A modeling method and a risk assessment system for a highway strong rainfall disaster risk assessment model

Abstract

In view of the problems that the existing achievements are mainly aimed at regional traffic meteorological risk assessment and the assessment model is influenced by expert knowledge, the application provides a highway heavy rainfall disaster risk assessment model modeling method and a risk assessment system. The application obtains the scores and weights of various factors through questionnaire design, investigation, reliability and validity analysis and other means, then finds out the factors directly and indirectly influencing the heavy rainfall disaster risk through path analysis, and further comprehensively considers the direct and indirect influences and the weights of the various influencing factors to establish a risk assessment model. Compared with the prior art, the application has the beneficial effects that the method is aimed at the specific section of the highway for assessment, and the influence of the experts on the model results can be overcome in the modeling process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of traffic safety, and more particularly, to a highway heavy rainfall disaster risk assessment model modeling method and a risk assessment system. BACKGROUND

[0002] Heavy rainfall will have an impact on highway driving safety. On the one hand, heavy rainfall will directly affect the drivers of the cars driving on the highway, affecting their vision and increasing the risk of driving; on the other hand, rainfall will change the road performance and reduce the friction coefficient between the vehicle and the road, especially in the case of partial water accumulation on the road, which can easily lead to uneven force between the tires of the vehicle and loss of control; in addition, heavy rainfall can also cause bridge collapse and landslides, which can lead to serious mass casualty accidents.

[0003] Therefore, we urgently need to know the real-time risk of the highway under heavy rainfall conditions to support the early issuance of warning information, interfere with or even break the accident chain, reduce the probability of accidents and the risk of traffic blockage and traffic congestion, improve the safety and efficiency of highway travel, and improve people's happiness when traveling.

[0004] There have been some research results on the risk assessment method of highways under heavy rainfall meteorological disasters. Generally, these methods divide the disaster-causing risk factors into five parts, namely the danger of the disaster-causing factor, the sensitivity of the disaster environment, the exposure of the disaster body, the vulnerability of the disaster body, and the disaster prevention and mitigation ability. Then use the analytic hierarchy process to obtain the weight of each factor, and then use the given formula to calculate the highway traffic meteorological disaster risk of a certain area.

[0005] Among them, the danger of the disaster-causing factor refers to the danger of heavy rainfall itself, such as the greater the rainfall, the higher the frequency, the more dangerous it is; the sensitivity of the disaster environment mainly refers to the road conditions, such as steep slope sections, which are more likely to induce accidents under heavy rainfall conditions; the exposure of the disaster body emphasizes the traffic participants exposed to heavy rainfall, mainly cars and their operating characteristics on the highway; the vulnerability of the disaster body emphasizes the past behavior of traffic participants, which can be regarded as the probability of accidents, the more accidents that have occurred at a certain place, the higher the risk under extreme weather conditions; the disaster prevention and mitigation ability emphasizes the ability of emergency rescue, the more developed the economy of a certain place, the denser the road network, the higher the ability of rescue.

[0006] But the existing research has two main problems, one is that the existing research focuses on regional risk assessment, and more attention is paid to the risk assessment of a city or province, which is very necessary and valuable for understanding the disaster risk of each region at the macro level. But when focusing on the risk assessment of a certain section of a highway, some disaster risk factors are no longer applicable. For example, although the road network density of a region will affect the disaster prevention and mitigation capacity of a certain section of a highway, the impact of the number of roads near the section is definitely greater. The second is that when using the analytic hierarchy process, expert opinions will be used. Experts have rich experience and should give reasonable scores to the weights of each influencing factor, but the study of the literature "Comparison and Analysis of Different Industry Expert Consultation Results by AHP Method" shows that there are significant differences between the scores of experts with different backgrounds and different regions. This makes it difficult for people to have confidence in the existing model. SUMMARY

[0007] In view of the shortcomings of the existing results, the present application provides a highway heavy rainfall disaster risk assessment model building method and a risk assessment system. The steps of establishing a heavy rainfall disaster risk model are as follows:

[0008] S1: Considering the five influencing factors of the hazard of the disaster-causing factor F1, the sensitivity of the disaster environment F2, the exposure of the disaster-bearing body F3, the vulnerability of the disaster-bearing body F4 and the disaster prevention and mitigation capacity F5, and the six factors of heavy rainfall disaster risk F6, considering the specific content of each factor, a five-level Richter scale is designed to design a questionnaire;

[0009] S2: Conduct a questionnaire survey, analyze the reliability and validity of the questionnaire, obtain the loading matrix A of all 6 factors and the characteristic roots after rotation of each factor;

[0010] S3: Calculate the scores of F1, F2, F3, F4, F5 and F6, and do path analysis to find out the factors that have direct and indirect effects on heavy rainfall disaster risk F6, determine the positive and negative signs of the standardized path coefficients of the direct influencing factors, and determine the intermediate variables between the indirect influencing factors and heavy rainfall disaster risk F6;

[0011] S4: Remove the heavy rainfall disaster risk F6 related items and corresponding results in the questionnaire of step S2, obtain a new questionnaire and corresponding questionnaire survey results, and analyze the reliability and validity of the new questionnaire, obtain the loading matrix B of the five influencing factors and the characteristic roots after rotation of each influencing factor;

[0012] S5: First, calculate the scores of F1, F2, F3, F4 and F5 using the same method as step S3, and then calculate the weights of each influencing factor using the entropy method;

[0013] S6: Considering the direct and indirect influence of the influencing factors on the disaster risk caused by heavy rain, and combining the weights of each influencing factor, a heavy rain disaster risk model is established.

