Method, device and equipment for determining road traffic capacity in multi-disaster scenario

Abstract

The application discloses a kind of method, device and equipment for determining road traffic capacity under multi-disaster scenario, it is related to highway traffic disaster prevention technical field, method includes: the disaster data of target section is collected, according to disaster data, the disaster type suffered by target section is divided into sustained disaster and instantaneous disaster;With disaster intensity parameter in disaster data as independent variable, the attenuation model corresponding to sustained disaster is constructed, with the plugging parameter in disaster data as independent variable, the attenuation model corresponding to instantaneous disaster is constructed;Based on attenuation model, the single-disaster loss coefficient of each disaster type is calculated, each single-disaster loss coefficient is weighted and summed, based on the coupling correction coefficient set according to preset rule is corrected to obtain comprehensive loss coefficient;Based on comprehensive loss coefficient and preset grading standard, the traffic capacity of target section is rated, to determine the traffic capacity of target section and generate corresponding control strategy.The application improves the accuracy of disaster evaluation on road traffic capacity.

Description

Technical Field

[0001] This application relates to the field of highway traffic disaster prevention technology, specifically to a method, device, and equipment for determining road traffic capacity under multi-hazard scenarios. Background Technology

[0002] Mountain roads, high-altitude and cold-weather roads, and canyon roads are often affected by disasters such as ice and snow, strong winds, and mudslides. These various disasters affect the road's traffic capacity in terms of road surface adhesion performance, driving stability, effective cross-section, and roadbed structure stability.

[0003] Existing road capacity disaster assessment technologies have the following shortcomings: First, most existing assessment models are static qualitative ratings, unable to achieve continuous quantitative calculations of capacity under different disaster intensities, making it difficult to support refined management and control decisions. Second, existing technologies fail to distinguish the essential differences in the mechanisms and attenuation patterns between persistent and transient disasters. Using a unified model for evaluation when multiple disasters coexist fails to accurately decompose the contribution of each disaster, resulting in distorted evaluation results. Third, when calculating the cumulative impact of multiple disasters, existing methods only perform a simple weighted summation of the losses from each disaster, without setting a coupling correction coefficient to correct for the synergistic amplification effect of multiple disasters, leading to calculation results that deviate from reality. Fourth, existing methods lack standardized grading criteria, making it difficult to generate clear traffic control strategies based on the calculation results. Summary of the Invention

[0004] This application provides a method, apparatus, and equipment for determining road capacity under multi-hazard scenarios, which can solve the technical problem of low accuracy in existing road capacity disaster assessment.

[0005] In a first aspect, embodiments of this application provide a method for determining road capacity under multi-hazard scenarios, the method comprising: Collect disaster data for the target road section, and classify the types of disasters suffered by the target road section into persistent disasters and transient disasters based on the disaster data; A decay model for persistent disasters is constructed using disaster intensity parameters from disaster data as independent variables, and a decay model for transient disasters is constructed using blockage parameters from disaster data as independent variables. The single-disaster loss coefficient for each disaster type is calculated based on the attenuation model corresponding to each disaster type. The single-disaster loss coefficients are then weighted and summed, and corrected based on the coupling correction coefficient set according to the preset rules to obtain the comprehensive loss coefficient. The traffic capacity of the target road segment is rated based on the comprehensive loss coefficient and the preset classification standard. The traffic capacity of the target road segment is determined according to the rating results and the corresponding control strategy is generated.

[0006] In conjunction with the first aspect, in one implementation method: The disaster intensity parameters in the disaster data include: road surface ice thickness, snow depth, duration of low temperature, instantaneous wind speed, and duration of strong wind; The blocking parameters in the disaster data include: road blockage width, siltation volume, and slope damage level; The persistent disasters include: snow and ice disasters and strong wind disasters; The instantaneous disasters include: debris flow disasters.

[0007] In conjunction with the first aspect, in one implementation, the step of constructing a decay model corresponding to a persistent disaster using disaster intensity parameters from disaster data as independent variables includes: By collecting data on the thickness of ice accumulation on the road surface and the corresponding measured road traffic loss from historical snow and ice disasters on the target road section, regression analysis was used to obtain the fitting constant, and snow depth and duration of low temperature were introduced to adjust the fitting constant. A quadratic polynomial fitting function is used to construct the attenuation model corresponding to snow and ice disasters, specifically:

[0008] in, This represents the traffic loss coefficient caused by ice and snow disasters. Indicates the thickness of ice on the road surface. , , All represent fitting constants; By collecting instantaneous wind speeds and corresponding measured road traffic losses from historical strong wind disasters along the target road section, regression analysis was used to obtain fitting constants, and the duration of strong winds was introduced to adjust the fitting constants. A quadratic polynomial fitting function is used to construct the attenuation model corresponding to the severe wind disaster, specifically:

[0009] in, This represents the traffic loss coefficient caused by strong winds. Indicates instantaneous wind speed. , , All of these represent fitting constants.

[0010] In conjunction with the first aspect, in one implementation, the measured road traffic loss data specifically refers to the true value of the comprehensive loss for a single disaster obtained by collecting traffic speed attenuation rate, effective road width loss rate, driving safety factor, and road surface bearing capacity remaining factor corresponding to historical disaster events, and then weighting and fusing them using the entropy weighting method. The specific calculation of the true value of the comprehensive loss for a single disaster is as follows: The system collects four sub-indicators corresponding to historical disaster events: traffic speed attenuation rate, effective road width loss rate, driving safety factor, and road surface load-bearing capacity remaining factor. Calculate the information entropy of each sub-indicator, and calculate the entropy weight of each sub-indicator based on its information entropy. Based on each sub-indicator and its entropy weight, the true value of the single-disaster comprehensive loss is calculated as follows:

[0011] in, This represents the true value of the overall loss in a single disaster. Indicates the rate of decrease in travel speed. Indicates the effective road width loss rate. Indicates the driving safety factor. Indicates the road surface bearing capacity remaining coefficient. , , , These represent the entropy weights corresponding to the speed attenuation rate, effective road width loss rate, driving safety factor, and road surface bearing capacity remaining factor, respectively.

[0012] In conjunction with the first aspect, in one implementation, the step of constructing an attenuation model corresponding to a transient disaster using the blocking parameters in the disaster data as independent variables includes: By collecting data on the blocked road width, standard road width, and corresponding measured road traffic loss of historical debris flow disasters in the target road section, the least squares method was used to fit the fitting constant, and the siltation volume and slope damage level were introduced to adjust the fitting constant. A quadratic polynomial fitting function is used to construct the attenuation model corresponding to debris flow disasters, specifically:

[0013] in, This represents the traffic loss coefficient due to debris flow disasters. This indicates that the road width is blocked. Indicates the standard road width. , , All of these represent fitting constants.

