CN114742444A - Game combination empowerment-based urban low-carbon passenger traffic structure evaluation method - Google Patents
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Abstract
The invention discloses a game combination empowerment-based urban low-carbon passenger traffic structure evaluation method, which comprises the following steps of: constructing an urban low-carbon passenger traffic structure evaluation system; based on the urban low-carbon passenger traffic structure evaluation system, obtaining subjective weights of all evaluation indexes by adopting a DEMATEL-G1 method, and obtaining objective weights of all evaluation indexes by adopting a CRITIC-entropy method; carrying out linear combination on the subjective weight and the objective weight of each evaluation index by adopting a game theory combination weighting method to obtain the balance weight of each evaluation index; acquiring data corresponding to each evaluation index in the urban low-carbon passenger traffic structure evaluation system of the city to be tested, and acquiring a comprehensive evaluation value of the urban low-carbon passenger traffic structure to be tested based on the data and the balance weight of each evaluation index. The invention realizes effective evaluation of the current situation of urban low-carbon passenger traffic structure development.
Description
Technical Field
The invention belongs to the technical field of urban passenger traffic planning, and particularly relates to an urban low-carbon passenger traffic structure evaluation method based on game combination empowerment.
Background
In the face of global warming, a common challenge in the human society at present, China puts forward a double-carbon target. The transportation industry is the second largest carbon emission industry of China next to industry, wherein urban passenger transport is closely related to civil life, and the optimization of the urban passenger transport transportation structure is an effective way for promoting urban low-carbon sustainable development. Effective evaluation on the development level of the urban low-carbon passenger transport traffic structure is an important basis for optimizing the urban passenger transport structure. Therefore, in order to effectively identify the bottleneck of urban low-carbon passenger traffic structure development, promote high-energy-efficiency and low-carbon development and digital transformation of urban traffic, the establishment of the urban low-carbon passenger traffic structure evaluation methodology under the double-carbon background has important significance for guiding urban passenger traffic structure planning and urban passenger traffic management.
Regarding the construction method of the urban passenger traffic evaluation system, the existing technical scheme evaluates the development level of urban passenger traffic from the perspective of traffic system composition, but due to lack of scientific framework support, the problems of incomplete evaluation index setting and insufficient human activity interaction are existed. In the prior art, the research for establishing an evaluation system based on a scientific framework is relatively lacked, the problem that the framework logic is not completed by combining a research subject of an urban low-carbon passenger traffic structure exists, and the long-term and staged characteristics of the construction process of urban traffic engineering are not reflected.
In addition, the determination of the index weight is a key step for obtaining the evaluation result, and the accuracy of the evaluation result is directly influenced. As for the weighting method of the evaluation index, part of scholars adopt a single weighting method and cannot reflect subjective experience and objective data of experts at the same time. The analytic hierarchy process and the entropy method are relatively mature evaluation methods and are widely applied to current-stage research. However, because the influence factors of the urban passenger transport traffic structure are numerous and complicated, the conventional evaluation method has a space for further optimization on the weighting method or the weight combination method, and a game combination weighting-based urban low-carbon passenger transport traffic structure evaluation method is urgently needed.
Disclosure of Invention
The invention aims to provide a game combination empowerment-based urban low-carbon passenger traffic structure evaluation method, which can effectively evaluate the current situation of urban low-carbon passenger traffic structure development and provide decision bases in the aspects of traffic planning, infrastructure, operation management and the like for promoting high-energy-efficiency and low-carbon development and digital transformation of urban traffic.
In order to realize the aim, the invention provides a game combination empowerment-based urban low-carbon passenger traffic structure evaluation method, which comprises the following steps of:
constructing an urban low-carbon passenger traffic structure evaluation system;
on the basis of the urban low-carbon passenger traffic structure evaluation system, subjective weights of all evaluation indexes are obtained by adopting a DEMATEL-G1 method, and objective weights of all evaluation indexes are obtained by adopting a CRITIC-entropy method;
carrying out linear combination on the subjective weight and the objective weight of each evaluation index by adopting a game theory combination weighting method to obtain the balance weight of each evaluation index;
acquiring data corresponding to each evaluation index in the urban low-carbon passenger traffic structure evaluation system of the city to be tested, and acquiring a comprehensive evaluation value of the urban low-carbon passenger traffic structure to be tested based on the data and the balance weight of each evaluation index.
