]

[0152] Wherein:

[0153] v opt is the recommended passing speed;

[0154] L is the distance from the intersection to the signal lamp area;

[0155] t g (i) is the green light remaining time of the current lane.

[0156] It should be noted that the recommended passing speed v optThrough dynamic calculation adjustment, the vehicle is smoothly passed through the intersection during the green light period, the number of stops is minimized, and the traffic efficiency is improved.

[0157] It can be understood that the signal light control unit, the yellow flashing light frequency modulation unit and the Internet of Vehicles communication unit in the dynamic execution module cooperate with each other to jointly execute the optimal control strategy and realize the dynamic guidance and control of the intersection traffic flow.

[0158] In some embodiments, the dynamic execution module can also interact with the feedback monitoring module in real time to receive traffic state feedback information and dynamically update the control parameters of the signal light and the yellow flashing light. For example, when it is monitored that the traffic flow density of a lane abnormally increases, the dynamic execution module can temporarily adjust the green light duration of the lane to preferentially guide the traffic flow and reduce the lane congestion.

[0159] In the embodiment, the feedback monitoring module is used to monitor the effect of the dynamic control of the highway flat intersection and feed the monitoring data to the data perception module and the optimal control module to form a closed-loop optimal control to realize the real-time adjustment and optimization of the system.

[0160] Specifically, the feedback monitoring module includes a traffic state monitoring unit and a system performance analysis unit, which are used to collect and evaluate the performance of the traffic operation state in real time and transmit the feedback data to the data perception module for cyclic optimization.

[0161] As an option, the traffic state monitoring unit evaluates the dynamic control effect of the signal light and the yellow flashing light by monitoring the key parameters such as the traffic speed, density and travel time in real time.

[0162] The traffic speed v(x, t) is obtained by jointing the laser radar and the visual sensor, and the traffic density p(x, t) is collected by the geomagnetic sensor in real time.

[0163] Exemplarily, the traffic travel time T c can be calculated by the time difference between the entrance and the exit, and the specific relationship is as follows:

[0164] T c = t exit -t entry

[0165] wherein, t exit is the time when the vehicle passes through the exit, and t entry is the time when the vehicle enters the intersection.

[0166] In a possible implementation manner, the feedback monitoring module can also monitor the lane congestion in the intersection area based on the above data. For example, if the traffic flow density p of a lane exceeds the threshold p threshold , it is considered that congestion occurs, and the traffic state monitoring unit will mark the congested lane in real time and output a congestion warning signal.

[0167] As an option, the system performance analysis unit is used to quantitatively evaluate the effect of dynamic regulation of the intersection, mainly including the calculation and analysis of indicators such as vehicle flow rate, vehicle delay time and conflict point accident rate.

[0168] Specifically, the vehicle flow rate Q represents the total number of vehicles passing through the intersection per unit time, and the calculation formula is:

[0169]

[0170] Wherein:

[0171] Q is the vehicle flow rate;

[0172] N is the number of vehicles passing through the intersection in the monitoring period T monitor

[0173] T monitor is the monitoring period time.

[0174] The vehicle delay time T d represents the average waiting time of vehicles in the intersection area, and is calculated as follows:

[0175] T d = T c -T f

[0176] Wherein:

[0177] T d is the vehicle delay time;

[0178] T c is the actual travel time of the vehicle;

[0179] T f is the ideal travel time (i.e. the time required to pass through the intersection without waiting).

[0180] 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 flow:

[0181]

[0182] Wherein:

[0183] R is the accident rate;

[0184] C is the number of conflict point events occurring in the monitoring period;

[0185] N is the total number of vehicles passing through the intersection.

[0186] ​It should be noted that the analysis result of the system performance analysis unit is fed back to the optimal control module to update the signal light duration allocation and yellow flashing light frequency regulation parameters, so as to realize dynamic optimization.

[0187] In a possible implementation, the feedback monitoring module can combine historical monitoring data and real-time monitoring data to perform trend analysis on the traffic flow state and predict future traffic flow changes of the intersection. For example, the feedback monitoring module can use a time series prediction method such as an ARIMA model or a deep learning model based on LSTM (Long Short-Term Memory Network) to realize short-term prediction of the traffic flow state.

[0188] Alternatively, the feedback monitoring module can also interact with the dynamic execution module to update the traffic flow state information in real time. For example, when the traffic flow state monitoring unit detects that a lane is congested, the system performance analysis unit marks the lane state as "high priority", and the dynamic execution module can temporarily extend the green light duration of the lane to preferentially guide the traffic flow.

[0189] It can be understood that the feedback monitoring module forms a closed-loop feedback control through real-time monitoring of the traffic flow state and system performance analysis, so as to ensure that the system can be dynamically adjusted according to the actual operation effect and improve the traffic efficiency and safety of the intersection.

[0190] In summary, the system of the present application collects real-time traffic speed, density and position information through the data perception module, constructs a traffic potential field model based on the traffic continuity equation and potential flow control equation through the traffic modeling module, describes the dynamic distribution state of the traffic flow, calculates the green light duration and the flashing frequency of the yellow flashing light according to the traffic potential field model through the optimal control module, generates an optimal control strategy, dynamically adjusts the signal light cycle and the yellow flashing light frequency through the dynamic execution module, and provides a traffic path and a recommended speed to the vehicle through the Internet of Vehicles, and the feedback monitoring module monitors the system operation effect in real time, and returns the traffic flow state data to the data perception module and the optimal control module to form a closed-loop optimization control.

[0191] Please refer to the accompanying Figure 2 The present application also provides a safety facility method suitable for a highway flat intersection with a large traffic volume. The specific implementation of each step will be described below in combination with the working process of the system of the present application.

