Modeling method of highway heavy rainfall disaster risk assessment model and risk assessment system

Abstract

The invention provides an expressway heavy rainfall disaster risk assessment model modeling method and a risk assessment system, aiming at the problems that the existing achievements mainly aim at regional traffic meteorological risk assessment and an assessment model is influenced by expert knowledge. According to the method, scores and weights of factors are obtained by means of questionnaire design, investigation, trust / validity analysis and the like, then factors having direct and indirect influences on the heavy rainfall disaster-causing risk are found out through path analysis, the direct and indirect influences and the weights of the influence factors are comprehensively considered, and a risk assessment model is established. Compared with the prior art, the method provided by the invention has the beneficial effects that the method is used for evaluating specific road sections of the expressway, and the influence of experts on a model result can be overcome in a modeling process.

Description

Technical Field

[0001] The present invention relates to the field of traffic safety, and more specifically, to a modeling method and a risk assessment system for a highway heavy rainfall disaster risk assessment model. Background Art

[0002] Heavy rainfall will have an impact on highway driving safety. On the one hand, heavy rainfall will directly affect the drivers of cars on the highway, affecting their vision and increasing the danger of driving; on the other hand, rainfall will change the road performance and reduce the friction coefficient between the car and the road. Especially when the road surface is partially flooded, it is easy to cause uneven force between the tires of the vehicle and lose control; furthermore, heavy rainfall may also collapse bridges and cause landslides, leading to serious mass casualties.

[0003] To this end, we urgently need to know the real-time risks of highways under heavy rainfall conditions to support the early release of warning information, interfere with or even interrupt the accident chain, reduce the probability of accidents and the risks of traffic blockages and congestion, improve highway travel safety and efficiency, and enhance people's sense of happiness when traveling.

[0004] There are some research results on highway risk assessment methods under heavy rainfall meteorological disasters. In general, these methods divide the factors affecting disaster risk into five parts, namely the danger of disaster factors, the sensitivity of the disaster-prone environment, the exposure of the disaster-bearing body, the vulnerability of the disaster-bearing body and the disaster prevention and mitigation capabilities. The weight of each factor is obtained by the hierarchical analysis method, and then the highway traffic meteorological disaster risk of a certain area is calculated using a given formula.

[0005] Among them, the danger of disaster-causing factors refers to the danger of heavy rainfall itself. For example, the greater the rainfall and the higher the frequency, the more dangerous it is; the sensitivity of the disaster-prone environment mainly refers to road conditions, such as steep slopes. Accidents are more likely to occur under heavy rainfall environments; the exposure of disaster-prone bodies emphasizes the traffic participants exposed to heavy rainfall, mainly cars and their operating characteristics on highways; the vulnerability of disaster-prone bodies emphasizes the past behavior of traffic participants, which can be regarded as the probability of accidents. The more accidents have occurred in a certain place, the higher the risk will naturally be under extreme weather conditions; disaster prevention and mitigation capabilities emphasize the ability of emergency rescue. The more developed the economy of a certain place and the denser the road network, the higher the rescue capacity will naturally be.

[0006] However, there are two main problems with existing research. First, existing research mainly focuses on regional risk assessment, and pays more attention to the risk assessment of a city or province. This is extremely necessary and valuable for understanding the disaster risks of various regions at the macro level. However, when focusing on the risk assessment of a certain section of a highway, some factors affecting disaster risks are no longer applicable. For example, although the road network density of a region will have an impact on the disaster prevention and mitigation capabilities of a certain section of a highway, this impact is certainly not as great as the impact of the number of roads near the section. Second, when using the hierarchical analysis method, expert opinions will be used. Experts have rich experience and should give reasonable scores to the weights of various influencing factors. However, the study in the literature "Comparative Analysis of Differences in Consultation Results of Experts in Different Industries Using the AHP Method" shows that there are significant differences between the scores of experts from different backgrounds and regions. This makes it difficult for people to build confidence in existing models. Summary of the invention

[0007] In view of the shortcomings of existing achievements, the present invention proposes a modeling method and risk assessment system for highway heavy rainfall disaster risk assessment model. The steps of establishing the heavy rainfall disaster risk model in the present invention are as follows:

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

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

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

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

[0012] S5: first, the scores of the five influencing factors F1, F2, F3, F4, and F5 are calculated by the same method as step S3, and then the weight of each influencing factor is calculated by the entropy method;

[0013] S6: Comprehensively consider the direct and indirect impacts of influencing factors on the risk of disasters caused by heavy rainfall, combine the weights of various influencing factors, and establish a heavy rainfall disaster risk model.

