Bus carbon emission factor library construction method for unit passengers, equipment and medium

Abstract

The invention discloses a method and equipment for constructing a bus carbon emission factor library of unit passengers, and a medium. The method comprises the steps: obtaining a bus network, and estimating bus attribute parameters including the bus passenger capacity, the bus speed and the bus size; calculating an energy consumption factor, a fuel consumption factor, a fuel combustion carbon emission factor, a cold start carbon emission factor and / or a lubricant carbon emission factor of the bus so as to estimate direct carbon emission of the bus; calculating the total carbon emission of the corresponding oil well to oil tank stage of the bus, the carbon emission factor of the corresponding oil well to oil tank stage of the electric bus, the carbon emission factor of the whole life cycle of the bus and / or the infrastructure carbon emission factor of the bus so as to estimate the indirect carbon emission of the bus; and deriving individual-level, grid-level, region-level, link-level and line-level bus carbon emission factors according to the estimated direct and indirect carbon emission of the bus.

Description

Technical Field

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

[0002] As the global need to address climate change and promote sustainable development continues to intensify, all sectors of society have paid close attention to the issue of carbon emissions in the transportation industry. As one of the main sources of greenhouse gas emissions, transportation accounts for about 20%-30% of the world's total emissions. In urban transportation, the public transportation system is one of the important sources of transportation carbon emissions due to its high operating frequency, large number of vehicles and wide coverage.

[0003] At present, there are two main methods for estimating carbon emissions from public transportation systems: top-down and bottom-up. The top-down method allocates carbon emissions to the public transportation system based on macro data (such as national or regional energy consumption and emission statistics). This method is suitable for preliminary analysis and macro assessment because the data sources are wide and easy to obtain, but its accuracy is limited and it is difficult to truly reflect the specific operating conditions of the public transportation system. In contrast, the bottom-up method is based on the operating data of a single bus or a specific line, and is aggregated step by step to the overall system, which can provide higher accuracy and detail. However, this method has high data requirements, including integrating GPS-recorded mileage, fuel efficiency indicators, and emission factors to calculate the emissions of a single bus.

[0004] Despite this, most existing studies focus on the absolute indicator of total carbon emissions (BCE) of the bus system, while paying insufficient attention to the relative indicator of bus carbon emission factor (PBF) per passenger. 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 cities of different sizes is difficult to fully reflect the carbon emission efficiency of the bus system. A lower BCE value may only reflect the smaller scale of the bus system rather than better environmental efficiency. In the temporal dimension, a decrease in BCE does not necessarily mean an increase in carbon efficiency. This may be due to the decline in bus service quality, resulting in the loss of passengers 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 fail to fully consider the dynamic changes of PBF in time and space; at the same time, most studies focus on PBF estimation at a single scale (macro or micro), lacking a multi-scale integration method, making it difficult to achieve cross-scale verification and comprehensive evaluation of emission reduction strategies. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a method, device and medium for constructing a public transportation carbon emission factor library per unit passenger.

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

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

[0008] 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, so as to estimate the direct carbon emission of the bus;

[0009] Based on the bus network and bus attribute parameters, calculate the total carbon emissions from the oil well to the tank corresponding to the bus, the carbon emission factor from the oil well to the tank corresponding to the electric bus, the carbon emission factor of the bus's entire life cycle and / or the carbon emission factor of the bus's infrastructure, so as to estimate the indirect carbon emissions of the bus;

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

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

[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 that can be executed 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 per unit passenger.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for constructing a public transportation carbon emission factor library per unit passenger.

[0016] In a fourth aspect, an embodiment of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for constructing a public transportation carbon emission factor library per unit passenger.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] The present invention provides a method for constructing a bus carbon emission factor library per unit passenger. By generating a bus network, estimating the bus passenger capacity, bus speed, and bus size, the direct carbon emissions and indirect carbon emissions of bus vehicles are estimated, and individual-level bus carbon emission factors, link-level bus carbon emission factors, line-level bus carbon emission factors, grid-level bus carbon emission factors, and regional-level bus carbon emission factors are derived. A dynamic multi-scale unit passenger bus carbon emission factor estimation method covering individuals, links, lines, and regions is constructed to achieve a comprehensive cross-scale evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0020] Figure 1 A flow chart of a method for constructing a public transportation carbon emission factor library per passenger provided in an embodiment of the present invention;

[0021] Figure 2 A relationship diagram between individual-level PBF and average travel distance provided in an embodiment of the present invention;

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

[0023] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] It should be noted that, in the absence of conflict, the features in the following embodiments and implementations may be combined with each other.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a method for constructing a public transportation carbon emission factor library per passenger, the method comprising the following steps:

[0027] Step S1, obtaining a bus network and estimating bus attribute parameters including bus passenger capacity, bus speed, and bus size.

