Traffic transportation industry operation efficiency evaluation method based on super-efficiency SBM model

Abstract

The invention discloses a super-efficiency SBM model-based traffic transportation industry operation efficiency evaluation method. The method at least comprises the steps of collecting input data, environmental influence data and output data corresponding to a region needing traffic transportation industry operation efficiency evaluation; preprocessing the input data, the environmental influence data and the output data; constructing a super-efficiency SBM model for evaluating the operation efficiency of the transportation industry; on the basis of the preprocessed input data, environmental influence data and output data, the operation efficiency of the transportation industry in the region is evaluated through a constructed super-efficiency SBM model; and making decision suggestions based on the evaluation result. The system has the advantages that multi-dimensional information such as energy input, environmental influence and expected output can be systematically integrated, the real efficiency level of the transportation industry in the aspects of energy utilization and environmental protection is accurately measured, and scientific and reliable data support and decision basis are provided for government departments, traffic enterprises and research institutions.

Description

Technical Field

[0001] The present invention relates to the fields of data analysis and economic policy research, and in particular to a method for evaluating the operating efficiency of the transportation industry based on a super-efficiency SBM model. Background Art

[0002] With the rapid development of the global economy, the transportation industry, as a solid pillar and key link of economic growth, has continued to expand in scale and demonstrated vigorous vitality. However, this rapid development is also accompanied by a series of severe challenges, especially in terms of energy consumption and environmental pollution.

[0003] In terms of energy consumption, the transportation industry is increasingly dependent on various energy sources such as oil and natural gas, and the total energy consumption is rising year by year, which not only exacerbates the tension between global energy supply and demand, but also poses a potential threat to energy supply security in various regions. At the same time, transportation activities have also become the main source of greenhouse gas emissions and various pollutants. The large-scale emission of pollutants such as carbon dioxide, nitrogen oxides, and particulate matter has seriously damaged air quality, ecological environment, and human health. This environmental pressure has attracted widespread attention from all walks of life and posed a severe challenge to sustainable development, prompting governments and regions to successively introduce strict environmental regulations and set energy conservation and emission reduction targets to curb the negative environmental externalities of the transportation industry.

[0004] Facing the dual challenges of energy and environment in the transportation industry, accurately measuring its energy and environmental efficiency has become a key prerequisite for formulating effective policies and taking reasonable measures. Traditional efficiency measurement methods are often limited to a single economic output or energy input indicator, which makes it difficult to fully and comprehensively reflect the complex input-output relationship in the transportation industry, especially failing to fully consider the environmental factor, which is a crucial variable. This leads to bias and incompleteness in the assessment of the actual operating efficiency of the transportation industry, and it is impossible to accurately reveal the root causes of energy waste and environmental damage, and thus it is impossible to provide decision makers with targeted and operational policy recommendations. Summary of the invention

[0005] The traditional efficiency measurement method is difficult to fully and comprehensively reflect the complex input-output relationship in the transportation industry, especially the failure to fully consider the environmental factor, which is a crucial variable, resulting in deviations and incompleteness in the evaluation of the actual operating efficiency of the transportation industry. The present invention proposes a transportation industry operating efficiency evaluation method based on the super-efficiency SBM model, which can systematically integrate multi-dimensional information such as energy input, environmental impact and expected output, accurately measure the real efficiency level of the transportation industry in energy utilization and environmental protection, and provide scientific and reliable data support and decision-making basis for government departments, transportation enterprises and research institutions.

[0006] The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model disclosed in the present invention at least includes:

[0007] Collect input data, environmental impact data and output data corresponding to the areas where transportation industry operation efficiency assessment is required;

[0008] Preprocessing the input data, environmental impact data and output data;

[0009] Construct a super-efficiency SBM model for evaluating the operational efficiency of the transportation industry;

[0010] Based on the pre-processed input data, environmental impact data and output data, the operating efficiency of the transportation industry in the region is evaluated by constructing the super-efficiency SBM model;

[0011] Based on the evaluation results, decision recommendations are made.

[0012] In a preferred embodiment, the input data includes the total energy consumption data, fixed asset investment data, and total number of employees in the transportation industry of each administrative district in the region each year;

[0013] The output data include annual passenger turnover data and cargo turnover data for each administrative district in the region;

[0014] The environmental impact data include annual carbon dioxide emissions data for each administrative district in the region.

