SUMO road network coordinate conversion method and device based on adaptive learning algorithm, electronic equipment and storage medium

Abstract

The application provides a SUMO road network coordinate conversion method and device based on an adaptive learning algorithm, electronic equipment and a storage medium, and belongs to the technical field of data processing. The method comprises the following steps: extracting node information, edge information and lane information from road network data; generating a point geometric object based on the node information; generating a line geometric object of a road edge based on the edge information; generating a surface geometric object based on the lane information; converting two-dimensional coordinates of the geometric object into WGS84 space coordinates by using an integrated learning model that integrates multiple heterogeneous machine learning algorithms; if the converted WGS84 space coordinates are abnormal, interpolation repair, repair based on a preset geometric constraint, or repair based on network topology connectivity is performed; and updating the road network data based on the WGS84 space coordinates. The application can capture the nonlinear deformation of a complex road network and improve the conversion accuracy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a SUMO road network coordinate transformation method, apparatus, electronic device, and storage medium based on an adaptive learning algorithm. Background Technology

[0002] SUMO, as a microscopic traffic simulation software, uses a local Cartesian coordinate system to describe traffic elements (lanes, nodes, traffic lights, etc.) in its road network data. However, practical applications require WGS84 geographic coordinates to support scenarios such as GIS analysis and high-precision map matching.

[0003] Traditional coordinate transformation methods require pre-defined, fixed transformation parameters. For example, the seven-parameter transformation method employs a multi-step transformation process (WGS84 → Cartesian → 7 parameters → Clarke80), but requires pre-determined fixed transformation parameters. Another example is the projection parameter transformation method, which uses the Proj4js library for coordinate system transformation and relies on pre-defined projection parameters. This approach depends on fixed parameters, and the transformation accuracy is limited by the accuracy of the pre-defined parameters. It lacks an adaptive optimization mechanism, resulting in limited coordinate transformation accuracy.

[0004] Therefore, improving the accuracy of SUMO road network coordinate transformation based on adaptive learning algorithms has become an urgent technical problem to be solved. Summary of the Invention

[0005] This invention provides a SUMO road network coordinate transformation method, device, electronic device, and storage medium based on an adaptive learning algorithm, which solves the defect in the prior art where the accuracy of SUMO road network coordinate transformation based on adaptive learning algorithm is limited by the accuracy of preset parameters, resulting in insufficient coordinate transformation accuracy.

[0006] This invention provides a SUMO road network coordinate transformation method based on an adaptive learning algorithm, comprising the following steps:

[0007] Node information, edge information, and lane information are extracted from road network data; the node information is the attribute information of intersections, the edge information is the attribute information of road edges, and the lane information is the attribute information of lanes.

[0008] Based on the node information, a point geometry object is generated;

[0009] Based on the edge information, a line geometry object of the road edge is generated;

[0010] Based on the line geometry object, generate the surface geometry object of the lane surface;

[0011] Using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometry, the line geometry of the road edge, and the surface geometry are converted into WGS84 spatial coordinates; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates.

[0012] If the converted WGS84 spatial coordinates are abnormal, interpolation is performed based on the WGS84 spatial coordinates of neighboring points, or based on preset geometric constraints, or based on network topology connectivity.

[0013] The road network data is updated based on the WGS84 spatial coordinates.

[0014] According to the present invention, a SUMO road network coordinate transformation method based on an adaptive learning algorithm is provided, wherein the ensemble learning model includes a base layer, a core layer, and a fusion layer;

[0015] The base layer expands the two-dimensional coordinates to a cubic polynomial feature space, resulting in a nine-dimensional feature vector.

[0016] The core layer includes a parallel support vector machine regression model, a random forest regression model, and a gradient boosting regression model. The support vector machine regression model, the random forest regression model, and the gradient boosting regression model generate prediction results based on the nine-dimensional feature vector, respectively.

[0017] The fusion layer includes a ridge regressor learner that generates WGS84 spatial coordinates based on the three prediction results.

[0018] According to the SUMO road network coordinate transformation method based on an adaptive learning algorithm provided by the present invention, before performing interpolation repair based on the WGS84 spatial coordinates of neighboring points, or repair based on preset geometric constraints, or repair based on network topology connectivity if the transformed WGS84 spatial coordinates are abnormal, the method further includes:

[0019] The continuity of the WGS84 spatial coordinates is detected in real time. If the WGS84 spatial coordinates change abruptly, it is determined that the WGS84 spatial coordinates are abnormal.

[0020] The geographic rationality of the WGS84 spatial coordinates is detected in real time. If the WGS84 spatial coordinates are not within the preset geographic range, the WGS84 spatial coordinates are determined to be abnormal.

[0021] The transformation accuracy of the WGS84 spatial coordinates is detected in real time. If the transformation accuracy is less than a preset accuracy threshold, the WGS84 spatial coordinates are determined to be abnormal.

[0022] According to the present invention, a SUMO road network coordinate transformation method based on an adaptive learning algorithm is provided, wherein the SUMO road network coordinate transformation method based on the adaptive learning algorithm further includes:

[0023] The updated road network data will be output as Shapefile format data.

[0024] According to the present invention, a SUMO road network coordinate transformation method based on an adaptive learning algorithm is provided, wherein the SUMO road network coordinate transformation method based on the adaptive learning algorithm further includes:

[0025] The vehicle trajectory data is parsed to identify the vehicle ID and trajectory point data;

[0026] Based on the vehicle ID and the trajectory point data, construct the driving trajectory of each vehicle;

[0027] Generate lane surface data based on Shapefile format data of the lane centerline;

[0028] Vehicle trajectory data is generated based on the driving trajectory and the lane surface data.

