Method, device, and medium for constructing a bus carbon emission factor library for a single passenger

Abstract

The present invention discloses a method, device, and medium for constructing a bus carbon emission factor library for individual passengers, including: obtaining a bus network, and estimating bus attribute parameters such as bus passenger capacity, bus speed, and bus size; calculating the energy consumption factor, fuel consumption factor, carbon emission factor of fuel combustion, cold start carbon emission factor, and / or lubricant carbon emission factor of a bus vehicle, so as to estimate the direct carbon emissions of the bus vehicle; calculating the total carbon emissions in the well-to-tank stage corresponding to the bus vehicle, the carbon emission factor in the well-to-tank stage corresponding to the electric bus vehicle, the carbon emission factor of the bus vehicle throughout its life cycle, and / or the infrastructure carbon emission factor of the bus vehicle, so as to estimate the indirect carbon emissions of the bus vehicle; and deriving the bus carbon emission factors at the individual level, grid level, regional level, link level, and line level according to the estimated direct and indirect carbon emissions of the bus vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban transportation carbon emission estimation, and particularly relates to a method, device, and medium for constructing a bus carbon emission factor library per passenger. Background Art

[0002] With the increasing demand for global response to climate change and promotion of sustainable development, all sectors of society have paid high attention to the carbon emission problem in the transportation industry. As one of the main sources of greenhouse gas emissions, the carbon emissions of transportation account for about 20%-30% of the global total emissions. In urban transportation, the bus system is an important source of traffic carbon emissions due to its high operation frequency, large number of vehicles, and wide coverage.

[0003] Currently, the estimation methods of bus system carbon emissions are mainly divided into two types: top-down and bottom-up. The top-down method is based on macro data (such as national or regional energy consumption and emission statistics) and allocates carbon emissions to the public transportation system. This method is suitable for preliminary analysis and macro evaluation because the data sources are extensive and easy to obtain, but its accuracy is limited and it is difficult to truly reflect the specific operation status of the bus system. In contrast, the bottom-up method is based on the operation data of individual buses or specific routes and summarizes them to the overall system step by step, which can provide higher accuracy and detail level. However, this method has high data requirements, including integrating driving mileage recorded by GPS, fuel efficiency indicators, and emission factors, etc., to calculate the emissions of individual buses.

[0004] Nevertheless, most existing studies focus on the absolute indicator of the total carbon emissions of the bus system (BCE), while paying insufficient attention to the relative indicator of the bus carbon emission factor per passenger (PBF). As an important reference for measuring carbon emission efficiency and environmental benefits, PBF has higher applicability in comparative analysis across cities, regions, and time dimensions. For example, in the spatial dimension, directly comparing the BCE values of different scale cities is difficult to comprehensively reflect the carbon emission efficiency of the bus system. A lower BCE value may only reflect a smaller scale of the bus system, rather than better environmental efficiency. In the time dimension, the decrease in BCE does not necessarily mean an increase in carbon efficiency, which may be due to the decline in bus service quality, resulting in passengers shifting to high-carbon emission modes such as private cars or taxis. In addition, existing studies generally assume that PBF is a constant value to estimate BCE, and do not fully consider the dynamic change characteristics of PBF in time and space; at the same time, the research mostly focuses on the estimation of PBF at a single scale (macro or micro), lacking a multi-scale integration method, and it is difficult to achieve cross-scale verification and comprehensive evaluation of emission reduction strategies. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the present invention provides a method, device, and medium for constructing a bus carbon emission factor library per passenger.

[0006] In a first aspect, an embodiment of the present invention provides a method for constructing a bus carbon emission factor library for a single passenger, the method comprising:

[0007] Obtain a bus network and estimate bus attribute parameters including bus passenger capacity, bus speed, and bus size;

[0008] According to the bus network and the bus attribute parameters, calculate the energy consumption factor, fuel consumption factor, carbon emission factor of fuel combustion, cold start carbon emission factor, and / or lubricant carbon emission factor of the bus vehicle, so as to estimate the direct carbon emissions of the bus vehicle;

[0009] According to the bus network and the bus attribute parameters, calculate the total carbon emissions in the well-to-tank stage corresponding to the bus vehicle, the carbon emission factor in the well-to-tank stage corresponding to the electric bus vehicle, the carbon emission factor of the bus vehicle throughout its life cycle, and / or the infrastructure carbon emission factor of the bus vehicle, so as to estimate the indirect carbon emissions of the bus vehicle;

[0010] According to the estimated direct carbon emissions of the bus vehicle and the indirect carbon emissions of the bus vehicle, calculate the individual-level bus carbon emission factor based on the multiple travel information of the same individual; based on the matching relationship between the individual and the link, estimate the link-level bus carbon emission factor and the line-level bus carbon emission factor; based on the spatial intersection operation, estimate the grid-level bus carbon emission factor and the regional-level bus carbon emission factor.