[0015] In a preferred embodiment, the collecting of input data, environmental impact data and output data corresponding to the area where the transportation industry operation efficiency assessment is required specifically includes the following steps:

[0016] Through the statistical data of the energy statistics department, the annual energy consumption data of each administrative district in the transportation industry is collected as the total energy consumption data, and the unit of the total energy consumption data is unified as 10,000 tons of standard coal;

[0017] Through the statistical data released by the transportation authorities and relevant financial departments, the total annual government investment data of each administrative district in the transportation industry is collected as the fixed asset investment data, and the fixed asset investment data is unified into 100 million yuan;

[0018] Through the human resources statistics of the transportation industry management department, the number of employees in each administrative district in the transportation industry in each year is collected as the total number of employees data, and the units of the total number of employees data are unified;

[0019] Collect the passenger turnover data of the transportation vehicles operated in each administrative district in the region each year through the transportation statistics yearbook, operation reports of each transportation enterprise and relevant transportation monitoring systems, and unify the units of the passenger turnover data;

[0020] Collect the cargo turnover data of the transportation vehicles operated in each administrative district in the region each year through logistics industry statistics, railway freight department statistics, road freight enterprise reporting data and port cargo transportation records, and unify the cargo turnover data units;

[0021] Through the carbon emission monitoring data released by the environmental monitoring department and the standard method for carbon emission accounting in the transportation industry, the annual carbon dioxide emissions data for each administrative district in the region is calculated based on the energy-based carbon footprint model.

[0022] In a preferred embodiment, the preprocessing of the input data, environmental impact data and output data specifically comprises the following steps:

[0023] Step S201, cleaning the collected input data, environmental impact data and output data, checking the consistency, completeness and accuracy of the data, removing obviously erroneous or abnormal data points, and supplementing missing or incomplete data by linear interpolation;

[0024] Step S202: based on the range normalization method, convert the input data, environmental impact data and output data into numerical values ​​in the interval [0, 1].

[0025] In a preferred embodiment, the construction of a super-efficiency SBM model for evaluating the operation efficiency of the transportation industry specifically includes the following steps:

[0026] Step S301, taking each administrative district in the region as a decision-making unit, wherein any of the decision-making units includes the input data, environmental impact data and output data of the transportation industry of the corresponding administrative district in different years;

[0027] Step S302: taking the input data as input variables, the input variables are expressed as:

[0028] X k =(x 1k , x 2k , x 3k )

[0029] Among them, X k is the input variable of the kth administrative district; x 1k is the total energy consumption data of the kth administrative district; x 2k is the fixed asset investment data of the kth administrative district; x 3kis the total number of employees in the kth administrative district;

[0030] The output data is used as the expected output variable, and the expected output variable is expressed as:

[0031] Y k =(y 1k ,y 2k )

[0032] Among them, Y k is the expected output variable of the kth administrative district, y 1k is the passenger turnover data of the kth administrative district, y 2k is the cargo turnover data of the kth administrative district;

[0033] The environmental impact data is used as an undesirable output variable, and the undesirable output variable is expressed as:

[0034] Z k

[0035] Among them, Z k is the carbon dioxide emission data in the kth administrative area;

[0036] Step S303: construct a linear programming form of the super-efficiency SBM model.

[0037] In a preferred embodiment, the operation efficiency of the transportation industry in the region is evaluated by constructing the super-efficiency SBM model based on the pre-processed input data, environmental impact data and output data, which specifically includes the following steps:

[0038] Step S401, inputting the pre-processed data into the super efficiency SBM model;

[0039] Step S402, based on a linear programming method, the transportation operation efficiency value of each administrative district in the region in different years is calculated by a super efficiency SBM model.

[0040] In a preferred embodiment, step S402 specifically includes the following steps:

[0041] Step S4021, constructing the objective function coefficient f, upper and lower bounds lb and ub, and equality constraints Aeq and beq through Matlab to solve the corresponding linear programming problem, and obtaining slack variables corresponding to the input variables, expected output variables, and non-expected output variables;

[0042] Step S4022, based on the input variables, expected output variables, undesired output variables, and corresponding slack variables, calculate the super efficiency score as the transportation industry operation efficiency value, and store it in the rho vector;

[0043] Step S4023, looping through each of the decision-making units to obtain the operating efficiency value corresponding to each of the decision-making units.