[0014] Preferably, a highway heavy rain disaster risk assessment model modeling method, the assessment model is for a certain section of the highway, the reference standard is "Technical Specification for Highway Traffic Meteorological Station Network Construction" (DB 53 / T1091-2022), and the evaluation unit is 10km.

[0015] Preferably, a highway heavy rain disaster risk assessment model modeling method, the risk F1 of the disaster-causing factor in step S1 refers to the risk of heavy rain on the highway section, which includes rainfall F11, rainfall duration F12, and rainfall frequency F13.

[0016] The sensitivity F2 of the disaster environment refers to the sensitivity of the road to heavy rain, including the degree of road section fluctuation F21, the degree of slope steepness F22, the number of tunnels F23, the number of bridges F24, the amount of surrounding vegetation F25, and the degree of road surface concave and convex F26.

[0017] The exposure F3 of the disaster body refers to the traffic participants exposed to the risk of heavy rain, specifically the vehicles on the section in the present application, including the average vehicle speed F31, the speed difference between vehicles F32, the overtaking situation between vehicles F33, the heavy load truck F34, and the traffic flow F35.

[0018] The vulnerability F4 of the disaster body refers to the historical accident, congestion and blockage data of the section, including the number of traffic accidents F41, the number of casualties caused by traffic accidents F42, the economic loss caused by traffic accidents F43, the number of traffic congestion F44, the duration of traffic congestion F45, the number of closures F46, and the duration of closures F47.

[0019] The disaster prevention and mitigation ability F5 refers to the ability of the local timely rescue after the accident of the section, including the per capita GDP F51, the per capita GDP F52, the annual financial budget F53, and the number of national / provincial / county roads around the section F54.

[0020] The rainfall disaster risk F6 refers to the public's cognitive attitude that heavy rain may exacerbate traffic risks, including traffic accidents F61, traffic congestion F62, traffic blockage F63, and rescue difficulty F64.

[0021] According to the 29 specific contents of the above 6 factors, 29 items can be designed, and a five-level Likert scale can be used for investigation. In the modeling practice, with the deepening of the understanding of the objective world, the specific content of each factor, the design form of the questionnaire and the object of the investigation can be updated to obtain a risk assessment model that can better reflect the objective reality.

[0022] Preferably, in the highway heavy rainfall disaster risk assessment model modeling method, no less than 40 pre-investigation questionnaires should be carried out before the questionnaire investigation in step S2; the object of the questionnaire investigation should be above the cultural level of university (including college students), and the subject background should be transportation, civil engineering, automobile and other science and technology background; the number of final questionnaires should be more than 400, and the reliability and validity test should be passed. The purpose of taking the public investigation is to make up for the lack of experts and the difference in investigation results caused by the knowledge background of experts, but the highway traffic meteorological disaster risk assessment is a professional work, so it is difficult to ensure the credibility of the results if the ordinary people and the personnel completely irrelevant to the professional participate in the investigation, therefore, the cultural level and professional background of the investigated object are limited. As for the number limitation of 40 pre-investigation and 400 questionnaires, it is a reasonable arrangement combined with a large number of research results on investigation samples and the long-term practice of the project group.

[0023] Preferably, in the highway heavy rainfall disaster risk assessment model modeling method, step S3 first calculates the scores of F1, F2, F3, F4, F5 and F6, which are as follows:

[0024] The results of the investigation of the 29 items are analyzed for validity, and under the pre-investigation, F1 to F6 are obtained, the connection between each item and each factor is established through the factor loading coefficient, the factor loading coefficient between the ith item and the jth influencing factor is set as a ij , all a ij form a loading matrix A of 6 factors; at the same time, the validity analysis also obtains the characteristic roots of the rotated influencing factors, and the characteristic root of the jth influencing factor after rotation is set as c j ;

[0025] Assuming that the score of the kth investigator for the ith item is x i , then the score z kj of the kth investigator for the jth influencing factor is

[0026]

[0027] The above formula can be used to obtain the score of each investigator for each factor, that is, the score of each factor is obtained;

[0028] Subsequently, conventional path analysis in statistics can be used to identify factors that directly affect the risk of disasters caused by heavy rainfall (F6). Simultaneously, factors with only indirect effects on F6 can be identified, and further mediation effect analysis can be conducted to obtain the mediating variables of these indirect factors' influence on F6. For example, path analysis might reveal that the hazard of the disaster-causing factor (F1), the sensitivity of the disaster-prone environment (F2), the exposure of the disaster-bearing body (F3), and the disaster prevention and mitigation capacity (F5) directly affect the risk of disasters caused by heavy rainfall, indicating that the vulnerability of the disaster-bearing body (F4) only has an indirect effect on the risk of disasters caused by heavy rainfall (F6). Further mediation effect analysis would then show that the vulnerability of the disaster-bearing body (F4) affects F6 through the hazard of the disaster-causing factor (F1), the sensitivity of the disaster-prone environment (F2), the exposure of the disaster-bearing body (F3), and the disaster prevention and mitigation capacity (F5), meaning that F1, F2, F3, and F5 are all mediating variables of F1's influence on F6. Similar studies can be easily conducted using statistical analysis software such as SPSSAU or AMOS.

