Urban traffic carbon emission calculation and key propagation path identification method and system

Abstract

The invention discloses an urban traffic carbon emission calculation and key propagation path identification method and system, and belongs to the technical field of urban traffic carbon emission calculation. In order to solve the problem of urban traffic carbon emission accurate path identification, the method comprises the following steps: constructing a complete traffic carbon emission calculation model for calculating traffic carbon emission and total traffic carbon emission corresponding to different moments; the traffic carbon emission intensity and the traffic carbon emission change rate are calculated to serve as basic data of key propagation path recognition; constructing a space-time distance calculation system; a regional coupling strength coefficient and a connection influence index are established, the regional coupling strength coefficient is established by considering the attenuation effect of the space-time distance, the difference of the emission strength and the collaboration of the change rate, and the connection influence index is used for being combined with the regional coupling strength coefficient to jointly determine the importance of the path; and calculating the association strength between the regions, and on the basis of the association strength, constructing a path importance scoring index for comprehensively evaluating the importance degrees of different propagation paths.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban traffic carbon emission calculation, and specifically relates to a method and system for urban traffic carbon emission calculation and key propagation path identification. Background Art

[0002] Traditional carbon emission calculation methods often use static, simplistic models that fail to accurately reflect the dynamic characteristics and complexity of transportation systems. These methods often overlook the interplay and interconnectedness between various elements of the transportation system, making it difficult to capture the dynamic patterns of actual operation. In particular, when calculating interregional transportation carbon emissions, existing methods fail to fully account for interregional interactions and transmission effects, resulting in significant deviations from actual results. These issues have severely hampered the in-depth development of transportation carbon emissions research and its practical application.

[0003] Identifying key carbon emission transmission pathways plays a crucial role in optimizing urban transportation systems. By doing so, we can gain a deeper understanding of the flow and diffusion mechanisms of carbon emissions within urban transportation systems, accurately grasping their spatial distribution and evolutionary trends. This identification helps pinpoint key carbon emission areas and key transmission pathways, providing a scientific basis for developing targeted emission reduction measures. Furthermore, by analyzing the structural characteristics of carbon emission transmission networks, we can optimize operational management strategies for urban transportation systems and enhance the relevance and effectiveness of emission reduction measures.

[0004] However, existing methods for identifying key carbon emission transmission pathways still face multiple technical challenges. Traditional identification methods are often oversimplified and fail to fully consider the interactions and complex connections between regions, making it difficult to accurately reflect the actual transmission characteristics of carbon emissions. In terms of analyzing spatiotemporal evolution characteristics, existing methods lack systematicity and comprehensiveness and cannot effectively capture the dynamic changes in carbon emissions. In addition, when identifying the key influencing factors of carbon emissions, existing methods have difficulty accurately quantifying the extent of the influence of each factor, nor can they effectively evaluate the carbon emission transmission effects between different regions. These problems have affected the accuracy and practicality of the identification results. Summary of the Invention

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

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A method for calculating urban traffic carbon emissions and identifying key transmission paths includes the following steps:

[0008] S1. Based on the basic model of urban transportation carbon emissions, a comprehensive transportation carbon emissions calculation model is constructed by integrating dynamic response mechanisms, regional correlation effects, time-dependent correlation characteristics, driving efficiency and losses, flow transmission mechanisms, and system evolution laws. This model is used to calculate the corresponding and total transportation carbon emissions at different times.

[0009] S2. Calculate the traffic carbon emissions in region j based on the traffic carbon emissions calculation model from step S1, and calculate the traffic carbon emissions intensity and traffic carbon emissions change rate as basic data for identifying key transmission paths;

[0010] S3. Build a spatiotemporal distance calculation system, standardize spatial and temporal distances, and then construct a comprehensive spatiotemporal distance index to reflect the physical connections between regions.

[0011] S4. Based on the transportation carbon emission intensity and transportation carbon emission change rate obtained in step S2 and the comprehensive spatiotemporal distance index obtained in step S3, establish a regional coupling intensity coefficient and a connection influence index. This coefficient is constructed by considering the attenuation effect of spatiotemporal distance, the differences in emission intensity, and the synergy of the change rate. The connection influence index is used in conjunction with the regional coupling intensity coefficient to determine the importance of a path.

[0012] S5. Based on the regional coupling strength coefficient and connection influence index obtained in step S4, calculate the correlation strength between regions, including direct correlation strength and indirect correlation strength. Then, based on the correlation strength, construct a path importance scoring index to comprehensively evaluate the importance of different propagation paths.

