Cold region expressway tunnel group collaborative speed limiting method based on variable cells

Abstract

The invention discloses a cold region expressway tunnel group collaborative speed limiting method based on variable cells, relates to the technical field of traffic safety, and aims to solve the problem that the congestion risk of a tunnel group is increased due to the fact that a driver cannot accurately limit the vehicle speed due to deterioration of a driving environment under the condition of rainy and snowy weather in a cold region. And various traffic flow indexes under the actual weather influence are calculated. And by utilizing the tunnel group speed limit control target function, based on the traffic condition and weather condition of the tunnel group, the tunnel group cooperative speed limit is dynamically adjusted, and a driver is assisted to limit the vehicle speed, so that the tunnel group congestion risk caused by the fact that the driver cannot accurately limit the vehicle speed under the cold region rain and snow weather condition is reduced.

Description

Technical Field

[0001] This application relates to the field of traffic safety technology, specifically a collaborative speed limit method for highway tunnel groups in cold regions based on variable cells. Background Technology

[0002] A tunnel cluster refers to a combination of adjacent tunnels with a distance between them not exceeding 6 seconds at the design speed. As a special section of highway, tunnel clusters are more prone to congestion. In cold and snowy weather conditions, the deteriorating driving environment increases driving difficulty, leading to improper speed control by drivers, increasing the risk of congestion in tunnel clusters, and posing a serious safety hazard to highway driving. Summary of the Invention

[0003] The purpose of this invention is to address the problem that the deteriorating driving environment in cold and snowy weather conditions can lead to drivers being unable to accurately limit their vehicle speed, thereby increasing the risk of congestion in tunnel clusters. This invention provides a cooperative speed limiting method for highway tunnel clusters in cold regions based on variable cells.

[0004] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0005] A cooperative speed limit method for highway tunnel groups in cold regions based on variable cells includes the following steps:

[0006] Step 1: Obtain traffic information, rain / snow weather information, and road information for each section of the tunnel group;

[0007] Step 2: Construct a density-driven variable cell transmission model using traffic and road information from each section of the tunnel group;

[0008] Step 3: Construct meteorological adjustment factors using rain and snow weather information, and use the meteorological adjustment factors to correct the variable cell transport model;

[0009] Step 4: Based on the traffic and road information of each section of the tunnel group, and using the modified variable cellular transport model, predict the cells. Speed ​​in the next period and the next period of time to drive out of the cell Number of vehicles ;

[0010] Step 5: Utilize predictions and Furthermore, by combining traffic information from each section of the tunnel group, a speed limit control objective function for the tunnel group is constructed.

[0011] Step 6: Adaptive genetic algorithm is used as the optimization solver, the speed limit control objective function of the tunnel group is used as the fitness evaluation criterion, and the optimal speed limit value of each section of the tunnel group is obtained based on the simulation feedback of the variable cell transmission model.

[0012] Furthermore, the traffic information for each section of the tunnel group includes density, speed, and flow rate; the rain and snow weather information includes rainfall or snowfall intensity, visibility, and icy and snowy conditions; and the road information includes the number of lanes and the slope of the road section.

[0013] Furthermore, the density-driven variable cell transport model is expressed as:

[0014] ,

[0015] ,

[0016] ,

[0017] ,

[0018] The cell density update formula is:

[0019] ,

[0020] ,

[0021] ,

[0022] in, for Time period drive out of cell The number of vehicles, for Time cell The latter half is directed towards the cell. The amount of transportation supply provided for Time cell The number of vehicles that the front section can accommodate. For cells Length, The recommended maximum safe speed for rainy / snowy weather. The time for the detector to update information, For cells exist Speed ​​of time period For cells exist Traffic density during different time periods This is the critical distance at which a vehicle can travel at free-flow speed. For maximum flow, The propagation speed of the reverse wave of traffic flow. for Time period drive out of cell The number of vehicles, For cells exist Traffic density during different time periods For cells exist Traffic density during the +1 hour period For cells The number of lanes, To control the cycle, For cells exist The number of vehicles merging into the ramp during a given time period; if a cell does not merge into the ramp, the value is 0. For cells exist The number of vehicles exiting the ramp during a given time period; if a cell did not exit the ramp, the number is 0. for +1 period to drive out of cell The number of vehicles, For cells exist Traffic density during the +1 hour period For cells The critical density, For road congestion density, This represents the free-flow velocity.