[0028] Specifically, the step S1 includes:

[0029] Step S101, constructing a bus network based on bus station GIS data and bus route GIS data.

[0030] Specifically, based on the bus stop GIS data, the bus stops are projected onto the bus routes, thereby correcting the bus stops in the bus route GIS data;

[0031] According to the corrected bus stops, the original bus routes are truncated to obtain the point sequence between bus stops, that is, the link of the bus network, which is recorded as ;

[0032] Calculate the length of each link based on the Haversine formula , determine the route Average speed , taking the travel time of each link as the link weight ; Get the first line and the second line The average transfer time between As a transfer weight.

[0033] The bus network is constructed based on the corrected bus stops, links of the bus network, link weights, and transfer weights, which are recorded as ;in, represents a set of bus stops, Indicates a link set.

[0034] Step S102, estimating the passenger's travel route, travel distance and travel time based on the smart transportation card data and the public transportation network.

[0035] Specifically, the step S102 includes:

[0036] Acquire smart transportation card data, where the smart transportation card data includes route information taken by the passenger during the jth trip;

[0037] For Bus Rapid Transit (BRT), boarding routes and drop-off lines May be different. Assuming a maximum of three transfers (covering most user situations), extract the bus routes containing the boarding routes from the bus network. and drop-off lines A subgraph of its connecting lines ;

[0038] According to the sub-graph , smart transportation card data, and use the Dijkstra algorithm to estimate the path of passenger u on his jth trip , thus obtaining the travel distance and travel time .

[0039] Step S103, based on the bus operation data, calculate the earliest and latest vehicle arrival times at each stop, and use this as a time window to screen candidate passengers; use the average travel speed of the candidate passengers as the approximate travel speed of the bus, and use the passengers who swipe their cards within the time interval between the arrival of two adjacent buses as the actual passengers of the previous bus, and calculate the bus passenger capacity based on the distribution of the starting and ending points of the actual passengers and the expansion coefficient.

[0040] Specifically, determine the bus routes based on bus operation data. Maximum operating speed and minimum operating speed Assume that the vehicles v run at their maximum speed and minimum operating speed Driving, calculating vehicles The earliest arrival time at station s and the latest arrival time At the earliest arrival time and the latest arrival time Passengers who board the bus within the time window are marked as candidate passengers. .

[0041] Adopt candidate passengers The average travel speed of vehicle v is taken as the approximate travel speed of the d-th departure In the time interval Passengers who swipe their cards at station s are considered to be the actual passengers of the dth departure of vehicle v. A preliminary ridership estimate is made based on the origin-destination (OD) distribution of the actual passengers. An expansion factor, calculated by the ratio of the total sample size to the total bus trips, is applied to the preliminary ridership to obtain the estimated ridership. .

[0042] Step S104, estimating the bus speed.

[0043] Among them, the speed of bus v when passing link k at its d-th departure is the ratio of the length of link k to the time required for all passengers to pass link k assuming a uniform speed, expressed as follows:

[0044]

[0045] In the formula, represents the speed of bus v when it passes link k during its d-th departure, represents the length of link k, represents the passenger set of bus v when it passes link k for its d-th departure, Denotes the time required for passenger 𝑢 to pass through link 𝑘 assuming uniform speed.

[0046] Step S105, based on the passenger capacity distribution of the buses, a maximum passenger capacity threshold is set, and the bus size is estimated by comparing the maximum passenger capacity threshold with the designed passenger capacity corresponding to each type of bus.

[0047] It should be noted that accurate estimation of bus size is crucial to determine PBF because carbon emissions vary significantly with bus size. For example, the carbon emissions of an articulated bus (AB) are 2-3 times that of a medium bus (MB). Since vehicle size information such as MB, standard bus (SB), and AB are not available in smart card data, most existing studies usually assume that SB models are used to estimate bus carbon emissions. However, this assumption may affect the accuracy of carbon emission estimates. The main challenge in estimating 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 at the same time, which may overestimate the maximum passenger capacity threshold. .

