A method and system for constructing a high spatiotemporal resolution mobile source emission inventory

Abstract

This invention discloses a method and system for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory, relating to the field of motor vehicle pollution monitoring technology. The method includes: acquiring vehicle driving information and regional electronic road network linear element files, preprocessing them to obtain a standardized motor vehicle activity dataset and a standardized road network file; matching vehicle trajectories with the regional road network to obtain the vehicle's travel volume on each road segment; determining the vehicle category on the road segment, acquiring historical travel volume data corresponding to the current category of vehicles, finding the historical travel volume most similar to the current category of vehicles from the historical travel volume data, and using the instantaneous pollutant emission rate corresponding to the historical travel volume as the instantaneous pollutant emission rate corresponding to the travel volume; and weighting the instantaneous pollutant emission rates of the road segment according to road length, road grade, and operational complexity to generate an emission inventory with kilometer-level spatial resolution.

Description

Technical Field

[0001] This invention belongs to the field of motor vehicle pollution monitoring technology, and more specifically, relates to a method and system for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory. Background Technology

[0002] In existing urban traffic emission monitoring and assessment technologies, traditional methods typically rely on statistical emission factors or regional average operating conditions for emission calculations. These methods use vehicle type, road grade, and average speed as primary inputs, directly estimating total emissions through static emission factor tables (such as g / km or g / s). The advantages of these methods are their simplicity, low data requirements, and ease of generating emission inventories quickly over large areas, making them suitable for macro-level pollution emission estimation. However, with the increasing complexity of urban traffic systems, especially in high-density urban areas, areas with significant differences in road grades, diverse vehicle types, and highly dynamic traffic flows, these methods exhibit significant limitations. First, traditional statistical emission methods struggle to reflect the instantaneous changes in actual traffic conditions, such as road congestion, frequent starts and stops, and vehicle acceleration and deceleration distribution. These factors have non-linear effects on pollutant emissions, but static factors cannot capture these dynamic effects. Second, traditional methods typically use low spatial resolution grids (such as 5km×5km or 10km×10km), making it difficult to reflect emission differences at the road level, resulting in insufficient accuracy in urban air quality simulation, micro-pollution source analysis, and traffic control decisions.

[0003] Therefore, there is an urgent need for a technical solution that can construct a pollutant emission inventory with high spatiotemporal resolution. Summary of the Invention

[0004] To address the above technical problems, this invention proposes a method for constructing a high spatiotemporal resolution vehicle pollutant emission inventory, comprising:

[0005] Step 101: Obtain vehicle driving information and regional electronic road network linear element files, and perform preprocessing to obtain standardized motor vehicle activity dataset and standardized road network files;

[0006] Step 102: Based on the standardized motor vehicle activity dataset and standardized road network file, match the vehicle trajectory with the regional road network to obtain the vehicle's travel volume on each road segment.

[0007] Step 103: Determine the vehicle category on each road segment, obtain historical driving volume data corresponding to the current category of vehicles, find the historical driving volume most similar to the current category of vehicles from the historical driving volume data, and use the instantaneous pollutant emission rate corresponding to the historical driving volume as the instantaneous pollutant emission rate corresponding to the driving volume.

[0008] Step 104: The instantaneous pollutant emission rate of the road segment is weighted according to the road length, road grade and operational complexity, and mapped to the regional regular grid cell to generate an emission inventory with a spatial resolution of kilometers.

[0009] Furthermore, the preprocessing of vehicle driving information in step 101 includes: receiving real-time short packet data according to a predetermined message format, and performing time standardization, anomaly removal, and short-term caching to obtain a time-slice-level sample set as a standardized motor vehicle activity dataset.

[0010] Furthermore, step 101 preprocesses the regional electronic road network linear feature file, including: traversing each road network line feature in the standardized road network file, dividing it according to boundary points, generating multiple road micro-segments, and saving the static attributes of each road micro-segment;

[0011] Record the IDs of the predecessor and successor road segments for each road segment to build a road network adjacency index and save it as a database table.