[0029] According to the SUMO road network coordinate transformation method based on an adaptive learning algorithm provided by the present invention, the generation of lane surface data from Shapefile format data based on lane centerlines includes:

[0030] Generate lane regions based on Shapefile format data of lane centerlines;

[0031] Project the WGS84 spatial coordinates of the lane centerline onto the UTM 50N coordinate system to obtain the UTM 50N spatial coordinates;

[0032] Based on the lane width information, a buffering operation is performed on the UTM 50N spatial coordinates to generate a lane surface;

[0033] After removing overlaps and repairing gaps in the lane surface, the repaired data is projected into the WGS84 space to obtain lane surface data.

[0034] The present invention also provides a SUMO road network coordinate transformation device based on an adaptive learning algorithm, comprising the following modules:

[0035] The information extraction module is used to extract node information, edge information, and lane information from road network data; the node information is the attribute information of intersections, the edge information is the attribute information of road edges, and the lane information is the attribute information of lanes.

[0036] The point generation module is used to: generate point geometric objects based on the node information;

[0037] The edge generation module is used to: generate line geometry objects of the road edge based on the edge information;

[0038] The central generation module is used to: generate a surface geometry object of the lane surface based on the line geometry object;

[0039] The coordinate transformation module is used to: convert the two-dimensional coordinates of the point geometric object, the line geometric object of the road edge, and the surface geometric object into WGS84 spatial coordinates using a pre-trained ensemble learning model; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates;

[0040] The anomaly repair module is used to: if the converted WGS84 spatial coordinates are abnormal, perform interpolation repair based on the WGS84 spatial coordinates of nearby points, or repair based on preset geometric constraints, or repair based on network topology connectivity.

[0041] The road network update module is used to update the road network data based on the WGS84 spatial coordinates.

[0042] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the SUMO road network coordinate transformation method based on the adaptive learning algorithm as described above.

[0043] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the SUMO road network coordinate transformation method based on the adaptive learning algorithm as described above.

[0044] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the SUMO road network coordinate transformation method based on the adaptive learning algorithm as described above.

[0045] This invention provides a SUMO road network coordinate transformation method, device, electronic device, and storage medium based on an adaptive learning algorithm. It extracts node information, edge information, and lane information from road network data. The node information refers to the attribute information of intersections, the edge information refers to the attribute information of road edges, and the lane information refers to the attribute information of lanes. Based on the node information, point geometric objects are generated; based on the edge information, line geometric objects of road edges are generated; based on the line geometric objects, surface geometric objects of lane surfaces are generated. Using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometric objects, the line geometric objects of the road edges, and the surface geometric objects are converted into WGS84 spatial coordinates. The ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates. If the converted WGS84 spatial coordinates are abnormal, interpolation repair is performed based on the WGS84 spatial coordinates of neighboring points, or repair is performed based on preset geometric constraints, or repair is performed based on network topology connectivity. Based on the WGS84 spatial coordinates, the road network data is updated. Traditional methods rely on preset fixed transformation parameters or standard projection models, making it difficult to handle issues such as local distortion and nonlinear deformation in SUMO road networks, resulting in large transformation errors. This invention, by introducing an ensemble learning model, can automatically learn local spatial transformation rules based on actual road network data, significantly improving the accuracy of coordinate transformation. This is particularly evident in scenarios with complex urban structures and elevated highway ramps, where it achieves significantly higher transformation accuracy than traditional methods. Traditional methods require manual adjustment of transformation parameters in different cities or terrain regions, lacking adaptability. In contrast, the ensemble learning model used in this invention possesses excellent generalization capabilities, supports joint training with samples from multiple cities and terrains, and can dynamically update model parameters through an online learning mechanism, significantly improving the system's adaptability and transferability. Furthermore, this invention reconstructs point geometric objects, line geometric objects at road edges, and surface geometric objects in a hierarchical manner. Different geometric objects exhibit different behavioral characteristics during spatial deformation. For example, intersection nodes are more suitable for precise point-to-point mapping, while lane centerlines are more suitable for linear fitting and smoothing. Through hierarchical modeling, a more suitable transformation strategy can be designed for each type of geometric object, avoiding the "one-size-fits-all" error caused by a globally uniform model and significantly improving the overall transformation accuracy. Moreover, the SUMO road network has a complex topological structure (such as connection relationships, lane affiliation, traffic light control, etc.). This topological information still needs to be maintained after geographic coordinate transformation. By modeling point, line, and surface objects separately and performing consistency verification, the topological relationships such as road connectivity, lane continuity, and intersection structure can be effectively maintained, avoiding problems such as "road breaks" and "misalignments". Attached Figure Description

[0046] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is one of the flowcharts of the SUMO road network coordinate transformation method based on adaptive learning algorithm provided by the present invention;

[0048] Figure 2 This is the second flowchart of the SUMO road network coordinate transformation method based on adaptive learning algorithm provided by the present invention;

[0049] Figure 3 This is the third flowchart of the SUMO road network coordinate transformation method based on adaptive learning algorithm provided by the present invention;

[0050] Figure 4 This is a schematic diagram of the SUMO road network coordinate transformation device based on adaptive learning algorithm provided by the present invention;

[0051] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention;

[0052] Figure 6 This is a schematic diagram of the line geometry of the lane centerline provided by the present invention;

[0053] Figure 7 This is a schematic diagram of the surface geometry of the lane surface provided by the present invention;

[0054] Figure 8 This is a schematic diagram of the linear geometry of a lane intersection provided by the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0056] It should be noted that in the description of the embodiments of the present invention, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly, for example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0057] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0058] The following is combined Figures 1-8 This invention describes the SUMO road network coordinate transformation method, apparatus, electronic device, and storage medium based on an adaptive learning algorithm provided in embodiments of the present invention.