[0048] Specifically, the step S105 includes:

[0049] Get bus routes passenger capacity distribution;

[0050] Isolation forest and local outlier factor (LOF) are used to identify bus routes. The passenger capacity threshold is obtained by and the second threshold ; Define the maximum passenger capacity threshold The first threshold and the second threshold The minimum value in is expressed as: , thus avoiding missing anomalies in passenger load factor detection. The combination of the isolation forest model and the local outlier factor enhances the anomaly identification of global and local passenger load.

[0051] Compare the maximum passenger capacity threshold with the design passenger capacity of each type of bus to estimate the bus size; including: the design passenger capacity of a medium-sized bus is recorded as , the design passenger capacity of a standard bus is , the design passenger capacity of the articulated bus is ; For the bus routes to be identified Bus size ,when , bus size is the size of a medium bus; When the bus size Take the dimensions of a standard bus; when When the bus size Take the size of the articulated bus; at the same time, the bus size constraint is used to exclude abnormal identification of bus passenger capacity, that is, .

[0052] Step S2, based on 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, so as to estimate the direct carbon emission of the bus.

[0053] It should be noted that direct carbon emissions from public transportation systems refer to the CO released into the atmosphere during bus operation. 2 , mainly from fuel combustion, cold start phase 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 down, resulting in CO2 emissions due to incomplete combustion and reduced efficiency. 2 Increased emissions. Lubricant emissions originate from evaporation or thermal degradation of lubricants during engine operation and, although their emissions are smaller than fuel combustion and cold start emissions, they still need to be considered in the comprehensive emissions calculation. In this example, the Road Transport Emissions Computer Program (COPERT) is used to estimate these emissions.

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

[0055]

[0056]

[0057] Wherein, e represents the energy type, which includes diesel or compressed natural gas; z represents the size of the bus; is the slope of link k, represents the speed of bus v when it passes through link k during its d-th departure; α, β, γ, δ, ε, ζ, η are COPERT model parameters, which are related to fuel type, bus size, load factor, road slope and emission standard, 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 load factors (0%, 50%, and 100%) and road slopes (-0.06 to 0.06, with an interval of 0.02), the Clough–Tocher method is used for interpolation in this example.

[0059] The calculation process of the fuel consumption factor (FC, g / km) includes: taking the product of the energy consumption factor of the public transport 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] The calculation process of the carbon emission factor (EF, g / km) of fuel combustion 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-to-carbon ratio, It represents the oxygen-carbon ratio; the hydrogen-carbon ratio and the oxygen-carbon ratio are related to the fuel type.

[0065] Among them, the cold start carbon emission factor ( The calculation process of (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 using a localized portable emission measurement system (PEMS).

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

[0069]

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

[0071] Step S3, based on the bus network and bus attribute parameters, calculate the total carbon emissions from the oil well to the fuel tank stage corresponding to the bus, the carbon emission factor from the oil well to the fuel tank stage corresponding to the electric bus, the carbon emission factor of the bus's entire life cycle, and the carbon emission factor of the bus's infrastructure, so as to estimate the indirect carbon emissions of the bus.

[0072] It should be noted that in the life cycle assessment of the public transportation system, although indirect carbon emissions do not directly come from the operation stage of the vehicle, they still occupy an important position in the overall carbon emissions. This part of emissions includes emissions in the WTT stage, such as carbon emissions generated during the mining, refining and transportation of fuels, as well as emissions in the production, maintenance and manufacturing stages of buses, and carbon emissions from infrastructure related to public transportation.

[0073] Among them, in the Well to Tank (WTT) stage corresponding to public transportation 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; the details are as follows:

[0074] The calculation process of the total carbon emissions in the crude oil extraction and transportation stage includes: first, collect the crude oil import and export and proportion data of the research city, and obtain the origin, export port and import port location of the crude oil. Calculate the pipeline transportation distance and measure the sea transportation distance in combination with the pipeline spatial distribution data. Obtain the refinery location data of the research city, and use the map platform to calculate the road transportation distance from the port to the refinery. Combined with the pipeline and sea transportation carbon emission factors, calculate the total carbon emissions in the crude oil extraction and transportation stage.