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

[0014] S1.1. Construct a basic model for urban transportation carbon emissions. Considering that urban transportation carbon emissions are primarily reflected in vehicle operation, different types of vehicles exhibit different emission characteristics due to their technical characteristics, usage patterns, and operating conditions, a basic model for urban transportation carbon emissions is constructed to obtain the vehicle's emissions at time t. ;

[0015] S1.2. Establish a comprehensive dynamic response mechanism, taking into account the typical cyclical changes of weekday morning and evening rush hours and weekend leisure travel peaks, and the nonlinear characteristics of traffic accidents and extreme weather. Use functional relationships to describe the comprehensive dynamic response mechanism and obtain the dynamic response coefficient of the urban transportation system at time t. ;

[0016] S1.3. Establish regional correlation effects. Considering that carbon emissions in urban transportation systems have significant spatial correlation characteristics, changes in traffic conditions in one region will affect surrounding areas through road network connections, forming a chain reaction. Using the three-dimensional spatial integration of the region, a model of carbon emissions generated by regional correlation effects is constructed to obtain the carbon emissions generated by regional correlation effects at time t. ;

[0017] S1.4. Establish a time-dependent correlation characteristic, considering that current emissions will have real-time impacts on the regional environment and travel behavior through multiple channels, including the immediate occupation of regional environmental capacity and the dynamic adjustment of travel behavior, and obtain the carbon emissions generated by the time-dependent correlation characteristic at time t. ;

[0018] S1.5. Establish a driving efficiency and loss model, taking into account the conversion loss during acceleration and deceleration, idle loss, and loss caused by slope during operation, and obtain the comprehensive effect value of the loss at time t. , and then calculate the efficiency loss emissions at time t ;

[0019] S1.6. Establish a flow transfer mechanism, taking into account the flow transfer process, including changes in the number of vehicles, the propagation of speed and density parameters. During the transfer process, the fluctuation, diffusion, and aggregation of the urban traffic system affect the characteristics of traffic carbon emissions. Calculate the flow transfer emissions at time t based on the established transfer equation. ;

[0020] S1.7. Establish a system evolution law. Consider that the orderliness of the urban transportation system changes over time due to increasing traffic chaos during peak hours, the expansion of congested areas, and the increasing uneven distribution of vehicles. Calculate the system evolution emissions at time t based on the established system description equation. ;

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

[0022]

[0023] in, is the emission of the stationary source at time t, obtained through experiments or from the traffic management department;

[0024] Get the total traffic carbon emissions corresponding to the total time T , the expression is:

[0025] .

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

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

[0028] S2.2. Calculate the traffic carbon emission intensity per unit area and obtain the traffic carbon emission intensity corresponding to time t in the jth area. for:

[0029]

[0030] in, 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 time t in region j for:

[0032]

[0033] in, is the traffic carbon emissions corresponding to time t-1 in the jth region.

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

[0035] S3.1. Through normalization, the spatial distance is uniformly mapped to the interval [0,1], and the normalized spatial distance between the jth region and the nth region is obtained. ;

[0036] S3.2. Through normalization, the time distance is uniformly mapped to the interval [0,1] to obtain the normalized time distance between the jth region and the nth region. ;

[0037] S3.3. Integrate the normalized spatial distance and normalized temporal distance to obtain the comprehensive temporal distance between the jth region and the nth region , the expression is:

[0038]

[0039] in, is the spatial distance weight, is the time distance weight, and Obtained through experiments, historical data analysis or expert experience.

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

[0041] S4.1. Consider the attenuation effect of temporal and spatial distance, the variability of emission intensities, and the synergy of the rate of change to construct a regional coupling intensity coefficient. The specific calculation formula is as follows:

[0042]

[0043] in, 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 through experiments, historical data analysis or expert experience; is the maximum value of transportation carbon emission intensity in all regions; is the traffic carbon emission intensity corresponding to time t in the nth region, is the carbon emission change rate corresponding to time t in the nth region;

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

[0045]

[0046] in, is the connection influence index of the middle region e; is the total number of shortest paths from the jth region to the nth region; is the number of paths that pass through the intermediate area e in the shortest path from area j to area n.

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

[0048] S5.1. The direct correlation strength is characterized by the regional coupling strength coefficient, which is expressed as:

[0049]

[0050] in, is the direct correlation strength;

[0051] S5.2. Considering the indirect influence between regions through intermediate nodes and the exponential decay of path length, construct the indirect connection strength using the following formula:

[0052]

[0053] in, is the indirect association strength; Sum all possible intermediate regions e; is the regional coupling strength coefficient between the jth region and the middle region e at time t; is the regional coupling strength coefficient between the middle region e and the nth region at time t; is the path attenuation coefficient, obtained through 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;

[0054] S5.3. Based on the direct and indirect association strengths, construct a path importance scoring index, which is expressed as:

[0055] in, Score the importance of paths; is the direct correlation strength weight coefficient, It is the indirect correlation strength weight coefficient, obtained through experiments, historical data analysis or expert experience.

[0056] A system for calculating urban traffic carbon emissions and identifying key propagation paths includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, the steps of the method for calculating urban traffic carbon emissions and identifying key propagation paths are implemented.

[0057] Beneficial effects of the present invention:

[0058] The method for calculating urban traffic carbon emissions and identifying key transmission paths described in the present invention establishes a carbon emission calculation model that includes multiple factors such as dynamic response mechanism, regional correlation effect, time correlation characteristics, driving efficiency and loss, flow transmission mechanism, and system evolution law, making the calculation results more accurate and close to reality.