Optionally, the method for constructing the urban low-carbon passenger transport traffic structure evaluation system based on the DPSIR model comprises the following steps: setting a first criterion layer based on a DPSIR model, wherein the first criterion layer comprises a driving force layer, a pressure layer, a state layer, an influence layer and a response layer; setting a second criterion layer based on a life cycle theory, wherein the second criterion layer comprises a construction stage layer and an operation stage layer; and constructing an urban low-carbon passenger transport traffic structure evaluation system based on the evaluation indexes of the first criterion layer and the second criterion layer.
Optionally, the dividing of the evaluation index according to the criterion layer includes:
driving force layer-engineering stage layer group: GDP per capita, traffic transportation storage and postal industry fixed asset investment acceleration, value increase of high and new technology industry and income domination of per capita;
drive power layer-operation stage layer group: energy-saving and environment-friendly public budget indication, resident traffic consumption price index, resident trip satisfaction and dead people in ten-thousand-vehicle traffic;
pressure layer-engineering stage layer group: road network density, pedestrian-based road area and greening coverage rate of a built-up area;
pressure layer-operation stage layer group: unit passenger capacity pollutant emission, traffic trunk noise average value, road cleaning and cleaning area, and commuting peak congestion coefficient;
state layer-engineering stage layer group: the system comprises the following components of urban rail transit line length, public vehicle and electric vehicle line network proportion, passenger average transfer coefficient and public charging facility setting rate;
state layer-operation stage layer group: the method comprises the following steps of (1) acquiring the owned quantity of hundreds of private cars, urban rail transit operating mileage, public electric car occupation ratio and public transport trip sharing rate;
influence layer-engineering stage layer group: the percentage of the total energy consumption of the production in the ten thousand yuan region, the total power consumption of the production in the ten thousand yuan region, the traffic transportation storage and the added value of the postal industry accounts for GDP;
influence layer-operation phase layer group: the percentage of the turnover of the passenger transport on the highway to the total passenger transport volume, the average commuting time consumption of residents and the excellent rate of the environmental air quality;
response layer-engineering stage layer group: the method comprises the following steps of (1) changing the density of a road network, changing the length of an urban rail transit line and changing the coverage of a bus line network;
response layer-operation phase layer group: the urban rail transit operation mileage changes, the unit passenger capacity carbon emission changes, and the number of public electric vehicles changes.
Optionally, the subjective weight of each evaluation index is obtained by using a DEMATEL-G1 method based on the urban low-carbon passenger traffic structure evaluation system, and the method comprises the following steps:
sequentially scoring the influence degrees among the evaluation indexes to obtain a direct influence matrix;
carrying out standardization processing on the index data by adopting a maximum value chord taking method to obtain a standard direct influence matrix;
acquiring a comprehensive influence matrix;
acquiring the centrality of each evaluation index;
sorting the indexes according to the centrality from big to small to obtain the sequence relation among all the evaluation indexes;
obtaining relative importance degree between indexes based on the order relation;
acquiring the subjective weight of the final evaluation index of the order relation based on the relative importance degree;
acquiring subjective weights of the rest other evaluation indexes;
and transmitting the obtained corresponding weight of the indexes to the indexes according to the sequence relation to obtain subjective weight coefficients of the indexes before and after sequencing.
