A tunnel emergency traffic control method, device, equipment and medium

Abstract

The present application belongs to the technical field of tunnel emergency management and control, and discloses a tunnel emergency traffic management and control method, device, equipment and medium, the method comprising: in response to a tunnel emergency, obtaining the accident location; taking the accident location as the reference, and demarcating a set distance downstream along the driving direction of the traffic flow in the tunnel as the data collection area; determining the traffic flow control device position and control parameters in the tunnel according to the current environmental state data, traffic operation state data and road structure data in the data collection area, obtaining multiple groups of candidate control strategies; simulating the traffic flow for each group of candidate control strategies, and evaluating the control effectiveness; determining the best control strategy according to the control effectiveness evaluation result. The present application realizes the optimization of the control strategy for the tunnel scene, and improves the blocking effect.

Description

Technical Field

[0001] This invention belongs to the field of traffic flow control technology, and in particular relates to a method, device, equipment and medium for emergency traffic control in tunnels. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, emergency management in tunnels mainly relies on conventional methods such as fixed signage, manual deployment of warning devices, or simple traffic lights. However, traffic conditions inside tunnels are affected by various factors such as environmental visibility, smoke concentration, lighting conditions, and road structure (such as curvature and longitudinal slope). Existing management strategies are difficult to dynamically adapt to the accident location, traffic density, vehicle speed, and environmental visibility conditions, resulting in a disconnect between management strategies and actual traffic needs. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, equipment and medium for emergency traffic control in tunnels, in order to optimize the blocking strategy and control strategy for tunnel scenarios and improve the blocking effect.

[0005] To achieve the above objectives, a first aspect of the present invention provides a tunnel emergency traffic control method, the method comprising the following steps: In response to a sudden tunnel accident, the location of the accident must be determined. Based on the location of the accident, a set distance downstream along the direction of traffic flow in the tunnel is designated as the data collection area; Based on the current environmental status data, traffic operation status data and road structure data within the data collection area, the location and control parameters of the traffic flow control device in the tunnel are determined, and multiple sets of candidate control strategies are obtained. Traffic flow simulations are performed for each group of candidate control strategies, and the effectiveness of the control measures is evaluated. Based on the evaluation results, the optimal control strategy is determined.

[0006] In some embodiments, the traffic flow control device includes a liftable barrier device, and the control parameters include the target luminous intensity and target height of the barrier cable; the method for determining multiple candidate control strategies is as follows: Determine the initial barrier device to be activated based on the location of the accident; Based on the current environmental status data, traffic operation status data, and road structure data, the control parameters of the barrier device are determined, and the initial control strategy is obtained. Multiple candidate control strategies are generated based on the initial control strategy. Each candidate control strategy includes the location of the barrier device to be activated and the control parameters.

[0007] In some embodiments, visibility impact, visible distance impact, and braking distance impact are calculated based on environmental state data, traffic operation status data, and road structure data, respectively; brightness adjustment intensity is calculated based on visibility impact, visible distance impact, and braking distance impact; and target luminous brightness is calculated based on the set base luminous brightness and brightness adjustment intensity.

[0008] In some embodiments, visibility impact items and visible distance impact items are calculated based on environmental state data, traffic operation state data, and road structure data, respectively. The height adjustment intensity is calculated based on the visible distance impact item. The target height of the arresting cable is calculated based on the effective height travel range of the arresting cable and the height adjustment intensity.

[0009] In some embodiments, a tunnel-based digital twin system performs blocking simulations for each set of candidate control strategies. The digital twin system includes a tunnel geometry model, a traffic flow evolution model, an environmental change model, and a physical response model of the blocking device.

[0010] In some embodiments, the blocking simulation for each group of candidate control strategies includes: injecting real-time environmental state data, traffic operation state data, and accident location and accident type into the digital twin system; simulating the process of each vehicle driver visually capturing the blocking device and performing braking operation based on the driver behavior simulation model; and simulating the braking process of the vehicle in response to the braking operation.

[0011] In some embodiments, the method further includes: after the blocking device is activated, continuously collecting traffic operation status data in the tunnel to determine whether it has entered the stable blocking stage; when in the stable blocking stage, adjusting the target luminous brightness according to the downstream traffic flow density and average vehicle speed of the blocking cable. In response to the command to resume traffic, the barrier cable is retracted, and the traffic resumption phase begins. During the traffic resumption phase, the target brightness is adjusted based on the rate of decrease in traffic density downstream of the barrier cable and the rate of increase in average vehicle speed.

[0012] A second aspect of the present invention provides a tunnel emergency traffic control device, wherein a barrier device is deployed at predetermined intervals along the longitudinal direction of the tunnel, the barrier device comprising a liftable barrier cable, the device comprising: The initialization module is configured to obtain the location of an accident in response to a sudden tunnel incident. The data acquisition module is configured to define a set distance downstream of the accident location along the direction of traffic flow in the tunnel as the data acquisition area. The strategy generation module is configured to determine the location and control parameters of the traffic flow control device in the tunnel based on the current environmental status data, traffic operation status data and road structure data within the data collection area, and to obtain multiple sets of candidate control strategies. The strategy optimization module is configured to perform traffic flow simulation for each group of candidate control strategies and evaluate the effectiveness of the control; based on the evaluation results of the control effectiveness, the optimal control strategy is determined.

