Highway influence quantification and grading method based on improved triangular fuzzy algorithm

Abstract

The invention discloses an expressway influence quantification and grade division method based on an improved triangular fuzzy algorithm, and relates to the field of expressway influence judgment, in particular to the expressway influence quantification and grade division method. The objective of the invention is to solve the problems that the coupling relation between expressway influence factors is not clear, influence indexes cannot be quantified, and the influence judgment accuracy on an expressway is low. The method comprises the steps of determining a criterion layer set and an index layer set; obtaining an evaluation set of the indexes based on the membership degree of the to-be-tested index values; determining a coupling relationship between the criteria and the indexes, and constructing a single-criterion evaluation matrix based on the evaluation index set of each criterion and the evaluation set of the indexes; designing a pairwise comparison expert scoring method questionnaire, and determining expressway influence criterion layer and index layer weight; calculating a single-criterion fuzzy comprehensive evaluation set according to the index weight and the evaluation matrix; and according to the criterion weight and the single-criterion fuzzy comprehensive evaluation set, performing expressway influence result analysis and grade determination.

Description

Technical Field

[0001] The present invention relates to the field of highway impact determination, and in particular to a method for quantifying and grading highway impact. Background Art

[0002] As an important part of the modern transportation network, the planning, design and operation management of highways have a profound impact on the development of surrounding areas. It not only greatly promotes the rapid circulation of people and materials, but also shows obvious construction benefits in multiple aspects, including the convenience of transportation, the vitality of regional economic development, the speed of social progress, the effectiveness of environmental protection, and the realization of energy conservation and emission reduction goals. However, since the impact of highway construction involves many aspects, its evaluation is extremely complex. Therefore, there is an urgent need for a method that can objectively and quantitatively integrate multiple complex indicators to achieve quantitative analysis and classification of the impact of highway construction. At present, the defect of traditional highway impact evaluation methods is that they only analyze key factors such as traffic flow, social benefits, economic benefits, environmental impact, energy consumption, etc. in isolation, ignoring the objective coupling relationship between various factors. In addition, the measurement units of various influencing factors are different, and it is impossible to quantify and compare various indicators across fields, resulting in one-sided and subjective analysis results. Summary of the invention

[0003] The purpose of the present invention is to solve the problems of unclear coupling relationship between highway influencing factors, inability to quantify influencing indicators, and low accuracy in judging the impact on highways, and to propose a highway impact quantification method and a grading system based on an improved triangular fuzzy algorithm.

[0004] A method for quantifying and grading highway impact based on improved triangular fuzzy algorithm. The specific process is as follows:

[0005] Step 1: Determine the criterion layer set and the indicator layer set;

[0006] Step 2: Obtain the historical data of each indicator in the indicator layer set, draw the cumulative probability curve, formulate different evaluation level cutoff values ​​according to the information of different points on the curve, and then construct the triangular fuzzy number membership function of each indicator;

[0007] Get the value L of the indicator to be measured, based on the indicator p j The membership function diagram and membership degree calculation formula of the triangular fuzzy number are used to calculate the membership degree of the index value L to be measured;

[0008] Based on the membership degree of the index value L to be measured, the index p is obtained j Evaluation set;

[0009] Step 3: Determine the coupling relationship between criteria and indicators, and construct a single criterion evaluation matrix based on the evaluation indicator set of each criterion and the evaluation set of indicators;

[0010] Step 4: Design a questionnaire for pairwise comparison expert scoring method to determine the weights of the highway impact criterion layer and indicator layer;

[0011] Step 5: Calculate the single criterion fuzzy comprehensive evaluation set according to the indicator weights of step 4 and the evaluation matrix of step 3;

[0012] Step 6: According to the criterion weights in step 4 and the single criterion fuzzy comprehensive evaluation set in step 5, the highway impact results are analyzed and the level is determined.

[0013] The beneficial effects of the present invention are:

[0014] The present invention collects real and objective historical data, considers the coupling relationship between the indicator layer and the criterion layer and the reusability of the indicators, and uses an improved triangular fuzzy comprehensive evaluation method to conduct a comprehensive and systematic analysis of the built expressways.

[0015] The present invention focuses on the impact assessment of highways, and systematically decomposes the impact issues of highways into a three-layer architecture including a target layer, a criterion layer, and an indicator layer, thereby simplifying complex and multi-dimensional issues. The project criterion layer is divided into five levels: traffic impact, social impact, economic impact, environmental impact, and energy-saving impact. The indicator layer adopts 6 quantitative indicators. Considering the coupling relationship between the indicator layer and the criterion layer, the reuse attributes of the indicator layer, etc., an improved triangular fuzzy comprehensive evaluation method is used to conduct a comprehensive and systematic analysis of the project impact. The invention has the characteristics of real data and solves the subjective problem of traditional fuzzy comprehensive evaluation methods; at the same time, the use of triangular fuzzy numbers can better fit the membership functions of various indicators; in addition, the loopholes in the traditional evaluation method that indicators act on the criteria alone are abandoned, and considering the problem that a single indicator may affect different criteria, an improved fuzzy comprehensive evaluation algorithm is constructed to further improve the accuracy of highway impact judgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of the method of the present invention; Figure 2 is the cumulative probability distribution curve; Figure 3 is the membership function diagram of triangular fuzzy numbers; Figure 4 For evaluation hierarchy diagram; Figure 5 It is the cumulative distribution probability curve of traffic accident rate; Figure 6 is the membership function diagram of traffic accident rate; Figure 7 is the cumulative distribution probability curve of toll rate; Figure 8 is the membership function diagram of toll rate; Fig. 9 This is the cumulative distribution probability curve of the debt repayment ratio; Fig.10 This is the membership function diagram of the debt repayment ratio; Fig.11 This is the cumulative distribution probability curve of heavy truck carbon emissions; Fig.12 It is the membership function diagram of heavy truck carbon emission; Fig.13 It is the probability curve of the cumulative distribution of the average daily traffic volume; Fig.14 is the membership function diagram of the average daily traffic volume; Fig.15 This is the cumulative distribution probability curve of environmental protection investment proportion; Fig.16 This is the membership function diagram of the proportion of environmental protection investment. DETAILED DESCRIPTION

[0017] Specific implementation method 1: This implementation method is a method for quantifying and grading highway impact based on an improved triangular fuzzy algorithm. The specific process is as follows:

[0018] Step 1: Identify and classify highway impact indicators and construct a hierarchical framework for highway impact assessment (the hierarchical framework includes a criterion layer set and an indicator layer set);

[0019] Determine the criteria layer set and the indicator layer set;

[0020] Step 2: Obtain the historical data of each indicator in the indicator layer set, draw the cumulative probability curve, formulate different evaluation level cutoff values ​​according to the information of different points on the curve, and then construct the triangular fuzzy number membership function of each indicator;

[0021] Get the value L of the indicator to be measured, based on the indicator p j The membership function diagram and membership degree calculation formula of the triangular fuzzy number are used to calculate the membership degree of the index value L to be measured;

[0022] Based on the membership degree of the index value L to be measured, the index p is obtained j 's evaluation set;

[0023] Step 3: Determine the coupling relationship between criteria and indicators, and construct a single criterion evaluation matrix based on the evaluation indicator set of each criterion and the evaluation set of indicators;

[0024] Step 4: Design a questionnaire for pairwise comparison expert scoring method to determine the weights of the highway impact criterion layer and indicator layer;

[0025] Step 5: Calculate the single criterion fuzzy comprehensive evaluation set according to the indicator weights of step 4 and the evaluation matrix of step 3;

[0026] Step 6: According to the criterion weights in step 4 and the single criterion fuzzy comprehensive evaluation set in step 5, the highway impact results are analyzed and the level is determined.

[0027] Specific implementation method 2: This implementation method is different from the specific implementation method 1 in that: in step 1, highway impact indicators are identified and classified to construct a hierarchical framework for highway impact assessment; a criterion layer set and an indicator layer set are determined; the specific process is as follows:

[0028] Step 11, criterion layer set U = {u1, u2, u3, u4, u5};

[0029] Among them, u i represents the i-th criterion, i = 1, 2, 3, 4, 5; u1 is the traffic impact, u2 is the social impact, u3 is the economic impact, u4 is the environmental impact, and u5 is the energy-saving impact;

[0030] The criterion set is a general set composed of various factors that affect the evaluation object (target layer), usually represented by U, U = {u1, u2, ... u5}. These criteria usually have different degrees of fuzziness.

[0031] Step 12, indicator layer set P = {p1, p2, p3, p4, p5, p6};

[0032] Among them, p j Indicates the influence on the i-th criterion u i The jth indicator, p1 is the traffic accident rate, p2 is the toll, p3 is the proportion of debt repayment, p4 is the carbon emission of heavy trucks, p5 is the average daily traffic volume, and p6 is the proportion of environmental protection investment;

[0033] The index set is a general set of elements consisting of all factors that affect the criterion, usually represented by P, where P = {p1, p2, ... p m}, where p j Representative impact criteria i The jth indicator of the project evaluation system is selected as the traffic accident rate, toll, debt repayment ratio, heavy truck carbon emissions, daily average traffic volume, and environmental protection investment ratio. These indicators are reusable, that is, a single indicator may affect different criteria.

[0034] The other steps and parameters are the same as those in the first embodiment.