[0023] Furthermore, the predicted cells in step four Speed ​​in the next period and the next period of time to drive out of the cell Number of vehicles Represented as:

[0024] ,

[0025] ,

[0026] ,

[0027] in, Intensity of rainfall / snowfall, For visibility, For the parameter to be estimated, It is a meteorological regulating factor.

[0028] Furthermore, the objective function for speed limit control of the tunnel group is expressed as:

[0029] ,

[0030] in, and The objective functions are for the downstream tunnel and the upstream control section, respectively. and These are the weighting coefficients for the downstream tunnel and the upstream control section targets, respectively.

[0031] Furthermore, the objective function of the downstream tunnel It includes target control functions for high-density periods and low-density periods. The target control function is used when the lane occupancy rate is greater than 0.55, and otherwise it is used when the lane occupancy rate is low-density.

[0032] Furthermore, the target control function for the high-density period is expressed as:

[0033] ,

[0034] ,

[0035] ,

[0036] in, This represents the total traffic capacity of the downstream tunnel section. The difference between the actual speed of the cell and the speed limit. and These are the weighting coefficients for the total traffic capacity target and the speed difference target, respectively. This refers to the cellular segments included in the downstream tunnel section. The number of statistical intervals for one control period. For Yuanbao section In the statistical time speed, For Yuanbao section The target control speed, For the duration of the statistical interval, For Yuanbao section In the statistical time Traffic capacity.

[0037] Furthermore, the target control function for the low-density period is expressed as:

[0038] ,

[0039] ,

[0040] ,

[0041] in, The headway between adjacent vehicles. and The speeds of adjacent vehicles are respectively. This represents the distance between the front ends of adjacent vehicles. and These are the weighting coefficients for the headway target and the speed difference target, respectively.

[0042] Furthermore, the objective function of the upstream control segment Represented as:

[0043] ,

[0044] ,

[0045] ,

[0046] ,

[0047] in, The difference between the vehicle speed and the speed limit in the upstream control area. This represents the number of cells in the upstream control region. Speed ​​limits are set at the starting point of the controlled area. Speed ​​limits are in place at the end of the controlled area. and These are the weighting coefficients for the headway target and the speed difference target, respectively. This represents the rate-limited change value between adjacent cells under ideal conditions.

[0048] Furthermore, the constraint conditions for the objective function of the tunnel group speed limit control are as follows:

[0049]

[0050]

[0051]

[0052] in, For Yuanbao section The target control speed, It is the covariant function when 5 is taken.

[0053] The beneficial effects of this invention are:

[0054] This application calculates various traffic flow indicators under the influence of actual weather by introducing a weather adjustment factor. Furthermore, it utilizes the tunnel group speed limit control objective function to dynamically adjust the coordinated speed limits of the tunnel group based on traffic conditions and weather conditions, assisting drivers in limiting vehicle speed. This reduces the risk of tunnel group congestion caused by drivers' inability to accurately limit vehicle speed under cold, rainy, or snowy weather conditions. Attached Figure Description

[0055] Figure 1 This is a flowchart for our organization;

[0056] Figure 2 Here is a flowchart of the adaptive genetic algorithm;

[0057] Figure 3 This is a schematic diagram of a specific embodiment of this application. Detailed Implementation

[0058] It should be noted that, where there is no conflict, the various embodiments disclosed in this application can be combined with each other.

[0059] Specific Implementation Method 1: The cooperative speed limiting method for highway tunnel groups in cold regions based on variable cells described in this implementation method includes the following steps:

[0060] Step 1: Obtain traffic information, rain / snow weather information, and road information for each section of the tunnel group;

[0061] Step 2: Construct a density-driven variable cell transmission model using traffic and road information from each section of the tunnel group;

[0062] Step 3: Construct meteorological adjustment factors using rain and snow weather information, and use the meteorological adjustment factors to correct the variable cell transport model;

[0063] Step 4: Based on the traffic and road information of each section of the tunnel group, and using the modified variable cellular transport model, predict the cells. Speed ​​in the next period and the next period of time to drive out of the cell Number of vehicles ;

[0064] Step 5: Utilize predictions and Furthermore, by combining traffic information from each section of the tunnel group, a speed limit control objective function for the tunnel group is constructed.

[0065] Step 6: Adaptive genetic algorithm is used as the optimization solver, the speed limit control objective function of the tunnel group is used as the fitness evaluation criterion, and the optimal speed limit value of each section of the tunnel group is obtained based on the simulation feedback of the variable cell transmission model.