[0011] In a second aspect, an embodiment of the present invention provides an electronic device, comprising:

[0012] At least one processor; and

[0013] A memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the above-mentioned method for constructing a bus carbon emission factor library for a single passenger.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and the computer program realizes the above-mentioned method for constructing a bus carbon emission factor library for a single passenger when being executed by a processor.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer program product, comprising a computer program / instructions, and the computer program / instructions realize the above-mentioned method for constructing a bus carbon emission factor library for a single passenger when being executed by a processor.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0018] The present invention provides a method for constructing a bus carbon emission factor library per passenger, which generates a bus network, estimates the bus passenger capacity, bus speed, and bus size, thereby estimating the direct carbon emissions and indirect carbon emissions of buses, and deducing the individual-level bus carbon emission factor, link-level bus carbon emission factor, line-level bus carbon emission factor, grid-level bus carbon emission factor, and regional-level bus carbon emission factor, constructing a dynamic multi-scale estimation method of bus carbon emission factor per passenger covering individuals, links, lines, and regions, and realizing a comprehensive evaluation across scales. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. 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.

[0020] Figure 1 is a flowchart of the method for constructing a bus carbon emission factor library per passenger provided by the embodiment of the present invention;

[0021] Figure 2 is a relationship diagram between the individual-level PBF and the average travel distance provided by the embodiment of the present invention;

[0022] Figure 3 is a time distribution diagram of the average PBF, average bus speed, and average passenger capacity provided by the embodiment of the present invention;

[0023] Figure 4 is a schematic diagram of an electronic device provided by the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0025] It should be noted that, without conflict, the features in the following embodiments and implementation manners can be combined with each other.

[0026] As Figure 1 shown, the embodiment of the present invention provides a method for constructing a bus carbon emission factor library per passenger, and the method includes the following steps:

[0027] Step S1: Obtain the bus network and estimate bus attribute parameters including bus passenger capacity, bus speed, and bus size.

[0028] Specifically, step S1 includes:

[0029] Step S101: Construct the bus network based on bus stop GIS data and bus line GIS data.

[0030] Specifically, project the bus stops onto the bus lines according to the bus stop GIS data to correct the bus stops in the bus line GIS data;

[0031] Truncate the original bus line according to the corrected bus stops to obtain the point sequence between bus stops, which is the link of the bus network, denoted as ;

[0032] Calculate the length of each link based on the Haversine formula , determine the average speed of line and use the travel time of each link as the link weight ; Obtain the average transfer time between the first line and the second line as the transfer weight. Construct the bus network according to the corrected bus stops, the links of the bus network, the link weights, and the transfer weights, denoted as

[0033] ; where represents the set of bus stops, and represents the set of links. represents the set of links.

[0034] Step S102: Estimate the travel path, travel distance, and travel time of passengers based on smart card data and the bus network.

[0035] Specifically, step S102 specifically includes:

[0036] Obtain the smart card data, which contains the line information that the passenger took during their j-th trip;

[0037] For the Bus Rapid Transit (BRT), the boarding line and the alighting line may be different. Under the assumption of at most three transfers (covering most user cases), extract the subgraph from the bus network that contains the boarding line and the alighting line and their connecting lines;

[0038] According to the sub - graph , intelligent transportation card data, and use the Dijkstra algorithm to estimate the path of passenger u during his / her j - th trip , so as to obtain the travel distance and travel time .

[0039] Step S103: According to the bus operation data, calculate the earliest and latest vehicle arrival times at each station, and use these as time windows to screen candidate passengers; take the average travel speed of the candidate passengers as the approximate driving speed of the bus, and take the passengers who swipe their cards within the time interval between the arrivals of two consecutive bus trips as the actual passengers of the previous bus trip. Based on the origin - destination distribution and expansion factor of the actual passengers, calculate the passenger capacity of the bus.

[0040] Specifically, according to the bus operation data, determine the bus line 's maximum operating speed and minimum operating speed . Assume that vehicle v travels at the maximum operating speed and minimum operating speed respectively, and calculate the earliest arrival time of vehicle at station s and the latest arrival time . Passengers who board within the time window between the earliest arrival time are marked as candidate passengers .

[0041] Use the average travel speed of the candidate passengers as the approximate driving speed of vehicle v for its d - th departure . Passengers who swipe their cards at station s within the time interval are identified as the actual passengers of vehicle v for its d - th departure. Make a preliminary estimate of the passenger capacity based on the origin - destination (OD) distribution of the actual passengers. Apply the expansion factor calculated by the ratio of the total sample size to the total bus travel volume to the preliminary passenger capacity to obtain the estimated passenger capacity .

[0042] Step S104: Estimate the bus speed

[0043] Among them, the speed of bus vehicle v when passing through link k during its d - th departure is the ratio of the length of link k to the time required for all passengers to pass through link k under the assumption of a uniform speed. The expression is as follows

[0044]

[0045] In the formula represents the speed of the bus vehicle v when passing through link k at its d-th departure, represents the length of link k, represents the set of passengers on the bus vehicle v when passing through link k at its d-th departure, represents the time required for passenger u to pass through link k under the assumption of a uniform speed.

[0046] Step S105: Based on the passenger capacity distribution of the buses, set the maximum passenger capacity threshold, and estimate the bus size by comparing the maximum passenger capacity threshold with the designed passenger capacity corresponding to each type of bus vehicle.