[0012] Furthermore, step 102 includes: in the standardized road network file, road micro-segments within a preset radius centered on vehicle trajectory points are selected as a set of candidate road micro-segments;

[0013] The trajectory points in adjacent time slices are combined to form short trajectory segments. The candidate road micro-segments corresponding to each trajectory point in the short trajectory segment are combined to generate candidate paths.

[0014] Calculate the confidence score of each candidate path and convert it into path probability weights. For each time slice of each candidate path, allocate the travel volume of the time slice to each road micro-segment on the candidate path according to the path probability weights, and generate the travel volume of vehicles in each road micro-segment within the time slice.

[0015] Furthermore, step 103 includes: summing the instantaneous pollutant emission rates of each type of vehicle within the road micro-segment and multiplying the sum by the total travel mileage to obtain the total pollutant emission rate of the road micro-segment.

[0016] Furthermore, step 104 includes: for each road micro-segment, the intersection length with the target grid cell is used as the allocation benchmark, and a dynamic influence factor is constructed by combining the low speed ratio, speed variance and truck ratio of the road micro-segment in the time slice.

[0017] The allocation weights are determined based on the dynamic impact factors and intersection lengths, and the pollutant emission rates of road segments are allocated to each intersecting grid cell according to the weights to generate the total pollutant emission rate of each regional regular grid cell in each time slice.

[0018] Furthermore, the emissions inventory with kilometer-level spatial resolution is visualized in the form of regional regular grid cells.

[0019] This invention also proposes a high spatiotemporal resolution vehicle pollutant emission inventory construction system, comprising:

[0020] The preprocessing module is used to acquire vehicle driving information and regional electronic road network linear feature files, and to preprocess them to obtain a standardized motor vehicle activity dataset and a standardized road network file.

[0021] The vehicle trajectories acquisition module is used to match vehicle trajectories with the regional road network based on the standardized motor vehicle activity dataset and standardized road network file to obtain the vehicle trajectories on each road segment.

[0022] The emission rate calculation module is used to determine the vehicle category on each road segment, obtain historical driving volume data corresponding to the current category of vehicles, find the historical driving volume most similar to the current category of vehicles from the historical driving volume data, and use the instantaneous pollutant emission rate corresponding to the historical driving volume as the instantaneous pollutant emission rate corresponding to the driving volume.

[0023] The mapping module is used to weight the instantaneous pollutant emission rate of road segments according to road length, road grade and operational complexity, and map it to regional rule grid cells to generate an emission inventory with kilometer-level spatial resolution.

[0024] Furthermore, the preprocessing module performs preprocessing on vehicle driving information, including receiving real-time short packet data according to a predetermined message format, and performing time standardization, anomaly removal, and short-term caching to obtain a time-slice-level sample set as a standardized motor vehicle activity dataset.

[0025] Furthermore, the preprocessing module preprocesses the regional electronic road network linear feature file by: traversing each road network line feature in the standardized road network file, dividing it by boundary points, generating multiple road micro-segments, and saving the static attributes of each road micro-segment;

[0026] Record the IDs of the predecessor and successor road segments for each road segment to build a road network adjacency index and save it as a database table.

[0027] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0028] The technical solution of this invention precisely couples micro-segment-level pollution emissions with a spatial grid, realizing adaptive projection from road segments to the grid. This allows the emissions of each road at different times to be allocated to 1km×1km grid cells according to their actual geometric location and dynamic operating conditions, thereby achieving the construction of a pollutant emission inventory with high spatiotemporal resolution. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0030] Figure 2 This is a system structure diagram of Embodiment 2 of the present invention. Detailed Implementation

[0031] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0032] The method provided by this invention can be implemented in a terminal environment that may include one or more of the following components: a processor, a storage medium, and a display screen. The storage medium stores at least one instruction, which is loaded and executed by the processor to implement the method described in the following embodiments.

