A method and system for calculating carbon emissions of urban traffic and identifying key propagation paths

Abstract

The application discloses a kind of urban traffic carbon emission calculation and key propagation path identification method and system, belong to urban traffic carbon emission calculation technical field.To solve the problem of accurate path identification of urban traffic carbon emission, the present application includes constructing a complete traffic carbon emission calculation model, for calculating the corresponding traffic carbon emission and traffic carbon emission total amount at different time;Calculate traffic carbon emission intensity and traffic carbon emission change rate as the basis data for key propagation path identification;Construct a space-time distance calculation system;Establish regional coupling strength coefficient and connection influence index, consider the attenuation effect of space-time distance, the difference of emission intensity, and the cooperativity of change rate to construct regional coupling strength coefficient, connection influence index is used to determine the importance of path together with regional coupling strength coefficient;Calculate the correlation strength between regions, on the basis of correlation strength, construct path importance score index, for comprehensive evaluation of the importance of different propagation paths.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of urban traffic carbon emission calculation, and particularly relates to a method and system for calculating urban traffic carbon emission and identifying key propagation paths. BACKGROUND

[0002] Traditional carbon emission calculation methods mostly use static and simplified models, which cannot accurately reflect the dynamic characteristics and complexity of the traffic system. These methods often ignore the mutual influence and relevance between the elements of the traffic system, making it difficult to capture the change rules in the actual operation process. In particular, when calculating inter-regional traffic carbon emission, existing methods fail to fully consider the interaction and transmission effect between regions, resulting in a large deviation between the calculation results and the actual situation. These problems seriously restrict the in-depth development and practical application effect of traffic carbon emission research.

[0003] The identification of key carbon emission propagation paths plays an important role in optimizing the urban traffic system. By identifying the key carbon emission propagation paths, the flow rules and diffusion mechanisms of carbon emission in the urban traffic system can be deeply understood, and the spatial distribution characteristics and evolution trend of carbon emission can be accurately grasped. This identification helps to determine the key areas and key propagation channels of carbon emission, and provides a scientific basis for formulating precise emission reduction measures. At the same time, by analyzing the structural characteristics of the carbon emission propagation network, the operation and management strategies of the urban traffic system can be optimized, and the pertinence and effectiveness of the emission reduction measures can be improved.

[0004] However, existing methods for identifying key carbon emission propagation paths still face multiple technical challenges. Traditional identification methods are often too simplified and do not fully consider the interaction and complex relationship between regions, making it difficult to accurately reflect the actual propagation characteristics of carbon emission. In terms of spatio-temporal evolution characteristics analysis, existing methods lack systematicness and comprehensiveness, and cannot effectively capture the dynamic change rules of carbon emission. In addition, in identifying the key influencing factors of carbon emission, existing methods cannot accurately quantify the degree of action of each factor, nor can they effectively evaluate the carbon emission transmission effect between different regions, which affects the accuracy and practicality of the identification results. SUMMARY

[0005] The problem to be solved by the present application is the accurate path identification of urban traffic carbon emission, and a method and system for calculating urban traffic carbon emission and identifying key propagation paths are proposed.

[0006] To achieve the above-mentioned purpose, the technical scheme is as follows:

[0007] A method for calculating urban traffic carbon emission and identifying key propagation paths, comprising the following steps:

[0008] S1. Based on the urban traffic carbon emission basic model, the complete traffic carbon emission calculation model is constructed by comprehensively considering the dynamic response mechanism, the regional correlation effect, the time and effect correlation characteristics, the driving efficiency and loss, the flow transmission mechanism and the system evolution law, which is used to calculate the traffic carbon emission at different time and the total traffic carbon emission;

[0009] S2. The traffic carbon emission of the jth region is calculated based on the traffic carbon emission calculation model of step S1, and the traffic carbon emission intensity and the traffic carbon emission change rate are calculated as the basic data for identifying the key transmission path;

[0010] S3. The space-time distance calculation system is constructed, the spatial distance and the time distance are standardized, and then the comprehensive space-time distance index is constructed, which is used to reflect the physical connection between regions;

[0011] S4. Based on the traffic carbon emission intensity and the traffic carbon emission change rate obtained in step S2, and the comprehensive space-time distance index obtained in step S3, the regional coupling strength coefficient and the connection influence index are established, the regional coupling strength coefficient is constructed considering the attenuation effect of space-time distance, the difference of emission intensity and the synergy of change rate, and the connection influence index is used to determine the importance of the path together with the regional coupling strength coefficient;

[0012] S5. Based on the regional coupling strength coefficient and the connection influence index obtained in step S4, the correlation strength between regions is calculated, including direct correlation strength and indirect correlation strength, and then the path importance score index is constructed based on the correlation strength, which is used to comprehensively evaluate the importance of different transmission paths.

