A method for analyzing and controlling time sequence evolution characteristics of urban traffic carbon emissions

Abstract

The application relates to a kind of urban traffic carbon emission time evolution characteristic analysis and control method, belongs to the technical field of urban traffic carbon emission control.To solve the problem of inaccurate analysis of urban traffic carbon emission time evolution characteristics.The application includes constructing long-term trend change coefficient of urban traffic carbon emission evolution, periodic fluctuation correction coefficient of urban traffic carbon emission evolution, intervention correction coefficient of major events on carbon emission;Construct a basic decomposition model of urban traffic carbon emission time evolution to calculate the total traffic carbon emission at different times;Through the synergy regulation and control of technology progress dimension, traffic structure dimension and energy structure dimension, a comprehensive control evaluation model is established to obtain the evaluation results of the comprehensive control evaluation model;By constructing a double-objective optimization function, the optimal emission reduction time is determined by considering the emission reduction target function and economic cost function, and the control of urban traffic carbon emission time evolution characteristics is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban traffic carbon emission control, and particularly relates to a method for analyzing and controlling time sequence evolution characteristics of urban traffic carbon emission. BACKGROUND

[0002] Urban traffic is an important source of urban carbon emission. With the acceleration of urbanization and the continuous growth of motor vehicle ownership, the total amount of carbon emission in the transportation field is rising. Traffic carbon emission not only has a large total amount, but also has obvious time sequence evolution characteristics. It is affected by factors such as travel behavior, road traffic conditions, energy structure, and weather changes, and shows dynamic characteristics such as trend, periodicity, and sudden fluctuation. In-depth analysis of the time sequence change rule of carbon emission helps to reveal the internal mechanism of carbon emission, identify key influencing factors, and provide a basis for policy making, system regulation, and fine management.

[0003] Although existing researches have focused on the change characteristics of urban traffic carbon emission, there are still many deficiencies in the time sequence evolution analysis. First, there is a lack of a systematic modeling framework, making it difficult to describe dynamic characteristics such as trend change, periodic fluctuation, and sudden disturbance. Second, the identification ability of periodic factors and non-periodic disturbances is limited, affecting the accuracy of carbon emission prediction. Third, the mechanism of various influencing factors is not clear, and there is a lack of effective decomposition method, making it difficult to quantify the contribution degree of different factors. In addition, existing researches mainly focus on the macro scale, ignoring regional differences and spatial heterogeneity, which limits the precise implementation of regulation strategies.

[0004] Carbon emission control is a key measure to achieve green and low-carbon development of cities, and scientific control of traffic carbon emission has important practical significance. By reasonably controlling carbon emission, energy consumption and environmental pollution can be effectively reduced, road transportation efficiency can be improved, transportation energy structure can be optimized, and economic, social and environmental development can be achieved. At the same time, carbon emission control is an important part of intelligent and fine management of urban traffic system, which provides decision support for formulating differentiated regulation policies, optimizing traffic organization and guiding green travel, and is one of the core means to improve urban comprehensive governance capacity and environmental quality.

[0005] Current urban traffic carbon emission control still faces many challenges in practice, lacks precise control strategies based on time sequence dynamics, regulation means is too extensive, and differentiated policies cannot be formulated according to time and place, making it difficult to achieve efficient emission reduction. In the control process, there is a lack of systematic evaluation and trade-off mechanism between carbon emission reduction and economic cost, which affects the feasibility and implementation effect of the measures. SUMMARY

[0006] The present application aims to solve the problem of inaccurate analysis of time sequence evolution characteristics of urban traffic carbon emission, and proposes a method for analyzing and controlling time sequence evolution characteristics of urban traffic carbon emission.

[0007] To achieve the above object, the present application is realized by the following technical solutions:

[0008] A city traffic carbon emission timing evolution characteristic analysis and control method, comprising the following steps:

[0009] S1. Constructing a long-term trend change coefficient of city traffic carbon emission evolution;

[0010] S2. Constructing a periodic fluctuation correction coefficient of city traffic carbon emission evolution;

[0011] S3. Constructing an intervention correction coefficient of major events on carbon emission;

[0012] S4. Based on the obtained long-term trend change coefficient of city traffic carbon emission evolution, periodic fluctuation correction coefficient of city traffic carbon emission evolution, and intervention correction coefficient of major events on carbon emission, constructing a city traffic carbon emission timing evolution basic decomposition model for calculating traffic carbon emission total amount at different times;

[0013] S5. Based on the traffic carbon emission total amount at different times obtained in step S4, through coordinated regulation and control in the dimensions of technological progress, traffic structure and energy structure, establishing a comprehensive regulation and control evaluation model to obtain comprehensive regulation and control evaluation model evaluation results;

[0014] S6. On the basis of traffic carbon emission amount, considering a reduction target function and an economic cost function, through constructing a double-objective optimization function, determining an optimal reduction time to realize control of city traffic carbon emission timing evolution characteristics.