[0059] Figure 1 This is one of the flowcharts illustrating the SUMO road network coordinate transformation method based on adaptive learning algorithm provided by this invention, such as... Figure 1 As shown, the method includes the following:

[0060] S110, extract node information, edge information and lane information from road network data; the node information is the attribute information of the intersection, the edge information is the attribute information of the road edge, and the lane information is the attribute information of the lane.

[0061] S120, Based on the node information, generate a point geometry object;

[0062] S130, Based on the edge information, generate a line geometry object of the road edge;

[0063] S140, Based on the line geometry object, generate the surface geometry object of the lane surface;

[0064] S150, using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometry object, the line geometry object of the road edge, and the surface geometry object are converted into WGS84 spatial coordinates; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates;

[0065] S160. If the converted WGS84 spatial coordinates are abnormal, then interpolation is performed based on the WGS84 spatial coordinates of the neighboring points, or based on preset geometric constraints, or based on network topology connectivity.

[0066] S170, Update the road network data based on the WGS84 spatial coordinates.

[0067] It should be noted that the execution subject of the SUMO road network coordinate transformation method based on adaptive learning algorithm provided in the embodiments of the present invention can be a server, computer device, such as a tablet computer, laptop computer, handheld computer, vehicle electronic device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc.

[0068] In S110, the road network data, i.e., the SUMO network file to be converted, is stored in XML format and contains a complex nested structure. Here, different types of road network elements are identified from the road network data. The parsing process first reads the location tag in the file header and extracts key information such as network offset, coordinate boundaries, and projection parameters. This information provides important references for subsequent coordinate transformation. Specifically, all junction tags are traversed, and internal type nodes used within the SUMO are automatically filtered out, retaining only the node data representing actual intersections. For each valid node, its ID, coordinates, node type, and other attribute information are extracted to obtain node information. Node types include priority, traffic light, and right-before-left, which are of great significance in GIS analysis. Edge labels are processed to identify road connection relationships. Each edge contains attributes such as the starting node ID, ending node ID, and road type to obtain edge information. Lane labels under the edge are parsed to obtain detailed lane-level information, including lane ID, speed limit, length, and precise geometric shape point sequence. The geometric shape is stored in string format and needs to be parsed into an array of coordinate points to obtain lane information.

[0069] In S120, the node layer creates point geometry objects. Each intersection corresponds to a Point geometry, retaining the original attribute information, such as node ID, coordinate position, and type.

[0070] In S130, the edge layer creates line geometry objects, generating LineString line geometry based on the coordinates of the start and end nodes of the edge.

[0071] Figure 6 This is a schematic diagram of the linear geometry of the lane centerline provided by the present invention, such as... Figure 6 As shown, each lane centerline corresponds to a line geometry object.

[0072] Figure 8 This is a schematic diagram of the linear geometry of a lane intersection provided by the present invention, such as... Figure 8 As shown, a lane intersection includes intersecting transverse and longitudinal line geometric objects.

[0073] In S140, such as Figure 7As shown, the lane layer creates refined line geometry objects. After generating high-precision lane centerlines based on the detailed geometric points of each lane, it is tiled and expanded into surface geometry objects for the lane surface. In essence, a hierarchical spatial data structure is constructed based on the parsed raw data. The execution order of S120, S130, and S140 is not strictly limited and can be determined according to actual usage requirements.

[0074] It should be noted that the original topological relationships are strictly maintained during the creation of geometric objects. Connections between edges and nodes are established through node IDs to ensure the connectivity of the road network. Simultaneously, the hierarchical relationship between edges and lanes is maintained to support multi-level spatial queries and analysis.

[0075] In S150, a trained ensemble learning model is applied to transform the coordinates of each geometric object. Point geometric objects directly transform their coordinates, while line geometric objects transform their shape point sequence point by point.

[0076] In S160, quality checks are performed in real time during the transformation process. For abnormal WGS84 spatial coordinates detected, interpolation repair is performed based on the transformation results of neighboring points; or, geometric constraints are used to maintain the shape characteristics of linear features (such as road curvature) for repair; or, topology checks are used to ensure the correctness of network connections.

[0077] In S170, the SUMO road network coordinates in the road network data are replaced with the corresponding WGS84 spatial coordinates to obtain the updated road network data.

[0078] The SUMO road network coordinate transformation method based on an adaptive learning algorithm provided in this invention extracts node information, edge information, and lane information from road network data. The node information refers to the attribute information of intersections, the edge information refers to the attribute information of road edges, and the lane information refers to the attribute information of lanes. Based on the node information, point geometric objects are generated; based on the edge information, line geometric objects of road edges are generated; based on the line geometric objects, surface geometric objects of lane surfaces are generated. Using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometric objects, the line geometric objects of road edges, and the surface geometric objects are converted into WGS84 spatial coordinates. The ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates. If the converted WGS84 spatial coordinates are abnormal, interpolation repair is performed based on the WGS84 spatial coordinates of neighboring points, or repair is performed based on preset geometric constraints, or repair is performed based on network topology connectivity. Based on the WGS84 spatial coordinates, the road network data is updated. This invention predicts and generates WGS84 spatial coordinates by integrating multiple heterogeneous machine learning algorithms into an ensemble learning model. This model can capture the nonlinear deformation of complex road networks, such as spiral ramps of interchanges, and achieve centimeter-level conversion accuracy of road networks. In addition, it performs hierarchical reconstruction of point geometric objects, line geometric objects of road edges, and surface geometric objects to ensure the integrity of the conversion data.

