Method for measuring and calculating traffic carbon emission of light passenger vehicle based on urban land function driving

Abstract

The embodiment of the invention provides a light passenger vehicle traffic carbon emission measuring and calculating method based on urban land function driving, and belongs to the technical field of calculation, and the method specifically comprises the steps: 1, dividing traffic zones according to the distribution and pattern of different land functions of a city, and calculating the influence variable of each traffic zone; step 2, according to the influence variable of each traffic zone, calculating the travel times of the passenger vehicles of each traffic zone; 3, calculating traffic travel distribution among the traffic cells according to the gravity model and the travel times of the passenger vehicles; 4, calculating urban traffic travel carbon emission according to the traffic travel distribution; and 5, performing multi-view analysis on the urban transportation carbon emission according to a preset rule to obtain a carbon emission region level distribution result. Through the scheme of the invention, the measurement and calculation efficiency, accuracy and adaptability are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of computing technology, and in particular to a method for calculating traffic carbon emissions of light passenger vehicles driven by urban land use functions. Background Art

[0002] At present, estimating travel demand to calculate traffic carbon emissions has become one of the important technical means for traffic carbon emission accounting, and the mixed-use development model provides an effective tool for connecting urban land use functions and travel demand. However, existing methods still have deficiencies in depicting the travel interaction patterns between regions and localizing the calibration of model parameters. There is still a lack of a fine-scale traffic carbon emission calculation method that can accurately depict the travel interaction patterns between regions and calibrate parameters based on actual traffic behavior data. The main disadvantages of the existing technology are as follows: The existing traffic carbon emission calculation method based on the mixed-use development model only considers the travel generation volume and average travel distance of a single region, ignoring the actual interaction patterns and travel flow distribution characteristics between regions, resulting in a significant deviation between the calculated results of the carbon emission spatial distribution and the actual situation; Most of the existing traffic carbon emission calculation methods analyze the attribution of carbon emissions from a single perspective, lacking a multi-perspective collaborative analysis framework of "departure place, arrival place, and moving process", making it difficult to comprehensively reflect the spatial attribution characteristics of traffic carbon emissions and unable to meet the diverse needs of different management entities for carbon emission responsibility allocation.

[0003] It can be seen that there is an urgent need for a method for calculating traffic carbon emissions of light passenger vehicles driven by urban land use functions with high calculation accuracy and adaptability. Summary of the Invention

[0004] In view of this, the embodiments of the present invention provide a method for calculating traffic carbon emissions of light passenger vehicles driven by urban land use functions, which at least partially solves the problem of poor calculation accuracy and adaptability in the existing technology.

[0005] The embodiments of the present invention provide a method for calculating traffic carbon emissions of light passenger vehicles driven by urban land use functions, including: Step 1, dividing traffic zones according to the distribution and pattern of different urban land use functions and calculating the influence variables of each traffic zone; Step 2, calculating the number of passenger vehicle trips of each traffic zone according to the influence variables of each traffic zone; Step 3, calculating the traffic travel distribution between each traffic zone according to the gravity model and the number of passenger vehicle trips; Step 4, calculating the urban traffic travel carbon emissions according to the traffic travel distribution; Step 5, performing multi-perspective analysis on the urban traffic travel carbon emissions according to preset rules to obtain the carbon emission regional-level allocation results.

[0006] According to a specific implementation manner of an embodiment of the present invention, step 1 specifically includes: Step 1.1, after the road network combination is completed, divide the urban area into multiple traffic zones according to the distribution and pattern of different land use functions in the city; Step 1.2, calculate the influence variables of each traffic zone, where the influence variables include land use, traffic accessibility, and socioeconomic factors.

[0007] According to a specific implementation manner of an embodiment of the present invention, step 2 specifically includes: Step 2.1, based on the influence variables, use the trip rate in the ITE Trip Generation Manual to calculate the total trip volume of each traffic zone; Step 2.2, decompose the total trip volume into internal trips and external trips and calculate the total external trip volume accordingly; Step 2.3, decompose the total external trip volume into walking trips, bicycle trips, public transportation trips, and vehicle trips, and calculate the probabilities of walking, bicycle, and public transportation in external trips through a preset formula; Step 2.4, subtract the probabilities of walking, bicycle, and public transportation from the total probability to obtain the trip probability of vehicles in external trips, and calculate the vehicle trip times of each traffic zone according to the total external trip volume and the trip probability of vehicles.

