Regional traffic carbon emission accounting method and device, electronic equipment and storage medium

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for calculating regional traffic carbon emissions. The method includes generating a resident travel origin-destination matrix based on mobile phone signaling location data to identify resident travel activity chains, and generating a freight travel origin-destination matrix based on vehicle global positioning system trajectory data to identify freight logistics networks. A total travel equivalent matrix is ​​generated based on the resident and freight travel origin-destination matrices; dynamic simulation of the regional traffic system is performed, and a road network state sequence is iteratively obtained. Based on the road network state sequence and a speed-dependent emission factor model in vehicle attribute and emission factor data, the traffic carbon emissions of each road segment within the target area are calculated. The traffic carbon emissions of each road segment are allocated according to the spatial mapping relationship between the road segment and spatial grid units to generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area, thereby improving the accuracy of regional traffic carbon emission calculation.

Description

Technical Field

[0001] This disclosure relates to the field of carbon emission accounting technology, and in particular to a method, apparatus, electronic device and storage medium for regional transportation carbon emission accounting. Background Technology

[0002] With accelerated urbanization and increased traffic intensity, the transportation sector has become one of the major sources of global carbon emissions, especially in urban clusters. Carbon emission accounting methods based on macro-level energy consumption are currently the mainstream approach for carbon emission assessment in the transportation sector both internationally and domestically, and are particularly suitable for national, provincial, or city-level macro-level carbon emission total accounting and trend analysis.

[0003] However, current carbon emission accounting methods rely mainly on historical data retrospective calculations and static extrapolation from energy terminals. This not only makes it difficult to adapt to the dynamic changes in urban traffic, but also makes it difficult to integrate multi-source data on actual traffic. It fails to capture the fluctuations and spatial heterogeneity of carbon emissions in urban traffic at the micro scale, ultimately reducing the accuracy of regional traffic carbon emission accounting. Summary of the Invention

[0004] The main objective of this disclosure is to provide a method, apparatus, electronic device, and storage medium for calculating regional transportation carbon emissions, which can improve the accuracy of regional transportation carbon emission calculation.

[0005] To achieve the above objectives, a first aspect of this disclosure provides a method for calculating regional transportation carbon emissions, comprising: Acquire multi-source spatiotemporal data of the target area, wherein the multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle global positioning system trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data; Based on the mobile phone signaling location data, the stop points are identified through spatiotemporal clustering and a resident travel activity chain is constructed. The destination type is labeled by combining the points of interest and land use data. The resident travel origin and destination matrix is ​​generated by expanding the sampling according to the mobile phone signaling sampling rate. The stop clusters are detected based on the vehicle global positioning system trajectory data. The logistics facilities are identified through land use function calibration and a freight logistics network is constructed. The freight travel origin and destination matrix is ​​generated by calibrating according to the grid freight intensity. The origin-destination matrix of residents' trips and the origin-destination matrix of freight trips are unified to the same spatial grid and time slice, and after being expanded, corrected and converted into standard car equivalents, a total trip equivalent matrix is ​​generated. The total trip equivalent matrix is ​​assigned to the road network corresponding to the road network vector data to obtain the initial flow and initial average speed of each road segment. The generalized trip cost between grids in the road network is determined, and a dynamic simulation of the regional traffic system is performed to obtain a new total trip equivalent matrix. The process is iterated again until the rate of change of the average flow of the road network between two adjacent iterations is less than a preset convergence threshold. The road network state sequence is obtained based on the flow and average speed of each road segment after iteration. According to the preset vehicle type and fuel composition ratio, the traffic flow of each road segment is decomposed into sub-flows of different vehicle types and fuel types. The average speed of each road segment is input into the speed-dependent emission factor model to calculate the real-time emission factor. Based on the sub-flow and the real-time emission factor, the carbon emissions of each vehicle type and fuel type in the corresponding road segment are calculated. The carbon emissions of all vehicle types and fuel types are accumulated to obtain the traffic carbon emissions of each road segment in the target area. The traffic carbon emissions of each road segment are allocated according to the spatial mapping relationship between the road segment and the spatial grid unit to generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area.

[0006] In some embodiments, the step of identifying stop points and constructing resident travel activity chains based on the mobile phone signaling location data through spatiotemporal clustering, labeling destination types by combining the points of interest and land use data, and generating a resident travel origin-destination matrix by expanding the sampling according to the mobile phone signaling sampling rate includes: The mobile phone signaling location data is subjected to spatiotemporal clustering to identify the user's stop points and construct the resident travel activity chain; Using the points of interest and land use data, the activity destination types of each stop in the residents' travel activity chain are identified, and the origin and destination attributes of residents' travel are marked. After the annotation is completed, the trip between adjacent stops is defined as a resident trip. The origin and destination grid of all resident trips is counted and expanded according to the mobile phone signaling sampling rate to generate a resident trip origin and destination matrix.

[0007] In some embodiments, the process of detecting dwell clusters based on vehicle GPS trajectory data, identifying logistics facilities and constructing a freight logistics network through land use function calibration, and generating a freight trip origin-destination matrix based on grid freight intensity calibration includes: The vehicle's GPS trajectory data is used to detect dwell clusters, and the detected dwell clusters are calibrated for land use function using the points of interest and land use data to identify the spatial location and type of logistics facilities and construct a freight logistics network. Based on the freight logistics network, the routes of vehicles between adjacent logistics facilities are extracted, and the data is calibrated according to the grid freight intensity to generate a freight trip origin-destination matrix.

[0008] In some embodiments, the method further includes: unifying the resident travel origin-destination matrix and the freight travel origin-destination matrix to the same spatial grid and time slice; performing expansion correction on the two types of origin-destination matrices based on mobile phone signaling sample coverage and freight vehicle trajectory sample coverage, respectively; converting the corrected resident travel origin-destination matrix and freight travel origin-destination matrix into standard passenger car equivalents to generate a total travel equivalent matrix; and performing fusion calibration on the resident travel origin-destination matrix and the freight travel origin-destination matrix based on the deviation between simulated traffic flow and observed traffic flow.

[0009] In some embodiments, the dynamic simulation of the regional traffic system yields a new total trip equivalent matrix, and the process is iterated until the rate of change of the average traffic flow in the road network between two adjacent iterations is less than a preset convergence threshold. A road network state sequence is then obtained based on the traffic flow and average speed of each road segment after iteration, including: The residential accessibility index and employment accessibility index of each grid are calculated based on the generalized travel cost, and the residential population distribution and employment distribution of each grid are updated according to the residential accessibility index and the employment accessibility index. An updated resident travel origin-destination matrix and an updated freight travel origin-destination matrix are generated based on the updated resident population distribution and the updated employment position distribution. The updated resident travel origin-destination matrix and the updated freight travel origin-destination matrix are converted into a new total travel equivalent matrix, and the process is iterated again until the average traffic flow change rate of the road network in two adjacent iterations is less than a preset convergence threshold. The road network state sequence is obtained based on the traffic flow and average speed of each road segment after iteration.

[0010] In some embodiments, generating updated resident travel origin-destination matrix and updated freight travel origin-destination matrix based on the updated resident population distribution and employment distribution includes: Based on the updated resident population distribution and employment distribution, the trip generation and attraction of each grid are recalculated using the trip generation rate; The origin and destination matrix of residents' trips is generated by inputting the trip generation and attraction into a preset dual-constraint gravity model, and the origin and destination matrix of freight trips is updated synchronously through the freight demand elasticity model.

[0011] In some embodiments, the allocation of traffic carbon emissions for each road segment according to the spatial mapping relationship between road segments and spatial grid units, generating a gridded spatiotemporal inventory of traffic carbon emissions within the target area, includes: Establish a spatial mapping relationship between road segments and spatial grid units, calculate the length ratio of each road segment in each intersecting grid, and allocate the traffic carbon emissions of each road segment to the corresponding spatial grid unit according to the length ratio. The total carbon emissions of the corresponding grid are obtained by summing up the carbon emissions of all road segments allocated in each spatial grid unit. A grid-level carbon emission matrix is ​​generated based on the total carbon emissions of each grid, and a gridded spatiotemporal inventory of transportation carbon emissions containing time, space and emission information is formed within the target area through the grid-level carbon emission matrix.

[0012] In some embodiments, after generating a gridded spatiotemporal inventory of traffic carbon emissions within the target area, the regional traffic carbon emission accounting method further includes: Obtain the emission reduction policy parameters to be evaluated, and rerun the regional transportation system dynamic simulation and carbon emission calculation process based on the emission reduction policy parameters to generate a gridded spatiotemporal inventory of target transportation carbon emissions within the target area under the policy scenario; By comparing the aforementioned spatiotemporal inventory of transportation carbon emissions with the target spatiotemporal inventory of transportation carbon emissions, a corresponding comprehensive policy assessment report is generated.

[0013] To achieve the above objectives, a second aspect of this disclosure provides a regional transportation carbon emission accounting device, comprising: The traffic data acquisition module is used to acquire multi-source spatiotemporal data of the target area. The multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle global positioning system trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data. The origin-destination matrix generation module is used to identify stop points and construct resident travel activity chains based on the mobile phone signaling positioning data through spatiotemporal clustering, label destination types by combining the points of interest and land use data, expand the sampling according to the mobile phone signaling sampling rate to generate a resident travel origin-destination matrix, detect stop clusters based on vehicle global positioning system trajectory data, identify logistics facilities and construct a freight logistics network through land use function calibration, and generate a freight travel origin-destination matrix according to grid freight intensity calibration. The total trip equivalent matrix generation module is used to unify the resident trip origin-destination matrix and the freight trip origin-destination matrix to the same spatial grid and time slice, and after expanding and correcting them respectively and converting them into standard car equivalents, generate the total trip equivalent matrix. The traffic dynamic simulation module is used to allocate the total trip equivalent matrix to the road network corresponding to the road network vector data, obtain the initial flow and initial average speed of each road segment, determine the generalized trip cost between grids in the road network, and perform dynamic simulation of the regional traffic system to obtain a new total trip equivalent matrix. It is then iterated until the rate of change of the average flow of the road network between two adjacent iterations is less than a preset convergence threshold. Based on the flow and average speed of each road segment after iteration, the road network state sequence is obtained. The carbon emission determination module is used to decompose the traffic flow of each road segment into sub-flows of different vehicle types and fuel types according to a preset vehicle type and fuel composition ratio, input the average speed of each road segment into the speed-dependent emission factor model to calculate the real-time emission factor, and calculate the carbon emission of each vehicle type and fuel type in the corresponding road segment based on the sub-flow and the real-time emission factor, and sum the carbon emission of all vehicle types and fuel types to obtain the traffic carbon emission of each road segment in the target area. The carbon emission accounting module is used to allocate the traffic carbon emissions of each road segment according to the spatial mapping relationship between the road segment and the spatial grid unit, and generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area.