[0013] A third aspect of the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer instructions that, when executed by the processor, cause the electronic device to perform the method described thereon.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method.

[0015] The above one or more technical solutions achieve adaptive determination of the required traffic flow control location and control parameters by integrating data on tunnel environmental conditions, traffic operation conditions and road structure. Based on digital twin simulation, the effectiveness of multiple candidate control strategies is evaluated, thereby selecting the optimal control strategy that is suitable for the current traffic environment conditions. This solves the technical problems of existing tunnel emergency control strategy generation relying on fixed plans or human experience and lacking multi-source data fusion analysis and simulation pre-verification methods. Attached Figure Description

[0016] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0017] Figure 1 This is a schematic diagram of the tunnel emergency traffic control method in an embodiment of the present invention; Figure 2 This is a flowchart of the tunnel emergency traffic control device program module in an embodiment of the present invention. Detailed Implementation

[0018] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0019] In the description of the embodiments of this application, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on".

[0020] The implementation process of the present invention will be described in detail below with reference to a specific example scenario. The example scenario is applied to a one-way tunnel, including monitoring equipment, traffic flow control devices, and a control center deployed inside the tunnel.

[0021] The monitoring equipment includes video surveillance cameras, environmental monitoring sensors, and traffic flow sensors. The video surveillance cameras capture vehicle driving status, accident characteristics, and accident locations. The environmental monitoring sensors collect real-time environmental data within the tunnel and traffic operation data related to vehicle movement. The video surveillance cameras are deployed at the tunnel ceiling. The environmental monitoring sensors, including visibility sensors, smoke sensors, and ambient lighting sensors, are deployed at corresponding locations in the center of the tunnel ceiling and on the sidewalls. The traffic flow sensors are buried beneath the road surface of each lane. All monitoring equipment is deployed in a distributed manner.

[0022] The traffic flow control device comprises multiple units, evenly distributed along the longitudinal direction of the tunnel, with one unit placed at predetermined intervals, without affecting the smooth flow of the tunnel's emergency and rescue passages. Each traffic flow control device is equipped with an independent control module, and achieves passage / prohibition / warning control through variable traffic lights, variable information boards, barrier bars, etc.

[0023] The control center is located in the monitoring room at the tunnel entrance. It pre-stores tunnel road structure data, which can be obtained by analyzing the tunnel design drawings. The control center also has a built-in main controller, data processing module, and digital twin simulation module, which are used to receive data from multi-source monitoring equipment, generate control strategies, and issue control commands. Data transmission between the control center and various monitoring equipment and traffic flow control devices is achieved through dual-mode communication of industrial Ethernet and wireless communication to ensure the real-time performance and stability of the transmission.

[0024] Figure 1 This diagram illustrates a tunnel emergency traffic control method provided by an embodiment of the present invention. The method includes the following steps: S101. In response to a sudden tunnel accident, obtain the accident location; S102. Based on the location of the accident, a set distance is delineated downstream along the direction of traffic flow in the tunnel as the data collection area. S103. Based on the current environmental status data, traffic operation status data and road structure data within the data collection area, determine the location and control parameters of the traffic flow control device in the tunnel, and obtain multiple sets of candidate control strategies. S104. Perform traffic flow simulation for each group of candidate control strategies and evaluate the effectiveness of the control measures; determine the optimal control strategy based on the evaluation results of the control effectiveness.

[0025] By integrating data on tunnel environmental conditions, traffic operation status, and road structure, the system enables adaptive determination of traffic flow control locations and parameters. Furthermore, it evaluates the effectiveness of multiple candidate control strategies based on digital twin simulation, thereby selecting the optimal control strategy that best suits the current traffic environment. This solves the technical problems of existing tunnel emergency control strategies relying on fixed plans or human experience and lacking multi-source data fusion analysis and simulation pre-verification methods.

[0026] In step S101, video surveillance cameras inside the tunnel capture images in real time. Image recognition algorithms identify accident characteristics, such as vehicle collisions, fire smoke, and obstacles blocking the road, to determine the accident type. When an accident is identified, the mileage marker at the accident location is located. Simultaneously, traffic flow data collected by traffic flow sensors can be used for auxiliary determination. By comparing normal traffic flow with the traffic flow after the accident, the accident location can be identified, such as the location where vehicle speed suddenly drops, followed by a traffic interruption.

[0027] It is understandable that accidents such as traffic accidents or fires can be identified based on existing image recognition algorithms, which will not be explained in detail here. Furthermore, different accidents require different safe distances, which can be set according to the type of accident.

[0028] In step S102, based on the accident location, a predetermined distance is defined downstream of the traffic flow direction within the tunnel as a data collection area to collect environmental status data, traffic operation status data, and road structure data. Step S102 specifically includes: S1021. Collect environmental status data, traffic operation status data, and road structure data, and perform preprocessing. The preprocessing includes time alignment and reliability verification. Specifically, configure sampling period and timestamp recording rules. For cases where sampling periods are inconsistent, alignment is achieved using the nearest time alignment and window aggregation method to reduce the risk of control lag caused by missing instantaneous accident information. Reliability verification includes missing data detection, anomaly removal, and smoothing filtering. When sensors are offline or data is saturated, short-term hold or reliability interval backtracking is used; when traffic speed experiences non-physical jumps, neighborhood statistics are used to replace and mark low-reliability states; when smoke obstruction causes visual detection to miss detections, consistency verification is performed in conjunction with other detection sources to improve input robustness.