[0008] According to a specific implementation manner of an embodiment of the present invention, step 3 specifically includes: Step 3.1, calculate the spatial interaction intensity between every two traffic zones of all traffic zones according to the vehicle trip times, where the expression of the spatial interaction intensity is ; where, represents the number of trips from traffic zone to traffic zone , represents the number of trips from traffic zone to traffic zone , and respectively represent the vehicle trip times of traffic zone and traffic zone ; Step 3.2, assume that the Euclidean distance between traffic zone and traffic zone is , and the spatial interaction intensity is , for the spatial interaction intensity and the Euclidean distance Perform logarithmic transformation, perform linear fitting on the transformed data, and calculate the distance decay exponent based on this. , where the expression of the linear fitting is ; Among them, represents the Euclidean distance between regions and ; represents the spatial interaction intensity, is the constant term, represents the distance decay exponent; Step 3.3, based on the distance decay exponent, use the gravity model formula to allocate the number of passenger vehicle trips in each traffic zone to different traffic zones to obtain the traffic travel distribution among traffic zones .

[0009] According to a specific implementation manner of an embodiment of the present invention, the specific steps of step 4 include: Based on the traffic travel distribution among traffic zones, use the vehicle emission calculation model to calculate the urban traffic travel carbon emissions according to the driving distance and the number of trips of passenger vehicles ; Among them, represents the carbon dioxide emissions of passenger vehicles, represents the travel distribution from traffic zone to traffic zone , represents the proportion of gasoline vehicles, represents the energy intensity of fuel, represents the carbon dioxide emission factor.

[0010] According to a specific implementation manner of an embodiment of the present invention, the preset rules include attributing the urban traffic travel carbon emissions to the area where the starting point is located from the perspective of the departure place, attributing the urban traffic travel carbon emissions to the area where the arrival point is located from the perspective of the arrival place, and dividing the urban traffic travel carbon emissions proportionally according to the areas actually passed by the vehicle along the actual path from the perspective of the moving process.

[0011] The light passenger vehicle traffic carbon emission measurement solution based on urban land use function drive in the embodiments of the present invention includes: Step 1, dividing traffic zones and calculating the influence variables of each traffic zone according to the distribution and pattern of different urban land uses; Step 2, calculating the number of passenger vehicle trips of each traffic zone according to the influence variables of each traffic zone; Step 3, calculating the traffic travel distribution between traffic zones according to the gravity model and the number of passenger vehicle trips; Step 4, calculating the urban traffic travel carbon emissions according to the traffic travel distribution; Step 5, performing multi-perspective analysis on the urban traffic travel carbon emissions according to preset rules to obtain the carbon emission regional-level allocation results.

[0012] The beneficial effects of the embodiments of the present invention are as follows: Through the solution of the present invention, the accurate measurement of the traffic travel flow distribution between regions driven by urban land use functions is achieved. On the basis of obtaining the regional travel generation volume through the mixed-use development model, the model parameters are locally calibrated by combining the gravity model and actual traffic behavior data to accurately depict the traffic travel interaction mode between regions, thereby improving the accuracy and reliability of the calculation of the spatial distribution of traffic carbon emissions; A multi-perspective collaborative traffic carbon emission attribution analysis method is established to perform spatial allocation of carbon emissions from three perspectives: the starting point, the ending point, and the moving process of the trip, enhancing the practical value of the carbon emission measurement results in urban low-carbon planning and carbon emission reduction decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0014] Figure 1 It is a flow schematic diagram of a light passenger vehicle traffic carbon emission measurement method based on urban land use function drive provided by the embodiments of the present invention; Figure 2 It is a specific implementation flow schematic diagram of a light passenger vehicle traffic carbon emission measurement method based on urban land use function drive provided by the embodiments of the present invention; Figure 3 It is a calculation result diagram of the number of vehicle trips provided by the embodiments of the present invention; Figure 4 It is a calculation result diagram of the traffic travel distribution between regions provided by the embodiments of the present invention; Figure 5 It is a calculation result diagram of traffic carbon emissions from the perspective of the departure place provided by the embodiments of the present invention; Figure 6 It is a calculation result diagram of traffic carbon emissions from the perspective of the arrival place provided by the embodiments of the present invention; Figure 7 A traffic carbon emission calculation result graph from the perspective during the movement process provided by an embodiment of the present invention. Specific implementation manners

[0015] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0017] It should be noted that the following describes various aspects of the embodiments within the scope of the appended claims. It should be obvious that the aspects described herein can be embodied in a wide variety of forms, and any specific structure and / or function described herein is illustrative only. Based on the present invention, those skilled in the art should understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects described herein can be used to implement the device and / or practice the method. In addition, this device and / or this method can be implemented using other structures and / or functions in addition to one or more of the aspects described herein.

