CN115496275A

CN115496275A - Urban traffic carbon emission prediction method, device, electronic equipment and storage medium

Info

Publication number: CN115496275A
Application number: CN202211128263.1A
Authority: CN
Inventors: 吴沐彦; 肖诚斌; 王博; 辛芳; 陈一锌; 赵彬
Original assignee: China Everbright Green Technology Innovation Research Institute Co ltd; Everbright Envirotech China Ltd; Everbright Environmental Protection Technology Research Institute Shenzhen Co Ltd
Current assignee: China Everbright Green Technology Innovation Research Institute Co ltd; Everbright Envirotech China Ltd; Everbright Environmental Protection Technology Research Institute Shenzhen Co Ltd
Priority date: 2022-09-16
Filing date: 2022-09-16
Publication date: 2022-12-20

Abstract

The application provides a method, a device, electronic equipment and a storage medium for predicting urban traffic carbon emission, wherein the method comprises the following steps: constructing an urban traffic carbon emission prediction model; acquiring historical urban traffic carbon emission related data; determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through an urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factors; and judging whether the future urban traffic carbon emission predicted value is accordant with a preset urban traffic carbon emission target value, and if not, adjusting the influence factor parameter and the urban traffic carbon emission target value so as to enable the future urban traffic carbon emission predicted value obtained based on the adjusted influence factor parameter to be accordant with the adjusted urban traffic carbon emission target value. The method and the device break through the limitation of related technologies and can provide quantitative reference for future policy making.

Description

Urban traffic carbon emission prediction method, device, electronic equipment and storage medium

Technical Field

The present application relates to the field of carbon emission technologies, and in particular, to a method and an apparatus for predicting carbon emission in urban traffic, an electronic device, and a storage medium.

Background

Global warming has become one of the biggest environmental threats facing mankind in the world today. Cities as economic activity centers of production and life are main sources of energy consumption and carbon emission, so the cities are also important carriers for realizing carbon peak reaching and carbon neutralization targets. The carbon neutralization of cities is realized, the greenhouse gas emission needs to be reduced by adopting corresponding policy measures and technical means, and when carbon reduction policies and measures are implemented, the carbon dioxide emission and future trends of the cities need to be accounted and predicted, so that quantitative basis is provided for the formulation of carbon neutralization policies and routes.

Transportation is an important carrier of urban energy consumption and also one of the main emission sources of urban carbon emission. In order to better establish a corresponding carbon reduction policy for transportation, the development of the transportation carbon reduction technology needs to account the carbon emission condition of urban transportation. In the related technology, the traffic carbon emission accounting method mainly calculates regional traffic carbon emission from a macroscopic level and mainly calculates based on three parameters of average carbon emission factors of different vehicles, the holding amount of motor vehicles and the average driving distance of different vehicles. The partial accounting method further considers the factors such as the traffic flow of the driving road section, the driving state of the motor vehicle and the like at the mesoscopic level, further perfects the method for calculating the carbon emission of the road traffic, and improves the accuracy of the accounting of the carbon emission of the road traffic.

However, the method mainly performs accounting on the existing carbon emission data, lacks prediction on the future trend of the carbon emission of transportation, and cannot provide quantitative reference for future policy making.

In view of the above problems, the present application proposes a new urban traffic carbon emission prediction method, apparatus, electronic device and storage medium to at least partially solve the above problems.

Disclosure of Invention

In this summary, concepts in a simplified form are introduced that are further described in the detailed description. The summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

An embodiment of the present application provides a method for predicting urban traffic carbon emission, including: constructing an urban traffic carbon emission prediction model; acquiring historical urban traffic carbon emission related data; determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and the historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factor; and judging whether the future urban traffic carbon emission predicted value is in accordance with a preset urban traffic carbon emission target value, and if not, adjusting the influence factor parameter and the urban traffic carbon emission target value so as to enable the future urban traffic carbon emission predicted value obtained based on the adjusted influence factor parameter to be in accordance with the adjusted urban traffic carbon emission target value.

In one example, the adjusting the impact factor parameter and the urban traffic carbon emission target value to make a future urban traffic carbon emission predicted value based on the adjusted impact factor parameter conform to the adjusted urban traffic carbon emission target value comprises: determining a first type urban traffic carbon emission influence factor based on the adjusted influence factor parameter and the historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factor; and judging whether the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value, if not, readjusting the influence factor parameter and the urban traffic carbon emission target value, and repeating the steps until the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value.

In one example, the constructing the urban traffic carbon emission prediction model comprises: classifying the traffic types of the cities; and constructing the urban traffic carbon emission prediction model according to the classification result.

In one example, the first type of urban traffic carbon emission impact factor comprises a future motorization proportion.

In one example, the impact factor parameter includes a final value of a motorization ratio of a motor vehicle at the urban traffic carbon emission target value.

In one example, the determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and the historical urban traffic carbon emission related data comprises: determining the historical motor vehicle electromotion proportion according to the number of the electric vehicles and the number of the non-electric vehicles in the historical motor vehicle; determining the future motor vehicle motoring proportion according to the historical motor vehicle motoring proportion and the final value of the motor vehicle motoring proportion.

In one example, the determining the future vehicle motoring proportion as a function of the historical vehicle motoring proportion and a final value of the vehicle motoring proportion includes: selecting an S-shaped curve model; calculating the future vehicle motorization ratio by the sigmoid curve model based on the historical vehicle motorization ratio and a final value of the vehicle motorization ratio.

In one example, the determining a predicted value of future urban traffic carbon emission by the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission impact factor comprises: determining a second type of urban traffic carbon emission influence factor, wherein the second type of urban traffic carbon emission influence factor comprises at least one of the following factors: direct emission intensity of motor vehicles, electrical emission intensity of motor vehicles, number of motor vehicles in the future; determining the urban traffic carbon emission prediction value through the urban traffic carbon emission prediction model based on the future motor vehicle motorization proportion and the second type urban traffic carbon emission influence factor.

In one example, the determining the urban traffic carbon emission prediction value by the urban traffic carbon emission prediction model based on the future motorvehicle motorization ratio and the second type urban traffic carbon emission impact factor comprises: calculating the carbon emission of the electric vehicle according to the future electric vehicle motorization proportion, the electric vehicle power consumption emission intensity and the future number of the electric vehicles; calculating the carbon emission of the non-electric vehicles according to the future motor vehicle electromotion proportion, the direct emission intensity of the motor vehicles and the future number of the motor vehicles; and calculating the urban traffic carbon emission predicted value according to the carbon emission of the electric vehicle and the carbon emission of the non-electric vehicle.

In one example, when the second type of urban traffic carbon emission impact factor is a direct emission intensity of a motor vehicle, the determining the second type of urban traffic carbon emission impact factor comprises: and calculating the direct emission intensity of the motor vehicle according to the average driving distance of the motor vehicle, the energy consumption per kilometer of the motor vehicle and the unit energy consumption carbon emission of the motor vehicle.

In one example, when the second type of urban traffic carbon emission impact factor is a power consumption emission intensity of a motor vehicle, the determining the second type of urban traffic carbon emission impact factor includes: determining the unit power consumption carbon emission of the future motor vehicle according to the unit power consumption carbon emission variation trend of the motor vehicle and the unit power consumption carbon emission of the historical motor vehicle; and calculating the power consumption emission intensity of the motor vehicle according to the average driving distance of the motor vehicle, the energy consumption per kilometer of the motor vehicle and the unit power consumption carbon emission of the future motor vehicle.

In one example, when the second type of urban traffic carbon emission impact factor is a number of future vehicles, the determining the second type of urban traffic carbon emission impact factor includes: when the type of the motor vehicle is a public vehicle, the number of the future motor vehicles is equal to the historical number of the motor vehicles; when the type of the motor vehicle is a private vehicle, the number of the future motor vehicles is determined according to the historical number of the motor vehicles.

In one example, the determining a future number of vehicles based on the historical number of vehicles includes: confirming a future automobile consumption capacity value; calculating a regression constant according to the historical number of the motor vehicles and the historical automobile consumption capacity value; and calculating the future number of the motor vehicles according to the regression constant and the future automobile consumption capacity value.

In one example, the confirming a future automobile consumption capability value includes: determining the average price of the future automobile according to the flatulence index and the historical average price of the automobile; determining a future domestic production total value of everyone according to the economic growth prediction rate and the historical domestic production total value of everyone; and determining the automobile consumption capacity value according to the future average domestic production total value of everyone and the future average price of the automobile.