[0044] In a preferred embodiment, making decision suggestions based on the evaluation results specifically includes the following steps:

[0045] Step S501, statistically analyzing the operation efficiency values ​​of the transportation industry in each administrative district in the region, and calculating statistical indicators of the operation efficiency values, wherein the statistical indicators include one or a combination of a mean, a median, and a standard deviation;

[0046] Step S502, based on the statistical indicators, analyzing the redundancy of the transportation industry in each administrative district in the region in terms of energy consumption, fixed asset investment, and employees, as well as the shortage of expected outputs such as passenger turnover and cargo turnover and the excess of undesired outputs such as carbon dioxide emissions;

[0047] Step S503, by changing the input variables, expected output variables, and variables among the unexpected output variables one by one, observing the change of the operating efficiency value, and obtaining the variables that have a significant impact on the operating efficiency value;

[0048] Step S504, according to the operation efficiency value of the transportation industry in each administrative district in the region and the analysis result, the corresponding administrative district is divided into three categories: high efficiency, medium efficiency and low efficiency;

[0049] Step S505, optimizing resource allocation based on the operation efficiency value of the transportation industry in each administrative district and the classification of the corresponding administrative district.

[0050] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program comprises instructions for executing any of the above methods.

[0051] The present invention also provides a storage medium, including a computer program / instruction, which, when executed by a processor, implements any of the above-mentioned methods for evaluating the operating efficiency of the transportation industry based on the super-efficiency SBM model.

[0052] Compared with the prior art, the transportation industry operation efficiency evaluation method based on the super-efficiency SBM model disclosed in the present invention has the following beneficial effects:

[0053] (1) The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model disclosed in the present invention collects the input data, environmental impact data and output data corresponding to the area where the operation efficiency of the transportation industry needs to be evaluated, and can systematically integrate multi-dimensional information such as energy input, environmental impact and expected output, and comprehensively reflect the real efficiency level of the transportation industry in terms of energy utilization and environmental protection. This comprehensive data integration method ensures the comprehensiveness and accuracy of the evaluation results, and avoids the one-sidedness that may be caused by single-dimensional evaluation in traditional methods. A super-efficiency SBM model for evaluating the operation efficiency of the transportation industry is constructed. The super-efficiency SBM model takes into account the slackness of input and output, and can more finely reflect the efficiency differences between various decision-making units, providing higher accuracy for the evaluation. Based on the pre-processed input data, environmental impact data and output data, the operation efficiency of the transportation industry in the region is evaluated through the constructed super-efficiency SBM model. By providing accurate efficiency evaluation results, it helps government departments, transportation enterprises and research institutions to make more scientific and reasonable decisions. These decisions can be optimized in terms of energy utilization, environmental protection and other aspects, and promote the development of the transportation industry in a more efficient, green and sustainable direction.

[0054] (2) In the transportation industry operation efficiency evaluation method based on the super-efficiency SBM model of the present invention, the input data include the total energy consumption data, fixed asset investment data, and total number of employees data of the transportation industry in each administrative district in the region each year. These data comprehensively reflect the resource input of the transportation industry. The total energy consumption data reflects the energy utilization efficiency, the fixed asset investment data reflects the capital investment level, and the total number of employees data shows the allocation of human resources. The output data include the passenger turnover data and cargo turnover data of each administrative district in the region each year. These data directly reflect the service capacity and economic benefits of the transportation industry and are important indicators for evaluating the operation efficiency of the transportation industry. The environmental impact data include the annual carbon dioxide emission data of each administrative district in the region, represented by carbon dioxide emissions, which is the main aspect of the impact of the transportation industry on the environment. Including it in the evaluation system helps to comprehensively consider the green development level of the transportation industry. The required data is relatively easy to obtain, and the evaluation process is operational, which is convenient for promotion and use in practical applications. At the same time, the evaluation results are practical and can be directly used to guide the planning, management and optimization of the transportation industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of an embodiment of the method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The term "or / and" used herein includes any and all combinations of one or more related listed items.

[0059] In addition, in the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes, and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only used to distinguish components or operations described with the same technical terms, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0060] The transportation industry operation efficiency evaluation method based on the super efficiency SBM model of this embodiment is as follows: Figure 1 As shown, including at least:

[0061] Step S1, collect the input data, environmental impact data and output data corresponding to the area where the transportation industry operation efficiency assessment is required. It can systematically integrate multi-dimensional information such as energy input, environmental impact and expected output, and comprehensively reflect the real efficiency level of the transportation industry in terms of energy utilization and environmental protection. This comprehensive data integration method ensures the comprehensiveness and accuracy of the assessment results, and avoids the one-sidedness that may be caused by single-dimensional assessment in traditional methods.