[0014] In conjunction with the first aspect, in one implementation, the step of calculating the single-disaster loss coefficient for each disaster type based on the attenuation model corresponding to each disaster type, weighting and summing the single-disaster loss coefficients, and correcting them based on a coupling correction coefficient set according to preset rules to obtain a comprehensive loss coefficient includes: Calculate the single-disaster loss coefficient for each disaster type based on the attenuation model corresponding to each disaster type; The Analytic Hierarchy Process (AHP) was used to construct pairwise judgment matrices for snow and ice disasters, strong wind disasters, and debris flow disasters. Subjective weights were obtained after passing the consistency test. The information entropy is calculated based on the degree of variation of each disaster data using the entropy weight method, and the objective weight is obtained. The final coupling weight is calculated based on the subjective and objective weights using a weight balance coefficient, specifically as follows:

[0015] in, Indicates the first The final coupling weight of disaster-like events, Indicates the first Subjective weighting of disaster types Indicates the first Objective weighting of disaster types Indicates the weighting balance coefficient; The loss coefficients of each individual disaster are weighted and summed based on the final coupling weights of each disaster type, and then corrected according to the coupling correction coefficient set according to preset rules to obtain the comprehensive loss coefficient, as follows:

[0016] in, Indicates the overall loss coefficient. Represents the coupling correction coefficient. This represents the traffic loss coefficient caused by ice and snow disasters. This represents the traffic loss coefficient caused by strong winds. This represents the traffic loss coefficient due to debris flow disasters. This represents the final coupling weight corresponding to the snow and ice disaster. This represents the final coupling weight corresponding to a severe wind disaster. This represents the final coupling weight corresponding to the debris flow disaster.

[0017] In conjunction with the first aspect, in one implementation method, the coupling correction coefficient is specifically set as follows: The coupling correction coefficient increases with the number of superimposed disasters, and the higher the disaster intensity or the lower the highway grade, the larger the value of the coupling correction coefficient.

[0018] In conjunction with the first aspect, in one implementation, the step of rating the traffic capacity of the target road segment based on a comprehensive loss coefficient and a preset grading standard, determining the traffic capacity of the target road segment based on the rating result, and generating a corresponding control strategy includes: The overall loss coefficient is divided into five level ranges to determine which level range the overall loss coefficient of the target road segment falls into: If the overall loss coefficient of the target road segment is at level one, it is determined to be safe to pass and the routine monitoring and control strategy is implemented. If the overall loss coefficient of the target road segment is at level two, it is determined to be normal traffic and a key monitoring and control strategy is implemented. If the comprehensive loss coefficient of the target road section is at level three, it is determined to be a speed-limited passage and an overtaking control strategy is implemented. If the comprehensive loss coefficient of the target road section is at level four, it is determined to be a restricted traffic zone, and a one-way traffic control strategy is implemented. If the overall loss coefficient of the target road section is at level 5, the road is deemed closed and a no-passage control strategy is implemented.

[0019] Secondly, embodiments of this application provide a device for determining road capacity under multi-hazard scenarios, the device comprising: The data acquisition module is used to collect disaster data of the target road section and classify the disaster types suffered by the target road section into continuous disasters and instantaneous disasters based on the disaster data; The module is used to construct attenuation models for persistent disasters using disaster intensity parameters from disaster data as independent variables, and to construct attenuation models for transient disasters using blockage parameters from disaster data as independent variables. The calculation module is used to calculate the single-disaster loss coefficient of each disaster type based on the attenuation model corresponding to each disaster type, sum the single-disaster loss coefficients by weight, and correct them based on the coupling correction coefficient set according to the preset rules to obtain the comprehensive loss coefficient. The rating module is used to rate the traffic capacity of target road segments based on the comprehensive loss coefficient and preset grading standards, determine the traffic capacity of target road segments based on the rating results, and generate corresponding control strategies.

[0020] Thirdly, embodiments of this application provide a device for determining road capacity under multi-hazard scenarios. The device for determining road capacity under multi-hazard scenarios includes a processor, a memory, and a program for determining road capacity under multi-hazard scenarios stored in the memory and executable by the processor. When the program for determining road capacity under multi-hazard scenarios is executed by the processor, it implements the steps of the method for determining road capacity under multi-hazard scenarios as described in any of the above embodiments.

[0021] The beneficial effects of the technical solutions provided in this application include: This application's embodiments classify disaster types into persistent and transient categories, and construct corresponding attenuation models using disaster intensity parameters and closure parameters as independent variables. This ensures the model structure matches the actual action mechanisms of different disaster types, improving the accuracy of single-disaster loss coefficient calculation. By calculating the single-disaster loss coefficient for each disaster type separately and then weighting and summing them, this application's embodiments quantify the independent contribution of each disaster type to traffic capacity, avoiding the deficiency of a unified model in multi-disaster coexistence scenarios where it cannot distinguish the impact of each disaster. This application's embodiments introduce a coupling correction coefficient set according to preset rules to correct the weighted summation result, rectifying the additional impact of the synergistic amplification effect between disasters on traffic capacity in multi-disaster superposition scenarios, making the comprehensive loss coefficient closer to the actual traffic conditions. This application's embodiments directly output the target road segment's traffic capacity level and corresponding control strategies by comparing the comprehensive loss coefficient with preset classification standards, providing clear and actionable decision-making basis for traffic management. Attached Figure Description

[0022] Figure 1 A flowchart illustrating the method for determining road capacity under multi-hazard scenarios provided in this application embodiment; Figure 2 Another flowchart illustrating the method for determining road capacity in multi-hazard scenarios provided in this application embodiment; Figure 3 This is a diagram illustrating the entropy weight-AHP combined weight calculation architecture provided in an embodiment of this application. Figure 4 This is a diagram showing the correspondence between road capacity rating and control strategy provided in an embodiment of this application. Figure 5 A schematic diagram of the functional modules of the device for determining road capacity under multi-hazard scenarios provided in the embodiments of this application; Figure 6 This is a schematic diagram of the hardware structure of the device for determining road traffic capacity under multi-hazard scenarios involved in the embodiments of this application. Detailed Implementation

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0025] This application provides a method, apparatus, and equipment for determining road capacity under multi-hazard scenarios, which can solve the technical problem of low accuracy in existing road capacity disaster assessment.

[0026] Firstly, embodiments of this application provide a method for determining road capacity in multi-hazard scenarios.