[0059] The method for calculating urban transportation carbon emissions and identifying key transmission paths addresses the problem of accurately identifying key carbon emission transmission paths. This method, based on inter-regional interactions, accurately identifies the carbon emission transmission relationships between different regions and reveals the propagation patterns and key paths of carbon emissions within urban transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a flow chart of a method for calculating urban traffic carbon emissions and identifying key transmission paths according to the present invention;

[0061] Figure 2The carbon emission bar chart at 9 moments calculated by the present invention;

[0062] Figure 3 This is the path importance scoring result diagram of the present invention. DETAILED DESCRIPTION

[0063] In order to make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present invention and are not intended to limit the present invention. That is, the specific embodiments described herein are only some embodiments of the present invention, not all embodiments. Generally, the components of the specific embodiments of the present invention described and illustrated in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0064] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected specific embodiments of the present invention. All other specific embodiments obtained by those skilled in the art based on the specific embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] In order to further understand the content, features and effects of the present invention, the following specific embodiments are given as examples, and the attached Figure 1 -Attached Figure 3 The detailed instructions are as follows:

[0066] Example 1:

[0067] A method for calculating urban traffic carbon emissions and identifying key transmission paths includes the following steps:

[0068] S1. Based on the basic model of urban transportation carbon emissions, a comprehensive transportation carbon emissions calculation model is constructed by integrating dynamic response mechanisms, regional correlation effects, time-dependent correlation characteristics, driving efficiency and losses, flow transmission mechanisms, and system evolution laws. This model is used to calculate the corresponding and total transportation carbon emissions at different times.

[0069] Furthermore, starting from the system's operational characteristics, transmission mechanisms, and evolutionary laws, and incorporating the nonlinear dynamic characteristics of the transportation system, a multi-dimensional coupled transportation carbon emissions calculation model system was constructed. This system comprises several core components: dynamic response mechanisms, regional correlation effects, time-dependent characteristics, driving efficiency and losses, flow transmission mechanisms, and system evolutionary laws. This framework design not only considers the system's apparent characteristics but also deeply analyzes its underlying mechanisms. By establishing a multi-level and multi-scale mathematical description, it achieves a comprehensive characterization of the dynamic evolution of urban transportation carbon emissions. In particular, the model considers various nonlinear effects within the transportation system, such as additional emissions under congestion conditions, the impact of extreme weather, and inter-regional cascading effects, which are important factors overlooked by traditional models.

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

[0071] S1.1. Construct a basic model for urban transportation carbon emissions. Considering that urban transportation carbon emissions are primarily reflected in vehicle operation, different types of vehicles exhibit different emission characteristics due to their technical characteristics, usage patterns, and operating conditions, a basic model for urban transportation carbon emissions is constructed to obtain the vehicle's emissions at time t. ;

[0072] Furthermore, the expression of vehicle emissions at time t is:

[0073]

[0074] in, is the vehicle’s emissions at time t; is the actual number of vehicles running at time t; is the emission coefficient of the vehicle at time t; is the average mileage at time t; is the system attenuation coefficient, reflecting the effect of performance degradation; where, 、 Obtained from on-site statistics or traffic management departments; The emission coefficient is obtained through experimental acquisition, historical data analysis, or expert experience. A vehicle's emission coefficient is not a fixed value, but changes dynamically with environmental conditions and operating status. Ambient temperature affects engine thermal efficiency and battery performance, while driving speed is directly related to energy consumption. To accurately describe this changing characteristic, a dynamic correction model for the emission coefficient is established:

[0075]

[0076] in, is the emission coefficient of the vehicle at time t, is the vehicle's baseline emission coefficient, which can be measured under standard operating conditions; is the temperature influence coefficient, which represents the intensity of the impact of temperature on carbon emissions; is the ambient temperature at time t; is the reference temperature, obtained from expert experience; is the speed influence coefficient, which represents the intensity of the influence of speed on emission; is the speed at time t; is the reference speed, obtained from expert experience; is the weather influencing factor; is the road condition factor. 、 、 、 Obtained through experiments, historical data analysis or expert experience.

[0077] S1.2. Establish a comprehensive dynamic response mechanism, taking into account the typical cyclical changes of weekday morning and evening rush hours and weekend leisure travel peaks, and the nonlinear characteristics of traffic accidents and extreme weather. Use functional relationships to describe the comprehensive dynamic response mechanism and obtain the dynamic response coefficient of the urban transportation system at time t. ;

[0078]

[0079] in, is the system dynamic response coefficient; is the periodic fluctuation amplitude, The frequency of the cycle can be determined by analyzing historical traffic data or expert experience; is the initial suppression coefficient; is the system recovery coefficient; The intensity of traffic flow impact; is the speed influence index, which describes the nonlinear characteristics of speed influence; The vehicle speed can be obtained through actual measurement or traffic management department; is the critical speed, which can be determined by expert experience; is the external interference correction term, taking into account the impact of unexpected events; 、 、 、 、 It can be obtained through experiments, historical data analysis or expert experience.