[0015] Further, in step S1, considering that different cities in different development stages will present differentiated emission growth patterns, a growth rate adjustment coefficient is established, considering that cities from rapid growth to stable development to mature stage correspond to different emission characteristics, a development stage adjustment coefficient is established, and a long-term trend change correction coefficient of carbon emission is obtained .

[0016] Further, in step S2, considering that city traffic activities present periodic changes in multiple time scales, including intraday travel fluctuations, differences between weekdays and weekends, monthly activity rules, and annual seasonal changes, a periodic fluctuation correction coefficient of city traffic carbon emission evolution is constructed based on the fluctuation amplitude and length of the cycle .

[0017] Further, in step S3, considering the process in which major events have an impact on city traffic carbon emission, an intervention correction coefficient of major events on carbon emission is constructed based on the initial impact intensity of major events, the decay coefficient of major event impact, and the system recovery coefficient .

[0018] Further, the specific implementation method of step S4 includes the following steps:

[0019] S4.1. In order to accurately describe the interaction relationship among the long-term trend change coefficient of urban traffic carbon emission evolution, the periodic fluctuation correction coefficient of urban traffic carbon emission evolution, and the intervention correction coefficient of major events on carbon emission, a coupling correction term is constructed by wavelet analysis method , obtaining:

[0020]

[0021] wherein, is the wavelet coherence coefficient of components i and j at time t and scale s, with a value range of 0~1; is the coupling strength weight at scale s, reflecting the importance of different scale coupling effects, which is determined by historical data analysis, statistical analysis or benchmark analysis or expert experience; scale s is determined by historical data analysis or expert experience;

[0022]

[0023] wherein, is the cross wavelet spectrum of components i and j; and are the continuous wavelet transform coefficients of components i and j; is the time-frequency smoothing operator, which is determined by historical data analysis or expert experience;

[0024] S4.2. A basic decomposition model of urban traffic carbon emission time series evolution is constructed, and the interaction among components is reflected by the coupling correction term, and the total traffic carbon emission at time t is calculated as follows:

[0025]

[0026] wherein, is the total traffic carbon emission at time t; is the baseline carbon emission, which is determined by specific requirements or expert experience.

[0027] Further, the specific implementation method of step S5 includes the following steps:

[0028] S5.1. Establish evaluation indexes of the technology progress dimension, which evaluates the comprehensive contribution of vehicle energy efficiency improvement, intelligent transportation system application and new energy vehicle technology progress factors. Vehicle energy efficiency improvement is reflected in energy-saving technology improvement of traditional fuel vehicles; intelligent transportation system application is reflected in optimization of traffic flow organization and reduction of idling and congestion; new energy vehicle technology progress is reflected in carbon emissions in the vehicle use stage. The specific calculation formula of the evaluation index value X1 of the technology progress dimension is as follows:

[0029]

[0030] wherein, is the weight coefficient of the gth technology, reflecting the relative importance of different technologies to emission reduction, obtained from historical data; is the efficiency improvement index of the gth technology, indicating the emission reduction effect brought by technology progress, obtained from historical data; is the technology diffusion coefficient, representing the technology popularization speed, obtained from historical data analysis;

[0031] S5.2. Establish evaluation indexes of the traffic structure dimension, which evaluates the influence of changes in urban traffic mode composition on emission reduction, including the improvement of public transport share, the improvement of slow traffic system and the promotion of shared transportation. At the same time, the influence of travel distance on the emission reduction effect of different traffic modes is considered, and a distance attenuation coefficient is introduced for correction. The specific calculation formula of the evaluation index value X2 of the traffic structure dimension is as follows:

[0032]

[0033] wherein, is the weight coefficient of the hth travel mode, reflecting the difference in emission reduction contribution of various traffic modes, determined from historical data; is the share of the hth travel mode, indicating the proportion of the travel mode in the total travel volume, determined from historical data; is the distance attenuation coefficient, representing the influence of travel distance on emission reduction effect, determined from historical data; is the average travel distance of the hth travel mode, determined from historical data; is the reference distance, determined from actual demand or expert experience;

[0034] S5.3. Establish evaluation indexes of the energy structure dimension, which evaluates the factors of the increase of new energy replacement rate and the change of clean energy use proportion. Energy structure dimension directly affects the carbon emissions of transportation tools and produces upstream emission reduction effect through energy supply chain. The specific calculation formula of the evaluation index value X3 of the energy structure dimension is as follows:

[0035]

[0036] wherein, is the weight coefficient of the kth energy, reflecting the relative importance of each type of energy to emission reduction, determined by historical data; is the proportion of the kth energy, determined by historical data; is the carbon emission factor of the kth energy, determined by historical data; is the energy conversion efficiency coefficient, reflecting the influence of energy utilization efficiency on emission reduction, determined by historical data; is the reference carbon emission factor, determined by actual demand or expert experience;

[0037] S5.4. Based on the evaluation indexes of the technology progress dimension, the evaluation indexes of the traffic structure dimension, and the evaluation indexes of the energy structure dimension obtained in steps S5.1-S5.3, a comprehensive control evaluation model is established to calculate the comprehensive emission reduction potential, expressed as:

[0038]

[0039] wherein, is the comprehensive emission reduction potential, representing the actual achievable carbon reduction amount after considering the coupling effects of each dimension; is the coupling coefficient between dimension c and dimension d, representing the mutual influence degree between the two dimensions, which can be determined by historical data analysis or expert experience; and are the evaluation indexes of dimension c and dimension d, respectively; and are the interaction intensity parameters between dimension c and dimension d, respectively, used to adjust the attenuation characteristics of the coupling strength, determined based on empirical research or model fitting; is the baseline carbon emission amount, determined by specific demand or expert experience; is the comprehensive emission reduction adjustment factor.