[0014] To achieve the above objectives, a third aspect of this disclosure provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the regional transportation carbon emission accounting method described in the first aspect embodiment.

[0015] To achieve the above objectives, a fourth aspect of the present disclosure provides a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the regional transportation carbon emission accounting method described in the first aspect of the present disclosure.

[0016] The beneficial effects of the embodiments disclosed herein include: By comprehensively acquiring multi-source spatiotemporal data covering resident travel, freight logistics, road networks, vehicle attributes and emission factors, points of interest, and land use, a complete data foundation covering the entire transportation chain was constructed. Subsequently, based on mobile phone signaling location data, a resident travel origin-destination matrix was generated by identifying resident travel activity chains, and based on vehicle GPS trajectory data, a freight logistics network origin-destination matrix was generated by identifying freight travel origin-destination matrices. This transformed the raw location data into structured data that accurately reflects real traffic demand, replacing the static travel assumptions based on statistical averaging in traditional methods. Next, dynamic simulation of the regional transportation system was conducted, and the road network state sequence was obtained iteratively. The system enables a dynamic depiction of the real-time operation of urban traffic, accurately adapting to dynamic changes in urban traffic. Based on the road network state sequence containing real-time vehicle speed information, and combined with a speed-dependent emission factor model, the system calculates the traffic carbon emissions of each road segment, fully considering the significant impact of vehicle speed on carbon emissions, and accurately capturing the carbon emission fluctuations of different road segments and time periods at the microscale. Finally, the carbon emissions of each road segment are allocated to grid cells according to spatial mapping relationships, generating a gridded spatiotemporal inventory of traffic carbon emissions, clearly presenting the spatial heterogeneity of carbon emissions within the region, and ultimately improving the accuracy of regional traffic carbon emission accounting. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of an application environment for the regional transportation carbon emission accounting method provided in this embodiment of the disclosure; Figure 2 This is a schematic flowchart of the regional transportation carbon emission accounting method provided in the embodiments of this disclosure; Figure 3 yes Figure 2 A flowchart further includes step S102; Figure 4 yes Figure 2 Another process diagram further included in step S102; Figure 5 yes Figure 2 A flowchart further includes step S104; Figure 6 yes Figure 5 A flowchart further includes step S401; Figure 7 yes Figure 5 A flowchart further includes step S402; Figure 8 yes Figure 2 A flowchart further includes step S106; Figure 9 yes Figure 2 A flowchart illustrating the further steps following step S106; Figure 10 This is a schematic diagram of the functional modules of the regional transportation carbon emission accounting device provided in this embodiment of the disclosure; Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this disclosure. Detailed Implementation

[0018] The accompanying drawings in the embodiments clearly and completely describe the technical solutions in the embodiments of this disclosure. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0019] It is understood that in the specific embodiments of this disclosure, which involve retrieving multi-source spatiotemporal data and related data, when the above embodiments of this disclosure are applied to specific products or technologies, permission or consent from the target is required, and the collection, use and processing of related data must comply with relevant laws, regulations and standards.

[0020] Furthermore, when this embodiment of the disclosure needs to retrieve multi-source spatiotemporal data and related data, it will obtain separate permission or separate consent for the multi-source spatiotemporal data and related data through pop-up windows or redirection to a confirmation page. After clearly obtaining separate permission or separate consent for the multi-source spatiotemporal data and related data, it will then obtain the necessary multi-source spatiotemporal data and related data for enabling the embodiments of this disclosure to operate normally.

[0021] In this disclosure, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0022] Before providing a further detailed description of the embodiments of this disclosure, the terms and concepts used in these embodiments are explained, and they are subject to the following interpretations: Emission factor: The amount of pollutants emitted per unit of activity level (e.g., driving 1 kilometer), which is usually related to vehicle type, fuel type, and driving speed.

[0023] Speed-emission model: A mathematical model that describes the relationship between vehicle emission rate and instantaneous speed, usually in the form of a polynomial or exponential function.

[0024] Broadly defined travel costs: comprehensively reflect the total cost of a trip, including time costs (converted to currency), monetary costs (such as fuel costs and toll fees), and additional costs associated with congestion.

[0025] User equilibrium allocation: a principle of traffic allocation that assumes all travelers choose the path most advantageous to them, eventually reaching a stable state in which no traveler can reduce their travel costs by unilaterally changing their path.

[0026] Bottom-up emissions inventory: A method for estimating total emissions by statistically analyzing the activity level and emission factors of each emission source and aggregating them step by step at the micro level, corresponding to the "top-down" approach in macroeconomic energy statistics.

[0027] Demand management: Regulating the spatial and temporal distribution of traffic demand through economic and administrative means, such as congestion pricing and parking management.

[0028] Origin-destination matrix: This refers to a matrix in which all traffic zones are sorted by rows (origin zones) and columns (destination zones), and the number of residents or vehicles traveling between any two zones (OD volume) are the elements.

[0029] With the acceleration of urbanization and the intensification of transportation activities, the transportation sector has become one of the major sources of global carbon emissions, especially in urban agglomerations. Currently, transportation carbon emissions in highly urbanized areas are characterized by a high base, rapid growth, and multiple drivers, urgently requiring high-precision, interpretable, and scalable technologies for quantifying transportation carbon emissions to support scientific decision-making and comprehensive governance.

[0030] Carbon emission accounting based on macroeconomic energy consumption is currently the mainstream method for assessing carbon emissions in the transportation sector both internationally and domestically. It is particularly suitable for calculating and analyzing total carbon emissions and trends at the national, provincial, or city levels. This method uses energy consumption statistics combined with emission factors to extrapolate total carbon emissions in the transportation sector. Its core logic is to treat the transportation sector as a "black box," inputting energy consumption figures such as gasoline, diesel, and aviation kerosene, multiplying them by the corresponding carbon emission factors, and outputting the total carbon emissions.

[0031] However, current carbon emission accounting technologies primarily rely on macro-level energy statistics, resulting in low spatiotemporal resolution and an inability to achieve dynamic monitoring at a fine scale. Existing methods typically calculate emissions based on annual or regional total energy consumption, making it difficult to capture carbon emission fluctuations and spatial heterogeneity at micro-scales such as road segments, time periods, and vehicle types. Consequently, they cannot support the refined management and decision-making needs for hotspot identification, peak-hour optimization, and travel behavior impact assessment.

[0032] Furthermore, current carbon emission accounting technologies are disconnected from transportation system elements, making it difficult to reveal the carbon emission formation mechanism under the complex interaction of people, vehicles, roads, and areas. Existing methods mostly rely on static estimations from energy end-use, failing to integrate multi-source data such as residents' travel behavior, real-time vehicle operating conditions, road network operation status, and land use structure. This results in the analysis of carbon emission drivers remaining at the macro-level of correlation, lacking a systematic analysis of the coupling effects and nonlinear impacts of multiple factors.

[0033] Furthermore, current carbon emission accounting technologies lack the ability to dynamically simulate future policy scenarios and system evolution. Existing methods primarily rely on historical data for retrospective accounting, which cannot simulate carbon emission responses and predict effects of policy interventions and system changes such as fleet electrification, road network optimization, traffic demand management, and spatial restructuring. This limits the quantitative design and dynamic optimization of multi-strategy, multi-stage emission reduction pathways.

[0034] Therefore, current carbon emission accounting methods ultimately reduce the accuracy of regional transportation carbon emission accounting. To address the above problems, this disclosure proposes a regional transportation carbon emission accounting method, apparatus, electronic device, and storage medium that can improve the accuracy of regional transportation carbon emission accounting.

[0035] Please see Figure 1 , Figure 1 A schematic diagram of the implementation environment for the regional transportation carbon emission accounting method provided in this embodiment of the disclosure includes: terminal 11 and server 12.

[0036] For example, server 12 can obtain multi-source spatiotemporal data of the target area from terminal 11. The multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle GPS trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data. Based on the mobile phone signaling positioning data, it identifies stop points through spatiotemporal clustering and constructs resident travel activity chains. Combined with points of interest and land use data, it labels destination types and generates a resident travel origin-destination matrix by expanding the sampling rate according to the mobile phone signaling. Based on the vehicle GPS trajectory data, it detects stop clusters, identifies logistics facilities through land use function calibration, and constructs a freight logistics network. It generates a freight travel origin-destination matrix by calibrating according to grid freight intensity. It unifies the resident travel origin-destination matrix and the freight travel origin-destination matrix to the same spatial grid and time slice, expands and corrects them respectively, and converts them into standard car equivalents to generate a total travel equivalent matrix. The total travel equivalent matrix is ​​then allocated to the corresponding road network vector data. The road network is analyzed to obtain the initial flow rate and initial average speed of each road segment. The generalized travel cost between grids in the road network is determined, and a new total travel equivalent matrix is ​​obtained through dynamic simulation of the regional traffic system. The simulation is iterated until the rate of change of the average flow rate of the road network between two adjacent iterations is less than a preset convergence threshold. The road network state sequence is obtained based on the flow rate and average speed of each road segment after iteration. The flow rate of each road segment is decomposed into sub-flow rates of different vehicle types and fuel types according to the preset vehicle type and fuel composition ratio. The average speed of each road segment is input into the speed-dependent emission factor model to calculate the real-time emission factor. Based on the sub-flow rate and the real-time emission factor, the carbon emissions of each vehicle type and fuel type in the corresponding road segment are calculated. The carbon emissions of all vehicle types and fuel types are accumulated to obtain the traffic carbon emissions of each road segment in the target area. The traffic carbon emissions of each road segment are allocated according to the spatial mapping relationship between the road segment and the spatial grid unit to generate a gridded spatiotemporal inventory of traffic carbon emissions in the target area.

[0037] Terminal 11 can be a mobile phone, computer, smart voice interaction device, smart wearable device, smart home appliance, vehicle terminal, etc., but is not limited to these. Terminal 11 can also independently execute the regional traffic carbon emission accounting method. Terminal 11 and server 12 can be directly or indirectly connected through wired or wireless communication, and this embodiment of the disclosure does not impose any limitations.

[0038] Server 12 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Additionally, server 12 can also be a node server in a blockchain network.

[0039] It should be noted that, Figure 1 The schematic diagram of the implementation environment shown is merely an example. The scenarios described in this disclosure are intended to more clearly illustrate the technical solutions of this disclosure and do not constitute a limitation on the technical solutions provided in this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new business scenarios, the technical solutions provided in this disclosure are also applicable to similar technical problems.

