A traffic simulation method and apparatus

Abstract

The present disclosure relates to a traffic simulation method and device. The method and device obtain multiple original road network data of a target traffic system based on different data sources, and correlate and fuse the original road network data to obtain target road network data with unified format and related standards. When simulating the target traffic system based on the target road network data, different sources of road network data can be conveniently and smoothly switched and called to meet different simulation requirements and expand the application range of the corresponding simulation system. In addition, different original road network data may respectively include road network data corresponding to different simulation levels such as micro, meso and macro. Therefore, the present disclosure integrates road network data corresponding to different simulation levels in the target road network data by fusing multiple original road network data, so that data can be obtained on demand, multi-level traffic simulation can be realized, seamless switching between simulation results at different levels can be realized, and the rules and characteristics of the target traffic system can be more comprehensively displayed.

Description

Technical Field

[0001] This disclosure relates to the field of digital information technology, specifically to a traffic simulation method and apparatus. Background Technology

[0002] Traffic simulation is the application of computer simulation in the field of traffic engineering. It aims to simulate and analyze the operating status of real-world traffic systems, providing a basis for road planning and design, optimization of intelligent transportation systems, and other related fields.

[0003] In related technologies, some traffic simulation systems simulate and analyze the overall characteristics of traffic flow from a macroscopic perspective, such as traffic volume and density, without focusing on detailed elements or behaviors such as the number of lanes and vehicle lane changes. Other traffic simulation systems, on the other hand, focus on simulating and analyzing the detailed behaviors and interactions of individual vehicles or lanes related to overtaking, lane changes, etc., from a microscopic perspective. These traffic simulation systems, focused on single application scenarios, struggle to reflect the characteristics of traffic systems at different levels, thus exhibiting certain limitations in application. Summary of the Invention

[0004] The purpose of this disclosure is to provide a traffic simulation method and apparatus that takes into account the characteristics of traffic systems at different levels and meets different application needs.

[0005] In a first aspect, embodiments of this disclosure provide a traffic simulation method, including:

[0006] Acquire multiple raw road network data of the target traffic system based on different data sources;

[0007] The various original road network data are correlated and fused to obtain the target road network data;

[0008] The target traffic system is simulated based on the target road network data.

[0009] In one optional implementation, the plurality of raw road network data includes at least one of micro road network data, meso road network data, and macro road network data corresponding to different simulation levels.

[0010] In one optional implementation, the association and fusion of the various original road network data includes at least one of the following:

[0011] The original location data from the various original road network data are transformed into coordinates to obtain target location data based on a preset coordinate system;

[0012] Based on the successive relationship data of road segments in the various original road network data, adjacent road segments in the target traffic system are associated.

[0013] Associate multiple sets of road segment data corresponding to the same road segment from the various original road network data;

[0014] The multiple sets of road segment data corresponding to the same road segment are classified and labeled according to different simulation levels.

[0015] In one optional implementation, the step of simulating the target traffic system based on the target road network data includes:

[0016] Obtain the simulation data corresponding to the simulation area from the target road network data;

[0017] The simulation image is rendered based on the simulation data.

[0018] In one optional implementation, the current simulation data includes at least one of the current microscopic simulation data, current mesoscopic simulation data, and current macroscopic simulation data corresponding to different simulation levels;

[0019] The microscopic simulation data includes at least one of the following: location, shape, width, number of lanes, and vehicle position for each road segment within the simulation area.

[0020] The mesoscopic simulation data includes at least one of the following: location, shape, and vehicle speed of each road segment within the simulation area.

[0021] The macroscopic simulation data includes at least one of the following: location, shape, and vehicle density of each road segment within the simulation area.

[0022] In one optional implementation, the step of rendering the simulation image based on the current simulation data includes at least one of the following:

[0023] Based on the microscopic simulation data, each lane and each vehicle in each road segment within the simulation area are rendered to obtain the microscopic simulation image.

[0024] Based on the mesoscopic simulation data, the vehicle queues on each road segment within the simulation area are rendered with different colors according to the different vehicle speed levels to obtain the mesoscopic simulation image.