[0080] S1.3. Establish regional correlation effects. Considering that carbon emissions in urban transportation systems have significant spatial correlation characteristics, changes in traffic conditions in one region will affect surrounding areas through road network connections, forming a chain reaction. Using the three-dimensional spatial integration of the region, a model of carbon emissions generated by regional correlation effects is constructed to obtain the carbon emissions generated by regional correlation effects at time t. ;

[0081] For example, congestion on arterial roads can divert traffic to secondary roads, changing the spatial distribution of emissions. This spatial correlation can be described by the diffusion-transport equation:

[0082]

[0083] in, Carbon emissions generated by regional linkage effects; 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 from field statistics or the traffic management department; Characterize the diffusion trend of concentration in space; is the time coupling coefficient; It is the partial derivative of the emission concentration at time t with respect to time t, indicating the rate of change of carbon emission concentration over time. is a volume element, and the integration range is the volume of the study area. It represents the three-dimensional spatial integration of the study area, that is, the accumulation of the area. 、 Obtained from experiments, historical data analysis or expert experience, the two parameters can also be used to adjust the dimension.

[0084] S1.4. Establish a time-dependent correlation characteristic, considering that current emissions will have real-time impacts on the regional environment and travel behavior through multiple channels, including the immediate occupation of regional environmental capacity and the dynamic adjustment of travel behavior, and obtain the carbon emissions generated by the time-dependent correlation characteristic at time t. ;

[0085] In order to accurately describe this time-dependent relationship, a time-dependent relationship model is established:

[0086]

[0087] in, Carbon emissions generated for time-dependent characteristics; is the initial intensity coefficient of environmental impact; is the attenuation coefficient of environmental impact; To correspond Emission coefficient at the time; is the conversion factor between behavior pattern indicator and emission factor; is the initial intensity coefficient of the travel mode; is the attenuation coefficient of the travel mode; To correspond Behavioral pattern indicators at each moment, among which, 、 、 、 、 、 Obtained through experiments, historical data analysis or expert experience.

[0088] in, and It is also used to adjust the dimensional differences between different physical quantities.

[0089] S1.5. Establish a driving efficiency and loss model, taking into account the conversion loss during acceleration and deceleration, idle loss, and loss caused by slope during operation, and obtain the comprehensive effect value of the loss at time t. , and then calculate the efficiency loss emissions at time t ;

[0090]

[0091] in, is the comprehensive effect value of 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 due to gravity; is the elevation change of the vehicle at time t, obtained from GPS data; is the slope influence coefficient. 、 Obtained through experiments, historical data analysis or expert experience.

[0092] The additional emissions from losses can be expressed as:

[0093]

[0094] in, is the efficiency loss emission; is the loss-emission conversion factor, It is the system efficiency correction factor obtained through experiments, historical data analysis or expert experience.

[0095] S1.6. Establish a flow transfer mechanism, taking into account the flow transfer process, including changes in the number of vehicles, the propagation of speed and density parameters. During the transfer process, the fluctuation, diffusion, and aggregation of the urban traffic system affect the characteristics of traffic carbon emissions. Calculate the flow transfer emissions at time t based on the established transfer equation. ;

[0096] Furthermore, the transfer equation is established:

[0097]

[0098] in, is the traffic flow density, is the vehicle speed, obtained from actual measurement or traffic management department; is the symbol of partial derivative; is the travel distance, obtained by actual measurement or traffic management department; is the diffusion coefficient at time t, For the driving distance The source and sink terms at time t reflect the interaction between the road system and the outside world, which is obtained through experiments, historical data analysis, or expert experience. The additional emissions caused by this transfer process can be expressed as:

[0099]

[0100] in, Transfer emissions for flows; is the transfer-emission conversion factor, To transfer effect factors, they are obtained through experiments, historical data analysis or expert experience.

[0101] S1.7. Establish a system evolution law. Consider that the orderliness of the urban transportation system changes over time due to increasing traffic chaos during peak hours, the expansion of congested areas, and the increasing uneven distribution of vehicles. Calculate the system evolution emissions at time t based on the established system description equation. ;

[0102] Furthermore, traffic systems exhibit distinct evolutionary characteristics during operation, and the system's orderliness changes over time. For example, traffic chaos increases during peak hours, congestion areas spread, and the uneven distribution of vehicles worsens. These phenomena all affect the system's emission characteristics. Based on system evolution theory, a descriptive equation is established:

[0103]

[0104] in, is the system state function, which indicates the orderliness of the traffic system. The larger it is, the more orderly the system is, that is, the smoother the traffic flow and the lower the congestion level. represents the external input items received by the system at time t, including the impact of new traffic, external intervention, etc. Represents the output items of the system at time t, including the effects of vehicle departure and system optimization.

[0105] Specifically,

[0106]

[0107] in, Traffic flow is obtained from actual measurement or traffic management department; is the flow penetration response coefficient; It is the system control intensity, which can be obtained by actual measurement or traffic management department; is the control response coefficient; is the system control intensity-flow conversion coefficient in the input item; is the road network load rate, which can be obtained from actual measurement or traffic management department; is the load sensitivity coefficient; It is the road network load-flow conversion coefficient in the input item. 、 、 、 、 It can be obtained through experiments, historical data analysis or expert experience.