[0018] It should also be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape and size of the components during actual implementation. The type, quantity and ratio of each component during its actual implementation can be an arbitrary change, and the component layout type may also be more complex.

[0019] In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0020] An embodiment of the present invention provides a method for calculating traffic carbon emissions of light passenger vehicles driven by urban land use functions. The method can be applied to the process of calculating traffic carbon emissions in traffic management.

[0021] See Figure 1 , which is a schematic flowchart of a method for calculating the traffic carbon emissions of light passenger vehicles driven by urban land use functions provided by an embodiment of the present invention. As Figure 1 and Figure 2 shown, the method mainly includes the following steps: Step 1: Divide traffic zones according to the distribution and pattern of different urban land use functions, and calculate the impact variables of each traffic zone; Further, the specific steps of Step 1 include: Step 1.1: Combine the road network end, and divide the urban area into multiple traffic zones according to the distribution and pattern of different urban land use functions; Step 1.2: Calculate the impact variables of each traffic zone, where the impact variables include land use, traffic accessibility, and socio-economic factors.

[0022] Specifically, when implementing, traffic zones can be divided according to the distribution and pattern of different urban land use functions. Based on the existing traffic network, fully consider the road network constraints to ensure that each zone can accurately reflect the internal traffic behavior characteristics. Each traffic zone is composed of two or more urban land use functions, and the sizes of each traffic zone are relatively consistent. By restricting the area of the region to be less than 3 square kilometers, it is ensured that within each traffic zone, walking or cycling is the main mode of travel, while between traffic zones, passenger cars are the main mode of travel, so as to facilitate the accurate estimation of the traffic volume between regions. At the same time, calculate the impact variables of each traffic zone. The impact variables include three types of parameters: land use, traffic accessibility, and socio-economic factors, and the values of these indicators are determined by the spatial planning policies under the overall framework.

[0023] Step 2: Calculate the number of passenger vehicle trips of each traffic zone according to the impact variables of each traffic zone; Based on the above embodiments, the specific steps of Step 2 include: Step 2.1: Based on the impact variables, use the trip rates in the ITE Trip Generation Manual to calculate the total trip volume of each traffic zone; Step 2.2: Decompose the total trip volume into internal trips and external trips and calculate the total external trip volume accordingly; Step 2.3: Decompose the total external trip volume into walking trips, bicycle trips, public transportation trips, and passenger vehicle trips, and calculate the probabilities of walking, bicycle, and public transportation in external trips through a preset formula; Step 2.4: Subtract the probabilities of walking, bicycle, and public transportation from the total probability to obtain the trip probability of passenger vehicles in external trips, and calculate the number of passenger vehicle trips of each traffic zone according to the total external trip volume and the trip probability of passenger vehicles.

[0024] In specific implementation, the specific process of calculating the number of passenger vehicle trips in each traffic zone can be as follows: 2.1 Estimate the total vehicle trips: Adopt the Institute of Transportation Engineers (ITE) model based on land use, and use the trip rates in the ITE Trip Generation Manual to estimate the total number of trips. The basic formula is shown in Equation (1) below: ; In the formula, is the total vehicle trips in area , is the area of urban land use function in area ; the function calculates the total trips of urban land use type starting from traffic zone . The specific calculation formula refers to the introduction of the ITE model. This process estimates the trips of each land use type through the influence degree of different land use function types on the trips.

[0025] 2.2 Decompose vehicle trips into internal and external vehicle trips ① Classify vehicle trip types into three categories according to purposes: home-work trips, home-other trips, and non-home trips. Decompose the total number of trips into the total number of trips under each trip purpose according to the NCHRP report. ② Decompose the estimated vehicle trips into internal and external vehicle trips: Calculate the logarithmic odds ratio of internal trips through Equation (2). ③ Exponentiate the logarithmic odds ratio to obtain the ratio of internal trips and convert it into a probability, as shown in Equation (3). ④ Subtract the internal trips from the total trips estimated according to the ITE Trip Generation Manual to obtain the total external trips, as shown in Equation (4).