[0013] Further, the specific implementation method of step S1 includes the following steps:

[0014] S1.1. The urban traffic carbon emission basic model is constructed, considering that the carbon emission of urban traffic is first reflected in the vehicle operation process, and different types of vehicles have different emission characteristics due to the differences in technical characteristics, use mode and operation condition. The urban traffic carbon emission basic model is constructed to obtain the emission amount of vehicle t at time t ;

[0015] S1.2. The comprehensive dynamic response mechanism is established, considering that the periodic change of morning and evening peak of weekdays and the leisure travel peak of weekends is a typical feature, and traffic accidents and extreme weather are nonlinear features. The function relationship is used to describe the comprehensive dynamic response mechanism, and the dynamic response coefficient of urban traffic system t at time t is obtained ;

[0016] S1.3. Establish regional correlation effect, considering that carbon emissions in urban traffic system have significant spatial correlation characteristics, the traffic condition change in a region will affect the surrounding region through road network connection, forming a chain reaction, using the three-dimensional spatial integral of the region to build the carbon emission model generated by the regional correlation effect, and obtaining the carbon emission generated by the regional correlation effect at time t ;

[0017] S1.4. Establish time-effect correlation characteristics, considering that the current emissions will have real-time effects on regional environment and travel behavior through various ways, including immediate occupation of regional environmental capacity and dynamic adjustment of travel behavior, obtaining the carbon emissions generated by time-effect correlation characteristics at time t ;

[0018] S1.5. Establish driving efficiency and loss model, considering the conversion loss during acceleration and deceleration of vehicles in operation, idling loss, and loss caused by slope, obtaining the comprehensive effect value of loss at time t , then calculating the efficiency loss emission at time t ;

[0019] S1.6. Establish flow transmission mechanism, considering that flow transmission includes changes in vehicle quantity, speed, and density parameters, and in the transmission process, the phenomena of fluctuation, diffusion, and aggregation of urban traffic system affect the characteristics of traffic carbon emissions, based on the established transmission equation to calculate the flow transmission emission at time t ;

[0020] S1.7. Establish system evolution law, considering that the order degree of urban traffic system will change over time due to the increase of peak traffic chaos, the diffusion of congestion areas, and the aggravation of vehicle distribution unevenness during the operation of urban traffic system, based on the established system description equation to calculate the system evolution emission at time t ;

[0021] S1.8. Build a complete traffic carbon emission calculation model, obtain the traffic carbon emission at time t , the expression is:

[0022]

[0023] where, is the emission of fixed sources at time t, obtained by experiment or from the traffic management department;

[0024] obtain the total traffic carbon emission amount corresponding to the total time T , the expression is:

[0025] .

[0026] Further, the specific implementation method of step S2 includes the following steps:

[0027] S2.1. Calculate the traffic carbon emission of the jth region at time t based on the traffic carbon emission calculation model of step S1 ;

[0028] S2.2. Calculate the traffic carbon emission intensity per unit area to obtain the traffic carbon emission intensity corresponding to the jth region at time t is:

[0029]

[0030] wherein, is the area of the jth region, j is the region number, j = 1, 2, 3, …, J, and J is the total number of regions;

[0031] S2.3. Calculate the emission change rate, the carbon emission change rate corresponding to the jth region at time t is:

[0032]

[0033] wherein, is the traffic carbon emission corresponding to the jth region at time t-1.

[0034] Further, the specific implementation method of step S3 includes the following steps:

[0035] S3.1. Through standardization processing, uniformly map the spatial distance to the interval [0, 1] to obtain the standardized spatial distance between the jth region and the nth region ;

[0036] S3.2. Through standardization processing, uniformly map the time distance to the interval [0, 1] to obtain the standardized time distance between the jth region and the nth region ;

[0037] S3.3. Integrate the standardized spatial distance and the standardized time distance to obtain the comprehensive space-time distance between the jth region and the nth region , the expression is:

[0038]

[0039] wherein, is the spatial distance weight, is the time distance weight, and are obtained by experiment, historical data analysis or expert experience.

[0040] Further, the specific implementation method of step S4 includes the following steps:

[0041] S4.1. Considering the attenuation effect of space-time distance, the difference of emission intensity, and the synergy of change rate, the regional coupling strength coefficient is constructed, and the specific calculation formula is as follows:

[0042]

[0043] Wherein, is the regional coupling strength coefficient between the jth region and the nth region at t time, is the emission intensity difference sensitivity parameter, is the change rate influence coefficient, and is obtained from test, historical data analysis or expert experience; is the maximum value of the traffic carbon emission intensity of all regions; is the traffic carbon emission intensity of the nth region at t time, is the carbon emission change rate of the nth region at t time;

[0044] S4.2. Select the shortest path as the calculation basis, and construct the connection influence index by calculating the frequency of edges in all shortest paths, and the specific calculation formula is:

[0045]

[0046] Wherein, is the connection influence index of the intermediate region e; is the total number of shortest paths from the jth region to the nth region; is the number of paths passing through the intermediate region e in the shortest path from the jth region to the nth region.

[0047] Further, the specific implementation method of step S5 includes the following steps:

[0048] S5.1. Directly related strength is represented by the regional coupling strength coefficient, and the expression is:

[0049]

[0050] Wherein, is the direct correlation strength;

[0051] S5.2. Considering the indirect influence generated by the intermediate node and the exponential attenuation of path length between regions, the indirect correlation strength is constructed, and the calculation formula is as follows:

[0052]

[0053] Wherein, is the indirect correlation strength; summing all possible intermediate regions e; is the region coupling strength coefficient between the jth region and the intermediate region e at time t; is the region coupling strength coefficient between the intermediate region e and the nth region at time t; is the path attenuation coefficient, obtained by experiment, historical data analysis or expert experience; is the shortest path length between the jth region and the nth region, is the maximum path length in the network;

[0054] S5.3. Based on the direct correlation strength and the indirect correlation strength, a path importance score index is constructed, expressed as:

[0055] wherein, is the path importance score; is the direct correlation strength weight coefficient, is the indirect correlation strength weight coefficient, obtained by experiment, historical data analysis or expert experience.