In another aspect, an embodiment of the present invention provides an urban traffic carbon emission prediction apparatus, including: the prediction model construction module is used for constructing an urban traffic carbon emission prediction model; the historical data acquisition module is used for acquiring historical urban traffic carbon emission related data; the influence factor determination module is used for determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and the historical urban traffic carbon emission related data; the carbon emission predicted value determining module is used for determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factors; and the judging module is used for judging whether the future urban traffic carbon emission predicted value is consistent with a preset urban traffic carbon emission target value or not, and adjusting the influence factor parameter and the urban traffic carbon emission target value when the future urban traffic carbon emission predicted value is not consistent with the preset urban traffic carbon emission target value, so that the future urban traffic carbon emission predicted value obtained based on the adjusted influence factor parameter is consistent with the adjusted urban traffic carbon emission target value.

In another aspect, an electronic device includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the method for predicting carbon emission in urban traffic.

Yet another aspect of the embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements a method for predicting carbon emissions from urban traffic according to any one of the above.

According to the urban traffic carbon emission prediction method, the urban traffic carbon emission prediction device, the electronic equipment and the storage medium, the influence factor parameter is introduced, the influence factor parameter and the urban traffic carbon emission target value can be continuously adjusted in the process of comparing the urban traffic carbon emission predicted value calculated by the traffic carbon emission prediction model with the urban traffic carbon emission target value until the urban traffic carbon emission predicted value is consistent with the urban traffic carbon emission target value, so that the urban traffic future carbon emission situation is predicted, the urban traffic carbon neutralization target can be formulated and traffic carbon reduction planning can be carried out under the target, the limitation that only existing carbon emission data can be subjected to accounting in the related technology is broken through, quantitative reference can be provided for future policy formulation, and the method of introducing and adjusting the influence factor parameter is higher in prediction accuracy compared with the method of predicting the future urban traffic carbon emission only through historical urban traffic carbon emission related data.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings may be obtained according to the drawings without inventive labor.

In the drawings:

FIG. 1 shows a schematic block diagram of an electronic device according to an embodiment of the application;

FIG. 2 shows a schematic flow diagram of a method for urban traffic carbon emission prediction according to an embodiment of the present application;

FIG. 3 shows a schematic flow diagram of a method for urban traffic carbon emission prediction according to another embodiment of the present application;

FIG. 4 shows a schematic block diagram of an urban traffic carbon emission prediction apparatus according to an embodiment of the present application;

fig. 5 shows a schematic block diagram of an electronic device according to an embodiment of the application.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, exemplary embodiments according to the present application will be described in detail below with reference to the accompanying drawings. It should be apparent that the described embodiments are only a few embodiments of the present application, and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the application described in the application without inventive step, shall fall within the scope of protection of the application.

In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present application. It will be apparent, however, to one skilled in the art, that the present application may be practiced without one or more of these specific details. In other instances, well-known features of the art have not been described in order to avoid obscuring the present application.

It is to be understood that the present application may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of the associated listed items.

In order to provide a thorough understanding of the present application, a detailed structure will be provided in the following description in order to explain the technical solution proposed in the present application. Alternative embodiments of the present application are described in detail below, however, the present application may have other implementations in addition to these detailed descriptions.

First, an example electronic device 100 for implementing the urban traffic carbon emission prediction method and apparatus according to an embodiment of the present invention is described with reference to fig. 1.

As shown in FIG. 1, electronic device 100 includes one or more processors 102, one or more memories 104, input devices 106, and output devices 108, which are interconnected by a bus system 110 and/or other form of connection mechanism (not shown). It should be noted that the components and structure of the electronic device 100 shown in fig. 1 are exemplary only, and not limiting, and the electronic device may have other components and structures as desired.

The processor 102 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 100 to perform desired functions.

The memory 104 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, etc. On which one or more computer program instructions may be stored that may be executed by processor 102 to implement client-side functionality (implemented by the processor) and/or other desired functionality in embodiments of the invention described below. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer-readable storage medium.

The input device 106 may be a device used by a user to input instructions and may include one or more of a keyboard, a mouse, a microphone, a touch screen, and the like.

The output device 108 may output various information (e.g., images or sounds) to the outside (e.g., a user), and may include one or more of a display, a speaker, and the like.

Exemplarily, an exemplary electronic device for implementing the urban traffic carbon emission prediction method and apparatus according to the embodiment of the present invention may be implemented as a terminal such as a smartphone, a tablet computer, a desktop computer, or the like.

Hereinafter, a method for predicting urban traffic carbon emissions according to an embodiment of the present invention will be described with reference to fig. 2. Fig. 2 is a schematic flow chart of a method 200 for predicting urban traffic carbon emission according to an embodiment of the present application. The urban traffic carbon emission prediction method according to the embodiment of the present application is applied to an urban traffic carbon emission prediction apparatus, where the urban traffic carbon emission prediction apparatus includes a processor, a memory, an input device, an output device, and the like, and the urban traffic carbon emission prediction apparatus may be implemented as the electronic device 100 described above. Specifically, the urban traffic carbon emission prediction method 200 according to the embodiment of the present application includes the following steps:

first, in step S210, a city traffic carbon emission prediction model is constructed.

In the embodiment of the invention, firstly, a city needing to be predicted and planned for traffic carbon emission is determined, and after the corresponding city is determined, an urban traffic carbon emission prediction model is constructed for the city. According to the constructed urban traffic carbon emission prediction model, the urban traffic carbon emission value of the city in a certain year, a certain month and other time can be predicted.

Specifically, the urban traffic carbon emission prediction model may be constructed in various ways, such as according to the urban traffic situation, according to the type of vehicle and type of combustion, according to the type of traffic pollution, and so on. Taking the construction of the urban traffic carbon emission prediction model according to the traffic types as an example, the urban traffic types need to be classified, and after the classification is finished, the urban traffic carbon emission prediction model is constructed according to the classification result. Traffic types such as cities include public traffic and non-public traffic.

In step S220, historical urban traffic carbon emission-related data is acquired.

In the embodiment of the invention, the urban traffic carbon emission related data comprises a series of data related to the traffic carbon emission, such as the number of vehicles, the driving mileage, the energy consumption, the carbon emission and the like, and the future urban traffic carbon emission is predicted by acquiring the data. It should be noted that the history here refers to any historical time period such as a past year, a month, etc.

In step S230, a first type urban traffic carbon emission influence factor is determined based on preset influence factor parameters and the historical urban traffic carbon emission related data.

In the embodiment of the invention, the first type urban traffic carbon emission influence factor is calculated by combining the influence factor parameter and historical urban traffic carbon emission related data. When the future urban traffic carbon emission is predicted only through historical urban traffic carbon emission related data, the prediction can be accurate only when the development trend of the future urban traffic carbon emission is consistent with the past trend, and when the development trend of the future urban traffic carbon emission is inconsistent with the past trend, the prediction value is far from the future actual value, so that the prediction of the future urban traffic carbon emission through the historical urban traffic carbon emission related data has great limitation. By setting the influence factor parameters, the trend obtained by predicting the historical urban traffic carbon emission related data can be corrected, so that the predicted value is consistent with the future actual value as much as possible.

The influence factor parameters are related to the acquired historical urban traffic carbon emission related data and the first type urban traffic carbon emission influence factor to be determined. For example, when the historical urban traffic carbon emission related data is a historical motorization ratio of motor vehicles and the first type of urban traffic carbon emission impact factor is a future motorization ratio of motor vehicles, then the impact factor parameter may be a final value of the motorization ratio of motor vehicles when the urban traffic carbon emission reaches the urban traffic carbon emission target value.

The historical motor vehicle electric proportion can be determined according to the historical number of the electric vehicles and the number of the non-electric vehicles in the motor vehicle, and the final value of the motor vehicle electric proportion is preset. In one example, determining a first type of urban traffic carbon emission impact factor based on preset impact factor parameters and the historical urban traffic carbon emission-related data comprises: determining the historical motor vehicle electromotion proportion according to the number of the electric vehicles and the number of the non-electric vehicles in the historical motor vehicle; determining the future motor vehicle motoring rate as a function of the historical motor vehicle motoring rate and the final value of the motor vehicle motoring rate.