[0108]

[0109] in, is the flow evacuation coefficient, To optimize the efficiency coefficient, is the system control intensity-flow conversion coefficient in the output item, is the network adjustment coefficient, The road network load-flow conversion coefficient in the output item can be obtained through experiments, historical data analysis or expert experience.

[0110] The emission contribution from system evolution can be expressed as:

[0111]

[0112] in, is the system evolution emission; is the evolution-emission conversion coefficient, It is an evolutionary effect factor, which can be obtained through experiments, historical data analysis or expert experience.

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

[0114]

[0115] in, is the emission of the stationary source at time t, obtained through experiments or from the traffic management department;

[0116] Get the total traffic carbon emissions corresponding to the total time T , the expression is:

[0117] .

[0118] S2. Calculate the traffic carbon emissions in region j based on the traffic carbon emissions calculation model from step S1, and calculate the traffic carbon emissions intensity and traffic carbon emissions change rate as basic data for identifying key transmission paths;

[0119] First, different areas are divided. The division method can be based on administrative division, functional division, transportation function division, specific area division, etc. The divided areas are numbered j, j = 1, 2, 3, ..., Na; Na is the total number of divided areas;

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

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

[0122] Furthermore, the aforementioned urban transportation carbon emission calculation model is used to calculate the transportation carbon emissions in different regions. The specific process is as follows:

[0123] Statistics are collected on the actual number of vehicles in different areas at time t, their average mileage, and the emission coefficient of the vehicles at time t. The system attenuation coefficient is determined through expert experience, and the emission of the vehicles in the jth area at time t is calculated. ;

[0124] Measure and count the vehicle's speed, and determine the periodic fluctuation amplitude, periodic frequency, initial suppression coefficient, system recovery coefficient, traffic flow impact intensity, speed impact index, critical speed, and external interference correction term based on expert experience, and calculate the system dynamic response coefficient of the jth area. ;

[0125] The carbon emission concentrations of different regions at time t are counted, and the second-order derivative and partial derivative of the carbon emission concentration are calculated. Then, the spatial diffusion coefficient and time coupling coefficient are obtained by historical data analysis. On this basis, combined with the volume of the study area, the carbon emissions generated by the association effect of the jth region are calculated. ;

[0126] Based on the emission coefficient of the vehicle at time t calculated above, 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 pattern index are obtained through comprehensive analysis of historical data analysis and expert experience. On this basis, the carbon emissions generated by the time-related effect are calculated. ;

[0127] The mass, driving speed, and frontal 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 GPS, and the speed conversion efficiency coefficient, slope influence coefficient, and air resistance coefficient are obtained from historical data analysis. On this basis, the comprehensive effect value of the loss is calculated; then, the loss-emission conversion coefficient and system efficiency correction factor are determined by expert experience. On this basis, the efficiency loss emission value of the jth area is calculated. ;

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

[0129] The traffic flow, system control intensity, and road network load rate are obtained from actual measurements. The flow penetration response coefficient, control response coefficient, load sensitivity coefficient, flow evacuation coefficient, optimization efficiency coefficient, network adjustment coefficient, system control intensity-flow conversion coefficient, and road network load-flow conversion coefficient are obtained from historical data analysis and expert experience. Then, the evolution-emission conversion coefficient and evolution effect factor are determined by experiments. On this basis, the j-th region is calculated. System evolution emissions;

[0130] S2.2. Calculate the traffic carbon emission intensity per unit area and obtain the traffic carbon emission intensity corresponding to time t in the jth area. for:

[0131]

[0132] in, 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;

[0133] When analyzing interregional carbon emissions transmission, the first task is to quantify the emission levels of each region. Because regions vary significantly in size, directly comparing total emissions can be misleading. Therefore, it is necessary to calculate emission intensity per unit area to objectively reflect the emission levels of each region.

[0134] S2.3. Calculate the emission change rate, the carbon emission change rate corresponding to time t in region j for:

[0135]

[0136] in, is the traffic carbon emissions corresponding to time t-1 in the jth region.

[0137] Beyond understanding regional emission intensities, it's also important to understand the dynamics of emissions. The emission change rate is a key indicator for measuring regional emission dynamics, reflecting the growth or decline of regional emissions. Using the relative rate of change in this calculation eliminates the impact of regional size differences.

[0138] This embodiment provides an actual example as follows, selecting information corresponding to 9 moments, and partial information is shown in Table 1:

[0139] Table 1

[0140]

[0141] The calculated carbon emissions at the 9 moments are as follows: Figure 2 shown.

[0142] S3. Build a spatiotemporal distance calculation system, standardize spatial and temporal distances, and then construct a comprehensive spatiotemporal distance index to reflect the physical connections between regions.