[0033] A processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the terminal, and performs various functions and processes data by running or executing instructions, programs, code sets or instruction sets stored in the storage medium, and by calling data stored in the storage medium.

[0034] Storage media can include random access memory (RAM) or read-only memory (ROM). Storage media can be used to store instructions, programs, code, code sets, or instructions.

[0035] The display screen is used to show the user interface of each application.

[0036] In addition, those skilled in the art will understand that the structure of the terminal described above does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include radio frequency circuits, input units, sensors, audio circuits, power supplies, and other components, which will not be described in detail here.

[0037] Example 1

[0038] like Figure 1 As shown in the figure, this embodiment proposes a method for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory, including:

[0039] Step 101: Obtain vehicle driving information and regional electronic road network linear element files, and perform preprocessing to obtain standardized motor vehicle activity dataset and standardized road network files;

[0040] Specifically, the preprocessing of vehicle driving information in step 101 includes: receiving real-time short packet data according to a predetermined message format, and performing time standardization, anomaly removal, and short-term caching to obtain a time-slice-level sample set as a standardized motor vehicle activity dataset.

[0041] Specifically, step 101 preprocesses the regional electronic road network linear element file, including: traversing each road network line element in the standardized road network file, dividing it according to boundary points, generating multiple road micro-segments, and saving the static attributes of each road micro-segment;

[0042] Record the IDs of the predecessor and successor road segments for each road segment to build a road network adjacency index and save it as a database table.

[0043] Step 102: Based on the standardized motor vehicle activity dataset and standardized road network file, match the vehicle trajectory with the regional road network to obtain the vehicle's travel volume on each road segment.

[0044] Specifically, step 102 includes: in the standardized road network file, road micro-segments within a preset radius centered on vehicle trajectory points are selected as a set of candidate road micro-segments;

[0045] The trajectory points in adjacent time slices are combined to form short trajectory segments. The candidate road micro-segments corresponding to each trajectory point in the short trajectory segment are combined to generate candidate paths.

[0046] Calculate the confidence score of each candidate path and convert it into path probability weights. For each time slice of each candidate path, allocate the travel volume of the time slice to each road micro-segment on the candidate path according to the path probability weights, and generate the travel volume of vehicles in each road micro-segment within the time slice.

[0047] Step 103: Determine the vehicle category on each road segment, obtain historical driving volume data corresponding to the current category of vehicles, find the historical driving volume most similar to the current category of vehicles from the historical driving volume data, and use the instantaneous pollutant emission rate corresponding to the historical driving volume as the instantaneous pollutant emission rate corresponding to the driving volume.

[0048] Specifically, step 103 includes summing the instantaneous pollutant emission rates of each type of vehicle within the road micro-segment and multiplying the sum by the total travel mileage to obtain the total pollutant emissions of the road micro-segment.

[0049] Step 104: The instantaneous pollutant emission rate of the road segment is weighted according to the road length, road grade and operational complexity, and mapped to the regional regular grid cell to generate an emission inventory with a spatial resolution of kilometers.

[0050] Specifically, step 104 includes: for each road micro-segment, the intersection length with the target grid cell is used as the allocation benchmark, and a dynamic influencing factor is constructed by combining the low speed ratio of the road micro-segment in the time slice (the proportion of vehicles in the road micro-segment that are in a low speed state to all vehicles in the road micro-segment, wherein when the vehicle speed is less than a preset speed threshold (e.g., 20km / h), the vehicle is determined to be in a low speed state), speed variance, and truck ratio.

[0051] The allocation weights are determined based on the dynamic impact factors and intersection lengths, and the pollutant emission rates of road segments are allocated to each intersecting grid cell according to the weights to generate the total pollutant emission rate of each regional regular grid cell in each time slice.