[0025] Based on the macroscopic simulation data, each road segment within the simulation area is rendered with different colors according to the different vehicle density levels, thus obtaining the macroscopic simulation image.

[0026] In an optional implementation, the traffic simulation method further includes:

[0027] Obtain interactive operation information;

[0028] The target road network data can be edited based on the interactive operation information, or at least one of the simulation area and simulation level can be determined based on the interactive operation information.

[0029] Secondly, embodiments of this disclosure provide a traffic simulation device, comprising:

[0030] The data collection unit is used to acquire various raw road network data of the target traffic system based on different data sources;

[0031] The data fusion unit is used to correlate and fuse the various original road network data to obtain target road network data;

[0032] The simulation rendering unit is used to simulate the target traffic system based on the target road network data.

[0033] Thirdly, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, characterized in that, when executed by a processor, the program implements the steps of the method described in the first aspect above.

[0034] Fourthly, embodiments of this disclosure provide a computing device, including:

[0035] Memory, used to store computer program products;

[0036] A processor for executing a computer program product stored in the memory, wherein, when the computer program product is executed, it implements the steps of the method described in the first aspect above.

[0037] In this embodiment of the disclosure, multiple original road network data from different sources are correlated and fused to form a standardized target road network data. Then, the target traffic system is simulated based on the target road network data. This not only allows for the acquisition of data from different sources from the target road network data to meet different simulation needs and expand the scope of simulation applications, but also ensures the uniformity of the simulation scene and prevents abrupt changes in the simulation image due to the use of data from different sources, thereby improving the user experience.

[0038] This disclosure embodiment performs correlation and fusion of different original road network data to obtain target road network data. It may include one or more processing methods such as coordinate transformation, correlation matching, classification labeling, and conflict handling on related data. This ensures that in the subsequent simulation process, data from different sources in the target road network data can be switched and called as needed, thereby ensuring simulation rendering speed and improving simulation effect.

[0039] In addition, in this embodiment of the present disclosure, since various original road network data of different formats and sources are processed through association and fusion to form standardized target road network data, during the simulation process, not only can the data required for different simulation levels be obtained from the target road network data, but the uniformity between these data can also be guaranteed. Thus, the simulation results corresponding to different simulation levels can be seamlessly and smoothly switched without any sense of disconnection, meeting the needs of multi-level traffic simulation and ensuring user experience.

[0040] This embodiment of the disclosure also enables the editing, management, and control of relevant data and simulation results of the target traffic system by acquiring interactive operation information, thereby meeting diverse traffic simulation needs and improving user experience. Attached Figure Description

[0041] Figure 1 A flowchart of a traffic simulation method provided by an embodiment of this disclosure is shown;

[0042] Figure 2 A flowchart of another traffic simulation method provided by an embodiment of this disclosure is shown;

[0043] Figure 3 A flowchart of another traffic simulation method provided by an embodiment of this disclosure is shown;

[0044] Figure 4 The illustration shows a rendering of a microscopic simulation image provided by an embodiment of the present disclosure;

[0045] Figure 5 A rendering of a mesoscopic simulation image provided by an embodiment of this disclosure is shown;

[0046] Figure 6 A rendering of a macroscopic simulation image provided by an embodiment of this disclosure is shown;

[0047] Figure 7 A rendering of the interactive panel provided in an embodiment of this disclosure is shown;

[0048] Figure 8 A schematic diagram of the structure of a traffic simulation device provided in an embodiment of this disclosure is shown;

[0049] Figure 9 A schematic diagram of the structure of a computing device provided by an embodiment of the present disclosure is shown. Detailed Implementation

[0050] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. Through these descriptions, the features and advantages of the present application will become clearer and more apparent.

[0051] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments. Although various aspects of embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless specifically indicated otherwise.