[0066] Step 1: Traffic, weather, and road information for each section of the tunnel complex:

[0067] Traffic information includes density, speed, and flow; weather information includes rainfall intensity, snowfall intensity, visibility, and icy / snowy conditions; road information includes the number of lanes and road gradient.

[0068] Step 2: Construct a density-driven variable cellular transport model and dynamically adjust the cellular model using meteorological factors;

[0069] (1) To avoid vehicles crossing multiple cells during the detector update time, the cell length of the variable cell transport model is set based on the maximum safe speed limit and the detector update time. The density-driven variable cell transport model is as follows:

[0070] (1)

[0071] (2)

[0072] (3)

[0073] (4)

[0074] The cell density update formula is:

[0075] (5)

[0076] (6)

[0077] (7)

[0078] in, for Time period drive out of cell The number of vehicles, for Time cell The latter half is directed towards the cell. The amount of transportation supply provided for Time cell The number of vehicles that the front section can accommodate. For cells Length, The recommended maximum safe speed for rainy / snowy weather. The time for the detector to update information, For cells exist Speed ​​of time period For cells exist Traffic density during different time periods This is the critical distance at which a vehicle can travel at free-flow speed. For maximum flow, The propagation speed of the reverse wave of traffic flow. for Time period drive out of cell The number of vehicles, For cells exist Traffic density during different time periods For cells exist Traffic density during the +1 hour period For cells The number of lanes, To control the cycle, For cells exist The number of vehicles merging into the ramp during a given time period; if there are no cells, the ramp count is 0. For cells exist The number of vehicles exiting the ramp during a given time period; if the cell has no ramps, the value is 0. for +1 period to drive out of cell The number of vehicles, For cells exist Traffic density during the +1 hour period For cells The critical density, For road congestion density, This represents the free-flow velocity.

[0079] Please refer to the following table for settings.

[0080] Table 1 Recommended Maximum Safe Speed ​​on Highways in Rainy Weather

[0081]

[0082] Table 2 Recommended Maximum Safe Speed ​​on Highways in Snowy Weather

[0083]

[0084]

[0085] (2) Variable cell transport model under rain and snow weather:

[0086] When encountering rain or snow, environmental changes such as reduced road visibility and decreased road surface friction coefficient will directly lead to significant changes in traffic flow characteristics. Introducing a weather-dependent factor (WAF) can dynamically adjust traffic flow parameters. , , The expression for the meteorological regulation factor is:

[0087] (8)

[0088] In the formula, Indicates the intensity of rainfall or snowfall. Indicates visibility. , , , The parameters to be estimated for rainy and snowy days are shown in the table below:

[0089] Table 3. Estimated parameter values ​​under rainy weather conditions.

[0090]

[0091] Table 4 Estimated values ​​of parameters to be estimated under snowy conditions

[0092]

[0093] In rainy or snowy weather, the cellular transport model for the highway section outside the tunnel is improved as follows:

[0094] (9)

[0095] (10)

[0096] Step 3: Based on the traffic conditions of the tunnel group, implement speed limits for each road segment. Vehicles in free-flow mode will travel at a speed not exceeding the speed limit, while vehicles in congested mode will not be affected. Furthermore, by adjusting the objective function for high / low density scenarios within the tunnel group, ensure smooth changes in speed limits.

[0097] (1) The speed limit control objective function of the tunnel group is divided into the downstream tunnel control objective function and the upstream control objective function.

[0098] (11)

[0099] In the formula, Let be the objective function for speed limit control of the tunnel group. and These are the objective functions for the downstream tunnel and the upstream control section, respectively. and These are the weighting coefficients for the downstream tunnel and the upstream control section targets, respectively.

[0100] (2) Downstream tunnel control objective function

[0101] The downstream tunnel control objective function is divided into high-density and low-density period objective control functions. The distinction between high and low density is made by using detector equipment to monitor lane occupancy and judging based on historical traffic flow data and Table 5. If the congestion level exceeds the lower limit of mild congestion, that is, the lane occupancy rate is greater than 0.55, it is the high-density period objective control function; otherwise, it is the low-density period objective control function.

[0102] Table 5. Lane occupancy thresholds for different levels of congestion.