[0062] In the implementation shown in the present invention, the input data includes the total energy consumption data, fixed asset investment data, and total number of employees in the transportation industry of each administrative district in the region each year;

[0063] The output data include annual passenger turnover data and freight turnover data for each administrative district in the region;

[0064] Environmental impact data include annual carbon dioxide emissions data for each administrative district in the region.

[0065] Step S1 specifically includes the following steps:

[0066] Step S101, through the statistical data of the energy statistics department, collect the annual energy consumption data of each administrative district in the transportation industry area as the total energy consumption data, and unify the unit of the total energy consumption data into 10,000 tons of standard coal, covering the energy consumption of various modes of transportation such as highway, railway, water transportation, aviation, etc.

[0067] Step S102, through the statistical data released by the transportation authorities and relevant financial departments, collect the annual total government investment data of each administrative district in the transportation industry as fixed asset investment data, and unify the fixed asset investment data unit into 100 million yuan, including investment information in transportation infrastructure construction, transportation vehicle purchase, transportation informatization construction, etc.

[0068] Step S103, through the human resources statistics of the transportation industry management department, the number of employees in each administrative district in the transportation industry is collected as the total number of employees, and the unit of the total number of employees is unified. Specifically, the number of employees in the transportation industry is counted accurately to the person, and classified and counted according to different modes of transportation (such as passenger transportation, freight transportation, urban public transportation, rail transportation, etc.) to facilitate the subsequent analysis of the staffing efficiency of different business types.

[0069] Step S104, through the transportation statistics yearbook, the operation reports of each transportation enterprise and the relevant traffic monitoring system, the passenger turnover data of each administrative district in the region is collected each year, and the unit of passenger turnover data is unified into 10,000 people-kilometers. Specifically, the passenger turnover of various modes of transportation such as road passenger transportation, railway passenger transportation, air passenger transportation, water passenger transportation, etc. is accumulated to ensure that the data covers all major passenger transportation activities.

[0070] Step S105 collects the cargo turnover data of the vehicles in operation in each administrative district in the region each year through logistics industry statistics, railway freight department statistics, highway freight enterprise reporting data and port cargo transportation records, and unifies the cargo turnover data unit into 10,000 tons-kilometers to accurately reflect the total cargo turnover of the vehicles in operation and comprehensively cover all types of freight transportation modes.

[0071] Step S106 calculates the annual carbon dioxide emissions data for each administrative district in the region based on the energy carbon footprint model through the carbon emission monitoring data released by the environmental monitoring department and the standard method for carbon emission accounting in the transportation industry. The specific calculation method is as follows:

[0072]

[0073] Among them, A is the carbon dioxide emission and all carbonaceous fuels burned, CCF is the carbon content factor, HE is the thermal energy equivalent, and COF is the carbon oxidation factor. In addition, It represents the ratio of the molecular weight of carbon 44 to the molecular weight of carbon 12, so, Indicates the carbon dioxide emission factor of a certain energy source. In 2007, the Energy Research Institute of the National Development and Reform Commission listed several types of carbonaceous fuels related to carbon emission factors, as shown in Table 1:

[0074] Table 1

[0075]

[0076]

[0077] In this embodiment, the administrative district within the provincial scope is taken as the evaluation object, and the energy data consumed by the transportation industry within the provincial scope each year is obtained from the provincial energy statistics department. The total government investment data within the provincial scope of the transportation industry is collected through the statistical data released by the provincial transportation authorities and relevant financial departments. According to the human resources statistics of the provincial transportation industry management department, the number of employees within the provincial scope of the transportation industry is counted. The total passenger turnover of operating vehicles within the provincial scope is extracted from the provincial transportation statistical yearbook, the operation report of each transportation enterprise, and the relevant traffic monitoring system. Integrate the cargo turnover data from different channels, including provincial logistics industry statistics, railway freight department statistics, highway freight enterprise reporting data, and port cargo transportation records. According to the carbon emission monitoring data released by the provincial environmental monitoring department and the standard method for carbon emission accounting in the transportation industry, the carbon dioxide emissions of the transportation department within the provincial scope are calculated in tons, and the carbon footprint model based on energy is used to measure the carbon dioxide emissions within the provincial scope.