[0040] Further, the specific implementation method of step S6 includes the following steps:

[0041] S6.1. Constructing an emission reduction objective function f1;

[0042] Considering the gradual characteristics of the emission reduction process, an exponential decay model is used to construct the emission reduction objective function, the target is decomposed in combination with the comprehensive emission reduction potential RP, and a time discount factor is introduced to reflect the importance of recent emission reduction, and the expression is:

[0043]

[0044] wherein, is the carbon emission amount of the planning target, determined by the management department; is the emission reduction intensity coefficient, obtained from historical data; is the time discount rate, reflecting the value weight of recent emission reduction, determined by historical data;

[0045] S6.2. Constructing economic cost function f2;

[0046] The economic cost of emission reduction measures includes direct investment cost and indirect opportunity cost, and presents a nonlinear growth characteristic. The economic cost function considers the cost difference of different regions and different types of measures, as well as the time value factor, and the expression is:

[0047]

[0048] wherein, is the unit emission reduction cost of the pth measure, determined by historical data; is the emission reduction amount of the pth measure, determined by historical data; is the cost elasticity coefficient, reflecting the marginal cost increasing characteristic, determined by historical data; (t) is the social discount rate, reflecting the time value of money, determined by historical data;

[0049]

[0050] wherein, is the basic discount rate, determined by historical data; is the discount rate growth coefficient, determined by historical data; is the time sensitivity parameter, determined by historical data or expert experience;

[0051] S6.3. Constructing a double-objective optimization function to comprehensively consider the two dimensions of emission reduction target and economic cost, and determining the optimal emission reduction time by constructing a double-objective optimization function, and the expression is:

[0052]

[0053] wherein, is the comprehensive optimization target function, representing the weighted result of emission reduction effect and economic cost; , is the weight coefficient of emission reduction target and economic cost; is the emission reduction target function, is the minimum value of the emission reduction target function, is the maximum value of the emission reduction target function; is the economic cost function, is the minimum value of the economic cost function, is the maximum value of the economic cost function.

[0054] Since and All are functions of time t, then the comprehensive optimization objective function Z is a function of t, taking Z as a function of t with other parameters known;

[0055] In order to achieve the optimal balance between emission reduction effect and economic cost, the time point that makes the value of Z minimum is calculated, that is:

[0056]

[0057] Wherein, Indicates the variable at the minimum value; That is, the optimal control time point, indicating that the traffic carbon emission control measures are implemented at this time point, and the overall system optimization is realized under the premise of considering carbon emission reduction effect and economic feasibility.

[0058] The beneficial effects of the present application are:

[0059] The urban traffic carbon emission time sequence evolution characteristic analysis and control method provided by the present application comprehensively considers trend, period and interference factors, constructs a complete time sequence analysis framework, can accurately reveal the evolution law of carbon emission, and provides data support and theoretical basis for prediction and regulation of traffic carbon emission. By introducing multi-dimensional factors such as economic cost and energy structure, the present application realizes the optimal selection of emission reduction time, ensures that the control cost is spent while meeting the emission reduction target, and provides a practical quantitative tool for policy making and traffic planning.

[0060] The urban traffic carbon emission time sequence evolution characteristic analysis and control method provided by the present application fully combines the complexity of the traffic system and economic constraints, and the carbon emission reduction path selection lacks scientific basis. The present application establishes a double-target optimization model taking emission reduction effect and economic cost as the target, clearly defines the control time point and emission reduction path, and improves the scientificity and operability of carbon emission management. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The flow chart of the urban traffic carbon emission time sequence evolution characteristic analysis and control method provided by the present application;

[0062] Figure 2 The future 5-year comprehensive optimization objective value column chart calculated by using the urban traffic carbon emission time sequence evolution characteristic analysis and control method provided by the present application. DETAILED DESCRIPTION

[0063] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application, that is, the specific embodiments described herein are only a part of the embodiments of the present application, but not all the specific embodiments. The components of the specific embodiments of the present application generally described and shown in the accompanying drawings can be arranged and designed in various different configurations, and the present application can also have other embodiments.