[0029] S1022. Extract environmental features, traffic features, and road features. The environmental features include visibility, smoke concentration, and lighting level, which are used to characterize the visibility conditions inside the tunnel. The traffic features include traffic density. The road features include tunnel curvature, longitudinal slope, tunnel cross-sectional dimensions, lane layout, and road alignment around the accident site.

[0030] The method for obtaining road features is as follows: vehicle type is identified by combining video cameras with image recognition algorithms, and vehicle speed and spacing data are collected by traffic flow sensors to calculate traffic density.

[0031] To quantify the actual effectiveness of the control strategy in slowing down traffic and mitigating risks, S101 also saves the baseline data before the accident as a reference.

[0032] In step S103, based on the current environmental status data, traffic operation status data and road structure data within the data collection area, the location and control parameters of the traffic flow control device in the tunnel are determined, and multiple sets of candidate control strategies are obtained.

[0033] For example, the traffic flow control device can be a variable signal light, an audible and visual warning device, etc. This application embodiment uses a lifting barrier device as an example for detailed description. The barrier device is driven to adjust the height and brightness of the barrier cable to achieve traffic control. The barrier cable can completely span the tunnel. For example, the barrier device includes two parallel lifting rods, the tops of which are respectively connected to one end of the barrier cable. As a specific implementation, the barrier device structure disclosed in patent document CN108360414A can be adopted. In the non-working state, the barrier cable is located at a high position and does not obstruct vehicles. In the working state, the lowering height of the barrier cable can be adjusted as needed. As another implementation, the tops of the two lifting rods are rotatably connected to a single-sided barrier cable via a rotating mechanism. The rotating mechanism can control the barrier cables on both sides to change from a vertical direction to a horizontal direction. In the non-working state, both sides of the barrier cables are in a vertical state. In the working state, both sides of the barrier cables rotate to a horizontal state. Simultaneously, through the lifting rods, the height of the barrier cables on both sides can be adjusted as needed. It is understandable that the liftable poles can be integrated into the support columns and fixed to the tunnel sidewalls via these columns. Linear light-emitting units, such as LED light strips, are installed on both sides of the arresting cables, embedded in grooves on the surface of the transverse arresting cable body. The LED light strips are evenly distributed along the length of the arresting cable, and their brightness is adjustable to adapt to different visibility conditions. A tensioning mechanism can also be integrated into the support columns, connected to both ends of the arresting cable body, to adjust the tension of the arresting cable and prevent it from swaying. Each arresting device has a control unit integrated into the support column or mounting base, connected to the tunnel control center via a communication module. This control unit can receive target height and target brightness commands from the control center and send control commands to the lifting mechanism and / or linear light-emitting units to control the height of the liftable poles and / or the brightness of the LED light strips. Furthermore, to achieve closed-loop control of position and brightness adjustment, a position sensor can be installed on one side of the liftable pole to provide real-time feedback on the current height of the arresting cable; and a brightness sensor can be installed on the arresting cable to provide real-time feedback on the current brightness.

[0034] Step S103 specifically includes: S1031. Based on the accident location, initially determine the blocking device to be activated in the upstream direction of oncoming traffic. For example, preset a safety distance threshold, extend the safety distance from the accident location along the upstream direction of oncoming traffic to determine the approximate range of the initial blocking location; then, in combination with tunnel road structure data, avoid key facilities such as fire-fighting facilities, monitoring equipment, and cable trenches in the tunnel to determine the specific blocking device location.

[0035] S1032. Based on the current environmental status data, traffic operation status data, and road structure data within the data collection area, determine the location and control parameters of the barrier device. The control parameters include the target luminous intensity and target height of the barrier cable. Specifically, calculate the required luminous intensity based on the environmental status data, traffic operation status data, and road structure data at the initial barrier location, and perform a weighted summation to obtain the target luminous intensity; calculate the target height based on the visibility of the barrier device.

[0036] Specifically, a weighted decision model for the target luminous intensity of the arresting cable is constructed as the control benchmark for the linear luminous unit. It is determined through the following weighting model:

[0037] in, This represents the target luminance, which can be mapped to control current or PWM duty cycle; The set base luminous intensity; , , The weighting coefficients satisfy the normalization constraint, i.e. .

[0038] This represents the visibility impact factor, reflecting the influence of visibility, smoke concentration, and lighting level within the tunnel on the need for luminous warnings. The worse the visibility, the denser the smoke, and the dimmer the lighting, the larger the value of this factor, and the stronger the need for increased luminous brightness. This can be further expressed as:

[0039] in, This represents the normalized parameter for tunnel visibility; This represents the normalized parameter for smoke concentration; This represents the normalized parameter of the original lighting brightness; These are internal weighting coefficients used to characterize the relative contributions of different environmental sub-factors to luminescence demand. .

[0040] The calculation method is as follows:

[0041] in, Indicates the real-time visibility of the data collection area; This indicates the highest visibility inside the tunnel; This indicates the minimum visibility inside the tunnel; an empirical value can be set. This represents the truncation function, which will... It is limited to the range [0,1].