[0056] A city traffic carbon emission calculation and key propagation path identification system, comprising a processor, a memory and a computer program stored in the memory and executable on the processor, the computer program realizes the steps of the city traffic carbon emission calculation and key propagation path identification method as described when running.

[0057] The beneficial effects of the present application are:

[0058] The city traffic carbon emission calculation and key propagation path identification method according to the present application, by establishing a carbon emission calculation model containing dynamic response mechanism, region correlation effect, time correlation characteristics, driving efficiency and loss, flow transmission mechanism, system evolution law and other factors, makes the calculation result more accurate and close to the actual situation.

[0059] The city traffic carbon emission calculation and key propagation path identification method according to the present application solves the problem of accurate identification of carbon emission key propagation path. The carbon emission propagation network analysis method based on the interaction between regions is proposed, which can accurately identify the carbon emission transmission relationship between different regions and reveal the propagation law and key path of carbon emission in the city traffic system. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is a flow chart of the city traffic carbon emission calculation and key propagation path identification method according to the present application;

[0061] Figure 2A carbon emission column chart of 9 time points calculated for the application;

[0062] Figure 3 A path importance score result chart for the application. DETAILED DESCRIPTION

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

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

[0065] In order to further understand the invention content, characteristics and effects of the present application, the following specific embodiments are exemplified, and the drawings are Figure 1 - the drawings Figure 3 are described as follows:

[0066] Example 1:

[0067] A city traffic carbon emission calculation and key propagation path identification method, comprising the following steps:

[0068] S1. Based on the city traffic carbon emission basic model, the dynamic response mechanism, the regional correlation effect, the time-effect correlation characteristics, the driving efficiency and loss, the flow transmission mechanism and the system evolution law are integrated to build a complete traffic carbon emission calculation model for calculating the traffic carbon emission at different time and the total traffic carbon emission;

[0069] Further, from the system operating characteristics, transmission mechanism and evolution law, combined with the nonlinear dynamic characteristics of the traffic system, a multi-dimensional coupled traffic carbon emission calculation model system is constructed. The system includes multiple core components: dynamic response mechanism, regional correlation effect, time and efficiency correlation characteristics, driving efficiency and loss, flow transmission mechanism, and system evolution law. This framework design not only considers the apparent characteristics of the system, but also deeply analyzes the internal mechanism, and through the establishment of multi-level and multi-scale mathematical description, the dynamic evolution process of urban traffic carbon emission is fully described. In particular, the model also considers various nonlinear effects in the traffic system, such as additional emissions under congestion conditions, the influence of extreme weather, and the cascading effect between regions, which are important factors ignored by traditional models.

[0070] Further, the specific implementation method of step S1 includes the following steps:

[0071] S1.1. Construct a basic model of urban traffic carbon emissions. The carbon emissions of urban traffic are first reflected in the operation of vehicles. Due to the differences in technical characteristics, usage patterns and operating conditions, different types of vehicles exhibit different emission characteristics. A basic model of urban traffic carbon emissions is constructed to obtain the emission amount of vehicle t at time t ;

[0072] Further, the expression of the emission amount of vehicle t at time t is:

[0073]

[0074] wherein, is the emission amount of vehicle at time t; is the actual number of vehicles at time t; is the emission coefficient of vehicle at time t; is the average driving distance at time t; is the system attenuation coefficient, reflecting the influence of performance degradation; wherein, , is obtained by field statistics or obtained by the traffic management department; is obtained by test, historical data analysis or expert experience. The emission coefficient of vehicle is not a fixed value, but a dynamic value that changes with environmental conditions and operating state. Environmental temperature affects engine thermal efficiency and battery performance, and driving speed is directly related to energy consumption rate. To accurately describe this change characteristic, a dynamic correction model of emission coefficient is established:

[0075]

[0076] wherein, is the emission coefficient of vehicle at time t, is the reference emission coefficient of vehicle, which can be measured under standard conditions; is a temperature influence coefficient, representing the influence intensity of temperature on carbon emissions; is the ambient temperature at time t; is a reference temperature, obtained from expert experience; is a speed influence coefficient, representing the influence intensity of speed on emissions; is the speed at time t; is a reference speed, obtained from expert experience; is a weather influence factor; is a road condition factor. Wherein, , , , is obtained by test, historical data analysis or expert experience.

[0077] S1.2. Establish a comprehensive dynamic response mechanism, considering the periodic changes of morning and evening peak of weekdays, weekend leisure travel peak as typical characteristics, traffic accidents, extreme weather as nonlinear characteristics, using function relationship to describe the comprehensive dynamic response mechanism, to get the dynamic response coefficient of urban traffic system at time t ;

[0078]

[0079] Wherein, is the system dynamic response coefficient; is the periodic fluctuation amplitude, is the periodic frequency, which can be determined by analyzing historical traffic data or expert experience; is the initial suppression coefficient; is the system recovery coefficient; is the traffic flow influence intensity; is the speed influence index, describing the nonlinear characteristics of speed influence; is the vehicle driving speed, which can be obtained by actual measurement or traffic management department; is the critical speed, which can be determined by expert experience; is the external disturbance correction term, considering the influence of sudden events; , , , , is obtained by test, historical data analysis or expert experience.