[0079] In an optional embodiment, the ensemble learning model includes a base layer, a core layer, and a fusion layer;

[0080] The base layer expands the two-dimensional coordinates to a cubic polynomial feature space, resulting in a nine-dimensional feature vector.

[0081] The core layer includes a parallel support vector machine regression model, a random forest regression model, and a gradient boosting regression model. The support vector machine regression model, the random forest regression model, and the gradient boosting regression model generate prediction results based on the nine-dimensional feature vector, respectively.

[0082] The fusion layer includes a ridge regressor learner that generates WGS84 spatial coordinates based on the three prediction results.

[0083] In this embodiment of the invention, a three-layer integrated learning architecture is constructed. The base layer employs a polynomial feature expansion technique to extend the original two-dimensional coordinate features (x, y) to a cubic polynomial feature space, generating a nine-dimensional feature vector including x, y, x², y², xy, x³, y³, x²y, and xy². This nonlinear feature expansion can capture complex coordinate mapping relationships and is particularly suitable for handling nonlinear transformations in geographic coordinate transformations.

[0084] In this embodiment of the invention, the core layer integrates three heterogeneous machine learning algorithms. Support Vector Regression (SVR) uses a radial basis function kernel, which can handle high-dimensional nonlinear mappings; Random Forest Regression utilizes an ensemble voting mechanism of multiple decision trees, exhibiting good anti-overfitting ability; and Gradient Boosting Regression (XGBoost) achieves high-precision prediction through iterative optimization of residuals. Each algorithm has its advantages: SVR performs excellently with small sample sizes, Random Forest is insensitive to outliers, and XGBoost has extremely high prediction accuracy.

[0085] In this embodiment of the invention, the fusion layer employs a stacked ensemble strategy, using the prediction results of three base models as input features to train a ridge regression meta-learner. The meta-learner can learn the weight allocation of different base models and automatically select the model combination that performs best in different spatial regions.

[0086] Optionally, when training the ensemble learning model, training sample data is obtained in the following manner:

[0087] The SUMO simulation coordinate system needs to be converted to the WGS84 standard coordinate system. First, a basic reference point dataset is established. Road center point information is located using GPS, and the SUMO local coordinates and corresponding WGS84 spatial coordinates of key geographical locations are obtained and packaged into CSV files. These reference points are distributed across different locations within the simulation area, including major intersections, road start and end points, and area boundary points. Specifically, the data includes: reference point 1 with local coordinates of (562.57, 1244.07) and corresponding WGS84 coordinates of (120.368655°E, 36.293704°N); reference point 2 with local coordinates of (19.14, 1690.72) and corresponding WGS84 coordinates of (120.362474°E, 36.297593°N), and so on.

[0088] To improve model training performance, data augmentation techniques are employed to expand the original n (e.g., 10) reference points to m (e.g., 50) training samples. Data augmentation methods include: generating intermediate points between known reference points using spatial interpolation algorithms; calculating the coordinates of interpolation points using distance-weighted inverse interpolation; and ensuring the geographical validity of the interpolation points through cross-validation. The augmented dataset better covers the entire transformation area, improving the model's generalization ability.

[0089] Optionally, Bayesian optimization techniques are employed for intelligent hyperparameter tuning of the ensemble learning model. Traditional grid search or random search methods are inefficient, while Bayesian optimization, based on Gaussian process modeling, can find the globally optimal parameter combination within a finite number of trials. In specific implementation, a hyperparameter search space is defined, with the SVR regularization parameter C being 10. -3 Logarithmically uniform distribution between 10³ and 10³, kernel function parameter gamma is within 10 -4 The number of trees in a random forest varies between 100 and 500 (integer distribution), and the maximum depth varies between 5 and 20. The learning rate of XGBoost varies between 0.01 and 0.3 (logarithmic distribution), the number of trees varies between 100 and 300, and the maximum depth varies between 3 and 15. The Bayesian optimizer constructs a probabilistic model of the objective function based on historical trial results, uses a collection function to balance exploration and utilization, and automatically selects the next set of most promising parameters for experimentation. After 100 optimization trials, the optimal parameter combination that minimizes the mean squared error of the 5-fold cross-validation is found.

[0090] Furthermore, to ensure the geographical reasonableness of the transformation results, a geographical constraint regularization mechanism is introduced: First, a reasonable geographical boundary is set to ensure that the transformed latitude and longitude coordinates fall within a reasonable range within my country (longitude 70°-140°, latitude 15°-55°). For transformation results exceeding this reasonable range, soft constraints are used for correction to avoid accuracy loss caused by hard truncation. Uncertainty quantification is achieved through the prediction variance of the integrated model: the standard deviation of the prediction results from the three base models is calculated as a measure of the reliability of the transformation results. Transformation results with high uncertainty are marked and manually reviewed to ensure data quality.