[0026] ; ; ; In the formula, represents the internal trip odds ratio from traffic zone ; represents the internal trip probability from traffic zone ; represents the total external trips from traffic zone ; is the influencing variable; , It is the correlation coefficient based on existing research. In existing research, the estimation of this coefficient is mainly obtained through regression of a large amount of real survey data. Since the survey data comes from cities in different regions and with different levels of development around the world, the reliability of this model is widely recognized and it is widely used in cities around the world.

[0027] 2.3 Modal split of external trips Decompose the estimated external trip distances into walking trips, bicycle trips, public transport trips, and vehicle trips. Calculate the probabilities of walking, cycling, and public transport in external trips through the equations in the model. The formulas are shown as (5) to (10): ; ; ; ; ; ; In the formula, 、 、 represent the occurrence ratios of walking trips, bicycle trips, and public transport trips of external trips from traffic zone ; 、 、 represent the probabilities of walking trips, bicycle trips, and public transport trips of external trips from traffic zone ; is the influencing variable; 、 、 、 、 、 The calculation method is the same as above.

[0028] 2.4 Calculation of external trips of light passenger vehicles Obtain the trip probability of light passenger vehicles in external trips by subtracting the probabilities of walking, cycling, and public transport from the total probability. The formula is shown as (11). Multiply the total external trip volume by this probability to obtain the total external trip volume generated by light passenger vehicles. The formula is shown as (12).

[0029] ; ; In the formula, represents the occurrence ratio of light passenger vehicles in external trips; represents the number of light passenger vehicle trips.

[0030] Step 3: Calculate the traffic trip distribution between each traffic zone according to the gravity model and the number of trips of passenger vehicles. Based on the above embodiments, the specific steps of Step 3 include: Step 3.1: Calculate the spatial interaction intensity between all pairs of traffic zones according to the number of trips of passenger vehicles. Among them, the expression of the spatial interaction intensity is ; Among them, represents the number of trips from traffic zone to traffic zone , represents the number of trips from traffic zone to traffic zone , and respectively represent the number of trips of passenger vehicles in traffic zone and traffic zone ; Step 3.2: Assume that the Euclidean distance between traffic zone and traffic zone is , and the spatial interaction intensity is . Perform logarithmic transformation on the spatial interaction intensity and the Euclidean distance , and perform linear fitting on the transformed data, and calculate the distance decay index accordingly. Among them, the expression of the linear fitting is ; Among them, represents the Euclidean distance between regions and , represents the spatial interaction intensity, is a constant term, represents the distance decay index; Step 3.3: Based on the distance decay index, use the gravity model formula to distribute the number of trips of passenger vehicles in each traffic zone to different traffic zones to obtain the traffic trip distribution between each traffic zone .

[0031] In specific implementation, the trip distribution method based on the gravity model is mainly used to calculate the trip number distribution between regions. The gravity model assumes that the travel demand is proportional to the attractiveness of the departure region and the arrival region, and inversely proportional to a certain power of the travel distance. By applying the gravity model, the travel demand distribution between regions can be estimated more accurately, providing basic data for further calculation of traffic travel carbon emissions. This process can be decomposed into the following three steps: 3.1 Calculation of Spatial Interaction between Regions Spatial interaction refers to the intensity of interaction between two regions. Generally, the spatial interaction between regions can be estimated through actual taxi trajectory data. The specific method is to analyze the travel frequency between different regions using taxi trajectory data, thereby quantifying the intensity of interaction between regions. Suppose there are two regions and , and the intensity of their interaction is calculated by the formula shown in Equation (13): ; In the formula, represents the number of trips from region to region , represents the number of trips from region to region , and respectively represent the total number of trips of region and region . By calculating the interaction intensity between each region through this step, basic data is provided for subsequent gravity model calculations.

[0032] 3.2 Calculation of Distance Decay Index The distance decay index is a measure of the rate of change of spatial interaction intensity with respect to geographical distance. Specifically, the distance decay index can be calculated by linearly fitting the spatial interaction intensity and distance between different regions in taxi trajectory data. Suppose the Euclidean distance between two regions and is , and the spatial interaction intensity is , then the distance decay index can be calculated through the following steps: First, perform logarithmic transformation on and , and then perform linear fitting on the transformed data. The fitting equation is shown in Formula (14): ; In the formula, is the Euclidean distance between region and ; is the spatial interaction intensity; is the constant term; is the distance decay exponent. The value obtained by fitting can be used for the calculation of the gravity model.