Specifically, the following formula can be adopted to calculate the historical motorization ratio of the motor vehicle:

wherein,

motor vehicle motorization ratio, Q, representing history _EV Number of electric vehicles in motor vehicles, Q, representing history _FV Non-electric vehicle in motor vehicle representing historyThe number of the cells. Taking the calculation of a certain type of motor vehicle in a certain year in the past as an example, historical urban traffic carbon emission related data, namely the number of electric vehicles and the number of non-electric vehicles in the motor vehicle of the type in the current year, are firstly obtained, and then the number of the electric vehicles and the number of the non-electric vehicles are substituted into the formula, so that the electric proportion of the motor vehicle of the type in the current year can be obtained. Similarly, the proportion of the motorization of the motor vehicle in any other time period such as a month in the past can be calculated through the formula. The historical motor vehicle electromotion proportion in the city can be determined by calculating the historical electromotion proportion of all types of motor vehicles. Of course, the number of electric vehicles and the number of non-electric vehicles in all types of motor vehicles can be directly obtained and substituted into the formula, so that the historical motor vehicle electromotion proportion in the city can be obtained.

In calculating the historical motor vehicle electromotion proportion

Then, will be based on

And substituting the preset final value of the motor vehicle electromotion proportion into a corresponding calculation model to obtain the future motor vehicle electromotion proportion. In one example, determining the future vehicle motorization ratio based on the historical vehicle motorization ratio and the final value of the vehicle motorization ratio includes: selecting an S-shaped curve model; calculating the future motor vehicle motoring ratio by the sigmoid curve model based on the historical motor vehicle motoring ratio and the final value of the motor vehicle motoring ratio. In the example, the proportion increase curve of the electric vehicle is simulated by an S-shaped curve, so that the future motorization proportion of the motor vehicle is predicted.

There are many sigmoid curve models for selection, such as Logistic curve model (logic distribution cumulative distribution curve model), gompertz curve model (goberz curve model), weibull curve model (weber distribution cumulative distribution curve model), and the like. Taking the Logistic curve model as an example, the future calculation formula of the motor vehicle electric proportion is as follows:

wherein,

representing the proportion of motorization of the motor vehicle in the future,

represents the final value of the motorization ratio of the vehicle, n represents the number of years since the city possessed the first electric vehicle, and γ represents the slope of the curve.

For the slope γ of the curve, the motor vehicle motorization ratio can be substituted into the history

And the number of years n from the city having the first electric vehicle ₀ Fitting is carried out to obtain that:

then, in step S240, a predicted value of the future urban traffic carbon emission is determined by the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factor.

In the embodiment of the invention, the first type of urban traffic carbon emission influence factor is substituted into the urban traffic carbon emission prediction model, so that the future urban traffic carbon emission prediction value is obtained. When the first type of urban traffic carbon emission influence factor is the future motor vehicle electromotion proportion, the future motor vehicle electromotion proportion is substituted into the calculation factor of the urban traffic carbon emission prediction model, so that the future urban traffic carbon emission is predicted according to the future motor vehicle electromotion proportion.

Besides, a second type urban traffic carbon emission influence factor related to the first type urban traffic carbon emission influence factor can be determined, and the first type urban traffic carbon emission influence factor and the second type urban traffic carbon emission influence factor are jointly used as calculation factors to be substituted into the urban traffic carbon emission prediction model. In one example, determining a future urban traffic carbon emission prediction value by the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission impact factor comprises: determining a second type of urban traffic carbon emission impact factor, the second type of urban traffic carbon emission impact factor comprising at least one of: direct emission intensity of motor vehicles, electrical emission intensity of motor vehicles, number of motor vehicles in the future; determining the urban traffic carbon emission prediction value through the urban traffic carbon emission prediction model based on the future motor vehicle motorization proportion and the second type urban traffic carbon emission influence factor.

The second type of urban traffic carbon emission influence factor is also related to the historical urban traffic carbon emission related data, so the second type of urban traffic carbon emission influence factors such as the direct emission intensity of the motor vehicles, the power consumption emission intensity of the motor vehicles, the number of future motor vehicles and the like can be obtained by calculating the historical urban traffic carbon emission related data obtained in step S220.

In some implementations, when the second type of urban traffic carbon emission influence factor includes a future number of vehicles, historical urban traffic carbon emission-related data such as a historical number of vehicles, a historical vehicle consumption capability value, a flatulence index, a historical average vehicle price, an economic growth prediction rate, and a historical total average domestic production value may be acquired in step S220, and the future number of vehicles may be calculated according to the acquired historical urban traffic carbon emission-related data, thereby determining the second type of urban traffic carbon emission influence factor.

Wherein, when the type of the motor vehicles is a bus such as a bus, a taxi, a van, etc., since the number of buses is relatively stable in a city, it can be assumed that the number of such vehicles is not changed, i.e., the number of future motor vehicles is equal to the historical number of motor vehicles, where the future number of motor vehicles and the historical number of motor vehicles refer to the number of buses.

When the type of the motor vehicle is a private car, the holding capacity of the private car can change along with various influence factors such as the price of the automobile, the GDP (total production per capita) per capita and the like. Therefore, the number of the vehicles in the future needs to be predicted by combining historical data of the number of the vehicles and the like.

Illustratively, determining a future number of vehicles based on the historical number of vehicles includes: confirming a future automobile consumption capacity value; calculating a regression constant according to the historical number of the motor vehicles and the historical automobile consumption capacity value; and calculating the future number of the motor vehicles according to the regression constant and the future automobile consumption capacity value. In this example, a unary linear regression approach is taken to predict the future number of vehicles, and the historical number of vehicles refer to the number of private cars. In the process of confirming the number of the future vehicles, the step of confirming the future vehicle consumption capacity value and the step of calculating the regression constant can have a sequence, can also be carried out simultaneously, and the symmetry is not limited.

Firstly, fitting is carried out according to the historical number of motor vehicles and the historical value of the automobile consumption capacity to obtain a regression constant, and the following calculation formula can be adopted:

a＝Q _{private car 0} /R ₀

Wherein a represents a regression constant, Q _{Private car 0} Number of motor vehicles, R, representing history ₀ Representing historical automobile consumption capacity values. R is ₀ The total value, namely R, can be produced by historical average automobile prices and historical per capita domestic production ₀ = historical per-person GDP/historical average price of car.

After the regression constant a is obtained, calculating the number of future motor vehicles by a unary linear regression mode based on the regression constant a and the future automobile consumption capacity value, wherein the calculation formula is as follows:

Q _{private car} ＝aR

Wherein Q _{Private car} Representing the number of future vehicles and R representing the future vehicle consumption capability value.

As for the future automobile consumption capacity value R, the future automobile consumption capacity value R can be obtained by calculation according to historical urban traffic carbon emission related data such as a flatulence index, historical automobile average price, economic growth prediction rate, historical domestic production total value of everyone and the like.

Illustratively, confirming future car consumption capability values includes: determining the average price of the future automobile according to the flatulence index and the historical average price of the automobile; determining future per capita domestic production total value according to the economic growth prediction rate and the historical per capita domestic production total value; and determining the automobile consumption capacity value according to the future average domestic production total value of everyone and the future average price of the automobile. In this example, the ability to consume cars by people is reflected by the average price of cars and the total value of domestic production by everyone. In the process of confirming the future automobile consumption capacity value, the step of determining the future average automobile price and the step of determining the future total domestic production value of everyone can have a sequence, can also be carried out simultaneously, and is not limited symmetrically.

The swelling index is also called the inflation rate, and refers to the rate of increase of the general total price level over a certain period of time (usually one year). In this example, the flatulence index may be chosen as a flatulence prediction index I issued for the city government. The formula for calculating the average price of the future vehicle (after n years) according to the flatulence prediction index I and the historical average price of the vehicle is as follows:

future average price of automobile = historical average price of automobile × (1 + I) ⁿ

The economic growth prediction rate is used for predicting the economic growth rate in a certain time in the future, and an economic prediction index x issued for the city government can be selected. The formula for calculating the future (after n years) total domestic production value of the everyone according to the economic prediction index x and the historical total domestic production value of the everyone is as follows:

future people GDP = historical people GDP x (1 + x) ⁿ

After the future average automobile price and the future total domestic production value of everyone are calculated, the future automobile consumption capacity value R can be further calculated, and the calculation formula is as follows:

r = future average people GDP/future average price of automobile

In some implementations, when the motor vehicle is a non-motor vehicle, the second type of urban traffic carbon emission influence factor includes a direct emission intensity of the motor vehicle, historical urban traffic carbon emission related data such as an average driving distance of the motor vehicle, energy consumption per kilometer of the motor vehicle, a unit energy consumption carbon emission of the motor vehicle, and the like may be acquired in step S220, and the direct emission intensity of the motor vehicle may be calculated according to the acquired average driving distance of the motor vehicle, energy consumption per kilometer of the motor vehicle, and the unit energy consumption carbon emission of the motor vehicle, so as to obtain the second type of urban traffic carbon emission influence factor. Specifically, the following formula can be adopted for calculation:

wherein,

representing the direct emission intensity of the motor vehicle, D _i Representing the mean driving distance, FC, of the motor vehicle _i Representing the energy consumption per kilometer of the vehicle, CEF represents the specific energy consumption carbon emission of the vehicle. When the average driving distance D _i The annual average driving distance can be calculated, and the direct emission intensity of the annual motor vehicle can be calculated; when the average driving distance D _i The monthly average driving distance or the driving distance of other time duration, and accordingly, the direct emission intensity of the motor vehicle of the monthly or other time duration can be calculated. And because the energy consumption per kilometer, the carbon emission per energy consumption and the like of different types of motor vehicles are different, the direct emission intensity of each type of motor vehicle can be respectively calculated by adopting the formula and then summarized to obtain the direct emission intensity of the urban motor vehicle.