[0091] The SUMO road network coordinate transformation method based on an adaptive learning algorithm provided in this invention integrates a learning model with a base layer to combat nonlinearity, a core layer to ensure robustness, and a fusion layer to optimize accuracy. These three layers collaborate in a hierarchical manner. A meta-learner adaptively adjusts the weight distribution of each sub-model based on local geographic features of the spatial region (such as terrain undulation, road density, and topological complexity), thereby dynamically optimizing the predictive performance of the integrated model. Compared to existing fixed-parameter models (such as the seven-parameter transformation method) or standard projection transformation methods (such as transformations based on the Proj4 library), this invention exhibits stronger adaptability and higher transformation accuracy when handling complex nonlinear deformations and local spatial distortions. It is particularly suitable for applications with stringent requirements for spatial mapping accuracy and consistency, such as geographic coordinate transformation, high-precision map matching, autonomous driving simulation, and urban digital twins.

[0092] In an optional embodiment, before performing interpolation repair based on the WGS84 spatial coordinates of neighboring points, or repair based on preset geometric constraints, or repair based on network topology connectivity if the converted WGS84 spatial coordinates are abnormal, the method further includes:

[0093] The continuity of the WGS84 spatial coordinates is detected in real time. If the WGS84 spatial coordinates change abruptly, it is determined that the WGS84 spatial coordinates are abnormal.

[0094] The geographic rationality of the WGS84 spatial coordinates is detected in real time. If the WGS84 spatial coordinates are not within the preset geographic range, the WGS84 spatial coordinates are determined to be abnormal.

[0095] The transformation accuracy of the WGS84 spatial coordinates is detected in real time. If the transformation accuracy is less than a preset accuracy threshold, the WGS84 spatial coordinates are determined to be abnormal.

[0096] In this embodiment of the invention, quality checks are performed in real time during the conversion process:

[0097] The continuity of coordinate transformation is checked to avoid abrupt changes, meaning the distance between adjacent coordinate points should conform to geographical reality. Abrupt changes are corrected through interpolation, for example, using cubic spline interpolation to reconstruct coordinates based on five adjacent valid points; or, through geometric constraints, the distance between points is forced to not exceed a preset threshold, such as the road segment's design speed multiplied by the sampling time interval (e.g., 0.5s interval).

[0098] The geographical rationality of the coordinates is checked to ensure that the coordinates fall within the expected geographical range. For example, the buffer zone is automatically expanded by a certain proportion based on the road network MBR (Minimum Bounding Rectangle) to obtain the expected geographical range.

[0099] Calculate the conversion accuracy index and evaluate the conversion reliability of each point.

[0100] The SUMO road network coordinate transformation method based on adaptive learning algorithm provided in this embodiment of the invention detects the transformation results in real time during the transformation process, automatically identifies abnormal coordinates, and performs data repair to ensure the reliability of the transformation results.

[0101] In an optional embodiment, the SUMO road network coordinate transformation method based on the adaptive learning algorithm further includes:

[0102] The updated road network data will be output as Shapefile format data.

[0103] In this embodiment of the invention, to address the issue of missing details in existing road network conversion methods when processing lane connection details at intersections, the road network data (XML data file) is converted to OpenDRIVE format (.xodr) with high fidelity. This method can completely preserve the static structural information of the road network and some dynamic traffic elements. The OpenDRIVE file uses... <planview>Elements and their interior <geometry>Sub-elements, precisely define the micro-geometry of the road with curvature characteristics. At the same time, this format also supports the standardized modeling of road facilities such as traffic lights, stop lines, lane boundaries, etc. Further, by using the TESSNG software to import and convert the generated.xodr file, with the help of its built-in geometric reconstruction and structure recognition algorithm, the road network information is further output as more rich Shapefile (.shp) format data. This file not only contains lane width, lane turning, stop line information, etc., but also accurately describes the complex lane connection relationship in the intersection, thereby effectively making up for the information loss problem of traditional methods in modeling complex intersection structures.

[0104] The SUMO road network coordinate conversion method based on the adaptive learning algorithm provided by the embodiment of the application converts the XML format road network data output by the SUMO simulation system into the OpenDRIVE standard format with high fidelity, completely retains the static structure information (such as road topology, lane division, intersection layout, etc.) in the original road network, and effectively retains part of the dynamic traffic elements (such as signal control logic, lane speed limit information, etc.), realizes semantic-level mapping from simulation modeling data to standardized automatic driving map format, and further converts the road network data in the OpenDRIVE format into the Shapefile geographic information system (GIS) format, realizes automatic completion and topological correction of missing or fuzzy lane connection line data through geometric modeling and logical reasoning of the lane connection relationship, and significantly improves the spatial expression accuracy and logical consistency of the road network data.

[0105] In an optional embodiment, the SUMO road network coordinate conversion method based on the adaptive learning algorithm further comprises:

[0106] The vehicle trajectory data is analyzed to identify the vehicle ID and the trajectory point data;

[0107] Based on the vehicle ID and the trajectory point data, the driving trajectory of each vehicle is constructed;

[0108] Based on the lane center line Shapefile format data, lane surface data is generated;

[0109] Based on the driving trajectory and the lane surface data, vehicle trajectory data is generated.

[0110] In the implementation process, the vehicle trajectory data is usually stored in CSV format, but different versions of SUMO output format may differ, therefore, the format is inferred by reading the first few rows of the file, and then a suitable parsing strategy is selected. Specifically, the separator type (comma, semicolon, tab, etc.) and column structure of the CSV file are identified, and for files containing column headers, the time, vehicle ID, coordinates, lane, etc. fields are identified directly according to the header names; for headerless files, the meanings of each column are automatically inferred according to the data types and value ranges. Data type conversion and outlier detection are performed during the parsing process to ensure the quality of the data for subsequent processing.