[0075] The calculation process of the total carbon emissions from diesel refining, transportation and storage includes: using local electricity and heat carbon emission factors to calculate the emissions from the crude oil refining stage. Using point of interest (POI) distribution data, the road transportation distance from the refinery to the diesel station is determined to estimate the emissions from the transportation stage. Combined with the freight carbon emission factor, the total carbon emissions from diesel refining, transportation and storage are calculated.

[0076] Although electric buses are almost CO2-free from the tank to wheels (TTW) stage, 2 Emissions, its WTT phase CO 2The emissions are still not negligible. In this example, the carbon emission factor of the oil well to oil tank stage corresponding to the electric bus is calculated based on the vehicle traction and the carbon emission factor of the local power grid; the expression is as follows:

[0077]

[0078]

[0079]

[0080] In the formula, represents the vehicle traction (N), which is affected by bus size, driving speed, road slope and passenger capacity; is the rolling resistance coefficient of the vehicle, is the vehicle mass (kg), Indicates the bus passenger capacity, is the mass of a single standard passenger (kg); is the local gravitational acceleration (m / s 2 ), represents the slope of link 𝑘, is the inertia adjustment factor; is the vehicle's air resistance coefficient; is the frontal area of ​​the vehicle (m²); is the local air density (kg / m³); represents the speed of bus v when it passes through link k during its d-th departure; is the carbon emission factor of the local power grid (g / Wh); It is energy Total power generation; It is energy Carbon emission factor per unit of electricity generation (g / Wh); , , are the efficiencies of the inverter, controller, and motor, respectively; is the auxiliary power (W); , They are grid transmission efficiency and charging efficiency respectively; is the regenerative braking energy factor (Wh / km).

[0081] The calculation process of the carbon emission factor of buses in their entire life cycle includes: based on the number of buses of target size running on the bus line, the total carbon emissions of buses of target size in the material production stage, the manufacturing stage, the maintenance stage, and the carbon emissions of the disposal stage, the average travel distance of passengers on the bus line, and the average daily passenger volume, the carbon emission factor of the bus life cycle is calculated; specifically, for the line The carbon emission factor of buses in their entire life cycle (i.e. production, maintenance and disposal stages) is ,g / (pax×km)), the expression is as follows:

[0082]

[0083] In the formula, Indicates line The size type that runs on The number of public transport vehicles; For size type Total carbon emissions from public transport during the material production and processing phase, including carbon emissions from the production and processing of various materials (such as steel, aluminum alloy, glass, plastic, batteries, etc.) used in the manufacturing of public transport; For size type Total carbon emissions of buses during the manufacturing and assembly process, including electricity consumption of production lines, welding, painting, heat treatment and other processes, as well as energy consumption required for the operation of manufacturing equipment; For size type Carbon emissions from public transport during the maintenance phase, including carbon emissions from the production, transportation and installation of parts (such as tires, refrigerants, lead-acid batteries, etc.) that are replaced or supplemented during the maintenance process; Indicates the size type is Carbon emissions from public transport vehicles during the disposal phase, including indirect carbon emissions from the recycling, treatment or disposal of waste (such as used parts, engine oil, coolant, etc.) generated during maintenance; For size type The expected service life of public transport vehicles; and Line The average distance travelled by passengers and the average daily passenger volume.

[0084] Among them, the infrastructure carbon emission factor of public transportation vehicles ( ,g / (pax×km)) calculation process includes: calculating the infrastructure carbon emission factor of public transportation vehicles according to the corresponding number of bus stations, parking lots and charging stations in the target city, the total carbon emissions of the infrastructure over its entire life cycle, and its service life; the expression is as follows:

[0085]

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

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

[0088] Among them, the calculation process of the individual-level public transportation carbon emission factor includes: calculating the individual-level public transportation carbon emission factor based on the life cycle emission factor of each individual trip and the total travel distance of each individual trip, and the life cycle emission factor of each individual trip is calculated based on the travel route, passenger capacity, carbon emission factor of fuel combustion, cold start carbon emission factor, lubricant carbon emission factor, carbon emission factor of the oil well to tank stage corresponding to the electric bus, carbon emission factor of the bus in the entire life cycle, and carbon emission factor of the bus infrastructure.