[0040] Please see Figure 2 , Figure 2 This is a flowchart illustrating the regional transportation carbon emission accounting method provided in this embodiment. This regional transportation carbon emission accounting method can be applied to the server in the above embodiment, or executed jointly by a terminal and a server. The regional transportation carbon emission accounting method includes steps S101 to S106: Step S101: Obtain multi-source spatiotemporal data of the target area. The multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle global positioning system trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data. Step S102: Based on mobile phone signaling location data, identify stop points through spatiotemporal clustering and construct resident travel activity chains. Combine points of interest and land use data to label destination types. Expand the sampling according to mobile phone signaling sampling rate to generate a resident travel origin-destination matrix. Detect stop clusters based on vehicle global positioning system trajectory data. Identify logistics facilities through land use function calibration and construct a freight logistics network. Generate a freight travel origin-destination matrix according to grid freight intensity calibration. Step S103: Unify the origin-destination matrix of residents' trips and the origin-destination matrix of freight trips to the same spatial grid and time slice, expand and correct them respectively, and convert them into standard car equivalents to generate the total trip equivalent matrix; Step S104: Assign the total trip equivalent matrix to the road network corresponding to the road network vector data, obtain the initial flow and initial average speed of each road segment, determine the generalized trip cost between grids in the road network, and perform dynamic simulation of the regional traffic system to obtain a new total trip equivalent matrix. Iterate again until the rate of change of the average flow of the road network between two adjacent iterations is less than the preset convergence threshold, and obtain the road network state sequence based on the flow and average speed of each road segment after iteration. Step S105: Decompose the traffic flow of each road segment into sub-flows of different vehicle types and fuel types according to the preset vehicle type and fuel composition ratio. Input the average speed of each road segment into the speed-dependent emission factor model to calculate the real-time emission factor. Based on the sub-flow and the real-time emission factor, calculate the carbon emissions of each vehicle type and fuel type in the corresponding road segment. Add up the carbon emissions of all vehicle types and fuel types to obtain the traffic carbon emissions of each road segment in the target area. Step S106: Allocate the traffic carbon emissions of each road segment according to the spatial mapping relationship between the road segment and the spatial grid unit to generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area.

[0041] Regarding step S101 above, multi-source spatiotemporal data refers to a data set with spatial and temporal attributes that covers the same target area and comes from different data sources. Multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle global positioning system trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data.

[0042] Among them, mobile phone signaling location data is location sequence data automatically generated by the mobile communication network during the interaction between user equipment and base stations, containing anonymous user identifiers, timestamps, and base station numbers, which can reflect the spatial distribution and movement trajectory of the population; vehicle global positioning system trajectory data refers to spatiotemporal sequence data recorded by vehicle-mounted or portable GPS devices, containing information such as unique vehicle identifiers, high-precision latitude and longitude coordinates, timestamps, instantaneous speed, and heading angle, which can accurately reflect the microscopic driving behavior of vehicles on the road network; road network vector data refers to geographic information data that digitally represents real road infrastructure using geometric objects such as points, lines, and surfaces and their topological relationships, usually containing semantic information such as road grade, number of lanes, design speed, direction, and toll attributes; vehicle attribute and emission factor data includes vehicle registration information in the region, such as vehicle type, fuel type, emission standard, etc., as well as the pollutant emission coefficient per unit mileage for vehicles of different vehicle types, fuel types, and emission standards at different driving speed ranges; point of interest data includes category information of various urban facilities, such as shopping malls, schools, hospitals, and residential areas; and land use data refers to land use type patch data classified according to land cover and human activity. These data collectively constitute the foundational data for the entire chain, from tracing travel demand and simulating road network conditions to calculating emission factors.

[0043] It should be noted that the embodiments disclosed herein obtain multi-source spatiotemporal data covering residents' travel, freight logistics, road infrastructure, and vehicle emission characteristics, providing complete data support for subsequent step-by-step analysis from the individual activity layer to the road network layer and then to the carbon emission layer, avoiding the one-sidedness and lag of traditional methods that rely solely on macroeconomic energy statistics.

[0044] Regarding step S102 above, the resident travel origin-destination matrix, or simply the resident travel OD matrix, is a two-dimensional matrix. Rows represent origin spatial units, and columns represent destination spatial units. The matrix element values ​​are the number of trips from the origin to the destination, quantifying the intensity of resident travel exchange between different areas within the city. The freight travel origin-destination matrix, or simply the freight travel OD matrix, is similar to the resident travel OD matrix, but reflects the origin and destination demand for freight transportation.

[0045] Furthermore, in this embodiment, the target area is divided into regular square grids, each with a unique identifier and geographic coordinates. By performing spatiotemporal clustering on mobile phone signaling data, user stop points are identified. Destination types are labeled by combining points of interest (POIs) and land use data. For example, when consecutive signaling points are located in the same or adjacent grids and the duration exceeds a threshold (e.g., 15 minutes), these stop points are sorted by time to form an activity chain, and movement between adjacent stop points is defined as a trip. The origin and destination grids of all trips are statistically analyzed, and the sampling is expanded according to the mobile phone signaling sampling rate to obtain a resident travel OD matrix. In addition, for freight, points in heavy truck GPS data with speeds below a threshold and sustained for a certain duration are spatially clustered to obtain stop clusters. The locations of logistics facilities are identified by combining POIs and land use data, and the journeys of vehicles between adjacent logistics facilities are extracted to form a freight travel OD matrix.

[0046] In addition to refined activity chain extraction methods based on mobile phone signaling and vehicle GPS trajectories, alternative solutions combining traditional traffic surveys with statistical models can be employed. Specifically, when mobile phone signaling data is unavailable or trajectory data coverage is insufficient, small-sample household surveys or intersection intercept surveys can be used to obtain OD sample data of residents' trips. Using a four-stage trip generation and distribution model, based on macro-statistical data such as population, employment, and land use, regression analysis can be used to predict the trip generation and attraction of each traffic zone. Then, a gravity model or growth coefficient model can be used to calculate the OD matrix between zones. For freight travel, economic statistical data, such as industrial output and logistics throughput, can be used in conjunction with industry linkage models for estimation.

[0047] It should be noted that the embodiments of this disclosure extract activity chains and OD matrices from the original location data, transforming abstract travel demands into quantifiable spatial interaction relationships. This provides demand inputs that conform to real travel patterns for subsequent traffic system simulation, overcoming the shortcomings of traditional methods that use fixed travel rates to replace actual travel distribution.

[0048] Regarding step S103 above, this embodiment of the disclosure can further unify the resident travel origin-destination matrix and the freight travel origin-destination matrix to the same spatial grid and time slice, and perform expansion and correction on the two types of origin-destination matrices based on the mobile phone signaling sample coverage rate and the freight vehicle trajectory sample coverage rate, respectively. The corrected resident travel origin-destination matrix and freight travel origin-destination matrix are then converted into standard passenger car equivalents to generate a total travel equivalent matrix. Furthermore, based on the deviation between the simulated traffic flow and the observed traffic flow of the road segment, the resident travel origin-destination matrix and the freight travel origin-destination matrix are fused and calibrated.

[0049] Regarding step S104 above, the dynamic simulation of the regional transportation system refers to simulating the interaction between various elements of the urban system and their evolution over time by coupling the iterative process of traffic assignment, population employment distribution, land use change, and travel demand updates. The road network state sequence refers to the time series of traffic flow and travel time or average speed on each road segment within each simulation period. The traffic flow on each road segment is the number of vehicles passing through per unit time. When the road network state sequence includes travel time but not average speed, the average speed can be obtained by dividing the road segment length by the travel time. When the road network state sequence includes average speed but not travel time, the travel time can be obtained by dividing the road segment length by the average speed. Therefore, subsequent embodiments assume that the road network state sequence includes both travel time and average speed, but it can also include one of the two.

[0050] Specifically, in this embodiment, the passenger and freight travel OD matrix is ​​first converted into a total travel equivalent matrix. For example, freight vehicles can be converted using the equivalent coefficient of a standard passenger car. Then, a user equilibrium allocation model is used to allocate travel demand to the road network, obtaining the initial flow and average speed of each road segment. Next, the generalized travel cost between grids is calculated based on the average speed of road segments, and then the residential accessibility and employment accessibility of each grid are calculated. Then, the spatial distribution of population and employment is updated through an attraction model. Changes in population and employment will change the travel generation and attraction of each grid. A new OD matrix is ​​generated through a dual-constraint gravity model, forming a closed-loop iteration. The above process is repeated until the rate of change of the average flow of the road network between two adjacent iterations is less than a preset convergence threshold. At this point, the converged road network state sequence is obtained.

[0051] In addition to constructing an endogenous coupled feedback model of land use, population, traffic, and carbon emissions, a static equilibrium model can be used as an alternative. When a complete dynamic feedback system cannot be constructed or computational resources are limited, land use and population employment distribution can be treated as exogenous givens, and a static traffic assignment model can be used to perform a single allocation on a given OD matrix to obtain the road network flow and speed distribution. This approach avoids complex iterative feedback processes, significantly reduces computational complexity, and can use the BPR function to calculate road segment impedance and allocate traffic volume based on user equilibrium or system optimality principles.

[0052] It should be noted that the embodiments disclosed herein simulate the long-term evolution of urban systems under policy intervention by establishing an endogenous feedback loop of land use, population, transportation and travel. This can predict traffic demand and road network status under different future scenarios, providing a dynamic simulation platform for long-term carbon emission prediction and policy evaluation.

[0053] Regarding step S105 above, the speed-dependent emission factor model refers to the functional relationship between the emission factor and vehicle speed. This model can be in polynomial form or use localized parameters obtained from a standard database. Based on the vehicle type-fuel composition ratio, the road segment traffic flow is decomposed into sub-flows of different vehicle type and fuel type. For each road segment, its average speed is obtained by dividing the segment length by the travel time. This speed is substituted into the emission factor model for the corresponding vehicle type and fuel type to obtain the real-time emission factor. The carbon emissions of a road segment are equal to the sub-flow multiplied by the segment length and then multiplied by the emission factor. Finally, the carbon emissions of all vehicle type and fuel type are summed to obtain the total carbon emissions of that road segment during the simulation period.

[0054] In addition to emission calculation methods based on vehicle trajectory and micro-driving behavior models, aggregated models based on traffic assignment results can be used as an alternative. When it is impossible to obtain vehicle trajectory data covering the entire area or when computing resources cannot support large-scale micro-simulation, the segment-level flow rate and average speed output by the traffic assignment model can be used, combined with the macro-average emission factor curves for different vehicle types, such as the speed-emission function in the COPERT standard emission factor database, to directly calculate the segment emissions. Specifically, the emission factor per unit mileage is obtained by looking up a table or substituting it into a preset polynomial function based on the segment's average speed, then multiplied by the segment length and flow rate, and accumulated to obtain the total emissions.

[0055] It should be noted that the embodiments disclosed herein employ a speed-dependent instantaneous emission model, which can capture emission fluctuations caused by micro-driving behaviors such as congestion, traffic lights, acceleration and deceleration, avoiding the large errors caused by using a fixed average emission factor, and significantly improving the spatiotemporal accuracy of carbon emission accounting.