[0033] 3.3 Trip Assignment Using the gravity model formula, the number of trips is assigned to each area. The specific formula is shown in Equation (15): ; In the formula, is the number of trips from area to area ; is the total number of trips in area ; is the Euclidean distance from the center of area to the center of area ; is the distance decay exponent.

[0034] Step 4, calculate the urban traffic travel carbon emissions according to the traffic travel distribution; Furthermore, the specific content of the said Step 4 includes: Based on the traffic travel distribution among traffic zones, using the vehicle emission calculation model, calculate the urban traffic travel carbon emissions according to the driving distance and the number of trips of passenger vehicles ; Among them, represents the carbon dioxide emissions of passenger vehicles, represents the travel distribution from traffic zone to traffic zone ; represents the proportion of gasoline vehicles, represents the energy intensity of fuel, represents the carbon dioxide emission factor.

[0035] In specific implementation, based on the estimated result of the spatial distribution of traffic travel volume according to the urban land use function, using the vehicle emission calculation model, calculate the carbon dioxide emissions through the driving distance and the number of trips of light-duty passenger vehicles. The formula for the total carbon dioxide emissions of light-duty passenger vehicles is shown in Equation (16): ; In the formula, is the carbon dioxide emissions of light-duty passenger vehicles; is the number of trips from area to area ; is area to the area Euclidean distance; is the proportion of gasoline vehicles; is the energy intensity of fuel; is the carbon dioxide emission factor.

[0036] Step 5, perform a multi-perspective analysis on the carbon emissions of urban traffic trips according to preset rules to obtain the carbon emission regional-level allocation results.

[0037] Furthermore, the preset rules include attributing the carbon emissions of urban traffic trips to the area where the starting point is located from the perspective of the starting point, attributing the carbon emissions of urban traffic trips to the area where the arrival point is located from the perspective of the arrival point, and, from the perspective of the moving process, dividing the carbon emissions of urban traffic trips proportionally according to the areas actually passed by the vehicle's path.

[0038] In specific implementation, when analyzing the area to which carbon emissions belong, existing research mainly focuses on the road network. Its advantage is that it can more accurately reflect the actual driving path of the vehicle and its corresponding carbon emissions. However, since China's management and planning are mainly based on administrative units, such as blocks and streets, attributing carbon emissions to the road network is not convenient for management and planning. In addition, the regional summary of traffic carbon emissions can help identify high-emission areas, so as to concentrate resources for optimization and improvement and improve the efficiency of carbon emission management. Therefore, the method of the present disclosure is based on the traffic zone scale and considers three perspectives to analyze traffic trip carbon emissions: the starting point perspective, the arrival point perspective, and the moving process perspective. The starting point perspective attributes the generated traffic trip carbon emissions to the area where the starting point is located; the arrival point perspective attributes the carbon emissions to the area where the arrival point is located; and the moving process perspective divides the carbon emissions proportionally according to the areas actually passed by the vehicle's path. The following will specifically explain and analyze the three perspectives: ① Starting point perspective: Under this perspective, the carbon emissions of traffic trips are attributed to the area where the starting point is located. This perspective believes that regardless of where the vehicle finally arrives, all emissions are considered to be contributed by the starting area. This perspective helps to understand the impact of traffic behavior starting from a certain area on carbon emissions and can effectively reflect the contribution of traffic behavior in each area to carbon emissions.

[0039] ② Arrival point perspective: Contrary to the starting point perspective, the arrival point perspective attributes the carbon emissions of traffic trips to the area where the arrival point is located. That is, regardless of where the vehicle starts, all emissions are attributed to the area where the vehicle finally arrives. This perspective helps to analyze the traffic carbon emission burden borne by a certain area as the destination. This attribution method is not only convenient for management and decision-making but also helps to formulate more targeted carbon emission reduction policies.

[0040] ③ Mobile process perspective: This perspective is more detailed and divides carbon emissions in proportion to the areas that the vehicle actually travels through on the road network. That is, the carbon emissions generated by the vehicle in each area during its travel are allocated to the corresponding area. This perspective can more comprehensively reflect the carbon emissions in each area during the transportation process and provide more accurate carbon emissions data.