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

[0090]

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

[0092]

[0093] In the formula, Indicates the travel path. represents the links 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 lubricant carbon emission factor, represents the carbon emission factor from the oil well to the oil tank corresponding to the electric bus, represents the carbon emission factor of public transport vehicles in their entire life cycle, represents the infrastructure carbon emission factor of public transport, Indicates passenger capacity.

[0096] It should be noted that this case study is about the impact of individual travel behavior (including travel distance, travel purpose and travel frequency, etc.) The impact of public transportation on the environment can be analyzed to develop emission reduction strategies that meet individual preferences. For example, by integrating with the Mobility as a Service (MaaS) platform, public transportation systems can be integrated with other modes of transportation to reduce emissions. Figure 2 The relationship between the average travel distance and the individual-level public transportation carbon emission factor (individual-level PBF) is presented. It can be found that the individual-level PBF roughly presents a log-normal distribution. About 79% of the individuals have a PBF of 10 1.5 Up to 10 2.1 g / (pax×km). A small number of individuals have PBF lower than 10 1.5 g / (pax×km), which is mainly due to the fact that these individuals are long-distance commuters. There is an inverse relationship between PBF and travel distance, which is expressed as: , and the R² value is 0.68. This inverse relationship may be related to individual travel behavior preferences and the phenomenon of separation of work and residence in cities.

[0097] Among them, the link-level bus carbon emission factor ( , g / (pax×km)) calculation process includes: based on the individual set passing through the target link during the target period, passenger capacity, carbon emission factor of fuel combustion, cold start carbon emission factor, lubricant carbon emission factor, carbon emission factor of the oil well to tank stage corresponding to electric buses, carbon emission factor of buses in the whole life cycle, and carbon emission factor of infrastructure of buses; the expression is as follows:

[0098]

[0099] In the formula, represents the bus carbon emission factor of link k in 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 from the oil well to the oil tank corresponding to the electric bus, represents the carbon emission factor of public transport vehicles in their entire life cycle, represents the infrastructure carbon emission factor of public transport, Indicates the passenger capacity, represents the set of individuals passing through link k within time period t.

[0100] It should be noted that this example calculates the spatial distribution of link-level PBF during peak and off-peak hours. During peak hours, the PBF of links 1-6 is significantly lower than that of other links, mainly due to the increase in cross-regional commuting demand, which improves the utilization efficiency of public transportation resources. In contrast, during off-peak hours, except for link 1, the PBF of these links increases significantly, because the passenger load factor 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; and (2) the frequency of bus departures decreases.

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

[0102]

[0103] In the formula, Indicates the line in time period t The line-level bus carbon emission factor.

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

[0105]

[0106] In the formula, represents the grid-level public transportation carbon emission factor corresponding to the grid g in time period t, Indicates link With Grid The spatial intersection operation.

[0107] It should be noted that the link With Grid Spatial intersection operation It is not a traditional collection operation. With Grid Spatial intersection operation For the link With Grid When the outer boundaries overlap, only the grid is retained. Inner part.

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

[0109] Among them, the regional public transportation carbon emission factor ( , g / (pax×km)) calculation process includes: the regional bus carbon emission factor of region r in time period t is consistent with the derivation process of line-level bus carbon emission factor and grid-level bus carbon emission factor. The regions include but are not limited to different scales such as community, street, county, city or province, so as to better analyze the spatial distribution pattern of PBF at different regional scales. This example calculates the PBF estimation results at the street and county levels, and the results show that the PBF in the surrounding areas of the old city increases significantly during peak hours. Figure 3 The temporal distribution characteristics of average PBF, average bus speed and average passenger volume are presented. The results show that the average PBF increases significantly during non-peak hours and decreases during peak hours. This finding is in contrast to current studies on bus carbon emission estimates, which usually use static carbon emission factors. This approach may lead to overestimation or underestimation of bus system emissions and cannot accurately reflect its spatiotemporal dynamic characteristics. At the same time, the average bus speed also shows a similar trend. However, the average passenger volume shows the opposite change pattern. Compared with the peak period, the average PBF during the off-peak period increased by 13.3%, the average bus speed increased by 13.5%, and the average passenger volume decreased by 21.1%.