[0029] Preferably, in a modeling method for assessing the risk of heavy rainfall-induced disasters on highways, the method for calculating the scores of the five influencing factors (F1, F2, F3, F4, and F5) in step S5 is the same as that in step S3. The difference is that after step S4, the questionnaire only has 25 items, and the eigenvalues ​​and loading matrices B of the five influencing factors obtained in step S4 are still required. Subsequently, the weights of each influencing factor can be calculated using the entropy method.

[0030] For the j-th influencing factor, define

[0031]

[0032] Let n represent the contribution of the k-th surveyor's score to the j-th factor; where n is the number of surveyors, which should be no less than 400; then the entropy value of the j-th influencing factor is:

[0033]

[0034] In the formula,

[0035] m = 1 / ln(n),

[0036] Furthermore define

[0037] f j =1-E j ,

[0038] The weight coefficient of the j-th influencing factor is then...

[0039]

[0040] Preferably, through steps S1, S2, S3, S4, S5 and S6, a highway heavy rainfall disaster risk assessment model can be obtained, and then a risk assessment system is developed to realize the application of the model to assess the heavy rainfall disaster risk. The risk assessment system includes three subsystems of information collection, risk assessment and disaster warning:

[0041] The information collection subsystem needs to collect the following contents:

[0042] In the hazard F1 of the disaster-causing factor, the rainfall F11, the rainfall duration F12 and the rainfall frequency F13 are collected. The rainfall is the hourly rainfall, which is divided into five levels according to the Highway Traffic Meteorological Condition Level (QX / T 111-2010), i.e. (0, 10mm], (10mm, 15mm], (15mm, 30mm], (30mm, 50mm] and (>50mm). In the actual measurement process, the real-time data of the measuring instrument can be converted into hourly rainfall, and each level corresponds to 1-5 points. The rainfall duration is the duration of a period of time, which is divided into five levels, i.e. 0.5 days or less, 0.5-2 days, 2 days-5 days, 5 days to 10 days and more than 10 days, which correspond to 1-5 points respectively. In a month, the ratio of the number of days with rainfall greater than 10mm to the total number of days is evaluated, which is divided into five levels, i.e. less than 10%, 10%-20%, 20%-50%, 50%-80% and more than 80%, which correspond to 1-5 points respectively. Unlike the three-year period evaluation in the regional heavy rainfall disaster risk assessment, the real-time evaluation of the road section is more emphasized, so the period is one month.

[0043] In the sensitivity F2 of the disaster environment, the road section fluctuation F21, the side slope steepness F22, the number of tunnels F23, the number of bridges F24, the surrounding vegetation F25 and the road surface concave-convex degree F26 are collected. The road section fluctuation F21 refers to the proportion of the road section meeting the provisions of 8.3.5 in JTG D20-2017 to the length of the evaluation unit. The side slope steepness F22 refers to the proportion of the road section with a side slope greater than 65° to the length of the evaluation unit. The number of tunnels F23 refers to the ratio of the number of tunnel entrances and exits in the evaluation unit to the number 5. The number of bridges F24 refers to the proportion of the length of the bridges in the evaluation unit to the length of the evaluation unit. The road surface concave-convex degree F26 refers to the ratio of the number of water accumulation positions on the road section after the rain to the number 10. Each of the above factors is divided into five levels, i.e. less than 10%, 10%-20%, 20%-50%, 50%-80% and more than 80%, which correspond to 1-5 points respectively. The surrounding vegetation F25 is divided into five levels, i.e. very low, low, medium, high and very high, which correspond to 1-5 points respectively.

[0044] The exposure of the disaster-bearing body F3 needs to collect the average speed of the road section F31, the speed difference between vehicles F32, the overtaking situation between vehicles F33, heavy trucks F34, and traffic flow F35, a total of 5 specific contents; the average speed F31 refers to the 85th speed of the interval speed in the evaluation unit, which is divided into 5 levels according to the speed limit standard of the road section, i.e. 20% lower than the speed limit, 10% lower than the speed limit but higher than 20%, 10% around the speed limit, 10% higher than the speed limit but lower than 20%, and 20% higher than the speed limit, corresponding to 1-5 points respectively; the speed difference between vehicles F32 refers to the difference between the speeds of vehicles in the evaluation unit within a period of time, and the traffic flow F35 refers to the traffic volume through the road section, which is divided into 5 levels, i.e. very low, low, medium, high, and very high, corresponding to 1-5 points respectively; the overtaking situation between vehicles F33 refers to the proportion of the number of vehicles with overtaking behavior in the total number of vehicles in the evaluation unit, and the heavy trucks F34 refers to the proportion of heavy trucks in the evaluation unit within a period of time, which is divided into 5 levels, i.e. lower than 10%, 10%-20%, 20%-50%, 50%-80%, and greater than 80%, corresponding to 1-5 points respectively;