[0047] It should be noted that accurately estimating the bus size is crucial for determining the PBF because the carbon emissions vary significantly with the bus size. For example, the carbon emissions of articulated buses (AB) are 2 - 3 times that of medium buses (MB). Since vehicle size information such as MB, standard buses (SB), and AB is not provided in the smart card data, most existing studies usually assume the use of SB models to estimate bus carbon emissions. However, this assumption may affect the accuracy of carbon emission estimation. The main challenge in estimating the bus size lies in accurately identifying the maximum passenger capacity threshold for a given bus route. Traffic congestion may cause two adjacent buses on the same route to arrive at the station simultaneously, thus potentially overestimating the maximum passenger capacity threshold. .

[0048] Specifically, the step S105 includes:

[0049] Obtain the passenger capacity distribution of the bus route ;

[0050] Use the Isolation Forest and Local Outlier Factor (LOF) respectively to identify the passenger capacity threshold of the bus route , and obtain the first threshold and the second threshold ; Define the maximum passenger capacity threshold as the minimum value of the first threshold and the second threshold , and the expression is: , so as to avoid missing abnormalities in the passenger occupancy rate detection. The combination of the Isolation Forest model and the Local Outlier Factor enhances the identification of global and local passenger capacity abnormalities.

[0051] Compare the maximum passenger capacity threshold with the designed passenger capacity corresponding to each type of bus vehicle, and estimate the bus size; including: The designed passenger capacity of the medium bus is denoted as , and the designed passenger capacity of the standard bus is denoted as , the designed passenger capacity of an articulated bus is denoted as ; for the bus size on the bus line to be recognized , when , the bus size is the size of a medium-sized bus; when , the bus size , the bus size takes the size of a standard bus; when , the bus size takes the size of an articulated bus; at the same time, the bus size is restricted to exclude the abnormal recognition of bus passenger capacity, that is .

[0052] Step S2, according to the bus network and bus attribute parameters, calculate the energy consumption factor, fuel consumption factor, carbon emission factor of fuel combustion, cold start carbon emission factor and / or lubricant carbon emission factor of the bus vehicle, so as to estimate the direct carbon emission of the bus vehicle.

[0053] It should be noted that the direct carbon emission of the bus system is the CO2 released into the atmosphere during the operation of the bus, mainly from fuel combustion, cold start stage and lubricant consumption. Fuel combustion is the main source, and its emissions are affected by fuel type, consumption and carbon content. Cold start emissions occur when the engine is restarted after cooling, and due to incomplete combustion and reduced efficiency, the CO2 emissions increase. Lubricant emissions result from the evaporation or thermal degradation of the lubricant during engine operation. Although its emissions are less than those of fuel combustion and cold start emissions, it still needs to be considered in the comprehensive emission calculation. In this example, the computer program for road transport emissions (COPERT) is used to estimate these emissions.

[0054] Among them, the calculation process of the energy consumption factor (EC, MJ / km) of the bus vehicle includes: calculating the energy consumption factor of the bus vehicle based on fuel type, bus size, load factor, road slope and emission standard, as well as bus speed and emission reduction factor; the expression is as follows:

[0055]

[0056]

[0057] In the formula, e represents the energy type, and the energy type includes diesel or compressed natural gas; z represents the bus size; is the slope of link k, Denote the speed of bus vehicle \(v\) passing through link \(k\) during its \(d\)-th departure. \(\alpha\), \(\beta\), \(\gamma\), \(\delta\), \(\varepsilon\), \(\zeta\), \(\eta\) are COPERT model parameters, which are related to fuel type, bus size, load factor, road gradient and emission standards, and are calibrated by a localized portable emission measurement system (PEMS); \(RF\) is the emission reduction factor.

[0058] Since the COPERT model only provides benchmark parameter values at specific passenger occupancy rates (0%, 50%, 100%) and road gradients (-0.06 to 0.06, interval 0.02), the Clough–Tocher method is adopted for interpolation in this example.

[0059] Among them, the calculation process of the fuel consumption factor (\(FC\), g / km) includes: taking the product of the energy consumption factor of the bus vehicle and the energy conversion factor as the fuel consumption factor; the expression is as follows:

[0060]

[0061] In the formula, is the energy conversion factor; the energy conversion factor is related to the fuel type.

[0062] Among them, the calculation process of the carbon emission factor of fuel combustion (\(EF\), g / km) includes: calculating the carbon emission factor of fuel combustion based on the fuel consumption factor, hydrogen-carbon ratio, and oxygen-carbon ratio; the expression is as follows:

[0063]

[0064] In the formula, represents the hydrogen-carbon ratio, represents the oxygen-carbon ratio; the hydrogen-carbon ratio and oxygen-carbon ratio are related to the fuel type.

[0065] Among them, the calculation process of the cold start carbon emission factor ( , g / km) includes: calculating the cold start carbon emission factor based on the bus speed, ambient temperature, and carbon emission factor of fuel combustion; the expression is as follows:

[0066]

[0067] In the formula, is the ambient temperature; \((A, B, C)\) are parameters of the computer program for road transport emissions (COPERT), which can be calibrated by a localized portable emission measurement system (PEMS).