Optionally, objective weights of all evaluation indexes are obtained by a CRITIC-entropy method based on the urban low-carbon passenger traffic structure evaluation system, where the objective weights include a first objective weight and a second objective weight, and the method includes the following steps:
constructing an initial data matrix according to an evaluation scheme and each evaluation index, wherein the evaluation scheme comprises calendar year index evaluation data;
carrying out non-dimensionalization processing on the basis of the initial data matrix to obtain a non-dimensionalized matrix;
obtaining a first objective weight of each evaluation index by adopting a CRITIC method;
and obtaining each evaluation index by an entropy method to obtain a second objective weight.
Optionally, based on the standard deviation δjAnd the index conflict index LjAnd calculating to obtain:
in the formula, zijIs an element of the ith row and the jth column in the non-dimensionalized matrix;
is the average of the jth column in the dimensionless matrix; ltjIs the correlation coefficient of the t index and the j index; m is the number of evaluation protocols; n is the number of evaluation indexes;
combining the comprehensive information quantity reflected by the contrast strength and the conflict, and obtaining the objective weight of the CRITIC method; objective weight value omega calculated based on CRITIC methodj CriticCan be expressed as:
in the formula, deltajIs the standard deviation of the jth index, LjIs the jth conflict index, and n is the number of evaluation indexes.
Optionally, a game theory combination weighting method is adopted to perform linear combination on the subjective weight and the objective weight of each evaluation index, and the method includes the following steps:
setting a linear combination weight coefficient according to a weight vector obtained by an objective weighting method, and obtaining a comprehensive weight value based on the combination of the weight vector and the linear combination weight coefficient;
optimizing the linear combination weight coefficient based on the deviation of the comprehensive weight value and the weight vector and a minimum optimization objective function to obtain an optimal weight coefficient which is the optimization objective function;
based on the optimization objective function, obtaining a linear equation of equivalent optimal to derivative conditions according to a matrix differential mode;
normalizing the linear combination weight coefficient based on the linear equation to obtain a normalized linear combination weight coefficient;
and on the basis of the normalized linear combination weight coefficient, obtaining a linear combination of the subjective weight and the objective weight by adopting a game theory combination weighting method.
Optionally, the normalized index data and the corresponding balance weights of the evaluation indexes are linearly combined to obtain a comprehensive evaluation value.
The invention has the technical effects that: the invention discloses a game combination empowerment-based urban low-carbon passenger traffic structure evaluation method, which can communicate a causal link between an urban low-carbon passenger traffic structure and an external environment and between the urban low-carbon passenger traffic structure and an urban passenger traffic system, and reflect the stage characteristics of the urban low-carbon passenger traffic structure; in addition, in the aspect of the weighting method, the game theory-based combined weighting method integrates the advantages of the DEMATEL-G1 method and the CRITIC-entropy value method, and balances the results obtained by different weighting methods; the method has the advantages that the subjective weight is determined by the DEMATEL-G1 method, the characteristics of simplicity, clear logic, strong operability and the like of the calculation process of the G1 method are reserved, and meanwhile, the defects that the order relation is difficult to determine by the G1 method and the objective logic is weak are overcome by combining the DEMATEL method; the CRITIC-entropy method is selected to obtain objective weight, the conflict among indexes, the variability in the indexes, the data dispersion degree and the stability are comprehensively considered, and the information breadth contained in the weight is improved; the evaluation result is high in effectiveness and reliability, the development current situation of the urban low-carbon passenger traffic structure can be effectively evaluated, a decision basis is provided for urban passenger traffic planning and management, the method has important significance for optimizing the urban passenger traffic structure and reducing carbon emission of urban traffic, and the method is favorable for promoting high-energy-efficiency and low-carbon development and digital transformation of urban traffic.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of an urban low-carbon passenger traffic structure evaluation method based on game combination empowerment in an embodiment of the invention;
fig. 2 is a schematic diagram of an urban low-carbon passenger transportation structure evaluation system constructed based on a DPSIR model and a life cycle theoretical idea according to an embodiment of the present invention;
FIG. 3 is a flow chart of a DEMATEL-G1 method according to an embodiment of the invention;
fig. 4 is a schematic diagram of the balance weight corresponding to each evaluation index in an urban low-carbon passenger transportation structure evaluation system based on data of the year 2015-2020 in Qingdao city provided by the embodiment of the present invention;
fig. 5 is a schematic diagram of a comprehensive evaluation value of urban low-carbon passenger transportation structure development based on data of the year 2015-2020 in Qingdao city provided by the embodiment of the invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
As shown in fig. 1 to 5, the embodiment provides a method for evaluating an urban low-carbon passenger transportation structure based on game combination empowerment, which includes the following steps:
step 1, setting a first criterion layer based on a DPSIR model (Drive-Pressure-State-Impact-Response, DPSIR), wherein the first criterion layer comprises a driving force layer (D), a Pressure layer (P), a State layer (S), an influence layer (I) and a Response layer (R);
step 2, setting a second criterion layer based on a life cycle theory, wherein the second criterion layer comprises a construction stage layer (C) and an operation stage layer (O);
step 7, acquiring data corresponding to each index in the urban low-carbon passenger traffic structure evaluation system;
and 8, obtaining a comprehensive evaluation value of the urban low-carbon passenger traffic structure to be evaluated based on the index data of the urban low-carbon passenger traffic structure to be evaluated.