[0042] The calculation method is as follows:

[0043] in, This indicates the real-time smoke concentration in the data acquisition area; This indicates the highest smoke concentration inside the tunnel; This indicates the lowest smoke concentration inside the tunnel; an empirical value can be set. This represents the truncation function, which will... It is limited to the range [0,1].

[0044] The calculation method is as follows:

[0045] in, Indicates the ambient lighting brightness in the data acquisition area; Indicates the maximum lighting brightness inside the tunnel; Indicates the minimum lighting brightness inside the tunnel; This represents the truncation function, which will... It is limited to the range [0,1].

[0046] This represents the visibility distance impact factor, reflecting the influence of the road curvature and longitudinal slope of the accident section on visibility distance and warning requirements. The greater the curvature and the steeper the longitudinal slope, the shorter the visibility distance. A higher value for this factor indicates a stronger need for increased luminous brightness. (Visibility Distance Impact Factor) Represented as:

[0047]

[0048] in, This indicates the curvature of the road segment in the data collection area; the sharper the bend, the greater the curvature. Indicates the preset reference curvature; For longitudinal slope influence, This indicates the design longitudinal slope of the data acquisition area. Indicates downhill. Indicates uphill or no slope; Indicates the maximum allowable longitudinal slope in the tunnel design; Represents the weighting coefficients, satisfying The curvature and longitudinal slope can both be read from the tunnel structure data.

[0049] This indicates the impact of braking distance, reflecting the influence of vehicle speed and traffic density in the accident area on braking distance and warning intensity. Higher vehicle speeds and denser traffic flow result in longer braking distances, leading to a larger value for this item and a stronger demand for increased luminous brightness. Represented as:

[0050] in, This represents the average speed of vehicles in the data collection area. This indicates the preset reference speed; This indicates the traffic flow density in the data collection area; This indicates the preset baseline density; Represents the weighting coefficients, satisfying .

[0051] The calculation results of E, R, and V are all normalized to If a value exceeds the specified range, it will be saturated with 0 or 1 to ensure stable brightness output and meet engineering feasibility requirements.

[0052] The target height needs to be adaptively adjusted based on the influence of visibility distance and braking distance, while meeting the constraints of tunnel clearance and device travel. Specifically, the height adjustment intensity is calculated based on the influence of visibility distance and braking distance, and the target height of the arresting cable is calculated based on the effective height travel range of the arresting cable. The influence of visibility distance includes one or more of the following: downstream visibility, current road curvature, and vehicle density; the influence of braking distance includes one or more of the following: road longitudinal slope, average vehicle speed, vehicle density, and the proportion of large vehicles. The formula is as follows:

[0053] in, Indicates the height of the arresting cable. Indicates the minimum allowable arresting cable height, used to ensure basic visual boundaries and the lower limit of safe operation of the device. This indicates the maximum allowable height of the arresting cable, which is constrained by tunnel clearance, safety margin, and device travel. This indicates the intensity of the height adjustment; a higher value indicates a stronger barrier is required. The formula for representing is as follows:

[0054]

[0055] in, This represents a limiting function that restricts the input to the range [0,1] to avoid unreasonable heights. This represents the weighting coefficient of each influencing factor. The normalized values ​​mentioned above are calculated based on the current values ​​of each influencing factor and the maximum and minimum values ​​within the tunnel.

[0056] This represents the normalized visibility measure; a larger value indicates better visibility, therefore it is used... This indicates the degree of visibility deterioration. For example, the formula for calculating the normalized visibility measure is:

[0057] in, The current measured visibility in the data acquisition area is given. The minimum and maximum visibility inside the tunnel are respectively... and .when hour, This indicates excellent visibility.

[0058] This represents the normalized effect of curvature, used to characterize the degree of visibility impairment caused by curves; the value is lower for straight segments and higher for larger curvature segments. The formula for calculating the normalized effect of curvature is:

[0059] in, The current road segment curvature in the data collection area is given, and the minimum and maximum curvatures within the tunnel are respectively... and Straight road section The greater the curvature, the higher the value.

[0060] This represents the normalized value of the longitudinal slope effect, used to characterize the degree of adverse impact on sight distance and braking caused by the longitudinal slope. The more unfavorable the slope, the higher the value. The formula for calculating the normalized value of the longitudinal slope effect is:

[0061] in, This is the longitudinal slope effect.

[0062] This represents the normalized amount of the impact of traffic speed, used to characterize the need for stronger advance constraints and visible boundaries as speeds increase. The formula for calculating the normalized amount of the impact of traffic speed is:

[0063] in, The current average vehicle speed in the data collection area The minimum and maximum speeds inside the tunnel are respectively The higher the speed, The larger the value, the stronger the constraint required.

[0064] This indicates the impact of traffic density on the normalized value; higher density increases the likelihood of visual obstruction, resulting in a higher value. The formula for calculating the impact of traffic density on the normalized value is:

[0065] The current traffic density in the data collection area. This represents the maximum vehicle density inside the tunnel; the higher the density, the closer the value is to 1.