[0035] Specific implementation method three: The difference between this implementation method and specific implementation method one or two is that: in step 2, the historical data of each indicator in the indicator layer set is obtained, and the cumulative probability curve is drawn. Different evaluation level cutoff values ​​are proposed according to the information of different points on the curve, and then the triangular fuzzy number membership function of each indicator is constructed; the value L of the indicator to be measured is obtained, and the cumulative probability curve is drawn according to the cumulative probability curve. j The membership function diagram and membership calculation formula of the triangular fuzzy number are used to calculate the membership of the index value L to be measured; based on the membership of the index value L to be measured, the index p is obtained. j The evaluation set of; the specific process is:

[0036] Step 21: Get the jth index p in the index layer set P = {p1, p2, p3, p4, p5, p6} jHistorical data of the indicator p j The cumulative probability of j The historical data is used as the horizontal axis, and the indicator p j The cumulative probability is used as the ordinate to form the index p j The corresponding cumulative probability curve is as follows: Figure 2 ;

[0037] Index p j The traffic accident rate p1, toll p2, debt repayment ratio p3, heavy truck carbon emissions p4, daily average traffic volume p5 or environmental protection investment ratio p6;

[0038] Select index p j The corresponding cumulative probability curve has the index value L corresponding to the cumulative probability of 15%, 30%, 50%, 65% and 85% j1 , L j2 , L j3 , L j4 , L j5 As the dividing value of evaluation grades A, B, C, D, and E;

[0039] A means excellent, B means good, C means average, D means poor, and E means bad;

[0040] Step 22: Based on the indicator value L j1 , L j2 , L j3 , L j4 , L j5 and the cutoff values ​​of evaluation levels A, B, C, D, and E, construct the index p j The triangular fuzzy number membership function diagram of Figure 3 .

[0041] Step 23: Get the value L of the indicator to be measured, based on the indicator p j The triangular fuzzy number membership function diagram and membership calculation formula are used to calculate the membership of the index value L to be measured;

[0042] Based on the membership degree of the index value L to be measured, the index p is obtained j Evaluation set.

[0043] The other steps and parameters are the same as those in the first or second embodiment.

[0044] Specific implementation method 4: This implementation method is different from any one of the specific implementation methods 1 to 3 in that: in step 23, the value L of the indicator to be measured is obtained based on the indicator p j The triangular fuzzy number membership function diagram and membership calculation formula are used to calculate the membership of the index value L to be measured; based on the membership of the index value L to be measured, the index p is obtained. j The specific process is as follows:

[0045] Get the value L of the indicator to be measured, based on the value L of the indicator to be measured in the indicator p j The level of the measured indicator value L is determined on the membership function diagram of the triangular fuzzy number; the evaluation levels are divided into A, B, C, D, and E; based on the level to which the measured indicator value L belongs, the measured indicator value L is substituted into the membership calculation formula of the corresponding level to calculate the membership of the measured indicator value L; based on the membership of the measured indicator value L, the single indicator p is obtained j The evaluation set L j ={L j1 ,L j2 ,L j3 ,L j4 ,L j5}.

[0046] The other steps and parameters are the same as those in Specific Embodiments 1 to 3.

[0047] Specific implementation method 5: This implementation method is different from the specific implementation methods 1 to 4 in that: the value L of the index to be measured is brought into the membership calculation formula of the corresponding level to calculate the membership of the value L of the index to be measured; the expression is:

[0048] The membership function of the indicator level A is:

[0049] Among them, L1 and L2 are the first and second indicators on the membership graph respectively; f A (L) is the membership degree of the index value L to be measured;

[0050] The membership function of the indicator level B is:

[0051] Among them, L1, L2, and L3 are the first, second, and third indicators on the membership graph respectively; f B (L) is the membership degree of the index value L to be measured;

[0052] The membership function of the indicator level C is:

[0053] Among them, L2, L3, and L4 are the second, third, and fourth indicators on the membership graph respectively; f C (L) is the membership degree of the index value L to be measured;

[0054] The membership function of the indicator level D is:

[0055] Among them, L3, L4, and L5 are the third, fourth, and fifth indicators on the membership graph respectively; f D (L) is the membership degree of the index value L to be measured;

[0056] The membership function of the indicator level E is:

[0057] Among them, L4 and L5 are the 4th and 5th indicators on the membership graph respectively; f E (L) is the degree of membership of the indicator value L to be measured.

[0058] The other steps and parameters are the same as those in Specific Embodiments 1 to 4.

[0059] Specific implementation method 6: This implementation method is different from the specific implementation methods 1 to 5 in that: in step 3, the coupling relationship between the criterion and the indicator is determined, based on the evaluation indicator set of each criterion and the evaluation set L of the indicator. j ={L j1 ,L j2 ,L j3 ,L j4 ,L j5}, construct a single criterion evaluation matrix; the specific process is:

[0060] Determine in turn whether all indicators in the indicator set affect the evaluation of all criteria in the criterion set, and draw the organizational structure of the project impact evaluation based on the objective coupling relationship, such as Figure 4 , and then clearly show the relationship between the indicator layer and the criterion layer, and explain the issue of the reusability of indicators mentioned in step 12.

[0061] Step 31: Analyze the evaluation index set of each criterion; the specific process is:

[0062] The evaluation index set of traffic impact is C 交通影响 = {accident rate, toll, daily average traffic volume, heavy truck carbon emissions}; the evaluation index set of social impact is C 社会影响 = {accident rate, heavy truck carbon emissions, environmental protection investment ratio}; the evaluation index set of economic impact is C 经济影响 = {toll, debt repayment ratio, daily average traffic volume, environmental protection investment ratio}; the evaluation index set of environmental impact is C 环境影响 = {accident rate, heavy truck carbon emissions, environmental protection investment ratio}; the evaluation index set of energy saving impact is C 节能影响 ={heavy truck carbon emissions, environmental protection investment ratio}; the link between the criteria and indicators can be found in Figure 4 .

[0063] Step 32: Combine the evaluation index set of each criterion with the evaluation set L of the index j ={L j1 ,L j2 ,L j3 ,L j4 ,L j5}, construct a single criterion evaluation matrix; the specific process is:

[0064] The evaluation matrix of traffic impact is:

[0065] Among them, L 11 L 12 L 13 L 14 L 15 is the evaluation set of accident rate, L 21 L 22 L 23 L 24 L 25 is the evaluation set of tolls, L 51 L 52 L 53 L 54 L 55 is the evaluation set of daily average traffic volume, L 41 L 42 L 43 L 44 L 45 is the evaluation set of carbon emissions of heavy trucks;

[0066] The evaluation matrix of social impact is:

[0067] Among them, L 11 L 12 L 13 L 14 L 15 is the evaluation set of accident rate, L 41 L 42 L 43 L 44 L 45 is the evaluation set of heavy truck carbon emissions, L 61 L 62 L 63 L 64 L 65 is the evaluation set of environmental protection investment proportion;

[0068] The evaluation matrix of economic impact is:

[0069] Among them, L 21 L 22 L 23 L 24 L 25 is the evaluation set of tolls, L 31 L 32 L 33 L 34 L 35is the evaluation set of debt repayment ratio, L 51 L 52 L 53 L 54 L 55 is the evaluation set of daily average traffic volume, L 61 L 62 L 63 L 64 L 65 is the evaluation set of environmental protection investment proportion;

[0070] The environmental impact assessment matrix is:

[0071] Among them, L 11 L 12 L 13 L 14 L 15 is the evaluation set of accident rate, L 41 L 42 L 43 L 44 L 45 is the evaluation set of heavy truck carbon emissions, L 61 L 62 L 63 L 64 L 65 is the evaluation set of environmental protection investment proportion;

[0072] The evaluation matrix of energy saving impact is:

[0073] Among them, L 41 L 42 L 43 L 44 L 45 is the evaluation set L of heavy truck carbon emissions 61 L 62 L 63 L 64 L 65 is the evaluation set of the proportion of environmental protection investment.

[0074] The other steps and parameters are the same as those in Specific Implementation Methods 1 to 5.

[0075] Specific implementation method 7: This implementation method is different from the specific implementation methods 1 to 6 in that: in step 4, a questionnaire for the expert scoring method of pairwise comparison is designed to determine the weights of the highway influencing criterion layer and the indicator layer; the specific process is:

[0076] Step 41: Design the questionnaire using the pairwise comparison expert scoring method, collect feedback, and use the approximate calculation method to determine the weight of the criterion layer; the specific process is:

[0077] Step 411: Score any two criteria in the criterion layer set U = {u1, u2, u3, u4, u5} to obtain 25 sets of scores, which are u 11 、u 12 、u 13 、u 14 、u 15 、u 21 、u 22 、u 23 、u 24 、u 25 、u 31 、u 32 、u 33 、u 34 、u 35 、u 41 、u 42 、u 43 、u 44 、u 45 、u 51 、u 52 、u 53 、u 54 、u 55 ;

[0078] u 11 、u 12 、u 13 、u 14 、u 15 For the first row, u 21 、u 22 、u 23 、u 24 、u 25 For the second line, u 31 、u 32 、u 33 、u 34 、u 35 For the third row, u 41 、u 42 、u 43 、u 44 、u 45 For the fourth row, u 51 、u 52 、u 53 、u 54 、u 55 For the fifth row;

[0079] Among them, u i represents the i-th criterion, u1 is the traffic impact, u2 is the social impact, u3 is the economic impact, u4 is the environmental impact, and u5 is the energy-saving impact;

[0080] Step 412: Perform normalization on the 25 sets of scores obtained to obtain the normalized u 11 、u 12 、u 13 、u 14 、u 15 、u 21 、u 22 、u 23 、u 24 、u 25 、u 31 、u 32 、u 33 、u 34 、u 35 、u 41 、u 42 、u 43 、u 44 、u 45 、u 51 、u 52 、u 53 、u 54 、u 55 ; The specific process is:

[0081] Will The value of u after normalization is 11 ;Will The value of u after normalization is 21 ;Will The value of u after normalization is 31 ;Will The value of u after normalization is 41 ;Will The value of u after normalization is 51 ;

[0082] Will The value of u after normalization is 12 ;Will The value of u after normalization is 22 ;Will The value of u after normalization is 32 ;Will The value of u after normalization is 42 ;Will The value of u after normalization is 52 ;

[0083] Will The value of u after normalization is 13 ;Will The value of u after normalization is 23 ;Will The value of u after normalization is33 ;Will The value of u after normalization is 43 ;Will The value of u after normalization is 53 ;

[0084] Will The value of u after normalization is 14 ;Will The value of u after normalization is 24 ;Will The value of u after normalization is 34 ;Will The value of u after normalization is 44 ;Will The value of u after normalization is 54 ;