In some implementations, when the motor vehicle is an electric vehicle, the second type of urban traffic carbon emission influence factor includes an electricity consumption emission intensity of the motor vehicle, historical urban traffic carbon emission related data such as a unit electricity consumption carbon emission variation trend of the motor vehicle, historical unit electricity consumption carbon emission of the motor vehicle, an average driving distance of the motor vehicle, energy consumption per kilometer of the motor vehicle and the like may be obtained in step S220, and the electricity consumption emission intensity of the motor vehicle is calculated according to the obtained unit electricity consumption carbon emission variation trend of the motor vehicle, historical unit electricity consumption carbon emission of the motor vehicle, the average driving distance of the motor vehicle, and energy consumption per kilometer of the motor vehicle, so as to obtain the second type of urban traffic carbon emission influence factor.

Specifically, the unit electricity consumption carbon emission of the future motor vehicle is determined according to the unit electricity consumption carbon emission change trend of the motor vehicle and the unit electricity consumption carbon emission of the historical motor vehicle. Taking the energy structure adjustment of china as an example, the variation trend of the unit carbon consumption emission of the motor vehicle can be set to decrease by 2.4% year by year, and then the calculation formula of the unit carbon consumption emission of the motor vehicle (after n years) in the future is as follows:

CEE＝CEE'×(1-2.4％) ⁿ

where CEE' represents the unit electricity consumption carbon emission of the historical motor vehicle, which is released by the country or the local place, and CEE represents the unit electricity consumption carbon emission of the future motor vehicle.

Then, calculating the power consumption emission intensity of the motor vehicle according to the average driving distance of the motor vehicle, the energy consumption per kilometer of the motor vehicle and the unit power consumption carbon emission of the future motor vehicle, wherein the calculation formula is as follows:

wherein,

representing the intensity of the electrical emissions of the motor vehicle, D _i Representing the average driving distance, EC, of the motor vehicle _i Representing the energy consumption per kilometre of the motor vehicle. When the average driving distance D _i The annual average driving distance can be calculated, and the annual power consumption emission intensity of the motor vehicle can be calculated;when the average driving distance D _i The average driving distance per month or the driving distance of other time duration, and accordingly, the power consumption emission intensity of the motor vehicle per month or other time duration can be calculated. And because the energy consumption per kilometer, the unit power consumption carbon emission and the like of different types of motor vehicles are different, the power consumption emission intensity of each type of motor vehicle can be respectively calculated by adopting the formula and then summarized to obtain the power consumption emission intensity of the urban motor vehicle.

After the second type of urban traffic carbon emission influence factor is determined, a future urban traffic carbon emission predicted value can be calculated through an urban traffic carbon emission prediction model based on the future motor vehicle electromotion proportion and the determined second type of urban traffic carbon emission influence factor.

In one example, determining the urban traffic carbon emission prediction value by the urban traffic carbon emission prediction model based on the future motorization ratio and the second type of urban traffic carbon emission impact factor comprises: calculating the carbon emission of the electric vehicle according to the future electric vehicle motorization proportion, the electric vehicle power consumption emission intensity and the future number of the electric vehicles; calculating the carbon emission of the non-electric vehicle according to the future electric vehicle motorization proportion, the direct emission intensity of the motor vehicle and the future number of the motor vehicles; and calculating the urban traffic carbon emission predicted value according to the carbon emission of the electric vehicle and the carbon emission of the non-electric vehicle. In this example, the predicted urban traffic carbon emission values are summarized by calculating the carbon emission of future electric vehicles and the carbon emission of non-electric vehicles, respectively. In the process of determining the urban traffic carbon emission predicted value, the step of calculating the carbon emission of the electric vehicle and the step of calculating the carbon emission of the non-electric vehicle can have a sequence, can also be carried out simultaneously, and are not limited symmetrically.

Specifically, a calculation formula for calculating and obtaining a predicted value of the carbon emission of urban traffic in the future (after n years) according to the carbon emission of the electric vehicle and the carbon emission of the non-electric vehicle is as follows:

wherein GHG represents a predicted value of urban traffic carbon emission in the future,

representing the future motorvehicle motorization ratio, Q _i Representing the number of vehicles in the future, can be obtained by summing the number of public vehicles in the future and the number of private vehicles in the future,

representing the direct emission intensity of the motor vehicle,

representing the intensity of the electricity used by the motor vehicle.

Finally, in step S250, it is determined whether the predicted future urban traffic carbon emission value matches a preset urban traffic carbon emission target value, and if not, the impact factor parameter and the urban traffic carbon emission target value are adjusted, so that the predicted future urban traffic carbon emission value obtained based on the adjusted impact factor parameter matches the adjusted urban traffic carbon emission target value.

In the embodiment of the invention, the impact factor parameter and the urban traffic carbon emission target value are adjusted until the calculated urban traffic carbon emission predicted value is consistent with the urban traffic carbon emission target value, so that the urban traffic carbon neutralization quantification target can be specified according to the calculated urban traffic carbon emission predicted value, and urban traffic is subdivided according to the urban traffic carbon emission target value and the impact factor to carry out carbon reduction planning.

In one example, as shown in fig. 3, when the predicted future urban traffic carbon emission value does not conform to the preset urban traffic carbon emission target value, repeating steps S230 to S250, i.e. the adjusting the impact factor parameter and the urban traffic carbon emission target value, so that the predicted future urban traffic carbon emission value based on the adjusted impact factor parameter conforms to the adjusted urban traffic carbon emission target value includes: determining a first type of urban traffic carbon emission influence factor based on the adjusted influence factor parameters and the historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factor; and judging whether the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value, if not, readjusting the influence factor parameter and the urban traffic carbon emission target value, and repeating the steps until the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value.

Based on the description, according to the urban traffic carbon emission prediction method provided by the embodiment of the invention, the influence factor parameter is introduced, and in the process of comparing the urban traffic carbon emission predicted value calculated by the traffic carbon emission prediction model with the urban traffic carbon emission target value, the influence factor parameter and the urban traffic carbon emission target value can be continuously adjusted until the urban traffic carbon emission predicted value is consistent with the urban traffic carbon emission target value, so that the prediction of the urban traffic future carbon emission situation is realized, the urban traffic carbon neutralization target can be formulated, the traffic carbon reduction planning is performed under the target, the limitation that only the existing carbon emission data can be accounted in the related technology is broken, the quantitative reference can be provided for the future policy formulation, and the method of introducing and adjusting the influence factor parameter is higher in prediction accuracy compared with the method of predicting the future urban traffic carbon emission through the historical urban traffic carbon emission related data.

In addition, on the basis of carrying out macroscopic calculation mainly based on the product of the motor vehicle reserves of different vehicle types and the carbon emission factor, factors such as the number of future motor vehicles, the future motor vehicle electromotion proportion, the direct emission intensity of the motor vehicles, the power consumption emission intensity of the motor vehicles and the like are introduced, so that the future change trend prediction of the urban traffic carbon emission is accurately realized.

The urban traffic carbon emission prediction method according to the embodiment of the invention is exemplarily described above. Illustratively, the urban traffic carbon emission prediction method according to the embodiment of the present invention may be implemented in a device, apparatus or system having a memory and a processor.

In addition, the urban traffic carbon emission prediction method can be conveniently deployed on local terminals such as smart phones, tablet computers and desktop computers. Alternatively, the urban traffic carbon emission prediction method according to the inventive embodiment can also be deployed on a server side (or cloud side). Alternatively, the urban traffic carbon emission prediction method according to the embodiment of the invention can also be distributively deployed at a server side (or a cloud side) and a local terminal.