Specifically, in step 3, the set evaluation indexes may be divided into 10 groups of indexes and 36 corresponding specific indexes according to a criterion layer, and specifically include:
drive power layer-engineering stage layer set (DC): GDP per capita, traffic transportation storage and postal industry fixed asset investment acceleration, value increase of high and new technology industry and income domination of per capita;
drive power layer-operational phase layer group (DO): energy-saving and environment-friendly public budget indication, resident traffic consumption price index, resident trip satisfaction and dead people in ten-thousand-vehicle traffic;
pressure layer-engineering stage layer set (PC): road network density, pedestrian-oriented road area and greening coverage rate of a built-up area;
pressure layer-operational stage layer set (PC): unit passenger capacity pollutant emission, traffic trunk noise average value, road cleaning and cleaning area, and commuting peak congestion coefficient;
state layer-engineering stage layer group (SC): the system comprises the following components of urban rail transit line length, public vehicle and electric vehicle line network proportion, passenger average transfer coefficient and public charging facility setting rate;
state layer-operation phase layer group (SO): the method comprises the following steps of (1) acquiring the owned quantity of hundreds of private cars, urban rail transit operating mileage, public electric car occupation ratio and public transport trip sharing rate;
influence layer-engineering stage layer group (IC): the total energy consumption of production in the ten thousand yuan region, the total power consumption of production in the ten thousand yuan region, the traffic transportation storage and postal industry added value account for the percentage of GDP;
influence layer-operation phase layer group (IO): the percentage of the turnover of the passenger transport on the highway to the total passenger transport volume, the average commuting time consumption of residents and the excellent rate of the environmental air quality;
response layer-engineering phase layer group (RC): the density of a road network is changed, the length of an urban rail transit line is changed, and the coverage of a bus and electric vehicle road network is changed;
response layer-operation phase layer group (RO): the urban rail transit operation mileage changes, the unit passenger capacity carbon emission changes, and the number of public electric vehicles changes;
the method G1 is also called as a sequence relation analysis method, the method DEMATEL-G1 described in the step 4 is improved by using a DEMATEL method (Decision-making triple and Evaluation Laboratory, DEMATEL) on the basis of the method G1, the characteristics of small calculated amount, clear logic and strong operability are reserved, and meanwhile, the problems that the order relation is difficult to determine, the expert opinion integration difficulty is strong, the objective logic is weak and the like in an Evaluation system with a large number of Evaluation indexes are solved.