[0041] The method for calculating carbon emissions from light passenger vehicle transportation based on urban land function drive provided in this embodiment, by combining a gravity model based on actual traffic behavior data such as taxi trajectory calibration, accurately depicts the interaction pattern of traffic travel between regions, achieves high-precision calculation of the spatial distribution of traffic carbon emissions, and improves the accuracy of carbon emission spatial attribution; it can achieve fine-scale transportation carbon emissions calculation based only on easily accessible urban land function distribution data and some travel behavior sample data without relying on a large amount of field survey data, thereby reducing calculation costs and improving calculation efficiency.

[0042] The method of the present invention will be further described below in conjunction with a specific embodiment, and the specific implementation process of the present invention will be described using a case study: (1) In this example, Area A is selected as the research area. The area is about 78.66 square kilometers, and the permanent population is about 1.521 million by the end of 2023. As a political, economic, and cultural center, the area has rich traffic variables and diverse land use functions, making it suitable for the study of transportation carbon emissions.

[0043] (2) Division of research units According to the land use distribution characteristics and traffic network structure of Area A, the study area is divided into 52 traffic communities, each with an area of ​​1.5-3 square kilometers and an average area of ​​2.2 square kilometers. The division process fully considers the separation effect of administrative boundaries, natural geographical boundaries and major traffic arteries to ensure that the land use function within each traffic community is relatively homogeneous and that light passenger vehicles are the main means of travel between traffic communities.

[0044] (3) Calculation of influencing variables The following influencing variables are calculated for each traffic zone: ① Land use mix: This factor is used to measure the diversity of land use types within the transportation zone. The calculation formula is: ; In the formula, Indicates the traffic zone The proportion of the coverage area of ​​this type of land use type to the total area of ​​the transportation zone.

[0045] ② Activity density: This factor is used to measure the density of crowd activities in a traffic zone. The calculation formula is: ; In the formula, Indicates traffic area The number of permanent residents in Indicates traffic area The number of employed people in Indicates traffic area area.

[0046] ③Intersection density: This factor is used to measure the density of road intersections in a traffic zone. The calculation formula is: ; In the formula, Indicates traffic area The number of permanent residents in Indicates traffic area The number of employed people in Indicates traffic area area.

[0047] ④ Ratio of employed population to permanent population: This factor is used to measure the ability of a transportation community to meet the travel needs of residents for work commuting. The value range is [0, 1]. When the value is 1, it means that the transportation community can meet the travel needs of all residents for work commuting.

[0048] ⑤ Accessibility index: calculate the average distance and accessibility of each transportation community to major city centers and public transportation stations.

[0049] (4) Calculation of vehicle trip times Based on the above influencing variables and equations 1-12 mentioned above, the internal travel ratio, walking travel ratio, bicycle travel ratio, public transportation travel ratio and light passenger vehicle travel ratio of each traffic zone are calculated, and then the travel volume generated by each traffic zone is calculated. The travel volume calculation results of each traffic zone in 2018 are as follows: Figure 3 As shown in the figure, the overall travel volume in the urban area of ​​Area A shows a trend of uniform distribution, with higher travel volumes in the northeast and southwest corners. The number of trips in traffic areas A and B in the northeast corner of the urban area of ​​Area A and traffic area C in the south is significantly higher than that in other areas.

[0050] (5) Calculation of inter-regional travel flow distribution Using the taxi trajectory data in September 2018, the origin and destination of each trip were extracted and matched to traffic zones to construct the actually observed OD matrix. Based on this matrix, the intensity of spatial interaction between regions was calculated, and the distance decay exponent was determined through linear fitting. Substituting the decay exponent into the gravity model and combining with the total number of light passenger vehicle trips in each traffic zone calculated in step (4), the distribution of light passenger vehicle trips between regions was calculated. The calculation results are as Figure 4 shown. The results show that the intensity of trips between regions exhibits obvious spatial heterogeneity, mainly concentrated among the central business district of Region A, Commercial Area B, and the surrounding areas of the bonded area in Region A, which is highly consistent with the actually observed travel patterns.