[0045] The vulnerability of the disaster-bearing body F4 needs to collect 7 specific contents within a period of time, i.e. the number of traffic accidents F41, the number of casualties caused by traffic accidents F42, the economic loss caused by traffic accidents F43, the number of traffic jams F44, the duration of traffic jams F45, the number of closures F46, and the duration of closures F47; all of these 7 contents are the ratio of the corresponding parameters of the road section to the corresponding average value of the local area in the past 3 years, for example, the ratio of the number of traffic accidents (unit: times / km) to the number of traffic accidents in the local area in the past 3 years (unit: times / km), which is divided into 5 levels, i.e. lower than 10%, 10%-20%, 20%-50%, 50%-80%, and greater than 80%, corresponding to 1-5 points respectively;

[0046] The disaster prevention and reduction capacity F5 needs to collect 4 specific contents, i.e. the per capita GDP of the location of the road section F51, the per capita GDP F52, the current fiscal budget F53, and the number of national / provincial / county roads around the road section F54; F51, F52, and F53 are the ratio of the value to the average value of the corresponding value nationwide, which is divided into 5 levels, i.e. lower than 10%, 10%-20%, 20%-50%, 50%-80%, and greater than 80%, corresponding to 1-5 points respectively; the number of national / provincial / county roads around the road section F54 is divided into 5 levels according to whether there is a road within 500 meters of the road section, i.e. no road, equal to a provincial road, county road, provincial road, and national road, corresponding to 1-5 points respectively;

[0047] The risk assessment subsystem calculates the disaster risk factor caused by heavy rain according to the information provided by the information collection subsystem, as follows:

[0048] Step S71: First, sort the information provided by the information collection subsystem according to the load matrix B obtained in step S4, so that the specific content of the information corresponds one-to-one with the corresponding items in B; for example, if the provided information is GDP per unit area, it needs to correspond to the GDP per unit area item in the questionnaire in B; then calculate the scores of the five influencing factors F1, F2, F3, F4, and F5 using the same method as in step S3.

[0049] Step S72: Calculate the highway heavy rainfall disaster risk factor γ1, which has a direct impact on the heavy rainfall disaster risk F6 among the influencing factors.

[0050]

[0051] In the formula, γ1 is the risk factor for disasters caused by heavy rainfall on highways that has a direct impact on F6, and H i Of the five influencing factors, n1 have a direct impact on F6; w i This represents the weights of factors that have a direct impact on F6; during the calculation, if the standardized path coefficient of an influencing factor is negative, the plus sign needs to be corrected to a minus sign.

[0052] Step S73: Calculate the highway heavy rainfall disaster risk factor γ2, which has an indirect impact on the heavy rainfall disaster risk F6.

[0053]

[0054] In the formula, V i Of the five influencing factors, n² have an indirect impact on F6; w i The weights of factors that indirectly affect F6; w j For V i The weights of the mediating variables affecting F6 are calculated, and there are n3 mediating variables. If the mediating effect value is negative, the sign of the corresponding term in the above formula needs to be corrected to a negative sign.

[0055] Step S74: Calculate the disaster risk factor γ caused by heavy rainfall on highways.

[0056] γ = γ1 + γ2

[0057] The disaster early warning subsystem obtains the disaster risk factors caused by heavy rainfall on highways from the risk assessment subsystem, and releases the disaster risk caused by heavy rainfall on a certain section of the highway through roadside equipment, navigation, data processing center, etc.

[0058] Before using this system, the Monte Carlo method should be used to automatically generate all the information that needs to be collected by the information collection subsystem. The Monte Carlo sample size should be no less than 10. 7 Then, obtain 10 through the risk assessment subsystem. 7A highway heavy rainfall disaster risk factor gamma, the first 20% is defined as high risk, the last 20% is low risk, the last 20% to 40% is lower risk, the last 40% to 60% is medium risk, and the last 60% to 80% is higher risk; The main reason for such processing is that although the influencing factors affecting F6 are obtained through a questionnaire survey, the degree is not given, so it is necessary to define the degree of risk first;

[0059] After the above operation, the highway heavy rainfall disaster risk factor obtained from the risk assessment subsystem is analyzed, and high risk, higher risk, medium risk, lower risk and low risk risk warning information is issued according to the interval it is in.

[0060] Compared with the prior art, the beneficial effects of the technical scheme of the present application are:

[0061] 1. Selection of research object and refinement unit: In the existing heavy rainfall disaster risk assessment, the assessment is usually carried out in units of large spatial scales such as regions and districts, and the assessment period is relatively long (such as 3 years), while the present application focuses on specific road sections of highways and refines the assessment unit to 10km, emphasizing fine real-time assessment at the road section level. The fine improvement of spatial and temporal scales meets the actual needs of real-time driving safety management of highways and fills the gap of traditional assessment models in the micro (road section level) real-time prevention level.