In step 4, the calculating the subjective weight of each evaluation index by using the DEMATEL-G1 method includes the following steps:
s41, inviting experts to sequentially score the influence degrees among all the evaluation indexes to obtain a direct influence matrix G, wherein the expression is as follows:
wherein, i ═ j ═ 1,2, …, n; gijRepresenting elements in a direct influence matrix; n is the number of evaluation indexes;
s42: in order to eliminate the influence of different orders and dimensions of each index, the maximum value chord taking method is used for carrying out standardization processing on index data to obtain a standard direct influence matrix P, and the expression is as follows:
in the formula, pijThe specification directly affects the value of the ith row and the jth column in the matrix;
s43: calculating a comprehensive influence matrix T, wherein the expression is as follows:
T＝(tij)n×n＝P/(I-P) (3)
wherein, i ═ j ═ 1,2, …, n; t is tijThe value of the ith row and the jth column in the comprehensive influence matrix; i is an n-order identity matrix;
s44: and calculating the centrality of each evaluation index. Center degree MiThe evaluation index system represents the function of the evaluation index in the evaluation index system, and the expression is as follows:
s45: sorting the indexes according to the central degree value from large to small to obtain the sequence relation among all the evaluation indexes;
s46: and calculating the relative importance degree between the indexes according to the order relation. The importance degree of the evaluation index to be calculated is the ratio of the centrality of the previous evaluation index in the order relation to the centrality of the evaluation index to be calculated; the larger the ratio is, the larger the importance degree of the previous evaluation index in the order relation relative to the evaluation index to be calculated is; relative degree of importance rjCan be calculated from equation (5):
rj＝Mj-1/Mj (5)
wherein j ═ is2，3，…，n；rjIs the relative importance degree of the j-1 th evaluation index to the j-th evaluation index; mj-1Is the centrality of the j-1 th evaluation index;
s47: the subjective weight of the evaluation index at the end of the order relation can be calculated according to the relative importance degree
The expression is as follows:
s48: the subjective weight of the remaining other evaluation indexes is calculated and can be obtained by using the formula (7):
wherein j is 2,3, …, n;
the subjective weight of the j-1 th evaluation index of other evaluation indexes except the evaluation index at the end of the order relation;
the subjective weight of the jth evaluation index which is the evaluation index other than the evaluation index at the end of the order relationship;
s49: finally, according to the order relation among the indexes, the obtained index C is usedi *The corresponding weight is passed to the index CiAnd after sorting, the subjective weight coefficient of each index before sorting can be obtained.
The CRITIC method (CRITIC) in step 5 is a relatively perfect objective weighting method, and the basic idea is to reflect and quantify the Inter-index conflict and the intra-index variability by using Correlation coefficients and standard deviations, but not considering the discreteness among data, and the entropy method can reflect the discreteness and stability of data by using the entropy of the index. The CRITIC method-entropy method calculates the objective weight of the index, and can more fully utilize the information contained in the index data.
In step 5, the calculating objective weights of the evaluation indexes by the CRITIC-entropy method includes the following steps:
s51: establishing an initial data matrix D (D) for m evaluation schemes and n evaluation index setsij)m×n. Wherein d isijIndicates the value of the j-th evaluation index in the i-th evaluation scheme.
S52: because the initial data dimensions are different, the initial matrix needs to be subjected to non-dimensionalization processing for the convenience of calculation. The dimensionless processing mode is to the benefit type index z+Forward processing is carried out to the cost type index z-And (5) carrying out reverse processing. The processed dimensionless matrix Z can be represented by equation (8):
wherein Z is a dimensionless matrix; z is a radical ofijIs an element in a dimensionless matrix; m is the number of evaluation protocols; n is the number of evaluation indexes; dijIs an element in the initial data matrix; z is a radical of+Is a benefit type index; z is a radical of-Is a cost-type indicator.
It should be noted that, since the value of the index with the initial value as a percentage is zero after non-dimensionalization, in order to avoid the unreasonable result that such value is weighted to be zero, consider to add a minimum value that does not affect the result to the total normalized values uniformly.