[0052] Specifically, the emissions inventory with kilometer-level spatial resolution will be visualized in the form of regional regular grid cells.

[0053] Example 2

[0054] like Figure 2 As shown, this embodiment proposes a high spatiotemporal resolution motor vehicle pollutant emission inventory construction system, including:

[0055] The preprocessing module is used to acquire vehicle driving information and regional electronic road network linear feature files, and to preprocess them to obtain a standardized motor vehicle activity dataset and a standardized road network file.

[0056] Specifically, the preprocessing module performs preprocessing on vehicle driving information, including receiving real-time short packet data according to a predetermined message format, and performing time standardization, anomaly removal, and short-term caching to obtain a time-slice-level sample set as a standardized motor vehicle activity dataset.

[0057] Specifically, the preprocessing module preprocesses the regional electronic road network linear feature file by: traversing each road network line feature in the standardized road network file, dividing it by boundary points, generating multiple road micro-segments, and saving the static attributes of each road micro-segment;

[0058] Record the IDs of the predecessor and successor road segments for each road segment to build a road network adjacency index and save it as a database table.

[0059] The vehicle trajectories acquisition module is used to match vehicle trajectories with the regional road network based on the standardized motor vehicle activity dataset and standardized road network file to obtain the vehicle trajectories on each road segment.

[0060] Specifically, the traffic acquisition module includes: in the standardized road network file, road micro-segments within a preset radius centered on vehicle trajectory points are used as a set of candidate road micro-segments;

[0061] The trajectory points in adjacent time slices are combined to form short trajectory segments. The candidate road micro-segments corresponding to each trajectory point in the short trajectory segment are combined to generate candidate paths.

[0062] Calculate the confidence score of each candidate path and convert it into path probability weights. For each time slice of each candidate path, allocate the travel volume of the time slice to each road micro-segment on the candidate path according to the path probability weights, and generate the travel volume of vehicles in each road micro-segment within the time slice.

[0063] The emission rate calculation module is used to determine the vehicle category on each road segment, obtain historical driving volume data corresponding to the current category of vehicles, find the historical driving volume most similar to the current category of vehicles from the historical driving volume data, and use the instantaneous pollutant emission rate corresponding to the historical driving volume as the instantaneous pollutant emission rate corresponding to the driving volume.

[0064] Specifically, the emission rate calculation module includes summing the instantaneous pollutant emission rates of each type of vehicle within a road micro-segment and multiplying the sum by the total travel distance to obtain the total pollutant emission rate of the road micro-segment.

[0065] The mapping module weights the instantaneous pollutant emission rates of road segments according to road length, road grade, and operational complexity, and maps them to regional rule grid cells to generate an emission inventory with kilometer-level spatial resolution.

[0066] Specifically, the mapping module includes: for each road micro-segment, the intersection length with the target grid cell is used as the allocation benchmark, and a dynamic influence factor is constructed by combining the low speed ratio, speed variance and truck ratio of the road micro-segment within the time slice;

[0067] The allocation weights are determined based on the dynamic impact factors and intersection lengths, and the pollutant emission rates of road segments are allocated to each intersecting grid cell according to the weights to generate the total pollutant emission rate of each regional regular grid cell in each time slice.

[0068] Specifically, the emissions inventory with kilometer-level spatial resolution will be visualized in the form of regional regular grid cells.

[0069] Example 3

[0070] This invention also proposes a storage medium storing multiple instructions for implementing the method for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory.

[0071] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0072] Optionally, in this embodiment, the storage medium is configured to store program code for performing the method steps of Embodiment 1.

[0073] Example 4

[0074] This invention also proposes an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, which can be loaded and executed by the processor to enable the processor to execute the method for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory.

[0075] Specifically, the electronic device in this embodiment can be a computer terminal, which may include one or more processors and a storage medium.