[0078] Step S2, pre-processing the input data, environmental impact data and output data.

[0079] The specific steps include:

[0080] Step S201, clean the collected input data, environmental impact data and output data, check the consistency, completeness and accuracy of the data, remove obviously erroneous or abnormal data points, and use linear interpolation to supplement the missing or incomplete data.

[0081] In this embodiment, if the data of a certain year fluctuates significantly compared with the adjacent years and there is no reasonable explanation, it is necessary to further verify the authenticity of the data; if there is missing data or incomplete records, it is supplemented by linear interpolation. Data cleaning can remove invalid, duplicate and erroneous data and fill in missing values, thereby improving the quality and credibility of the data. Through consistency checks, the unity and coordination of data in different dimensions and levels are ensured, providing an accurate basis for subsequent analysis. The integrity check ensures that the data covers all necessary information and variables to avoid analytical deviations caused by missing data. Accurate data can more truly reflect the actual situation and improve the accuracy and reliability of subsequent analysis.

[0082] Step S202: based on the range normalization method, the input data, environmental impact data and output data are converted into numerical values ​​in the interval [0, 1].

[0083] Specifically, due to the different dimensions of input-output indicators, in order to eliminate the impact of the dimensions on the model calculation results, a suitable standardization method is used to process the data. The range standardization method is used to convert the data of each indicator into a value in the interval [0, 1]. For positive indicators (such as passenger turnover and cargo turnover), standardization is performed according to formula (1) a) i) (1); for negative indicators (such as carbon dioxide emissions), standardization is performed using formula (1) a) i) (2).

[0084] Positive indicators: Negative indicators: Where X ij is the original value of the jth indicator of the ith decision-making unit (region), X ij * is the normalized value, minX j and minX j are the minimum and maximum values ​​of the j-th indicator respectively.

[0085] Step S3, constructing a super-efficiency SBM model for evaluating the operation efficiency of the transportation industry.

[0086] The specific steps include:

[0087] Step S301, taking each administrative district in the region as a decision-making unit, each decision-making unit includes the input data, environmental impact data and output data of the transportation industry of the corresponding administrative district in different years.

[0088] In this embodiment, each administrative district within the provincial scope is taken as a decision-making unit, and the input-output data of the transportation industry of each administrative district in different years constitutes a DMU, ​​which is recorded as DMU k (k=1, 2, ..., n), where n is the number of administrative regions.

[0089] Step S302: Use the input data as input variables, where the input variables are expressed as:

[0090] X k =(x 1k , x 2k , x 3k )

[0091] Among them, X k is the input variable of the kth administrative district; x 1k is the total energy consumption data of the kth administrative district; x 2k is the fixed asset investment data of the kth administrative district; x 3k is the total number of employees in the kth administrative district;

[0092] The output data is taken as the expected output variable, and the expected output variable is expressed as:

[0093] Y k =(y 1k ,y 2k )

[0094] Among them, Y k is the expected output variable of the kth administrative district, y 1k is the passenger turnover data of the kth administrative district, y 2k is the cargo turnover data of the kth administrative district;

[0095] Taking environmental impact data as undesirable output variables, the undesirable output variables are expressed as:

[0096] Z k

[0097] Among them, Z k is the carbon dioxide emission data in the kth administrative district.

[0098] Step S303: construct a linear programming form of the super-efficiency SBM model.

[0099] Among them, the objective function is: Constraints:

[0100]

[0101] λ j ≥0, j=1,2,…,n; j≠k

[0102]

[0103]

[0104] Among them, ρ * is the efficiency value of the super-efficiency SBM model, m is the number of input indicators, s 1 is the expected number of output indicators, s 2 is the number of undesirable output indicators, λ j is the weight vector, To input redundant variables, is the expected output deficiency variable, is the unexpected excess output variable.

[0105] Step S4, based on the pre-processed input data, environmental impact data and output data, the operating efficiency of the transportation industry in the region is evaluated through the constructed super-efficiency SBM model.

[0106] The specific steps include:

[0107] Step S401, input the preprocessed data into the super efficiency SBM model.