S53: and calculating objective weight of each evaluation index by using a CRITIC method. The CRITIC method is mainly used for calculating the standard deviation deltajTo reflect index variability, and by calculating a conflict index LjTo reflect index conflicts. Standard deviation deltajAnd index conflict index LjCalculated from equations (9) to (10):
in the formula (I), the compound is shown in the specification,
is the average of the jth column in the dimensionless matrix; ltjIs the correlation coefficient of the t index and the j index;
combining the comprehensive information quantity reflected by the contrast strength and the conflict, and obtaining the objective weight of the CRITIC method; therefore, the objective weight value omega calculated based on the CRITIC methodj CriticCan be expressed as:
in the formula, deltajIs the standard deviation of the jth index, LjIs the jth conflict index, and n is the number of evaluation indexes.
S54: and calculating the information entropy. In the process of calculating the objective weight based on the entropy method, the information entropy reflects the discrete degree and stability of data. Information entropy EjThe following equations (12) to (13) can be used:
in the formula oijThe index value of the jth evaluation index under the ith evaluation scheme is represented as the proportion;
s55: objective weight value omega calculated based on entropy methodj Entropy of the entropyCan be calculated according to equation (14):
the game theory combination weighting method in the step 6 takes Nash equilibrium as a theoretical basis, and the core idea is to assume a linear combination weight coefficient and optimize. The balance weight of each evaluation index refers to the optimal combination weight which is obtained by solving the optimal linear combination coefficient and achieves a Nash balance state among different weighting methods.
In step 6, the step of linearly combining the subjective weight and the objective weight of each evaluation index based on the game theory combination weighting method is as follows:
s61: the weight vector obtained by the kth weighting method is Wk＝ {ωk1,ωk2,ωk3,…,ωknDenotes, let a bekFor linear combination of the weight coefficients, the comprehensive weight value W after the weight vectors are arbitrarily combined can be expressed as:
wherein L is the number of the adopted weighting methods and the number of the obtained weight vectors;
s62: with W and WkTargeting the minimum sum of dispersion, on the linear weight coefficient αkOptimizing to obtain an optimal weight coefficient, wherein an optimization objective function f can be represented by formula (16):
Wk Trefers to the transposition of the weight vector obtained by the kth weighting method; k is the number of weight vectors obtained by the weighting method; w is a group ofp TA weight set for the pth weight method;
representing the deviation of the integrated weights for each weighting method; (
Is for all alphakWk TSummation, i.e. the composite weight in equation (15)
S63: from the matrix differential properties, the linear equation for the equivalent optimal derivative condition, which can be derived from the objective function, is given by equation (17):
s64: combination coefficient α obtained based on equation (17)kNormalization is carried out to combine the coefficient alphakThe normalization process is performed in the following manner:
in the formula of alphak *Is a normalized combination coefficient;
s65: thus, the W of the combined empowerment based on the game theory*Can be calculated from equation (19):
in step 8, the method for calculating the comprehensive evaluation value of the urban low-carbon passenger traffic structure to be evaluated is to linearly combine the standardized index data with corresponding balance weights of all evaluation indexes. The composite evaluation value can be expressed as:
wherein j is 1,2, …, n. U shapeiIs the comprehensive evaluation value of the ith evaluation scheme; wiIs the ith evaluation schemeA corresponding weight; z is a radical ofkIs the value of the dimensionless matrix;
as shown in fig. 4, the lengths of the SC1 urban rail lines, the changes of the RC2 urban rail transit lines, the PO4 commuting peak congestion coefficient, the holding capacity of the SO1 hundred-family private cars, the SO2 urban rail transit operating mileage, and the balance weight of the changes of the RO1 urban rail transit operating mileage are obviously higher than other indexes, and are key influencing factors for the optimization of the urban low-carbon passenger transport structure; the rules of the first rule layer are sequentially a state S, a pressure P, a response R, a driving force D and an influence I according to the weight values from large to small; in the second criterion layer, the weight value of the construction stage is slightly higher than that of the operation stage. The method shows that in the current development stage, the state of the structure is the key point for evaluating the urban low-carbon passenger traffic structure, the self pressure of the urban traffic system has a remarkable promoting effect on the urban low-carbon passenger traffic structure, and the current urban low-carbon passenger traffic development is still in the stage mainly based on construction and development, so that the method has a further perfect space.