[0062] 2. Multi-factor comprehensive consideration and structured factor model construction: The present application starts from five influencing factors of the risk of disaster-causing factors, the sensitivity of disaster-pregnant environment, the exposure of disaster-bearing body, the vulnerability of disaster-bearing body and the disaster prevention and mitigation ability, and finally constructs a comprehensive influence model of heavy rainfall disaster risk (F1-F5 influence on F6). Compared with the traditional assessment method which only relies on a few natural factors (such as rainfall, terrain conditions) or simply traffic indicators, the present application significantly expands the dimension and connotation of the influencing factors. Not only meteorological conditions and road surface environment are considered, but also traffic characteristics (traffic volume, speed difference, overtaking ratio, etc.), road network structure (tunnel, bridge distribution), historical accident and congestion records, economic and social resources (per capita GDP, fiscal budget) and public perception and attitude are considered. Through the comprehensive analysis of the interaction of multiple factors, the complexity and dynamic characteristics of the heavy rainfall disaster risk of highways can be more accurately reflected.

[0063] 3. The comprehensive statistical modeling method of introducing factor analysis, path analysis and mediation effect analysis: the traditional risk assessment model is often based on empirical formula, weight superposition or simple expert scoring method, the present application obtains data by questionnaire survey, and extracts potential structural factors by factor analysis, and then determines the direct and indirect influence path by path analysis and mediation effect analysis, the introduction of the multi-level statistical analysis method makes the model not only qualitatively describe the influence of various factors on the risk, but also quantitatively decompose the influence path and the intermediate variable, which is less common in the existing expressway meteorological disaster risk assessment model, and improves the explanatory power and theoretical depth of the model;

[0064] 4. Objective weight assignment based on entropy method: compared with the commonly used subjective weighting method (such as expert scoring method), the present application introduces entropy method when quantitatively calculating the weight of each influencing factor, and the entropy method objectively determines the weight according to the dispersion degree of the survey data, thereby reducing the influence of subjective conjecture in the weight determination, and helping to establish a more objective and fair weight distribution mechanism;

[0065] 5. Quantifying risk level and issuing early warning information by Monte Carlo method: the present application introduces Monte Carlo simulation (more than 10 7 times sampling) into the risk level definition process, and defines the interval range of high risk, higher risk, medium risk, lower risk and low risk by the distribution characteristics of large-scale simulated data, thereby providing a scientific and repeatable quantitative reference for risk assessment and early warning release under the condition of lacking clear quantitative standard, which is more rigorous and objective compared with the traditional experience-dependent or simple experience-based grading method;

[0066] 6. Systematic design for real application: not only the modeling method is proposed, but also a risk assessment system framework integrating information collection, risk assessment and disaster warning is provided, from data acquisition (such as meteorological data, road condition data, economic and social data, traffic operation data) to final risk factor calculation and warning level release, there is a clear technical path and operability, and the systematicness and practicality are obviously improved;

[0067] In summary, the modeling method and risk assessment system of the present application for expressway disaster risk assessment caused by heavy rain realizes stronger real-time emphasis on finer granularity and time period, organically integrates social economy, traffic operation, public awareness, meteorological conditions and road structure environment, has more comprehensive factor dimensions, improves the scientificity and objectivity of the model by introducing rigorous statistical analysis methods (factor analysis, path analysis, mediation effect analysis, entropy method), improves the rationality of risk classification by objectively determining the risk level division by using Monte Carlo method, and provides a whole-process integrated solution from data collection to early warning information release. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 Figure 1 is a flow chart of a modeling method of a highway heavy rainfall disaster-causing risk assessment model according to the present application. DETAILED DESCRIPTION

[0069] The accompanying drawings are only used for illustrative purposes and should not be construed as limiting the present patent;

[0070] The technical solutions of the present application will be further described below in combination with embodiments.

[0071] As Figure 1 shown, to establish a disaster-causing risk model of a certain section of a certain highway under heavy rainfall, according to the process described in the present application, the following work is carried out:

[0072] Step S1: considering the risk of disaster-causing factors F1, the sensitivity of disaster-creating environment F2, the exposure of disaster-bearing body F3, the vulnerability of disaster-bearing body F4, the disaster prevention and mitigation ability F5 and the heavy rainfall disaster-causing risk F6, a total of six factors, considering the specific content of each factor, a five-level Richter scale is used to design a questionnaire, and the designed questionnaire is shown in Table 1:

[0073] Table 1. Questionnaire and validity analysis results

[0074]

[0075] Step S2: carry out questionnaire survey, analyze the reliability and validity of the questionnaire, obtain the loading matrix A of all 6 factors and the characteristic roots after rotation of each factor. Through the design of the questionnaire and the investigation, 1245 valid questionnaires are finally recovered, the proportion of people with college education or above is 93.49%, and the proportion of students majoring in transportation, civil engineering and machinery is 86.51%; At the same time, through the reliability analysis, the Cronbach α coefficient of the scale is 0.970; Through the validity analysis, the loading matrix A of 6 factors and the characteristic roots after rotation of each factor are obtained, and the related results are listed in Table 1.