[0064] Therefore, the detailed description of the specific embodiments of the present application provided below in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected specific embodiments of the present application. Based on the specific embodiments of the present application, all other specific embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0065] In order to further understand the inventive content, characteristics and effects of the present application, the following specific embodiments are exemplified, and the accompanying drawings are combined Figure 1 - the accompanying drawings Figure 2 The detailed description is as follows:

[0066] Embodiment 1:

[0067] A city traffic carbon emission timing evolution characteristic analysis and control method, comprising the following steps:

[0068] S1. Constructing a long-term trend change coefficient of urban traffic carbon emission evolution;

[0069] Further, in step S1, considering that different cities will show different emission growth patterns in different development stages, a growth rate adjustment coefficient is established, considering that cities from rapid growth to stable development to mature stage correspond to different emission characteristics, a development stage adjustment coefficient is established, and a long-term trend change correction coefficient of carbon emission is obtained ; The calculation formula of the long-term trend change correction coefficient of carbon emission is as follows:

[0070]

[0071] Wherein, is a basic emission growth coefficient, is a growth rate adjustment coefficient, is a development stage adjustment coefficient, which can be determined by historical data analysis, statistical analysis, benchmarking analysis or expert experience; is a calculation time point; The reference time point is determined by specific requirements or expert experience. Long-term trend changes are the basic characteristics of the evolution of urban traffic carbon emissions, reflecting the changes in the development process of the urban traffic system. The long-term trend change reflects the long-term evolution characteristics of urban traffic carbon emissions. When constructing the long-term trend change, two key factors need to be considered: first, the nonlinear characteristics of urban development, different cities will show different emission growth patterns at different development stages; second, the difference between development stages, from rapid growth to stable development to mature stage, each stage has its specific emission characteristics.

[0072] S2. Constructing a periodic fluctuation correction coefficient of the evolution of urban traffic carbon emissions;

[0073] Further, in step S2, the periodic changes of urban traffic activities are considered, including daily travel fluctuations, differences between weekdays and weekends, monthly activity patterns, and annual seasonal changes. Based on the fluctuation amplitude and the length of the cycle, a periodic fluctuation correction coefficient of the evolution of urban traffic carbon emissions is constructed The calculation method of the periodic fluctuation correction coefficient of carbon emissions is as follows:

[0074]

[0075] wherein, is the fluctuation amplitude coefficient of the cycle, which can be determined by historical data analysis, statistical analysis, benchmarking analysis or expert experience; is the length of the cycle, which is determined by specific requirements or expert experience; is the phase difference of the cycle, with a value range of 0~2 can be obtained by statistical analysis of historical data; is the decay coefficient of the cycle, which can be obtained by statistical analysis of historical data; is the annual cycle adjustment coefficient, with a value range of 0~1, which can be obtained by statistical analysis of historical data; is the length of the annual cycle, usually taking the value of 365. Periodic changes are one of the important characteristics of urban traffic carbon emissions, reflecting the regularity of human activities and the periodic characteristics of social and economic operation. This periodicity is reflected in multiple time scales, from daily commuting to seasonal changes, each of which has its specific characteristics. Urban traffic activities show periodic changes in multiple time scales, including daily travel fluctuations, differences between weekdays and weekends, monthly activity patterns, and annual seasonal changes. Each cycle has its specific fluctuation amplitude and phase characteristics.

[0076] S3. Constructing an intervention correction coefficient of major events on carbon emissions;

[0077] Further, the process of considering the influence of the major event on the urban traffic carbon emission in step S3 is based on the initial influence intensity of the major event, the attenuation coefficient of the influence of the major event, and the system recovery coefficient to construct an intervention correction coefficient of the major event on the carbon emission ; the intervention correction coefficient of the major event on the carbon emission is calculated according to the following formula:

[0078]

[0079] wherein, is a time point of the event occurrence. is the initial influence intensity of the major event, reflecting the direct influence degree of the event on the emission; is the attenuation coefficient of the event influence, reflecting the duration of the influence; is the system recovery coefficient, reflecting the adaptation ability of the system. The several coefficients can be determined through historical data analysis, statistical analysis, benchmarking analysis or expert experience. The intervention term reflects the short-term impact effect of the sudden event or the major policy on the urban traffic carbon emission. Such influence has the characteristics of suddenness, short-termness and uncertainty, and needs to be described by a specific mathematical model to describe the influence process and the recovery mechanism. The major event will have a significant but temporary influence on the urban traffic carbon emission. Such influence usually has obvious time characteristics: the influence is the largest in the initial period of the event, then gradually decreases, and finally the system will recover to the normal state.