[0056] Regarding step S106 above, the spatial mapping relationship refers to the geometric relationship between each road segment and the grid cells it passes through. When establishing the spatial mapping relationship between road segments and spatial grid cells, the road network vector data and spatial grid data are first uniformly converted to a projected coordinate system suitable for length calculation. For any road segment a, its geometric line object is obtained. And obtain the set of candidate grids that intersect with the outer rectangle of the road segment. For each candidate grid g, calculate the intersection of the road segment geometry and the grid polygon: ; If the intersection is empty, no emissions will be allocated to this grid for that road segment; if the intersection is not empty, the length of the intersection segment will be calculated. ; The percentage of the length of road segment a within grid g is: ; in, This represents the proportion of road segment a's length within grid g. If the road vector data is complete and free of clipping errors, then... ,in Let be the total length of road segment 'a'. If the sum of the intersection lengths of all grids is inconsistent with the total length of the road segment due to road network trimming, boundary fitting, or topology errors, then 'a' is adopted. Normalization is performed to ensure that the sum of emissions allocated to all grids for the same road segment equals the original emissions for that road segment.

[0057] Carbon emissions of road segment a in time slice τ The emissions allocated to grid g are: ; The traffic carbon emissions of grid g in time slice τ are: ; in, Let g be the set of road segments that intersect with grid g. For road segments located on grid boundaries, they are allocated according to the geometric intersection length; for long road segments spanning multiple grids, they are allocated according to their actual intersection lengths within each grid; for road segments located entirely within a single grid, their length percentage is 1.

[0058] The total carbon emissions of a road segment are multiplied by this ratio to obtain the carbon emissions allocated to the grid. The total carbon emissions of the grid are obtained by summing the allocations of all road segments in the same grid. The carbon emissions of each grid in each simulation period are organized into a matrix to obtain the grid period carbon emission matrix, which is the gridded spatiotemporal inventory of traffic carbon emissions. This inventory can be queried and visualized by time and space dimensions, intuitively showing the dynamic distribution and hotspots of carbon emissions.

[0059] In addition to allocating emissions based on the spatial mapping relationship between road segments and regular grids, other spatial units or allocation weights can be used as alternatives. For example, the study area can be divided into traffic analysis zones or administrative division units, and carbon emissions can be allocated based on the spatial overlap ratio between road segments and these units. When detailed road network geometry data is lacking, an inverse distance weighted interpolation method based on population density or land use type can be used to spatialize road segment emissions onto the grid. Furthermore, allocation weights can be adjusted based on auxiliary data such as the density of points of interest and nighttime light intensity in the area where the road segment is located, making the spatial distribution of carbon emissions closer to the actual emission source locations.

[0060] It should be noted that the embodiments of this disclosure generate a high spatial resolution carbon emission map by allocating road segment-level emissions to a grid according to the length ratio, which clearly reveals the spatial heterogeneity and hotspot areas of carbon emissions, providing a scientific basis for targeted governance and refined management.

[0061] In summary, this embodiment of the present disclosure, by executing the regional transportation carbon emission accounting method in steps S101 to S106, comprehensively acquires multi-source spatiotemporal data covering resident travel, freight logistics, road networks, vehicle attributes and emission factors, points of interest, and land use data, constructing a complete data foundation covering the entire transportation chain. Subsequently, based on mobile phone signaling location data, it identifies resident travel activity chains to generate a resident travel origin-destination matrix, and based on vehicle global positioning system trajectory data, it identifies freight logistics networks to generate a freight travel origin-destination matrix, transforming the raw location data into structured data that accurately reflects real traffic demand, replacing the static travel assumption of statistical averaging in traditional methods; then... The regional transportation system is dynamically simulated and iterated to obtain a road network state sequence, which realizes a dynamic characterization of the real-time operation of urban traffic and can accurately adapt to the dynamic changes of urban traffic. Based on the road network state sequence containing real-time vehicle speed information, the carbon emissions of each road segment are calculated by combining a speed-dependent emission factor model. This fully considers the significant impact of vehicle speed on carbon emissions and accurately captures the carbon emission fluctuations of different road segments and time periods at the micro scale. Finally, the carbon emissions of each road segment are allocated to grid cells according to spatial mapping relationships to generate a gridded spatiotemporal inventory of traffic carbon emissions, which clearly presents the spatial heterogeneity of carbon emissions in the region and ultimately improves the accuracy of regional traffic carbon emission accounting.

[0062] Please see Figure 3 , Figure 3 yes Figure 2 The flowchart further included in step S102, in one embodiment, includes steps S201 to S203, which are steps in the process of identifying stop points and constructing resident travel activity chains based on mobile phone signaling location data through spatiotemporal clustering, labeling destination types by combining points of interest and land use data, and expanding the sample according to the mobile phone signaling sampling rate to generate a resident travel origin-destination matrix: Step S201: Perform spatiotemporal clustering processing on the mobile phone signaling location data to identify the user's stop points and construct the resident travel activity chain; Step S202: Use points of interest and land use data to identify the activity destination types of each stop in the residents' travel activity chain, and mark the origin and destination attributes of residents' travel. Step S203: After the annotation is completed, the trip between adjacent stops is defined as a resident trip. The origin and destination grid of all resident trips is counted and the sampling is expanded according to the mobile phone signaling sampling rate to generate a resident trip origin and destination matrix.

[0063] In the above steps, this embodiment of the disclosure can perform spatiotemporal clustering processing on mobile phone signaling location data to identify user dwell points and construct resident travel activity chains. A dwell point refers to a location where a user's dwell time within a certain grid exceeds a preset threshold. Assume that consecutive points in user u's signaling sequence are located in the same or adjacent grids, and the total dwell time... Exceeding the threshold If so, it is identified as a stop point. ,in, This is the k-th stop point object for user u, containing spatial location and time interval information. Let the grid number be the location of user u during its k-th stop. Let this be the timestamp of the start of user u's kth stay. Let this be the end timestamp of user u's kth stay. It can be set to 15 minutes.

[0064] Sort all stops by time, define movement between adjacent stops as a trip, and obtain the set of all users' trips, which constitute the resident travel activity chain. ,in, For user u's m-th trip, Let this be the starting and stopping point for user u's m-th trip. Let m be the destination stop for user u's m-th trip. Through spatiotemporal clustering, discrete signaling events can be transformed into semantically meaningful stop and move sequences.

[0065] Subsequently, this embodiment of the disclosure utilizes points of interest (POIs) and land use data to identify the activity destination types of each stop in the resident travel activity chain. It labels the origin and destination attributes of resident trips, spatially associates the location of each stop with surrounding POIs, and, combined with land use types, determines the activity type of that stop, such as home, work, shopping, or entertainment. For example, the grid where residents most frequently stop between 0:00 and 6:00 AM is labeled as their residence, and the grid where residents most frequently stop between 10:00 and 4:00 PM on weekdays is labeled as their workplace. These attributes enable the model to distinguish travel behaviors for different purposes, such as commuting, business, and leisure.

[0066] Next, after annotation, this embodiment defines a trip between adjacent stops as a resident trip. It then counts the origin and destination grids of all resident trips and performs expansion processing based on the mobile signaling sampling rate to generate a resident trip origin and destination matrix. Specifically, it counts the number of trips from origin grid i to destination grid j to obtain the original counting matrix. Since mobile signaling data only covers a portion of users, i.e., only users using specific operator services, it needs to be expanded based on the sampling rate, such as the proportion of signaling users to the total population of the area. After expansion, the resident population of grid g is summarized as follows: ,in In addition to the user's residential grid, the system can also perform employment statistics, identifying the grids where the user spends the most time within a certain period. For example, it can track the grids where the user spends the most time between 10:00 AM and 4:00 PM on weekdays and record them as the user's work grid. After expansion, the number of jobs obtained for grid g is: Multiplying the count by the expansion factor yields an approximate full-sample resident travel OD matrix. When expanding the sample, it is also necessary to consider the differences in mobile phone usage rates at different times and in different areas. A stratified expansion method can be adopted, and the OD matrix of residents' travel can be obtained by combining grid data. It can also obtain grid attribute vectors, including the resident population. Job positions .

[0067] It should be noted that the embodiments of this disclosure transform abstract signaling trajectories into activity chains with semantic labels through spatiotemporal clustering and point of interest association, enabling traffic demand to be mapped with land use and providing a real and detailed demand basis for subsequent traffic allocation.

[0068] Please see Figure 4 , Figure 4 yes Figure 2 Another flowchart further included in step S102, in one embodiment, in the process of detecting dwell clusters based on vehicle GPS trajectory data, identifying logistics facilities and constructing a freight logistics network through land use function calibration, and generating a freight trip origin-destination matrix according to grid freight intensity calibration, may also include steps S301 to S302: Step S301: Detect dwell clusters in the vehicle's GPS trajectory data, and use points of interest and land use data to calibrate the land use functions of the detected dwell clusters, identify the spatial location and type of logistics facilities, and construct a freight logistics network. Step S302: Extract the vehicle's journey between adjacent logistics facilities based on the freight logistics network, and calibrate according to the grid freight intensity to generate a freight trip origin-destination matrix.

[0069] In the above steps, this embodiment of the disclosure performs dwell cluster detection on vehicle GPS trajectory data, and uses points of interest and land use data to calibrate the land use functions of the detected dwell clusters, identify the spatial location and type of logistics facilities, and construct a freight logistics network. Specifically, for heavy truck GPS trajectories, speeds below a threshold value are considered. And lasting longer than the allotted time Points are marked as dwell points. These dwell points are spatially clustered to obtain dwell clusters. Based on the types of points of interest surrounding the dwell cluster location, such as logistics parks, ports, factories, warehouses, etc., and land use data, such as industrial land, warehousing land, port land, etc., it is determined whether the cluster is a logistics facility. The facility clusters that pass the calibration are retained. The number of facility clusters in each grid is calculated, i.e., the grid freight facility density. The connections between the facilities form a freight logistics network, with nodes representing a facility grid and edges representing the travel paths of vehicles between facilities.

[0070] Next, this embodiment extracts the vehicle's journey between adjacent logistics facilities based on the freight logistics network and calibrates it according to the grid freight intensity to generate a freight trip origin-destination matrix. Specifically, for each vehicle, the journey from one facility cluster grid i to the next facility cluster grid j is extracted, and the number of trips for all vehicles is counted. Considering that some vehicles also stop in non-facility areas, such as for temporary loading and unloading, it is necessary to expand the sample for correction according to the grid freight intensity. The grid freight intensity can be comprehensively determined by the freight facility density, industrial and logistics land area, etc. of the grid. The number of freight trips from grid i to grid j is related to the freight facility density of grid i. The freight demand of grid j is positively correlated, and the freight travel OD matrix is ​​obtained after calibration. The freight travel OD matrix is ​​obtained by combining grid data. .