[0076] Step S3: calculate the scores of F1, F2, F3, F4, F5 and F6, and do path analysis to find out the factors that have direct and indirect influence on heavy rainfall disaster-causing risk F6, determine the positive and negative signs of the standardized path coefficients of the direct influence factors, and determine the intermediate variables between the indirect influence factors and heavy rainfall disaster-causing risk F6, as follows:

[0077] Assuming that the score of the ith item given by the kth respondent is xi, then the score of the jth influence factor by the kth respondent is zk j for

[0078]

[0079] The score of each factor for each investigator can be obtained by the above formula, i.e. the score of each factor is obtained;

[0080] Then, path analysis is performed, and the results are shown in Table 2:

[0081] Table 2. Path analysis results

[0082]

[0083] The evaluation indexes of the model in Table 2 are chi-square degree of freedom ratio = 0.09, GFI = 1, RMSEA = 0, RMR = 0.001, CFI = 1, NFI = 1, and NNFI = 1.002, indicating that the model fitting is very good. Table 2 clearly shows that the risk of the disaster-causing factor, the sensitivity of the disaster environment, the exposure of the disaster-bearing body, and the disaster prevention and mitigation ability have a direct impact on the disaster risk of heavy rain, but the vulnerability of the disaster-bearing body has no direct impact on the disaster risk of heavy rain. Further analysis of the intermediate variables of the vulnerability of the disaster-bearing body on the disaster risk of heavy rain is shown in Table 3:

[0084] Table 3. Summary of mediation effect test results

[0085]

[0086] Note: * p < 0.05 ** p < 0.01

[0087] Step S4: Remove the related items of the heavy rain disaster risk F6 in the questionnaire in step S2 and the corresponding results to obtain a new questionnaire. Then, the reliability and validity of the new questionnaire are analyzed to obtain the loading matrix B of the 5 influencing factors and the characteristic roots after rotation of each influencing factor. The results are shown in Table 4:

[0088] Table 4. Validity analysis results

[0089]

[0090] Step S5: The scores of the 5 influencing factors F1, F2, F3, F4 and F5 are calculated by the same method as in step S3. Then, the weights of the influencing factors are calculated by the entropy method,

[0091] For the jth influencing factor, define

[0092]

[0093] which represents the contribution of the score of the kth investigator to the jth factor. In the formula, n is the number of investigators, and n = 1245 in this embodiment. Then, the entropy value of the jth influencing factor is:

[0094]

[0095] wherein,

[0096] m = 1 / ln(n),

[0097] further defined as

[0098] f j = 1 - E j ,

[0099] The weight coefficient of the jth influencing factor is

[0100]

[0101] Step S6: Considering the direct and indirect effects of the influencing factors on the disaster risk caused by heavy rain, and combining the weights of the influencing factors, a heavy rain disaster risk model is established. The calculation formula of the highway heavy rain disaster risk factor γ is as follows:

[0102] γ = γ1 + γ2

[0103] wherein, γ1 is the highway heavy rain disaster risk factor directly affecting the heavy rain disaster risk F6, and γ2 is the highway heavy rain disaster risk factor indirectly affecting the heavy rain disaster risk F6, which are obtained by the following formulas respectively:

[0104]

[0105] wherein, γ1 is the highway heavy rain disaster risk factor directly affecting F6, H1 is the hazard of the disaster-causing factor, H2 is the sensitivity of the disaster environment, H3 is the exposure of the disaster-bearing body, H4 is the disaster prevention and mitigation ability, and n1 = 4; wherein, the standardized path coefficient of the disaster prevention and mitigation ability is negative, so the plus sign needs to be corrected to minus;

[0106]

[0107] wherein, V1 is the vulnerability of the disaster-bearing body, n2 = 1; w j is the weight of the intermediate variable affecting F6, which is the weight of the hazard of the disaster-causing factor, the sensitivity of the disaster environment, the exposure of the disaster-bearing body, and the disaster prevention and mitigation ability, i.e. n3 = 4. Among them, the intermediate effect of the disaster prevention and mitigation ability item is negative, and its sign should be corrected to negative.

[0108] After obtaining the above model, a risk assessment system can be further developed to realize the use of the model to assess the heavy rain disaster risk. The risk assessment system includes three subsystems of information collection, risk assessment and disaster warning:

[0109] An information collection subsystem is required to collect information on the risk of disaster-causing factors, the sensitivity of disaster-pregnant environment, the exposure of disaster-bearing bodies, the vulnerability of disaster-bearing bodies, and the ability of disaster prevention and mitigation;

[0110] A risk assessment subsystem calculates the risk factors of heavy rainfall causing disasters according to the information provided by the information collection subsystem;

[0111] A disaster warning subsystem obtains the risk factors of heavy rainfall causing disasters on highways from the risk assessment subsystem and publishes the risk of heavy rainfall causing disasters on a certain section of the highway through roadside equipment, navigation, and data processing centers;

[0112] The disaster warning subsystem first automatically generates all information required to be collected by the information collection subsystem using the Monte Carlo method, and the sample size of the Monte Carlo method is not less than 10 7 Then, the risk assessment subsystem obtains 10 7 highway risk factors of heavy rainfall causing disasters γ, and the top 20% are defined as high risk, the next 20% are low risk, the next 20% to 40% are relatively low risk, the next 40% to 60% are medium risk, and the next 60% to 80% are relatively high risk;

[0113] Then, the highway risk factors of heavy rainfall causing disasters obtained from the risk assessment subsystem are analyzed, and high-risk, relatively high-risk, medium-risk, relatively low-risk, and low-risk warning information is published according to the interval in which they are located.