[0108] Data initialization: Here the input matrix X has three columns corresponding to the three input variables, the expected output matrix Y has two columns corresponding to the two expected outputs, and the unexpected output z is a single column vector.

[0109] Step S402, based on the linear programming method, the super-efficiency SBM model is used to calculate the transportation operation efficiency value of each administrative district in the region in different years.

[0110] In this embodiment, Matlab tool is used to solve the problem, which specifically includes the following steps:

[0111] Step S4021, construct the objective function coefficient f, upper and lower bounds lb and ub, and equality constraints Aeq and beq through Matlab to solve the corresponding linear programming problem, and obtain the slack variables corresponding to the input variables, expected output variables, and non-expected output variables.

[0112] Step S4022, based on the input variables, the expected output variables, the unexpected output variables, and the corresponding slack variables, the super efficiency score is calculated as the operation efficiency value of the transportation industry and stored in the rho vector.

[0113] Step S4023, loop through each decision unit to obtain the operation efficiency value corresponding to each decision unit.

[0114] In this embodiment, 30 decision-making units are set, and the following method is used to implement it:

[0115]

[0116]

[0117] Substitute the preprocessed data into the super-efficiency SBM model, and by solving the linear programming problem, obtain the operating efficiency value of the transportation industry in each administrative district in the region in different years. The higher the efficiency value, the better the degree to which the transportation industry in the region achieves the maximization of expected output and the minimization of unexpected output under given input, and the higher the energy utilization efficiency and environmental performance; conversely, the lower the efficiency value, the greater the room for improvement of the transportation industry in the region, and the need to optimize the input-output structure, reduce energy consumption and carbon emissions, and improve operational efficiency.

[0118] Step S5: Make decision recommendations based on the evaluation results.

[0119] The specific steps include:

[0120] Step S501, statistically analyzing the operation efficiency values ​​of the transportation industry in each administrative district in the region, and calculating statistical indicators of the operation efficiency values, the statistical indicators including one or more combinations of mean, median, and standard deviation.

[0121] In this embodiment, the operation efficiency values ​​of the transportation industry in each administrative region are statistically analyzed, and statistical indicators such as the mean, median, and standard deviation of the efficiency values ​​are calculated to understand the overall distribution of efficiency levels. At the same time, a time series graph and a regional distribution graph of the efficiency values ​​are drawn to intuitively display the changing trend of the energy and environmental efficiency of the transportation industry in each region over time and the efficiency differences between different regions, providing basic data support for further in-depth analysis.

[0122] Step S502, based on statistical indicators, analyze the redundancy of energy consumption, fixed asset investment, and employees in the transportation industry of each administrative district in the region, as well as the shortage of expected outputs such as passenger turnover and cargo turnover and the excess of undesired outputs such as carbon dioxide emissions.

[0123] In this embodiment, based on statistical indicators, the redundancy of energy consumption, fixed asset investment, and employees in the transportation industry of each administrative region is analyzed, as well as the shortage of expected outputs such as passenger turnover and cargo turnover, and the excess of undesired outputs such as carbon dioxide emissions. By identifying these inefficient links and potential improvement directions, specific data basis and decision-making reference are provided for each administrative region to formulate targeted transportation industry development policies and energy-saving and emission reduction measures. If there is a large redundancy in energy consumption in a certain administrative region, it means that there is waste in energy utilization in the administrative region, and it is necessary to strengthen energy management and technological innovation, and promote energy-saving vehicles and transportation organization methods; if the output of cargo turnover in a certain administrative region is insufficient, it may be necessary to optimize the logistics distribution network, improve transportation efficiency, and reduce transportation costs, so as to enhance the supporting role of the transportation industry in economic development.

[0124] Step S503, by changing the input variables, expected output variables, and variables in the unexpected output variables one by one, observing the change of the operating efficiency value, and obtaining the variables that have a significant impact on the operating efficiency value.

[0125] In this embodiment, in order to evaluate the influence of input-output variable indicators on energy and environmental efficiency values, a sensitivity analysis is performed. By changing the values ​​of input-output variables one by one and observing the changes in efficiency values, it is determined which indicators have a more significant impact on efficiency values ​​and which indicators are relatively stable. The results of sensitivity analysis can help decision makers grasp key factors more accurately, allocate resources reasonably, and take measures to optimize and adjust indicators with higher sensitivity when formulating development plans and policies for the transportation industry, so as to more effectively improve energy and environmental efficiency.