[0103]

[0104] High-density target control function:

[0105] During peak traffic hours or sudden congestion, the core objective of downstream tunnel traffic operation is to maximize road capacity by quickly alleviating backlogged traffic through optimized scheduling strategies. The target control function for high-density periods is:

[0106] (12)

[0107] (13)

[0108] (14)

[0109] In the formula, This represents the total traffic capacity of the downstream tunnel section. The difference between the actual speed of the cell and the speed limit. This refers to the cellular segments included in the downstream tunnel section. The number of statistical intervals for one control period. Indicates cell section The target control speed, For the duration of the statistical interval, For Yuanbao section In the statistical time Traffic capacity, For Yuanbao section In the statistical time speed, and These are the weighting coefficients for the total traffic capacity target and the speed difference target, respectively.

[0110] Target control function for low-density periods:

[0111] Under low-density tunnel operation conditions, the core control objective is the coordinated optimization of traffic safety and traffic efficiency. The target control function during low-density periods includes the minimum vehicle speed difference and the maximum headway, as follows:

[0112] (15)

[0113] (16)

[0114] (17)

[0115] In the formula, The headway between adjacent vehicles. and The speeds of the two vehicles are respectively. The distance between the front ends of the two vehicles. and These are the weighting coefficients for the headway target and the speed difference target, respectively.

[0116] (3) The objective function of the upstream road section control area is:

[0117] In the tunnel entrance control strategy design, the upstream road segment is divided into M gradient control units. To address the deviation between the actual control speed profile and the ideal smooth curve, the objective function of the upstream road segment control region is as follows: This is achieved by minimizing the difference between the actual control speed and the planned control speed in each cell, as well as the difference between the vehicle's actual speed and the actual control speed.

[0118] (18)

[0119] (19)

[0120] (20)

[0121] (twenty one)

[0122] In the formula, The speed difference between the vehicle speed and the speed limit in the upstream control area This represents the number of cells in the upstream control region. Speed ​​limits are set at the starting point of the controlled area. Speed ​​limits are in place at the end of the controlled area. and These are the weighting coefficients for the headway target and the speed difference target, respectively. This represents the rate-limited change value between adjacent cells under ideal conditions.

[0123] In step three, the constraints of the objective function include the difference in running speed between adjacent cells in the same control cycle, the variation range of the variable speed limit value between adjacent control cycles of the same cell, and the rationality constraint of the speed limit value. The calculation formulas for the above constraints are as follows:

[0124] (twenty two)

[0125] (twenty three)

[0126] (twenty four)

[0127] In the formula, It is the covariant function when 5 is taken.

[0128] Step 4: Adaptive genetic algorithm is used to solve the variable speed limit control model for the tunnel group.

[0129] An adaptive genetic algorithm is used as the optimization solver, and the speed limit control objective function of the tunnel group is used as the fitness evaluation criterion. Based on the simulation feedback of the variable cell transmission model, the optimal speed limit value of each section of the tunnel group is solved.

[0130] (1) Using the speed limit value of each cell as a decision variable, a set of speed limit schemes is randomly generated. Twenty speed limit schemes are randomly selected as the initial population individuals, encoded, and input into the variable cell transmission model constructed in step two. The model uses the received speed limit value as a constraint on the free flow velocity of the cell, dynamically adjusts the relevant traffic flow parameters, and simulates and deduces the traffic flow state of the tunnel group under the speed limit scheme.

[0131] (2) Based on the objective function and constraints set in step three, calculate the fitness value corresponding to the speed limit scheme. According to the fitness value of each individual, use the roulette wheel selection method to select individuals. Individuals with higher fitness are more likely to be retained, while individuals with lower fitness are more likely to be eliminated. The probability of each speed limit value being selected is as follows:

[0132] (25)

[0133] in, For each generation The adaptability of a speed limit value.

[0134] (3) Crossover and variation

[0135] In adaptive genetic algorithms, the crossover rate and mutation rate are dynamically generated based on the fitness of individuals in each generation. This mechanism is used to adjust the genomic composition of parent individuals, generating a new generation of individuals through crossover and mutation processes. The calculation formula is as follows:

[0136] (26)

[0137] (27)

[0138] in, Crossover rate, The variability rate, For maximum fitness, This represents the average fitness.

[0139] (4) The emergence of new populations

[0140] After selection, crossover, and mutation operations are performed on the parent individuals, a new population of the next generation is generated, and this process is repeated for iterative optimization.