[0076] The storage medium can be used to store software programs and modules, such as the high spatiotemporal resolution motor vehicle pollutant emission inventory construction method in this embodiment of the invention. The corresponding program instructions / modules are executed by the processor through running the software programs and modules stored in the storage medium, thereby performing various functional applications and data processing, thus realizing the aforementioned high spatiotemporal resolution motor vehicle pollutant emission inventory construction method. The storage medium may include high-speed random access storage media, and may also include non-volatile storage media, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage media. In some instances, the storage medium may further include storage media remotely configured relative to the processor, which can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0077] The processor can execute the method steps of Embodiment 1 by calling the information and application stored in the storage medium through the transmission system.

[0078] Example 5

[0079] This embodiment provides a specific example of a method for constructing a high spatiotemporal resolution vehicle pollutant emission inventory, as shown below:

[0080] 1. Data Preparation

[0081] 1.1 Obtain and standardize regional road network elements: Obtain the linear element files of the regional electronic road network (line strings, intersection labels, road grades, number of lanes, speed limits, slopes or elevation points, etc.), and convert them into a unified format within the system (unique segment_id, directional geometry, etc.). Directed geometry refers to breaking down the geometric shape of the road (usually a polyline) into geometric objects with directional attributes according to the direction of vehicle traffic, rather than simply undirected line segments.

[0082] Output: Standardized road network (set of line features); fields must include at least segment_id (unique identifier of road segment), geometry (spatial geometry of road segment), road_class (road class, such as expressway, national highway, provincial highway, urban arterial road, secondary arterial road, local road, etc.), lanes (number of lanes in road segment), speed_limit (maximum speed limit stipulated by the road), and elevation_profile (elevation or slope information of road segment).

[0083] 1.2 Determine the sampling source interface specifications: Define the reporting fields, time granularity and secure transmission protocol for mobile nodes (Flux-Mobile) and fixed nodes (Flux-Anchor), and stipulate that each message includes a timestamp, location, speed, acceleration, vehicle type label and irreversible aggregate signature.

[0084] Output: Message specification document (e.g., JSON schema description).

[0085] 2. Road network segmentation: The standardized road network is divided into the smallest directed micro-segments that can ensure homogeneity of operating conditions, providing the basic geometric units for trajectory allocation and emission estimation.

[0086] 2.1 Traverse each road network element and divide it according to the following boundary points: intersection points, speed limit change points, lane number change points, significant gradient change points (absolute gradient change >1% as threshold), traffic light or parking area locations. If the road segment is very long, force a segment every 200–500 meters to control the micro-segment length.

[0087] 2.2 Static attributes are saved for each micro-segment (road micro-segment):

[0088] length_m: Precise line segment length (meters).

[0089] directed_geometry: a directed line string.

[0090] nominal_speed: The speed limit for this section of road or the average historical speed for this section.

[0091] lanes, grade_mean (average slope), intersection_flag (whether it is close to an intersection).

[0092] 2.3 Construct a road network adjacency index: Record the predecessor and successor micro-segment IDs for each micro-segment for path candidate generation and continuity checks. Accelerate the nearest neighbor retrieval from a point to a micro-segment through a spatial index (e.g., a quadtree or a grid index).

[0093] 2.4 Save the micro-segment as a database table.

[0094] Output: Database tables, road network adjacency index.

[0095] 3. Data reception and preliminary cleaning

[0096] 3.1 Receiving and Unpacking: Receive short packets, verify message signatures and integrity, and deduplicate duplicate messages.

[0097] 3.2 Time standardization: unify the timestamp to UTC, and then round down by Δt (time slice or time slice) (e.g., 15:07:32 → 15:07:00, if Δt = 1 minute), while recording the original time for auditing.

[0098] 3.3 Validity and Anomaly Removal: Records with velocity or acceleration exceeding physical thresholds are removed (velocity <0 or >120m / s; absolute acceleration >10 m / s² is considered anomaly, etc.), and the anomaly entries are written to the anomaly queue for manual inspection.