Fig. 4 shows a schematic block diagram of an urban traffic carbon emission prediction apparatus according to an embodiment of the present invention. As shown in fig. 4, the urban traffic carbon emission prediction apparatus 400 according to the embodiment of the present invention includes a prediction model construction module 410, a historical data acquisition module 420, an influence factor determination module 430, a carbon emission prediction value determination module 440, and a judgment module 450. The prediction model building module 410 is used for building an urban traffic carbon emission prediction model, and the historical data acquisition module 420 is used for acquiring historical urban traffic carbon emission related data; the influence factor determining module 430 is used for determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and the historical urban traffic carbon emission related data; the carbon emission predicted value determining module 440 is used for determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type urban traffic carbon emission influence factor; the determining module 450 is configured to determine whether the future carbon emission prediction value of the urban traffic conforms to a preset carbon emission target value of the urban traffic, and if not, adjust the impact factor parameter and the carbon emission target value of the urban traffic, so that the future carbon emission prediction value obtained based on the adjusted impact factor parameter conforms to the adjusted carbon emission target value of the urban traffic.

The prediction model building module 410, the historical data obtaining module 420, the influence factor determining module 430, the carbon emission prediction value determining module 440 and the judging module 450 can be implemented by the processor 102 in the electronic device 100 shown in fig. 1 running program instructions stored in the memory 104, and can execute corresponding steps in the urban traffic carbon emission prediction method 200 according to the embodiment of the invention. Only the main functions of the respective modules of the urban traffic carbon emission prediction apparatus will be described below, and details that have been described above will be omitted.

In the embodiment of the invention, firstly, a city needing to be predicted and planned for traffic carbon emission is determined, and after the corresponding city is determined, a city traffic carbon emission prediction model is constructed for the city. According to the constructed urban traffic carbon emission prediction model, the urban traffic carbon emission value of the city in a certain year, a certain month and the like in the future can be predicted.

Specifically, the urban traffic carbon emission prediction model may be constructed in various ways, such as according to the traffic situation of the city, according to the type of vehicle and the type of combustion, according to the type of traffic pollution, and so on. Taking the example of constructing the urban traffic carbon emission prediction model according to the traffic types, the urban traffic types need to be classified, and after the classification is finished, the urban traffic carbon emission prediction model is constructed according to the classification result. Traffic types such as cities include public traffic and non-public traffic.

In the embodiment of the invention, the urban traffic carbon emission related data comprises a series of data related to the traffic carbon emission, such as the number of vehicles, the driving mileage, the energy consumption, the carbon emission and the like, and the future urban traffic carbon emission is predicted by acquiring the data. It should be noted that the history here refers to any historical time period such as a past year, a month, and the like.

The historical motorization ratio of the motor vehicle can be determined according to the historical number of the electric vehicles and the number of the non-electric vehicles in the motor vehicle, and the final value of the motorization ratio of the motor vehicle is preset. In one example, the determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and the historical urban traffic carbon emission related data comprises: determining the historical motor vehicle electromotion proportion according to the number of the electric vehicles and the number of the non-electric vehicles in the historical motor vehicle; determining the future motor vehicle motoring proportion according to the historical motor vehicle motoring proportion and the final value of the motor vehicle motoring proportion.

wherein,

motor vehicle motorization ratio, Q, representing history _EV Number of electric vehicles in motor vehicles, Q, representing history _FV Representing the number of non-electric vehicles in the historical automobile. Taking the calculation of a certain type of motor vehicle in a certain year in the past as an example, historical urban traffic carbon emission related data, namely the number of electric vehicles and the number of non-electric vehicles in the motor vehicle of the type in the current year, are firstly obtained, and then the number of the electric vehicles and the number of the non-electric vehicles are substituted into the formula, so that the electric proportion of the motor vehicle of the type in the current year can be obtained. Similarly, the proportion of the motorization of the motor vehicle in any other time period such as a past month can be calculated through the formula. The historical motor vehicle electromotion proportion in the city can be determined by calculating the historical electromotion proportion of all types of motor vehicles. Of course, the number of electric vehicles and the number of non-electric vehicles in the motor vehicle of the type can be directly obtained and substituted into the formula, so that the historical motor vehicle electromotion proportion in the city can be obtained.

In calculating the historical motor vehicle electromotion proportion

Then, will be based on

And substituting the preset final value of the motor vehicle electromotion proportion into a corresponding calculation model to obtain the future motor vehicle electromotion proportion. In one example, determining the future vehicle motorization ratio based on the historical vehicle motorization ratio and the final value of the vehicle motorization ratio includes: selecting an S-shaped curve model; calculating the future vehicle motorization ratio by the sigmoid curve model based on the historical vehicle motorization ratio and a final value of the vehicle motorization ratio. In the example, the proportion increase curve of the electric vehicle is simulated by an S-shaped curve, so that the future electric proportion of the motor vehicle is predicted.

There are many sigmoid curve models for selection, such as Logistic curve model (logic distribution cumulative distribution curve model), gompertz curve model (gobez curve model), weibull curve model (weber distribution cumulative distribution curve model), and the like. Taking a Logistic curve model as an example, a calculation formula of the future motor vehicle electromotion proportion is as follows:

wherein,

representing the proportion of motorization of the motor vehicle in the future,

For the slope γ of the curve, the motor vehicle motorization ratio can be obtained by substituting into the history

Besides, a second type urban traffic carbon emission influence factor related to the first type urban traffic carbon emission influence factor can be determined, and the first type urban traffic carbon emission influence factor and the second type urban traffic carbon emission influence factor are jointly used as calculation factors to be substituted into the urban traffic carbon emission prediction model. In one example, determining a future urban traffic carbon emission prediction value by the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission impact factor comprises: determining a second type of urban traffic carbon emission influence factor, wherein the second type of urban traffic carbon emission influence factor comprises at least one of the following factors: direct emission intensity of motor vehicles, electrical emission intensity of motor vehicles, number of motor vehicles in the future; determining the urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the future motor vehicle electric proportion and the second type urban traffic carbon emission influence factor.

The second type of urban traffic carbon emission influence factor is also related to historical urban traffic carbon emission related data, so that the second type of urban traffic carbon emission influence factors such as direct emission intensity of motor vehicles, power consumption emission intensity of motor vehicles, and the number of future motor vehicles can be obtained by calculating the historical urban traffic carbon emission related data obtained in the historical data obtaining module 420.

In some implementations, when the second type of urban traffic carbon emission influence factor includes the number of future vehicles, historical urban traffic carbon emission related data such as the historical number of vehicles, the historical consumption capacity value of vehicles, the flatulence index, the historical average price of vehicles, the economic growth prediction rate, and the historical total domestic production value of people can be obtained in the historical data obtaining module 420, and the number of future vehicles can be calculated according to the obtained historical urban traffic carbon emission related data, so as to determine the second type of urban traffic carbon emission influence factor.

Here, when the type of the motor vehicle is a bus such as a bus, a taxi, a van, or the like, since the number of the bus is relatively stable in a city, it can be assumed that the number of such vehicles is not changed, that is, the number of future motor vehicles is equal to the number of historical motor vehicles, where the number of future motor vehicles and the number of historical motor vehicles refer to the number of the bus.

Exemplary, determining a future number of vehicles based on the historical number of vehicles includes: confirming a future automobile consumption capacity value; calculating a regression constant according to the historical number of the motor vehicles and the historical automobile consumption capacity value; and calculating the future number of the motor vehicles according to the regression constant and the future automobile consumption capacity value. In this example, a unary linear regression approach is taken to predict the future number of vehicles, and the historical number of vehicles refer to the number of private cars. In the process of confirming the number of the future automobiles, the step of confirming the future automobile consumption capacity value and the step of calculating the regression constant can have a sequence, can also be carried out simultaneously, and the symmetry is not limited.

Firstly, fitting according to the historical number of motor vehicles and the historical value of the consumption capacity of the motor vehicles to obtain a regression constant, wherein the following calculation formula can be adopted:

a＝Q _{private car 0} /R ₀

Wherein a represents a regression constant, Q _{Private car 0} Number of motor vehicles, R, representing history ₀ Representing historical automobile consumption capacity values. R ₀ The total value, namely R, can be produced by historical average automobile prices and historical per capita domestic production ₀ = historical per-person GDP/historical average price of car.

Q _{private car} ＝aR

Wherein Q _{Private car} Representing the number of future vehicles, R representing future vehiclesA consumption capacity value.