[0111] Further, based on the parsed trajectory point data, complete driving trajectories are constructed by grouping according to vehicle ID. The trajectory points of each vehicle are sorted by timestamp to form an ordered spatio-temporal sequence. Quality checks are performed on the trajectory data: consistency of time intervals is detected to identify abnormal time jumps; spatial position continuity is verified to avoid unreasonable instantaneous phenomena; driving speed and acceleration are calculated to identify abnormal motion states. A coordinate conversion model is applied to each trajectory point to convert the SUMO local coordinates to WGS84 space longitude and latitude coordinates. The converted trajectory points construct LineString line geometry objects to form complete vehicle driving trajectory lines. The time properties of the trajectories are also preserved to support spatio-temporal analysis and dynamic visualization.

[0112] In the embodiments of the present application, multiple formats of trajectory data are generated according to different analysis requirements. The point format output contains detailed information of each trajectory point, suitable for fine-grained behavior analysis; the line format output contains complete trajectory lines, suitable for path analysis and flow statistics; the aggregated format output contains statistical information aggregated by time window or spatial region, suitable for macroscopic traffic analysis. All output data uses standard geographic information format, sets the correct coordinate reference system (EPSG: 4326), and ensures compatibility with mainstream GIS software. The attribute table contains rich vehicle and trajectory information, supporting complex spatial queries and statistical analysis.

[0113] The SUMO road network coordinate conversion method based on adaptive learning algorithm provided in the embodiments of the present application automatically processes the differences between multiple versions of SUMO output, eliminating the need for manual configuration; the lane surface and trajectory line spatial association support fine-grained behavior analysis; point, line, and aggregated data form a complete spatio-temporal analysis system.

[0114] In an optional embodiment, the lane center line based Shapefile format data generates lane surface data, including:

[0115] The lane center line based Shapefile format data generates lane area;

[0116] Project the WGS84 space coordinates of the lane center line to the UTM 50N coordinate system to obtain UTM 50N space coordinates;

[0117] Perform a buffer operation on the UTM 50N space coordinates based on the lane width information to generate a lane surface;

[0118] After performing overlap removal and gap repair on the lane surface, project the repaired data to the WGS84 space to obtain lane surface data.

[0119] In the embodiment of the application, the lane area in the form of a surface is generated based on the lane center line Shapefile data through a buffer construction method. The lane line data in the original WGS84 coordinate system is projected to the UTM 50N coordinate system suitable for geometric operation, and the center line is subjected to a buffer operation according to the width information of each lane to generate a lane surface with an actual width. During the buffer process, the flat end points and acute angle connections are used to more realistically restore the actual road form, and the generated buffer polygon is projected back to the WGS84 coordinate system.

[0120] Further, the lane surface is subjected to overlap removal and gap repair to ensure the spatial continuity and topological correctness of the generated result. Through traversal, spatial difference set operation is performed on each lane surface and the existing lane set to remove the overlapping parts. To deal with small gaps, a "expansion-contraction" micro-buffer strategy is adopted, the lane surface is subjected to small-scale buffer after being projected to the local UTM coordinate system, the gaps caused by boundary errors are effectively filled, the repaired data is projected back to WGS84, and continuous and complete, geometrically effective lane surface data is output.

[0121] The SUMO road network coordinate conversion method based on the adaptive learning algorithm provided in the embodiment of the application projects the data to the UTM 50N coordinate system for processing, which is suitable for geometric operation processing; the lane surface is subjected to overlap removal and gap repair to ensure the spatial continuity and topological correctness of the generated result. Compared with the common direct conversion or simple geometric processing method in the prior art, the present application effectively solves the problems of spatial discontinuity, topological fracture and geometric redundancy commonly encountered in the process of cross-coordinate system conversion and geometric modeling of SUMO road network data by introducing a standardized projection coordinate system and an overlap and gap optimization strategy, and significantly improves the spatial consistency and geometric integrity of the road network data.

[0122] Figure 2 is a second flowchart of the SUMO road network coordinate conversion method based on the adaptive learning algorithm provided by the application, Figure 3 is a third flowchart of the SUMO road network coordinate conversion method based on the adaptive learning algorithm provided by the application, as Figure 2 and Figure 3 As shown, data checking and optimization are performed in real time during the conversion process. Specifically, a three-dimensional evaluation system including statistical accuracy, spatial accuracy, and robustness evaluation is established. Statistical accuracy is measured by traditional indicators such as R² determination coefficient, root mean square error (RMSE), and mean absolute error (MAE); spatial accuracy calculates the spatial distribution characteristics of conversion error based on geographic distance; and robustness evaluation tests the model performance stability under different noise levels.

[0123] In the embodiments of the present application, a certain city traffic simulation project is taken as the background. The project contains more than 1000 traffic nodes, road edges and nearly ten thousand lanes. It is necessary to convert the SUMO simulation coordinate system into the WGS84 standard coordinate system for subsequent traffic platform visualization and analysis. The integrated learning model achieves a fitting degree of R²=0.99996 in longitude conversion and R²=0.99994 in latitude conversion, far exceeding the 0.9980 and 0.9975 of the traditional linear regression model. The RMSE error is controlled within 0.12 meters, and the accuracy is improved by 85% compared with the 0.8 meter error of the traditional method. Under 10% Gaussian noise interference, the model accuracy retention rate reaches 96%, showing excellent robustness.