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

[0015] Preferably, in a modeling method for a highway heavy rainfall disaster risk assessment model, the danger F1 of the disaster factor in step S1 refers to the danger of heavy rainfall on a highway section, including three specific contents: rainfall F11, rainfall duration F12, and rainfall frequency F13;

[0016] The sensitivity of the disaster-prone environment F2 refers to the sensitivity of the road to heavy rainfall, including the road section undulation F21, the steepness of the slope F22, the number of tunnels F23, the number of bridges F24, the amount of surrounding vegetation F25 and the degree of unevenness of the road surface F26, a total of 6 specific contents;

[0017] The exposure of the disaster-bearing body F3 refers to the traffic participants exposed to the risk of heavy rainfall, and in this invention, specifically refers to the vehicles on the road section, including the average speed of the road section F31, the speed difference between vehicles F32, the overtaking situation between vehicles F33, heavy trucks F34, and the traffic volume F35, a total of 5 specific contents;

[0018] The vulnerability of the disaster-bearing body F4 refers to the data of historical accidents, congestion and obstruction of the road section, including the number of traffic accidents F41, the number of casualties caused by traffic accidents F42, the economic losses 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 within a period of time, a total of 7 specific contents;

[0019] Disaster prevention and mitigation capability F5 refers to the ability of the local area to provide timely rescue after an accident occurs on a road section, including the per capita GDP of the road section F51, the per capita GDP of the area F52, the fiscal budget of the year F53, and the number of national / provincial / county roads around the road section F54, a total of 4 specific contents;

[0020] Rainfall-induced disaster risk F6 refers to the public's cognitive attitude toward the possibility that heavy rainfall may increase traffic risks, including traffic accidents F61, traffic congestion F62, traffic jams F63, and rescue difficulty F64, a total of 4 specific contents;

[0021] According to the 29 specific contents of the above 6 factors, 29 items can be designed and investigated using a five-level Likert scale. In modeling practice, as the understanding of the objective world deepens, the specific contents of the above factors, the design form of the questionnaire, and the objects of the investigation can be updated to obtain a risk assessment model that better reflects objective reality.

[0022] Preferably, in a modeling method for assessing the risk of heavy rainfall-induced disasters on highways, there should be no less than 40 pre-survey questionnaires before conducting the questionnaire survey in step S2; the subjects of the questionnaire survey should be college students (including college students) with a cultural level or above, and the subject background should be transportation, civil engineering, automobile and other science and engineering backgrounds; the final number of questionnaires should be more than 400, and they must pass the reliability and validity test. An important purpose of adopting the method of mass survey is to make up for the lack of experts and the differences in survey results caused by the lack of experts' knowledge background, but the risk assessment of meteorological disasters in highway traffic is a professional job after all. It is difficult to ensure the credibility of the results when ordinary people and people who are completely unrelated to their profession participate in the survey, so the cultural level and professional background of the subjects are limited. As for the number of 40 pre-surveys and 400 questionnaires, it is a reasonable arrangement made in combination with a large number of research results on survey samples and the long-term practice of the project team.

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

[0024] The validity analysis of the survey results of 29 items was carried out. With the support of the preliminary survey, a total of 6 factors from F1 to F6 were obtained. Each factor and each item were connected through the factor loading coefficient. 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 load matrix A of the six factors is composed; at the same time, the validity analysis will also obtain the characteristic roots of each influencing factor after rotation, and the characteristic root of the jth influencing factor after rotation is set as c j ;

[0025] Assume that the kth respondent gives the i-th item a score of x i , then the score z given by the kth respondent to the jth influencing factor kj for

[0026]

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

[0028] Subsequently, conventional path analysis in statistics can be used to find out the factors that have a direct impact on the heavy rainfall disaster risk F6. At the same time, factors that only have an indirect impact on F6 can be found. Further mediation effect analysis can be performed to obtain the mediating variables of the indirect influencing factors on F6. For example, through path analysis, it is found that the hazard factor F1, the sensitivity of the disaster-prone environment F2, the exposure of the disaster-prone body F3, and the disaster prevention and mitigation capacity F5 have a direct effect on the heavy rainfall disaster risk, indicating that the vulnerability F4 of the disaster-prone body has only an indirect effect on the heavy rainfall disaster risk F6; after further mediation effect analysis, it can be finally obtained that the vulnerability F4 of the disaster-prone body acts on F6 through the hazard factor F1, the sensitivity of the disaster-prone environment F2, the exposure of the disaster-prone body F3 and the disaster prevention and mitigation capacity F5, which means that F1, F2, F3, and F5 are all mediating variables of F1 acting on F6. Statistical analysis software such as SPSSAU or AMOS can be selected to easily implement similar studies.