[0141] (5) Output the optimal solution

[0142] Determine whether the current iteration count has reached the preset maximum iteration count. If not, proceed to step (3) to continue iterative optimization; otherwise, terminate the iteration and output the current optimal solution as the speed limit value published for each road segment.

[0143] Step 5: Speed ​​limit value release and feedback adjustment.

[0144] After generating the optimal speed limit scheme, the system will dynamically publish and optimize speed limits in real time through a feedback mechanism. Intelligent variable message signs deployed within the tunnel complex will update speed limits for each section using high-brightness LED displays, simultaneously pushing speed limit changes to radio stations and mainstream navigation service providers via API interfaces, ensuring drivers receive speed limit information before entering the tunnel complex. Millimeter-wave radar and video detectors deployed at tunnel entrances, exits, and key sections will collect core parameters such as traffic flow, vehicle speed distribution, and headway in real time, continuously comparing the measured average vehicle speed with the target speed limit. Based on the magnitude of the speed deviation, the system will iteratively update the speed limits for the tunnel complex in the next control cycle. This entire process forms a closed-loop feedback chain of "publishing-monitoring-analysis-optimization," ensuring that the speed limit strategy remains dynamically adapted to real-time traffic conditions and environmental changes, effectively improving the traffic efficiency and operational safety of the tunnel complex.

[0145] Example:

[0146] This embodiment uses a highway tunnel complex in a cold region as the implementation object. The tunnel complex includes three continuous tunnels (A, B, and C) and two connecting road sections (S1 and S2), with a total length of 11.2 kilometers. The entire line adopts a two-way separated lane design, with two lanes for traffic in each direction. The specific implementation process is as follows:

[0147] Step 1: Data Acquisition and Processing

[0148] Real-time conditions of each road section were obtained through roadside monitoring equipment: Tunnel A entrance density was 28 vehicles / km, average speed was 72 km / h, and flow rate was 1750 vehicles / h; connecting section S1 density was 32 vehicles / km, speed was 68 km / h, and flow rate was 1820 vehicles / h; Tunnel B density was 35 vehicles / km, speed was 65 km / h, and flow rate was 1700 vehicles / h; connecting section S2 density was 38 vehicles / km, speed was 62 km / h, and flow rate was 1720 vehicles / h; Tunnel C exit density was 45 vehicles / km, speed was 58 km / h, and flow rate was 1680 vehicles / h, triggering a high-density scenario. Snowfall intensity was 1.8 mm / min, visibility was 150 m, and the road surface icy and snowy conditions were compacted snow. The longitudinal slope of section S2 was 4.2%, and the slopes of the remaining sections were ≤3%.

[0149] The entire tunnel complex is divided into 20 cells. The detector updates information every 30 seconds. Based on a visibility of 150m, a compacted snow surface, and a slope of y=5%, the safe speed limit benchmark value outside the tunnel is determined by referring to Table 2. Safety speed limit benchmark value inside the tunnel Calculations show that the longest cell length inside the tunnel is 667m, and the longest cell length outside the tunnel is 541m. For road sections shorter than 500m, the maximum value is taken as the cell length for that section, and the control cycle is used. =1min; Based on the snow weather parameter table, the meteorological adjustment factor can be calculated. For the section outside the tunnel, the meteorologically corrected flow-velocity relationship is used to limit the free-flow velocity to [value missing]. The basic model of the road section inside the tunnel was preserved due to the stable environment.

[0150] Step 3: Dynamic configuration of the objective function

[0151] Tunnel section C has a traffic density of 45 vehicles / km and a lane occupancy rate greater than 0.75, triggering the high-density objective function; the remaining sections are optimized using a low-density mode. The objective for downstream tunnel C is to maximize traffic capacity. and minimizing vehicle speed deviation The goal of the upstream connecting section is to set a speed-limiting gradient from S1 to S2 to ensure a smooth speed transition.

[0152] Step 4: Solve using an adaptive genetic algorithm

[0153] Generate 20 rate-limiting schemes, each containing a rate-limiting value of 28 cells. Use the objective function G as the evaluation criterion, where downstream weights... Upstream Crossover probability Probability of mutation The speed limit difference between adjacent cells is dynamically adjusted (constrained to ≤10km / h). After 100 iterations, the optimal speed limit scheme is selected.