[0080] S4. Based on the obtained long-term trend change coefficient of the evolution of the urban traffic carbon emission, the periodic fluctuation correction coefficient of the evolution of the urban traffic carbon emission, and the intervention correction coefficient of the major event on the carbon emission, a basic decomposition model of the time sequence evolution of the urban traffic carbon emission is constructed, which is used to calculate the total traffic carbon emission at different times;

[0081] Further, the specific implementation method of step S4 includes the following steps:

[0082] S4.1. In order to accurately describe the interaction relationship of the long-term trend change coefficient of the evolution of the urban traffic carbon emission, the periodic fluctuation correction coefficient of the evolution of the urban traffic carbon emission, and the intervention correction coefficient of the major event on the carbon emission, a coupling correction term is constructed by a wavelet analysis method , obtaining:

[0083]

[0084] wherein, is the wavelet coherence coefficient of components i and j at time t and scale s, and the value range is 0-1; is the coupling strength weight on scale s, reflecting the importance of the coupling effect of different scales, determined by historical data analysis, statistical analysis or benchmark analysis or expert experience; scale s is determined by historical data analysis or expert experience;

[0085]

[0086] wherein, is the cross wavelet spectrum of components i and j; and is the continuous wavelet transform coefficient of components i and j; is the time-frequency smoothing operator, determined by historical data analysis or expert experience;

[0087] The introduction of the coupling correction term is to accurately describe the interaction relationship between the influencing factors. Such interaction has complex time-frequency characteristics, and needs to be analyzed by using advanced mathematical tools. Through the wavelet analysis method, the coupling relationship between components can be investigated in both time and frequency dimensions. The interaction between components has obvious time-frequency characteristics, and the coupling strength and phase relationship on different scales may be different. Wavelet transform can analyze signal characteristics in both time and frequency domains, and is particularly suitable for studying the mutual relationship between non-stationary time series.

[0088] S4.2. Build a basic decomposition model of urban traffic carbon emission time evolution, and reflect the interaction between components through the coupling correction term to obtain the total traffic carbon emission calculation formula at time t as follows:

[0089]

[0090] wherein, is the total traffic carbon emission at time t; is the baseline carbon emission, determined by specific requirements or expert experience. In building the basic decomposition model, the complexity and dynamic characteristics of the urban traffic carbon emission system need to be fully recognized. The system will produce periodic fluctuations with the change of human activity rules and external environment. Urban traffic carbon emission, as a complex dynamic system, has time evolution characteristics in multiple dimensions. The basic decomposition model includes a trend term, a periodic term and an intervention term, and reflects the interaction between components through a coupling correction term.

[0091] S5. Based on the total traffic carbon emission at different times obtained in step S4, the comprehensive control and evaluation model is established through the coordinated control of the technology progress dimension, the traffic structure dimension and the energy structure dimension, and the comprehensive control and evaluation model evaluation result is obtained;

[0092] Further, the specific implementation method of step S5 includes the following steps:

[0093] S5.1. Establish evaluation indexes of the technology progress dimension, which evaluates the comprehensive contribution of vehicle energy efficiency improvement, intelligent transportation system application, and new energy vehicle technology progress factors. Vehicle energy efficiency improvement is reflected in energy-saving technical improvements of traditional fuel vehicles. Intelligent transportation system application is reflected in optimizing traffic flow organization and reducing idling and congestion. New energy vehicle technology progress is reflected in carbon emissions during the vehicle use stage. The specific calculation formula of the evaluation index value X1 of the technology progress dimension is as follows:

[0094]

[0095] wherein, is the weight coefficient of the gth technology, reflecting the relative importance of different technologies to emission reduction, obtained from historical data; is the efficiency improvement index of the gth technology, indicating the emission reduction effect brought by technology progress, obtained from historical data; is the technology diffusion coefficient, representing the technology popularization speed, obtained from historical data analysis;

[0096] S5.2. Establish evaluation indexes of the traffic structure dimension, which evaluates the influence of changes in urban traffic mode composition on emission reduction, including the improvement of public transportation share, the improvement of slow traffic system, and the promotion of shared transportation. At the same time, the influence of travel distance on the emission reduction effect of different traffic modes is considered, and a distance attenuation coefficient is introduced for correction. The specific calculation formula of the evaluation index value X2 of the traffic structure dimension is as follows:

[0097]

[0098] wherein, is the weight coefficient of the hth travel mode, reflecting the emission reduction contribution difference of various traffic modes, determined from historical data; is the share of the hth travel mode, indicating the proportion of the travel mode in the total travel volume, determined from historical data; is the distance attenuation coefficient, representing the influence of travel distance on emission reduction effect, determined from historical data; is the average travel distance of the hth travel mode, determined from historical data; is the reference distance, determined from actual demand or expert experience;

[0099] S5.3. Establish evaluation indexes of the energy structure dimension, which evaluates the factors of new energy replacement rate improvement and clean energy use proportion change. The energy structure dimension directly affects the carbon emissions of transportation tools and produces upstream emission reduction effects through the energy supply chain. The specific calculation formula of the evaluation index value X3 of the energy structure dimension is as follows:

[0100]

[0101] wherein, is the weight coefficient of the kth energy, reflecting the relative importance of each type of energy to emission reduction, determined by historical data; is the proportion of the kth energy, determined by historical data; is the carbon emission factor of the kth energy, determined by historical data; is the energy conversion efficiency coefficient, reflecting the influence of energy utilization efficiency on emission reduction, determined by historical data; is the reference carbon emission factor, determined by actual demand or expert experience;