[0027] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the method for determining road capacity in multi-hazard scenarios provided in this application embodiment. Figure 1 As shown, the method for determining road capacity in multi-hazard scenarios includes the following steps: Step S1: Collect disaster data of the target road section, and classify the disaster types suffered by the target road section into continuous disasters and instantaneous disasters based on the disaster data.

[0028] In the embodiments of this application: the disaster intensity parameters in the disaster data include: road surface ice thickness, snow depth, duration of low temperature, instantaneous wind speed, and duration of strong wind; the blocking parameters in the disaster data include: road blockage width, siltation volume, and slope damage level; persistent disasters include: snow and ice disasters and strong wind disasters; instantaneous disasters include: debris flow disasters.

[0029] Specifically, the capacity loss from persistent disasters exhibits a continuous and smooth decline as the disaster intensity and duration increase. The capacity loss from transient disasters decreases sharply in a stepped manner as the volume of silt, the width of the blockage, and the degree of roadbed damage increase. Snow and ice disasters primarily reduce the road surface friction coefficient, affecting vehicle adhesion and braking safety. Strong wind disasters mainly affect vehicle lateral stability and reduce safe driving speeds. Debris flow disasters primarily compress the effective road width, damage the roadbed structure, and in severe cases, directly block traffic. By distinguishing between persistent and transient disasters, subsequent steps can construct differentiated attenuation models based on the mechanisms of action of different types of disasters.

[0030] Step S2: Construct a decay model for persistent disasters using the disaster intensity parameter in the disaster data as the independent variable, and construct a decay model for instantaneous disasters using the blockage parameter in the disaster data as the independent variable.

[0031] In this embodiment of the application, step S2, which involves constructing a decay model corresponding to a persistent disaster using the disaster intensity parameter in the disaster data as the independent variable, specifically includes the following steps: Step S211: By collecting the road surface ice thickness and corresponding measured road traffic loss data of historical ice and snow disasters of the target road section, regression analysis is used to obtain the fitting constant, and snow depth and duration of low temperature are introduced to adjust the fitting constant.

[0032] In this embodiment of the application, the measured road traffic loss data is specifically the true value of the comprehensive loss of a single disaster obtained by collecting the corresponding traffic speed attenuation rate, effective road width loss rate, driving safety factor and road surface bearing capacity remaining factor in historical disaster events, and using the entropy weight method to weight and fuse the data.

[0033] Figure 2 This is another flowchart illustrating the method for determining road capacity in multi-hazard scenarios provided in this application embodiment. See also... Figure 2 The specific calculation of the true value of the comprehensive loss in a single disaster is as follows: Step A1: Collect four sub-indicators corresponding to historical disaster events: traffic speed attenuation rate, effective road width loss rate, driving safety factor, and road surface load-bearing capacity remaining factor.

[0034] Specifically, the traffic speed reduction rate is the percentage decrease in actual traffic speed under disaster conditions relative to the free-flow speed under normal weather conditions. The effective road width loss rate is the percentage of the standard road width that becomes impassable due to a disaster. The driving safety factor is the degree to which the safety margin for vehicle operation is reduced under disaster conditions compared to normal conditions. The pavement bearing capacity remaining factor is the percentage of the pavement structure's remaining bearing capacity under disaster conditions relative to its design bearing capacity. These four sub-indicators need to be normalized before subsequent fusion calculations.

[0035] Step A2: Calculate the information entropy of each sub-indicator, and calculate the entropy weight of each sub-indicator based on the information entropy of each sub-indicator.

[0036] Specifically, information entropy is a statistic that measures the dispersion of an indicator, with a value range of [0,1]. For a given indicator, if the observed values ​​in different historical disaster events differ significantly, the information entropy is low; if the observed values ​​differ slightly or tend to be consistent, the information entropy is high. For a given indicator, the higher the information entropy, the lower the entropy weight; the lower the information entropy, the higher the entropy weight. The four entropy weights calculated in this way satisfy the normalization condition.

[0037] Step A3: Based on each sub-indicator and its entropy weight, calculate the true value of the single-disaster comprehensive loss, specifically as follows:

[0038] in, This represents the true value of the overall loss in a single disaster. Indicates the rate of decrease in travel speed. Indicates the effective road width loss rate. Indicates the driving safety factor. Indicates the road surface bearing capacity remaining coefficient. , , , These represent the entropy weights corresponding to the speed attenuation rate, effective road width loss rate, driving safety factor, and road surface bearing capacity remaining factor, respectively.

[0039] The true value of the comprehensive loss of a single disaster is used as the dependent variable in the regression analysis fitting process. It forms a data pair with the corresponding disaster intensity parameters and is substituted into a quadratic polynomial model for regression fitting to determine the fitting constants in each disaster attenuation model. By collecting four sub-indicators—traffic speed attenuation rate, effective road width loss rate, traffic safety factor, and road surface bearing capacity remaining factor—the impact of disasters on traffic capacity is comprehensively reflected from four dimensions: traffic efficiency, space resources, traffic safety, and structural stability. This provides multi-dimensional information support for the true value of the comprehensive loss of a single disaster.

[0040] Step S212: Construct a decay model corresponding to snow and ice disasters using a quadratic polynomial fitting function, specifically as follows:

[0041] in, This represents the traffic loss coefficient caused by ice and snow disasters. Indicates the thickness of ice on the road surface. , , All of these represent fitting constants.

[0042] Specifically, data on road surface ice thickness from multiple historical snow and ice disasters on the target road section, as well as measured road traffic loss data for each event, were collected. With road surface ice thickness as the independent variable and measured road traffic loss data as the dependent variable, regression analysis was used to fit the three undetermined coefficients of the quadratic polynomial to minimize the deviation between the function curve and the measured data, thereby determining the specific values ​​of the fitting constants.

[0043] After obtaining the basic values ​​of the fitting constants through regression analysis, snow depth and duration of low temperatures are used to adjust these constants. Snow depth and duration of low temperatures are converted into adjustment coefficients, which are then multiplied by the fitting constants to obtain the corrected values ​​of the fitting constants. The standard for setting the adjustment coefficients is: the deeper the snow or the longer the duration of low temperatures, the larger the adjustment coefficient, and the larger the fitting constant, resulting in a larger traffic loss coefficient for snow and ice disasters calculated under the same ice thickness.

[0044] When the ice thickness on the road surface is small, the road surface friction coefficient decreases slowly, resulting in minimal loss of traffic capacity. As the ice thickness continues to increase, the friction coefficient decreases more rapidly, and the rate of increase in traffic capacity loss gradually rises. The function form of a quadratic polynomial can effectively characterize this nonlinear, accelerating growth pattern.