Illustratively, confirming future car consumption capability values includes: determining the average price of the future automobile according to the flatulence index and the historical average price of the automobile; determining future per capita domestic production total value according to the economic growth prediction rate and the historical per capita domestic production total value; and determining the automobile consumption capacity value according to the future average price of the automobiles and the future total domestic production value of everyone. In this example, the ability to consume a car by people is reflected by the average price of the car and the total value of the domestic production by everyone. In the process of confirming the future automobile consumption capacity value, the step of determining the future average automobile price and the step of determining the future total domestic production value of everyone can have a sequence, can also be carried out simultaneously, and is not limited symmetrically.

The flatulence index, also known as the inflation rate, refers to the rate at which the general total price level rises over a period of time (usually a year). In this example, the flatulence index may be chosen as a flatulence prediction index I issued for the city government. The formula for calculating the average price of the future (after n years) of the automobile according to the flatulence prediction index I and the historical average price of the automobile is as follows:

future people GDP = historical people GDP x (1 + x) ⁿ

After the average price of the future automobile and the total domestic production value of the future everyone are calculated, the future automobile consumption capacity value R can be further calculated, and the calculation formula is as follows:

r = mean future average GDP/mean future price of cars

In some implementations, when the motor vehicle is a non-electric vehicle, the second type of urban traffic carbon emission influence factor includes a direct emission intensity of the motor vehicle, historical urban traffic carbon emission related data such as an average travel distance of the motor vehicle, energy consumption per kilometer of the motor vehicle, and a unit energy consumption carbon emission amount of the motor vehicle may be acquired in the historical data acquisition module 420, and the direct emission intensity of the motor vehicle is calculated according to the acquired average travel distance of the motor vehicle, energy consumption per kilometer of the motor vehicle, and the unit energy consumption carbon emission amount of the motor vehicle, so as to obtain the second type of urban traffic carbon emission influence factor. Specifically, the following formula can be adopted for calculation:

wherein,

In some implementations, when the motor vehicle is an electric vehicle, the second type of urban traffic carbon emission influence factor includes an electricity consumption emission intensity of the motor vehicle, historical urban traffic carbon emission related data such as a unit electricity consumption carbon emission variation trend of the motor vehicle, a historical unit electricity consumption carbon emission of the motor vehicle, an average driving distance of the motor vehicle, energy consumption per kilometer of the motor vehicle, and the like can be acquired in the historical data acquisition module 420, and the electricity consumption emission intensity of the motor vehicle is calculated according to the acquired unit electricity consumption carbon emission variation trend of the motor vehicle, the historical unit electricity consumption carbon emission of the motor vehicle, the average driving distance of the motor vehicle, and the energy consumption per kilometer of the motor vehicle, so as to obtain the second type of urban traffic carbon emission influence factor.

Specifically, the unit electricity consumption carbon emission of the future motor vehicle is determined according to the unit electricity consumption carbon emission change trend of the motor vehicle and the unit electricity consumption carbon emission of the historical motor vehicle. The unit carbon consumption emission amount changes with the adjustment of the energy structure, taking the adjustment of the energy structure of china as an example, the change trend of the unit carbon consumption emission amount of the motor vehicle can be set to decrease by 2.4% year by year, and then the calculation formula of the unit carbon consumption emission amount of the motor vehicle (after n years) in the future is as follows:

CEE＝CEE'×(1-2.4％) ⁿ

wherein,

representing the intensity of the electrical emissions of the motor vehicle, D _i Representing the average driving distance, EC, of the motor vehicle _i Representing the energy consumption per kilometre of the motor vehicle. When the average driving distance D _i Is the annual average driving distanceCalculating the annual power consumption emission intensity of the motor vehicle; when the average driving distance D _i The average driving distance per month or the driving distance of other time periods, and accordingly, the power consumption emission intensity of the motor vehicle per month or other time periods can be calculated. And because the energy consumption per kilometer, the unit power consumption carbon emission and the like of different types of motor vehicles are different, the power consumption emission intensity of each type of motor vehicle can be respectively calculated by adopting the formula and then summarized to obtain the power consumption emission intensity of the urban motor vehicle.

Specifically, a calculation formula for calculating and obtaining a predicted value of the carbon emission of the urban traffic in the future (after n years) according to the carbon emission of the electric vehicle and the carbon emission of the non-electric vehicle is as follows:

representing the future motorization ratio, Q _i Representing the number of vehicles in the future, can be obtained by summing the number of public vehicles in the future and the number of private vehicles in the future,

representing the direct emission intensity of the motor vehicle,

representing the intensity of the electricity used by the motor vehicle.

In the embodiment of the invention, the carbon reduction plan for subdividing the urban traffic can be divided according to the urban traffic carbon emission predicted value and the impact factor by adjusting the impact factor parameter and the urban traffic carbon emission target value until the calculated urban traffic carbon emission predicted value is in accordance with the urban traffic carbon emission target value, so that the urban traffic carbon neutralization quantification target can be specified according to the calculated urban traffic carbon emission predicted value.

In one example, when the future predicted urban traffic carbon emission value does not correspond to the preset urban traffic carbon emission target value, the repeatedly operating the impact factor determination module 430, the carbon emission predicted value determination module 440 and the judgment module 450, namely, the adjusting the impact factor parameter and the urban traffic carbon emission target value, so that the future predicted urban traffic carbon emission value obtained based on the adjusted impact factor parameter corresponds to the adjusted urban traffic carbon emission target value comprises: determining a first type of urban traffic carbon emission influence factor based on the adjusted influence factor parameters and the historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factor; judging whether the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value, if not, readjusting the influence factor parameter and the urban traffic carbon emission target value, and repeating the steps until the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value.

Based on the description, according to the urban traffic carbon emission prediction device provided by the embodiment of the invention, the influence factor parameter is introduced, and in the process of comparing the urban traffic carbon emission predicted value calculated by the traffic carbon emission prediction model with the urban traffic carbon emission target value, the influence factor parameter and the urban traffic carbon emission target value can be continuously adjusted until the urban traffic carbon emission predicted value is consistent with the urban traffic carbon emission target value, so that the prediction of the urban traffic future carbon emission situation is realized, the urban traffic carbon neutralization target can be formulated, the traffic carbon reduction planning is performed under the target, the limitation that only the existing carbon emission data can be checked in the related technology is broken, the quantitative reference can be provided for the future policy formulation, and the method of introducing and adjusting the influence factor parameter is higher in prediction accuracy compared with the method of predicting the future urban traffic carbon emission through the historical urban traffic carbon emission related data.

Furthermore, those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

FIG. 5 shows a schematic block diagram of an electronic device according to an embodiment of the invention. The electronic device 500 includes 510 a memory and a processor 520.

Wherein the memory 510 stores a program for implementing the respective steps in the urban traffic carbon emission prediction method according to the embodiment of the present invention. The processor 520 is used for operating the program stored in the memory 510 to perform the corresponding steps of the urban traffic carbon emission prediction method according to the embodiment of the invention, and is used for implementing the corresponding modules in the urban traffic carbon emission prediction device according to the embodiment of the invention.

In one embodiment, the program, when executed by the processor 520, causes the urban traffic carbon emissions prediction unit 500 to perform the steps of: constructing an urban traffic carbon emission prediction model; acquiring historical urban traffic carbon emission related data; determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through an urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factors; and judging whether the future urban traffic carbon emission predicted value is in accordance with a preset urban traffic carbon emission target value or not, and if not, adjusting the influence factor parameter and the urban traffic carbon emission target value so as to enable the future urban traffic carbon emission predicted value obtained based on the adjusted influence factor parameter to be in accordance with the adjusted urban traffic carbon emission target value.

In one embodiment, the adjusting the impact factor parameter and the urban traffic carbon emission target value to make the future urban traffic carbon emission predicted value obtained based on the adjusted impact factor parameter conform to the adjusted urban traffic carbon emission target value comprises: determining a first type of urban traffic carbon emission influence factor based on the adjusted influence factor parameters and historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through an urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factors; and judging whether the future urban traffic carbon emission predicted value is in accordance with the adjusted urban traffic carbon emission target value, if not, readjusting the influence factor parameter and the urban traffic carbon emission target value, and repeating the steps until the future urban traffic carbon emission predicted value is in accordance with the adjusted urban traffic carbon emission target value.

In one embodiment, the constructing the urban traffic carbon emission prediction model comprises the following steps: classifying the traffic types of the cities; and constructing an urban traffic carbon emission prediction model according to the classification result.

In one embodiment, the first type of urban traffic carbon emission impact factor comprises a future motorvehicle motorization rate.

In one embodiment, the impact factor parameter comprises a final value of the motorization ratio of the motor vehicle at the urban traffic carbon emission target value.