[0102] S5.4. Based on the evaluation indexes of the technology progress dimension, the evaluation indexes of the traffic structure dimension, and the evaluation indexes of the energy structure dimension obtained in steps S5.1-S5.3, a comprehensive control evaluation model is established to calculate the comprehensive emission reduction potential, expressed as:

[0103]

[0104] wherein, is the comprehensive emission reduction potential, representing the actual achievable carbon reduction amount after considering the coupling effects of each dimension; is the coupling coefficient between dimension c and dimension d, representing the mutual influence degree between the two dimensions, which can be determined by historical data analysis or expert experience; and are the evaluation indexes of dimension c and dimension d, respectively; and are the interaction intensity parameters between dimension c and dimension d, respectively, used to adjust the attenuation characteristics of the coupling strength, determined based on empirical research or model fitting; is the baseline carbon emission amount, determined by specific demand or expert experience; is the comprehensive emission reduction adjustment factor. Based on the total traffic carbon emission amount E(t) at time t, a comprehensive control evaluation model needs to be established to achieve the carbon emission control target. This model determines the optimal emission reduction path through the coordinated control of the three dimensions of technology progress, traffic structure, and energy structure. For example, the progress of new energy vehicle technology will directly promote the optimization of energy structure, while driving the transformation of traffic structure; the optimization of traffic structure (such as the increase of public transportation share) will promote the popularization of new energy buses, and further promote technological innovation and energy structure adjustment.

[0105] S6. Based on the comprehensive emission reduction potential obtained in step S5, the traffic carbon emission amount, the emission reduction objective function, and the economic cost function are comprehensively considered, and a double-objective optimization function is constructed to determine the optimal emission reduction time, thereby achieving the control of the time evolution characteristics of urban traffic carbon emission.

[0106] Further, the specific implementation method of step S6 includes the following steps:

[0107] S6.1. Constructing the emission reduction target function f1;

[0108] Considering the gradual characteristics of the emission reduction process, an exponential decay model is used to construct the emission reduction target function, combined with the comprehensive emission reduction potential RP for target decomposition, and a time discount factor is introduced to reflect the importance of recent emission reduction, and the expression is:

[0109]

[0110] wherein, is the planned target carbon emissions, determined by the management department; is the emission reduction intensity coefficient, obtained from historical data; is the time discount rate, reflecting the value weight of recent emission reduction, determined by historical data;

[0111] S6.2. Constructing the economic cost function f2;

[0112] The economic cost of emission reduction measures includes direct investment cost and indirect opportunity cost, and presents a nonlinear growth characteristic, and the economic cost function considers the cost difference of different regions and different types of measures, as well as the time value factor, and the expression is:

[0113]

[0114] wherein, is the unit emission reduction cost of the pth measure, determined by historical data; is the emission reduction amount of the pth measure, determined by historical data; is the cost elasticity coefficient, reflecting the marginal cost increasing characteristic, determined by historical data; (t) is the social discount rate, reflecting the time value of money, determined by historical data;

[0115]

[0116] wherein, is the basic discount rate, determined by historical data; is the discount rate growth coefficient, determined by historical data; is the time sensitivity parameter, determined by historical data or expert experience;

[0117] S6.3. Constructing a double target optimization function, considering both the emission reduction target and the economic cost dimension, through the construction of a double target optimization function, the optimal emission reduction time is determined, and the expression is:

[0118]

[0119] wherein, Z is a comprehensive optimization objective function representing the weighted result of emission reduction effect and economic cost; , is a weight coefficient of emission reduction target and economic cost; is an emission reduction objective function, is a minimum value of the emission reduction objective function, is a maximum value of the emission reduction objective function; is an economic cost function, is a minimum value of the economic cost function, is a maximum value of the economic cost function.

[0120] Since and are functions of time t, the comprehensive optimization objective function Z is a function of t, and Z is regarded as a function of t with other parameters known;

[0121] In order to achieve the optimal balance between emission reduction effect and economic cost, the time point at which the value of Z is minimized is calculated, that is:

[0122]

[0123] wherein, represents the variable at the minimum value; that is, the optimal control time point, representing the time point at which traffic carbon emission control measures are implemented, achieving the overall optimization of the system under the premise of considering carbon emission reduction effect and economic feasibility.

[0124] Further, by using the above method, the time sequence evolution characteristics analysis and control of traffic carbon emission of a certain region are carried out. The planning target carbon emission of the selected region is 1000 tons, the current carbon emission is 1150 tons, the emission reduction is 150 tons, the time discount rate is 0.05, the cost elasticity coefficient is 1.1, and the basic discount rate is 0.035. The carbon emission control is carried out by using energy saving technology reconstruction and new energy vehicle promotion, and the comprehensive optimization objective value of the next 5 years is calculated as shown in Figure 2

[0125] ​It has to be noted that the terms "first", "second", and the like in connection with an entity or action refer to this entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises", "comprising", or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without further constraints, exclude the presence of additional elements of the process, method, article, or apparatus.