As shown in fig. 5, the evaluation comprehensive value of the low-carbon passenger traffic structure in the city of Qingdao city shows an overall growth situation within the research period, and 2 important nodes can be observed according to the variation trend. Since 2016, the composite value enters a high-speed growth stage from a steady state, and has a tendency of decreasing slightly after 2019. The Qingdao subway realizes the full-line traffic of the first line at the end of 2016, becomes a key point for breaking through the bottleneck of low-carbon development of the Qingdao urban passenger traffic system, and forms a first turning point in the development process of the low-carbon passenger traffic of the Qingdao urban. Therefore, the evaluation results are consistent with the actual conditions, and the feasibility of the urban low-carbon passenger transport traffic structure evaluation method based on the DPSIR model and the game theory combined empowerment method is verified.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A game combination empowerment-based urban low-carbon passenger transport traffic structure evaluation method is characterized by comprising the following steps:
constructing an urban low-carbon passenger traffic structure evaluation system;
on the basis of the urban low-carbon passenger traffic structure evaluation system, subjective weights of all evaluation indexes are obtained by adopting a DEMATEL-G1 method, and objective weights of all evaluation indexes are obtained by adopting a CRITIC-entropy method;
carrying out linear combination on the subjective weight and the objective weight of each evaluation index by adopting a game theory combination weighting method to obtain the balance weight of each evaluation index;
acquiring data corresponding to each evaluation index in the urban low-carbon passenger traffic structure evaluation system of the city to be tested, and acquiring a comprehensive evaluation value of the urban low-carbon passenger traffic structure to be tested based on the data and the balance weight of each evaluation index.
2. The urban low-carbon passenger transportation structure evaluation method based on game combination empowerment of claim 1, wherein the construction of the urban low-carbon passenger transportation structure evaluation system based on the DPSIR model comprises: setting a first criterion layer based on a DPSIR model, wherein the first criterion layer comprises a driving force layer, a pressure layer, a state layer, an influence layer and a response layer; setting a second criterion layer based on a life cycle theory, wherein the second criterion layer comprises a construction stage layer and an operation stage layer; and constructing an urban low-carbon passenger transport traffic structure evaluation system based on the evaluation indexes of the first criterion layer and the second criterion layer.
3. The game combination empowerment-based urban low-carbon passenger traffic structure evaluation method according to claim 2, wherein the evaluation indexes are divided according to criteria layers and comprise the following steps:
driving force layer-engineering stage layer group: GDP per capita, investment acceleration of transportation and storage and postal industry fixed assets, added value of high and new technology industry and income per capita dominability;
drive power layer-operation stage layer group: energy-saving and environment-friendly public budget indication, resident traffic consumption price index, resident trip satisfaction and dead people in ten-thousand-vehicle traffic;
pressure layer-engineering stage layer group: road network density, pedestrian-oriented road area and greening coverage rate of a built-up area;
pressure layer-operation stage layer group: unit passenger capacity pollutant emission, traffic trunk noise average value, road cleaning and cleaning area, and commuting peak congestion coefficient;
state layer-engineering stage layer group: the system comprises the following components of urban rail transit line length, public vehicle and electric vehicle line network proportion, passenger average transfer coefficient and public charging facility setting rate;
state layer-operation stage layer group: the method comprises the following steps of (1) acquiring the owned quantity of hundreds of private cars, urban rail transit operating mileage, public electric car occupation ratio and public transport trip sharing rate;
influence layer-engineering stage layer group: the total energy consumption of production in the ten thousand yuan region, the total power consumption of production in the ten thousand yuan region, the traffic transportation storage and postal industry added value account for the percentage of GDP;
influence layer-operation stage layer group: the percentage of the turnover of the passenger transport on the highway to the total passenger transport volume, the average commuting time consumption of residents and the excellent rate of the environmental air quality;
response layer-engineering stage layer group: the density of a road network is changed, the length of an urban rail transit line is changed, and the coverage of a bus and electric vehicle road network is changed;
response layer-operation phase layer group: the urban rail transit operation mileage changes, the unit passenger capacity carbon emission changes, and the number of public electric vehicles changes.