[0045] Step S213: By collecting the instantaneous wind speed and corresponding measured road traffic loss data of historical strong wind disasters in the target road section, regression analysis is used to obtain the fitting constant, and the duration of strong winds is introduced to adjust the fitting constant.

[0046] Step S214: Construct the attenuation model corresponding to the severe wind disaster using a quadratic polynomial fitting function, specifically as follows:

[0047] in, This represents the traffic loss coefficient caused by strong winds. Indicates instantaneous wind speed. , , All of these represent fitting constants.

[0048] Instantaneous wind speed data and corresponding measured road traffic loss data from multiple historical strong wind disasters were collected for the target road section. With instantaneous wind speed as the independent variable and measured road traffic loss data as the dependent variable, regression analysis was used to fit the three undetermined coefficients of the quadratic polynomial to obtain the specific values ​​of the fitting constant.

[0049] After obtaining the basic values ​​of the fitting constants through regression analysis, the duration of strong winds is used to adjust these constants. The duration of strong winds is converted into an adjustment coefficient and multiplied by the fitting constants to obtain the corrected values ​​of the fitting constants. The standard for setting the adjustment coefficient is: the longer the strong winds last, the larger the adjustment coefficient, and the greater the fitting constant, resulting in a larger traffic loss coefficient for strong wind disasters calculated under the same instantaneous wind speed.

[0050] The impact of strong winds on vehicle stability increases non-linearly with increasing wind speed. When the wind speed is low, the lateral deviation of the vehicle is small, and the loss of traffic capacity is limited; as the wind speed increases to a higher level, the lateral stability of the vehicle decreases rapidly, the required safe driving speed decreases more, and the rate of increase in traffic capacity loss increases accordingly.

[0051] In this embodiment of the application, the numbers of steps S211 to S214 do not represent the execution order, but can be understood as the construction process of different types of disaster attenuation models respectively.

[0052] In this embodiment of the application, step S2, which uses the blocking parameters in the disaster data as independent variables to construct the attenuation model corresponding to the instantaneous disaster, specifically includes the following steps: Step S221: By collecting data on the blocked road width, standard road width, and corresponding measured road traffic loss of historical debris flow disasters in the target road section, the least squares method is used to fit the fitting constant, and the siltation volume and slope damage level are introduced to adjust the fitting constant. Step S222: Construct a decay model corresponding to debris flow disaster using a quadratic polynomial fitting function, specifically as follows:

[0053] in, This represents the traffic loss coefficient due to debris flow disasters. This indicates that the road width is blocked. Indicates the standard road width. , , All of these represent fitting constants.

[0054] Using the proportion of road closures as the independent variable and measured road traffic loss data as the dependent variable, the least squares method is used to fit the three undetermined coefficients of a quadratic polynomial, minimizing the sum of squared residuals between the function curve and the measured data, thereby determining the specific values ​​of the fitting constants. After obtaining the basic values ​​of the fitting constants, the sediment volume is used to adjust these fitting constants. Specifically, the sediment volume is converted into adjustment coefficients and superimposed on the fitting constants. The standard for setting the adjustment coefficients is: the larger the sediment volume, the larger the adjustment coefficient, and the correspondingly larger the fitting constant, resulting in a larger debris flow hazard traffic loss coefficient calculated under the same road closure proportion. Slope damage level refers to the degree of damage caused by debris flows to road slopes, which is divided into three levels—minor damage, moderate damage, and severe damage—based on field investigations. Each damage level corresponds to a preset adjustment coefficient, and the adjustment coefficients for different levels are different. The higher the damage level, the larger the adjustment range, resulting in a larger debris flow hazard traffic loss coefficient calculated under the same road closure proportion. The above two auxiliary correction parameters can be used individually or superimposed. When the volume of siltation is large and the slope damage level is high, the combined effects of the two corrections reflect the multi-factor comprehensive impact of debris flow disasters on traffic capacity.

[0055] The same blockage width results in different capacity losses on roads with varying widths. For narrow roads, the same blockage width occupies a larger proportion of the road width, leading to a more significant reduction in capacity; for wide roads, the impact of the same blockage width is relatively smaller. Using the proportion of blocked road width as the independent variable allows the model to adapt to road conditions of different levels and widths, improving the model's transferability across different target road segments.

[0056] The impact of debris flow disasters on traffic capacity exhibits a non-linear, accelerating growth as the proportion of blocked road width increases. When the blocked road width is relatively small, vehicles can pass through the remaining road width in an orderly manner, and the increase in traffic capacity loss is relatively gradual. When the blocked road width proportion increases to a high level, the remaining road width is insufficient to meet the normal traffic demand, and the traffic capacity loss increases rapidly. When the blocked road width proportion approaches 1, the road is completely blocked, and the traffic capacity loss approaches 1. The function form of a quadratic polynomial can effectively characterize this non-linear, accelerating growth until saturation.

[0057] Furthermore, in this embodiment of the application, after the attenuation model is constructed, the goodness of fit of each model is calculated. ,like If the value is ≤0.98, then the regression fitting should be repeated by increasing the sample size, removing outliers, or adjusting the adjustment coefficient of the auxiliary correction parameter until the value is ≤0.98. A value >0.98 indicates the model is valid.

[0058] Step S3: Calculate the single-disaster loss coefficient for each disaster type based on the attenuation model corresponding to each disaster type, sum the single-disaster loss coefficients by weight, and correct them based on the coupling correction coefficient set according to the preset rules to obtain the comprehensive loss coefficient.

[0059] Figure 3 This is a diagram illustrating the entropy weight-AHP combined weight calculation architecture provided in an embodiment of this application. See also... Figure 3 In this embodiment of the application, step S3 specifically includes the following steps: Step S31: Calculate the single-disaster loss coefficient for each disaster type based on the attenuation model corresponding to each disaster type.

[0060] Specifically, the value range of each single-disaster loss coefficient is [0,1]. If a certain type of disaster does not exist in the current disaster event, the single-disaster loss coefficient for that type of disaster is 0.

[0061] Step S32: Use the analytic hierarchy process (AHP) to construct pairwise judgment matrices for snow and ice disasters, strong wind disasters, and debris flow disasters. After passing the consistency test, obtain the subjective weights.