In one embodiment, the determining the first type urban traffic carbon emission influence factor based on the preset influence factor parameters and the historical urban traffic carbon emission related data comprises: determining the historical motor vehicle electromotion proportion according to the number of the electric vehicles and the number of the non-electric vehicles in the historical motor vehicle; and determining the future motor vehicle electric proportion according to the historical motor vehicle electric proportion and the final value of the motor vehicle electric proportion.

In one embodiment, the determining a future vehicle motorization ratio based on the historical vehicle motorization ratio and a final value of the vehicle motorization ratio includes: selecting an S-shaped curve model; and calculating the future motor vehicle electromotion proportion through an S-shaped curve model based on the historical motor vehicle electromotion proportion and the final value of the motor vehicle electromotion proportion.

In one embodiment, the determining a future urban traffic carbon emission prediction value by an urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission impact factor comprises: determining a second type of urban traffic carbon emission impact factor, the second type of urban traffic carbon emission impact factor comprising at least one of: direct emission intensity of motor vehicles, electrical emission intensity of motor vehicles, number of future motor vehicles; and determining the urban traffic carbon emission predicted value through an urban traffic carbon emission prediction model based on the future motor vehicle electromotion proportion and the second type of urban traffic carbon emission influence factor.

In one embodiment, the determining the urban traffic carbon emission prediction value through an urban traffic carbon emission prediction model based on the future motor vehicle electric proportion and the second type urban traffic carbon emission influence factor comprises: calculating the carbon emission of the electric vehicle according to the future electric proportion of the motor vehicle, the power consumption emission intensity of the motor vehicle and the future number of the motor vehicles; calculating the carbon emission of the non-electric vehicles according to the future electric proportion of the motor vehicles, the direct emission intensity of the motor vehicles and the future number of the motor vehicles; and calculating the predicted value of the urban traffic carbon emission according to the carbon emission of the electric vehicle and the carbon emission of the non-electric vehicle.

In one embodiment, when the second type of urban traffic carbon emission impact factor is a direct emission intensity of a motor vehicle, determining the second type of urban traffic carbon emission impact factor comprises: and calculating the direct emission intensity of the motor vehicle according to the average driving distance of the motor vehicle, the energy consumption per kilometer of the motor vehicle and the unit energy consumption carbon emission of the motor vehicle.

In one embodiment, when the second type of urban traffic carbon emission influencing factor is the intensity of electricity emission of the motor vehicle, determining the second type of urban traffic carbon emission influencing factor comprises: determining the unit power consumption carbon emission of the future motor vehicle according to the unit power consumption carbon emission variation trend of the motor vehicle and the unit power consumption carbon emission of the historical motor vehicle; and calculating the power consumption emission intensity of the motor vehicle according to the average driving distance of the motor vehicle, the energy consumption per kilometer of the motor vehicle and the unit power consumption carbon emission of the motor vehicle in the future.

In one embodiment, when the second type of urban traffic carbon emission impact factor is a number of future vehicles, determining the second type of urban traffic carbon emission impact factor comprises: when the type of the motor vehicle is a public vehicle, the number of the motor vehicles in the future is equal to the historical number of the motor vehicles; when the type of the motor vehicle is a private vehicle, the number of future motor vehicles is determined according to the historical number of motor vehicles.

In one embodiment, determining a future number of vehicles based on the historical number of vehicles includes: confirming a future car consumption capacity value; calculating a regression constant according to the historical number of the motor vehicles and the historical automobile consumption capacity value; and calculating the number of the future vehicles according to the regression constant and the future vehicle consumption capacity value.

In one embodiment, confirming future automobile consumption capability values comprises: determining the average price of the future automobile according to the flatulence index and the historical average price of the automobile; determining future per capita domestic production total value according to the economic growth prediction rate and the historical per capita domestic production total value; and determining the automobile consumption capacity value according to the future average price of the automobile and the future total domestic production value of everyone.

Further, according to an embodiment of the present invention, there is also provided a storage medium on which a computer program is stored, which, when executed by a computer or a processor, is used to execute the urban traffic carbon emission prediction method according to an embodiment of the present invention and to implement corresponding modules in the urban traffic carbon emission prediction apparatus according to an embodiment of the present invention. The storage medium may include, for example, a memory card of a smart phone, a storage component of a tablet computer, a hard disk of a personal computer, a Read Only Memory (ROM), an Erasable Programmable Read Only Memory (EPROM), a portable compact disc read only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer readable storage medium may be any combination of one or more computer readable storage media, for example, one computer readable storage medium containing computer readable program code for constructing a model for predicting carbon emissions from urban traffic, another computer readable storage medium containing computer readable program code for determining a first type of urban traffic carbon emission impact factor.

In one embodiment, the computer program may implement the functional modules of the urban traffic carbon emission prediction apparatus according to the embodiment of the present invention when being executed by a computer, and/or may perform the urban traffic carbon emission prediction method according to the embodiment of the present invention.

In one embodiment, the computer program, when executed by a computer or processor, causes the computer or processor to perform the steps of: constructing an urban traffic carbon emission prediction model; acquiring historical urban traffic carbon emission related data; determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and historical urban traffic carbon emission related data; determining a future urban traffic carbon emission predicted value through an urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factors; and judging whether the future urban traffic carbon emission predicted value is accordant with a preset urban traffic carbon emission target value, and if not, adjusting the influence factor parameter and the urban traffic carbon emission target value so as to enable the future urban traffic carbon emission predicted value obtained based on the adjusted influence factor parameter to be accordant with the adjusted urban traffic carbon emission target value.

In one embodiment, the constructing the urban traffic carbon emission prediction model comprises: classifying the traffic types of the cities; and constructing an urban traffic carbon emission prediction model according to the classification result.

In one embodiment, the determining the first type of urban traffic carbon emission influence factor based on the preset influence factor parameters and historical urban traffic carbon emission related data comprises: determining the historical motor vehicle electromotion proportion according to the number of the electric vehicles and the number of the non-electric vehicles in the historical motor vehicle; the future motor vehicle motorization ratio is determined according to the historical motor vehicle motorization ratio and the final value of the motor vehicle motorization ratio.

In one embodiment, the determining the urban traffic carbon emission prediction value through an urban traffic carbon emission prediction model based on the future motor vehicle electric proportion and the second type urban traffic carbon emission influence factor comprises: calculating the carbon emission of the electric vehicle according to the future electric proportion of the motor vehicle, the electric emission intensity of the motor vehicle and the future number of the motor vehicles; calculating the carbon emission of the non-electric vehicle according to the future electric proportion of the motor vehicle, the direct emission intensity of the motor vehicle and the future number of the motor vehicles; and calculating the predicted value of the urban traffic carbon emission according to the carbon emission of the electric vehicle and the carbon emission of the non-electric vehicle.

In one embodiment, confirming future automobile consumption capability values comprises: determining the average price of the future automobile according to the flatulence index and the historical average price of the automobile; determining a future domestic production total value of everyone according to the economic growth prediction rate and the historical domestic production total value of everyone; and determining the automobile consumption capacity value according to the future average price of the automobile and the future total domestic production value of everyone.

The modules in the urban traffic carbon emission prediction apparatus according to the embodiment of the present invention may be implemented by the processor of the electronic device according to the embodiment of the present invention running a computer program stored in a memory, or may be implemented when a computer program stored in a computer-readable storage medium of a computer program product according to the embodiment of the present invention is run by a computer.

In addition, according to the embodiment of the present invention, a computer program may be stored on a storage medium in a cloud or a local area. When being executed by a computer or a processor, the computer program is used for executing the corresponding steps of the urban traffic carbon emission prediction method of the embodiment of the invention and realizing the corresponding modules in the urban traffic carbon emission prediction device according to the embodiment of the invention.

Based on the above description, according to the urban traffic carbon emission prediction method, the urban traffic carbon emission prediction device, the electronic device and the storage medium of the embodiment of the invention, by introducing the influence factor parameter, in the process of comparing the urban traffic carbon emission predicted value calculated by the traffic carbon emission prediction model with the urban traffic carbon emission target value, the influence factor parameter and the urban traffic carbon emission target value can be continuously adjusted until the urban traffic carbon emission predicted value is consistent with the urban traffic carbon emission target value, so that the prediction of the urban traffic future carbon emission situation is realized, the urban traffic carbon neutralization target can be formulated and the traffic carbon reduction planning is performed under the target, the limitation that only the existing carbon emission data can be accounted in the related technology is broken, the quantitative reference can be provided for the future policy formulation, and the method of introducing and adjusting the influence factor parameter is higher in prediction accuracy compared with the method of predicting the future urban traffic carbon emission only through the historical urban traffic carbon emission related data.