[0066] S1033. Expanding the scope of control based on three dimensions—blocking location, target luminous intensity, and target height—and considering engineering feasibility and safety constraints, multiple candidate control strategies with different parameter combinations are generated. Specifically, the expansion of the blocking location is centered on the initial blocking location and extends along the longitudinal upstream and downstream directions of the tunnel. One or more blocking devices in the upstream and downstream directions are selected as candidate blocking locations, eliminating those whose distance from the accident location does not meet the safety buffer distance requirements. The expansion of the target luminous intensity is based on the initial target luminous intensity and extends vertically according to a preset brightness gradient. The expansion range matches the brightness adjustment capability of the LED light strip of the blocking device. Simultaneously, the luminous intensity must not exceed the upper limit for driver visibility safety within the tunnel to avoid glare and light curtain effects leading to misjudgment. The expansion of the target height is based on the initial target height and extends vertically according to a preset height gradient. All height parameters after expansion must not exceed safety limits.

[0067] After completing the independent expansion of each parameter, the expanded blocking position, target luminous intensity, and target height are matched and combined in pairs to generate multiple sets of candidate control strategies with different parameter combinations. Each strategy clearly includes specific blocking position, target luminous intensity value, and target height value.

[0068] In step S104, a blocking simulation is performed on each group of candidate control strategies using a digital twin model, and the control effectiveness is evaluated. Based on the control effectiveness evaluation results, the optimal control strategy is determined. Specifically, step S104 includes the following steps: S1041. Retrieve the digital twin system of the current tunnel. To ensure that the simulation scenario can reproduce the actual working conditions of the tunnel, a dedicated digital twin system matching the real tunnel scenario is constructed based on the actual engineering parameters of the current tunnel and the hardware parameters of the pre-installed barrier devices. This system includes a tunnel geometric model, a traffic flow evolution model, an environmental change model, and a barrier device physical response model. The tunnel geometric model, through in-depth analysis of the tunnel design drawings, reconstructs the tunnel's length, cross-sectional dimensions, lane layout, longitudinal slope, curve radius, and other structural features. Simultaneously, the location, installation parameters, and hardware performance parameters of all barrier devices are entered to ensure that the model is completely consistent with the actual tunnel structure and barrier device layout. The traffic flow evolution model, based on historical tunnel traffic data and combined with real-time traffic characteristics, constructs a realistic model of vehicle driving, following, and braking behavior, thereby simulating vehicle operating states under different traffic flow densities and vehicle speeds, providing realistic traffic scenario support for simulation. The environmental change model focuses on simulating the dynamic changes in environmental parameters such as visibility, smoke concentration, and ambient lighting brightness within the tunnel, ensuring that the environmental conditions during the simulation closely match the actual accident scenario. The physical response model of the barrier device is constructed based on the mechanical structure, luminous characteristics, and control logic of the pre-installed barrier device. It is used to recreate the barrier device's actions after receiving commands, such as height adjustment, brightness adjustment, deployment, and retraction, ensuring that the response characteristics of the barrier device during the simulation are consistent with the real equipment.

[0069] After completing the basic construction of this tunnel digital twin system, to ensure it can accurately reproduce the tunnel's physical and operational characteristics during daily operation, a virtual-real consistency calibration can be performed based on the tunnel's normal operating data. This eliminates deviations between the model and the actual tunnel under normal operating conditions, providing an accurate foundational environment for subsequent simulations of emergency scenarios. The virtual-real consistency calibration targets include tunnel environmental parameters, traffic flow characteristic parameters, and barrier device response parameters. If environmental parameter deviations exceed limits, adjust parameters such as the light attenuation coefficient and sensor response delay in the environmental change model; if device response deviations exceed limits, correct parameters such as the mechanical friction coefficient and LED luminous efficiency model in the barrier device's physical response model; if traffic flow deviations exceed limits, fine-tune parameters such as following distance and speed threshold in the traffic flow evolution model.

[0070] S1042. Real-time environmental and traffic operation data are injected into the digital twin system. Simultaneously, accident events, including accident location and type, are also injected to achieve virtual-real synchronization between the simulation scenario and the actual tunnel scenario. All types of data collected in real-time within the tunnel are comprehensively injected into the digital twin model, covering three main categories: environmental status data, traffic operation data, and road structure data. Environmental status data mainly includes parameters such as real-time visibility, smoke concentration, and ambient lighting brightness. Traffic operation data includes core parameters such as average vehicle speed, traffic flow density, and the proportion of large vehicles. Road structure data includes fixed parameters such as tunnel clearance dimensions, road section curve radius, and longitudinal slope, as well as real-time correction data. At the same time, the barrier location information corresponding to each pre-installed barrier device is synchronously entered into the model to ensure that the barrier positions in the model correspond to the actual pre-installed device positions. This effectively avoids a disconnect between the simulation scenario and actual working conditions, laying a solid data foundation for the subsequent simulation and deduction of candidate control strategies, and ensuring that the simulation process can realistically reflect the actual operating status of the tunnel site.

[0071] S1043. Based on each group of candidate control strategies, the control effect is simulated and deduced sequentially in the digital twin model. During the simulation and deduction process, the blocking position, target luminous brightness, and target height included in each group of candidate control strategies are input into the model one by one, driving the digital twin of the blocking device at the corresponding blocking position in the model to complete the height adjustment and luminous brightness adjustment according to the set parameters, thereby forming a lateral visual blocking interface across the lane.