[0124] A real-time quality monitoring system is established to continuously monitor the conversion quality during the data conversion process. The monitoring indicators include: real-time statistics of conversion accuracy, detection frequency of abnormal values, violation of geographical constraints, processing speed and resource consumption, etc. When quality abnormalities are detected, the system automatically triggers the repair mechanism or sends an early warning. The automatic repair strategy includes: repairing isolated abnormal points based on adjacent point interpolation; repairing abnormal points in the trajectory using time series smoothing technology; repairing network connection relationships through topological constraints; repairing missing data based on historical statistical information. The repair process is recorded throughout the process, and a detailed quality report is generated for user reference.

[0125] The following describes the SUMO road network coordinate conversion device based on the adaptive learning algorithm provided in the embodiments of the present application. The SUMO road network coordinate conversion device based on the adaptive learning algorithm described below can be correspondingly referred to the SUMO road network coordinate conversion method based on the adaptive learning algorithm described above.

[0126] Figure 4 FIG. 1 is a structural schematic diagram of the SUMO road network coordinate conversion device based on the adaptive learning algorithm provided in the embodiments of the present application. As shown in FIG. 1, the SUMO road network coordinate conversion device based on the adaptive learning algorithm can include but is not limited to: Figure 4

[0127] The information extraction module 410 is configured to extract node information, edge information and lane information from the road network data; the node information is attribute information of an intersection, the edge information is attribute information of a road edge, and the lane information is attribute information of a lane.​

[0128] The point generation module 420 is used to: generate point geometric objects based on the node information;

[0129] Edge generation module 430 is used to: generate line geometry objects of the road edge based on the edge information;

[0130] The center generation module 440 is used to: generate a surface geometry object of the lane surface based on the line geometry object;

[0131] The coordinate transformation module 450 is used to: convert the two-dimensional coordinates of the point geometric object, the line geometric object of the road edge, and the surface geometric object into WGS84 spatial coordinates using a pre-trained ensemble learning model; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates;

[0132] The anomaly repair module 460 is used to: if the converted WGS84 spatial coordinates are abnormal, perform interpolation repair based on the WGS84 spatial coordinates of nearby points, or perform repair based on preset geometric constraints, or perform repair based on network topology connectivity.

[0133] The road network update module 470 is used to update the road network data based on the WGS84 spatial coordinates.

[0134] It should be noted that the SUMO road network coordinate transformation device based on adaptive learning algorithm provided in this embodiment of the invention can execute the SUMO road network coordinate transformation method based on adaptive learning algorithm described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0135] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communications interface 520, and the memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute a SUMO road network coordinate transformation method based on an adaptive learning algorithm, which includes:

[0136] Node information, edge information, and lane information are extracted from road network data; the node information is the attribute information of intersections, the edge information is the attribute information of road edges, and the lane information is the attribute information of lanes.

[0137] Based on the node information, a point geometry object is generated;

[0138] Based on the edge information, a line geometry object of the road edge is generated;

[0139] Based on the line geometry object, generate the surface geometry object of the lane surface;

[0140] Using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometry, the line geometry of the road edge, and the surface geometry are converted into WGS84 spatial coordinates; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates.

[0141] If the converted WGS84 spatial coordinates are abnormal, interpolation is performed based on the WGS84 spatial coordinates of neighboring points, or based on preset geometric constraints, or based on network topology connectivity.

[0142] The road network data is updated based on the WGS84 spatial coordinates.

[0143] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a 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 various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the SUMO road network coordinate transformation method based on the adaptive learning algorithm provided by the above methods, the method including:

[0145] Node information, edge information, and lane information are extracted from road network data; the node information is the attribute information of intersections, the edge information is the attribute information of road edges, and the lane information is the attribute information of lanes.

[0146] Based on the node information, a point geometry object is generated;

[0147] Based on the edge information, a line geometry object of the road edge is generated;

[0148] Based on the line geometry object, generate the surface geometry object of the lane surface;

[0149] Using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometry, the line geometry of the road edge, and the surface geometry are converted into WGS84 spatial coordinates; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates.

[0150] If the converted WGS84 spatial coordinates are abnormal, interpolation is performed based on the WGS84 spatial coordinates of neighboring points, or based on preset geometric constraints, or based on network topology connectivity.

[0151] The road network data is updated based on the WGS84 spatial coordinates.

[0152] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the SUMO road network coordinate transformation method based on the adaptive learning algorithm provided by the above methods, the method comprising:

[0153] Node information, edge information, and lane information are extracted from road network data; the node information is the attribute information of intersections, the edge information is the attribute information of road edges, and the lane information is the attribute information of lanes.

[0154] Based on the node information, a point geometry object is generated;

[0155] Based on the edge information, a line geometry object of the road edge is generated;

[0156] Based on the line geometry object, generate the surface geometry object of the lane surface;

[0157] Using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometry, the line geometry of the road edge, and the surface geometry are converted into WGS84 spatial coordinates; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates.

[0158] If the converted WGS84 spatial coordinates are abnormal, interpolation is performed based on the WGS84 spatial coordinates of neighboring points, or based on preset geometric constraints, or based on network topology connectivity.

[0159] The road network data is updated based on the WGS84 spatial coordinates.