[0110] Furthermore, this example also conducts cross-city validation analysis; specifically, due to differences in city size, direct comparison of bus carbon emissions (BCE) at the city level is usually meaningless. Therefore, the PBF indicator provided in this example is more suitable for cross-city validation analysis. If there is no significant order of magnitude difference between the PBFs of different cities, the estimation method can be considered reliable, which is an advantage that the BCE indicator cannot provide. Although bus accessibility and service quality in different cities may lead to slight differences in 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 ​​of scopes I, II, and III are 50.35, 55.99, and 72.41 g / (pax×km), respectively. The PBF of scope 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 of scope III is 43.81% higher than that of scope I, which shows that only considering the fuel combustion emissions of the bus system and the WTT phase emissions of electric buses will lead to significant underestimation.

[0111] Table 1. Average PBF comparison table

[0112]

[0113] Step S5, calculating the carbon inequality index according to the number of individuals in the sample, the life cycle unit passenger public transportation carbon emission factor corresponding to each individual, the average life cycle unit passenger public transportation carbon emission factor, the carbon fairness benchmark value, and the individual's comprehensive carbon weight.

[0114] Specifically, the Carbon Inequality Index The expression is as follows:

[0115]

[0116] In the formula, is the total number of individuals in the sample, is the life cycle unit passenger public transportation carbon emission factor of individual u, is the life cycle unit passenger public transport carbon emission factor of individual u', is the average life cycle unit passenger bus carbon emission factor of the sample, is the regional or national carbon equity benchmark value (in this example, the carbon emission factor per passenger per capita of public transportation that meets the carbon standards 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 is The expression is as follows:

[0118]

[0119] Among them, the comprehensive carbon weight of individual u is The expression is 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 of travel, α>0, β>0; is the average monthly travel frequency of individual u, is the life cycle unit passenger public transportation carbon emission factor of individual j, is the average monthly travel frequency of individual j.

[0122] It should be noted that in order to more comprehensively reflect the uneven distribution of PBF among different individuals, this example proposes a carbon inequality index based on the classic Gini coefficient, which is denoted as The carbon inequality index combines the two dimensions of individual carbon emission intensity and behavioral activity. The specific improvements include the following three points: (1) introducing a logarithmic scaling factor to enhance the sensitivity of carbon emissions at different orders of magnitude, especially the ability to capture the differences between low-carbon and high-carbon individuals; (2) introducing a carbon fairness benchmark value As a comparison baseline, it is used to measure the degree of "carbon excess" or "carbon saving" of each individual; (3) The dual sensitivity parameters α and β are introduced to adjust the weights of high carbon emission individuals and high frequency travel individuals, respectively, so as 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 through the weight function Flexibly adjust the level of attention given to different groups of people to support the design of more equitable and targeted emission reduction policies.

[0123] Furthermore, existing studies have mostly focused on the assessment of the emission reduction potential of BCE, while less attention has been paid to the emission reduction potential of PBF. This example can evaluate the emission reduction potential of PBF under different emission reduction strategies by setting relevant model parameters. For example, by setting the proportion of electric buses to 100%, the PBF emission reduction potential of the full electrification of the bus system can be estimated. In addition, by increasing the proportion of wind, solar and hydropower in the power grid, the PBF emission reduction potential under the decarbonization scenario of the power grid 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 phase. However, this may lead to increased carbon emissions in the production and scrapping stages. By setting parameters such as the weight of high-strength materials and carbon emission factors in the model, the PBF emission reduction potential of lightweight vehicles can be evaluated.

[0124] Accordingly, the present application also provides an electronic device, comprising: 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 above-mentioned method for constructing a bus carbon emission factor library per unit passenger. Figure 4 As shown, a hardware structure diagram of any device with data processing capability for the method for constructing a unit passenger public transportation carbon emission factor library provided by an embodiment of the present invention is shown, except Figure 4 In addition to the processor, memory and network interface shown, any device with data processing capability in which the apparatus in the embodiment is located may also include other hardware, generally based on the actual functions of the device with data processing capability, which will not be described in detail.

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

[0126] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for constructing a public transportation carbon emission factor library per passenger, characterized in that: The method comprises: Obtain the bus network and estimate bus attribute parameters including bus passenger capacity, bus speed, and bus size; 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, so as to estimate the direct carbon emission of the bus; Based on the bus network and bus attribute parameters, calculate the total carbon emissions from the oil well to the tank corresponding to the bus, the carbon emission factor from the oil well to the tank corresponding to the electric bus, the carbon emission factor of the bus's entire life cycle and / or the carbon emission factor of the bus's infrastructure, so as to estimate the indirect carbon emissions of the bus; According to the estimated direct carbon emissions and indirect carbon emissions of buses, the individual-level bus carbon emission factor is calculated based on the multiple travel information of the same individual; based on the matching relationship between individuals and links, the link-level bus carbon emission factor and line-level bus carbon emission factor are estimated; based on the spatial intersection operation, the grid-level bus carbon emission factor and regional-level bus carbon emission factor are estimated.