[0029] Preferably, in a modeling method for a highway heavy rainfall disaster risk assessment model, the method for calculating the scores of the five influencing factors F1, F2, F3, F4, and F5 in step S5 is consistent with the method in step S3, except that after step S4, the questionnaire has only 25 items, and the characteristic roots obtained by step S4 and the load matrix B of the five influencing factors are also required; then the entropy method can be used to calculate the weight of each influencing factor:

[0030] For the jth influencing factor, define

[0031]

[0032] It represents the contribution of the score of the kth investigator to the jth factor; where n is the number of respondents, which should be no less than 400; the entropy value of the jth influencing factor is:

[0033]

[0034] In the formula,

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

[0036] Further definition

[0037] f j =1-E j ,

[0038] Then the weight coefficient of the jth influencing factor is

[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 use the model to assess the heavy rainfall disaster risk. The risk assessment system includes three subsystems: information collection, risk assessment and disaster warning:

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

[0042] The hazard factor F1 needs to collect three specific contents: rainfall F11, rainfall duration F12, and rainfall frequency F13; rainfall refers to hourly rainfall, refer to the "Highway Traffic Meteorological Conditions Level" (QX / T 111-2010), divided into five levels: (0, 10mm], (10mm, 15mm], (15mm, 30mm], (30mm, 50mm], (>50mm). In the actual measurement process, it can be converted into hourly rainfall according to the real-time data of the measuring instrument, and each level corresponds to 1-5 points; the duration of rainfall is the duration of a period of time, divided into five levels: less than 0.5 days, 0.5-2 days, 2 days-5 days, 5 days to 10 days, and more than 10 days, corresponding to 1-5 points respectively; in a period of one month, the proportion of days with rainfall greater than 10mm in the assessment unit to the total number of days is divided into five levels: less than 10%, 10%-20%, 20%-50%, 50%-80%, and greater than 80%, corresponding to 1-5 points respectively. Different from the three-year assessment of regional heavy rainfall disaster risk assessment, the assessment of road sections emphasizes its real-time nature, so the period here is one month.

[0043] The sensitivity of the disaster-prone environment F2 requires the collection of six specific contents: road undulation F21, slope steepness F22, number of tunnels F23, number of bridges F24, surrounding vegetation F25, and road surface unevenness F26. The road undulation F21 refers to the road undulation that meets the JTG The ratio of the road section specified in 8.3.5 of D20-2017 to the length of the assessment unit; the steepness of the slope F22 refers to the ratio of the road section with a roadside slope greater than 65° to the length of the assessment unit; the number of tunnels F23 refers to the ratio of the number of tunnel entrances and exits in the assessment unit to the number 5; the number of bridges F24 refers to the ratio of the length of bridges in the assessment unit to the length of the assessment unit; the degree of road surface unevenness F26 refers to the ratio of the number of waterlogging locations on the road section after rain to the number 10. The above factors are divided into five levels: less than 10%, 10%-20%, 20%-50%, 50%-80%, and greater than 80%, corresponding to 1-5 points respectively; the amount of surrounding vegetation F25 is divided into five levels: very low, low, medium, high, and very high, corresponding to 1-5 points;

[0044] The exposure of the disaster-prone body F3 needs to collect five specific contents, namely, the average speed of the road section F31, the speed difference between vehicles F32, the overtaking situation between vehicles F33, the heavy truck F34, and the traffic volume F35; the average speed F31 refers to the 85th speed of the interval speed within the assessment unit, which is divided into five levels according to the speed limit standard designed for the road section: 20% lower than the speed limit, 10% lower than the speed limit but higher than 20%, around 10% of 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 and roads F32 refers to the difference in vehicle speeds within a period of time within the assessment unit. Traffic volume F35 refers to the volume of traffic passing through the road section, which is divided into five levels: very low, low, medium, high, and very high, corresponding to 1-5 points respectively. Overtaking between vehicles F33 refers to the proportion of vehicles with overtaking behavior in the assessment unit to the total number of vehicles. Heavy-duty trucks F34 refers to the proportion of heavy-duty trucks in the assessment unit within a period of time, which is divided into five levels: less 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 seven specific contents, including the number of traffic accidents F41, the number of casualties caused by traffic accidents F42, the economic losses 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 within a period of time; these seven contents are the ratios of the corresponding parameters of the road section to the corresponding average values ​​of the previous three years in the local area. For example, the number of traffic accidents is the ratio of the number of accidents on the road section (in times / km) to the number of traffic accidents in the previous three years in the local area (in times / km). The local area should be divided according to provincial and higher units, or according to the characteristics of the road, such as mountain expressways and plain expressways, which are divided into five levels: less than 10%, 10%-20%, 20%-50%, 50%-80%, and greater than 80%, corresponding to 1-5 points respectively;

[0046] Disaster prevention and mitigation capacity F5 requires the collection of four specific items: GDP per capita F51, GDP per area F52, fiscal budget for the current year F53, and the number of national / provincial / county roads around the road section F54. F51, F52, and F53 are the ratios of the value to the average of the corresponding national value, which are divided into five levels: less 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 five levels according to no roads within 500 meters around the road section, there are non-standard highways, there are county and township roads, there are provincial roads, and there are national roads, corresponding to 1-5 points respectively.