[0068] Among them, the calculation process of the carbon emission factor of lubricant ( , g / km) includes: taking the product of the consumption ratio of lubricant to fuel and the fuel consumption factor as the carbon emission factor of lubricant; the expression is as follows:

[0069]

[0070] In the formula, is the consumption ratio of lubricant to fuel.

[0071] Step S3: According to the bus network and bus attribute parameters, calculate the total carbon emissions in the well-to-tank stage corresponding to the bus vehicles, the carbon emission factor in the well-to-tank stage corresponding to the electric bus vehicles, the carbon emission factor in the whole life cycle of the bus vehicles, and the infrastructure carbon emission factor of the bus vehicles, so as to estimate the indirect carbon emissions of the bus vehicles.

[0072] It should be noted that in the life cycle assessment of the bus system, although the indirect carbon emissions do not directly come from the operation stage of the vehicles, they still play an important role in the overall carbon emissions. This part of the emissions includes the emissions in the WTT stage, such as the carbon emissions generated during the extraction, refining and transportation of fuels, and also covers the emissions in the production, maintenance and manufacturing stages of the buses, as well as the carbon emissions of the bus-related infrastructure.

[0073] Among them, in the well-to-tank stage (WTT) corresponding to the bus vehicles, the carbon emission calculation methods of diesel and compressed natural gas (CNG) are similar; taking diesel as an example, it is specifically divided into two parts: the total carbon emissions in the crude oil extraction and transportation stage, and the total carbon emissions in the diesel refining, transportation and storage stage; specifically as follows:

[0074] The calculation process of the total carbon emissions in the crude oil extraction and transportation stage includes: First, collect the data of the import and export of crude oil and their proportions in the research city, and obtain the origin, export port and import port locations of the crude oil. Combine the pipeline spatial distribution data to calculate the pipeline transportation distance, and measure the sea transportation distance. Obtain the location data of the refineries in the research city, and use the map platform to calculate the road transportation distance from the port to the refinery. Then, combine the pipeline and sea transportation carbon emission factors to calculate the total carbon emissions in the crude oil extraction and transportation stage.

[0075] The calculation process of the total carbon emissions in the diesel refining, transportation and storage stage includes: Use the local electricity and heat energy carbon emission factors to calculate the emissions in the crude oil refining stage. Determine the road transportation distance from the refinery to the diesel station through the point of interest (POI) distribution data to estimate the emissions in the transportation stage. Combine the freight carbon emission factors to calculate the total carbon emissions in the diesel refining, transportation and storage stage.

[0076] Although electric buses achieve almost zero CO2 emissions at the Tank to Wheels (TTW) stage, the CO2 emissions at the Well to Tank (WTT) stage cannot be ignored. In this example, the carbon emission factor corresponding to the Well to Tank stage of electric buses is calculated based on vehicle traction and the local grid carbon emission factor; the expression is as follows:

[0077]

[0078]

[0079]

[0080] In the formula, represents the vehicle traction force (N), which is affected by the bus size, driving speed, road slope, and passenger capacity; is the rolling resistance coefficient of the vehicle, is the vehicle mass (kg), represents the passenger capacity of the bus, is the mass (kg) of a single standard passenger; is the local acceleration due to gravity (m / s 2 ), represents the slope of link 𝑘, is the inertia adjustment coefficient; is the air resistance coefficient of the vehicle; is the frontal area of the vehicle (m²); is the local air density (kg / m³); represents the speed of bus vehicle v passing through link k at its d-th departure; is the local grid carbon emission factor (g / Wh); is the energy total power generation; is the energy unit power generation carbon emission factor (g / Wh); 、 、 are the efficiencies of the inverter, controller, and motor, respectively; is the auxiliary power (W); 、 are the grid transmission efficiency and charging efficiency, respectively; is the regenerative braking energy factor (Wh / km).

[0081] Among them, the calculation process of the carbon emission factor of a bus vehicle in its whole life cycle includes: based on the number of bus vehicles of the target size operating on a bus line, the total carbon emissions of the bus vehicles of the target size in the material production stage, manufacturing stage, maintenance stage, and disposal stage, the average travel distance of passengers on the bus line and the daily passenger volume, calculate the carbon emission factor of the bus vehicle in its whole life cycle; specifically, for the line of buses, the carbon emission factor ( , g / (pax×km)) in the whole life cycle (i.e., production, maintenance, and disposal stages) is expressed as follows:

[0082]

[0083] In the formula, represents the number of bus vehicles of size type operating on the line ; is the total carbon emissions of bus vehicles of size type in the material production and processing stage, including the carbon emissions generated during the production and processing of various materials (such as steel, aluminum alloy, glass, plastic, battery, etc.) used in the manufacture of bus vehicles; is the total carbon emissions of bus vehicles of size type in the manufacturing and assembly process stage, including the power consumption of the production line, processes such as welding, painting, heat treatment, etc., and the energy consumption required for the operation of manufacturing equipment; is the carbon emissions of bus vehicles of size type in the maintenance stage, including the carbon emissions generated during the production, transportation, and installation of replacement or supplementary parts (such as tires, refrigerants, lead-acid batteries, etc.) during maintenance; represents the carbon emissions of bus vehicles of size type in the disposal stage, including the indirect carbon emissions generated during the recycling, treatment, or disposal of waste (such as waste parts, engine oil, coolant, etc.) generated during maintenance; is the expected service life of bus vehicles of size type ; and are the average travel distance of passengers and the daily passenger volume of the line respectively.