[0080] S1.3. Establish the regional correlation effect. The carbon emissions in urban traffic system have significant spatial correlation characteristics. The traffic condition change in a region will affect the surrounding regions through the connection of the road network, forming a chain reaction. The three-dimensional spatial integral of the region is used to build the carbon emission model generated by the regional correlation effect, and the carbon emission generated by the regional correlation effect at time t is obtained ;

[0081] For example, the congestion of the trunk road will cause the traffic to be diverted to the secondary road, changing the spatial distribution pattern of emissions. This spatial correlation can be described by the diffusion-transmission equation:

[0082]

[0083] where, is the carbon emission generated by the regional correlation effect; is the spatial diffusion coefficient; is the Laplace operator, which is used to describe the second-order derivative of space; is the carbon emission concentration at time t, which can be obtained by field statistics or obtained by the traffic management department; represents the diffusion trend of the concentration in space; is the time coupling coefficient; is the partial derivative of the emission concentration at time t with respect to time t, which represents the rate of change of carbon emission concentration with time. is the volume element, and the integral range is the volume of the study area. represents the three-dimensional spatial integral of the study area, that is, the accumulation of the region. Among them, , is obtained by experiment, historical data analysis or expert experience, and the two parameters can also be used to adjust the dimension.

[0084] S1.4. Establish the time-effect correlation characteristics. The current emissions will have real-time effects on the regional environment and travel behavior through various ways, including the immediate occupation of the regional environmental capacity and the dynamic adjustment of travel behavior. The carbon emission generated by the time-effect correlation characteristics at time t is obtained ;

[0085] To accurately describe this time-effect correlation, a time-effect correlation model is established:

[0086]

[0087] where, is the carbon emission generated by the time-effect correlation characteristics; is the initial intensity coefficient of environmental impact; is the attenuation coefficient of environmental impact; is the emission coefficient corresponding to time t. is the conversion coefficient of the behavior pattern index-emission factor; is the initial intensity coefficient of the travel mode; is the decay coefficient of the travel mode; is the corresponding behavior pattern index at time t, wherein, , , , , , is obtained by experiment, historical data analysis or expert experience.

[0088] is the conversion coefficient of the behavior pattern index-emission factor; and are also used to adjust the dimensional difference between different physical quantities.

[0089] S1.5. Establish a driving efficiency and loss model, considering the conversion loss in the acceleration and deceleration process of the vehicle during operation, the idle consumption in the idling state, and the loss caused by the slope, to obtain the comprehensive effect value of the loss at time t , and then calculate the efficiency loss emission amount at time t ;

[0090]

[0091] is the comprehensive effect value of the loss at time t; is the mass of the vehicle, is the speed of the vehicle at time t, obtained by actual measurement or traffic management department; is the speed conversion efficiency coefficient; is the acceleration of gravity; is the change in elevation of the vehicle at time t, obtained from GPS data; is the slope influence coefficient. Wherein, , is obtained by experiment, historical data analysis or expert experience. The additional emission amount generated by the loss can be expressed as:

[0092]

[0093]

[0094] is the efficiency loss emission amount; is the loss-emission conversion coefficient, is the system efficiency correction factor, obtained by experiment, historical data analysis or expert experience.

[0095] ​​S1.6. Establish a flow transfer mechanism, considering the changes in vehicle quantity, speed, and density parameters. During the transfer process, fluctuations, diffusion, and aggregation phenomena in the urban transportation system affect the characteristics of traffic carbon emissions. Based on the established transfer equation, calculate the flow transfer emissions at time t. ;

[0096] Furthermore, the transfer equation is established:

[0097]

[0098] in, Traffic flow density, The vehicle speed is determined by actual measurement or by traffic management authorities. The sign for partial derivatives; The distance traveled is determined by actual measurement or by traffic management authorities. Let be the diffusion coefficient at time t. To travel distance The source and sink terms at time t reflect the interaction between the road system and the external environment, obtained through experiments, historical data analysis, or expert experience. The additional emissions resulting from this transfer process can be expressed as:

[0099]

[0100] in, Emissions are transferred through the flow; For the transmission-emission conversion factor, These are transmission effect factors, obtained through experiments, historical data analysis, or expert experience.

[0101] S1.7. Establish the system evolution law, considering that the orderliness of the urban transportation system changes over time due to increased traffic flow disorder during peak hours, the spread of congested areas, and the exacerbation of uneven vehicle distribution. Calculate the system evolution emissions at time t based on the established system description equation. ;

[0102] Furthermore, transportation systems exhibit significant evolutionary characteristics during operation, with the system's orderliness changing over time. For example, traffic flow becomes more chaotic during peak hours, congestion areas spread, and uneven vehicle distribution intensifies. These phenomena all affect the system's emission characteristics. Based on system evolution theory, a descriptive equation is established:

[0103]

[0104] in, This is the system state function, representing the degree of orderliness of the traffic system. The greater, the more orderly the system, the smoother the traffic flow, and the lower the congestion level. represents the external input received by the system at time t, including new traffic flow, external intervention, etc. represents the output of the system at time t, including traffic flow leaving, system optimization, etc.

[0105] Specifically,

[0106]

[0107] wherein, is the traffic flow, obtained by actual measurement or traffic management department; is the flow penetration response coefficient; is the system control strength, which can be obtained by actual measurement or traffic management department; is the control response coefficient; is the system control strength-flow conversion coefficient in the input; is the road network load rate, which can be obtained by actual measurement or traffic management department; is the load sensitivity coefficient; is the road network load-flow conversion coefficient in the input. , , , , can be obtained by test, historical data analysis or expert experience.