[0072] Based on this, the vehicle operation status under accident scenarios is simulated synchronously. Vehicle twins are added according to the traffic flow and distribution of large vehicles in each lane to simulate vehicle operation status, specifically including driver behavior simulation and vehicle braking process simulation. The driver behavior simulation is achieved through a pre-set driver behavior simulation model, which simulates the driver's process from visual capture to the barrier interface to the execution of braking. This model pre-sets the driver's maximum visual capture distance, recognition delay rules under visibility influence, braking reaction time, and braking delay rules related to visibility. It monitors in real time the distance between downstream vehicles and the barrier device, visibility, target brightness of the barrier device, and target height to determine the driver's visual capture timing, i.e., the theoretical time when the driver can see the barrier device. Then, the vehicle braking process is simulated by combining vehicle type, braking performance parameters, and road longitudinal slope. After receiving the braking command output by the driver behavior simulation model, the vehicle braking system simulation is initiated, calculating braking acceleration and braking distance in real time, recreating the process of the vehicle gradually reducing from its current speed to a stop. To take into account the differences in braking characteristics of different vehicle types, and considering the large braking inertia and long braking distance of large vehicles, differentiated braking parameters are preset to reasonably extend the braking distance of large vehicles, ensuring that the braking process closely matches the braking law of real vehicles and improving the realism of the simulation.

[0073] S1044. Based on a multi-index comprehensive evaluation method, the effectiveness of each group of candidate control strategies is evaluated. Specifically, the evaluation indicators include at least one of the following: probability of secondary accidents, driver reaction failure rate, and vehicle deceleration efficiency. Based on key data recorded during simulation and combined with preset scoring criteria, each indicator of each candidate strategy is scored individually. Then, reasonable weighting coefficients are set according to the importance of each indicator, and the comprehensive evaluation score of each candidate strategy is calculated through weighted summation. For example, the evaluation time window is defined with the deployment of the blocking device as the starting point. Within the evaluation time window, each indicator is scored.

[0074] The probability of a secondary accident is used to quantify the probability of a vehicle entering a hazard area within a tunnel and causing a secondary collision after the implementation of control strategies. The evaluation time window is used as the data collection period. The total number of vehicles entering the data collection area and the number of conflict events where the minimum distance between vehicles in that area is less than a set safety threshold are recorded. The ratio of the number of conflict events to the total number of vehicles is defined as the probability of a secondary accident, with a value ranging from 0 to 1. The formula is:

[0075] in, This indicates the probability of a secondary accident. Indicates the number of conflict events. This indicates the total number of vehicles arriving.

[0076] The driver reaction failure rate quantifies the probability of a driver's recognition of the obstruction interface and the failure of their braking response. The driver reaction failure rate is defined as the ratio of the number of vehicles whose deceleration fails to reach the effective braking threshold within the statistical evaluation time window to the total number of approaching vehicles, with a value ranging from 0 to 1. The formula is:

[0077] in, Indicates the driver's reaction failure rate. Indicates the total number of vehicles arriving; This indicates whether the deceleration of the i-th vehicle has reached the effective braking threshold. If it has, the value is 1; otherwise, the value is 0.

[0078] Vehicle deceleration efficiency quantifies the effectiveness of a vehicle in reducing its speed from its current travel speed to a safe stopping speed after recognizing an obstacle. The speeds of oncoming vehicles within the data collection area are statistically analyzed, and the average speed of each vehicle at the start and end of the evaluation time window is calculated. The reduction in average speed per unit time from the start to the end is recorded as the vehicle deceleration efficiency. The formula is:

[0079] in, Indicates the vehicle's deceleration efficiency. Indicates the start time of the evaluation time window. Indicates the duration of the evaluation time window. and These represent the start and end times of the evaluation time window, respectively. and termination time The average speed of the vehicle.

[0080] In some embodiments, the method further includes step S105, determining the barrier device to be activated according to the optimal control strategy, sending the target luminous intensity and target height to the barrier device to be activated, controlling the barrier device to deploy, raising / lowering the barrier cable to the target height, and adjusting the brightness to the target luminous intensity.

[0081] In some embodiments, the method further includes step S106, which, after the optimal control strategy is executed, evaluates the actual blocking effect and compares the blocking effect evaluation indicators for consistency between the virtual and real data. The differences in the comparison serve as the basis for correcting the driver behavior simulation model. Specifically, if the secondary accident probability deviation exceeds the standard, the traffic flow evolution model in the digital twin model needs to be calibrated first, adjusting parameters such as vehicle following distance and braking response threshold. At the same time, the braking delay rules in the driver behavior simulation model are verified to ensure that the vehicle braking decision in the simulation scenario is consistent with the actual driver behavior. If the driver reaction failure rate deviation exceeds the standard, the driver behavior simulation model needs to be calibrated, focusing on adjusting parameters such as the maximum visual capture distance and the recognition delay rules under the influence of visibility. If the vehicle deceleration efficiency deviation exceeds the standard, the vehicle braking-related parameters in the digital twin model need to be calibrated. Preset parameters such as braking acceleration and braking distance are adjusted in combination with the actual vehicle type (small car, large car). At the same time, the influence coefficient of road longitudinal slope on braking effect is calibrated to ensure that the vehicle deceleration process in the simulation matches the actual vehicle braking pattern.