[0062] Specifically, the Analytic Hierarchy Process (AHP) is a subjective weighting method based on expert experience. Its core lies in decomposing the decision problem into levels such as objectives, criteria, and solutions, and then conducting qualitative and quantitative analysis based on these levels. "Determining the weights of each disaster type" is used as the objective level, and snowstorms, strong winds, and debris flows are used as the criteria level. Using a 1-9 scale (i.e., using values ​​from 1 to 9 to represent the relative importance of two factors), experts in road engineering, meteorological disasters, and traffic management are invited to conduct pairwise comparisons of the relative importance of the three types of disasters in affecting road capacity, constructing a 3×3 judgment matrix. Taking the ratio of snowstorms to strong winds as an example: if experts believe that the impact of snowstorms on traffic capacity is three times that of strong winds, then in the judgment matrix, the position corresponding to snowstorms and strong winds is filled with 3, and the position corresponding to strong winds and snowstorms is filled with 1 / 3. After constructing the judgment matrix, the largest eigenvalue of the matrix and its corresponding eigenvector are calculated. After normalizing the eigenvector, the subjective weights corresponding to snow and ice disasters, strong wind disasters, and debris flow disasters are obtained respectively. , , ,and Simultaneously, a consistency check needs to be performed on the judgment matrix, i.e., the consistency ratio is calculated. When the consistency ratio is less than 0.1, the pairwise comparison judgments of the experts are logically consistent, and the subjective weights are effective. If the consistency check fails, the expert judgment data needs to be collected again, and the judgment matrix needs to be adjusted until the consistency check passes.

[0063] Step S33: Calculate the information entropy based on the degree of variation of each disaster data using the entropy weight method to obtain the objective weight.

[0064] Specifically, for a certain disaster parameter, if the observed values ​​differ significantly across historical disaster events, then this parameter contains more information and should be assigned a higher weight in the comprehensive evaluation; conversely, if the observed values ​​tend to be consistent, then this parameter contributes less to the differentiation of the comprehensive evaluation and should be assigned a lower weight. Observational data for three types of disaster parameters—ice and snow disasters (road surface ice thickness), strong wind disasters (instantaneous wind speed), and debris flow disasters (percentage of road closures)—were collected from multiple sets of historical disaster events. First, the three types of parameters were normalized to eliminate dimensional differences; then, the degree of numerical variation for each type of parameter was calculated, measured by information entropy; finally, the corresponding objective weight was calculated based on the information entropy of each parameter. The lower the information entropy (the greater the data dispersion), the higher the corresponding weight; the higher the information entropy (the smaller the data dispersion), the lower the corresponding weight. Thus, the objective weights corresponding to ice and snow disasters, strong wind disasters, and debris flow disasters were calculated. , , ,and .

[0065] Step S34: Calculate the final coupling weight based on subjective and objective weights using a weight balancing coefficient, specifically as follows:

[0066] in, Indicates the first The final coupling weight of disaster-like events, Indicates the first Subjective weighting of disaster types Indicates the first Objective weighting of disaster types This represents the weight balance coefficient. Furthermore, the coupled weights satisfy the normalization condition: .

[0067] The role of the weighting balancing coefficient is to adjust the proportion of subjective and objective weights in the final result. When its value is large, subjective judgment contributes more, which is suitable for scenarios with insufficient historical data; when its value is small, objective data contributes more, which is suitable for scenarios with sufficient data accumulation. The weighting balancing coefficient ranges from [0,1], with a preferred value of 0.5. By combining the analytic hierarchy process (AHP) and the entropy weighting method to calculate the final coupling weights for each disaster type, the weights incorporate both expert experience in judging the relative importance of disasters and reflect the statistical characteristics of historical disaster data, avoiding subjective or data biases caused by a single weighting method.

[0068] Step S35: Based on the final coupling weights of each disaster type, the loss coefficients of each single disaster are weighted and summed, and then corrected according to the coupling correction coefficient set according to preset rules to obtain the comprehensive loss coefficient, specifically:

[0069] in, Indicates the overall loss coefficient. Represents the coupling correction coefficient. This represents the traffic loss coefficient caused by ice and snow disasters. This represents the traffic loss coefficient caused by strong winds. This represents the traffic loss coefficient due to debris flow disasters. This represents the final coupling weight corresponding to the snow and ice disaster. This represents the final coupling weight corresponding to a severe wind disaster. This represents the final coupling weight corresponding to the debris flow disaster.

[0070] In this embodiment of the application, the coupling correction coefficient is specifically set as follows: The coupling correction coefficient increases with the number of superimposed disasters, and the higher the disaster intensity or the lower the highway grade, the larger the value of the coupling correction coefficient.

[0071] Specifically, when multiple disasters act simultaneously on the same road segment, the impact of each disaster on traffic capacity is not independent and superimposed; rather, there is a synergistic amplification effect between the disasters. That is, the traffic loss under the condition of multiple disasters coexisting is greater than the sum of the losses caused by each disaster acting alone. However, this excess is not unlimited but has a certain growth limit, and the weighted summation result needs to be corrected by a coupling correction coefficient.

[0072] The preset rule is as follows: only when any one of the following disasters exists: snowstorm, strong wind, or mudslide. =1, meaning no correction is made, and the overall loss coefficient is equal to the loss coefficient of the single disaster multiplied by the corresponding coupling weight. When any two of the three types of disasters exist simultaneously, The value is taken as a number between 1.05 and 1.15. The specific value within this range is selected based on the actual combination of the two disaster intensities and the highway grade of the target road section. The higher the disaster intensity or the lower the highway grade, the larger the value. When snowstorms, strong winds, and mudslides occur simultaneously... The value is a number between 1.15 and 1.25. The specific value within this range is selected based on the actual combination of the three disaster intensities and the highway grade of the target road section. The higher the disaster intensity or the lower the highway grade, the larger the value.

[0073] The above The value range is determined based on experimental observations and engineering experience statistics of the synergistic amplification effect of traffic capacity under the coupling effect of multiple disasters. It can effectively correct the underestimation of loss caused by simple weighted summation in multi-disaster scenarios. The comprehensive loss coefficient after being corrected by the coupling correction coefficient has a value range of [0,1]. Through the design of the range of values ​​for the coupling correction coefficient, it can be specifically adjusted within the preset range according to the combination of disaster intensity and highway grade, realizing differentiated adaptation of the correction intensity for different disaster scenarios and different road conditions.

[0074] Step S4: Rate the traffic capacity of the target road segment based on the comprehensive loss coefficient and the preset grading standard, determine the traffic capacity of the target road segment based on the rating results, and generate the corresponding control strategy.

[0075] In this embodiment of the application, step S4 specifically includes the following steps: Figure 4 A diagram showing the correspondence between road capacity rating and control strategies provided in this application's embodiments. See also... Figure 4The comprehensive loss coefficient is divided into five levels to determine the level of the target road segment: if the comprehensive loss coefficient of the target road segment is at level 1, it is determined to be safe to pass and the regular monitoring and control strategy is implemented; if the comprehensive loss coefficient of the target road segment is at level 2, it is determined to be normal to pass and the key monitoring and control strategy is implemented; if the comprehensive loss coefficient of the target road segment is at level 3, it is determined to be speed-limited to pass and the overtaking control strategy is implemented; if the comprehensive loss coefficient of the target road segment is at level 4, it is determined to be restricted to pass and the one-way traffic control strategy is implemented; if the comprehensive loss coefficient of the target road segment is at level 5, it is determined to be road closed and the passage control strategy is implemented.