[0126] While the application has been described with reference to specific implementations thereof, it should be understood that various modifications and substitutions can be made by those skilled in the art without departing from the scope of the present application. In particular, any one of the features of the present application disclosed above can be utilized independently of any other and the scope of the application should not be limited by the specific embodiments disclosed herein, but should be given the widest coverage possible in its true scope.

Claims

1. A method for analyzing and controlling the evolution of urban traffic carbon emissions over time, characterized in that, Comprise the following steps: S1. Constructing the long-term trend change coefficient of urban traffic carbon emission evolution; S2. Constructing the periodic fluctuation correction coefficient of urban traffic carbon emission evolution; S3. Constructing the intervention correction coefficient of carbon emission of major events; S4. Based on the obtained long-term trend change coefficient of urban traffic carbon emission evolution, the periodic fluctuation correction coefficient of urban traffic carbon emission evolution, the intervention correction coefficient of carbon emission of major events, constructing the basic decomposition model of urban traffic carbon emission time evolution, used for calculating the total traffic carbon emission at different time; The specific implementation method of step S4 comprises the following steps: S4.

1. In order to accurately describe the interaction relationship among the long-term trend change coefficient of urban traffic carbon emission evolution, the periodic fluctuation correction coefficient of urban traffic carbon emission evolution, and the intervention correction coefficient of major events on carbon emission, the coupling correction term is constructed by wavelet analysis method , which is obtained as follows: ； wherein, is the wavelet coherence coefficient of component i and j at time t and scale s, with a value range of 0~1; is the coupling strength weight at scale s, reflecting the importance of the coupling effect of different scales, which is determined by historical data analysis, statistical analysis or benchmark analysis or expert experience; scale s is determined by historical data analysis or expert experience; ； wherein, is the cross wavelet spectrum of components i and j; and is the continuous wavelet transform coefficient of components i and j; is a time-frequency smoothing operator determined from historical data analysis or expert experience; S4.

2. Constructing the basic decomposition model of urban traffic carbon emission time evolution, reflecting the interaction between each component through coupling correction term, the total traffic carbon emission calculation formula at time t is as follows: ； wherein, is the total traffic carbon emission at time t; is the baseline carbon emission, determined by specific requirements or expert experience; is the long-term trend change correction coefficient of carbon emission, is the periodic fluctuation correction coefficient of urban traffic carbon emission evolution, I(t) is the intervention correction coefficient of major events on carbon emission; S5. Based on the total traffic carbon emission at different time obtained in step S4, through the coordinated control of technology progress dimension, traffic structure dimension and energy structure dimension, establishing the comprehensive control evaluation model, obtaining the evaluation results of the comprehensive control evaluation model; The specific implementation method of step S5 comprises the following steps: S5.

1. Establishing the evaluation index of technology progress dimension, the comprehensive contribution of vehicle energy efficiency improvement, intelligent transportation system application and new energy vehicle technology progress factor is evaluated in technology progress dimension; Vehicle energy efficiency improvement is reflected in the energy saving technology improvement of traditional fuel vehicles; Intelligent transportation system application is reflected in optimizing traffic flow organization, reducing idling and congestion; New energy vehicle technology progress is reflected in carbon emission in vehicle use stage; The specific calculation formula of the evaluation index value X1 of technology progress dimension is as follows: ； wherein, is the weight coefficient of the gth technology, reflecting the relative importance of different technologies to emission reduction, obtained from historical data; is the efficiency improvement index of the gth technology, representing the emission reduction effect brought by technological progress, obtained from historical data; is the technology diffusion coefficient, representing the technology popularization speed, obtained from historical data analysis; S5.

2. Establishing the evaluation index of traffic structure dimension, the influence of the change of urban traffic mode composition on emission reduction is evaluated in traffic structure dimension, including the promotion of public transportation share, the improvement of slow traffic system, the promotion of shared transportation, at the same time, considering the influence of travel distance on the emission reduction effect of different traffic modes, introducing distance attenuation coefficient for correction; The specific calculation formula of the evaluation index value X2 of traffic structure dimension is as follows: ； wherein, is the weight coefficient of the hth travel mode, reflecting the difference in emission reduction contribution of various traffic modes, determined by historical data; is the hth travel mode share, indicating the proportion of the travel mode in the total travel volume, determined by historical data; is the distance attenuation coefficient, representing the influence degree of travel distance on emission reduction effect, determined by historical data; is the average travel distance of the hth travel mode, determined by historical data; is the reference distance, determined by actual demand or expert experience; S5.

3. Establishing the evaluation index of energy structure dimension, the promotion of new energy replacement rate and the change factor of clean energy use proportion are evaluated in energy structure dimension; Energy structure dimension directly affects the carbon emission of transportation tools and produces upstream emission reduction effect through energy supply chain, the specific calculation formula of the evaluation index value X3 of energy structure dimension is as follows: ； wherein, is the weight coefficient of the kth energy source, reflecting the relative importance of each type of energy to emission reduction, determined by historical data; is the proportion of the kth energy source, determined by historical data; is the carbon emission factor of the kth energy source, determined by historical data; is the energy conversion efficiency coefficient, reflecting the influence of energy utilization efficiency on emission reduction, determined by historical data; is the reference carbon emission factor, determined by actual demand or expert experience; S5.