[0126] Step S504, based on the transportation operation efficiency value and analysis results of each administrative district in the region, the corresponding administrative district is divided into three categories: high efficiency, medium efficiency and low efficiency.

[0127] In this embodiment, administrative regions are divided into three categories: high efficiency, medium efficiency and low efficiency, based on the measurement results and analysis and evaluation of the energy and environmental efficiency of the transportation industry in each administrative region. For high-efficiency administrative regions, their successful experiences and advanced practices are summarized to form models and cases that can be promoted, providing reference for other administrative regions; for medium-efficiency regions, their main problems and improvement directions are analyzed, and targeted optimization suggestions are put forward to help them further improve their energy and environmental efficiency; for low-efficiency regions, detailed improvement plans and support policies are formulated, and investment in infrastructure construction, technological innovation, talent training and other aspects is increased to promote the rapid development and efficiency improvement of their transportation industry.

[0128] Step S505, optimizing resource allocation based on the operation efficiency value of the transportation industry in each administrative district and the classification of the corresponding administrative district.

[0129] In this embodiment, based on the input-output efficiency, optimization suggestions are provided for the resource allocation of the transportation industry in each administrative region. In terms of fixed asset investment, transportation infrastructure construction projects are rationally planned to avoid blind investment and duplication, and priority is given to supporting projects that have a significant effect on improving energy and environmental efficiency, such as the construction of intelligent transportation systems and the transformation and upgrading of comprehensive transportation hubs; in terms of human resource allocation, according to the development needs of transportation business and the level of labor productivity in each administrative region, the structure of employees is adjusted, vocational skills training is strengthened, the quality of employees is improved, and efficient use of human resources is achieved; in terms of energy supply, the energy structure is optimized, the proportion of clean energy supply is increased, the construction of energy distribution networks is strengthened, the energy demand of the transportation industry is guaranteed, and the energy supply cost and carbon emission level are reduced at the same time.

[0130] The embodiment shown in the present invention also provides a computer device, including a memory and a processor, the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program includes instructions for executing any of the above methods.

[0131] The embodiment shown in the present invention also provides a storage medium, including a computer program / instruction, which, when executed by a processor, implements any of the above-mentioned methods for evaluating the operating efficiency of the transportation industry based on the super-efficiency SBM model.

[0132] In short, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The transportation industry operation efficiency evaluation method based on the super efficiency SBM model includes at least the following steps: Collect input data, environmental impact data and output data corresponding to the areas where transportation industry operation efficiency assessment is required; Preprocessing the input data, environmental impact data and output data; Construct a super-efficiency SBM model for evaluating the operational efficiency of the transportation industry; Based on the pre-processed input data, environmental impact data and output data, the operating efficiency of the transportation industry in the region is evaluated by constructing the super-efficiency SBM model; Based on the evaluation results, decision recommendations are made.

2. The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model according to claim 1 is characterized by: The input data include the total energy consumption data, fixed asset investment data, and total number of employees in the transportation industry of each administrative district in the region each year; The output data include annual passenger turnover data and cargo turnover data for each administrative district in the region; The environmental impact data include annual carbon dioxide emissions data for each administrative district in the region.

3. The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model according to claim 2 is characterized in that: The collection of input data, environmental impact data and output data corresponding to the area where the transportation industry operation efficiency assessment is required specifically includes the following steps: Through the statistical data of the energy statistics department, the annual energy consumption data of each administrative district in the transportation industry is collected as the total energy consumption data, and the unit of the total energy consumption data is unified as 10,000 tons of standard coal; Through the statistical data released by the transportation authorities and relevant financial departments, the total annual government investment data of each administrative district in the transportation industry is collected as the fixed asset investment data, and the fixed asset investment data is unified into 100 million yuan; Through the human resources statistics of the transportation industry management department, the number of employees in each administrative district in the transportation industry in each year is collected as the total number of employees data, and the units of the total number of employees data are unified; Collect the passenger turnover data of the transportation vehicles operated in each administrative district in the region each year through the transportation statistics yearbook, operation reports of each transportation enterprise and relevant transportation monitoring systems, and unify the units of the passenger turnover data; Collect the cargo turnover data of the transportation vehicles operated in each administrative district in the region each year through logistics industry statistics, railway freight department statistics, road freight enterprise reporting data and port cargo transportation records, and unify the cargo turnover data units; Through the carbon emission monitoring data released by the environmental monitoring department and the standard method for carbon emission accounting in the transportation industry, the annual carbon dioxide emissions data for each administrative district in the region is calculated based on the energy-based carbon footprint model.