[0076] Specifically, a higher overall loss coefficient indicates a more severe loss of traffic capacity. The grading standard divides the [0,1] interval into five consecutive half-open and half-closed sub-intervals, with each sub-interval corresponding to a loss level.

[0077] Level 1 Loss Range: The comprehensive loss coefficient ranges from 0 to less than 0.2, corresponding to a minor loss level. When the comprehensive loss coefficient of the target road section falls within this range, it indicates that the overall loss to traffic capacity caused by various disasters is low, the road traffic condition is close to normal, and safety hazards are within a controllable range. The evaluation result corresponding to this level is "Safe Passage," and the corresponding control strategy is "Routine Monitoring," meaning that routine traffic monitoring work is carried out according to daily management standards, without the need for additional restrictive control measures.

[0078] The second level of loss ranges from 0.2 to less than 0.4, corresponding to a mild loss level. When the comprehensive loss coefficient of the target road section falls into this range, it indicates that various disasters have had an observable impact on the overall loss of traffic capacity, but have not yet reached the level requiring traffic restrictions. The evaluation result corresponding to this level is "normal traffic," and the corresponding control strategy is "key monitoring," which means increasing the frequency and intensity of monitoring on the basis of routine monitoring, closely tracking the changing trends of disaster parameters, and accumulating data support for possible escalation of control measures.

[0079] Level 3 Loss Range: The comprehensive loss coefficient ranges from 0.4 to less than 0.6, corresponding to a moderate loss level. When the comprehensive loss coefficient of the target road segment falls into this range, it indicates that the comprehensive loss of traffic capacity due to various disasters has reached a significant level, and the road traffic safety margin under disaster conditions no longer meets the requirements for normal traffic. Active control measures are required to manage safety risks. The evaluation result corresponding to this level is "speed limit passage," and the corresponding control strategy is "no overtaking," which means controlling vehicle speed by reducing the maximum speed limit on the road segment and prohibiting overtaking to avoid lateral instability or collisions caused by lane changes and overtaking.

[0080] Level 4 Loss Range: The comprehensive loss coefficient ranges from 0.6 to less than 0.8, corresponding to a severe loss level. When the comprehensive loss coefficient of the target road segment falls into this range, it indicates that the comprehensive loss of traffic capacity due to various disasters has reached a severe level, the road segment's traffic capacity has decreased significantly, and there is a significant safety risk under two-way traffic conditions. The evaluation result corresponding to this level is "restricted traffic," and the corresponding control strategy is "one-way traffic," that is, two-way traffic is prohibited simultaneously, and one-way traffic is opened alternately according to a preset release cycle to reduce the risk of oncoming conflicts and ensure the orderly passage of vehicles under remaining traffic conditions.

[0081] Level 5 Loss Range: The comprehensive loss coefficient ranges from 0.8 to 1.0, corresponding to the most severe loss level. When the comprehensive loss coefficient of the target road segment falls into this range, it indicates that the comprehensive loss of traffic capacity due to various disasters has approached or reached a level of complete blockage, and the road segment no longer meets the conditions for safe passage. The evaluation result corresponding to this level is "road closure," and the corresponding control strategy is "no passage," that is, closing the road segment to traffic and prohibiting all social vehicles from entering until the disaster conditions improve and the comprehensive loss coefficient falls back to Level 4 or below before the closure can be lifted.

[0082] Furthermore, based on measured data, historical disaster data, and simulation test data, Pearson correlation analysis can be used to screen key sensitive parameters for each disaster type. Significance tests can be used to determine whether there is a significant impact relationship between key disaster parameters and traffic capacity loss. An iterative error correction algorithm can be used to iteratively calibrate the classification thresholds.

[0083] During the iterative calibration process, the error between the loss value calculated by the model and the actual traffic conditions on site is stably converged to less than or equal to 4%, and the error of multiple consecutive sets of samples remains stably converged. Furthermore, there is a significant influence relationship between the key disaster parameters and the traffic capacity loss. When all three conditions are met, the threshold is deemed valid, and the disaster physical critical threshold and the comprehensive loss classification threshold are output.

[0084] The differentiated threshold system established based on the convergence results includes: Snow and ice disasters: ice thickness safety threshold, speed limit threshold, and no-passage threshold; Gale disasters: wind speed warning threshold, speed limit control threshold, and road closure control threshold; Debris flow disasters: road closure ratio warning threshold, traffic restriction threshold, and road closure threshold.

[0085] In one specific embodiment, based on historical disaster data of the target road section, disaster intensity parameters and corresponding measured road traffic loss data for various disaster events are collected. Specifically, the measured road traffic loss data is obtained by collecting four sub-indicators from historical disaster events: traffic speed attenuation rate, effective road width loss rate, driving safety factor, and road surface bearing capacity remaining factor. After normalizing each sub-indicator, the entropy weight method is used to calculate the entropy weight of each indicator, and the results are fused to obtain the true value of the comprehensive loss for a single disaster.

[0086] Using the main independent variables of various disasters as inputs and the true value of the comprehensive loss of a single disaster as the dependent variable, the least squares method was used to perform regression fitting on the attenuation models of the three types of disasters. Taking the measured data of a target road section as an example, the fitting constants of the attenuation models of the three types of disasters are as follows: Snow and ice disaster model: =0.0022, =0.015, =0.003; High wind disaster model: =0.0018, =0.012, =0.002; Debris flow disaster model: =0.85, =0.12, =0.01.

[0087] Model fit It should be greater than 0.98 to meet the accuracy requirements for engineering quantitative calculations.

[0088] A third-order judgment matrix for snow and ice disasters, strong wind disasters, and debris flow disasters was constructed using the analytic hierarchy process (AHP). After passing expert scoring and consistency checks, the subjective weights of the three types of disasters were obtained as follows: =0.38, =0.32, =0.3 The entropy weight method was used to collect observational data of three types of disaster parameters in historical disaster events, calculate the information entropy of each parameter, and obtain the objective weights based on the information entropy: =0.35, =0.33, =0.32 Take the weight balance coefficient =0.5, calculate the final combination weight: =0.5×0.38+0.5×0.35=0.365; =0.5×0.32+0.5×0.33=0.325; =0.5×0.3+0.5×0.32=0.31.