[0154]

[0155] Step 5: Speed ​​Limit Announcement and Feedback Adjustment

[0156] After determining the optimal speed limit scheme, the system will display the dynamic speed limit values ​​for each road segment on various variable message signs within the tunnel complex, and push the dynamic speed limit information to drivers via FM radio and navigation service providers. It will also collect traffic information in real time through traffic flow detectors, compare the actual vehicle speeds with the target speed limits set by the decision engine, and dynamically adjust the published speed limit values ​​for the following week based on the feedback.

[0157] Implementation effect verification

[0158]

[0159] The verification data in this embodiment effectively proves the scientific nature and engineering applicability of this application: Under the condition of snowfall in cold regions, the coordinated speed limit control for the high-density tunnel C section significantly improves the traffic capacity by 21.8%, reduces the speed standard deviation by 50%, and reduces the risk of rear-end collision by 41%. Furthermore, the upstream connecting section S2 achieves a simultaneous increase in traffic capacity of 15.3% and a reduction in risk of 36% through gradient speed limit. This effect verifies the technical advantages of the three-in-one approach of variable cell model dynamically adapting to weather, genetic algorithm co-optimizing speed limit, and gradient constraint ensuring driving stability.

[0160] The focus of this application is:

[0161] (1) Weather adjustment factor

[0162] Under severe weather conditions, the impact of weather on traffic flow can be simulated by adjusting key parameters in traffic flow models. To this end, a Weather Adjustment Factor (WAF) is introduced. This factor is calculated by multiplying the baseline traffic parameters under clear weather conditions to determine various traffic flow indicators under actual weather conditions. The value of the WAF is closely related to three main variables reflecting weather severity: visibility, rainfall intensity, and snowfall intensity. These variables collectively determine the magnitude of the WAF, thus quantitatively describing the combined impact of different severe weather conditions on traffic flow.

[0163] (2) Variable speed limit target for tunnel group

[0164] The goal of variable speed limits in tunnel groups is to ensure traffic safety while improving traffic efficiency. The objective function for speed limit control in tunnel groups can be divided into two parts: downstream tunnel control objectives and upstream control objectives.

[0165] Downstream tunnel control is categorized into high-density and low-density scenarios based on traffic density. Under high-density conditions, a multi-objective function is constructed, maximizing total capacity and minimizing speed difference. Total capacity refers to the sum of the capacities of all segments in the downstream tunnel; a larger value indicates more vehicles passing through per unit time, thus helping to alleviate congestion. Speed ​​difference represents the difference between the actual vehicle speed and the speed limit. Minimizing this difference allows vehicle speeds to approach the target speed limit, ensuring driving safety and improving compliance with speed limit enforcement.

[0166] Under low-density conditions, the objective function of variable speed limits primarily focuses on headway and speed difference. Headway reflects the distance between vehicles; in low-density conditions, vehicles travel at higher speeds, and if the headway is too small, the risk of collision increases. Therefore, increasing the headway effectively reduces the probability of accidents and improves safety. The speed difference objective continues to ensure that vehicles travel at the speed limit as safely as possible.

[0167] The primary objective of the upstream control zone is to ensure that the actual controlled speed closely follows the ideal speed curve, thereby guaranteeing consistency between driving behavior and control commands. This zone has two sub-objectives: minimizing the difference between the actual controlled speed and the planned controlled speed at each cell, and reducing the difference between the vehicle's actual speed and the actual controlled speed. Due to factors such as weather and cell dynamic limitations, the actual speed limit of some cells may not perfectly follow changes in the ideal speed limit. Optimization aims to make the actual speed limit as close to the ideal curve as possible. The speed difference objective remains focused on ensuring safety while encouraging vehicles to drive at the actual speed limit.

[0168] It should be noted that the specific embodiments are merely explanations and illustrations of the technical solution of the present invention and should not be used to limit the scope of protection. Any modifications made in accordance with the claims and specification of the present invention that are only partial should still fall within the protection scope of the present invention.

Claims

1. A cooperative speed limiting method for highway tunnel groups in cold regions based on variable cells, characterized in that... Includes the following steps: Step 1: Obtain traffic information, rain / snow weather information, and road information for each section of the tunnel group; Step 2: Construct a density-driven variable cell transmission model using traffic and road information from each section of the tunnel group; Step 3: Construct meteorological adjustment factors using rain and snow weather information, and use the meteorological adjustment factors to correct the variable cell transport model; Step 4: Based on the traffic and road information of each section of the tunnel group, and using the modified variable cellular transport model, predict the cells. Speed ​​in the next period and the next period of time to drive out of the cell Number of vehicles ; Step 5: Utilize predictions and Furthermore, by combining traffic information from each section of the tunnel group, a speed limit control objective function for the tunnel group is constructed. Step 6: Adaptive genetic algorithm is used as the optimization solver, the speed limit control objective function of the tunnel group is used as the fitness evaluation criterion, and the optimal speed limit value of each section of the tunnel group is obtained based on the simulation feedback of the variable cell transmission model.