[0082] If all performance indicators are within acceptable limits, the effectiveness of the optimal control strategy is confirmed. At the same time, the parameters of this operating condition, the actual output data, and the model parameters are associated and stored to provide a benchmark for the generation of strategies for similar scenarios in the future, and to achieve continuous optimization of the model.

[0083] After the control and blocking device is activated, it continuously collects traffic operation data within the tunnel to determine whether a stable blocking phase has been entered. If a stable blocking phase is entered, it reports to the control center that the blocking is complete and waits for the incident to be handled before the user issues a command to restore traffic. For example, the criteria for determining the stable blocking phase are: when the average vehicle speed is lower than a preset speed threshold, the average lane density is higher than a preset density threshold, and the queue length exceeds a preset length threshold, the stable blocking phase is determined to have been entered.

[0084] After the incident is resolved, the user issues a command to restore passage. In response to the command, the blocking device is retrieved, and the passage is restored.

[0085] To enhance the warning and guidance functions of the barrier device, the linear light-emitting unit maintains brightness during both the stabilizing barrier phase and the resumption of traffic phase. Specifically, a higher brightness is maintained during the stabilizing barrier phase to strengthen the warning, while the brightness is gradually reduced to normal levels during the resumption of traffic phase to avoid glare interference. A smooth transition is achieved through a phase brightness coefficient. The target brightness for each phase is calculated as follows: During the stabilization and blocking phase, the warning effect is enhanced, and the target brightness is adjusted as follows:

[0086] in, This represents the maximum brightness of the linear light-emitting device. This is the brightness enhancement factor. This parameter can be dynamically adjusted based on the real-time traffic density and vehicle speed within the tunnel. The higher the vehicle density downstream of the barrier device, the longer the queue. The larger.

[0087] During the traffic resumption phase, to avoid excessive glare and guide traffic flow smoothly, the target brightness will be gradually reduced to normal levels:

[0088] in, The brightness attenuation coefficient is a parameter that increases or decreases linearly according to the disengagement process, ensuring a smooth transition of brightness from warning level to normal lighting level. After the barrier device is retracted, the downstream vehicle density gradually decreases, while the average vehicle speed gradually increases. The brightness attenuation coefficient is determined based on the rate of decrease in vehicle density and the rate of increase in average vehicle speed. .

[0089] Furthermore, during the stable blocking phase, the height of the blocking device also needs to be more closely correlated with the traffic flow status to reduce the impact of environmental factors. The weight calculation formula for the i-th factor is as follows:

[0090] in, For stage enhancement coefficient, Set corresponding environmental factors ; Set the corresponding traffic flow status factor. The adjusted height is recorded as .

[0091] Furthermore, to prevent abrupt changes in control commands caused by stage switching, a first-order low-pass filter or a rate-of-change limitation method is used to ensure the continuity of the control process. The highly smooth formula is:

[0092] in, This indicates the new height of the arresting cable issued to the arresting device; Indicates the current height of the arresting cable; Indicates the high smoothness coefficient. The larger the value, the faster the response; the smaller the value, the smoother the response. .

[0093] To ensure smooth brightness changes and avoid sudden changes that could cause driver discomfort, a first-order low-pass filter is also used for smoothing.

[0094] in, This is the brightness smoothing coefficient. ; This indicates the new luminous intensity sent to the blocking device; This represents the current brightness.

[0095] Based on the above method, one or more embodiments of the present invention also provide a tunnel emergency traffic control device, wherein a barrier device is deployed at predetermined intervals along the longitudinal direction of the tunnel, and the barrier device includes a liftable barrier cable, such as... Figure 2 As shown, the device includes: an initialization module 201 configured to acquire the location of an accident in response to a sudden accident in the tunnel; a data acquisition module 202 configured to define a data acquisition area downstream of the accident location along the direction of traffic flow in the tunnel; a strategy generation module 203 configured to determine the location and control parameters of the traffic flow control device in the tunnel based on the current environmental status data, traffic operation status data, and road structure data within the data acquisition area, and obtain multiple sets of candidate control strategies; and a strategy optimization module 204 configured to perform traffic flow simulation on each set of candidate control strategies and evaluate the control effectiveness; and determine the optimal control strategy based on the control effectiveness evaluation results.

[0096] One or more embodiments of the present invention also provide an electronic device that can be used to implement the tunnel emergency traffic control method described in the above embodiments. The electronic device includes one or more processors, one or more memories coupled to the processors, and a communication module coupled to the processors.

[0097] The memory may include one or more non-volatile memories and one or more volatile memories. Examples of non-volatile memories include, but are not limited to, at least one of the following: read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, hard disk, compact disc (CD), digital video disc (DVD), or other magnetic and / or optical storage. Examples of volatile memories include, but are not limited to, at least one of the following: random access memory (RAM) or other volatile memories that do not persist during the duration of a power outage. The computer program may be stored in the ROM. When the processor executes the computer program, it implements the above-described tunnel emergency traffic control method.