[0108]

[0109] wherein, is the flow evacuation coefficient, is the optimization efficiency coefficient, is the system control strength-flow conversion coefficient in the output, is the network adjustment coefficient, is the road network load-flow conversion coefficient in the output, which can be obtained by test, historical data analysis or expert experience.

[0110] The emission contribution generated by the evolution of the system can be represented as:

[0111]

[0112] wherein, is the system evolution emission; is the evolution-emission conversion coefficient, is the evolution effect factor, which can be obtained by test, historical data analysis or expert experience.

[0113] S1.8. Build a complete traffic carbon emission calculation model to obtain the traffic carbon emission amount corresponding to t time , the expression is:

[0114]

[0115] , wherein, is the emission amount of the fixed source at t time, obtained by test or from the traffic management department;

[0116] Obtain the total traffic carbon emission amount corresponding to total time T , the expression is:

[0117] .

[0118] S2. Calculate the traffic carbon emission amount of the jth region based on the traffic carbon emission calculation model of step S1, calculate the traffic carbon emission intensity and the traffic carbon emission change rate as the basic data for identifying the key transmission path;

[0119] First, divide different regions, and the division method can be according to administrative division, functional division, traffic function division, specific region division, etc. The region number after division is j, j = 1, 2, 3, …, Na; Na is the total number of divided regions;

[0120] Further, the specific implementation method of step S2 includes the following steps:

[0121] S2.1. Calculate the traffic carbon emission amount of the jth region at t time based on the traffic carbon emission calculation model of step S1 ;

[0122] Further, the traffic carbon emission amount of different regions is calculated by using the aforementioned proposed urban traffic carbon emission calculation model; the specific process is as follows:

[0123] The actual running quantity and average driving distance of vehicles in different regions at t time are counted, the emission coefficient of vehicles at t time is calculated, and the system attenuation coefficient is determined by expert experience to calculate the emission amount of vehicles in the jth region at t time ;

[0124] The driving speed of vehicles is measured and counted, and the values of periodic fluctuation amplitude, periodic frequency, initial suppression coefficient, system recovery coefficient, traffic flow influence intensity, speed influence index, critical speed, and external interference correction term are determined by expert experience to calculate the system dynamic response coefficient of the jth region ;

[0125] The carbon emission concentration of different regions at time t is counted, and the second derivative and partial derivative of the carbon emission concentration are calculated, and then the spatial diffusion coefficient and the time coupling coefficient are obtained by analyzing the historical data, and on this basis, the carbon emission amount generated by the correlation effect of the jth region is calculated ;

[0126] Based on the emission factor of the vehicle at time t calculated in the foregoing, the initial intensity coefficient of environmental impact, the attenuation coefficient of environmental impact, the emission conversion coefficient corresponding to the unit behavior index, the initial intensity coefficient of travel mode, the attenuation coefficient of travel mode, and the behavior mode index are obtained by analyzing the historical data and expert experience, and on this basis, the carbon emission amount generated by the time-effect correlation effect is calculated ;

[0127] The mass, driving speed and vehicle windward area of the vehicle are measured and counted, the air density is obtained from the meteorological management department, the vehicle elevation information is obtained from the GPS, the speed conversion efficiency coefficient, the slope influence coefficient and the air resistance coefficient are obtained by analyzing the historical data, and on this basis, the comprehensive effect value of the loss is calculated; then, the loss-emission conversion coefficient and the system efficiency correction factor are determined by expert experience, and on this basis, the efficiency loss emission amount of the jth region is calculated ;

[0128] The traffic flow density, vehicle driving speed and driving distance are measured and counted, the diffusion coefficient, source and sink term, transfer-emission conversion coefficient and transfer effect factor are obtained by analyzing the historical data and expert experience, and on this basis, the flow transfer emission amount of the jth region is calculated ;

[0129] The traffic flow, system control intensity and road network load rate are actually measured and calculated, the flow penetration response coefficient, control response coefficient, load sensitivity coefficient, flow dispersion coefficient, optimization efficiency coefficient, network adjustment coefficient, system control intensity-flow conversion coefficient and road network load-flow conversion coefficient are obtained by analyzing the historical data and expert experience, and then the evolution-emission conversion coefficient and evolution effect factor are determined by experiment, and on this basis, the system evolution emission amount of the jth region is calculated ;

[0130] S2.2. Calculate the traffic carbon emission intensity per unit area to obtain the traffic carbon emission intensity of the jth region at time t :

[0131]

[0132] wherein, Area of the jth region, j is the region number, j = 1, 2, 3, …, J, J is the total number of regions;

[0133] When conducting inter-regional carbon emission propagation analysis, the first task is to quantify the emission level of each region. Due to the significant difference in area of different regions, direct comparison of total emissions may be misleading. Therefore, it is necessary to calculate the emission intensity per unit area to objectively reflect the emission level of each region.

[0134] S2.3. Calculate the emission change rate, the carbon emission change rate corresponding to the jth region at time t is:

[0135]

[0136] Wherein, is the traffic carbon emission of the jth region at time t-1.

[0137] On the basis of mastering the emission intensity of the region, it is also necessary to understand the dynamic change characteristics of the emission. The emission change rate is a key indicator for measuring the dynamic characteristics of the region emission, which can reflect the growth or decline trend of the regional emission. The relative change rate is used for calculation, which can eliminate the influence of the difference in regional scale.