[0071] It should be noted that the embodiments of this disclosure accurately identify the nodes and edges of the urban logistics network by fusing heavy truck GPS trajectory and point of interest data, providing a reliable data foundation for tracing the source and reconstructing the path of freight carbon emissions, and making up for the shortcomings of traditional freight statistics that rely solely on macro freight turnover.

[0072] Furthermore, after generating the resident travel origin-destination matrix and the freight travel origin-destination matrix, this embodiment of the disclosure also performs fusion calibration processing on the two types of origin-destination matrices. First, the target area is uniformly divided into a set of spatial grids, and the calculation period is divided into several time slices. The resident travel origin-destination matrix and the freight travel origin-destination matrix are projected onto the same spatial grids and time slices, respectively. Second, based on the mobile phone signaling sample coverage rate, operator market share, and the resident or active population of the grid, the resident travel origin-destination matrix is ​​expanded and corrected; based on the freight vehicle GPS sample coverage rate, vehicle type composition ratio, logistics facility density, industrial warehousing land area, or freight station throughput, the freight travel origin-destination matrix is ​​expanded and corrected. Third, the corrected resident travel volume and freight travel volume are uniformly converted into standard passenger car equivalents to obtain the total travel equivalent matrix.

[0073] Specifically, let the origin-destination matrix of residents' trips from grid i to grid j within time slice τ be . The origin-destination matrix for freight travel is as follows: For the origin-destination matrix of residents' trips, a resident trip correction coefficient is applied. Expand the sample to obtain ,in, The number of valid mobile signaling users within grid i, demographic data, and signaling sample coverage are used. For the freight trip origin-destination matrix, a freight correction coefficient is applied. and freight GPS sample coverage Expand the sample to obtain ,in, The determination is based on the density of logistics facilities, industrial warehousing land area, freight terminal throughput, or observed truck traffic flow in the origin-destination grid.

[0074] Subsequently, the corrected origin-destination matrix for resident trips and the origin-destination matrix for freight trips are converted into a unified total trip equivalent matrix: ; in, The total travel equivalent from grid i to grid j within time slice τ. The conversion factor for passenger cars used for residential travel, where h represents the vehicle type for freight transport. The standard passenger car equivalent coefficient for vehicle type h. The proportion of freight vehicles of model h within time slice τ.

[0075] To further reduce sampling bias in multi-source data, this embodiment of the disclosure also initially allocates the total travel equivalent matrix to the road network to obtain simulated traffic flow for each road segment. The simulated traffic flow for each road segment is then compared with the measured traffic flow or the observed traffic flow calculated from vehicle GPS trajectories. The resident travel correction coefficient and freight travel correction coefficient are updated based on the deviation between the two until the average relative error between the simulated and observed traffic flow for each road segment is less than a preset calibration error threshold, or the maximum number of calibrations is reached. This ensures that the resident travel demand reflected in mobile phone signaling data and the freight travel demand reflected in vehicle GPS data are consistent with the actual operating status of the road network.

[0076] Please see Figure 5 , Figure 5 yes Figure 2 The flowchart further includes step S104. In some embodiments, during the process of obtaining a new total trip equivalent matrix through dynamic simulation of the regional traffic system, iterating again until the average traffic flow change rate of the road network between two adjacent iterations is less than a preset convergence threshold, and obtaining the road network state sequence based on the traffic flow and average speed of each road segment after iteration, steps S401 to S403 may be included: Step S401: Update the residential population distribution and employment distribution of each grid based on the generalized travel cost; Step S402: Generate an updated resident travel origin-destination matrix and an updated freight travel origin-destination matrix based on the updated resident population distribution and employment distribution; Step S403: Convert the updated resident travel origin-destination matrix and the updated freight travel origin-destination matrix into a new total travel equivalent matrix, and iterate again until the average traffic flow change rate of the road network in two adjacent iterations is less than the preset convergence threshold. Obtain the road network state sequence based on the traffic flow and average speed of each road segment after iteration.

[0077] In the above steps, this embodiment of the disclosure can allocate the total trip equivalent matrix to the road network corresponding to the road network vector data to obtain the initial flow and initial average speed of each road segment. Specifically, the total trip equivalent is used as demand and allocated to the road network using the Beckmann user equilibrium model. The Beckmann model solves for the equilibrium state by minimizing the total system trip impedance function. ; Where Z represents the total travel impedance of the system, which is the sum of the time costs of all travelers; a is the road segment index; and E is the set of all road segments. The traffic flow (standard vehicles / hour) is for road segment a. The average speed of road segment a is the travel time of road segment a when the flow rate is w, and can be expressed in hours. The average speed of road segment a can be calculated by dividing the length of road segment a by the travel time. Initially, the total trip equivalent matrix is ​​the initial total trip equivalent matrix, and the above flow rate and average speed are the initial flow rate and average speed, which need to be continuously optimized and updated through subsequent steps. The model must satisfy the constraints of flow conservation and non-negativity: ; ; ; in, Let r be the flow rate of the k-th path from the starting point r to the ending point s. Let r be the set of all paths from r to s. Let r be the total travel demand from r to s, which is also the total travel equivalent. This is a path-segment association variable; it is 1 if segment 'a' is on path 'k', and 0 otherwise. This indicates that the constraint holds for all origin-end point pairs. Solving this convex programming problem yields the flow rates for each road segment. The travel time for a road segment is calculated using the BPR function: ; in, For road segment a, the traffic flow The following travel time can be expressed in hours. This refers to free-flow time, which can be expressed in hours. This refers to the road segment's traffic capacity (standard vehicles per hour). and For the BPR function parameters, usually take... =0.15, =4, this formula reflects the nonlinear relationship that increased traffic volume leads to longer travel time.

[0078] This disclosure also allows for the determination of generalized travel costs between grids in a road network, and the updating of the residential population distribution and employment distribution of each grid based on these generalized travel costs. The generalized travel costs are... This represents the comprehensive cost of traveling from grid i to grid j, including time cost, distance cost, and toll cost. The calculation formula is as follows: ; in, The monetary value of time can be determined based on the average income level of residents in the region. Let be the free-flow time between ij, read from the road network by the shortest path selection algorithm. The introduction of generalized travel costs allows the model to comprehensively evaluate the impact of traffic policies, such as congestion pricing, on travel behavior. Then, during the location accessibility feedback process, the generalized travel cost is updated using the following formula: ; in, Let t be the generalized travel cost (in yuan) from grid i to grid j during period t. This represents the shortest path (set of road segments) from i to j under equilibrium conditions. For the value of time, This is the distance cost coefficient (yuan / km). Let be the length (in kilometers) of road segment a. It is the charging cost coefficient. This refers to transportation costs from i to j, such as public transportation fares, taxi fares, and toll fees.

[0079] Next, this embodiment of the disclosure generates updated resident travel origin-destination matrix and updated freight travel origin-destination matrix based on the updated resident population distribution and employment distribution. Changes in population and employment distribution will alter the travel generation and attraction of each grid. Travel generation refers to the total number of trips originating from grid i, and travel attraction refers to the total number of trips arriving at grid j. Travel generation and attraction are recalculated using a regression model and then input into a dual-constraint gravity model to generate a new OD matrix.

[0080] Subsequently, the updated resident travel origin-destination matrix and the updated freight travel origin-destination matrix are converted into a new total travel equivalent matrix, and the process is iterated again until the average traffic flow change rate of the road network in two adjacent iterations is less than a preset convergence threshold. The road network state sequence is obtained based on the traffic flow and average speed of each road segment after iteration. The average traffic flow change rate is the ratio of the sum of the absolute values ​​of the traffic flow differences between each road segment in two adjacent iterations to the sum of the traffic flow in the previous iteration; the preset convergence threshold is determined based on the target area scale and calculation accuracy. Specifically, in this embodiment, the newly generated resident travel OD matrix and freight travel OD matrix are combined into a new total travel equivalent matrix, and then traffic allocation is performed again. Steps S402 and S403 are repeated to form a closed-loop iteration. The average traffic flow change rate of the road network in two adjacent iterations is calculated. After the nth iteration, the average traffic flow change rate of the road network is determined according to the following formula: ; Where E is the set of road segments in the road network. Let be the traffic flow of road segment a after the nth iteration. Let the traffic flow of segment a be after the (n-1)th iteration. To prevent extremely small positive numbers with a denominator of zero. If Less than the preset convergence threshold If this is the case, then the road network state has converged. In one specific embodiment, Take 0.01; in high-precision accounting scenarios, Take 0.005; in scenarios involving rapid evaluation over large-scale regions, The value should be between 0.02 and 0.03. To avoid misjudgments caused by accidental fluctuations in a single iteration, it can be set to satisfy the condition in two consecutive iterations. Then stop iterating. If the number of iterations reaches the maximum number of iterations... Then, the current iteration result is output as the approximate convergence result, where, Depending on the required accuracy, the number of iterations can be taken from 50 to 100. In this case, the traffic flow and travel time or average speed of each road segment constitute a road network state sequence with a period of t. This sequence reflects the equilibrium state under given population, employment, and road network conditions, and is the basis for subsequent carbon emission calculations.

[0081] It should be noted that the embodiments of this disclosure use an iterative feedback mechanism involving multiple elements to couple land use, population migration, traffic demand and road network status into a unified computational framework. This enables the model to simulate the complete causal chain, such as land development leading to separation of work and residence, increased commuting distance and carbon emissions, and rising commuting costs that suppress the attractiveness of distant residences. This overcomes the prediction bias caused by the fragmentation of system correlation in traditional assessment methods.

[0082] Please see Figure 6 , Figure 6 yes Figure 5 The flowchart further includes step S401. In some embodiments, the process of updating the residential population distribution and employment distribution of each grid based on generalized travel costs may also include steps S501 to S502: Step S501: Determine the generalized travel cost between grids in the road network, and calculate the residential accessibility index and employment accessibility index for each grid based on the generalized travel cost; Step S502: Update the residential population distribution and employment distribution of each grid according to the residential accessibility index and the employment accessibility index.

[0083] In the above steps, this embodiment of the disclosure determines the generalized travel cost between grids in the road network, and calculates the residential accessibility index and employment accessibility index for each grid based on the generalized travel cost. As described in step S402, the generalized travel cost... Having already obtained the shortest path search and cost accumulation, and based on generalized travel costs, we further calculate the residential accessibility index and employment accessibility index for each grid. Residential accessibility reflects the ease with which residents can access employment opportunities, while employment accessibility reflects the ease with which businesses can access labor. ; ; in, Let i be the residential accessibility index. The employment accessibility index of grid i Let J be the number of residents in grid j during period t-1. Let μ be the number of jobs in grid j during period t-1, and μ be the impedance decay parameter, controlling the degree of influence of travel costs on accessibility. The larger μ is, the faster the distance decays. The exponential decay form assumes that travelers' sensitivity to cost decreases exponentially with increasing cost. This represents the total number of grid cells.