[0047] The risk assessment subsystem calculates the heavy rainfall disaster risk factor based on 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 to the corresponding items in B one by one; for example, if the information provided is GDP per capita, it needs to correspond to the item of GDP per capita in the questionnaire item in B; then calculate the scores of the five influencing factors F1, F2, F3, F4, and F5 in the same way as step S3;

[0049] Step S72, calculating the highway heavy rainfall disaster risk factor γ1 that has a direct impact on the heavy rainfall disaster risk F6 among the influencing factors,

[0050]

[0051] Where γ1 is the highway heavy rainfall disaster risk factor that has a direct impact on F6, H i Among the five influencing factors, there are n1 factors that have a direct impact on F6; w i is the weight of the influencing factor that has a direct impact on F6; in the calculation, if the standardized path coefficient of the influencing factor is negative, the plus sign needs to be corrected to a minus sign;

[0052] Step S73, calculating the highway heavy rainfall disaster risk factor γ2 that has an indirect impact on the heavy rainfall disaster risk F6 among the influencing factors,

[0053]

[0054] Where V i Among the five influencing factors, there are n2 factors that have an indirect impact on F6; w i is the weight of the factors that have an indirect impact on F6; w j V i The weight of the mediating variable affecting F6, 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, calculating the highway heavy rainfall disaster risk factor γ

[0056] γ=γ1+γ2

[0057] The disaster warning subsystem obtains the highway heavy rainfall disaster risk factor from the risk assessment subsystem, and publishes the heavy rainfall disaster risk of a certain section of the highway through roadside equipment, navigation, data processing center, etc.

[0058] Before using the system, the Monte Carlo method is used to automatically generate all the information to be collected by the information collection subsystem. The sample size of the Monte Carlo method is not less than 10 7 , and then obtain 10 through the risk assessment subsystem 7The first 20% of the highway heavy rainfall disaster risk factor γ is defined as high risk, the second 20% as low risk, the second 20% to 40% as low risk, the second 40% to 60% as medium risk, and the second 60% to 80% as high risk. The main reason for this treatment is that although the path of the influencing factors affecting F6 is obtained through the questionnaire survey, its degree is not given, so it is necessary to define the degree of risk first.

[0059] After the above operations, the heavy rainfall disaster risk factors for 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 are issued according to the intervals in which they are located.

[0060] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0061] 1. Selection of research objects and detailed units: In the existing heavy rainfall disaster risk assessment, the assessment is mostly carried out at larger spatial scales such as regions and districts, and the assessment cycle is relatively long (such as 3 years). However, the present invention targets specific sections of highways and refines the assessment unit to 10km, emphasizing the refined real-time assessment at the section level. This refinement 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 microscopic (section-level) real-time prevention level.

[0062] 2. Comprehensive consideration of multiple factors and construction of structured factor model: Starting from five major influencing factors, namely, the danger of disaster-causing factors, the sensitivity of disaster-prone environments, the exposure of disaster-bearing bodies, the vulnerability of disaster-bearing bodies, and the ability to prevent and reduce disasters, the present invention finally constructs a comprehensive impact model for the risk of disasters caused by heavy rainfall (the impact of F1-F5 on F6). Compared with the traditional evaluation method that only relies on a few natural factors (such as rainfall, terrain conditions) or simple traffic indicators, the present invention significantly expands the dimensions and connotations of the influencing factors. It not only pays attention to meteorological conditions and road conditions, but also considers traffic characteristics (traffic volume, speed difference, overtaking ratio, etc.), road network structure (tunnel, bridge distribution), historical accident and congestion records, economic and social resources (GDP per capita, fiscal budget) and public perception and attitude. Through the comprehensive analysis of the interaction of multiple factors, the complexity and dynamic characteristics of the risk of disasters caused by heavy rainfall on highways can be more accurately reflected;