[0089] Normalized verification: 0.365 + 0.325 + 0.31 = 1, which meets the weight calculation requirements.

[0090] Select a dual-hazard coupled scenario: the current road surface ice thickness is 4mm, the instantaneous wind speed is 24m / s, there is no debris flow disaster, and the coupling correction coefficient is set to 1.1 according to the dual-hazard scenario.

[0091] First, calculate the single-disaster loss coefficient:

[0092]

[0093]

[0094] Then calculate the overall loss factor:

[0095] According to the five-level grading standard It falls under the category of minor damage, corresponding to safe passage and routine monitoring and control strategies.

[0096] This application's embodiments classify disaster types into persistent and transient categories, and construct corresponding attenuation models using disaster intensity parameters and closure parameters as independent variables. This ensures the model structure matches the actual action mechanisms of different disaster types, improving the accuracy of single-disaster loss coefficient calculation. By calculating the single-disaster loss coefficient for each disaster type separately and then weighting and summing them, this application's embodiments quantify the independent contribution of each disaster type to traffic capacity, avoiding the deficiency of a unified model in multi-disaster coexistence scenarios where it cannot distinguish the impact of each disaster. This application's embodiments introduce a coupling correction coefficient set according to preset rules to correct the weighted summation result, rectifying the additional impact of the synergistic amplification effect between disasters on traffic capacity in multi-disaster superposition scenarios, making the comprehensive loss coefficient closer to the actual traffic conditions. This application's embodiments directly output the target road segment's traffic capacity level and corresponding control strategies by comparing the comprehensive loss coefficient with preset classification standards, providing clear and actionable decision-making basis for traffic management.

[0097] Secondly, embodiments of this application also provide a device for determining road traffic capacity in multi-hazard scenarios.

[0098] In one embodiment, reference is made to Figure 5 , Figure 5This is a schematic diagram of the functional modules of the road capacity determination device under multi-hazard scenarios provided in an embodiment of this application. Figure 5 As shown, the device for determining road capacity under multi-hazard scenarios includes: The data acquisition module is used to collect disaster data of the target road section and classify the disaster types suffered by the target road section into continuous disasters and instantaneous disasters based on the disaster data; The module is used to construct attenuation models for persistent disasters using disaster intensity parameters from disaster data as independent variables, and to construct attenuation models for transient disasters using blockage parameters from disaster data as independent variables. The calculation module is used to calculate the single-disaster loss coefficient of each disaster type based on the attenuation model corresponding to each disaster type, sum the single-disaster loss coefficients by weight, and correct them based on the coupling correction coefficient set according to the preset rules to obtain the comprehensive loss coefficient. The rating module is used to rate the traffic capacity of target road segments based on the comprehensive loss coefficient and preset grading standards, determine the traffic capacity of target road segments based on the rating results, and generate corresponding control strategies.

[0099] The functions of each module in the above-mentioned device for determining road capacity under multiple disaster scenarios correspond to the steps in the above-mentioned method embodiment for determining road capacity under multiple disaster scenarios. Their functions and implementation processes will not be described in detail here.

[0100] Thirdly, embodiments of this application provide a device for determining road capacity under multi-hazard scenarios. The device for determining road capacity under multi-hazard scenarios includes a processor, a memory, and a program for determining road capacity under multi-hazard scenarios stored in the memory and executable by the processor. When the program for determining road capacity under multi-hazard scenarios is executed by the processor, it implements the steps of the method for determining road capacity under multi-hazard scenarios as described in any of the above embodiments.

[0101] The equipment used to determine road capacity in multi-hazard scenarios can be personal computers (PCs), laptops, servers, or other devices with data processing capabilities.

[0102] Reference Figure 6 , Figure 6 This is a schematic diagram of the hardware structure of the device for determining road capacity under multi-hazard scenarios in the embodiments of this application. In the embodiments of this application, the device for determining road capacity under multi-hazard scenarios may include a processor, a memory, a communication interface, and a communication bus.

[0103] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0104] Communication interfaces include input / output (I / O) interfaces, physical interfaces, and logical interfaces. These interfaces are used for interconnecting internal components of the device used to determine road capacity under multi-hazard scenarios, as well as for interconnecting the device with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0105] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0106] The processor can be a general-purpose processor, which can call the road capacity determination program for multi-hazard scenarios stored in memory and execute the road capacity determination method for multi-hazard scenarios provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the road capacity determination program for multi-hazard scenarios is called can refer to the various embodiments of the road capacity determination method for multi-hazard scenarios in this application, and will not be repeated here.

[0107] Those skilled in the art will understand that Figure 6 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0108] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0110] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0111] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0112] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0113] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0114] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for determining road capacity under multi-hazard scenarios, characterized in that, The methods for determining road capacity under multi-hazard scenarios include: Collect disaster data for the target road section, and classify the types of disasters suffered by the target road section into persistent disasters and transient disasters based on the disaster data; A decay model for persistent disasters is constructed using disaster intensity parameters from disaster data as independent variables, and a decay model for transient disasters is constructed using blockage parameters from disaster data as independent variables. The single-disaster loss coefficient for each disaster type is calculated based on the attenuation model corresponding to each disaster type. The single-disaster loss coefficients are then weighted and summed, and corrected based on the coupling correction coefficient set according to the preset rules to obtain the comprehensive loss coefficient. The traffic capacity of the target road segment is rated based on the comprehensive loss coefficient and the preset classification standard. The traffic capacity of the target road segment is determined according to the rating results and the corresponding control strategy is generated.

2. The method for determining road capacity under multi-hazard scenarios according to claim 1, characterized in that: The disaster intensity parameters in the disaster data include: road surface ice thickness, snow depth, duration of low temperature, instantaneous wind speed, and duration of strong wind; The blocking parameters in the disaster data include: road blockage width, siltation volume, and slope damage level; The persistent disasters include: snow and ice disasters and strong wind disasters; The instantaneous disasters include: debris flow disasters.

3. The method for determining road capacity under multi-hazard scenarios according to claim 2, characterized in that, The method of constructing a decay model corresponding to persistent disasters using disaster intensity parameters from disaster data as independent variables includes: By collecting data on the thickness of ice accumulation on the road surface and the corresponding measured road traffic loss from historical snow and ice disasters on the target road section, regression analysis was used to obtain the fitting constant, and snow depth and duration of low temperature were introduced to adjust the fitting constant. A quadratic polynomial fitting function is used to construct the attenuation model corresponding to snow and ice disasters, specifically: in, This represents the traffic loss coefficient caused by ice and snow disasters. Indicates the thickness of ice on the road surface. , , All represent fitting constants; By collecting instantaneous wind speeds and corresponding measured road traffic losses from historical strong wind disasters along the target road section, regression analysis was used to obtain fitting constants, and the duration of strong winds was introduced to adjust the fitting constants. A quadratic polynomial fitting function is used to construct the attenuation model corresponding to the severe wind disaster, specifically: in, This represents the traffic loss coefficient caused by strong winds. Indicates instantaneous wind speed. , , All of these represent fitting constants.