[0085] Will The value of u after normalization is 15 ;Will The value of u after normalization is 25 ;Will The value of u after normalization is 35 ;Will The value of u after normalization is 45 ;Will The value of u after normalization is 55 ;

[0086] Step 413: The normalized u 11 、u 12 、u 13 、u 14 、u 15 Add and obtain the combined value corresponding to u1;

[0087] After normalization, u 21 、u 22 、u 23 、u 24 、u 25 Add and obtain the combined value corresponding to u2;

[0088] After normalization, u 31 、u 32 、u 33 、u 34 、u 35 Add and obtain the combined value corresponding to u3;

[0089] After normalization, u 41 、u 42 、u43 、u 44 、u 45 Add and obtain the combined value corresponding to u4;

[0090] After normalization, u 51 、u 52 、u 53 、u 54 、u 55 Add and obtain the combined value corresponding to u5;

[0091] Step 414: sum the combined value corresponding to u1, the combined value corresponding to u2, the combined value corresponding to u3, the combined value corresponding to u4, and the combined value corresponding to u5 again to obtain α; divide the combined value corresponding to u1 by α to obtain the weight corresponding to u1;

[0092] The combined values ​​corresponding to u1, u2, u3, u4 and u5 are added again to obtain α; the combined value corresponding to u2 is divided by α to obtain the weight corresponding to u2;

[0093] The combined values ​​corresponding to u1, u2, u3, u4 and u5 are added again to obtain α; the combined value corresponding to u3 is divided by α to obtain the weight corresponding to u3;

[0094] The combined values ​​corresponding to u1, u2, u3, u4, and u5 are added again to obtain α; the combined value corresponding to u4 is divided by α to obtain the weight corresponding to u4;

[0095] The combined values ​​corresponding to u1, u2, u3, u4 and u5 are added again to obtain α; the combined value corresponding to u5 is divided by α to obtain the weight corresponding to u5;

[0096] Step 415: based on the weight corresponding to u1, the weight corresponding to u2, the weight corresponding to u3, the weight corresponding to u4, and the weight corresponding to u5, a weight set A of the criterion layer is obtained;

[0097] Step 42: Design a questionnaire using the pairwise comparison expert scoring method. After collecting feedback, use the approximate calculation method to determine the weight set of the indicator layer. The specific process is as follows:

[0098] Step 421: Determine the weight set of the traffic impact assessment index in the index layer by using the approximate calculation method; the specific process is as follows:

[0099] Step 4211: Traffic impact assessment indicators include traffic accident rate p1, toll p2, heavy truck carbon emissions p4, and average daily traffic volume p5;

[0100] Score any two indicators in the traffic impact assessment index to obtain 16 sets of scores;

[0101] Step 4212: normalize the obtained 16 groups of scores to obtain 16 groups of scores after normalization. The 16 groups of scores are p 11 、p 12 、p 14 、p 15 、p 21 、p 22 、p 24 、p 25 、p 41 、p 42 、p 44 、p 45 、p 51 、p 52 、p 54 、p 55 ;

[0102] Step 4213: Normalize the 16 sets of scores obtained to obtain the normalized p 11 、p 12 、p 14 、p 15 、p 21 、p 22 、p 24 、p 25 、p 41 、p 42 、p 44 、p 45 、p 51 、p 52 、p 54 、p 55 ; The specific process is:

[0103] Will The value of p after normalization is 11 ;Will The value of p after normalization is 21 ;Will The value of p after normalization is 41 ;Will The value of p after normalization is 51 ;

[0104] Will The value of p after normalization is 12 ;Will The value of p after normalization is 22 ;Will The value of p after normalization is 42 ;Will The value of p after normalization is 52 ;

[0105] Will The value of p after normalization is 14 ;Will The value of p after normalization is 24 ;Will The value of p after normalization is 44 ;Will The value of p after normalization is 54 ;

[0106] Will The value of p after normalization is 15 ;Will The value of p after normalization is 25 ;Will The value of p after normalization is 45 ;Will The value of p after normalization is 55 ;

[0107] Step 4214: The normalized p 11 、p 12 、p 14 、p 15 Add and obtain the combined value corresponding to p1;

[0108] After normalization, p 21 、p 22 、p 24 、p 25 Add and obtain the combined value corresponding to p2;

[0109] After normalization, p 41 、p 42 、p 44 、p 45 Add and obtain the combined value corresponding to p4;

[0110] After normalization, p 51 、p 52 、p 54 、p 55 Add and obtain the combined value corresponding to p5;

[0111] Step 4215: sum the combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 again to obtain β; divide the combined value corresponding to p1 by β to obtain the weight corresponding to p1;

[0112] The combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 are added again to obtain β; the combined value corresponding to p2 is divided by β to obtain the weight corresponding to p2;

[0113] The combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 are added again to obtain β; the combined value corresponding to p4 is divided by β to obtain the weight corresponding to p4;

[0114] The combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 are added again to obtain β; the combined value corresponding to p5 is divided by β to obtain the weight corresponding to p5;

[0115] Step 4216: Based on the weight corresponding to p1, the weight corresponding to p2, the weight corresponding to p4, and the weight corresponding to p5, a weight set A1 of traffic impact assessment indicators is obtained;

[0116] Step 422: Determine the weight set of social impact evaluation indicators in the indicator layer using an approximate calculation method; the specific process is:

[0117] Step 4221: social impact assessment indicators include traffic accident rate p1, heavy truck carbon emissions p4, and environmental protection investment proportion p6;

[0118] Score any two indicators in the social impact assessment indicators to obtain 9 sets of scores;

[0119] The 9 groups of scores obtained are normalized to obtain 9 groups of scores after normalization. The 9 groups of scores are p 11 、p 14 、p 16 、p 41 、p 44 、p 46 、p 61 、p 64 、p 66 ;

[0120] Step 4222: Normalize the obtained 9 groups of scores to obtain the normalized p 11 、p 14 、p 16 、p 41 、p 44 、p46 、p 61 、p 64 、p 66 ; The specific process is:

[0121] Will The value of p after normalization is 11 ;Will The value of p after normalization is 41 ;Will The value of p after normalization is 61 ;

[0122] Will The value of p after normalization is 14 ;Will The value of p after normalization is 44 ;Will The value of p after normalization is 64 ;

[0123] Will The value of p after normalization is 16 ;Will The value of p after normalization is 44 ;Will The value of p after normalization is 66 ;

[0124] Step 4223: The normalized p 11 、p 14 、p 16 Add and obtain the combined value corresponding to p1;

[0125] After normalization, p 41 、p 44 、p 46 Add and obtain the combined value corresponding to p4;

[0126] After normalization, p 61 、p 64 、p 66 Add and obtain the combined value corresponding to p6;

[0127] Step 4224: sum the combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 again to obtain δ; divide the combined value corresponding to p1 by δ to obtain the weight corresponding to p1;

[0128] The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain δ; the combined value corresponding to p4 is divided by δ to obtain the weight corresponding to p4;

[0129] The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain δ; the combined value corresponding to p6 is divided by δ to obtain the weight corresponding to p6;

[0130] Step 4225: Based on the weight corresponding to p1, the weight corresponding to p4, and the weight corresponding to p6, a weight set A2 of social impact evaluation indicators is obtained;

[0131] Step 423: Determine the weight set of economic impact evaluation indicators in the indicator layer using an approximate calculation method; the specific process is:

[0132] Step 4231: economic impact evaluation indicators include tolls p2, debt repayment ratio p3, daily average traffic volume p5, and environmental protection investment ratio p6;

[0133] Score any two indicators in the economic impact evaluation index to obtain 16 sets of scores;

[0134] Step 4232: Perform normalization on the 16 sets of scores obtained to obtain 16 sets of scores after normalization. The 16 sets of scores are p 22 、p 23 、p 25 、p 26 、p 32 、p 33 、p 35 、p 36 、p 52 、p 53 、p 55 、p 56 、p 62 、p 63 、p 65 、p 66 ; The specific process is:

[0135] Will The value of p after normalization is 22 ;Will The value of p after normalization is 32 ;Will The value of p after normalization is 52 ;Will The value of p after normalization is 62 ;

[0136] Will The value of p after normalization is 23 ;Will The value of p after normalization is 33 ;Will The value of p after normalization is53 ;Will The value of p after normalization is 63 ;

[0137] Will The value of p after normalization is 25 ;Will The value of p after normalization is 35 ;Will The value of p after normalization is 55 ;Will The value of p after normalization is 65 ;

[0138] Will The value of p after normalization is 26 ;Will The value of p after normalization is 36 ;Will The value of p after normalization is 56 ;Will The value of p after normalization is 66 ;

[0139] Step 4233: The normalized p 22 、p 23 、p 25 、p 26 Add and obtain the combined value corresponding to p2;

[0140] After normalization, p 32 、p 33 、p 35 、p 36 Add and obtain the combined value corresponding to p3;

[0141] After normalization, p 52 、p 53 、p 55 、p 56 Add and obtain the combined value corresponding to p5;

[0142] After normalization, p 62 、p 63 、p 65 、p 66 Add and obtain the combined value corresponding to p6;

[0143] Step 4234: sum the combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 again to obtain γ; divide the combined value corresponding to p2 by γ to obtain the weight corresponding to p2;

[0144] The combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 are added again to obtain γ; the combined value corresponding to p3 is divided by γ to obtain the weight corresponding to p3;

[0145] The combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 are added again to obtain γ; the combined value corresponding to p5 is divided by γ to obtain the weight corresponding to p5;

[0146] The combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 are added again to obtain γ; the combined value corresponding to p6 is divided by γ to obtain the weight corresponding to p6;

[0147] Step 4235: Based on the weight corresponding to p2, the weight corresponding to p3, the weight corresponding to p5, and the weight corresponding to p6, a weight set A3 of economic impact assessment indicators is obtained;

[0148] Step 424: Determine the weight set of the environmental impact assessment indicators in the indicator layer using an approximate calculation method; the specific process is:

[0149] Step 4241: Economic impact evaluation indicators include traffic accident rate p1, heavy truck carbon emissions p4, and environmental protection investment proportion p6;

[0150] Score any two indicators in the environmental impact assessment index to obtain 9 sets of scores;

[0151] The 16 obtained scores are normalized to obtain 9 scores, which are p 11 、p 14 、p 16 、p 41 、p 44 、p 46 、p 61 、p 64 、p 66 ;

[0152] Step 4242: Normalize the obtained 9 groups of scores to obtain the normalized p 11 、p 14 、p 16 、p 41 、p 44 、p 46 、p 61 、p 64 、p 66 ;

[0153] The specific process is:

[0154] Will The value of p after normalization is 11 ;Will The value of p after normalization is 41 ;Will The value of p after normalization is 61 ;

[0155] Will The value of p after normalization is 14 ;Will The value of p after normalization is 44 ;Will The value of p after normalization is 64 ;

[0156] Will The value of p after normalization is 16 ;Will The value of p after normalization is 46 ;Will The value of p after normalization is 66 ;

[0157] Step 4243: The normalized p 11 、p 14 、p 16 Add and obtain the combined value corresponding to p1;

[0158] After normalization, p 41 、p 44 、p 46 Add and obtain the combined value corresponding to p4;

[0159] After normalization, p 61 、p 64 、p 66 Add and obtain the combined value corresponding to p6;

[0160] Step 4244: sum the combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 again to obtain φ; divide the combined value corresponding to p1 by φ to obtain the weight corresponding to p1;

[0161] The combined values ​​corresponding to p1, p4, and p6 are added again to obtain φ; the combined value corresponding to p4 is divided by γ to obtain the weight corresponding to p4;

[0162] The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain φ; the combined value corresponding to p6 is divided by γ to obtain the weight corresponding to p6;

[0163] Step 4245: Based on the weight corresponding to p1, the weight corresponding to p4, and the weight corresponding to p6, a weight set A4 of environmental impact assessment indicators is obtained;

[0164] Step 425: Determine the weight set of energy-saving impact evaluation indicators in the indicator layer by using an approximate calculation method; the specific process is as follows:

[0165] Step 4251: Energy conservation impact evaluation indicators include heavy truck carbon emissions p4 and environmental protection investment proportion p6;

[0166] Score any two indicators in the energy-saving impact evaluation index to obtain 4 sets of scores;

[0167] The four groups of scores obtained are normalized to obtain four groups of scores after normalization. The four groups of scores are p 44 、p 46 、p 64 、p 66 ;

[0168] Step 4252: Perform normalization on the four sets of scores obtained to obtain the normalized p 44 、p 46 、p 64 、p 66 ; The specific process is:

[0169] Will The value of p after normalization is 44 ;Will The value of p after normalization is 64 ;

[0170] Will The value of p after normalization is 46 ;Will The value of p after normalization is 66 ;

[0171] Step 4253: The normalized p 44 、p 46 Add and obtain the combined value corresponding to p4;

[0172] After normalization, p 64 、p 66 Add and obtain the combined value corresponding to p6;

[0173] Step 4254: sum the combined value corresponding to p4 and the combined value corresponding to p6 again to obtain ε; divide the combined value corresponding to p4 by ε to obtain the weight corresponding to p4;

[0174] The combined value corresponding to p4 and the combined value corresponding to p6 are added again to obtain ε; the combined value corresponding to p6 is divided by ε to obtain the weight corresponding to p6;

[0175] Step 4255: Based on the weight corresponding to p4 and the weight corresponding to p6, obtain the weight set A5 of energy-saving impact evaluation indicators.

[0176] Table 1. Primary evaluation index pairwise comparison importance scoring table

[0177] Project impact evaluation <![CDATA[u1]]> <![CDATA[u2]]> <![CDATA[u3]]> <![CDATA[u4]]> <![CDATA[u5]]> <![CDATA[u1]]> <![CDATA[u2]]> <![CDATA[u3]]> <![CDATA[u4]]> <![CDATA[u5]]>

[0178] Table 2. Primary evaluation index pairwise comparison importance scoring table

[0179] <![CDATA[u i ]]> <![CDATA[p1]]> <![CDATA[p2]]> … <![CDATA[p m ]]> <![CDATA[p1]]> <![CDATA[p2]]> … <![CDATA[p m ]]>

[0180] Table 3 Index relative importance score table

[0181] Scale meaning 1 Indicates that two factors are equally important. 3 Indicates that one factor is slightly more important than the other factor. 5 Indicates that one factor is significantly more important than the other factor when compared with the other factor. 7 Indicates that one factor is more important than the other. 9 Indicates that one factor is extremely more important than the other. 2，4，6，8，10 The median of the two adjacent judgments above reciprocal If the scale of A compared to B is 3, then B is 1 / 3 compared to A.

[0182] The other steps and parameters are the same as those in Specific Implementations 1 to 6-1.

[0183] Specific implementation eight: This implementation differs from any one of specific implementations one to seven in that: in step 5, a single-criteria fuzzy comprehensive evaluation set is calculated according to the indicator weights of step 4 and the evaluation matrix of step 3; the specific process is:

[0184] Combining the index weight set and the evaluation matrix, the fuzzy comprehensive evaluation set of the five criteria of traffic impact, social impact, economic impact, environmental impact and energy-saving impact is calculated respectively;

[0185] The fuzzy comprehensive evaluation set B1 of traffic impact is: B1 = A1·M 交通影响

[0186] Where: · represents dot product;

[0187] The fuzzy comprehensive evaluation set B2 of social impact is: B2 = A2·M 社会影响

[0188] The fuzzy comprehensive evaluation set B3 of economic impact is: B3 = A3·M 经济影响

[0189] The fuzzy comprehensive evaluation set B4 of environmental impact is: B4 = A4·M 环境影响

[0190] The fuzzy comprehensive evaluation set B5 of energy saving effect is: B5 = A5·M 节能影响 .

[0191] The other steps and parameters are the same as those in Specific Implementation 1 to 7-1.

[0192] Specific implementation method 9: This implementation method is different from any one of the specific implementation methods 1 to 8 in that: in step 6, the highway impact result analysis and grade determination are determined according to the criterion weights of step 4 and the single criterion fuzzy comprehensive evaluation set of step 5; the specific process is:

[0193] Step 61: Calculate the multi-criteria fuzzy comprehensive evaluation set according to the fuzzy comprehensive evaluation set of each criterion and the weight set A of the criterion layer; the expression is:

[0194]

[0195] Step 62: Set the evaluation level set V = {excellent, good, medium, poor, bad};

[0196] The evaluation level set is a set of various possible results that the evaluator may make on the evaluation object, usually represented by V;

[0197] Step 63: Based on the multi-criteria fuzzy comprehensive evaluation set B, determine the highway impact level using the maximum membership method.

[0198] The other steps and parameters are the same as those in Specific Embodiments 1 to 8.

[0199] Specific implementation method 10: This implementation method is different from the specific implementation methods 1 to 9 in that: in step 63, based on the multi-criteria fuzzy comprehensive evaluation set B, the highway impact level is determined by the maximum membership method; the specific process is:

[0200] Select the element Y with the largest absolute value in the multi-criteria fuzzy comprehensive evaluation set B:

[0201] If Y is the first element in the set, the highway impact level is excellent;

[0202] If Y is the second element in the set, the highway impact level is good;

[0203] If Y is the third element in the set, the highway impact level is medium;

[0204] If Y is the fourth element in the set, the highway impact level is poor;

[0205] If Y is the fifth element in the set, the highway impact level is poor.

[0206] The other steps and parameters are the same as those in Specific Embodiments 1 to 9.

[0207] The following examples are used to verify the beneficial effects of the present invention:

[0208] Embodiment 1:

[0209] like Figure 1 FIG. 1 is a flowchart of a highway project impact evaluation method based on an improved triangular fuzzy comprehensive evaluation method according to the present invention, comprising the following steps:

[0210] Step 1: Systematically analyze the influencing factors of highway construction project evaluation, divide the criterion layer set and indicator layer set, and construct a hierarchical framework for project impact evaluation;

[0211] The project criteria set is U = {u1, u2, ...u n}, where u1 is traffic impact, u2 is social impact, u3 is economic impact, u4 is environmental impact, and u5 is energy-saving impact. Six data items, including traffic accident rate, toll, debt repayment ratio, heavy truck carbon emission, daily average traffic volume, and environmental protection investment ratio, are selected as the indicator set P = {p1, p2, ... p6} of the project evaluation system.

[0212] Step 2: Obtain the historical data of each indicator in the indicator layer, draw the cumulative probability curve, formulate different evaluation level cutoff values ​​according to the information of different points on the curve, and then construct the triangular fuzzy number membership function of the indicator, and calculate the corresponding membership degree to obtain the evaluation set of the indicator;

[0213] The definition of each indicator, historical data source, cumulative probability curve drawing, triangular fuzzy number membership function and membership degree calculation are as follows:

[0214] (1) Accident rate

[0215] The accident rate refers to the frequency or number of accidents that occur in a system or unit within a certain period of time. It is usually expressed as "number of accidents / time unit" and the calculation formula is:

[0216]

[0217] Where: N i is the predicted accident rate on accident prediction unit i, that is, the number of accidents per unit mileage per year, in units of times / 100 million vehicle kilometers; N Bi is the basic accident rate predicted on the accident prediction unit i, that is, the accident rate under ideal geometric conditions and only affected by highway traffic volume, with the unit of times / 100 million vehicle kilometers; j It is the correction coefficient of the j-th linear indicator to the basic accident rate.