[0063] 3. Introducing a comprehensive statistical modeling method of factor analysis, path analysis and mediation effect analysis: Traditional risk assessment models are often based on empirical formulas, weight superposition or simple expert scoring methods. The present invention obtains data through questionnaire surveys, extracts potential structural factors through factor analysis, and then clarifies direct and indirect impact paths through path analysis and mediation effect analysis. The introduction of this multi-level statistical analysis method enables the model to not only qualitatively describe the impact of various factors on risks, but also quantitatively decompose the impact paths and mediating variables, which is relatively rare in existing highway meteorological disaster risk assessment models, 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 invention introduces the entropy method when quantitatively calculating the weights of various influencing factors. The entropy method objectively determines the weights according to the discreteness of the survey data, thereby reducing the influence of subjective assumptions in weight determination and helping to establish a more objective and fair weight allocation mechanism;

[0065] 5. Quantify the risk level and issue early warning information using the Monte Carlo method: The present invention uses Monte Carlo simulation (greater than 10 7 In the process of risk level definition, the distribution characteristics of large-scale simulated data are used to define the interval ranges of high risk, relatively high risk, medium risk, relatively low risk and low risk. In the absence of clear quantitative standards, this provides a scientific and repeatable quantitative reference for risk assessment and early warning release. This is more rigorous and objective than the traditional method of relying on experience or simply empirical classification.

[0066] 6. Systematic design for real-world applications: It not only proposes a modeling method, but also provides a risk assessment system framework that integrates information collection, risk assessment and disaster warning. From data acquisition (such as meteorological data, road condition data, economic and social data, and traffic operation data) to the final risk factor calculation and warning level release, there is a clear technical path and operability, and the systematization and practicality are significantly improved;

[0067] In summary, the modeling method of the highway heavy rainfall disaster risk assessment model and the risk assessment system of the present invention achieve finer granularity and real-time emphasis in the assessment scale and time period, organically integrate social economy, traffic operation, public cognition, meteorological conditions and road structure environment, and have a more comprehensive factor dimension. By introducing rigorous statistical analysis methods (factor analysis, path analysis, mediation effect analysis, entropy method), the scientificity and objectivity of the model are improved. By using the Monte Carlo method to objectively determine the risk level division, the rationality of risk classification is improved, and a full-process integrated solution from data collection to warning information release is provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 The present invention is a flowchart of a method for modeling a highway heavy rainfall disaster risk assessment model. DETAILED DESCRIPTION

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

[0070] The technical solution of the present invention is further described below in conjunction with embodiments.

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

[0072] Step S1: Considering the five influencing factors of the hazard factor F1, the sensitivity of the disaster-prone environment F2, the exposure of the disaster-prone body F3, the vulnerability of the disaster-prone body F4 and the disaster prevention and mitigation capacity F5, and the heavy rainfall disaster risk F6, a total of six factors, considering the specific content of each factor, the five-level Likert scale is used to design the questionnaire. The designed questionnaire is shown in Table 1:

[0073] Table 1. Questionnaire and validity analysis results

[0074]

[0075] Step 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 characteristic roots of each factor after rotation. Through the design of the questionnaire and the survey, 1245 valid questionnaires were finally collected, of which 93.49% were college graduates and 86.51% were students majoring in transportation, civil engineering, and machinery. At the same time, through reliability analysis, the Cronbach α coefficient of the scale was 0.970. Through validity analysis, the loading matrix A of the 6 factors and the characteristic roots of each factor after rotation were obtained, and the relevant results are listed in Table 1.

[0076] Step S3: Calculate the scores of the six factors, F1, F2, F3, F4, F5, and F6, and perform path analysis to find out the factors that have direct and indirect effects on the heavy rainfall disaster risk F6, determine the positive and negative signs of the standardized path coefficients of the direct influencing factors, and determine the mediating variables between the indirect influencing factors and the heavy rainfall disaster risk F6, as follows:

[0077] Assuming that the score given by the kth respondent to the i-th item is xi, then the score given by the kth respondent to the j-th influencing factor is zk j for

[0078]

[0079] The above formula can be used to obtain the score of each factor given by each investigator, that is, the score of each factor;

[0080] Then do path analysis, the results are shown in Table 2:

[0081] Table 2. Path analysis results

[0082]

[0083] The evaluation indicators of the model in Table 2 are chi-square freedom ratio = 0.09, GFI = 1, RMSEA = 0, RMR = 0.001, CFI = 1, NFI = 1, NNFI = 1.002, indicating that the model fits very well. Table 2 clearly shows that the danger of disaster factors, the sensitivity of the disaster-prone environment, the exposure of the disaster-prone body, and the disaster prevention and mitigation capabilities have a direct impact on the risk of heavy rainfall disasters, but the vulnerability of the disaster-prone body has no direct impact on the risk of heavy rainfall disasters. Further analysis of the mediating variables that affect the vulnerability of the disaster-prone body on the risk of heavy rainfall disasters is listed in Table 3:

[0084] Table 3. Summary of mediation test results

[0085]

[0086] Remark: * p<0.05 ** p<0.01,

[0088] Step S4: Remove the items and corresponding results related to heavy rainfall disaster risk F6 in the questionnaire of step S2 to obtain a new questionnaire. Then analyze the reliability and validity of the new questionnaire to obtain the loading matrix B of the five influencing factors and the characteristic roots of each influencing factor after rotation. The results are shown in Table 4:

[0089] Table 4. Validity analysis results

[0090]

[0091] .