[0052] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0053] The digital twin model editing method and apparatus provided in this disclosure will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0054] Figure 1 This is a flowchart of a traffic simulation method provided in an embodiment of this disclosure, see below. Figure 1 The method may include the following steps:

[0055] Step 10: Obtain various raw road network data of the target traffic system based on different data sources;

[0056] The target transportation system mentioned above is a real transportation system used as the simulation object; it can be the urban transportation system of a city or the transportation system of any geographical area.

[0057] In some optional embodiments, the aforementioned raw road network data can be obtained through an access interface provided by a relevant data source. For example, the data source providing the aforementioned raw road network data can be an existing simulation system related to the target traffic system.

[0058] In some alternative embodiments, the format of the raw road network data provided by different data sources may be different, for example, it may be OpenDrive, OSM, SHP and other formats.

[0059] Step 20: The above-mentioned multiple original road network data are correlated and fused to obtain the target road network data;

[0060] Step 30: Simulate the target traffic system based on the target road network data described above.

[0061] The above-mentioned simulation of the target traffic system is also known as visualization of the relevant data of the target traffic system. For example, the simulation image is obtained by rendering through a rendering engine, so that relevant personnel can view and understand the patterns and characteristics of the target traffic system.

[0062] During traffic simulation, the various original road network data obtained in step 10 come from different data sources and may use different data formats or correspond to different simulation levels. As a result, the description information of the same traffic element may also be different in different original road network data. If traffic simulation is performed directly based on multiple original road network data, serious delays and sudden changes in the simulation screen may occur when calling different original road network data.

[0063] In step 20 of the above embodiment, the various original road network data are correlated and fused to form a standardized target road network data. Then, in step 30, the target traffic system is simulated based on the target road network data. This not only allows the acquisition of data from different sources from the target road network data to meet different simulation needs and expand the scope of simulation applications, but also ensures the uniformity of the simulation scene and prevents abrupt changes in the simulation image due to the use of data from different sources, thereby improving the user experience.

[0064] In some optional embodiments of this disclosure, the different raw road network data obtained from different data sources can be data corresponding to different simulation levels, including but not limited to: micro road network data corresponding to micro-level simulation, meso road network data corresponding to meso-level simulation, and macro road network data corresponding to macro-level simulation.

[0065] Different simulation levels, such as micro, meso, and macro, require different data types and precision. Therefore, single road network data is insufficient to meet the simulation needs of different levels. However, the above embodiment can acquire different original road network data corresponding to different simulation levels when obtaining raw road network data. Then, based on subsequent steps, these raw road network data can be correlated and fused, allowing for easy switching and calling of data at different levels to obtain simulation results at different levels. This enables multi-level simulation of the target traffic system, expands the scope of simulation applications, and improves the user experience.

[0066] In some optional embodiments of this disclosure, the traffic simulation method described above, after step 10, i.e. after obtaining various raw road network data, may further include the following steps:

[0067] Configure corresponding priorities for each type of raw road network data.

[0068] For example, the priority of each type of raw road network data can be determined by combining factors such as data volume, data importance, and data reliability.

[0069] In subsequent steps, when redundancy or conflicts occur between different original road network data, the data processing strategy can be determined based on their corresponding priorities to ensure the smooth execution of the traffic simulation process.

[0070] In some optional embodiments of this disclosure, such as Figure 2 As shown, step 20 above involves linking and fusing multiple types of original road network data, which may specifically include at least one of the following steps 21 to 24.

[0071] Step 21: Perform coordinate transformation on the original location data from various original road network data to obtain target location data based on a preset coordinate system;

[0072] Different original road network data may use different coordinate systems, such as the Universal Transverse Mercator Grid System (UTM), the China Geodetic Coordinate System 2000 (CGCS2000), and some regionally independently developed Cartesian coordinate systems.

[0073] In view of this, in some embodiments, the original location data in each type of original road network data can be filtered out separately, and relevant coordinate transformation tools can be used to transform the coordinates of these original location data to a preset coordinate system, so that the coordinate system of original road network data from different sources is unified, which facilitates the association, matching and calculation of related data.