[0084] Residential Accessibility Index The Employment Accessibility Index measures the ease with which residents can access all employment opportunities from grid i. From the enterprise's perspective, the ease of access from grid i to all workers is measured using a negative exponential decay formula, reflecting the non-linear decay of travel costs with accessibility.

[0085] Next, the residential population distribution and employment distribution of each grid are updated based on the residential accessibility index and the employment accessibility index. The population and employment updates are based on the attractiveness model, and residential attractiveness is calculated using the following formula: ; in, The residential attractiveness index for grid i, , , As pre-defined weighted parameters, residential attractiveness is determined by the employment accessibility index, which reflects job opportunities, and housing prices. The reciprocal housing cost and environmental quality Joint decision, A parameter sensitive to crowding. Let i be the population of grid i in period t-1. Let i be the residential land area in period t-1. Let i be the average housing price in grid i during period t-1. The environmental quality index for grid i can be represented by the density or area of ​​park green space POIs.

[0086] Furthermore, in the denominator As a congestion penalty item, Population density is a factor; the higher the density, the lower the attractiveness.

[0087] Employment attractiveness is jointly determined by factors reflecting labor supply, including residential accessibility, employment land density, and tax incentives. It is calculated using the following formula: ; Among them, For grid j, the employment attractiveness index For pre-calibrated weight parameters, Let be the residential accessibility index for grid j. Let grid j be the area of ​​employment land used in period t-1, including commercial and industrial land. As a parameter sensitive to employment density, Let j be the number of jobs in grid j during period t-1. The tax incentive index for grid j is usually set to 0, but for regions with special tax policies, the specific proportion of the tax incentive is determined.

[0088] Then update population and employment using the following formula: ; ; in, Let i be the population of grid i in period t. Let j be the number of jobs in period t. To adjust the speed parameters and control the speed at which population employment responds to changes in attractiveness; and These are the average residential attractiveness and average employment attractiveness of all grids, used for normalization. The formula indicates that when a grid's attractiveness is above average, its population or employment will increase, and vice versa, if there are regional total constraints, such as the planned total population. and General Position The updated population and employment figures also need to be scaled proportionally: ; It should be noted that the embodiments disclosed herein couple transportation accessibility with factors such as land value, housing cost, and environmental quality through two-way feedback between residential attractiveness and employment attractiveness. This enables the spatial distribution of population and employment to adaptively respond to changes in transportation infrastructure and policies, thereby improving the model's ability to simulate the evolution of urban spatial structure.

[0089] Furthermore, embodiments of this disclosure can also perform land use feedback, calculating land use conversion probabilities using a cellular automata model: ; in, This represents the probability that grid i will be converted from land use type l to type k. Let i be the land use type in period t. The Markov transition probability from type l to type k can be derived from historical data. Let K be the suitability score of grid i for land use type k, where K is the total number of land use types.

[0090] The land use conversion process is simulated using the following formula: ; in, This indicates taking the value of k that maximizes the value inside the parentheses. U(0,1) is a random disturbance intensity parameter, usually taken as a small value such as 0.1, and U(0,1) is a uniformly distributed random number in the interval [0,1].

[0091] The land area is updated according to the following formula: ; in, Let be the area of ​​grid i for the k-th land use category at period t. Let be the total area of ​​grid i. Based on this, after obtaining the area, the updated distribution of the residential population and the distribution of employment positions can be obtained based on the total population and total positions mentioned above.

[0092] Please see Figure 7 , Figure 7 yes Figure 5The flowchart further includes step S402. In some embodiments, the process of generating the updated resident travel origin-destination matrix and the updated freight travel origin-destination matrix based on the updated resident population distribution and employment distribution may also include steps S601 to S602: Step S601: Based on the updated resident population distribution and employment distribution, recalculate the trip generation and attraction of each grid using the trip generation rate; Step S602: Based on the input of travel generation and attraction, input into the preset dual-constraint gravity model to generate an updated resident travel origin-destination matrix, and synchronously update the freight travel origin-destination matrix through the freight demand elasticity model.

[0093] In the above steps, this embodiment of the disclosure can, after obtaining the updated resident population distribution and employment distribution, recalculate the trip generation and attraction of each grid based on relevant population and employment parameters and using the trip generation rate. Trip generation and attraction are determined by population, employment, and various land use areas. ; ; in, Let i be the number of trips generated per day (person-trips / day) in grid i during period t. This refers to the collection of land use types that affect travel, such as residential and commercial. Let be the trip generation rate (person-times / km² / day) per unit area of ​​land use category k. The average employment accessibility index, Let J be the number of trips attracted by grid j in period t (person-trips / day). This refers to a set of land use types that influence travel attraction, such as employment and commerce. Let k be the travel attraction rate per unit area of ​​land use type k. This is the average residential accessibility index, used to adjust for the impact of accessibility on travel demand. and The regression coefficients are determined using historical traffic survey data.

[0094] Next, in this embodiment, the origin-destination matrix of resident travel can be generated by inputting the travel generation and attraction volumes into a preset dual-constraint gravity model, and the origin-destination matrix of freight travel can be updated synchronously through a freight demand elasticity model. The dual-constraint gravity model simultaneously satisfies the constraints of total travel generation and total attraction volumes. The formula for calculating the resident travel OD matrix is ​​as follows: ; in, For period t, the number of resident trips from grid i to grid j. and As a balancing factor, to ensure constraints on the generation and attraction of travel. The travel cost impedance coefficient reflects a traveler's sensitivity to cost and can be determined using historical OD data. The balance factor is solved iteratively. ; ; Iterate until convergence condition: ,in To achieve convergence tolerance, such as 0.01.

[0095] The freight travel OD matrix is ​​updated based on changes in industrial and logistics land use, using a freight demand elasticity model: ; in, Let be the number of freight trips from grid i to grid j during period t, and ν be the freight demand elasticity coefficient. This reflects the sensitivity of changes in industrial land area to freight demand; Let be the total industrial and logistics land area of ​​grid i in period t. The above formula assumes that freight demand changes in a positive correlation with the industrial and logistics land area.

[0096] It should be noted that the embodiments disclosed herein use a gravity model and elasticity formula to dynamically transform changes in population, employment, and land use into updates to travel demand, thereby achieving the linkage between transportation demand and various elements of urban development and ensuring the closed-loop nature of the simulation.

[0097] Furthermore, in this embodiment, the calculation results from the above steps are substituted into the model as the new base year input, thus enabling automatic iterative updates of the model. Simultaneously, it accepts external inputs such as population distribution, land use, and road network attributes at any iteration node. Ultimately, the output data is a sequence of travel distribution matrices for each period. Road network state sequence for each period , Grid attribute sequence of each period .

[0098] In some embodiments, step S105, in the process of decomposing the traffic flow of each road segment into sub-flows of different vehicle types and fuel types according to a preset vehicle type and fuel composition ratio, inputting the average speed of each road segment into a speed-dependent emission factor model to calculate the real-time emission factor, and calculating the carbon emissions of each vehicle type and fuel type in the corresponding road segment based on the sub-flows and the real-time emission factor, and summing the carbon emissions of all vehicle types and fuel types to obtain the traffic carbon emissions of each road segment in the target area, may further include: (1) Based on the preset vehicle type and fuel composition ratio, the traffic flow of each road segment is decomposed into sub-flows of different vehicle type and fuel type; (2) Obtain the speed-dependent emission factor model from the vehicle attributes and emission factor data corresponding to the fuel type of each vehicle model, and input the average speed of each road segment into the speed-dependent emission factor model to calculate the real-time emission factor; (3) Based on sub-flow and real-time emission factor, calculate the carbon emissions of each vehicle fuel type on the corresponding road segment, and sum the carbon emissions of all vehicle fuel types to obtain the traffic carbon emissions of each road segment in the target area.

[0099] In the above steps, this embodiment of the disclosure can decompose the traffic flow of each road segment into sub-flows of different vehicle types and fuel types according to a preset vehicle type and fuel composition ratio. Specifically, the input data for the carbon emission process is a road network state sequence. The data includes the proportion of vehicles, including cars, buses, heavy trucks, etc., and the types of fuels, including gasoline, diesel, and pure electric vehicles. Let the total flow rate of road segment a during period t be... The vehicle-fuel composition ratio is Then, using k as the vehicle type index and f as the fuel type index, the sub-flow... for: ; in, It can be determined based on regional vehicle registration data, annual inspection data, or traffic surveys, and can change over time, such as setting a curve for the annual increase in electrification rate to simulate the impact of electrification policies.

[0100] Next, this embodiment of the present disclosure obtains the speed-dependent emission factor model from the vehicle attributes and emission factor data corresponding to the fuel type of each vehicle model, and inputs the average speed of each road segment into the speed-dependent emission factor model to calculate the real-time emission factor.

[0101] Specifically, the speed-dependent emission factor model is established separately for vehicle type, fuel type, and emission standard. Let vehicle type be k, fuel type be f, emission standard be s, and average road speed be v, then the corresponding CO2 emission factor per unit mileage is... Determined according to the following model: ; in, The unit for is grams per kilometer, and the unit for v is kilometers per hour. To prevent extremely small positive numbers from causing computational anomalies due to zero speed, , , , The fitting parameters are corresponding to vehicle model, fuel type, and emission standard. The vehicle attribute and emission factor data pre-store fitting parameter tables corresponding to different vehicle models, fuel types, and emission standards. The fitting parameter tables include at least the vehicle model field, fuel type field, emission standard field, applicable speed range field, fitting parameter field, and unit field.

[0102] Fitting parameters can be determined using measured emission samples or a standard emission factor database. Specifically, the observed CO2 emissions per unit mileage for vehicles corresponding to vehicle type k, fuel type f, and emission standard s at different average speed ranges are obtained to form a sample set. And solve it using the least squares method: ; When measured emission samples cannot be obtained for the target area, the speed and emission factor curve parameters of the corresponding vehicle type, fuel type, and emission standard are retrieved from the vehicle attribute and emission factor database or the standard emission factor database. These parameters are then combined with local vehicle registration data, annual inspection data, or traffic survey data to perform localized calibration of the vehicle type composition ratio. For pure electric vehicles, if the calculation method is direct road emissions, the direct CO2 emission factor during road operation is set to 0; if the calculation method is indirect electricity emissions or life-cycle emissions, the equivalent CO2 emission factor is calculated based on unit electricity consumption and grid emission factors.