4. The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model according to any one of claims 1 to 3 is characterized in that: The preprocessing of the input data, environmental impact data and output data specifically includes the following steps: Step S201, cleaning the collected input data, environmental impact data and output data, checking the consistency, completeness and accuracy of the data, removing obviously erroneous or abnormal data points, and supplementing missing or incomplete data by linear interpolation; Step S202: based on the range normalization method, convert the input data, environmental impact data and output data into numerical values ​​in the interval [0, 1].

5. The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model according to claim 2 or 3 is characterized in that: The construction of the super-efficiency SBM model for evaluating the operation efficiency of the transportation industry specifically includes the following steps: Step S301, taking each administrative district in the region as a decision-making unit, wherein any of the decision-making units includes the input data, environmental impact data and output data of the transportation industry of the corresponding administrative district in different years; Step S302: taking the input data as input variables, the input variables are expressed as: X k =(x lk ,x 2k ,x 3k ) Among them, X k is the input variable of the kth administrative district; x 1k is the total energy consumption data of the kth administrative district; x 2k is the fixed asset investment data of the kth administrative district; x 3k is the total number of employees in the kth administrative district; The output data is used as the expected output variable, and the expected output variable is expressed as: AND k (and 1k ,and 2k ) Among them, Y k is the expected output variable of the kth administrative district, y 1k is the passenger turnover data of the kth administrative district, y 2k is the cargo turnover data of the kth administrative district; The environmental impact data is used as an undesirable output variable, and the undesirable output variable is expressed as: Z k Among them, Z k is the carbon dioxide emission data in the kth administrative area; Step S303: construct a linear programming form of the super-efficiency SBM model.

6. The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model according to claim 5 is characterized in that: The evaluation of the operation efficiency of the transportation industry in the region based on the pre-processed input data, environmental impact data and output data is performed by constructing the super-efficiency SBM model, which specifically includes the following steps: Step S401, inputting the pre-processed data into the super efficiency SBM model; Step S402, based on a linear programming method, the transportation operation efficiency value of each administrative district in the region in different years is calculated by a super efficiency SBM model.

7. The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model according to claim 6 is characterized in that: The step S402 specifically includes the following steps: Step S4021, constructing the objective function coefficient f, upper and lower bounds lb and ub, and equality constraints Aeq and beq through Matlab to solve the corresponding linear programming problem, and obtaining slack variables corresponding to the input variables, expected output variables, and non-expected output variables; Step S4022, based on the input variables, expected output variables, undesired output variables, and corresponding slack variables, calculate the super efficiency score as the transportation industry operation efficiency value, and store it in the rho vector; Step S4023, looping through each of the decision-making units to obtain the operating efficiency value corresponding to each of the decision-making units.

8. The method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model according to claim 7 is characterized in that: The decision-making recommendations based on the evaluation results specifically include the following steps: Step S501, statistically analyzing the operation efficiency values ​​of the transportation industry in each administrative district in the region, and calculating statistical indicators of the operation efficiency values, wherein the statistical indicators include one or a combination of a mean, a median, and a standard deviation; Step S502, based on the statistical indicators, analyzing the redundancy of the transportation industry in each administrative district in the region in terms of energy consumption, fixed asset investment, and employees, as well as the shortage of expected outputs such as passenger turnover and cargo turnover and the excess of undesired outputs such as carbon dioxide emissions; Step S503, by changing the input variables, expected output variables, and variables among the unexpected output variables one by one, observing the change of the operating efficiency value, and obtaining the variables that have a significant impact on the operating efficiency value; Step S504, according to the operation efficiency value of the transportation industry in each administrative district in the region and the analysis result, the corresponding administrative district is divided into three categories: high efficiency, medium efficiency and low efficiency; Step S505, optimizing resource allocation based on the operation efficiency value of the transportation industry in each administrative district and the classification of the corresponding administrative district.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, the computer program is configured to be executed by the processor, and the computer program comprises instructions for executing the method according to any one of claims 1 to 8.

10. A storage medium comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for evaluating the operation efficiency of the transportation industry based on the super-efficiency SBM model as described in any one of claims 1 to 8 is implemented.

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