[0099] 3.4 Spatial filtering: Discard points outside the region; label records with extremely poor GPS accuracy (such as HDOP exceeding the threshold) with low confidence.

[0100] 3.5 Short-term cache: Write the processed samples to a short-term time cache (e.g., using an in-memory database or lightweight file storage) according to Δt, and retain the original samples from the most recent 24–72 hours for backtracking.

[0101] Output: The validated Δt-level sample set (short-term cache).

[0102] 4. Trajectory-Road Network Coupling

[0103] 4.1 Generation of candidate road micro-segments: For each sample point (time t, latitude and longitude, speed and direction information) in the Δt-level sample set, micro-segments within a radius R (e.g., 50–100 meters) from that point are retrieved from the spatial index as a set of candidate road micro-segments. The selection of R can be dynamically adjusted according to the road density (R is set smaller when the urban density is high), and the number of candidate road micro-segments is limited to ensure computational efficiency.

[0104] 4.2 Path Confidence Score: Multiple consecutive sample points within the same Δt (e.g., segments of adjacent time windows) are combined into short trajectory segments. For these short trajectory segments, several micro-segment paths are concatenated from the candidate road micro-segment set of the sample points that make up the short trajectory segments according to the road adjacency relationship (micro-segment adjacency index) as candidate paths. Each path is a series of micro-segments.

[0105] For each candidate path, a confidence score is calculated, which consists of three parts: the geometric projection distance (the shortest distance from the sample point to the line), the lateral deviation (the angle between the direction of travel of the sample point and the direction of the candidate road segment), and the speed consistency factor (the absolute value of the difference between the speed of the sample point and the nominal_speed of the segment).

[0106] Preferably, in this embodiment, calculating the confidence score for each candidate path specifically includes:

[0107] For each candidate path Calculate the three types of deviation, namely distance deviation: (The shortest distance from the point (the coordinates of the sample point in the Δt-level sample set) to the line (the geometric center line of the candidate path), along the bias) Speed ​​deviation The biases are mapped to [0,1] using a Gaussian kernel:

[0108] ,

[0109] in, The Gaussian kernel for distance deviation, For the Gaussian kernel with along-direction bias, For the Gaussian kernel of velocity deviation, Using distance as the scale, For directional scale, For velocity scale.

[0110] Calculate the confidence score for each candidate path:

[0111] ,

[0112] in, Candidate paths confidence score, The weights of the Gaussian kernel for distance deviation, The weights of the Gaussian kernel for the along-biased direction are... The fraction of the Gaussian kernel for velocity deviation. , and The sum is 1.

[0113] Normalize to path probability weights and sort:

[0114] ,

[0115] in, Candidate paths Path probability weights, The number of candidate paths, For the first The score of each candidate path will Sort by size from largest to smallest.

[0116] 4.3 Allocation Iteration: For each candidate path within a time slice, the travel volume of that time slice is proportionally allocated to each candidate road segment on the candidate path according to the path probability weight.

[0117] After performing the above allocation on all samples within a time window in batches, the travel volume of each candidate road segment in that Δt is obtained.

[0118] Preferably, the travel volume of the time slice is proportionally allocated to each candidate road segment on the candidate path according to the path probability weight, including: multiplying the travel volume of the time slice by the path probability weight of each candidate path. The current travel volume of the candidate path is obtained; the current micro-segment is multiplied by the current travel volume of the candidate path to obtain the current micro-segment travel volume.

[0119] 5. Obtain total pollutant emissions

[0120] The data includes: speed, acceleration, vehicle low-speed ratio (the percentage of vehicles whose speed is below the speed threshold in a given time segment), climbing power, and vehicle type on each road segment.