[0098] In some embodiments, the program may be tangibly contained in a computer-readable medium, which may include a device (such as a memory) or other storage device accessible by the device. The program may be loaded from the computer-readable medium into RAM for execution. The computer-readable medium may include any type of tangible non-volatile memory, such as ROM, EPROM, flash memory, hard disk, whereby the computer-readable storage medium stores a computer program that, when executed by a processor, implements the aforementioned tunnel emergency traffic control method.

[0099] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a server or terminal, they generate, in whole or in part, the processes or functions described in the embodiments of this application. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic cable, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to the server or terminal, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, and magnetic tape), an optical medium (e.g., digital video disk (DVD), etc.), or a semiconductor medium (e.g., solid-state drive).

[0100] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0101] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A method for emergency traffic control in tunnels, characterized in that, The method includes the following steps: In response to a sudden tunnel accident, the location of the accident must be determined. Based on the location of the accident, a set distance downstream along the direction of traffic flow in the tunnel is designated as the data collection area; Based on the current environmental status data, traffic operation status data and road structure data within the data collection area, the location and control parameters of the traffic flow control device in the tunnel are determined, and multiple sets of candidate control strategies are obtained. Traffic flow simulations are performed for each group of candidate control strategies, and the effectiveness of the control measures is evaluated. Based on the evaluation results, the optimal control strategy is determined. The traffic flow control device includes a liftable barrier device, and the control parameters include the target luminous intensity and target height of the barrier cable; the method for determining multiple candidate control strategies is as follows: Determine the initial barrier device to be activated based on the location of the accident; Based on the current environmental status data, traffic operation status data, and road structure data, the control parameters of the barrier device are determined, and the initial control strategy is obtained. Multiple candidate control strategies are generated based on the initial control strategy. Each candidate control strategy includes the location of the barrier device to be activated and the control parameters. Based on environmental status data, traffic operation status data, and road structure data, the visibility impact item, the visibility distance impact item, and the braking distance impact item are calculated respectively; the brightness adjustment intensity is calculated based on the visibility impact item, the visibility distance impact item, and the braking distance impact item; and the target luminous brightness is calculated based on the set base luminous brightness and brightness adjustment intensity.

2. The tunnel emergency traffic control method as described in claim 1, characterized in that, Based on environmental status data, traffic operation status data, and road structure data, the visibility impact item and the visual distance impact item are calculated respectively. The height adjustment intensity is calculated based on the visual distance impact item. Based on the effective height travel range of the arresting cable, the target height of the arresting cable is calculated based on the height adjustment intensity.

3. The tunnel emergency traffic control method as described in claim 1, characterized in that, A tunnel-based digital twin system is used to simulate the blocking of each candidate control strategy. The digital twin system includes a tunnel geometric model, a traffic flow evolution model, an environmental change model, and a physical response model of the blocking device.

4. The tunnel emergency traffic control method as described in claim 3, characterized in that, Traffic flow simulation for each candidate control strategy includes: injecting real-time environmental status data, traffic operation status data, and accident location and type into the digital twin system; simulating the process of each vehicle driver visually capturing the blocking device and performing braking operation based on the driver behavior simulation model; and simulating the braking process of the vehicle in response to the braking operation.

5. The tunnel emergency traffic control method as described in claim 1, characterized in that, The method also includes: after the blocking device is activated, it continuously collects traffic operation status data in the tunnel to determine whether it has entered the stable blocking stage. When it is in the stable blocking stage, it adjusts the target luminous brightness according to the traffic flow density and average vehicle speed downstream of the blocking cable. In response to the command to resume traffic, the barrier cable is retracted, and the traffic resumption phase begins. During the traffic resumption phase, the target brightness is adjusted based on the rate of decrease in traffic density downstream of the barrier cable and the rate of increase in average vehicle speed.

6. A tunnel emergency traffic control device, characterized in that, include: The initialization module is configured to obtain the location of an accident in response to a sudden tunnel incident. The data acquisition module is configured to define a set distance downstream of the accident location along the direction of traffic flow in the tunnel as the data acquisition area. The strategy generation module is configured to determine the location and control parameters of the traffic flow control device in the tunnel based on the current environmental status data, traffic operation status data and road structure data within the data collection area, and to obtain multiple sets of candidate control strategies. The strategy optimization module is configured to perform traffic flow simulation for each group of candidate control strategies and evaluate the effectiveness of the control. Based on the results of the control effectiveness assessment, determine the optimal control strategy; The traffic flow control device includes a liftable barrier device, and the control parameters include the target luminous intensity and target height of the barrier cable. The method for determining multiple candidate control strategies is as follows: Determine the initial barrier device to be activated based on the location of the accident; Based on the current environmental status data, traffic operation status data, and road structure data, the control parameters of the barrier device are determined, and the initial control strategy is obtained. Multiple candidate control strategies are generated based on the initial control strategy. Each candidate control strategy includes the location of the barrier device to be activated and the control parameters. Based on environmental status data, traffic operation status data, and road structure data, the visibility impact item, the visibility distance impact item, and the braking distance impact item are calculated respectively; the brightness adjustment intensity is then calculated based on the visibility impact item, the visibility distance impact item, and the braking distance impact item. The target luminance is calculated based on the set base luminance and luminance adjustment intensity.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method according to any one of claims 1 to 5.

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