[0074] For example, the aforementioned raw location data can be any coordinate-related data, which can be used to describe the location, direction, and related vectors of various elements or behaviors in the target traffic system; such as the starting point location and extension direction of a road segment.

[0075] It should be noted that, in order to facilitate the distinction between the data before and after coordinate transformation, the position data obtained after coordinate transformation in this embodiment of the disclosure, that is, the position data based on the preset coordinate system, is referred to as the target position data.

[0076] For example, in step 21 above, open-source libraries for coordinate transformation, such as Geospatial Data Abstraction Library (GDAL), can be used to implement coordinate transformation.

[0077] In some optional embodiments, the aforementioned preset coordinate system may be a pre-defined coordinate system.

[0078] For example, WGS-84 (World Geodetic System 1984 Coordinate System) can be used as the preset coordinate system, that is, the original location data in each type of original road network data is converted into target location data in the WGS-84 coordinate system.

[0079] In some optional embodiments, to reduce the amount of data processing, the above-mentioned preset coordinate system can adopt the original coordinate system of a certain original road network data.

[0080] For example, based on the priority configuration for each type of raw road network data described in the previous example, the coordinate system used by the raw road network data with the highest priority can be used as the preset coordinate system.

[0081] In this way, the highest priority original road network data does not need to be transformed. Only the original location data in other original road network data needs to be transformed to achieve coordinate consistency between different original road network data, thereby reducing the amount of data processing related to coordinate transformation.

[0082] Step 22: Based on the successive relationship data of road segments in various original road network data, associate adjacent road segments in the target traffic system;

[0083] The road network of the target transportation system is a complex network structure formed by the interconnection of multiple road segments. The connection between road segments is crucial. Once it changes, the corresponding road network structure will also be different to a large or small extent, and thus the available paths between two points in the road network may also change.

[0084] In view of this, in some optional embodiments, adjacent road segments in the target traffic system can be associated based on the successor-successor relationship data of road segments recorded in various original road network data; in this way, the missing successor-successor relationship data of road segments in one type of original road network data can be filled in by another type of original road network data, thereby obtaining the complete road network structure corresponding to the target traffic system.

[0085] Step 23: Associate multiple sets of road segment data corresponding to the same road segment from various original road network data.

[0086] The aforementioned road segment data includes various attributes of the corresponding road segment, such as its length, width, and geometry. For the same road segment, corresponding road segment data may be recorded in different original road network datasets, but the road segment data recorded in different original road network datasets may contain different information. For example, some road segment data may only contain the length and geometry of the corresponding road segment, while other road segment data may also include the width, number of lanes, etc.

[0087] In the above embodiments, associating multiple sets of road segment data from different original road network data with the same road segment can not only ensure the integrity of the data, but also improve the speed of data filtering and calling by calling road segment data in groups during subsequent simulation, thereby improving the simulation rendering speed.

[0088] In some embodiments, it is possible to determine which road segment data corresponds to the same road segment by matching the road segment data recorded in different original road network information, such as the starting point coordinates, ending point coordinates, length, width, and geometric shape of the road segments.

[0089] Step 24: Classify and label multiple sets of road segment data corresponding to the same road segment according to different simulation levels.

[0090] Based on step 23 above, it can be seen that the same road segment in the target traffic system may be associated with multiple sets of road segment data; each set of road segment data may be applicable to different levels of simulation requirements. Therefore, each set of road segment data can be classified and labeled according to the applicable simulation level so as to quickly call the corresponding data during the simulation process.

[0091] For example, based on the data source, data precision, and data granularity, it can be determined which simulation level each set of road segment data associated with the same road segment is suitable for, and then labeled accordingly. For instance, according to the order of data precision from high to low and granularity from fine to coarse, each set of road segment data can be labeled as data suitable for micro, meso, and macro simulation levels.

[0092] In the above embodiments, by classifying and labeling multiple sets of data corresponding to the same road segment according to the simulation level, the required road segment data can be quickly determined according to the actual simulation level. This avoids calling road segment data with too low precision when simulating at the micro level or calling road segment data with too fine granularity when simulating at the macro level, ensuring that the data precision and granularity match the simulation level and guaranteeing the simulation effect.