[0121] Find the historical mileage data of the same category that is most similar to the current micro-segment mileage. Use the instantaneous pollutant emission rate corresponding to the historical mileage as the instantaneous pollutant emission rate corresponding to the current micro-segment mileage. Sum the instantaneous pollutant emission rates of each type of vehicle in the current micro-segment and multiply by the total mileage to obtain the total pollutant emissions of the current micro-segment.

[0122] 6. Projection of road segment to grid (projected onto a 1km×1km road grid unit)

[0123] For each micro-segment Obtain its intersections with all intersecting grids. Intersection length ;

[0124] For time slices Each micro-segment Calculate dynamic impact factors This is used to reflect the amplification or reduction of the impact of the current operating conditions of the micro-segment on the surrounding air quality;

[0125] Calculate dynamic impact factors include:

[0126] ,

[0127] in, The weighting of the vehicle's low-speed ratio. For time slices Inner microsegments The average low-speed ratio of the vehicles. As the weight of the velocity variance, For time slices Inner microsegments The mean of the variance of the speeds of the vehicles. The weighting of the proportion of trucks, For time slices Inner microsegments The proportion of trucks loaded with goods is used in calculating the dynamic impact factor. Previously, for and After normalization, it falls into (0,1).

[0128] For each intersecting cell Calculate the weights:

[0129] ,

[0130] in, For grid The weight, For grid .

[0131] Calculate grid-level pollutant emissions:

[0132] ,

[0133] in, For time slices inner grid Total pollutant emissions For time slices Inner microsegments Total pollutant emissions.

[0134] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0135] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0136] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The system embodiments described above are merely illustrative; for example, the division of units 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 system, 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, indirect coupling or communication connection between units or modules, and may be electrical or other forms.

[0137] The units described 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.

[0138] Furthermore, the functional units in the various embodiments of the present invention 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.

[0139] 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 the present invention, 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 several 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 described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, optical disks, and other media capable of storing program code.

[0140] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for constructing a high spatiotemporal resolution vehicle pollutant emission inventory, characterized in that, include: Step 101: Obtain vehicle driving information and regional electronic road network linear element files, and perform preprocessing to obtain standardized motor vehicle activity dataset and standardized road network files; Step 102: Based on the standardized motor vehicle activity dataset and standardized road network file, match the vehicle trajectory with the regional road network to obtain the vehicle's travel volume on each road segment. The amount of time a vehicle travels on each road segment is obtained by: in the standardized road network file, road segments within a preset radius centered on the vehicle trajectory point are used as a set of candidate road segments; The trajectory points in adjacent time slices are combined to form short trajectory segments. The candidate road micro-segments corresponding to each trajectory point in the short trajectory segment are combined to generate candidate paths. Calculate the confidence score of each candidate path and convert it into path probability weights. For each time slice of each candidate path, allocate the travel volume of the time slice to each road micro-segment on the candidate path according to the path probability weights, and generate the travel volume of vehicles in each road micro-segment within the time slice. Step 103: Determine the vehicle category on each road segment, obtain historical driving volume data corresponding to the current category of vehicles, find the historical driving volume most similar to the current category of vehicles from the historical driving volume data, and use the instantaneous pollutant emission rate corresponding to the historical driving volume as the instantaneous pollutant emission rate corresponding to the driving volume. Step 104: The instantaneous pollutant emission rate of the road segment is weighted according to the road length, road grade and operational complexity, and mapped to the regional regular grid cell to generate an emission inventory with a spatial resolution of kilometers. For each road micro-segment, the intersection length with the target grid cell is used as the allocation benchmark. At the same time, a dynamic influence factor is constructed by combining the low speed ratio, speed variance and truck ratio of the road micro-segment within the time slice. The allocation weights are determined based on the dynamic impact factors and intersection lengths, and the pollutant emission rates of road segments are allocated to each intersecting grid cell according to the weights to generate the total pollutant emission rate of each regional regular grid cell in each time slice.