4. Based on the evaluation index of technology progress dimension, the evaluation index of traffic structure dimension and the evaluation index of energy structure dimension obtained in step S5.1-step S5.3, establishing the comprehensive control evaluation model to calculate the comprehensive emission reduction potential, the expression is as follows: ； wherein, is the actual achievable carbon reduction amount after considering the coupling effect of each dimension, representing the comprehensive emission reduction potential; is the coupling coefficient between dimension c and dimension d, representing the degree of mutual influence between the two dimensions, which can be determined by historical data analysis or expert experience; and are the evaluation indexes of dimension c and dimension d, respectively; and are the interaction intensity parameters between dimension c and dimension d, respectively, used to adjust the attenuation characteristics of coupling strength, which are determined based on empirical research or model fitting; is the baseline carbon emission amount, which is determined by specific requirements or expert experience; is the comprehensive emission reduction adjustment factor; S6. On the basis of traffic carbon emission, considering the emission reduction objective function and economic cost function, through constructing the double objective optimization function, determining the optimal emission reduction time, realizing the control of urban traffic carbon emission time evolution characteristics.

2. The urban traffic carbon emission timing evolution feature analysis and control method according to claim 1, characterized in that, In step S1, the growth rate adjustment coefficient is established considering that different cities in different development stages will show different emission growth patterns, the development stage adjustment coefficient is established considering that cities correspond to different emission characteristics from rapid growth to stable development to mature stage, and the long-term trend change correction coefficient of carbon emission is obtained .

3. The urban traffic carbon emission timing evolution feature analysis and control method according to claim 1 or 2, characterized in that, The periodicity of urban traffic activities in step S2 presents multiple time scales, including intra-day travel fluctuations, differences between weekdays and weekends, monthly activity patterns, and annual seasonal changes. Based on the fluctuation amplitude and the length of the cycle, a periodic fluctuation correction coefficient for the evolution of urban traffic carbon emissions is constructed .

4. The urban traffic carbon emission timing evolution feature analysis and control method according to claim 3, characterized in that, The process of considering the major event in step S3 has an impact on the carbon emissions of urban traffic, and the intervention correction coefficient I(t) of the major event on the carbon emissions is constructed based on the initial influence intensity of the major event, the attenuation coefficient of the major event influence and the system recovery coefficient.

5. The urban traffic carbon emission timing evolution feature analysis and control method according to claim 4, characterized in that, The specific implementation method of step S6 includes the following steps: S6.

1. Constructing an emission reduction target function f1; Considering that the emission reduction process has a gradual characteristic, an exponential decay model is used to construct the emission reduction target function, the target is decomposed in combination with the comprehensive emission reduction potential RP, and a time discount factor is introduced to reflect the importance of recent emission reduction, so that the expression is: ； Wherein, The carbon emission amount for planning target is determined by the management department; The emission reduction intensity coefficient is obtained from historical data; The time discount rate reflects the value weight of recent emission reduction and is determined from historical data; S6.

2. Constructing an economic cost function f2; The economic cost of the emission reduction measures includes direct investment cost and indirect opportunity cost, and presents a nonlinear growth characteristic, the economic cost function considers the cost difference of different regions and different types of measures, and the time value factor, so that the expression is: ； wherein, is the unit abatement cost of the pth measure, determined from historical data; is the abatement amount of the pth measure, determined from historical data; is the cost elasticity coefficient, reflecting the marginal cost increasing property, determined from historical data; (t) is the social discount rate, reflecting the time value of money, determined from historical data; ； wherein, is a base discount rate determined from historical data; is a discount rate growth coefficient determined from historical data; is a time sensitivity parameter determined from historical data or expert experience; S6.

3. Constructing a double-target optimization function, considering two dimensions of emission reduction target and economic cost, determining the optimal emission reduction time by constructing a double-target optimization function, so that the expression is: ； wherein, is a comprehensive optimization objective function, representing the weighted result of emission reduction effect and economic cost; , are weight coefficients of emission reduction target and economic cost; is an emission reduction target function, is a minimum value of the emission reduction target function, is a maximum value of the emission reduction target function; is an economic cost function, is a minimum value of the economic cost function, is a maximum value of the economic cost function; Since and are functions of time t, the overall optimization objective function Z is a function of t, with other parameters known, Z is considered as a function of t with t as variable; In order to achieve the optimal balance between emission reduction effect and economic cost, the time point that makes the Z value minimum is calculated, that is: ； wherein, denotes the variable at the minimum; is the optimal control time point, which means that the traffic carbon emission control measures are implemented at this time point, and the overall system optimization is achieved under the premise of considering the carbon emission reduction effect and economic feasibility.

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