[0160] The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0161] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.< / geometry> < / planview>

Claims

1. A SUMO road network coordinate transformation method based on an adaptive learning algorithm, characterized in that, include: Extract node information, edge information, and lane information from road network data; The node information is the attribute information of the intersection, the edge information is the attribute information of the road edge, and the lane information is the attribute information of the lane. Based on the node information, point geometry objects are generated, with one point geometry object corresponding to each intersection, and the point geometry object retains the original attribute information of the intersection; Based on the edge information, a line geometry object of the road edge is generated; Based on the line geometry object, generate the surface geometry object of the lane surface; Using a pre-trained ensemble learning model, the two-dimensional coordinates of the point geometry, the line geometry of the road edge, and the surface geometry are converted into WGS84 spatial coordinates; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates. If the converted WGS84 spatial coordinates are abnormal, interpolation is performed based on the WGS84 spatial coordinates of neighboring points, or based on preset geometric constraints, or based on network topology connectivity. The road network data is updated based on the WGS84 spatial coordinates; The step of generating a line geometry object for the road edge based on the edge information includes: Generate line geometry objects based on the coordinates of the starting and ending nodes at the road edge; The process of generating a surface geometry object for the lane surface based on the line geometry object includes: After generating a linear geometry object of the lane centerline based on the geometric points of each lane, it is tiled and expanded into a surface geometry object of the lane surface.

2. The SUMO road network coordinate transformation method based on adaptive learning algorithm according to claim 1, characterized in that, The ensemble learning model includes a base layer, a core layer, and a fusion layer; The base layer expands the two-dimensional coordinates to a cubic polynomial feature space, resulting in a nine-dimensional feature vector. The core layer includes a parallel support vector machine regression model, a random forest regression model, and a gradient boosting regression model. The support vector machine regression model, the random forest regression model, and the gradient boosting regression model generate prediction results based on the nine-dimensional feature vector, respectively. The fusion layer includes a ridge regressor learner that generates WGS84 spatial coordinates based on the three prediction results.

3. The SUMO road network coordinate transformation method based on adaptive learning algorithm according to claim 1, characterized in that, Before performing interpolation repair based on the WGS84 spatial coordinates of neighboring points, or repair based on preset geometric constraints, or repair based on network topology connectivity if the converted WGS84 spatial coordinates are abnormal, the method further includes: The continuity of the WGS84 spatial coordinates is detected in real time. If the WGS84 spatial coordinates change abruptly, it is determined that the WGS84 spatial coordinates are abnormal. The geographic rationality of the WGS84 spatial coordinates is detected in real time. If the WGS84 spatial coordinates are not within the preset geographic range, the WGS84 spatial coordinates are determined to be abnormal. The transformation accuracy of the WGS84 spatial coordinates is detected in real time. If the transformation accuracy is less than a preset accuracy threshold, the WGS84 spatial coordinates are determined to be abnormal.

4. The SUMO road network coordinate transformation method based on adaptive learning algorithm according to claim 1, characterized in that, The SUMO road network coordinate transformation method based on the adaptive learning algorithm also includes: The updated road network data will be output as Shapefile format data.

5. The SUMO road network coordinate transformation method based on adaptive learning algorithm according to claim 4, characterized in that, The SUMO road network coordinate transformation method based on the adaptive learning algorithm also includes: The vehicle trajectory data is parsed to identify the vehicle ID and trajectory point data; Based on the vehicle ID and the trajectory point data, construct the driving trajectory of each vehicle; Generate lane surface data based on Shapefile format data of the lane centerline; Vehicle trajectory data is generated based on the driving trajectory and the lane surface data.

6. The SUMO road network coordinate transformation method based on adaptive learning algorithm according to claim 5, characterized in that, The Shapefile format data based on the lane centerline is used to generate lane surface data, including: Generate lane regions based on Shapefile format data of lane centerlines; Project the WGS84 spatial coordinates of the lane centerline onto the UTM 50N coordinate system to obtain the UTM 50N spatial coordinates; Based on the lane width information, the UTM 50N spatial coordinates are buffered to generate the lane surface; After removing overlaps and repairing gaps in the lane surface, the repaired data is projected into the WGS84 space to obtain lane surface data.

7. A SUMO road network coordinate transformation device based on an adaptive learning algorithm, characterized in that, include: The information extraction module is used to extract node information, edge information, and lane information from road network data. The node information is the attribute information of the intersection, the edge information is the attribute information of the road edge, and the lane information is the attribute information of the lane. The point generation module is used to: generate point geometric objects based on the node information; each intersection corresponds to one point geometric object, and the point geometric object retains the original attribute information of the intersection. The edge generation module is used to: generate line geometry objects of the road edge based on the edge information; The central generation module is used to: generate a surface geometry object of the lane surface based on the line geometry object; The coordinate transformation module is used to: convert the two-dimensional coordinates of the point geometric object, the line geometric object of the road edge, and the surface geometric object into WGS84 spatial coordinates using a pre-trained ensemble learning model; the ensemble learning model integrates the prediction results of multiple heterogeneous machine learning algorithms to generate WGS84 spatial coordinates; The anomaly repair module is used to: if the converted WGS84 spatial coordinates are abnormal, perform interpolation repair based on the WGS84 spatial coordinates of nearby points, or repair based on preset geometric constraints, or repair based on network topology connectivity. The road network update module is used to update the road network data based on the WGS84 spatial coordinates. The edge generation module is specifically used for: Generate line geometry objects based on the coordinates of the starting and ending nodes at the road edge; The central generation module is specifically used for: After generating a linear geometry object of the lane centerline based on the geometric points of each lane, it is tiled and expanded into a surface geometry object of the lane surface.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the SUMO road network coordinate transformation method based on the adaptive learning algorithm as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the SUMO road network coordinate transformation method based on the adaptive learning algorithm as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the SUMO road network coordinate transformation method based on the adaptive learning algorithm as described in any one of claims 1 to 6.

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