[0084] Among them, the carbon emission factor of the bus vehicle infrastructure ( , the calculation process of g / (pax×km) includes: calculating the infrastructure carbon emission factor of bus vehicles according to the quantities, total carbon emissions during the whole life cycle of the infrastructure, and service life of bus stops, parking lots, and charging stations in the target city; the expression is as follows:

[0085]

[0086] In the formula, , and respectively represent the quantities of bus stops, parking lots, and charging stations in the target city. , and respectively represent the total carbon emissions of bus stops, parking lots, and charging stations during the production, operation, and disposal stages. , and respectively represent the service lives of bus stops, parking lots, and charging stations.

[0087] Step S4, according to the estimated direct carbon emissions of bus vehicles and the indirect carbon emissions of bus vehicles, calculate the individual-level bus carbon emission factor based on the multiple travel information of the same individual; based on the matching relationship between the individual and the link, estimate the link-level bus carbon emission factor and the route-level bus carbon emission factor; based on the spatial intersection operation, estimate the grid-level bus carbon emission factor and the regional-level bus carbon emission factor.

[0088] Among them, the calculation process of the individual-level bus carbon emission factor includes: calculating the individual-level bus carbon emission factor based on the life cycle emission factor of each individual's travel and the total travel distance of each individual's travel, and the life cycle emission factor of each individual's travel is calculated based on the travel path, passenger capacity, carbon emission factor of fuel combustion, cold start carbon emission factor, lubricant carbon emission factor, carbon emission factor from well to tank for electric bus vehicles, carbon emission factor of bus vehicles during the whole life cycle, and the infrastructure carbon emission factor of bus vehicles.

[0089] The life cycle bus carbon emission factor of individual u ( , g / (pax×km)) is expressed as follows:

[0090]

[0091] In the formula, represents the life cycle emission factor of the j-th travel of individual u, represents the total travel distance of the j-th travel of individual u;

[0092]

[0093] In the formula, represents the travel path, represents the link of the bus network;

[0094]

[0095] In the formula, represents the carbon emission factor of fuel combustion, represents the cold start carbon emission factor, represents the carbon emission factor of lubricant, represents the carbon emission factor from well to tank for electric bus vehicles, represents the carbon emission factor of bus vehicles over the full life cycle, represents the carbon emission factor of bus vehicle infrastructure, represents the passenger capacity.

[0096] It should be noted that this case study focuses on the impact of individual travel behavior (including travel distance, travel purpose, travel frequency, etc.) on in order to develop emission reduction strategies that meet individual preferences. For example, through integration with the Mobility as a Service (MaaS) platform, it promotes collaborative emission reduction between the bus system and other transportation modes. Figure 2 presents a relationship graph between the average travel distance and the individual-level bus carbon emission factor (individual-level PBF). It can be found that the individual-level PBF generally follows a lognormal distribution. Approximately 79% of individuals' PBFs are concentrated between 10 1.5 and 10 2.1 g / (pax×km). A small number of individuals have a PBF lower than 10 1.5 g / (pax×km), mainly because these individuals are long-distance commuters. There is an inverse relationship between PBF and travel distance, and its expression is: , and the R² value is 0.68. This inverse relationship may be related to individual travel behavior preferences and the phenomenon of job-housing separation in the city.

[0097] Among them, the calculation process of the link-level bus carbon emission factor ( , g / (pax×km)) includes: calculating based on the set of individuals passing through the target link during the target time period, passenger capacity, carbon emission factor of fuel combustion, cold start carbon emission factor, carbon emission factor of lubricant, carbon emission factor from well to tank for electric bus vehicles, carbon emission factor of bus vehicles over the full life cycle, and carbon emission factor of bus vehicle infrastructure; the expression is as follows:

[0098]

[0099] In the formula, represents the bus carbon emission factor of link k during time period t, represents the carbon emission factor of fuel combustion represents the carbon emission factor of cold start represents the carbon emission factor of lubricant represents the carbon emission factor of the well-to-tank stage corresponding to electric bus vehicles represents the carbon emission factor of bus vehicles over the full life cycle represents the infrastructure carbon emission factor of bus vehicles represents the passenger capacity represents the set of individuals passing through link k within time period t

[0100] It should be noted that in this example, the spatial distribution of link-level PBF during peak and off-peak periods is calculated. During peak hours, the PBF of links 1-6 is significantly lower than that of other links, mainly because the increased cross-regional commuting demand improves the utilization efficiency of bus resources. Relatively speaking, during off-peak hours, except for link 1, the PBF of these links increases significantly because the occupancy rate of buses decreases significantly. The PBF of other links changes relatively smoothly, mainly because: (1) the traffic on these sections is less affected by commuting activities; (2) the bus departure frequency decreases.