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

[0144] S3.1. Through normalization, the spatial distance is uniformly mapped to the interval [0,1], and the normalized spatial distance between the jth region and the nth region is obtained. ;

[0145] Furthermore, after determining the basic emission characteristics, it is necessary to establish spatial correlations between regions. Spatial distance is the primary factor affecting the spread of carbon emissions, but the original distance data has dimensionality issues, which is not conducive to integration with other indicators. Through standardization, spatial distances can be uniformly mapped to the [0,1] interval. This not only solves the dimensionality problem but also provides a comparable basis for subsequent comprehensive distance calculations. The maximum and minimum value standardization method is selected to standardize spatial distances, as follows:

[0146]

[0147] in, is the standardized spatial distance; is the actual spatial distance from region j to region n; is the maximum spatial distance between all pairs of regions; is the minimum spatial distance between all pairs of regions;

[0148] S3.2. Through normalization, the time distance is uniformly mapped to the interval [0,1] to obtain the normalized time distance between the jth region and the nth region. ;

[0149] Furthermore, in addition to spatial distance, temporal correlation is also an important characteristic of carbon emission transmission. Temporal distance reflects the lag effect of emission impacts, which is prevalent in urban transportation systems. In order to integrate it with spatial distance, standardization is also required. This standardization allows the time lag effect to be quantitatively incorporated into the transmission path analysis. Using the same standardization method as spatial distance ensures comparability between the two dimensions. The specific calculation formula is as follows:

[0150]

[0151] in, is the normalized time distance; is the actual time distance from region j to region n; is the maximum temporal distance between all pairs of regions; is the minimum time distance between all pairs of regions;

[0152] S3.3. Integrate the normalized spatial distance and normalized temporal distance to obtain the comprehensive temporal distance between the jth region and the nth region , the expression is:

[0153]

[0154] in, is the spatial distance weight, is the time distance weight, and Obtained through experiments, historical data analysis or expert experience.

[0155] After obtaining standardized spatial and temporal distances, they need to be integrated into a single composite indicator. This integration takes into account the spatial and temporal characteristics of emission propagation and better reflects actual propagation mechanisms. By adjusting the weighting parameters, the relative importance of spatial and temporal factors can be flexibly controlled, which is of great significance for studies of different scales and types.

[0156] S4. Based on the transportation carbon emission intensity and transportation carbon emission change rate obtained in step S2 and the comprehensive spatiotemporal distance index obtained in step S3, establish a regional coupling intensity coefficient and a connection influence index. This coefficient is constructed by considering the attenuation effect of spatiotemporal distance, the differences in emission intensity, and the synergy of the change rate. The connection influence index is used in conjunction with the regional coupling intensity coefficient to determine the importance of a path.

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

[0158] S4.1. Consider the attenuation effect of temporal and spatial distance, the variability of emission intensities, and the synergy of the rate of change to construct a regional coupling intensity coefficient. The specific calculation formula is as follows:

[0159]

[0160] in, 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 through experiments, historical data analysis or expert experience; is the maximum value of transportation carbon emission intensity in all regions; is the traffic carbon emission intensity corresponding to time t in the nth region, is the carbon emission change rate corresponding to time t in the nth region;

[0161] Traditional fixed-weight calculation methods struggle to capture the dynamic nature of interregional connections. Therefore, a method for calculating the regional coupling intensity coefficient was proposed. This method simultaneously considers three key factors: the attenuation effect of temporal and spatial distance, the heterogeneity of emission intensities, and the synergy of rates of change. This multi-factor approach more accurately captures the actual degree of interregional connectivity.

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

[0163]

[0164] in, is the connection influence index of the middle region e; is the total number of shortest paths from the jth region to the nth region; is the number of paths that pass through the intermediate area e in the shortest path from area j to area n.

[0165] The connection influence index is a key metric for identifying key transmission pathways within a network. It measures the frequency of an edge's appearance across all shortest paths, reflecting the importance of that edge's transit function within the network. In carbon emission transmission networks, a high connection influence index indicates that the link plays a crucial role as a bridge in interregional transmission. This metric is combined with the regional coupling strength coefficient to determine path importance.

[0166] S5. Based on the regional coupling strength coefficient and connection influence index obtained in step S4, calculate the correlation strength between regions, including direct correlation strength and indirect correlation strength. Then, based on the correlation strength, construct a path importance scoring index to comprehensively evaluate the importance of different propagation paths.

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

[0168] S5.1. The direct correlation strength is characterized by the regional coupling strength coefficient, which is expressed as:

[0169]

[0170] in, is the direct correlation strength;

[0171] Direct correlation strength describes the immediate impact relationship between regions. This association is based on actual physical proximity and is the most basic and direct transmission channel. This invention directly uses the regional coupling strength coefficient to characterize the direct correlation strength;

[0172] S5.2. Considering the indirect influence between regions through intermediate nodes and the exponential decay of path length, construct the indirect connection strength using the following formula:

[0173]

[0174] in, is the indirect association strength; Sum all possible intermediate regions e; is the regional coupling strength coefficient between the jth region and the middle region e at time t; is the regional coupling strength coefficient between the middle region e and the nth region at time t; is the path attenuation coefficient, obtained through 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 connections, regions also experience indirect influences through intermediary nodes. While these indirect connections are typically weaker than direct connections, they play an important complementary role in the overall network structure, particularly in explaining long-distance transmission. By considering the exponential decay of path length, we can reasonably describe the weakening of indirect influences with increasing distance.