2. The method for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory as described in claim 1, characterized in that, Step 101 involves preprocessing vehicle driving information, including receiving real-time short packet data according to a predetermined message format, and performing time standardization, anomaly removal, and short-term caching to obtain a time-slice-level sample set as a standardized motor vehicle activity dataset.

3. The method for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory as described in claim 2, characterized in that, Step 101 preprocesses the regional electronic road network linear feature file, including: traversing each road network line feature in the standardized road network file, dividing it by boundary points, generating multiple road micro-segments, and saving the static attributes of each road micro-segment; Record the IDs of the predecessor and successor road segments for each road segment to build a road network adjacency index and save it as a database table.

4. The method for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory as described in claim 3, characterized in that, Step 103 includes: summing the instantaneous pollutant emission rates of each type of vehicle within the road micro-segment and multiplying the sum by the total travel mileage to obtain the total pollutant emissions of the road micro-segment.

5. The method for constructing a high spatiotemporal resolution motor vehicle pollutant emission inventory as described in claim 1, characterized in that, The emissions inventory with kilometer-level spatial resolution is visualized in the form of regional regular grid cells.

6. A system for constructing a high spatiotemporal resolution vehicle pollutant emission inventory, characterized in that, include: The preprocessing module is used to acquire vehicle driving information and regional electronic road network linear feature files, and to preprocess them to obtain a standardized motor vehicle activity dataset and a standardized road network file. The vehicle trajectories acquisition module is used to match vehicle trajectories with the regional road network based on the standardized motor vehicle activity dataset and standardized road network file to obtain the vehicle trajectories on each road segment. The amount of time a vehicle travels on each road segment is obtained by: in the standardized road network file, road segments within a preset radius centered on the vehicle trajectory point are used as a set of candidate road segments; The trajectory points in adjacent time slices are combined to form short trajectory segments. The candidate road micro-segments corresponding to each trajectory point in the short trajectory segment are combined to generate candidate paths. Calculate the confidence score of each candidate path and convert it into path probability weights. For each time slice of each candidate path, allocate the travel volume of the time slice to each road micro-segment on the candidate path according to the path probability weights, and generate the travel volume of vehicles in each road micro-segment within the time slice. The emission rate calculation module is used to determine the vehicle category on each road segment, obtain historical driving volume data corresponding to the current category of vehicles, find the historical driving volume most similar to the current category of vehicles from the historical driving volume data, and use the instantaneous pollutant emission rate corresponding to the historical driving volume as the instantaneous pollutant emission rate corresponding to the driving volume. The mapping module is used to weight the instantaneous pollutant emission rate of road segments according to road length, road grade and operational complexity, and map it to regional regular grid cells to generate an emission inventory with kilometer-level spatial resolution. For each road micro-segment, the intersection length with the target grid cell is used as the allocation benchmark. At the same time, a dynamic influence factor is constructed by combining the low speed ratio, speed variance and truck ratio of the road micro-segment within the time slice. The allocation weights are determined based on the dynamic impact factors and intersection lengths, and the pollutant emission rates of road segments are allocated to each intersecting grid cell according to the weights to generate the total pollutant emission rate of each regional regular grid cell in each time slice.

7. The high spatiotemporal resolution motor vehicle pollutant emission inventory construction system as described in claim 6, characterized in that, The preprocessing module performs preprocessing on vehicle driving information, including receiving real-time short packet data according to a predetermined message format, and performing time standardization, anomaly removal, and short-term caching to obtain a time-slice-level sample set as a standardized motor vehicle activity dataset.

8. The high spatiotemporal resolution motor vehicle pollutant emission inventory construction system as described in claim 7, characterized in that, The preprocessing module preprocesses the linear feature files of the regional electronic road network, including: traversing each road network line feature in the standardized road network file, dividing it according to boundary points, generating multiple road micro-segments, and saving the static attributes of each road micro-segment; Record the IDs of the predecessor and successor road segments for each road segment to build a road network adjacency index and save it as a database table.

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