[0051] (6)Calculation and multi-perspective analysis of traffic carbon emissions According to the distribution of trips between regions and the travel distance, the total driving distance of light passenger vehicles was calculated. Based on the current development status of new energy vehicles in Region A, it was set that the proportion of gasoline vehicles in light passenger vehicles was 80%, and the proportion of new energy vehicles was 20%. Referring to the energy consumption parameters in relevant research literature, the energy intensity of gasoline vehicles was 0.08 kgce / km, and the CO 2 emission factor was 2.08 kgCO 2 / kgce; for new energy vehicles, the direct emissions were zero (indirect emissions were not considered). Based on Formula 18, the carbon dioxide emissions of light passenger vehicles generated in each traffic zone were calculated, and attribution analysis was carried out from three perspectives: the perspective of the departure place, the perspective of the arrival place, and the perspective of the moving process. The calculation results are as Figure 5 、 Figure 6 、 Figure 7 shown.

[0052] It should be understood that each part of the present invention can be implemented by hardware, software, firmware, or a combination thereof.

[0053] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for calculating carbon emissions from light passenger vehicle transportation based on urban land use function, characterized in that: include: Step 1: Divide the traffic zones according to the distribution and pattern of different land use functions in the city and calculate the influencing variables of each traffic zone; Step 2, calculate the number of passenger vehicle trips in each traffic zone according to the influencing variables of each traffic zone; Step 3, calculate the traffic travel distribution between each traffic zone according to the gravity model and the number of passenger vehicle trips; Step 4, calculate the carbon emissions of urban transportation according to the distribution of transportation; Step 5: Perform a multi-perspective analysis of urban transportation carbon emissions according to preset rules to obtain regional-level carbon emission allocation results.

2. The method according to claim 1, characterized in that The step 1 specifically includes: Step 1.1, combined with the end of the road network, divide the urban area into multiple traffic zones according to the distribution and pattern of different land use functions in the city; Step 1.2, calculating the influencing variables of each traffic zone, wherein the influencing variables include land use, traffic accessibility and socioeconomic factors.

3. The method according to claim 2, characterized in that The step 2 specifically includes: Step 2.1, based on the influencing variables, calculate the total number of trips in each traffic zone using the travel rates in the ITE travel generation manual; Step 2.2, decompose the total travel volume into internal trips and external trips and calculate the total external travel volume accordingly; Step 2.3, decompose the total external trips into walking trips, bicycle trips, public transportation trips and passenger vehicle trips, and calculate the probabilities of walking, cycling and public transportation in external trips by using a preset formula; In step 2.4, the probability of walking, cycling and public transportation is subtracted from the total probability to obtain the travel probability of passenger vehicles in external trips. The number of passenger vehicle trips in each traffic zone is calculated based on the total number of external trips and the travel probability of passenger vehicles.

4. The method according to claim 3, characterized in that: The step 3 specifically includes: Step 3.1, calculate the spatial interaction intensity between every two traffic areas of all traffic areas according to the number of passenger vehicle trips, where the expression of the spatial interaction intensity is: in, Indicates that from the traffic area To the traffic area The number of trips, Indicates that from the traffic area To the traffic area The number of trips, and Traffic Zone and traffic area Number of passenger vehicle trips; Step 3.2, assuming traffic zone and traffic area The Euclidean distance between , the spatial interaction strength is , for the spatial interaction intensity and Euclidean distance Perform logarithmic transformation, perform linear fitting on the transformed data, and calculate the distance decay index based on it , where the linear fitting expression is in, Indicates area and The Euclidean distance between represents the spatial interaction strength, is a constant term, Represents the distance decay exponent; Step 3.3, based on the distance decay index, using the gravity model formula, the number of passenger vehicle trips in each traffic zone is allocated to different traffic zones to obtain the traffic travel distribution between each traffic zone 。 5. The method according to claim 4, characterized in that The step 4 specifically includes: Based on the distribution of traffic travel between each traffic zone, the vehicle emission calculation model is used to calculate the carbon emissions of urban traffic travel according to the driving distance and number of trips of passenger vehicles. in, represents the carbon dioxide emissions of passenger vehicles, Indicates traffic area To the traffic area The travel distribution of represents the proportion of gasoline vehicles, represents the energy intensity of fuel, represents the carbon dioxide emission factor.

6. The method according to claim 5, characterized in that The preset rules include attributing the carbon emissions of urban transportation to the area where the departure point is located from the perspective of the departure point, attributing the carbon emissions of urban transportation to the area where the arrival point is located from the perspective of the arrival point, and, from the perspective of the movement process, dividing the carbon emissions of urban transportation in proportion according to the areas through which the vehicle actually passes.

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