[0092] Step S5: First, the scores of the five influencing factors F1, F2, F3, F4, and F5 are calculated using the same method as step S3, and then the weight of each influencing factor is calculated using the entropy method.

[0093] For the jth influencing factor, define

[0094]

[0095] represents the contribution of the score of the kth investigator to the jth factor; where n is the number of respondents, and in this embodiment n=1245; the entropy value of the jth influencing factor is:

[0096]

[0097] In the formula,

[0098] m=1 / ln(n),

[0099] Further definition

[0100] f j =1-E j ,

[0101] Then the weight coefficient of the jth influencing factor is

[0102]

[0103] Step S6: Comprehensively consider the direct and indirect impacts of the influencing factors on the risk of heavy rainfall disasters, and combine the weights of each influencing factor to establish a heavy rainfall disaster risk model. The calculation formula of the highway heavy rainfall disaster risk factor γ is as follows:

[0104] γ=γ1+γ2

[0105] Where γ1 is the highway heavy rainfall disaster risk factor that has a direct impact on the heavy rainfall disaster risk F6, and γ2 is the highway heavy rainfall disaster risk factor that has an indirect impact on the heavy rainfall disaster risk F6, which are calculated by the following formulas:

[0106]

[0107] In the formula, γ1 is the risk factor of heavy rainfall disaster on highways that has a direct impact on F6, H1 is the danger of the disaster-causing factor, H2 is the sensitivity of the disaster-prone environment, H3 is the exposure of the disaster-bearing body, H4 is the disaster prevention and mitigation capability, and n1=4; among them, if the standardized path coefficient of the disaster prevention and mitigation capability is negative, its plus sign needs to be corrected to a minus sign;

[0108]

[0109] Where V1 is the vulnerability of the hazard-bearing body, n2 = 1; w j is the weight of the mediating variable of V1's influence on F6, which are the weights of the danger of disaster-causing factors, the sensitivity of the disaster-prone environment, the exposure of the disaster-bearing body, and the disaster prevention and mitigation capabilities, that is, n3 = 4. Among them, the mediating effect of the disaster prevention and mitigation capabilities is a negative value, and its sign should be corrected to a negative sign.

[0110] After obtaining the above model, a risk assessment system can be further developed to use the model to assess the risk of heavy rainfall disasters. The risk assessment system includes three subsystems: information collection, risk assessment, and disaster warning:

[0111] The information collection subsystem needs to collect information on specific contents such as the danger of disaster-causing factors, the sensitivity of the disaster-prone environment, the exposure of the disaster-prone body, the vulnerability of the disaster-prone body, and the disaster prevention and mitigation capabilities;

[0112] The risk assessment subsystem calculates the risk factor of heavy rainfall disasters based on the information provided by the information collection subsystem;

[0113] The disaster warning subsystem obtains the highway heavy rainfall disaster risk factor from the risk assessment subsystem, and publishes the heavy rainfall disaster risk of a certain section of the highway through roadside equipment, navigation, and data processing center;

[0114] The disaster warning subsystem first uses the Monte Carlo method to automatically generate all the information that needs to be collected by the information collection subsystem. The sample size of the Monte Carlo method is not less than 10 7 , and then obtain 10 through the risk assessment subsystem 7 The first 20% of the highway heavy rainfall disaster risk factor γ is defined as high risk, the second 20% as low risk, the second 20% to 40% as relatively low risk, the second 40% to 60% as medium risk, and the second 60% to 80% as relatively high risk;

[0115] Then, the heavy rainfall disaster risk factors for 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 are issued according to the intervals in which they are located.

[0116] The present invention proposes a modeling method and risk assessment system for highway heavy rainfall disaster risk assessment model. The invention obtains the scores and weights of each factor by means of questionnaire design, investigation, reliability / validity analysis, etc., and then finds out the factors that have direct and indirect effects on the heavy rainfall disaster risk through path analysis, and then comprehensively considers the direct and indirect effects of the influencing factors on the heavy rainfall disaster risk and the weights of each influencing factor to establish a risk assessment model. The method carries out assessment on specific sections of highways, and can overcome the influence of experts on the model results during the modeling process.