[0138] The actual example of this embodiment is as follows, 9 time points corresponding information is selected, part of the information is shown in Table 1:

[0139] Table 1

[0140]

[0141] The carbon emission of 9 time points calculated is shown in Table 1. Figure 2

[0142] S3. Construct a space-time distance calculation system, standardize the space distance and time distance, and then construct a comprehensive space-time distance index for reflecting the physical connection between regions;

[0143] Further, the specific implementation method of step S3 includes the following steps:

[0144] S3.1. Through standardization, the space distance is uniformly mapped to the interval [0, 1] to obtain the standardized space distance between the jth region and the nth region ;

[0145] ​Further, after determining the basic emission characteristics, it is necessary to establish the spatial correlation between regions. Spatial distance is the primary factor affecting carbon emission propagation, but the original distance data has a dimension problem, which is not conducive to comprehensive with other indicators. Through standardization processing, the spatial distance can be uniformly mapped to the [0, 1] interval, which not only solves the dimension problem, but also provides a comparable basis for subsequent comprehensive distance calculation. The maximum and minimum value standardization method is selected to standardize the spatial distance, as follows:

[0146]

[0147] wherein, is the standardized spatial distance; is the actual spatial distance from the jth region to the nth region; is the maximum spatial distance between all region pairs; is the minimum spatial distance between all region pairs;

[0148] S3.2. Through standardization processing, the time distance is uniformly mapped to the [0, 1] interval, and the standardized time distance between the jth region and the nth region is obtained .

[0149] Further, in addition to spatial distance, the correlation of time dimension is also an important feature of carbon emission propagation. Time distance reflects the lag effect of emission impact, which is common in urban transportation systems. In order to be comprehensive with spatial distance, the same standardization processing is also needed. This standardization processing makes the time lag effect can be quantitatively included in the propagation path analysis. The same standardization method as spatial distance is adopted to ensure the comparability of the two dimensions. The specific calculation formula is as follows:

[0150]

[0151] wherein, is the standardized time distance; is the actual time distance from the jth region to the nth region; is the maximum time distance between all region pairs; is the minimum time distance between all region pairs;

[0152] S3.3. Integrating the standardized spatial distance and the standardized time distance, the comprehensive space-time distance between the jth region and the nth region is obtained , the expression is:

[0153]

[0154] wherein, is the weight of spatial distance, is the weight of time distance, and obtained by experiments, historical data analysis or expert experience.

[0155] After obtaining the standardized spatial distance and temporal distance, it is necessary to integrate them into a comprehensive index. This integration takes into account the spatio-temporal characteristics of emission propagation, which is more in line with the actual propagation mechanism. Through the adjustment of the weight parameter, the relative importance of spatial and temporal factors can be flexibly controlled, which is of great significance for research of different scales and types.

[0156] S4. Based on the traffic carbon emission intensity and traffic carbon emission change rate obtained in step S2, and the comprehensive spatio-temporal distance index obtained in step S3, establish a regional coupling strength coefficient and a connection influence index, construct a regional coupling strength coefficient considering the attenuation effect of spatio-temporal distance, the difference of emission intensity, and the synergy of change rate, and the connection influence index is used to determine the importance of the path together with the regional coupling strength coefficient;

[0157] Further, the specific implementation method of step S4 includes the following steps:

[0158] S4.1. Construct a regional coupling strength coefficient considering the attenuation effect of spatio-temporal distance, the difference of emission intensity, and the synergy of change rate, and the specific calculation formula is as follows:

[0159]

[0160] wherein, is the regional coupling strength coefficient between the jth region and the nth region at time t, is the emission intensity difference sensitivity parameter, is the change rate influence coefficient, and obtained by experiments, historical data analysis or expert experience; is the maximum value of the traffic carbon emission intensity of all regions; is the traffic carbon emission intensity corresponding to the nth region at time t, is the carbon emission change rate corresponding to the nth region at time t;

[0161] The traditional fixed weight calculation method is difficult to reflect the dynamic characteristics of the correlation between regions. Therefore, a regional coupling strength coefficient calculation method is proposed, which considers three key factors: the attenuation effect of spatio-temporal distance, the difference of emission intensity, and the synergy of change rate. This multi-factor comprehensive calculation method can more accurately depict the actual correlation degree between regions.

[0162] S4.2. Select the shortest path as the basis for calculation, construct the connection influence index by calculating the frequency of edges in all shortest paths, and the specific calculation formula is:

[0163]

[0164] Wherein, is the connection influence index of the intermediate region e; is the total number of shortest paths from the jth region to the nth region; is the number of paths passing through the intermediate region e in the shortest path from the jth region to the nth region.

[0165] The connection influence index is an important indicator for identifying key transmission channels in the network. It reflects the importance of the edge in the whole network by calculating the frequency of the edge in all shortest paths. In the carbon emission propagation network, a higher connection influence index means that the connection plays a key role as a bridge in the inter-regional propagation process. This indicator will be combined with the regional coupling strength coefficient to determine the importance of the path.

[0166] S5. Based on the regional coupling strength coefficient and the connection influence index obtained in step S4, the correlation strength between regions is calculated, including direct correlation strength and indirect correlation strength, and then based on the correlation strength, the path importance score index is constructed to comprehensively evaluate the importance of different propagation paths.