[0176] S5.3. Based on the direct and indirect association strengths, construct a path importance scoring index, which is expressed as:

[0177]

[0178] in, Score the importance of paths; is the direct correlation strength weight coefficient, It is the indirect correlation strength weight coefficient, obtained through experiments, historical data analysis or expert experience.

[0179] The path importance score, the final output of the entire analytical framework, integrates the characteristics of both direct and indirect correlations. This two-tiered scoring approach comprehensively reflects the role of paths in the emissions transmission network, avoiding the bias inherent in considering only one level. By adjusting the weighting coefficients of the direct and indirect layers, the evaluation criteria can be flexibly adjusted to meet research needs. This comprehensive score will be directly used to identify critical transmission paths, providing a scientific basis for traffic management decisions.

[0180] Setting the area based on this embodiment To area There are four carbon emission transmission paths between → → 、 → → 、 → → 、 → → .in, → → The corresponding path importance scores are shown below, and the corresponding information for each region is shown in Table 2:

[0181] Table 2

[0182]

[0183] On this basis, the comprehensive spatiotemporal distance is calculated by normalizing the spatial distance and the temporal distance. Then, the correlation 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, we can calculate → → 、 →→ 、 → → The path importance scores of the three carbon emission transmission paths are shown in Table 4 and Figure 3 As shown:

[0187] Table 4

[0188]

[0189] Comparing the path importance scores of the four paths, we can see that the path → → The corresponding path has the highest importance score, and this path is the key transmission path of carbon emissions.

[0190] This example establishes a comprehensive method for calculating urban transportation carbon emissions, organically integrating 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 method for identifying carbon emission transmission paths has strong practical guidance and can be directly applied to the optimization and management of urban transportation systems. By identifying key transmission paths, it can provide clear guidance for the development of targeted emission reduction measures, thereby improving their effectiveness.

[0191] Example 2:

[0192] A system for calculating urban traffic carbon emissions and identifying key propagation paths includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed, the steps of a method for calculating urban traffic carbon emissions and identifying key propagation paths as described in Example 1 are implemented.

[0193] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0194] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and components may be substituted with equivalents without departing from the scope of the present application. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of these combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions within the scope of the claims.

Claims

1. A method for calculating urban traffic carbon emissions and identifying key transmission paths, characterized in that: The steps include: S1. Based on the basic model of urban transportation carbon emissions, a comprehensive transportation carbon emissions calculation model is constructed by integrating dynamic response mechanisms, regional correlation effects, time-dependent correlation characteristics, driving efficiency and losses, flow transmission mechanisms, and system evolution laws. This model is used to calculate the corresponding and total transportation carbon emissions at different times. S2. Calculate the traffic carbon emissions in region j based on the traffic carbon emissions calculation model from step S1, and calculate the traffic carbon emissions intensity and traffic carbon emissions change rate as basic data for identifying key transmission paths; S3. Build a spatiotemporal distance calculation system, standardize spatial and temporal distances, and then construct a comprehensive spatiotemporal distance index to reflect the physical connections between regions. S4. Based on the transportation carbon emission intensity and transportation carbon emission change rate obtained in step S2 and the comprehensive spatiotemporal distance index obtained in step S3, establish a regional coupling intensity coefficient and a connection influence index. This coefficient is constructed by considering the attenuation effect of spatiotemporal distance, the differences in emission intensity, and the synergy of the change rate. The connection influence index is used in conjunction with the regional coupling intensity coefficient to determine the importance of a path. S5. Based on the regional coupling strength coefficient and connection influence index obtained in step S4, calculate the correlation strength between regions, including direct correlation strength and indirect correlation strength. Then, based on the correlation strength, construct a path importance scoring index to comprehensively evaluate the importance of different propagation paths.

2. The method for calculating urban traffic carbon emissions and identifying key transmission paths according to claim 1 is characterized in that: The specific implementation method of step S1 includes the following steps: S1.

1. Construct a basic model for urban transportation carbon emissions. Considering that urban transportation carbon emissions are primarily reflected in vehicle operation, different types of vehicles exhibit different emission characteristics due to their technical characteristics, usage patterns, and operating conditions, a basic model for urban transportation carbon emissions is constructed to obtain the vehicle's emissions at time t. ; S1.

2. Establish a comprehensive dynamic response mechanism, taking into account the typical cyclical changes of weekday morning and evening rush hours and weekend leisure travel peaks, and the nonlinear characteristics of traffic accidents and extreme weather. Use functional relationships to describe the comprehensive dynamic response mechanism and obtain the dynamic response coefficient of the urban transportation system at time t. ; S1.