4. The urban low-carbon passenger transport traffic structure evaluation method based on game combination empowerment of claim 3, wherein the subjective weights of all evaluation indexes are obtained by a DEMATEL-G1 method based on the urban low-carbon passenger transport structure evaluation system, and the method comprises the following steps:
sequentially scoring the influence degrees among the evaluation indexes to obtain a direct influence matrix;
carrying out standardization processing on the index data by adopting a maximum value chord taking method to obtain a standard direct influence matrix;
acquiring a comprehensive influence matrix;
acquiring the centrality of each evaluation index;
sorting the indexes according to the centrality from large to small to obtain the order relation among the evaluation indexes;
obtaining relative importance degree between indexes based on the order relation;
acquiring the subjective weight of the final evaluation index of the order relation based on the relative importance degree;
acquiring subjective weights of the rest other evaluation indexes;
and transmitting the obtained corresponding weight of the indexes to the indexes according to the sequence relation to obtain subjective weight coefficients of the indexes before and after sequencing.
5. The urban low-carbon passenger traffic structure evaluation method based on game combination weighting as claimed in claim 4, wherein objective weights of evaluation indexes are obtained by a CRITIC-entropy method based on the urban low-carbon passenger traffic structure evaluation system, the objective weights comprise a first objective weight and a second objective weight, and the method comprises the following steps:
constructing an initial data matrix according to an evaluation scheme and various evaluation indexes, wherein the evaluation scheme comprises calendar year index evaluation data;
carrying out non-dimensionalization processing on the basis of the initial data matrix to obtain a non-dimensionalized matrix;
obtaining a first objective weight of each evaluation index by adopting a CRITIC method;
and obtaining each evaluation index by an entropy method to obtain a second objective weight.
6. The urban low-carbon passenger traffic structure evaluation method based on game combination empowerment as claimed in claim 5, wherein the standard deviation δ is based onjAnd the index conflict index LjAnd calculating to obtain:
in the formula, zijIs an element of the ith row and the jth column in the non-dimensionalized matrix;
is the average of the jth column in the dimensionless matrix; ltjIs the correlation coefficient of the t index and the j index; m is the number of evaluation protocols; n is the number of evaluation indexes;
combining the contrast intensity and the comprehensive information quantity reflected by the conflict, and obtaining the objective weight of the CRITIC method; objective weight value omega calculated based on CRITIC methodj CriticCan be expressed as:
in the formula, deltajIs the standard deviation of the jth index, LjIs the jth conflict index, and n is the number of evaluation indexes.
7. The urban low-carbon passenger transport traffic structure evaluation method based on game combination weighting as claimed in claim 6, wherein a game theory combination weighting method is adopted to linearly combine subjective weights and objective weights of each evaluation index, and the method comprises the following steps:
setting a linear combination weight coefficient according to a weight vector obtained by a subjective and objective weighting method, and obtaining a comprehensive weight value based on the combination of the weight vector and the linear combination weight coefficient;
optimizing the linear combination weight coefficient based on the deviation of the comprehensive weight value and the weight vector and a minimum optimization objective function to obtain an optimal weight coefficient which is the optimization objective function;
based on the optimization objective function, obtaining a linear equation of equivalent optimal to derivative conditions according to a matrix differential mode;
normalizing the linear combination weight coefficient based on the linear equation to obtain a normalized linear combination weight coefficient;
and on the basis of the normalized linear combination weight coefficient, obtaining a linear combination of the subjective weight and the objective weight by adopting a game theory combination weighting method.
8. The game combination empowerment-based urban low-carbon passenger traffic structure evaluation method according to claim 7, wherein the standardized index data and the corresponding balance weights of the evaluation indexes are linearly combined to obtain a comprehensive evaluation value.
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