[0101] Among them, the route-level bus carbon emission factor ( , g / (pax×km)) is expressed as follows:

[0102]

[0103] In the formula, represents the route-level bus carbon emission factor of route during time period t.

[0104] Among them, the grid-level bus carbon emission factor ( , g / (pax×km)) is expressed as follows:

[0105]

[0106] In the formula, represents the grid-level bus carbon emission factor corresponding to grid g during time period t, represents link and grid 's spatial intersection operation.

[0107] It should be noted that the spatial intersection operation of link and grid is not a traditional set operation. The spatial intersection operation of link and grid ​​ When the link overlaps with the outer boundary of the grid , only the part within the grid will be intercepted and retained.

[0108] This example calculates the spatial distribution of grid-level PBF during peak and off-peak hours. Compared with link-level PBF, the decline of grid-level PBF during peak hours is more significant. In addition, during off-peak hours, the PBF in the old urban area has decreased, which may be related to the increase in the demand for leisure travel.

[0109] Among them, the calculation process of the regional-level bus carbon emission factor ( , g / (pax×km)) includes: The derivation process of the regional-level bus carbon emission factor of region r during time period t is the same as that of the line-level and grid-level bus carbon emission factors. The regions include, but are not limited to, different scales such as communities, streets, counties, cities, or provinces, etc., in order to better analyze the spatial distribution patterns of PBF at different regional scales. This example calculates the PBF estimation results at the street and county levels, and the results show that during peak hours, the PBF in the areas surrounding the old urban area has increased significantly. Figure 3 Shows the time distribution characteristics of the average PBF, average bus speed, and average passenger load. The results show that the average PBF increases significantly during off-peak hours and decreases during peak hours. This finding is in contrast to current studies on bus carbon emission estimation, which usually adopt static carbon emission factors. This method may lead to overestimation or underestimation of the emissions of the bus system and cannot accurately reflect its spatio-temporal dynamic characteristics. At the same time, the average bus speed also shows a similar trend. However, the average passenger load shows an opposite change pattern. Compared with the peak period, the average PBF during the off-peak period has increased by 13.3%, the average bus speed has increased by 13.5%, while the average passenger load has decreased by 21.1%.

[0110] Furthermore, cross-city verification analysis was also conducted in this example. Specifically, due to the differences in city sizes, directly comparing the total bus carbon emissions (BCE) at the city level usually lacks practical significance. Therefore, the PBF indicator provided in this example is more suitable for cross-city verification analysis. If there are no significant differences in the order of magnitude between the PBFs of different cities, it can be considered that the estimation method is reliable, which is an advantage that the BCE indicator cannot provide. Although the bus accessibility and service quality in different cities may lead to slight differences in the average PBF, these differences are usually not statistically significant. Table 1 shows the comparison of the PBF values estimated in existing studies and this study. In this example, the PBF values in ranges I, II, and III are 50.35, 55.99, and 72.41 g / (pax×km), respectively. The PBF in range I is consistent with the results of existing studies, further verifying the reliability of the PBF estimation method we proposed. It is worth noting that the PBF in range III is 43.81% higher than that in range I, indicating that only considering the fuel combustion emissions of the bus system and the emissions in the WTT stage of electric buses will lead to a significant underestimation.

[0111] Table 1. Comparison Table of Average PBF

[0112]

[0113] Step S5: Calculate the carbon inequality index based on the number of individuals in the sample, the life-cycle unit passenger bus carbon emission factor corresponding to each individual, the average life-cycle unit passenger bus carbon emission factor, the carbon fairness benchmark value, and the comprehensive carbon weight of the individual.

[0114] Specifically, the carbon inequality index has the following expression:

[0115]

[0116] In the formula, is the total number of individuals in the sample, is the life-cycle unit passenger bus carbon emission factor of individual u, is the life-cycle unit passenger bus carbon emission factor of individual u', is the average life-cycle unit passenger bus carbon emission factor of the sample, is the carbon fairness benchmark value at the regional or national level (in this example, the carbon emission factor per unit passenger of the bus for carbon compliance can be selected), is the comprehensive carbon weight of individual u, is the comprehensive carbon weight of individual u'.

[0117] Among them, the average life-cycle unit passenger bus carbon emission factor of the sample has the following expression:

[0118]

[0119] Among them, the comprehensive carbon weight of individual u is expressed as follows:

[0120]

[0121] In the formula, α represents the sensitivity parameter to high carbon emissions, β represents the sensitivity parameter of individuals with high-frequency travel, α > 0, β > 0; is the average monthly travel frequency of individual u, is the life-cycle per-passenger bus carbon emission factor of individual j, is the average monthly travel frequency of individual j.