3. Establish regional correlation effects. Considering that carbon emissions in urban transportation systems have significant spatial correlation characteristics, changes in traffic conditions in one region will affect surrounding areas through road network connections, forming a chain reaction. Using the three-dimensional spatial integration of the region, a model of carbon emissions generated by regional correlation effects is constructed to obtain the carbon emissions generated by regional correlation effects at time t. ; S1.

4. Establish a time-dependent correlation characteristic, considering that current emissions will have real-time impacts on the regional environment and travel behavior through multiple channels, including the immediate occupation of regional environmental capacity and the dynamic adjustment of travel behavior, and obtain the carbon emissions generated by the time-dependent correlation characteristic at time t. ; S1.

5. Establish a driving efficiency and loss model, taking into account the conversion loss during acceleration and deceleration, idle loss, and loss caused by slope during operation, and obtain the comprehensive effect value of the loss at time t. , and then calculate the efficiency loss emissions at time t ; S1.

6. Establish a flow transfer mechanism, taking into account the flow transfer process, including changes in the number of vehicles, the propagation of speed and density parameters. During the transfer process, the fluctuation, diffusion, and aggregation of the urban traffic system affect the characteristics of traffic carbon emissions. Calculate the flow transfer emissions at time t based on the established transfer equation. ; S1.

7. Establish a system evolution law. Consider that the orderliness of the urban transportation system changes over time due to increasing traffic chaos during peak hours, the expansion of congested areas, and the increasing uneven distribution of vehicles. Calculate the system evolution emissions at time t based on the established system description equation. ; S1.

8. Build a complete traffic carbon emissions calculation model to obtain the corresponding traffic carbon emissions at time t , the expression is: in, is the emission of the stationary source at time t, obtained through experiments or from the traffic management department; Get the total traffic carbon emissions corresponding to the total time T , the expression is: 。 3. The method for calculating urban traffic carbon emissions and identifying key transmission paths according to claim 2 is characterized in that: The specific implementation method of step S2 includes the following steps: S2.

1. Calculate the traffic carbon emissions in the jth region at time t based on the traffic carbon emissions calculation model in step S1 ; S2.

2. Calculate the traffic carbon emission intensity per unit area and obtain the traffic carbon emission intensity corresponding to time t in the jth area. for: in, 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; S2.

3. Calculate the emission change rate, the carbon emission change rate corresponding to time t in region j for: in, is the traffic carbon emissions corresponding to time t-1 in the jth region.

4. The method for calculating urban traffic carbon emissions and identifying key transmission paths according to claim 3 is characterized in that: The specific implementation method of step S3 includes the following steps: S3.

1. Through normalization, the spatial distance is uniformly mapped to the interval [0,1], and the normalized spatial distance between the jth region and the nth region is obtained. ; S3.

2. Through normalization, the time distance is uniformly mapped to the interval [0,1] to obtain the normalized time distance between the jth region and the nth region. ; S3.

3. Integrate the normalized spatial distance and normalized temporal distance to obtain the comprehensive temporal distance between the jth region and the nth region , the expression is: in, is the spatial distance weight, is the time distance weight, and Obtained through experiments, historical data analysis or expert experience.

5. The method for calculating urban traffic carbon emissions and identifying key transmission paths according to claim 4 is characterized in that: The specific implementation method of step S4 includes the following steps: S4.

1. Consider the attenuation effect of temporal and spatial distance, the variability of emission intensities, and the synergy of the rate of change to construct a regional coupling intensity coefficient. The specific calculation formula is as follows: in, 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 through experiments, historical data analysis or expert experience; is the maximum value of transportation carbon emission intensity in all regions; is the traffic carbon emission intensity corresponding to time t in the nth region, is the carbon emission change rate corresponding to time t in the nth region; S4.

2. Select the shortest path as the basis for calculation and construct the connection influence index by calculating the frequency of edges appearing in all shortest paths. The specific calculation formula is: in, is the connection influence index of the middle region e; is the total number of shortest paths from the jth region to the nth region; is the number of paths that pass through the intermediate area e in the shortest path from area j to area n.

6. The method for calculating urban traffic carbon emissions and identifying key transmission paths according to claim 5 is characterized in that: The specific implementation method of step S5 includes the following steps: S5.

1. The direct correlation strength is characterized by the regional coupling strength coefficient, which is expressed as: in, is the direct correlation strength; S5.

2. Considering the indirect influence between regions through intermediate nodes and the exponential decay of path length, construct the indirect connection strength using the following formula: in, is the indirect association strength; Sum all possible intermediate regions e; is the regional coupling strength coefficient between the jth region and the middle region e at time t; is the regional coupling strength coefficient between the middle region e and the nth region at time t; is the path attenuation coefficient, obtained through 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 and indirect association strengths, construct a path importance scoring index, which is expressed as: in, Score the importance of paths; is the direct correlation strength weight coefficient, It is the indirect correlation strength weight coefficient, obtained through experiments, historical data analysis or expert experience.

7. A system for calculating urban traffic carbon emissions and identifying key transmission paths, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed, the steps of a method for calculating urban traffic carbon emissions and identifying key propagation paths are implemented as described in any one of claims 1 to 6.

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