[0167] Further, the specific implementation method of step S5 includes the following steps:

[0168] S5.1. The regional coupling strength coefficient is used to represent the direct correlation strength, and the expression is:

[0169]

[0170] Wherein, is the direct correlation strength;

[0171] The direct correlation strength describes the immediate influence relationship between regions. This correlation is based on actual physical adjacency and is the most basic and direct propagation channel. The present application directly uses the regional coupling strength coefficient to represent the direct correlation strength;

[0172] S5.2. Considering the indirect influence between regions through intermediate nodes and the exponential decay of path length, the indirect correlation strength is constructed, and the calculation formula is as follows:

[0173]

[0174] Wherein, is the indirect correlation strength; Summing up all possible intermediate regions e; is the region coupling strength coefficient between the jth region and the intermediate region e at time t; is the region coupling strength coefficient between the intermediate region e and the nth region at time t; is the path attenuation coefficient, obtained by experiments, historical data analysis or expert experience; is the shortest path length between the jth region and the nth region, is the maximum path length in the network;

[0175] In addition to direct association, there is also indirect influence between regions through intermediate nodes. Although the strength of indirect association is usually lower than that of direct association, it plays an important complementary role in the overall network structure, especially in explaining long-distance propagation phenomena. By considering the exponential attenuation of path length, the characteristics of indirect influence weakening with increasing distance can be reasonably described.

[0176] S5.3. Based on the direct association strength and the indirect association strength, construct the path importance score index, expressed as:

[0177]

[0178] wherein, is the path importance score; is the direct association strength weight coefficient, is the indirect association strength weight coefficient, obtained by experiments, historical data analysis or expert experience.

[0179] The path importance score is the final output of the entire analysis framework, which integrates the association characteristics of both direct and indirect levels. This double-layer scoring method can comprehensively reflect the role of the path in the emission propagation network and avoid the one-sidedness caused by considering only a single level. By adjusting the weight coefficients of the direct and indirect levels, the evaluation standard can be flexibly adjusted according to the research needs. This comprehensive score will be directly used to determine the key propagation path and provide a scientific basis for traffic management decisions.

[0180] Based on the region setting of the present embodiment to region , there are 4 carbon emission propagation paths between them, respectively → → , → → , → → , → → . Among them, → → Examples of corresponding path importance scores are shown below, and the information for each region is shown in Table 2:

[0181] Table 2

[0182]

[0183] Based on this, the comprehensive spatiotemporal distance is calculated through spatial distance standardization and temporal distance standardization. Then, the association strength is calculated, and finally, the path importance score is calculated. The calculation results are shown in Table 3:

[0184] Table 3

[0185]

[0186] Similarly, it can be calculated that → → , →→ , → → The importance scores of the three carbon emission propagation pathways are shown in Table 4 and Figure 3 As shown:

[0187] Table 4

[0188]

[0189] Comparing the path importance scores of the four paths reveals that the path... → → The path with the highest importance score is the key propagation path of carbon emissions.

[0190] This embodiment establishes a complete method for calculating urban traffic carbon emissions, organically combining multiple dimensions such as traffic flow characteristics, vehicle characteristics, and environmental factors. Through the collaborative operation of multiple sub-models, the accuracy and reliability of the calculation results are significantly improved. The proposed carbon emission propagation path identification method has strong practical guiding significance and can be directly applied to the optimization and management of urban transportation systems. By identifying key propagation paths, it can provide clear directional guidance for formulating targeted emission reduction measures, thereby improving the effectiveness of emission reduction measures.

[0191] Example 2:

[0192] A system for urban traffic carbon emission calculation and key propagation path identification, comprising a processor, a memory and a computer program stored in the memory and executable in the processor, the computer program implements the steps of a method for urban traffic carbon emission calculation and key propagation path identification as described in embodiment 1 when executed.

[0193] It should be noted that the terms "first", "second" and the like, such relational terms are used only to distinguish one entity or action from another, and do not necessarily require or imply that these entities or actions are in any way mutually exclusive or in any way arranged in sequence. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0194] Although the present application has been described above with reference to specific embodiments, various modifications can be made to it and equivalents can be substituted for elements thereof without departing from the scope of the present application. In particular, features of the specific embodiments disclosed herein can be combined together in any manner, provided that there is no structural conflict. The fact that these combinations are not explicitly described in the specification is merely due to the consideration of omitting unnecessary descriptions and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for urban traffic carbon emission calculation and key propagation path identification, characterized in that, Comprising the following steps: S1. The specific implementation method of step S1 comprises the following steps: S1.

1. The carbon emissions of urban traffic are first reflected in the operation of vehicles. Different types of vehicles have different emission characteristics due to differences in technical features, usage patterns, and operating conditions. A basic model of urban traffic carbon emissions is constructed to obtain the emission amount of a vehicle at time t ; S1.

2. Considering the periodic variation of morning and evening peak of weekdays, the leisure travel peak of weekends as the typical characteristics, traffic accidents, extreme weather as nonlinear characteristics, using function relationship to describe the comprehensive dynamic response mechanism, the dynamic response coefficient of urban traffic system at time t is obtained ; S1.

3. Considering that carbon emissions in urban transportation systems have significant spatial correlation characteristics, changes in the traffic conditions of an area can affect surrounding areas through road network connection relationships, forming a chain reaction. A model of carbon emissions generated by regional correlation effects is constructed using three-dimensional spatial integration, and the carbon emissions generated by regional correlation effects at time t are obtained ; S1.

4. Considering the current emissions will have real-time impacts on regional environment and travel behavior patterns through various ways, including the immediate occupation of regional environmental capacity and the dynamic adjustment of travel behavior, the carbon emissions generated by the time-dependent correlation characteristics at time t are obtained ; S1.

5. Consider the conversion loss of the vehicle during acceleration and deceleration, the idling state, the loss caused by the slope, and obtain the comprehensive effect value of the loss at time t , and then based on Calculate the efficiency loss and emission at time t ; S1.