[0122] It should be noted that to more comprehensively reflect the uneven distribution of PBF among different individuals, in this example, based on the classical Gini coefficient, a carbon inequality index is proposed, denoted as . This carbon inequality index integrates two dimensions of individual carbon emission intensity and behavior activity. The specific improvements include the following three points: (1) Introduce a logarithmic scaling factor to enhance the sensitivity to carbon emissions at different orders of magnitude, especially the ability to capture the differences between low-carbon and high-carbon individuals; (2) Introduce a carbon fairness benchmark value as a comparison baseline to measure the degree of "carbon excess" or "carbon savings" of each individual; (3) Introduce dual sensitivity parameters α and β, which are used to adjust the weights of high-carbon emission individuals and high-frequency travel individuals respectively, in order to more fairly reflect their impact on overall carbon inequality. This carbon inequality index can not only measure the differences in carbon emissions at the individual level, but also flexibly adjust the degree of attention to different populations through the weight function , thus supporting the design of more fair and targeted emission reduction policies.

[0123] Furthermore, existing research has mostly focused on the assessment of the emission reduction potential of BCE, while paying less attention to the emission reduction potential of PBF. In this example, by setting relevant model parameters, the emission reduction potential of PBF under different emission reduction strategies can be evaluated. For example, by setting the proportion of electric buses to 100%, the emission reduction potential of PBF for the full electrification of the bus system can be estimated. In addition, by increasing the proportion of wind energy, solar energy, and hydropower in the power grid, the emission reduction potential of PBF under the scenario of power grid decarbonization can be evaluated. Finally, high-strength materials (such as high-strength steel, aluminum alloy, carbon fiber, etc.) are used to replace traditional materials to reduce the vehicle's own weight, thereby reducing carbon emissions during the operation stage. However, this may lead to an increase in carbon emissions during the production and scrapping stages. By setting parameters such as the weight and carbon emission factor of high-strength materials in the model, the emission reduction potential of PBF for vehicle lightweighting can be evaluated.

[0124] Correspondingly, the present application further provides an electronic device, including: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method for constructing a bus carbon emission factor library for a single passenger as described above. As Figure 4 shown, it is a hardware structure diagram of any device with data processing capabilities where the method for constructing a bus carbon emission factor library for a single passenger provided by an embodiment of the present invention is located. In addition to Figure 4 the processors, memory, and network interfaces shown, any device with data processing capabilities where the device in the embodiment is located usually may further include other hardware according to the actual functions of the any device with data processing capabilities, which will not be elaborated herein.

[0125] Correspondingly, the present application further provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed by a processor, the method for constructing a bus carbon emission factor library for a single passenger as described above is implemented. The computer-readable storage medium may be an internal storage unit of any device with data processing capabilities described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a Smart Media Card (SMC), an SD card, a Flash Card, etc. equipped on the device. Further, the computer-readable storage medium may also include both an internal storage unit of any device with data processing capabilities and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the any device with data processing capabilities, and may also be used to temporarily store data that has been output or will be output.

[0126] The foregoing are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for constructing a bus carbon emission factor library for a single passenger, characterized in that, The method includes: Obtaining a bus network and estimating bus attribute parameters including bus passenger capacity, bus speed, and bus size; Calculating the energy consumption factor, fuel consumption factor, carbon emission factor of fuel combustion, cold start carbon emission factor, and / or lubricant carbon emission factor of a bus vehicle according to the bus network and the bus attribute parameters, so as to estimate the direct carbon emissions of the bus vehicle; Calculating the total carbon emissions of the well-to-tank stage corresponding to the bus vehicle, the carbon emission factor of the well-to-tank stage corresponding to the electric bus vehicle, the carbon emission factor of the full life cycle of the bus vehicle, and / or the infrastructure carbon emission factor of the bus vehicle according to the bus network and the bus attribute parameters, so as to estimate the indirect carbon emissions of the bus vehicle; Calculating the individual-level bus carbon emission factor based on the estimated direct carbon emissions and indirect carbon emissions of the bus vehicle and the multiple travel information of the same individual; estimating the link-level bus carbon emission factor and the line-level bus carbon emission factor based on the matching relationship between the individual and the link; estimating the grid-level bus carbon emission factor and the regional-level bus carbon emission factor based on the spatial intersection operation; Among them, the expression of the individual-level bus carbon emission factor is as follows: ； In the formula, represents the life cycle bus carbon emission factor of individual u, represents the life cycle emission factor of the j-th trip of individual u, represents the total travel distance of the j-th trip of individual u; The expression of the link-level bus carbon emission factor is as follows: ； In the formula, represents the bus carbon emission factor of link k within the time period t, represents the carbon emission factor of fuel combustion, represents the cold start carbon emission factor, represents the lubricant carbon emission factor, represents the carbon emission factor of the well-to-tank stage corresponding to the electric bus vehicle, represents the carbon emission factor of the bus vehicle in the whole life cycle, represents the infrastructure carbon emission factor of the bus vehicle, represents the passenger capacity, represents the set of individuals passing through link k within the time period t; The expression of the line-level bus carbon emission factor is as follows: ； In the formula, represents the line-level bus carbon emission factor of line during the time period t, the length of link k; The expression of the grid-level bus carbon emission factor is as follows: ； In the formula, represents the grid-level bus carbon emission factor corresponding to grid g within time period t, represents the link and the grid for the spatial intersection operation; The calculation process of the regional-level bus carbon emission factor includes: according to the link Perform a spatial intersection operation with region r to obtain the regional-level bus carbon emission factor and the line-level bus carbon emission factor of region r within time period t.