4. The method for determining road capacity under multi-hazard scenarios according to claim 3, characterized in that, The measured road traffic loss data specifically refers to the true value of the comprehensive loss for a single disaster obtained by collecting traffic speed attenuation rate, effective road width loss rate, driving safety factor, and road surface bearing capacity remaining factor corresponding to historical disaster events, and then using the entropy weight method for weighted fusion calculation. The specific calculation of the true value of the comprehensive loss for a single disaster is as follows: The system collects four sub-indicators corresponding to historical disaster events: traffic speed attenuation rate, effective road width loss rate, driving safety factor, and road surface load-bearing capacity remaining factor. Calculate the information entropy of each sub-indicator, and calculate the entropy weight of each sub-indicator based on its information entropy. Based on each sub-indicator and its entropy weight, the true value of the single-disaster comprehensive loss is calculated as follows: in, This represents the true value of the overall loss in a single disaster. Indicates the rate of decrease in travel speed. Indicates the effective road width loss rate. Indicates the driving safety factor. Indicates the road surface bearing capacity remaining coefficient. , , , These represent the entropy weights corresponding to the speed attenuation rate, effective road width loss rate, driving safety factor, and road surface bearing capacity remaining factor, respectively.

5. The method for determining road capacity under multi-hazard scenarios according to claim 2, characterized in that, The method of constructing an attenuation model corresponding to an instantaneous disaster using the blocking parameters in disaster data as independent variables includes: By collecting data on the blocked road width, standard road width, and corresponding measured road traffic loss of historical debris flow disasters in the target road section, the least squares method was used to fit the fitting constant, and the siltation volume and slope damage level were introduced to adjust the fitting constant. A quadratic polynomial fitting function is used to construct the attenuation model corresponding to debris flow disasters, specifically: in, This represents the traffic loss coefficient due to debris flow disasters. This indicates that the road width is blocked. Indicates the standard road width. , , All of these represent fitting constants.

6. The method for determining road capacity in multi-hazard scenarios according to claim 2, characterized in that, The method involves calculating the single-disaster loss coefficient for each disaster type based on the attenuation model corresponding to each disaster type, weighting and summing the single-disaster loss coefficients, and then correcting them based on a coupling correction coefficient set according to preset rules to obtain the comprehensive loss coefficient, including: Calculate the single-disaster loss coefficient for each disaster type based on the attenuation model corresponding to each disaster type; The Analytic Hierarchy Process (AHP) was used to construct pairwise judgment matrices for snow and ice disasters, strong wind disasters, and debris flow disasters. Subjective weights were obtained after passing the consistency test. The information entropy is calculated based on the degree of variation of each disaster data using the entropy weight method, and the objective weight is obtained. The final coupling weight is calculated based on the subjective and objective weights using a weight balance coefficient, specifically as follows: in, Indicates the first The final coupling weight of disaster-like events, Indicates the first Subjective weighting of disaster types Indicates the first Objective weighting of disaster types This represents the weighting balance coefficient; The loss coefficients of each individual disaster are weighted and summed based on the final coupling weights of each disaster type, and then corrected according to the coupling correction coefficient set according to preset rules to obtain the comprehensive loss coefficient, as follows: in, Indicates the overall loss coefficient. Represents the coupling correction coefficient. This represents the traffic loss coefficient caused by ice and snow disasters. This represents the traffic loss coefficient caused by strong winds. This represents the traffic loss coefficient due to debris flow disasters. This represents the final coupling weight corresponding to the snow and ice disaster. This represents the final coupling weight corresponding to a severe wind disaster. This represents the final coupling weight corresponding to the debris flow disaster.

7. The method for determining road capacity in multi-hazard scenarios according to claim 6, characterized in that, The coupling correction coefficient is set as follows: The coupling correction coefficient increases with the number of superimposed disasters, and the higher the disaster intensity or the lower the highway grade, the larger the value of the coupling correction coefficient.

8. The method for determining road capacity in multi-hazard scenarios according to claim 2, characterized in that, The process of rating the traffic capacity of target road segments based on comprehensive loss coefficients and preset grading standards, determining the traffic capacity of target road segments based on the rating results, and generating corresponding control strategies includes: The overall loss coefficient is divided into five level ranges to determine which level range the overall loss coefficient of the target road segment falls into: If the overall loss coefficient of the target road segment is at level one, it is determined to be safe to pass and the routine monitoring and control strategy is implemented. If the overall loss coefficient of the target road segment is at level two, it is determined to be normal traffic and a key monitoring and control strategy is implemented. If the comprehensive loss coefficient of the target road section is at level three, it is determined to be a speed-limited passage and an overtaking control strategy is implemented. If the comprehensive loss coefficient of the target road section is at level four, it is determined to be a restricted traffic zone, and a one-way traffic control strategy is implemented. If the overall loss coefficient of the target road section is at level 5, the road is deemed closed and a no-passage control strategy is implemented.

9. A device for determining road traffic capacity under multi-hazard scenarios, characterized in that, The device for determining road capacity under multi-hazard scenarios includes: The data acquisition module is used to collect disaster data of the target road section and classify the disaster types suffered by the target road section into continuous disasters and instantaneous disasters based on the disaster data; The module is used to construct attenuation models for persistent disasters using disaster intensity parameters from disaster data as independent variables, and to construct attenuation models for transient disasters using blockage parameters from disaster data as independent variables. The calculation module is used to calculate the single-disaster loss coefficient of each disaster type based on the attenuation model corresponding to each disaster type, sum the single-disaster loss coefficients by weight, and correct them based on the coupling correction coefficient set according to the preset rules to obtain the comprehensive loss coefficient. The rating module is used to rate the traffic capacity of target road segments based on the comprehensive loss coefficient and preset grading standards, determine the traffic capacity of target road segments based on the rating results, and generate corresponding control strategies.

10. A device for determining road capacity under multi-hazard scenarios, characterized in that, The device for determining road capacity under multi-hazard scenarios includes a processor, a memory, and a program for determining road capacity under multi-hazard scenarios stored in the memory and executable by the processor. When the program for determining road capacity under multi-hazard scenarios is executed by the processor, it implements the steps of the method for determining road capacity under multi-hazard scenarios as described in any one of claims 1 to 8.

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