[0114] The present application proposes a modeling method for a risk assessment model of heavy rainfall causing disasters on highways and a risk assessment system. The application obtains the scores and weights of various factors through questionnaire design, investigation, and reliability and validity analysis, and then finds out the factors that have direct and indirect effects on the risk of heavy rainfall causing disasters through path analysis. Then, the direct and indirect effects of the influencing factors on the risk of heavy rainfall causing disasters and the weights of the influencing factors are comprehensively considered to establish a risk assessment model. The method is used to evaluate specific sections of highways, and the influence of experts on the model results can be overcome during the modeling process.

[0115] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the embodiments of the present application. Based on the above description, other different forms of changes or variations can be made by those of ordinary skill in the art. Here, it is not necessary or possible to exhaust all embodiments. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application should be included in the protection scope of the claims of the present application.

Claims

1. A modeling method for assessing the risk of disasters caused by heavy rainfall on highways, characterized in that: The process involves designing and conducting a questionnaire survey, first analyzing the direct and indirect relationships between various factors, and then establishing a risk assessment model. The steps are as follows: S1: Considering five influencing factors (F1, the risk of disaster-causing factors), F2, the sensitivity of the disaster-prone environment, F3, the exposure of the disaster-bearing body, F4, and the disaster prevention and mitigation capacity (F5), and six factors (F6, the risk of disaster caused by heavy rainfall), the questionnaire was designed using a five-point Likert scale, taking into account the specific content of each factor. The hazard factor F1 refers to the danger of heavy rainfall on highway sections, which includes three specific items: rainfall amount F11, rainfall duration F12, and rainfall frequency F13. The sensitivity of the disaster-prone environment F2 refers to the sensitivity of roads to heavy rainfall, including six specific items: road section undulation F21, slope steepness F22, number of tunnels F23, number of bridges F24, surrounding vegetation volume F25, and road surface unevenness F26. S2: Conduct a questionnaire survey, analyze the reliability and validity of the questionnaire, and obtain the loading matrix A of all 6 factors and the eigenvalues ​​of each factor after rotation. S3: Calculate the scores of the six factors F1, F2, F3, F4, F5, and F6, and perform path analysis to identify the factors that have direct and indirect impacts on the risk of disaster caused by heavy rainfall F6. Determine the sign of the standardized path coefficients of the direct impact factors and identify the mediating variables between the indirect impact factors and the risk of disaster caused by heavy rainfall F6. S4: Remove the relevant items and corresponding results of the F6 question on the risk of disaster caused by heavy rainfall from the questionnaire in step S2, obtain the new questionnaire and the corresponding questionnaire survey results, and then analyze the reliability and validity of the new questionnaire to obtain the loading matrix B of the five influencing factors and the eigenvalues ​​of each influencing factor after rotation. S5: First, use the same method as in step S3 to calculate the scores of the five influencing factors, F1, F2, F3, F4, and F5, and then use the entropy method to calculate the weight of each influencing factor. S6: Taking into account the direct and indirect impacts of influencing factors on the risk of disasters caused by heavy rainfall, and combining the weights of each influencing factor, a risk model for disasters caused by heavy rainfall is established. Through steps S1, S2, S3, S4, S5, and S6, a risk assessment model for heavy rainfall-induced disasters on highways can be obtained. Then, a risk assessment system is developed to apply this model to assess the risk of heavy rainfall-induced disasters. The risk assessment system includes three subsystems: information collection, risk assessment, and disaster early warning. The risk assessment subsystem calculates the disaster risk factors caused by heavy rainfall based on the information provided by the information collection subsystem, as follows: Step S71: First, sort the information provided by the information acquisition subsystem according to the load matrix B obtained in step S4, so that the specific content of the information corresponds one-to-one with the corresponding items in B; then calculate the scores of the five influencing factors F1, F2, F3, F4, and F5 using the same method as in step S3. Step S72: Calculate the highway heavy rainfall disaster risk factor γ1, which has a direct impact on the heavy rainfall disaster risk F6 among the influencing factors. In the formula, γ1 is the risk factor for disasters caused by heavy rainfall on highways that has a direct impact on F6, and H i Of the five influencing factors, n1 have a direct impact on F6; w i This represents the weights of factors that have a direct impact on F6; during the calculation, if the standardized path coefficient of an influencing factor is negative, the plus sign needs to be corrected to a minus sign. Step S73: Calculate the highway heavy rainfall disaster risk factor γ2, which has an indirect impact on the heavy rainfall disaster risk F6. In the formula, V i Among the five influencing factors, n² are those that have an indirect impact on F6; y i The weights of factors that indirectly affect F6; w j For V i The weights of the mediating variables affecting F6, there are n3 mediating variables; Step S74: Calculate the disaster risk factor γ caused by heavy rainfall on highways. The disaster early warning subsystem obtains the disaster risk factors caused by heavy rainfall on highways from the risk assessment subsystem, and releases the disaster risk caused by heavy rainfall on a certain section of the highway through roadside equipment, navigation, and data processing center.