2. The method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells according to claim 1, characterized in that... Traffic information for each section of the tunnel group includes density, speed, and flow rate; rain and snow weather information includes rainfall or snowfall intensity, visibility, and icy / snow conditions; and road information includes the number of lanes and road gradient.

3. The method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells according to claim 2, characterized in that... The density-driven variable cell transport model is expressed as: ， ， ， ， The cell density update formula is: ， ， ， in, for Time period drive out of cell The number of vehicles, for Time cell The latter half is directed towards the cell. The amount of transportation supply provided for Time cell The number of vehicles that the front section can accommodate. For cells Length, The recommended maximum safe speed for rainy / snowy weather. The time for the detector to update information, For cells exist Speed ​​of time period For cells exist Traffic density during different time periods This is the critical distance at which a vehicle can travel at free-flow speed. For maximum flow, The propagation speed of the reverse wave of traffic flow. for Time period drive out of cell The number of vehicles, For cells exist Traffic density during different time periods For cells exist Traffic density during the +1 hour period For cells The number of lanes, To control the cycle, For cells exist The number of vehicles merging into the ramp during a given time period; if a cell does not merge into the ramp, the value is 0. For cells exist The number of vehicles exiting the ramp during a given time period; if a cell did not exit the ramp, the number is 0. for +1 period to drive out of cell The number of vehicles, For cells exist Traffic density during the +1 hour period For cells The critical density, For road congestion density, This represents the free-flow velocity.

4. The method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells according to claim 3, characterized in that... The predicted cells in step four Speed ​​in the next period and the next period of time to drive out of the cell Number of vehicles Represented as: ， ， ， in, Intensity of rainfall / snowfall, For visibility, For the parameter to be estimated, It is a meteorological regulating factor.

5. A method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells, as described in claim 4, is characterized in that... The objective function for speed limit control of the tunnel group is expressed as follows: ， in, and The objective functions are for the downstream tunnel and the upstream control section, respectively. and These are the weighting coefficients for the downstream tunnel and the upstream control section targets, respectively.

6. The method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells according to claim 5, characterized in that... The objective function of the downstream tunnel It includes target control functions for high-density periods and low-density periods. The target control function is used when the lane occupancy rate is greater than 0.55, and otherwise it is used when the lane occupancy rate is low-density.

7. A method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells, as described in claim 6, is characterized in that... The target control function for the high-density period is expressed as follows: ， ， ， in, This represents the total traffic capacity of the downstream tunnel section. The difference between the actual speed of the cell and the speed limit. and These are the weighting coefficients for the total traffic capacity target and the speed difference target, respectively. This refers to the cellular segments included in the downstream tunnel section. The number of statistical intervals for one control period. For Yuanbao section In the statistical time speed, For Yuanbao section The target control speed, For the duration of the statistical interval, For Yuanbao section In the statistical time Traffic capacity.

8. The method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells according to claim 7, characterized in that... The target control function for the low-density period is expressed as follows: ， ， ， in, The headway between adjacent vehicles. and The speeds of adjacent vehicles are respectively. This represents the distance between the front ends of adjacent vehicles. and These are the weighting coefficients for the headway target and the speed difference target, respectively.

9. A method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells, as described in claim 8, is characterized in that... The objective function of the upstream control section Represented as: ， ， ， ， in, The difference between the vehicle speed and the speed limit in the upstream control area. This represents the number of cells in the upstream control region. Speed ​​limits are set at the starting point of the controlled area. Speed ​​limits are in place at the end of the controlled area. and These are the weighting coefficients for the headway target and the speed difference target, respectively. This represents the rate-limited change value between adjacent cells under ideal conditions.

10. A method for collaborative speed limiting of highway tunnel groups in cold regions based on variable cells, as described in claim 9, is characterized in that... The constraints of the objective function for speed limit control of the tunnel group are: in, For Yuanbao section The target control speed, It is the covariant function when 5 is taken.

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