[0218] The historical data of accident rate in the project adopts the actual accident rate data of 100 million vehicle kilometers on 9 representative expressways, including Shenda Expressway, Shanghai-Nanjing Expressway, Beijing-Zhuhai Expressway, Chengyu Expressway, Shenyang-Shantou Expressway, Shenyang-Dalian Expressway, Shenyang-Dandong Expressway, Changshen Expressway, Pingkang Expressway, etc. The cumulative probability distribution curve is drawn as shown in the figure. Figure 5 , select the values ​​corresponding to the cumulative probabilities of 15%, 30%, 50%, 65%, and 85% as 18.58, 22.54, 31, 44.17, and 57.80 billion vehicle kilometers, and according to the membership function formula, obtain the membership function of a set of triangular fuzzy numbers of the accident rate, see Figure 6 .

[0219] Based on the accident prediction model developed by Harbin Institute of Technology, the predicted accident rate of 33.82 times / 100 million vehicle-kilometers in 2050 is used as the evaluation index value. Combined with the membership function curve, the single index evaluation set of the accident rate index is calculated.

[0220] Since 33.82 is between v3 and v4, the membership calculation needs to call the membership function of the indicator level C and D:

[0221] L 13 =(44.17-33.82) / (44.17-31)=0.79 (6)

[0222] L 14 =(33.82-31) / (44.17-31)=0.21 (7)

[0223] Then, L1=[0 0 0.79 0.21 0].

[0224] (2) Tolls

[0225] Tolls are fees that vehicles must pay to travel on highways. This fee is set mainly to recover the cost of building highways and the funds needed to maintain their daily operations.

[0226] The historical data in this project adopts the toll data of national expressways in the "2013-2021 National Toll Highway Statistical Bulletin". In order to ensure that the project data is comparable with the current number of expressway construction projects at the same level, the toll value per 10,000 kilometers is calculated. The calculation formula is as follows:

[0227]

[0228] Plotting the cumulative probability distribution curve Figure 7, select the values ​​corresponding to the cumulative probabilities of 15%, 30%, 50%, 65%, and 85%, which are 308.50, 330.28, 358.42, 370.90, and 387.66 billion yuan / 10,000 kilometers. According to the membership function formula, a set of triangular fuzzy number membership functions of tolls are obtained. Figure 8 .

[0229] Using the toll predicted in the feasibility report, the predicted traffic volume is divided by the actual mileage of the completed Harbin-Zhaoqing Expressway as the evaluation index value, and the calculation results are as follows:

[0230]

[0231] Combined with the membership function curve, the single index evaluation set of the toll index is calculated:

[0232] Since 250.56<v1, the membership calculation needs to call the membership function with index level A:

[0233] L 21 =1 (10)

[0234] Then, L2=[1 0 0 0 0].

[0235] (3) Proportion of debt repayment

[0236] The debt repayment ratio refers to the ratio of the amount of debt that should be repaid by a highway construction project to the total expenditure on highway construction within a certain period of time.

[0237] The historical data for this project uses the highway debt data and total highway expenditure data in the "2013-2021 National Toll Highway Statistical Bulletin". The calculation formula for the debt repayment ratio is as follows:

[0238]

[0239] Plotting the cumulative probability distribution curve Fig. 9 , select the values ​​0.36, 0.38, 0.41, 0.45, and 0.65 corresponding to the cumulative probabilities of 15%, 30%, 50%, 65%, and 85%, and according to the membership function formula, obtain the membership function of a set of triangular fuzzy numbers of the proportion of debt repayment, see Fig.10 .

[0240] According to the debt repayment of 4.852 billion yuan and the total project investment of 7.581 billion yuan in the project final report, the debt repayment ratio of the Harbin-Zhaoqing Expressway construction project is calculated to be 0.64. Combined with the membership function curve, the single indicator evaluation set of the debt repayment ratio indicator is calculated.

[0241] Since 0.64 is between v4 and v5, the membership calculation needs to call the membership function of the indicator level D and E:

[0242] L 34 =(0.65-0.64) / (0.65-0.45)=0.03 (12)

[0243] L 35 =(0.64-0.45) / (0.65-0.45)=0.97 (13)

[0244] Then, L3 = [0 0 0 0.03 0.97].

[0245] (4) Carbon emissions from heavy trucks

[0246] Heavy truck carbon emissions refer to the carbon dioxide emissions released by heavy trucks, commonly known as heavy trucks, including trucks, semi-trailers, dump trucks, semi-trailer tractors, special-purpose vehicles, etc. over a certain period of time.

[0247] The historical data of this project adopts the total carbon emissions of heavy trucks from 2018 to 2023. In order to ensure that the project data is comparable with the current number of highway construction projects at the same level, the carbon emissions of heavy trucks per 10,000 kilometers are calculated. The calculation formula is as follows:

[0248]

[0249] Plotting the cumulative probability distribution curve Fig.11 , the values ​​corresponding to the cumulative probabilities of 15%, 30%, 50%, 65%, and 85% are selected as 974, 1155, 1213, 1252, and 1431 tons / km. According to the membership function formula, a set of triangular fuzzy number membership functions of heavy truck carbon emissions are obtained, see Fig.12 .

[0250] Four sections of Harbin-Zhaoqing Expressway were selected for traffic flow composition analysis, and it was found that the average proportion of heavy trucks was 10.7%. Combined with the traffic volume data of 29058pcu / d predicted by the prediction model, the average daily number of heavy trucks was calculated to be about 3109 vehicles. The calculation formula is as follows:

[0251] 29058×10.7%=3109 vehicles (15)

[0252] Since the fuel consumption of heavy trucks is 33kg per 100km, the carbon emission factor is 2.6765kg / L. According to the carbon emission calculation formula, it is calculated that the carbon emissions of heavy trucks on Harbin-Zhaoqing Expressway are approximately 329 tons / km.

[0253] Heavy truck carbon emissions = fuel consumption per 100 kilometers × mileage × carbon emission factor × number of heavy trucks

[0254]

[0255] Since 329<v1, the membership calculation needs to call the membership function with index level A:

[0256] L 21 =1 (17)

[0257] Then, L4=[1 0 0 0 0].

[0258] (5) Average daily traffic volume

[0259] The average daily traffic volume is an indicator specifically used to describe the traffic congestion on highways. It refers to the sum of all vehicles passing through a certain section of road or a toll station within a specific period of time (such as a day). This value is averaged to obtain the daily average value.

[0260] The historical data used in this project is the average daily traffic volume of expressways in the "2013-2021 National Toll Highway Statistical Bulletin". The cumulative probability distribution curve is shown in Fig.13 , we select the values ​​corresponding to the cumulative probabilities of 15%, 30%, 50%, 65%, and 85% as 2.22, 2.25, 2.63, 2.72, and 2.89 thousand pcu / d, and according to the membership function formula, we get the membership function of a set of triangular fuzzy numbers of the daily average traffic volume, see Fig.14 .

[0261] Based on the traffic volume prediction model mentioned above in this report, the predicted average daily traffic volume of 29,058 pcu / d in 2050 is used as the evaluation index value, and combined with the membership function curve, the single index evaluation set of the average daily traffic volume is calculated.

[0262] Since 2.9058>v5, the membership calculation needs to call the membership function of the index level E:

[0263] L 55 =1 (18)

[0264] Then, L5=[0 0 0 01].

[0265] (6) Proportion of environmental protection investment

[0266] The proportion of environmental protection investment refers to the proportion of environmental costs in the total investment of highway construction projects. The historical data of this project adopts the environmental protection investment amount and total highway investment in the "Statistical Bulletin on the Development of the Transportation Industry 2013-2021", and the calculation formula is as follows:

[0267]

[0268] Plotting the cumulative probability distribution curve Fig.15, selecting the values ​​0.019, 0.022, 0.023, 0.026, and 0.036 corresponding to the cumulative probabilities of 15%, 30%, 50%, 65%, and 85%, and according to the membership function formula, we get the membership function of a set of triangular fuzzy numbers of the proportion of environmental protection investment, see Fig.16 .

[0269] Using the social cost analysis model developed by Harbin Institute of Technology, it is predicted that the environmental cost in 2050 will be 155.763 million yuan. The ratio of the estimated environmental cost value to the total highway investment is used as the evaluation index value. The calculation formula is as follows:

[0270]

[0271] Since 0.021 is between v1 and v2, the membership calculation needs to call the membership function of the indicator level A and B:

[0272] L 61 =(0.022-0.021) / (0.022-0.019)=0.41 (21)

[0273] L 62 =(0.021-0.019) / (0.022-0.019)=0.59 (22)

[0274] Then, L6=[0.41 0.59 0 0 0].

[0275] Step 3: Determine whether the indicator has an impact on the criterion, and then determine the coupling relationship between the elements of each criterion layer and the indicator layer, and combine the indicator evaluation set calculated in step 2 to form the evaluation matrix of each criterion;

[0276] First, the impact indicators of the analysis criteria are analyzed. The evaluation index set of traffic impact is C 交通影响 = {accident rate, toll, daily average traffic volume, heavy truck carbon emissions}, the social impact evaluation index set is C 社会影响 = {accident rate, heavy truck carbon emissions, environmental protection investment ratio}, the evaluation index set of economic impact is C 经济影响 = {toll, debt repayment ratio, daily average traffic volume, environmental protection investment ratio}, the environmental impact evaluation index set is C 环境影响 = {accident rate, heavy truck carbon emissions, environmental protection investment ratio}, the evaluation index set of energy saving impact is C 节能影响 ={heavy truck carbon emissions, environmental protection investment ratio}, the link between the criteria and indicators can be found in Figure 4 .

[0277] Secondly, the evaluation index set of each criterion and the evaluation set of the indicators are combined to construct a single criterion evaluation matrix.

[0278] The evaluation matrix of traffic impact is:

[0279] The evaluation matrix of social impact is:

[0280] The evaluation matrix of economic impact is:

[0281] The environmental impact assessment matrix is:

[0282] The evaluation matrix of energy saving impact is:

[0283] Step 4: Based on the intrinsic relationship between the criteria and indicators obtained in step 3, the questionnaire was designed using the pairwise comparison expert scoring method. After collecting feedback, the weights of the criteria and indicators were determined using the approximate calculation method;

[0284] Firstly, an impact assessment questionnaire for the construction of Harbin-Zhaoqing Expressway was designed and sent to eight types of units involved in the project construction, including the owner unit, main survey and design unit, building survey and design unit, supervision unit, construction unit, central laboratory, post-evaluation unit and environmental impact assessment unit. A total of 27 related companies were included after the questionnaire was distributed. A total of 349 valid feedback information was collected.