[0117] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for modeling a highway heavy rainfall disaster risk assessment model, characterized by: Through questionnaire design and investigation, we first analyze the direct and indirect relationships between various factors, and then establish a risk assessment model. The steps are as follows: S1: Considering the five influencing factors of the hazard factor F1, the sensitivity of the disaster-prone environment F2, the exposure of the disaster-prone body F3, the vulnerability of the disaster-prone body F4, and the disaster prevention and mitigation capacity F5, and the heavy rainfall disaster risk F6, a total of six factors, considering the specific content of each factor, the five-level Likert scale was used to design the questionnaire; S2: Conduct a questionnaire survey, analyze the reliability and validity of the questionnaire, and obtain the loading matrix A of all six factors and the characteristic roots of each factor after rotation; S3: Calculate the scores of the six factors, namely F1, F2, F3, F4, F5 and F6, and conduct path analysis to find out the factors that have direct and indirect effects on the heavy rainfall disaster risk F6, determine the positive and negative signs of the standardized path coefficients of the direct influencing factors, and determine the mediating variables between the indirect influencing factors and the heavy rainfall disaster risk F6; S4: Remove the items related to heavy rainfall disaster risk F6 and the corresponding results in the questionnaire of step S2, obtain a new questionnaire and the corresponding questionnaire survey results, analyze the reliability and validity of the new questionnaire, and obtain the loading matrix B of the five influencing factors and the characteristic roots of each influencing factor after rotation; S5: firstly, the scores of the five influencing factors F1, F2, F3, F4, and F5 are calculated by the same method as step S3, and then the weight of each influencing factor is calculated by the entropy method; S6: Comprehensively consider the direct and indirect impacts of influencing factors on the risk of disasters caused by heavy rainfall, combine the weights of various influencing factors, and establish a heavy rainfall disaster risk model.

2. The method for modeling a highway heavy rainfall disaster risk assessment model according to claim 1 is characterized by: The established highway heavy rainfall disaster risk assessment model is aimed at a certain section of the highway, and the assessment unit is 10km.

3. The method for modeling a highway heavy rainfall disaster risk assessment model according to claim 1 is characterized by: The danger F1 of the disaster factor in step S1 refers to the danger of heavy rainfall on the highway section, including three specific contents: rainfall amount F11, rainfall duration F12, and rainfall frequency F13; The sensitivity of the disaster-prone environment F2 refers to the sensitivity of the road to heavy rainfall, including the road section undulation F21, the steepness of the slope F22, the number of tunnels F23, the number of bridges F24, the amount of surrounding vegetation F25 and the degree of unevenness of the road surface F26, a total of 6 specific contents; The exposure of the disaster-bearing body F3 refers to the vehicles exposed to the risk of heavy rainfall, including the average speed of the road section F31, the speed difference between vehicles F32, the overtaking situation between vehicles F33, heavy trucks F34, and the traffic volume F35, a total of five specific contents; The vulnerability of the disaster-bearing body F4 refers to the historical accidents, congestion and obstruction of the road section, including the number of traffic accidents on the road section within a period of time F41, the number of casualties caused by traffic accidents F42, the economic losses 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, a total of 7 specific contents; Disaster prevention and mitigation capability F5 refers to the ability of the local area to provide timely rescue after an accident occurs on a road section, including the per capita GDP of the road section F51, the per capita GDP of the area F52, the fiscal budget of the year F53, and the number of national / provincial / county roads around the road section F54, a total of 4 specific contents; Rainfall-induced disaster risk F6 refers to the public's cognitive attitude toward the possibility that heavy rainfall may increase traffic risks, including traffic accidents F61, traffic congestion F62, traffic jams F63, and rescue difficulty F64, a total of 4 specific contents; According to the 29 specific contents of the above 6 factors, 29 items can be designed and the five-level Likert scale can be used for investigation.

4. The method for modeling a highway heavy rainfall disaster risk assessment model according to claim 1 is characterized by: There should be no less than 40 pre-survey questionnaires before conducting the questionnaire survey in step S2; the subjects of the questionnaire survey should be college graduates (including college students) or above, and their subject background should be science and engineering background such as transportation, civil engineering, and automobiles; the final number of questionnaires should be more than 400, and they must pass the reliability and validity test.