2. The method for constructing a public transportation carbon emission factor library per unit passenger according to claim 1, characterized in that: The process of obtaining the bus network includes: Based on the bus stop GIS data, the bus stops are projected onto the bus routes, thereby correcting the bus stops in the bus route GIS data; According to the corrected bus stops, the bus routes in the bus route GIS data are truncated to obtain the links of the bus network; Obtain 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; obtain the average transfer time between two bus lines and use this as the transfer weight; The bus network is constructed based on the corrected bus stops, links of the bus network, link weights, and transfer weights.

3. The method for constructing a public transportation carbon emission factor library per unit passenger according to claim 1, characterized in that: The process of estimating bus ridership includes: Determine the maximum and minimum operating speeds of bus routes based on bus operation data; Assuming that bus v runs at the maximum speed and the minimum speed respectively, calculate the earliest arrival time and the latest arrival time of bus v at station s; the passengers who get on the bus within the time window between the earliest arrival time and the latest arrival time are marked as candidate passengers; The average travel speed of the candidate passengers is taken as the approximate travel speed of the bus, and the passengers who swipe their cards within the time interval between the arrival of two adjacent buses are taken as the actual passengers of the previous bus. The passenger capacity of the bus is calculated based on the distribution of the starting and ending points of the actual passengers and the expansion coefficient.

4. The method for constructing a public transportation carbon emission factor library per unit passenger according to claim 1 or 3, characterized in that: The process of estimating bus size includes: Get the passenger capacity distribution of bus routes; Isolation forest and local outlier factor are used to identify the passenger capacity threshold of bus lines, and the first threshold and the second threshold are obtained; defining the maximum passenger capacity threshold as the minimum value of the first threshold and the second threshold; Obtain the design passenger capacity of medium-duty buses, the design passenger capacity of standard buses, and the design passenger capacity of articulated buses; For the bus size on the bus route to be identified, when the maximum passenger capacity threshold is less than or equal to the design passenger capacity of a medium-sized bus, the bus size shall be the design passenger capacity of a medium-sized bus; when the maximum passenger capacity threshold is greater than the design passenger capacity of a medium-sized bus and less than or equal to the design passenger capacity of a standard bus, the bus size shall be the design passenger capacity of a standard bus; when the maximum passenger capacity threshold is greater than the design passenger capacity of a standard bus and less than or equal to the design passenger capacity of an articulated bus, the bus size shall be the design passenger capacity of an articulated bus.

5. The method for constructing a public transportation carbon emission factor library per unit passenger according to claim 1, characterized in that: The energy consumption factor of the public transport vehicle is calculated based on the fuel type, bus size, load factor, road slope and emission standard, bus speed, and emission reduction factor; The fuel consumption factor is the product of the energy consumption factor of the public transport vehicle and the energy conversion factor; The carbon emission factor of the fuel combustion is calculated based on the fuel consumption factor, the hydrogen-carbon ratio, and the oxygen-carbon ratio; The cold start carbon emission factor is calculated based on the vehicle delivery speed, ambient temperature, and the carbon emission factor of fuel combustion; The lubricant carbon emission factor is the product of the lubricant and fuel consumption ratio and the fuel consumption factor.

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

7. The method for constructing a public transportation carbon emission factor library per unit passenger according to claim 1, characterized in that: The method further comprises: The carbon inequality index is calculated based on the number of individuals in the sample, the life cycle unit passenger public transportation carbon emission factor corresponding to each individual, the average life cycle unit passenger public transportation carbon emission factor, the carbon fairness benchmark value, and the individual's comprehensive carbon weight.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs that can be executed 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 public transportation carbon emission factor library per unit 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: When the computer program is executed by a processor, the computer program implements the method for constructing a public transportation carbon emission factor library per unit passenger according to any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the method for constructing a public transportation carbon emission factor library per unit passenger as described in any one of claims 1-7 is implemented.

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