Although the illustrative embodiments have been described herein with reference to the accompanying drawings, it is to be understood that the above-described illustrative embodiments are only exemplary, and are not intended to limit the scope of the present application thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the scope or spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as claimed in the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one type of logical functional division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another device, or some features may be omitted, or not executed.

In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the application may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it should be appreciated that in the description of exemplary embodiments of the present application, various features of the present application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the application and aiding in the understanding of one or more of the various inventive aspects. However, the method of the present application should not be construed to reflect the intent: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.

It will be understood by those skilled in the art that all of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where such features are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Moreover, those of skill in the art will understand that although some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.

Various component embodiments of the present application may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some of the modules according to the embodiments of the application. The present application may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present application may be stored on a computer readable medium or may be in the form of one or more signals. Such a signal may be downloaded from an internet website, or provided on a carrier signal, or provided in any other form.

It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The application may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

The above description is only for the specific embodiments of the present application or descriptions thereof, and the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and should be covered by the protection scope of the present application. The protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for predicting urban traffic carbon emission is characterized by comprising the following steps:

constructing an urban traffic carbon emission prediction model;

acquiring historical urban traffic carbon emission related data;

determining a first type of urban traffic carbon emission influence factor based on preset influence factor parameters and the historical urban traffic carbon emission related data;

determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factor;

and judging whether the future urban traffic carbon emission predicted value is accordant with a preset urban traffic carbon emission target value, and if not, adjusting the influence factor parameter and the urban traffic carbon emission target value so as to enable the future urban traffic carbon emission predicted value obtained based on the adjusted influence factor parameter to be accordant with the adjusted urban traffic carbon emission target value.

2. The urban traffic carbon emission prediction method according to claim 1, wherein the adjusting the impact factor parameter and the urban traffic carbon emission target value so that a future urban traffic carbon emission predicted value obtained based on the adjusted impact factor parameter coincides with the adjusted urban traffic carbon emission target value comprises:

determining a first type urban traffic carbon emission influence factor based on the adjusted influence factor parameter and the historical urban traffic carbon emission related data;

and judging whether the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value, if not, readjusting the influence factor parameter and the urban traffic carbon emission target value, and repeating the steps until the future urban traffic carbon emission predicted value is consistent with the adjusted urban traffic carbon emission target value.

3. The method for predicting urban traffic carbon emission according to claim 1, wherein the constructing the urban traffic carbon emission prediction model comprises:

classifying the traffic types of the cities;

and constructing the urban traffic carbon emission prediction model according to the classification result.

4. The method of predicting urban traffic carbon emissions according to claim 1, wherein said first type of urban traffic carbon emission influencing factor comprises a future motorization proportion.

5. The method of predicting urban traffic carbon emissions according to claim 4, wherein said impact factor parameter comprises a final value of a motorization ratio of a motor vehicle at said urban traffic carbon emission target value.

6. The method for predicting urban traffic carbon emission according to claim 5, wherein the determining a first type of urban traffic carbon emission influence factor based on the preset influence factor parameters and the historical urban traffic carbon emission related data comprises:

determining the historical motor vehicle electromotion proportion according to the number of the electric vehicles and the number of the non-electric vehicles in the historical motor vehicle;

determining the future motor vehicle motoring rate as a function of the historical motor vehicle motoring rate and the final value of the motor vehicle motoring rate.

7. The method of predicting urban traffic carbon emissions according to claim 6, wherein said determining said future motorization ratio based on said historical motorization ratio and a final value of said motorization ratio comprises:

selecting an S-shaped curve model;

calculating the future vehicle motorization ratio by the sigmoid curve model based on the historical vehicle motorization ratio and a final value of the vehicle motorization ratio.

8. The urban traffic carbon emission prediction method according to any one of claims 4 to 7, wherein the determining a predicted value of future urban traffic carbon emission by the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influencing factor comprises:

determining a second type of urban traffic carbon emission influence factor, wherein the second type of urban traffic carbon emission influence factor comprises at least one of the following factors: direct emission intensity of motor vehicles, electrical emission intensity of motor vehicles, number of future motor vehicles;

determining the urban traffic carbon emission prediction value through the urban traffic carbon emission prediction model based on the future motor vehicle motorization proportion and the second type urban traffic carbon emission influence factor.

9. The method of predicting urban traffic carbon emission according to claim 8, wherein said determining the urban traffic carbon emission prediction value based on the future ratio of motorization of the motor vehicle and the second type of urban traffic carbon emission impact factor by the urban traffic carbon emission prediction model comprises:

calculating the carbon emission of the electric vehicle according to the future electric vehicle motorization proportion, the electric vehicle power consumption emission intensity and the future number of the electric vehicles;

calculating the carbon emission of the non-electric vehicles according to the future motor vehicle electromotion proportion, the direct emission intensity of the motor vehicles and the future number of the motor vehicles;

and calculating the urban traffic carbon emission predicted value according to the carbon emission of the electric vehicle and the carbon emission of the non-electric vehicle.

10. The method for predicting carbon emission in urban traffic according to claim 8, wherein when the second type of influence factor for carbon emission in urban traffic is the direct emission intensity of motor vehicles, the determining the second type of influence factor for carbon emission in urban traffic comprises:

and calculating the direct emission intensity of the motor vehicle according to the average driving distance of the motor vehicle, the energy consumption per kilometer of the motor vehicle and the unit energy consumption carbon emission of the motor vehicle.

11. The method of predicting urban traffic carbon emission according to claim 8, wherein when the second type of urban traffic carbon emission influencing factor is the intensity of electricity emission of a motor vehicle, the determining the second type of urban traffic carbon emission influencing factor comprises:

determining the unit power consumption carbon emission of the future motor vehicle according to the unit power consumption carbon emission variation trend of the motor vehicle and the unit power consumption carbon emission of the historical motor vehicle;

and calculating the power consumption emission intensity of the motor vehicle according to the average driving distance of the motor vehicle, the energy consumption per kilometer of the motor vehicle and the unit power consumption carbon emission of the future motor vehicle.

12. The method for predicting carbon emission in urban traffic according to claim 8, wherein when the second type of urban traffic carbon emission influence factor is a number of future vehicles, the determining the second type of urban traffic carbon emission influence factor comprises:

when the type of the motor vehicle is a public vehicle, the number of the future motor vehicles is equal to the historical number of the motor vehicles;

when the type of the motor vehicle is a private vehicle, the number of future motor vehicles is determined according to the historical number of motor vehicles.

13. The method for predicting urban traffic carbon emission according to claim 12, wherein said determining a future number of vehicles based on a historical number of vehicles comprises:

confirming a future car consumption capacity value;

calculating a regression constant according to the historical number of the motor vehicles and the historical automobile consumption capacity value;

and calculating the future number of the motor vehicles according to the regression constant and the future vehicle consumption capacity value.

14. The method of predicting urban traffic carbon emissions according to claim 13, wherein said identifying future value of automobile consumption capability comprises:

determining the average price of the future automobile according to the flatulence index and the historical average price of the automobile;

determining future per capita domestic production total value according to the economic growth prediction rate and the historical per capita domestic production total value;

and determining the automobile consumption capacity value according to the future average price of the automobiles and the future total domestic production value of everyone.

15. An urban traffic carbon emission prediction device, comprising:

the prediction model construction module is used for constructing an urban traffic carbon emission prediction model;

the historical data acquisition module is used for acquiring historical urban traffic carbon emission related data;

the influence factor determination module is used for determining a first type of urban traffic carbon emission influence factor based on a preset influence factor parameter and the historical urban traffic carbon emission related data;

the carbon emission predicted value determining module is used for determining a future urban traffic carbon emission predicted value through the urban traffic carbon emission prediction model based on the first type of urban traffic carbon emission influence factors;

and the judging module is used for judging whether the future urban traffic carbon emission predicted value is consistent with a preset urban traffic carbon emission target value or not, and adjusting the influence factor parameter and the urban traffic carbon emission target value when the future urban traffic carbon emission predicted value is not consistent with the preset urban traffic carbon emission target value, so that the future urban traffic carbon emission predicted value obtained based on the adjusted influence factor parameter is consistent with the adjusted urban traffic carbon emission target value.

16. An electronic device comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor when executing the program implements the urban traffic carbon emission prediction method of any of claims 1 to 14.

17. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for urban traffic carbon emission prediction according to any one of claims 1 to 14.