5. The method for modeling a highway heavy rainfall disaster risk assessment model according to claim 1 is characterized by: Step S3 first calculates the scores of the six factors, namely, F1, F2, F3, F4, F5, and F6, as follows: The validity analysis of the survey results of 29 items was conducted, and a total of 6 factors from F1 to F6 were obtained. Each factor and each item were connected through the factor loading coefficient. 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 of the six factors is composed; at the same time, the validity analysis will also obtain the characteristic roots of each influencing factor after rotation, and the characteristic root of the jth influencing factor after rotation is set as c j ; Assume that the kth respondent gives the i-th item a score of x i , then the score z given by the kth respondent to the jth influencing factor kj for The above formula can be used to obtain the score of each factor given by each investigator, that is, the score of each factor; Subsequently, conventional path analysis methods in statistics were used to identify the influencing factors that have a direct impact on the heavy rainfall disaster risk F6, and the standardized path coefficients of each factor were obtained. At the same time, factors that only have an indirect impact on F6 were found, and further mediation effect analysis was conducted to obtain the mediating variables of the indirect impact factors on F6.

6. The method for modeling a highway heavy rainfall disaster risk assessment model according to claim 1 is characterized by: 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 has only 25 items, and the characteristic roots obtained in step S4 and the loading matrix B of the five influencing factors are also required; then the entropy method can be used to calculate the weight of each influencing factor: For the jth influencing factor, define It represents the contribution of the score of the kth investigator to the jth factor; where n is the number of respondents, which should be no less than 400; the entropy value of the jth influencing factor is: In the formula, m=1 / ln(n), Further define f j =1-E j , Then the weight coefficient of the jth influencing factor is 7. The method for modeling a highway heavy rainfall disaster risk assessment model according to claim 1 is characterized by: 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 use the model to assess the heavy rainfall disaster risk. The risk assessment system includes three subsystems: information collection, risk assessment and disaster warning: The information collection subsystem needs to collect the following contents: The hazard factor F1 needs to collect three specific contents: rainfall F11, rainfall duration F12, and rainfall frequency F13; The sensitivity of the disaster-prone environment F2 needs to collect six specific contents, including the road section undulation F21, the slope steepness F22, the number of tunnels F23, the number of bridges F24, the amount of surrounding vegetation F25 and the road surface unevenness F26; The exposure of the disaster-prone body F3 requires the collection of five specific items: average vehicle speed on the road section F31, speed difference between vehicles F32, overtaking between vehicles F33, heavy trucks F34, and traffic volume F35; The vulnerability of the disaster-bearing body F4 needs to collect seven specific contents, including the number of traffic accidents on the road section F41, the number of casualties caused by traffic accidents F42, the economic losses 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 within a period of time; Disaster prevention and mitigation capabilities F5 need to collect four specific items: GDP per capita F51, GDP per area F52, fiscal budget for the year F53, and the number of national / provincial / county roads around the road section F54; The risk assessment subsystem calculates the heavy rainfall disaster risk factor based on the information provided by the information collection subsystem, as follows: 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 to the corresponding items in B one by one; then calculate the scores of the five influencing factors F1, F2, F3, F4, and F5 using the same method as step S3; Step S72, calculating the highway heavy rainfall disaster risk factor γ1 that has a direct impact on the heavy rainfall disaster risk F6 among the influencing factors, Where γ1 is the highway heavy rainfall disaster risk factor that has a direct impact on F6, H i Among the five influencing factors, there are n1 factors that have a direct impact on F6; w i is the weight of the influencing factor that has a direct impact on F6; in the calculation, if the standardized path coefficient of the influencing factor is negative, the plus sign needs to be corrected to a minus sign; Step S73, calculating the highway heavy rainfall disaster risk factor γ2 that has an indirect impact on the heavy rainfall disaster risk F6 among the influencing factors, Where V i Among the five influencing factors, there are n2 factors that have an indirect impact on F6; w i is the weight of the factors that have an indirect impact on F6; w j V i The weights of the mediating variables affecting F6, there are n3 mediating variables; Step S74, calculating the highway heavy rainfall disaster risk factor γ γ=γ1+γ2 The disaster warning subsystem obtains the highway heavy rainfall disaster risk factor from the risk assessment subsystem, and publishes the heavy rainfall disaster risk of a certain section of the highway through roadside equipment, navigation, and data processing center; The system first uses the Monte Carlo method to automatically generate all the information that needs to be collected by the information collection subsystem. The sample capacity of the Monte Carlo method is not less than 10 7 , and then obtain 10 through the risk assessment subsystem 7 The first 20% of the highway heavy rainfall disaster risk factor γ is defined as high risk, the second 20% as low risk, the second 20% to 40% as relatively low risk, the second 40% to 60% as medium risk, and the second 60% to 80% as relatively high risk; Then, the heavy rainfall disaster risk factors for 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 are issued according to the intervals in which they are located.

Images