2. The modeling method for a highway heavy rainfall disaster risk assessment model according to claim 1, characterized in that: The established highway heavy rainfall disaster risk assessment model is designed for a certain section of the highway, with an assessment unit of 10km.

3. The modeling method for a highway heavy rainfall disaster risk assessment model according to claim 1, characterized in that: The exposure of disaster-bearing bodies (F3) refers to vehicles exposed to the risk of heavy rainfall, including five specific items: average vehicle speed on road sections (F31), speed difference between vehicles (F32), overtaking situation between vehicles (F33), heavy-duty trucks (F34), and traffic flow (F35). The vulnerability of the disaster-bearing body F4 refers to the historical accidents, congestion and blockage of the road section, including seven specific items: number of traffic accidents on the road section F41, number of casualties caused by traffic accidents F42, economic losses caused by traffic accidents F43, number of traffic congestion F44, duration of traffic congestion F45, number of closures F46, and duration of closures F47 within a certain period. F5 refers to the local area's ability to provide timely rescue after an accident occurs on the road section. Rainfall-induced disaster risk F6 refers to the public's perception and attitude towards the possibility that heavy rainfall may exacerbate traffic risks, including four specific aspects: traffic accidents F61, traffic congestion F62, traffic jams F63, and rescue difficulties F64. Based on the 29 specific items of the 6 factors, 29 items can be designed, and a five-point Likert scale can be used for the survey.

4. The modeling method for a highway heavy rainfall disaster risk assessment model according to claim 1, characterized in that: Before conducting the questionnaire survey in step S2, there should be no fewer than 40 pre-survey questionnaires; the survey subjects should have a college degree or above and their academic background should be in STEM fields such as transportation, civil engineering, and automotive engineering; the final number of questionnaires should be more than 400 and must pass the reliability and validity test.

5. The modeling method for a highway heavy rainfall disaster risk assessment model according to claim 1, characterized in that: Step S3 first calculates the scores for the six factors F1, F2, F3, F4, F5, and F6, as follows: Validity analysis was performed on the survey results of 29 items, resulting in 6 factors from F1 to F6. The factors and each item were linked through factor loading coefficients. The factor loading coefficient between the i-th item and the j-th influencing factor can be set as a. ij , all a ij The loading matrix A comprises six factors; simultaneously, the validity analysis will also obtain the eigenvalues ​​of each influencing factor after rotation, and the eigenvalue of the j-th influencing factor after rotation will be set as c. j ; Suppose that the k-th respondent rates the i-th item with a score of x. i Then, the score z given by the k-th respondent to the j-th influencing factor kj for ， The above formula can be used to obtain each researcher's score for each factor, that is, the score for each factor; Subsequently, using conventional path analysis methods in statistics, we identified the influencing factors that directly affect the disaster risk F6 caused by heavy rainfall, and obtained the standardized path coefficients of each factor. At the same time, we discovered factors that only have an indirect impact on F6, and further conducted mediation effect analysis to obtain the mediating variables of the indirect influencing factors on F6.

6. The modeling method for a highway heavy rainfall disaster risk assessment model according to claim 1, characterized in that: The method for calculating the scores of the five influencing factors F1, F2, F3, F4, and F5 in step S5 is the same as that in step S3. The difference is that after step S4, the questionnaire only has 25 items, and the eigenvalues ​​and loading matrix B of the five influencing factors obtained in step S4 are still required. Subsequently, the weight of each influencing factor can be calculated using the entropy method.

7. The modeling method for a highway heavy rainfall disaster risk assessment model according to claim 1, characterized in that: The information collection subsystem needs to collect the following information: The hazard factor F1 needs to collect three specific items: rainfall amount F11, rainfall duration F12, and rainfall frequency F13. The sensitivity of the disaster-prone environment F2 requires the collection of six specific items: road section undulation degree F21, slope steepness F22, number of tunnels F23, number of bridges F24, surrounding vegetation amount F25, and road surface unevenness F26. The exposure of the disaster-bearing body (F3) requires the collection of five specific items: average vehicle speed of the road section (F31), speed difference between vehicles (F32), overtaking situation between vehicles (F33), heavy-duty trucks (F34), and traffic flow (F35). The vulnerability of the disaster-bearing body in F4 requires the collection of seven specific items over a period of time: number of traffic accidents on the road section (F41), number of casualties caused by traffic accidents (F42), economic losses caused by traffic accidents (F43), number of traffic congestion events (F44), duration of traffic congestion (F45), number of road closures (F46), and duration of road closures (F47). The risk assessment system first uses the Monte Carlo method to automatically generate all the information that needs to be collected by the information collection subsystem, with a Monte Carlo sample size of no less than 10. 7 Then, obtain 10 through the risk assessment subsystem. 7 The risk factors γ for heavy rainfall disasters on highways are defined as follows: the first 20% are high risk, the last 20% are low risk, the last 20% to 40% are relatively low risk, the last 40% to 60% are medium risk, and the last 60% to 80% are relatively high risk. Then, the risk factors for disasters caused by heavy rainfall on highways obtained from the risk assessment subsystem are analyzed, and risk warning information of high risk, relatively high risk, medium risk, relatively low risk, and low risk is issued according to their location range.

Images