[0192] S1, real-time collection of traffic speed, density and position information;

[0193] In this step, the data perception module obtains the vehicle flow speed, density and position information of the flat intersection area through laser radar, geomagnetic sensor and visual sensor. Laser radar is used to measure the real-time speed and spatial position of vehicles, geomagnetic sensor is used to monitor the vehicle flow density per unit time, and visual sensor assists in identifying the dynamic position of vehicles and lane occupation through image recognition technology. The collected data is fused and processed to provide basic input for the vehicle flow modeling module.

[0194] S2, a vehicle flow potential field model is established based on the collected data to describe the vehicle flow density and speed distribution state;

[0195] In this step, the vehicle flow modeling module establishes a vehicle flow potential field model based on the vehicle flow speed, density and position information provided by the data perception module. The vehicle flow potential field model reflects the dynamic distribution state of vehicle flow in the flat intersection area, including the spatial distribution of vehicle flow density, the change trend of speed field and the distribution of potential field gradient. The generated vehicle flow potential field model provides data support for subsequent optimal control.

[0196] S3, calculate the optimal path of vehicle flow and the green light duration and yellow flashing light frequency of signal light according to the vehicle flow potential field model; in this step, the optimal control module calculates the optimal passing path of each lane, the green light duration and the flashing frequency of the yellow flashing light according to the vehicle flow potential field model. The allocation of green light duration is based on the real-time vehicle flow density of each lane, and high-density vehicle flow is preferentially guided. The yellow flashing light frequency is dynamically adjusted according to the vehicle flow density, and when the density is high, the flashing frequency is increased to remind the driver to slow down and reduce the risk of conflict.

[0197] S4, dynamically adjust the signal light duration and yellow flashing light frequency according to the optimal control result;

[0198] In this step, the dynamic execution module receives the control results generated by the optimal control module to real-time control the signal light cycle and yellow flashing light frequency of each lane of the intersection. The signal light control unit dynamically allocates the green light duration of each lane to ensure the orderly guidance of vehicle flow. The yellow flashing light frequency adjusting unit adjusts the flashing frequency according to the change of vehicle flow density to realize the early warning of vehicle flow. At the same time, the vehicle networking communication unit provides the optimal passing path and recommended passing speed to the vehicles approaching the intersection to guide the vehicles to pass through the intersection smoothly.

[0199] S5, monitor the vehicle flow state and system performance, and optimize in real-time through feedback data;

[0200] In this step, the feedback monitoring module monitors the control effect of the system in real time, collects traffic state data, including traffic speed, traffic density, travel time and system performance indicators (such as vehicle delay time, traffic passing 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 optimal control. By continuously iterating and updating the signal light duration and the yellow flashing light frequency, the dynamic regulation and control ability of the system on the traffic flow is improved.

[0201] Through the above steps, the method of the present application realizes dynamic monitoring, modeling, optimal control, execution and feedback optimization of the traffic flow at the flat intersection, improves the traffic efficiency at the intersection, reduces the risk of traffic conflict, and guarantees the safety and smoothness of the highway flat intersection.

[0202] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which 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 intersection traffic speed, density, and location; 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 based on 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; Feedback monitoring module, used to monitor the traffic flow control effect and transmit feedback data to the data perception module to form closed-loop optimization control; 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: describing the relationship between traffic flow density and speed based on the traffic flow continuity equation: 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, Indicates the spatial change rate of traffic flow; Relating the traffic flow speed to the potential field gradient, the traffic flow speed satisfies: 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 flow potential function, Indicates the speed of traffic; 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, is the square of the gradient of the traffic flow potential function, and C is the constant energy value of the potential field; Solving the traffic flow potential field model by numerical methods to obtain a dynamic distribution state of traffic flow density and speed, and outputting the distribution state to an optimal control module; The optimal control module is used to calculate the green light duration of the traffic light according to the traffic flow potential field model, specifically including: Based on the traffic density ρ of the lane, the green light duration of each lane is calculated so that the green light duration is proportional to the traffic density, satisfying: Among them, t g (i) is the green light duration of lane i, ρ i is the traffic density of lane i, is the sum of 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; Output the calculated optimal green light duration to the dynamic execution module for dynamically allocating the green light duration of the traffic light; 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, the flashing frequency of the yellow flashing light is increased; 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.

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

3. The safety facility system suitable for high-traffic highway intersections according to claim 1, characterized in that: The numerical method includes a finite difference method or a finite element method.

4. The safety facility system for high-traffic highway intersections according to claim 1, characterized in that: The dynamic execution module includes: Traffic light control unit, used to dynamically allocate the green light duration of traffic lights according to the optimal control strategy; Yellow flashing light frequency modulation unit, used to dynamically adjust the flashing frequency of the yellow flashing light according to 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.

5. A safety facility system suitable for high-traffic highway intersections according to claim 4, characterized in that: The vehicle network communication unit sends signals to vehicles through V2I communication, forming a green wave band, allowing vehicles to pass through the intersection at a uniform speed and reducing the number of emergency stops.

6. The safety facility system for high-traffic highway intersections according to claim 1, characterized in that: 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, conflict point accident rate and vehicle delay time.

7. A safety facility method suitable for high-traffic highway intersections, applied to the system according to any one of claims 1 to 6, characterized in that: The following steps are involved: Real-time collection of traffic speed, density and location information; Based on the collected data, a traffic potential field model is established to describe the traffic density and speed distribution; Based on the traffic potential field model, calculate the optimal traffic path, the duration of the green light, and the flashing frequency of the yellow flashing light; According to the optimal control results, the traffic light duration and the frequency of the yellow flashing light are dynamically adjusted; Monitor traffic status and system performance, and perform real-time optimization through feedback data.

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