[0093] In addition, to improve the data quality of the final fused target road network data, step 20 may include other processing operations such as mapping fields with different names but the same substantive meaning in different original road network data, or modifying them to a unified field name. These will not be listed one by one in this embodiment.

[0094] In some optional embodiments, step 20 may further include:

[0095] Conflicting data between different original road network data are processed according to preset rules.

[0096] In practical applications, due to differences in the generation time and accuracy of data, there may be conflicts in the data recorded for the same attribute of the same element or behavior in different original road network data. The reasons for conflicting data may be multiple, such as unreliable data sources (i.e., the data is erroneous) or the data not being updated with the actual reconstruction of the traffic system (i.e., the data is outdated).

[0097] For example, one type of original road network data records the successor relationship data of road segment A as "the successor road segment includes road segment A1", while another type of original road network data records the successor relationship data of road segment A as "the successor road segments include road segments A1 and A2". In this case, a data conflict will occur when performing step 22 above, that is, it is impossible to determine whether the successor road segment of road segment A includes road segment A2.

[0098] In view of this, the embodiments of this disclosure resolve the data conflict problem that may occur during the association and fusion of various original road network data by using preset rules.

[0099] For example, the preset rules for handling data conflicts mentioned above can be rules set in combination with the priority, export time, etc. of the original road network data.

[0100] For example, when there is a conflict between related data in two original road network data sets, Data1 and Data2, the corresponding data in Data1, which has a higher priority, can be retained according to a preset rule based on priority; or, the corresponding data in Data2, which was exported later, can be retained according to a preset rule based on export time.

[0101] In the above embodiments, different original road network data are correlated and fused to obtain target road network data. This may include one or more processing methods such as coordinate transformation, correlation matching, classification labeling, and conflict handling of related data. This ensures that in the subsequent simulation process, data from different sources in the target road network data can be switched and called as needed, thereby ensuring simulation rendering speed and improving simulation effect.

[0102] In some optional embodiments of this disclosure, such as Figure 3 As shown, step 30 above, which involves simulating the target traffic system based on the target road network data, may specifically include:

[0103] Step 31: Obtain the simulation data corresponding to the simulation area from the target road network data;

[0104] Step 32: Render the simulation image based on the simulation data.

[0105] During traffic simulation, depending on different needs, it may be necessary to display the traffic flow of the entire target traffic system, or it may only be necessary to display the traffic flow of a certain local area of ​​the target traffic system. Therefore, in the actual simulation process, it is not necessary to use all the data in the target road network data. Instead, data related to the simulation area can be extracted from the target road network data as the data required for this simulation, i.e., the simulation data. Then, simulation rendering can be performed to obtain the static or dynamic simulation image corresponding to the simulation area, i.e., the simulation image.

[0106] Compared to rendering based on all target road network data, the above embodiment obtains the corresponding simulation data based on the current simulation area, which can greatly reduce the amount of data processing in the simulation rendering process, improve the simulation rendering speed, and thus enhance the user experience.

[0107] In some optional embodiments, in step 31, in addition to data filtering and extraction based on the current simulation area, road segment data corresponding to the current simulation level can also be extracted from the target road network data based on the classification labels in step 24 above, thereby further reducing the amount of data in the current simulation data and improving the speed of simulation rendering.

[0108] Depending on the applicable simulation level, the aforementioned simulation data may include at least one of the following: microscopic simulation data, mesoscopic simulation data, and macroscopic simulation data.

[0109] In some optional embodiments, the aforementioned microscopic simulation data may include at least one of the following: location, shape, width, number of lanes, and vehicle position of each road segment within the aforementioned simulation area.

[0110] In some optional embodiments, the aforementioned mesoscopic simulation data may include at least one of the following: location, shape, and vehicle speed of each road segment within the aforementioned simulation area.