[0103] Subsequently, this embodiment calculates the carbon emissions of each vehicle type and fuel type on the corresponding road segment based on sub-flow rate and real-time emission factor, and sums the carbon emissions of all vehicle types and fuel types to obtain the traffic carbon emissions of each road segment within the target area. Specifically, the total carbon emissions of road segment a in period t... for: ; The formula multiplies traffic flow (vehicles / hour), road segment length (km), and emission factor (g / km) to obtain the hourly carbon emissions (g) of the road segment. Finally, the carbon emissions of each road segment are summed over time to obtain emission data at different time scales such as day, month, and year.

[0104] It should be noted that by correlating the emission factor with real-time speed, the embodiments of this disclosure can accurately reflect the emission differences under different traffic conditions such as congestion and smooth traffic, avoiding the large deviation caused by using a fixed average emission factor, thereby improving the micro-accuracy of carbon emission accounting.

[0105] Please see Figure 8 , Figure 8 yes Figure 2The flowchart further includes step S106. In some embodiments, the process of allocating traffic carbon emissions of each road segment according to the spatial mapping relationship between road segments and spatial grid units to generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area may also include steps S701 to S703: Step S701: Establish the spatial mapping relationship between road segments and spatial grid units, calculate the length ratio of each road segment in each intersecting grid, and allocate the traffic carbon emissions of each road segment to the corresponding spatial grid unit according to the length ratio. Step S702: Accumulate the carbon emissions of all road segments allocated in each spatial grid unit to obtain the total carbon emissions of the corresponding grid. Step S703: Generate a grid-level carbon emission matrix based on the total carbon emissions of each grid, and form a gridded spatiotemporal inventory of transportation carbon emissions within the target area containing time, space and emission information through the grid-level carbon emission matrix.

[0106] In the above steps, this embodiment of the present disclosure can establish a spatial mapping relationship between road segments and spatial grid units, calculate the length proportion of each road segment in each intersecting grid, and allocate the traffic carbon emissions of each road segment to the corresponding spatial grid unit according to the length proportion. Then, this embodiment of the present disclosure accumulates the carbon emissions of all road segments in each spatial grid unit after allocation to obtain the total carbon emissions of the corresponding grid. Specifically, the study area is divided into regular square grids, such as 1km×1km, and each grid g has a unique spatial index. For road segment a, the length of its line segment located in grid g is calculated using the spatial overlay analysis method. The traffic carbon emissions allocated to grid g are: ; in, Let g be the total carbon emissions of grid g at period t. Let be the total carbon emissions of road segment a during period t. This allocation assumes that carbon emissions are uniformly distributed along the road segment.

[0107] Finally, a grid-level carbon emission matrix is ​​generated based on the total carbon emissions of each grid. This grid-level carbon emission matrix is ​​then used to form a gridded spatiotemporal inventory of transportation carbon emissions within the target area, containing temporal, spatial, and emission information. Specifically, the grid carbon emission vector for each period t, i.e., the grid-period carbon emission matrix, is generated. Organized chronologically to form a spatiotemporal list. This list can be exported to raster or vector formats supported by geographic information systems for visualizing carbon emission heat maps, identifying emission hotspots, analyzing the spatiotemporal evolution of emissions, and can be overlaid with layers such as land use and population density for in-depth analysis.

[0108] It should be noted that the embodiments of this disclosure use spatial allocation technology to transform discrete road segment emission data into continuous grid surface data, which facilitates the integration and analysis with data such as urban planning and environmental monitoring, and provides intuitive and high-resolution support for the study of carbon emission driving mechanisms and spatial targeting of emission reduction policies.

[0109] Please see Figure 9 , Figure 9 yes Figure 2 The flowchart further includes steps S106. In some embodiments, after generating a gridded spatiotemporal inventory of traffic carbon emissions within the target area, the regional traffic carbon emission accounting method may further include steps S801 to S802: Step S801: Obtain the emission reduction policy parameters to be evaluated, and rerun the regional transportation system dynamic simulation and carbon emission calculation process based on the emission reduction policy parameters to generate a gridded spatiotemporal inventory of target transportation carbon emissions within the target area under the policy scenario. Step S802: Compare the spatiotemporal inventory of transportation carbon emissions with the target spatiotemporal inventory of transportation carbon emissions, and generate a corresponding comprehensive policy assessment report.

[0110] In the above steps, this embodiment of the disclosure can obtain the emission reduction policy parameters to be evaluated, and rerun the regional transportation system dynamic simulation and carbon emission calculation process based on the emission reduction policy parameters to generate a gridded spatiotemporal inventory of target traffic carbon emissions within the target area under the policy scenario. Specifically, the emission reduction policy parameters may include electrification policies to define the curves of the electrification rate of each vehicle type increasing over time; road network optimization policies to define the geometric attributes and capacity of newly added or expanded roads; demand management policies to define additional travel costs for specific areas or time periods, such as congestion pricing; and land use optimization policies to provide mandatory future land use planning maps. The above parameters are then rerun to rerun the regional transportation system dynamic simulation and carbon emission calculation process, that is, to rerun the complete dynamic simulation from the initial state to the target period T.

[0111] For example, emissions reduction policies may include electrification policies. This is used to define the curve function for the increase in the electrification rate of each vehicle model over time. This directly affects the vehicle composition ratio in the above embodiments. It may also include road network optimization policies. Used to define a set of newly added or expanded roads. By changing the road network g and the corresponding free-flow time in the above embodiments. And capacity; it may also include demand management policies. Used to define additional travel costs for a specific area or time period. This is directly added to the generalized travel cost in the above embodiments. It can also include land use optimization policies. Used to provide mandatory future land use planning maps In the feedback loop of the above embodiments, the endogenous evolution results are replaced; in addition, other policies that are set autonomously according to the needs of carbon emission control are also supported.

[0112] In policy effect simulation, for each policy scenario s (or combination of policies) By rerunning the regional transportation system dynamic simulation and carbon emission calculation process, and rerunning the complete simulation from the initial state to the target period T, the output under the policy scenario is obtained: ; Finally, by comparing the spatiotemporal inventory of transportation carbon emissions with the target spatiotemporal inventory of transportation carbon emissions, a corresponding comprehensive policy evaluation report is generated. Specifically, by comparing the spatiotemporal inventory of transportation carbon emissions under the baseline scenario without policy intervention with the target spatiotemporal inventory of transportation carbon emissions under the policy scenario, the core emission reduction effect is calculated: ; in, For policy scenario s, the cumulative total emission reductions As a baseline, the spatiotemporal inventory of traffic carbon emissions for period t. Let T be the spatiotemporal inventory of target transportation carbon emissions for period t under policy scenario s, where T is the total number of simulation periods.

[0113] It can also calculate the spatial distribution of emission reductions. , The cumulative emission reduction for grid g under policy scenario s is defined, and carbon leakage risk areas are identified. In areas where the policy was implemented, emissions actually increased. Furthermore, synergistic benefits can be assessed, including congestion mitigation, i.e., changes in average travel time. Estimate the economic costs of implementing the policy. The impact of changes in travel costs on fairness among different income groups and the cost-benefit ratio ,in, SCC stands for Social Carbon Cost (RMB / ton CO2), which is the socioeconomic loss caused by each ton of CO2 emissions. Total implementation cost (RMB) for policy scenario s. This represents the set of parameters for policy scenario s. Finally, by integrating the above indicators, a quantitative comprehensive policy evaluation report is generated, providing policymakers with multi-dimensional and interpretable feedback on policy effectiveness.

[0114] It should be noted that the embodiments disclosed herein, by parameterizing policies and embedding them into a dynamic simulation system, enable the ex-ante quantitative assessment and comparison of emission reduction measures, making up for the shortcomings of traditional methods that can only perform ex-post accounting and cannot predict policy chain reactions, and providing a forward-looking and systematic decision support tool for low-carbon transportation planning.

[0115] Please see Figure 10 This disclosure also provides a regional transportation carbon emission accounting device, which can implement the above-mentioned regional transportation carbon emission accounting method. The regional transportation carbon emission accounting device includes: The traffic data acquisition module 1001 is used to acquire multi-source spatiotemporal data of the target area. The multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle global positioning system trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data. The origin-destination matrix generation module 1002 is used to identify stop points and construct residents' travel activity chains based on mobile phone signaling positioning data through spatiotemporal clustering, label destination types by combining points of interest and land use data, expand the sampling according to the mobile phone signaling sampling rate to generate residents' travel origin-destination matrix, detect stop clusters based on vehicle global positioning system trajectory data, identify logistics facilities and construct freight logistics network through land use function calibration, and generate freight travel origin-destination matrix according to grid freight intensity calibration. The total trip equivalent matrix generation module 1003 is used to unify the origin-destination matrix of residents' trips and the origin-destination matrix of freight trips to the same spatial grid and time slice, and after expanding and correcting them respectively and converting them into standard car equivalents, the total trip equivalent matrix is ​​generated. The traffic dynamic simulation module 1004 is used to allocate the total trip equivalent matrix to the road network corresponding to the road network vector data, obtain the initial flow and initial average speed of each road segment, determine the generalized trip cost between grids in the road network, and perform dynamic simulation of the regional traffic system to obtain a new total trip equivalent matrix. It is then iterated until the rate of change of the average flow of the road network between two adjacent iterations is less than the preset convergence threshold. Based on the flow and average speed of each road segment after iteration, the road network state sequence is obtained. The carbon emission determination module 1005 is used to decompose the traffic flow of each road segment into sub-flows of different vehicle types and fuel types according to the preset vehicle type and fuel composition ratio, input the average speed of each road segment into the speed-dependent emission factor model to calculate the real-time emission factor, and calculate the carbon emission of each vehicle type and fuel type in the corresponding road segment based on the sub-flow and the real-time emission factor, and accumulate the carbon emission of all vehicle types and fuel types to obtain the traffic carbon emission of each road segment in the target area. The carbon emission accounting module 1006 is used to allocate the traffic carbon emissions of each road segment according to the spatial mapping relationship between the road segment and the spatial grid unit, and generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area.

[0116] In summary, the regional transportation carbon emission accounting device, by executing the regional transportation carbon emission accounting method described in the above embodiments, comprehensively acquires multi-source spatiotemporal data covering resident travel, freight logistics, road networks, vehicle attributes and emission factors, points of interest, and land use data, constructing a complete data foundation covering the entire transportation chain. Subsequently, it identifies resident travel activity chains based on mobile phone signaling location data to generate a resident travel origin-destination matrix, and identifies freight logistics networks based on vehicle global positioning system trajectory data to generate a freight travel origin-destination matrix. This transforms the raw location data into structured data that accurately reflects real traffic demand, replacing the static travel assumptions based on statistical averaging in traditional methods. Then, it conducts regional... The dynamic simulation and iteration of the regional traffic system yields a road network state sequence, enabling a dynamic depiction of the real-time operation of urban traffic and accurately adapting to dynamic changes in urban traffic. Based on the road network state sequence containing real-time vehicle speed information, and combined with a speed-dependent emission factor model, the carbon emissions of each road segment are calculated, fully considering the significant impact of vehicle speed on carbon emissions and accurately capturing carbon emission fluctuations at different road segments and time periods at the microscale. Finally, the carbon emissions of each road segment are allocated to grid cells according to spatial mapping relationships, generating a gridded spatiotemporal inventory of traffic carbon emissions. This clearly presents the spatial heterogeneity of carbon emissions within the region, ultimately improving the accuracy of regional traffic carbon emission accounting.