2. The method for constructing a bus carbon emission factor library for a single passenger according to claim 1, wherein The process of obtaining the bus network includes: Projecting bus stops onto bus lines according to bus stop GIS data, so as to correct the bus stops in the bus line GIS data; Truncating the bus lines in the bus line GIS data according to the corrected bus stops to obtain the links of the bus network; Obtaining the length of each link and the average speed of the bus line, so as to calculate the travel time of each link and use this as the link weight; obtaining the average transfer time between two bus lines and using this as the transfer weight; Constructing a bus network according to the corrected bus stops, the links of the bus network, the link weight, and the transfer weight.

3. A method for constructing a bus carbon emission factor library for a single passenger according to claim 1, characterized in that, The process of estimating bus passenger capacity includes: Determining the maximum operating speed and the minimum operating speed of a bus line according to bus operation data; Assuming that bus vehicle v travels at the maximum operating speed and the minimum operating speed respectively, calculating the earliest arrival time and the latest arrival time of bus vehicle v at station s; passengers getting on the bus within the time window between the earliest arrival time and the latest arrival time are marked as candidate passengers; Taking the average travel speed of the candidate passengers as the approximate driving speed of the bus, and taking the passengers swiping their cards within the time interval between the arrivals of two adjacent bus trips as the actual passengers of the previous bus trip, and calculating the passenger capacity of the bus based on the origin-destination distribution and the sampling expansion coefficient of the actual passengers.

4. A method for constructing a bus carbon emission factor library for a single passenger according to claim 1 or 3, characterized in that, The process of estimating bus size includes: Obtaining the passenger capacity distribution of the bus line; Using isolation forest and local outlier factor to identify the passenger capacity thresholds of the bus line respectively, and obtaining the first threshold and the second threshold; Defining the maximum passenger capacity threshold as the minimum value of the first threshold and the second threshold; Obtaining the designed passenger capacity of medium-sized buses, the designed passenger capacity of standard buses, and the designed passenger capacity of articulated buses; For the size of the bus on the bus line to be recognized, when the maximum passenger capacity threshold is less than or equal to the designed passenger capacity of a medium-sized bus, the bus size is taken as the designed passenger capacity of the medium-sized bus; when the maximum passenger capacity threshold is greater than the designed passenger capacity of the medium-sized bus and less than or equal to the designed passenger capacity of a standard bus, the bus size is taken as the designed passenger capacity of the standard bus; when the maximum passenger capacity threshold is greater than the designed passenger capacity of the standard bus and less than or equal to the designed passenger capacity of an articulated bus, the bus size is taken as the designed passenger capacity of the articulated bus.

5. A method for constructing a bus carbon emission factor library for a single passenger according to claim 1, characterized in that, The energy consumption factor of the bus is calculated based on the fuel type, bus size, load factor, road gradient, emission standard, bus speed, and emission reduction factor. The fuel consumption factor is the product of the energy consumption factor of the bus and the energy conversion factor. The carbon emission factor of fuel combustion is calculated based on the fuel consumption factor, hydrogen-carbon ratio, and oxygen-carbon ratio. The cold start carbon emission factor is calculated based on the bus speed, ambient temperature, and carbon emission factor of fuel combustion. The lubricant carbon emission factor is the product of the consumption ratio of lubricant to fuel and the fuel consumption factor.

6. The method for constructing a bus carbon emission factor library for a single passenger according to claim 1, characterized in that, The total carbon emissions of the bus in the well-to-tank stage include the total carbon emissions in the crude oil extraction and transportation stage, and the total carbon emissions in the diesel refining, transportation, and storage stage. The carbon emission factor of the electric bus in the well-to-tank stage is calculated based on the vehicle traction force and the local power grid carbon emission factor. The carbon emission factor of the bus in its whole life cycle is calculated based on the number of buses of the target size operating on the bus line, the total carbon emissions of the buses of the target size in the material production stage, manufacturing stage, maintenance stage, and disposal stage, the average travel distance of passengers on the bus line, and the daily passenger volume. The infrastructure carbon emission factor of the bus is calculated based on the number of bus stops, parking lots, and charging stations corresponding in the target city, the total carbon emissions of the infrastructure in its whole life cycle, and its service life.

7. A method for constructing a bus carbon emission factor library for a single passenger according to claim 1, characterized in that, The method further includes: Calculating the carbon inequality index according to the number of individuals in the sample, the life cycle unit passenger bus carbon emission factor corresponding to each individual, the average life cycle unit passenger bus carbon emission factor, the carbon fairness benchmark value, and the comprehensive carbon weight of the individual.

8. An electronic device, characterized in that, Including: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can execute the method for constructing a bus carbon emission factor library per passenger as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when executed by the processor, implements the method for constructing a bus carbon emission factor library per passenger as described in any one of claims 1-7.

10. A computer program product, comprising a computer program / instructions, characterized in that, The computer program / instructions, when executed by the processor, implement the method for constructing a bus carbon emission factor library per passenger as described in any one of claims 1-7.

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