[0285] Secondly, based on the questionnaire, we obtained the pairwise comparison judgment matrix of the criterion set and the evaluation index set, and calculated the weight set of each criterion at the criterion level and each item at the index level that affects the evaluation index through the approximate calculation method. The calculation process is shown in Tables 4 to 9.

[0286] Table 4 Approximate calculation weight set of criterion layer

[0287]

[0288] The criterion layer weight set is shown in Table 5, and the data summed by row is normalized, and the calculation formula is as follows:

[0289]

[0290] The weight set obtained is A = [0.42 0.16 0.07 0.25 0.10].

[0291] Table 5 Approximate calculation weight set of traffic impact assessment indicators

[0292]

[0293] The indicator layer weight set of traffic impact is shown in Table 6. The data summed by row is normalized and the calculation formula is as follows:

[0294]

[0295] The calculated weight set is A1 = [0.09 0.43 0.32 0.16].

[0296] Table 6 Approximate calculation weights of social impact evaluation indicators

[0297]

[0298] The indicator layer weight set of social impact is shown in Table 7. The data summed by row is normalized and the calculation formula is as follows:

[0299]

[0300] The calculated weight set is A2 = [0.11 0.31 0.58].

[0301] Table 7 Approximate calculation weights of economic impact evaluation indicators

[0302]

[0303]

[0304] The indicator layer weight set of economic impact is shown in Table 8. The data summed by row is normalized and the calculation formula is as follows:

[0305]

[0306] The calculated weight set is A3 = [0.29 0.12 0.07 0.52].

[0307] Table 8 Approximate calculation weights of environmental impact assessment indicators

[0308]

[0309] The indicator layer weight set of environmental impact is shown in Table 9. The data summed by row is normalized and the calculation formula is as follows:

[0310]

[0311] The calculated weight set is A4 = [0.12 0.50 0.38].

[0312] Table 9 Approximate calculation weight set of energy-saving impact evaluation indicators The indicator layer weight set for energy saving impact is shown in Table 10. The data summed by row is normalized and the calculation formula is as follows:

[0313]

[0314] The calculated weight set is A5 = [0.25 0.75].

[0315] Step 5: The indicator weights calculated in step 4 are combined with the corresponding evaluation matrix in step 3 to calculate the single-criterion fuzzy comprehensive evaluation set; combined with the indicator weights and the evaluation matrix, the comprehensive evaluation sets of the five criteria of traffic impact, social impact, economic impact, environmental impact and energy-saving impact are calculated respectively.

[0316] The fuzzy comprehensive evaluation set B1 of traffic impact is:

[0317]

[0318] The fuzzy comprehensive evaluation set B2 of social impact is:

[0319]

[0320] The fuzzy comprehensive evaluation set B3 of economic impact is:

[0321]

[0322] The fuzzy comprehensive evaluation set B4 of environmental impact is:

[0323]

[0324] The fuzzy comprehensive evaluation set B5 of energy-saving impact is:

[0325]

[0326] Step 6: The criterion weights calculated in step 4 are combined with the corresponding single criterion fuzzy comprehensive evaluation set in step 5 to calculate the multi-criteria fuzzy comprehensive evaluation set, and the maximum membership method is used to determine the impact performance of the highway construction project.

[0327] Firstly, the multi-criteria fuzzy comprehensive evaluation set is calculated according to the fuzzy comprehensive evaluation set and weight of each criterion.

[0328]

[0329] Secondly, the impact level of the expressway is determined according to the maximum membership method.

[0330] Since the largest element in B is the first element, according to the maximum membership method, the impact level of Harbin-Zhaoqing Expressway is "excellent".

[0331] The present invention may also have many other embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding changes and modifications based on the present invention, but these corresponding changes and modifications should all fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for quantifying and grading highway impact based on an improved triangular fuzzy algorithm, characterized by: The specific process of the method is: Step 1: Determine the criterion layer set and the indicator layer set; Step 2: Obtain the historical data of each indicator in the indicator layer set, draw the cumulative probability curve, formulate different evaluation level cutoff values ​​according to the information of different points on the curve, and then construct the triangular fuzzy number membership function of each indicator; Get the value L of the indicator to be measured, based on the indicator p j The membership function diagram and membership degree calculation formula of the triangular fuzzy number are used to calculate the membership degree of the index value L to be measured; Based on the membership degree of the index value L to be measured, the index p is obtained j 's evaluation set; Step 3: Determine the coupling relationship between criteria and indicators, and construct a single criterion evaluation matrix based on the evaluation indicator set of each criterion and the evaluation set of indicators; Step 4: Design a questionnaire for pairwise comparison expert scoring method to determine the weights of the highway impact criterion layer and indicator layer; Step 5: Calculate the single criterion fuzzy comprehensive evaluation set according to the indicator weights of step 4 and the evaluation matrix of step 3; Step 6: According to the criterion weights in step 4 and the single criterion fuzzy comprehensive evaluation set in step 5, the highway impact results are analyzed and the level is determined.

2. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 1 is characterized by: In step 1, the criterion layer set and the indicator layer set are determined; the specific process is: Step 11, criterion layer set U = {u1, u2, u3, u4, u5}; Among them, u i represents the i-th criterion, i = 1, 2, 3, 4, 5; u1 is the traffic impact, u2 is the social impact, u3 is the economic impact, u4 is the environmental impact, and u5 is the energy-saving impact; Step 12, indicator layer set P = {p1, p2, p3, p4, p5, p6}; Among them, p j Indicates the influence on the i-th criterion u i The jth indicator is: p1 is the traffic accident rate, p2 is the toll, p3 is the proportion of debt repayment, p4 is the carbon emission of heavy trucks, p5 is the average daily traffic volume, and p6 is the proportion of environmental protection investment.

3. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 2 is characterized by: In the step 2, the historical data of each indicator in the indicator layer set is obtained, a cumulative probability curve is drawn, and different evaluation level cutoff values ​​are proposed according to different point information on the curve, thereby constructing a triangular fuzzy number membership function of each indicator; Get the value L of the indicator to be measured, based on the indicator p j The membership function diagram and membership degree calculation formula of the triangular fuzzy number are used to calculate the membership degree of the index value L to be measured; Based on the membership degree of the index value L to be measured, the index p is obtained j The evaluation set of The specific process is: Step 21: Get the jth index p in the index layer set P = {p1, p2, p3, p4, p5, p6} j Historical data of the indicator p j The cumulative probability of j The historical data is used as the horizontal axis, and the indicator p j The cumulative probability is used as the ordinate to form the index p j The corresponding cumulative probability curve; Index p j The traffic accident rate p1, toll p2, debt repayment ratio p3, heavy truck carbon emissions p4, daily average traffic volume p5 or environmental protection investment ratio p6; Select index p j The corresponding cumulative probability curve has the index value L corresponding to the cumulative probability of 15%, 30%, 50%, 65% and 85%. j1 , L j2 , L j3 , L j4 , L j5 As the dividing value of evaluation grades A, B, C, D, and E; A means excellent, B means good, C means average, D means poor, and E means bad; Step 22: Based on the indicator value L j1 , L j2 , L j3 , L j4 , L j5 and the cutoff values ​​of evaluation levels A, B, C, D, and E, construct the index p j The triangular fuzzy number membership function diagram of ; Step 23: Get the value L of the indicator to be measured, based on the indicator p j The triangular fuzzy number membership function diagram and membership calculation formula are used to calculate the membership of the index value L to be measured; Based on the membership degree of the index value L to be measured, the index p is obtained j Evaluation set.

4. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 3 is characterized by: In step 23, the value L of the index to be measured is obtained, based on the index p j The triangular fuzzy number membership function diagram and membership calculation formula are used to calculate the membership of the index value L to be measured; Based on the membership degree of the index value L to be measured, the index p is obtained j The specific process is as follows: Get the value L of the indicator to be measured, based on the value L of the indicator to be measured in the indicator p j Determine the level to which the value L of the index to be measured belongs on the membership function diagram of the triangular fuzzy number; The evaluation levels are A, B, C, D, and E; Based on the level to which the value L of the indicator to be measured belongs, the value L of the indicator to be measured is substituted into the membership calculation formula of the corresponding level to calculate the membership of the value L of the indicator to be measured; Based on the membership degree of the index value L to be measured, the single index p is obtained. j The evaluation set L j ={L j1 ,L j2 ,L j3 ,L j4 ,L j5 }.

5. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 4 is characterized by: The membership degree of the index value L to be measured is calculated by bringing the value L of the index to be measured into the membership degree calculation formula of the corresponding level; the expression is: The membership function of the indicator level A is: Among them, L1 and L2 are the first and second indicators on the membership graph respectively; f A (L) is the degree of membership of the value L of the index to be measured; The membership function of the indicator level B is: Among them, L1, L2, and L3 are the first, second, and third indicators on the membership graph respectively; f B (L) is the degree of membership of the value L of the index to be measured; The membership function of the indicator level C is: Among them, L2, L3, and L4 are the second, third, and fourth indicators on the membership graph respectively; f C (L) is the degree of membership of the value L of the index to be measured; The membership function of the indicator level D is: Among them, L3, L4, and L5 are the third, fourth, and fifth indicators on the membership graph respectively; f D (L) is the degree of membership of the value L of the index to be measured; The membership function of the indicator level E is: Among them, L4 and L5 are the 4th and 5th indicators on the membership graph respectively; f E (L) is the degree of membership of the indicator value L to be measured.

6. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 5 is characterized by: In step 3, the coupling relationship between the criteria and the indicators is determined, based on the evaluation indicator set of each criterion and the evaluation set L of the indicators. j ={L j1 ,L j2 ,L j3 ,L j4 ,L j5 }, construct a single criterion evaluation matrix; the specific process is: Step 31: Analyze the evaluation index set of each criterion; the specific process is: The evaluation index set of traffic impact is C 交通影响 ={accident rate, toll, average daily traffic volume, carbon emissions of heavy trucks}; The evaluation index set of social impact is C 社会影响 ={accident rate, carbon emissions of heavy trucks, proportion of environmental protection investment}; The evaluation index set of economic impact is C 经济影响 ={tolls, debt repayment ratio, daily average traffic volume, environmental protection investment ratio}; The evaluation index set of environmental impact is C 环境影响 ={accident rate, carbon emissions of heavy trucks, proportion of environmental protection investment}; The evaluation index set of energy saving impact is C 节能影响 ={Carbon emissions from heavy trucks, percentage of environmental protection investment}; Step 32: Combine the evaluation index set of each criterion with the evaluation set L of the index j ={L j1 ,L j2 ,L j3 ,L j4 ,L j5 }, construct a single criterion evaluation matrix; The specific process is: The evaluation matrix of traffic impact is: Among them, L 11 L 12 L 13 L 14 L 15 is the evaluation set of accident rate, L 21 L 22 L 23 L 24 L 25 is the evaluation set of tolls, L 51 L 52 L 53 L 54 L 55 is the evaluation set of daily average traffic volume, L 41 L 42 L 43 L 44 L 45 is the evaluation set of carbon emissions of heavy trucks; The evaluation matrix of social impact is: Among them, L 11 L 12 L 13 L 14 L 15 is the evaluation set of accident rate, L 41 L 42 L 43 L 44 L 45 is the evaluation set of heavy truck carbon emissions, L 61 L 62 L 63 L 64 L 65 is the evaluation set of environmental protection investment proportion; The evaluation matrix of economic impact is: Among them, L 21 L 22 L 23 L 24 L 25 is the evaluation set of tolls, L 31 L 32 L 33 L 34 L 35 is the evaluation set of debt repayment ratio, L 51 L 52 L 53 L 54 L 55 is the evaluation set of daily average traffic volume, L 61 L 62 L 63 L 64 L 65 is the evaluation set of environmental protection investment proportion; The environmental impact assessment matrix is: Among them, L 11 L 12 L 13 L 14 L 15 is the evaluation set of accident rate, L 41 L 42 L 43 L 44 L 45 is the evaluation set of heavy truck carbon emissions, L 61 L 62 L 63 L 64 L 65 is the evaluation set of environmental protection investment proportion; The evaluation matrix of energy saving impact is: Among them, L 41 L 42 L 43 L 44 L 45 is the evaluation set L of heavy truck carbon emissions 61 L 62 L 63 L 64 L 65 is the evaluation set of the proportion of environmental protection investment.

7. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 6 is characterized by: In step 4, a pairwise comparison expert scoring method questionnaire is designed to determine the weights of the highway impact criterion layer and the indicator layer; The specific process is: Step 41: Design the questionnaire using the pairwise comparison expert scoring method, collect feedback, and use the approximate calculation method to determine the weights of the criterion layer; The specific process is: Step 411: The set U = {u1,u2,u3,u4,u5} for any two partial strokes, get 25 pairs of fractions, 25 pairs of fractions separately u 11 , u 12 , u 13 , u 14 , u 15 , u 21 , u 22 , u 23 , u 24 , u 25 , u 31 , u 32 , u 33 , u 34 , u 35 , u 41 , u 42 , u 43 , u 44 , u 45 , u 51 , u 52 , u 53 , u 54 , u 55 ; Among them, u i represents the i-th criterion, u1 is the traffic impact, u2 is the social impact, u3 is the economic impact, u4 is the environmental impact, and u5 is the energy-saving impact; Step 412: 25 pairs of fractions to get the right calculation, get the correct calculation after the u 11 , u 12 , u 13 , u 14 , u 15 , u 21 , u 22 , u 23 , u 24 , u 25 , u 31 , u 32 , u 33 , u 34 , u 35 , u 41 , u 42 , u 43 , u 44 , u 45 , u 51 , u 52 , u 53 , u 54 , u 55 ; The specific process is: Will The value of u after normalization is 11 ; Will The value of u after normalization is 21 ; Will The value of u after normalization is 31 ; Will The value of u after normalization is 41 ; Will The value of u after normalization is 51 ; Will The value of u after normalization is 12 ; Will The value of u after normalization is 22 ; Will The value of u after normalization is 32 ; Will The value of u after normalization is 42 ; Will The value of u after normalization is 52 ; Will The value of u after normalization is 13 ; Will The value of u after normalization is 23 ; Will The value of u after normalization is 33 ; Will The value of u after normalization is 43 ; Will The value of u after normalization is 53 ; Will The value of u after normalization is 14 ; Will The value of u after normalization is 24 ; Will The value of u after normalization is 34 ; Will The value of u after normalization is 44 ; Will The value of u after normalization is 54 ; Will The value of u after normalization is 15 ; Will The value of u after normalization is 25 ; Will The value of u after normalization is 35 ; Will The value of u after normalization is 45 ; Will The value of u after normalization is 55 ; Step 413: After normalization, u 11 、u 12 、u 13 、u 14 、u 15 Add and obtain the combined value corresponding to u1; After normalization, u 21 、u 22 、u 23 、u 24 、u 25 Add and obtain the combined value corresponding to u2; After normalization, u 31 、u 32 、u 33 、u 34 、u 35 Add and obtain the combined value corresponding to u3; After normalization, u 41 、u 42 、u 43 、u 44 、u 45 Add and obtain the combined value corresponding to u4; After normalization, u 51 、u 52 、u 53 、u 54 、u 55 Add and obtain the combined value corresponding to u5; Step 414: The combined values ​​corresponding to u1, u2, u3, u4 and u5 are added again to obtain α; the combined value corresponding to u1 is divided by α to obtain the weight corresponding to u1; The combined values ​​corresponding to u1, u2, u3, u4 and u5 are added again to obtain α; the combined value corresponding to u2 is divided by α to obtain the weight corresponding to u2; The combined values ​​corresponding to u1, u2, u3, u4 and u5 are added again to obtain α; the combined value corresponding to u3 is divided by α to obtain the weight corresponding to u3; The combined values ​​corresponding to u1, u2, u3, u4, and u5 are added again to obtain α; the combined value corresponding to u4 is divided by α to obtain the weight corresponding to u4; The combined values ​​corresponding to u1, u2, u3, u4 and u5 are added again to obtain α; the combined value corresponding to u5 is divided by α to obtain the weight corresponding to u5; Step 415: based on the weight corresponding to u1, the weight corresponding to u2, the weight corresponding to u3, the weight corresponding to u4, and the weight corresponding to u5, a weight set A of the criterion layer is obtained; Step 42: Design a questionnaire using the pairwise comparison expert scoring method. After collecting feedback, use the approximate calculation method to determine the weight set of the indicator layer. The specific process is as follows: Step 421: Determine the weight set of the traffic impact assessment index in the index layer by using the approximate calculation method; the specific process is as follows: Step 4211: Traffic impact assessment indicators include traffic accident rate p1, toll p2, heavy truck carbon emissions p4, and average daily traffic volume p5; Score any two indicators in the traffic impact assessment index to obtain 16 sets of scores; Step 4212: normalize the obtained 16 groups of scores to obtain 16 groups of scores after normalization. The 16 groups of scores are p 11 、p 12 、p 14 、p 15 、p 21 、p 22 、p 24 、p 25 、p 41 、p 42 、p 44 、p 45 、p 51 、p 52 、p 54 、p 55 ; Step 4213: Normalize the 16 sets of scores obtained to obtain the normalized p 11 、p 12 、p 14 、p 15 、p 21 、p 22 、p 24 、p 25 、p 41 、p 42 、p 44 、p 45 、p 51 、p 52 、p 54 、p 55 ; The specific process is: Will The value of p after normalization is 11 ; Will The value of p after normalization is 21 ; Will The value of p after normalization is 41 ; Will The value of p after normalization is 51 ; Will The value of p after normalization is 12 ; Will The value of p after normalization is 22 ; Will The value of p after normalization is 42 ; Will The value of p after normalization is 52 ; Will The value of p after normalization is 14 ; Will The value of p after normalization is 24 ; Will The value of p after normalization is 44 ; Will The value of p after normalization is 54 ; Will The value of p after normalization is 15 ; Will The value of p after normalization is 25 ; Will The value of p after normalization is 45 ; Will The value of p after normalization is 55 ; Step 4214: After normalization, p 11 、p 12 、p 14 、p 15 Add and obtain the combined value corresponding to p1; After normalization, p 21 、p 22 、p 24 、p 25 Add and obtain the combined value corresponding to p2; After normalization, p 41 、p 42 、p 44 、p 45 Add and obtain the combined value corresponding to p4; After normalization, p 51 、p 52 、p 54 、p 55 Add and obtain the combined value corresponding to p5; Step 4215: The combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 are added again to obtain β; the combined value corresponding to p1 is divided by β to obtain the weight corresponding to p1; The combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 are added again to obtain β; the combined value corresponding to p2 is divided by β to obtain the weight corresponding to p2; The combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 are added again to obtain β; the combined value corresponding to p4 is divided by β to obtain the weight corresponding to p4; The combined value corresponding to p1, the combined value corresponding to p2, the combined value corresponding to p4, and the combined value corresponding to p5 are added again to obtain β; the combined value corresponding to p5 is divided by β to obtain the weight corresponding to p5; Step 4216: Based on the weight corresponding to p1, the weight corresponding to p2, the weight corresponding to p4, and the weight corresponding to p5, a weight set A1 of traffic impact assessment indicators is obtained; Step 422: Determine the weight set of social impact evaluation indicators in the indicator layer using an approximate calculation method; the specific process is: Step 4221: social impact assessment indicators include traffic accident rate p1, heavy truck carbon emissions p4, and environmental protection investment proportion p6; Score any two indicators in the social impact assessment indicators to obtain 9 sets of scores; The 9 groups of scores obtained are normalized to obtain 9 groups of scores after normalization. The 9 groups of scores are p 11 、p 14 、p 16 、p 41 、p 44 、p 46 、p 61 、p 64 、p 66 ; Step 4222: Normalize the obtained 9 groups of scores to obtain the normalized p 11 、p 14 、p 16 、p 41 、p 44 、p 46 、p 61 、p 64 、p 66 ; The specific process is: Will The value of p after normalization is 11 ; Will The value of p after normalization is 41 ; Will The value of p after normalization is 61 ; Will The value of p after normalization is 14 ; Will The value of p after normalization is 44 ; Will The value of p after normalization is 64 ; Will The value of p after normalization is 16 ; Will The value of p after normalization is 44 ; Will The value of p after normalization is 66 ; Step 4223: After normalization, p 11 、p 14 、p 16 Add and obtain the combined value corresponding to p1; After normalization, p 41 、p 44 、p 46 Add and obtain the combined value corresponding to p4; After normalization, p 61 、p 64 、p 66 Add and obtain the combined value corresponding to p6; Step 4224: The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain δ; the combined value corresponding to p1 is divided by δ to obtain the weight corresponding to p1; The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain δ; the combined value corresponding to p4 is divided by δ to obtain the weight corresponding to p4; The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain δ; the combined value corresponding to p6 is divided by δ to obtain the weight corresponding to p6; Step 4225: Based on the weight corresponding to p1, the weight corresponding to p4, and the weight corresponding to p6, the weight set A2 of the social impact evaluation index is obtained; Step 423: Determine the weight set of economic impact evaluation indicators in the indicator layer using an approximate calculation method; the specific process is: Step 4231: economic impact evaluation indicators include tolls p2, debt repayment ratio p3, daily average traffic volume p5, and environmental protection investment ratio p6; Score any two indicators in the economic impact evaluation index to obtain 16 sets of scores; Step 4232: Perform normalization on the 16 sets of scores obtained to obtain 16 sets of scores after normalization. The 16 sets of scores are p 22 、p 23 、p 25 、p 26 、p 32 、p 33 、p 35 、p 36 、p 52 、p 53 、p 55 、p 56 、p 62 、p 63 、p 65 、p 66 ; The specific process is: Will The value of p after normalization is 22 ; Will The value of p after normalization is 32 ; Will The value of p after normalization is 52 ; Will The value of p after normalization is 62 ; Will The value of p after normalization is 23 ; Will The value of p after normalization is 33 ; Will The value of p after normalization is 53 ; Will The value of p after normalization is 63 ; Will The value of p after normalization is 25 ; Will The value of p after normalization is 35 ; Will The value of p after normalization is 55 ; Will The value of p after normalization is 65 ; Will The value of p after normalization is 26 ; Will The value of p after normalization is 36 ; Will The value of p after normalization is 56 ; Will The value of p after normalization is 66 ; Step 4233: After normalization, p 22 、p 23 、p 25 、p 26 Add and obtain the combined value corresponding to p2; After normalization, p 32 、p 33 、p 35 、p 36 Add and obtain the combined value corresponding to p3; After normalization, p 52 、p 53 、p 55 、p 56 Add and obtain the combined value corresponding to p5; After normalization, p 62 、p 63 、p 65 、p 66 Add and obtain the combined value corresponding to p6; Step 4234: The combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 are added again to obtain γ; the combined value corresponding to p2 is divided by γ to obtain the weight corresponding to p2; The combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 are added again to obtain γ; the combined value corresponding to p3 is divided by γ to obtain the weight corresponding to p3; The combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 are added again to obtain γ; the combined value corresponding to p5 is divided by γ to obtain the weight corresponding to p5; The combined value corresponding to p2, the combined value corresponding to p3, the combined value corresponding to p5, and the combined value corresponding to p6 are added again to obtain γ; the combined value corresponding to p6 is divided by γ to obtain the weight corresponding to p6; Step 4235: Based on the weight corresponding to p2, the weight corresponding to p3, the weight corresponding to p5, and the weight corresponding to p6, the weight set A3 of the economic impact evaluation index is obtained; Step 424: Determine the weight set of the environmental impact assessment indicators in the indicator layer using an approximate calculation method; the specific process is: Step 4241: Economic impact evaluation indicators include traffic accident rate p1, heavy truck carbon emissions p4, and environmental protection investment proportion p6; Score any two indicators in the environmental impact assessment index to obtain 9 sets of scores; The 16 obtained scores are normalized to obtain 9 scores, which are p 11 、p 14 、p 16 、p 41 、p 44 、p 46 、p 61 、p 64 、p 66 ; Step 4242: Normalize the obtained 9 groups of scores to obtain the normalized p 11 、p 14 、p 16 、p 41 、p 44 、p 46 、p 61 、p 64 、p 66 ; The specific process is: Will The value of p after normalization is 11 ; Will The value of p after normalization is 41 ; Will The value of p after normalization is 61 ; Will The value of p after normalization is 14 ; Will The value of p after normalization is 44 ; Will The value of p after normalization is 64 ; Will The value of p after normalization is 16 ; Will The value of p after normalization is 46 ; Will The value of p after normalization is 66 ; Step 4243: After normalization, p 11 、p 14 、p 16 Add and obtain the combined value corresponding to p1; After normalization, p 41 、p 44 、p 46 Add and obtain the combined value corresponding to p4; After normalization, p 61 、p 64 、p 66 Add and obtain the combined value corresponding to p6; Step 4244: The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain φ; the combined value corresponding to p1 is divided by φ to obtain the weight corresponding to p1; The combined values ​​corresponding to p1, p4, and p6 are added again to obtain φ; the combined value corresponding to p4 is divided by γ to obtain the weight corresponding to p4; The combined value corresponding to p1, the combined value corresponding to p4, and the combined value corresponding to p6 are added again to obtain φ; the combined value corresponding to p6 is divided by γ to obtain the weight corresponding to p6; Step 4245: Based on the weight corresponding to p1, the weight corresponding to p4, and the weight corresponding to p6, the weight set A4 of the environmental impact assessment index is obtained; Step 425: Determine the weight set of energy-saving impact evaluation indicators in the indicator layer by using an approximate calculation method; the specific process is as follows: Step 4251: Energy conservation impact evaluation indicators include heavy truck carbon emissions p4 and environmental protection investment proportion p6; Score any two indicators in the energy-saving impact evaluation index to obtain 4 sets of scores; The four groups of scores obtained are normalized to obtain four groups of scores after normalization. The four groups of scores are p 44 、p 46 、p 64 、p 66 ; Step 4252: Normalize the obtained four groups of scores to obtain the normalized p 44 、p 46 、p 64 、p 66 ; The specific process is: Will The value of p after normalization is 44 ; Will The value of p after normalization is 64 ; Will The value of p after normalization is 46 ; Will The value of p after normalization is 66 ; Step 4253: After normalization, p 44 、p 46 Add and obtain the combined value corresponding to p4; After normalization, p 64 、p 66 Add and obtain the combined value corresponding to p6; Step 4254: sum the combined value corresponding to p4 and the combined value corresponding to p6 again to obtain ε; divide the combined value corresponding to p4 by ε to obtain the weight corresponding to p4; The combined value corresponding to p4 and the combined value corresponding to p6 are added again to obtain ε; the combined value corresponding to p6 is divided by ε to obtain the weight corresponding to p6; Step 4255: Based on the weight corresponding to p4 and the weight corresponding to p6, obtain the weight set A5 of energy-saving impact evaluation indicators.

8. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 7 is characterized by: In step 5, a single criterion fuzzy comprehensive evaluation set is calculated according to the indicator weights of step 4 and the evaluation matrix of step 3; the specific process is: Combining the index weight set and the evaluation matrix, the fuzzy comprehensive evaluation set of the five criteria of traffic impact, social impact, economic impact, environmental impact and energy-saving impact is calculated respectively; The fuzzy comprehensive evaluation set B1 of traffic impact is: B1=A1·M 交通影响 Where: · represents dot product; The fuzzy comprehensive evaluation set B2 of social impact is: <h2 style=";text-align:left;direction:ltr">B2=A2·M<h2 style=";text-align:left;direction:ltr"> 社会影响 The fuzzy comprehensive evaluation set B3 of economic impact is: <h2 style=";text-align:left;direction:ltr">B3=A3·M<h2 style=";text-align:left;direction:ltr"> 经济影响 The fuzzy comprehensive evaluation set B4 of environmental impact is: B4=A4·M 环境影响 The fuzzy comprehensive evaluation set B5 of energy-saving impact is: <h2 style=";text-align:left;direction:ltr">B5=A5·M<h2 style=";text-align:left;direction:ltr"> 节能影响 <h2 style=";text-align:left;direction:ltr"> 。 9. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 8 is characterized by: In step 6, the highway impact result analysis and grade determination are determined according to the criterion weights of step 4 and the single criterion fuzzy comprehensive evaluation set of step 5; the specific process is: Step 61: Calculate the multi-criteria fuzzy comprehensive evaluation set according to the fuzzy comprehensive evaluation set of each criterion and the weight set A of the criterion layer; the expression is: Step 62: Set the evaluation level set V = {excellent, good, medium, poor, bad}; Step 63: Based on the multi-criteria fuzzy comprehensive evaluation set B, determine the highway impact level using the maximum membership method.

10. The method for quantifying and grading highway impact based on improved triangular fuzzy algorithm according to claim 9, characterized in that: In step 63, based on the multi-criteria fuzzy comprehensive evaluation set B, the highway impact level is determined according to the maximum membership method; The specific process is: Select the element Y with the largest absolute value in the multi-criteria fuzzy comprehensive evaluation set B: If Y is the first element in the set, the highway impact level is excellent; If Y is the second element in the set, the highway impact level is good; If Y is the third element in the set, the highway impact level is medium; If Y is the fourth element in the set, the highway impact level is poor; If Y is the fifth element in the set, the highway impact level is poor.

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