[0117] The specific implementation method of the regional transportation carbon emission accounting device is basically the same as the specific embodiment of the regional transportation carbon emission accounting method described above, and will not be repeated here. Subject to meeting the requirements of the embodiments of this disclosure, the regional transportation carbon emission accounting device may also be equipped with other functional modules to implement the regional transportation carbon emission accounting method described above.

[0118] This disclosure also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned regional transportation carbon emission accounting method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0119] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 1101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this disclosure. The memory 1102 can be implemented in the form of read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1102 can store operating devices and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1102 and is called and executed by the processor 1101 to execute the regional traffic carbon emission accounting method of the embodiments of this disclosure. Input / output interface 1103 is used to implement information input and output; The communication interface 1104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1105 transmits information between various components of the device (e.g., processor 1101, memory 1102, input / output interface 1103, and communication interface 1104); The processor 1101, memory 1102, input / output interface 1103 and communication interface 1104 are connected to each other within the device via bus 1105.

[0120] This disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described regional transportation carbon emission accounting method.

[0121] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0122] The embodiments described in this disclosure are intended to illustrate the technical solutions of this disclosure more clearly, and do not constitute a limitation on the technical solutions provided by this disclosure. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by this disclosure are also applicable to similar technical problems.

[0123] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this disclosure, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0124] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Those skilled in the art will understand that all or some of the steps, apparatuses, or functional modules / units in the methods disclosed above can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0126] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such orders can be interchanged where appropriate so that embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0127] It should be understood that in this disclosure, "at least one item" means one or more, and "more than one" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0128] In the several embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0129] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] Furthermore, the functional units in the various embodiments of this disclosure can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this disclosure. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0132] The preferred embodiments of the present disclosure have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present disclosure. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present disclosure shall be within the scope of the claims of the present disclosure.

Claims

1. A method for calculating carbon emissions from regional transportation, characterized in that, include: Acquire multi-source spatiotemporal data of the target area, wherein the multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle global positioning system trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data; The mobile phone signaling location data is subjected to spatiotemporal clustering to identify user stop points and construct resident travel activity chains. The activity destination types of each stop point in the resident travel activity chain are identified using the points of interest and land use data. The origin and destination attributes of resident travel are labeled. After labeling, the trip between adjacent stop points is defined as a resident travel. The origin and destination grid of all resident travel is counted and expanded according to the mobile phone signaling sampling rate to generate a resident travel origin and destination matrix. Based on vehicle GPS trajectory data, stop clusters are detected, logistics facilities are identified through land use function calibration, and a freight logistics network is constructed. A freight trip origin-destination matrix is ​​generated by calibrating according to grid freight intensity. The origin-destination matrix of residents' trips and the origin-destination matrix of freight trips are unified to the same spatial grid and time slice, and after being expanded, corrected and converted into standard car equivalents, a total trip equivalent matrix is ​​generated. The total trip equivalent matrix is ​​assigned to the road network corresponding to the road network vector data to obtain the initial flow and initial average speed of each road segment. The generalized trip cost between grids in the road network is determined, and a dynamic simulation of the regional traffic system is performed to obtain a new total trip equivalent matrix. The process is iterated again until the rate of change of the average flow of the road network between two adjacent iterations is less than a preset convergence threshold. The road network state sequence is obtained based on the flow and average speed of each road segment after iteration. According to the preset vehicle type and fuel composition ratio, the traffic flow of each road segment is decomposed into sub-flows of different vehicle types and fuel types. The average speed of each road segment is input into the speed-dependent emission factor model to calculate the real-time emission factor. Based on the sub-flow and the real-time emission factor, the carbon emissions of each vehicle type and fuel type in the corresponding road segment are calculated. The carbon emissions of all vehicle types and fuel types are accumulated to obtain the traffic carbon emissions of each road segment in the target area. The traffic carbon emissions of each road segment are allocated according to the spatial mapping relationship between the road segment and the spatial grid unit to generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area.

2. The regional transportation carbon emission accounting method according to claim 1, characterized in that, The process of detecting dwell clusters based on vehicle GPS trajectory data, identifying logistics facilities through land use function calibration, constructing a freight logistics network, and generating a freight trip origin-destination matrix based on grid freight intensity calibration includes: The vehicle's GPS trajectory data is used to detect dwell clusters, and the detected dwell clusters are calibrated for land use function using the points of interest and land use data to identify the spatial location and type of logistics facilities and construct a freight logistics network. Based on the freight logistics network, the routes of vehicles between adjacent logistics facilities are extracted, and the data is calibrated according to the grid freight intensity to generate a freight trip origin-destination matrix.

3. The regional transportation carbon emission accounting method according to claim 1, characterized in that, The dynamic simulation of the regional traffic system yields a new total trip equivalent matrix. The simulation is iterated until the rate of change of the average traffic flow in the road network between two consecutive iterations is less than a preset convergence threshold. Based on the traffic flow and average speed of each road segment after iteration, a road network state sequence is obtained, including: The residential accessibility index and employment accessibility index of each grid are calculated based on the generalized travel cost, and the residential population distribution and employment distribution of each grid are updated according to the residential accessibility index and the employment accessibility index. An updated resident travel origin-destination matrix and an updated freight travel origin-destination matrix are generated based on the updated resident population distribution and the updated employment position distribution. The updated resident travel origin-destination matrix and the updated freight travel origin-destination matrix are converted into a new total travel equivalent matrix, and the process is iterated again until the average traffic flow change rate of the road network in two adjacent iterations is less than a preset convergence threshold. The road network state sequence is obtained based on the traffic flow and average speed of each road segment after iteration.

4. The regional transportation carbon emission accounting method according to claim 3, characterized in that, The process of generating updated resident travel origin-destination matrix and updated freight travel origin-destination matrix based on the updated resident population distribution and employment distribution includes: Based on the updated resident population distribution and employment distribution, the trip generation and attraction of each grid are recalculated using the trip generation rate; The origin and destination matrix of residents' trips is generated by inputting the trip generation and attraction into a preset dual-constraint gravity model, and the origin and destination matrix of freight trips is updated synchronously through the freight demand elasticity model.

5. The regional transportation carbon emission accounting method according to claim 1, characterized in that, The process of allocating traffic carbon emissions for each road segment according to the spatial mapping relationship between road segments and spatial grid units, and generating a gridded spatiotemporal inventory of traffic carbon emissions within the target area, includes: Establish a spatial mapping relationship between road segments and spatial grid units, calculate the length ratio of each road segment in each intersecting grid, and allocate the traffic carbon emissions of each road segment to the corresponding spatial grid unit according to the length ratio. The total carbon emissions of the corresponding grid are obtained by summing up the carbon emissions of all road segments allocated in each spatial grid unit. A grid-level carbon emission matrix is ​​generated based on the total carbon emissions of each grid, and a gridded spatiotemporal inventory of transportation carbon emissions containing time, space and emission information is formed within the target area through the grid-level carbon emission matrix.

6. The regional transportation carbon emission accounting method according to claim 1, characterized in that, After generating the gridded spatiotemporal inventory of traffic carbon emissions within the target area, the regional traffic carbon emission accounting method further includes: Obtain the emission reduction policy parameters to be evaluated, and rerun the regional transportation system dynamic simulation and carbon emission calculation process based on the emission reduction policy parameters to generate a gridded spatiotemporal inventory of target transportation carbon emissions within the target area under the policy scenario; By comparing the aforementioned spatiotemporal inventory of transportation carbon emissions with the target spatiotemporal inventory of transportation carbon emissions, a corresponding comprehensive policy assessment report is generated.

7. A regional transportation carbon emission accounting device, characterized in that, include: The traffic data acquisition module is used to acquire multi-source spatiotemporal data of the target area. The multi-source spatiotemporal data includes at least mobile phone signaling positioning data, vehicle global positioning system trajectory data, road network vector data, vehicle attribute and emission factor data, and points of interest and land use data. The origin-destination matrix generation module is used to perform spatiotemporal clustering processing on the mobile phone signaling positioning data, identify user stop points and construct resident travel activity chains, identify the activity destination type of each stop point in the resident travel activity chain using the points of interest and land use data, and label the origin-destination attributes of resident travel. After labeling, the journey between adjacent stop points is defined as a resident travel, the origin-destination grid of all resident travel is counted and expanded according to the mobile phone signaling sampling rate to generate a resident travel origin-destination matrix; and based on vehicle global positioning system trajectory data, stop clusters are detected, logistics facilities are identified through land use function calibration and a freight logistics network is constructed, and a freight travel origin-destination matrix is ​​generated according to the grid freight intensity calibration. The total trip equivalent matrix generation module is used to unify the resident trip origin-destination matrix and the freight trip origin-destination matrix to the same spatial grid and time slice, and after expanding and correcting them respectively and converting them into standard car equivalents, generate the total trip equivalent matrix. The traffic dynamic simulation module is used to allocate the total trip equivalent matrix to the road network corresponding to the road network vector data, obtain the initial flow and initial average speed of each road segment, determine the generalized trip cost between grids in the road network, and perform dynamic simulation of the regional traffic system to obtain a new total trip equivalent matrix. It is then iterated until the rate of change of the average flow of the road network between two adjacent iterations is less than a preset convergence threshold. Based on the flow and average speed of each road segment after iteration, the road network state sequence is obtained. The carbon emission determination module is used to decompose the traffic flow of each road segment into sub-flows of different vehicle types and fuel types according to a preset vehicle type and fuel composition ratio, input the average speed of each road segment into the speed-dependent emission factor model to calculate the real-time emission factor, and calculate the carbon emission of each vehicle type and fuel type in the corresponding road segment based on the sub-flow and the real-time emission factor, and sum the carbon emission of all vehicle types and fuel types to obtain the traffic carbon emission of each road segment in the target area. The carbon emission accounting module is used to allocate the traffic carbon emissions of each road segment according to the spatial mapping relationship between the road segment and the spatial grid unit, and generate a gridded spatiotemporal inventory of traffic carbon emissions within the target area.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the regional transportation carbon emission accounting method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the regional transportation carbon emission accounting method according to any one of claims 1 to 6.

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