6. Considering the flow transmission includes the change of vehicle quantity, the spread of speed and density parameters, the fluctuation, diffusion and aggregation of urban traffic system in the transmission process affect the traffic carbon emission characteristics, and the flow transmission emission at time t is calculated based on the transmission equation ; S1.

7. Considering that the order degree of the urban traffic system will change over time during operation, based on the increasing degree of chaos of peak traffic flow, the spread of congestion areas, and the increasing unevenness of vehicle distribution, the system evolution emission at time t is calculated based on the system description equation ; S1.

8. Build a complete traffic carbon emission calculation model to obtain the traffic carbon emission at time t The expression is: ； wherein, is the emission of the stationary source at time t, obtained from tests or from the traffic management department; obtaining total traffic carbon emission amount corresponding to total time T The expression is: ； S2. Calculate the traffic carbon emissions of the jth region based on the traffic carbon emission calculation model of step S1, calculate the traffic carbon emission intensity and the traffic carbon emission change rate, as the basic data for key propagation path identification; S3. Build a space-time distance calculation system, standardize the spatial distance and time distance, and then build a comprehensive space-time distance index for reflecting the physical connection between regions; S4. Based on the traffic carbon emission intensity and the traffic carbon emission change rate obtained in step S2, and the comprehensive space-time distance index obtained in step S3, establish the regional coupling strength coefficient and the connection influence index; The specific implementation method of step S4 comprises the following steps: S4.

1. Considering the attenuation effect of space-time distance, the difference of emission intensity, and the synergy of change rate, build the regional coupling strength coefficient, the specific calculation formula is as follows: ； wherein, is the region coupling strength coefficient between the jth region and the nth region at time t, is the emission intensity difference sensitivity parameter, is the change rate influence coefficient, and obtained from experiments, historical data analysis or expert experience; is the maximum value of the traffic carbon emission intensity of all regions; is the traffic carbon emission intensity corresponding to the nth region at time t, is the carbon emission change rate corresponding to the nth region at time t; is the jth region and the nth region comprehensive space-time distance, the traffic carbon emission intensity corresponding to the jth region at time t , is the carbon emission change rate corresponding to the jth region at time t; S4.

2. Select the shortest path as the calculation basis, build the connection influence index by calculating the frequency of edges in all shortest paths, the specific calculation formula is as follows: ； wherein, is a connection influence index of the intermediate region e; is a total number of shortest paths from the jth region to the nth region; is a number of paths passing through the intermediate region e among the shortest paths from the jth region to the nth region; S5. Based on the regional coupling strength coefficient and the connection influence index obtained in step S4, calculate the correlation strength between regions, including direct correlation strength and indirect correlation strength, then build the path importance score index based on the correlation strength, for comprehensive evaluation of the importance of different propagation paths.

2. The method of claim 1, wherein, The specific implementation method of step S2 comprises the following steps: S2.

1. Calculate the traffic carbon emission of the j-th region at time t based on the traffic carbon emission calculation model of step S1 ; S2.

2. Calculate the traffic carbon emission intensity per unit area, and obtain the traffic carbon emission intensity of the jth region at time t is: ； wherein, Ajis an area of the jth region, j is a region number, j = 1, 2, 3, …, J, and J is a total number of regions. S2.

3. Calculate the emission change rate, the carbon emission change rate corresponding to the jth region at time t is: ； wherein, is the traffic carbon emission amount corresponding to the jth region at time t-1.

3. The method of claim 2, wherein, The specific implementation method of step S3 comprises the following steps: S3.

1. Through standardization processing, the spatial distance is uniformly mapped to the interval [0, 1] to obtain the standardized spatial distance of the jth region and the nth region ; S3.

2. Through standardization processing, the time distance is uniformly mapped to the interval [0, 1] to obtain the standardized time distance of the jth region and the nth region ; S3.

3. Integrate the standardized spatial distance and the standardized time distance to obtain the comprehensive spatio-temporal distance between the jth region and the nth region The expression is: ； wherein, is a spatial distance weight, is a temporal distance weight, and is obtained from experiments, historical data analysis or expert experience.

4. The method of claim 3, wherein, The specific implementation method of step S5 comprises the following steps: S5.

1. Use the regional coupling strength coefficient to represent the direct correlation strength, the expression is as follows: ； wherein is the direct correlation strength; S5.

2. Considering the indirect influence between regions through intermediate nodes and the exponential attenuation of path length, build the indirect correlation strength, the calculation formula is as follows: ； wherein, is the indirect correlation strength; summing over all possible intermediate regions e; is the region coupling strength coefficient between the jth region and the intermediate region e at time t; is the region coupling strength coefficient between the intermediate region e and the nth region at time t; is the path attenuation coefficient, obtained from experiments, historical data analysis or expert experience; is the shortest path length between the jth region and the nth region, is the maximum path length in the network; S5.

3. Based on the direct correlation strength and the indirect correlation strength, a path importance score index is constructed, and the expression is: ; wherein, is a path importance score; is a direct correlation strength weight coefficient, is an indirect correlation strength weight coefficient, obtained from experiments, historical data analysis or expert experience.

5. A system for urban traffic carbon emission calculation and key propagation path identification, characterized in that, A processor, a memory, and a computer program stored in the memory and executable on the processor, the computer program realizes the steps of a city traffic carbon emission calculation and key propagation path identification method according to any one of claims 1-4 when running.

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