[0111] In some optional embodiments, the aforementioned macroscopic simulation data may include at least one of the following: location, shape, and vehicle density of each road segment within the aforementioned simulation area.

[0112] For example, when only micro-level simulation of the current simulation area is required, the micro-level data corresponding to the current simulation area can be extracted from the target road network data to obtain the current micro-simulation data. Based on this micro-simulation data, the simulation image corresponding to the current simulation area can be rendered, i.e., the current simulation image. Similarly, by obtaining the current meso-level simulation data and the current macro-level simulation data, the current simulation area can be simulated at the meso-level and macro-level respectively to obtain the corresponding simulation images.

[0113] For example, when it is necessary to perform multi-level simulation of the current simulation area, the current micro-simulation data, current meso-simulation data, and current macro-simulation data can be extracted separately and rendered separately to obtain simulation images corresponding to different simulation levels.

[0114] In some optional embodiments, the process of rendering the simulation image based on the simulation data in step 32 above may specifically include at least one of the following a to c:

[0115] a. Microscopic level simulation

[0116] That is, based on the microscopic simulation data mentioned above, each lane and each vehicle in each road segment within the simulation area are rendered separately to obtain lane-level microscopic simulation images.

[0117] Figure 4 The illustrations show rendered images of microscopic simulations provided in some embodiments of this disclosure. For example... Figure 4 As shown, in microscopic simulation images, using a large scale, such as 1:500, can accurately display a variety of detailed information about the relevant road sections, including geometry, number of lanes, different vehicles in each lane, road markings, and environmental information such as buildings and trees.

[0118] In addition, during the microscopic simulation process, dynamic microscopic simulation images can be generated to simulate the following, lane changing, and overtaking behaviors of various vehicles in each lane.

[0119] b, Mesoscopic level simulation

[0120] That is, based on the above-mentioned mesoscopic simulation data, the vehicle queues of each road segment in the simulation area are rendered with different colors according to the different vehicle speed levels to obtain the mesoscopic simulation image.

[0121] Figure 5 The illustrations show rendered images of mesoscopic simulations provided in some embodiments of this disclosure. For example... Figure 5 As shown, in the mesoscopic simulation image, a medium scale, such as 1:100,000, is used. Linear highlights represent roads, and buildings or plots of land surrounding the roads are represented by bounding boxes. Figure 4 The microscopic simulation images shown are different. Figure 5 The mesoscopic simulation images shown do not specifically display local microscopic information such as individual lanes and vehicles, but rather display the directions of various road segments over a larger area, as well as the interrelationships between different road segments.

[0122] In addition, due to limitations in the format of the attached figures, Figure 5 What is not explicitly shown is that different colors can also be used to represent different speed levels on different road segments in the mesoscopic simulation image. Specifically, in the mesoscopic simulation process, bounding boxes can be used to represent vehicle queues formed by multiple adjacent vehicles. Then, based on the speed level of each vehicle queue, different colors are assigned to the corresponding bounding boxes. Bounding boxes of different colors are rendered onto the corresponding road segments, so that the final mesoscopic simulation image can display the speed levels of different road segments.

[0123] c. Macro-level simulation

[0124] That is, based on the aforementioned macroscopic simulation data, each road segment within the simulation area is rendered with different colors according to different vehicle density levels, thus obtaining the macroscopic simulation image.

[0125] Figure 6 The illustrations show rendered macroscopic simulation images provided in some embodiments of this disclosure. For example... Figure 6 As shown, in macroscopic simulation images, using a small scale, such as 1:1,000,000, can display the road distribution over a larger area, that is, it can display the overall road network structure of the target traffic system; it can also use different colors of each road segment in the road network to represent the traffic status of the corresponding road segment, such as red for congestion, yellow for busy, and green for smooth traffic.

[0126] According to the above embodiments and Figures 4-6 It can be seen that simulation images rendered at different simulation levels, using different scales, can simulate the target traffic system from different levels of detail and dimensions, obtaining simulation results at different levels in the same scene, which makes it easier for relevant personnel to have a more comprehensive understanding of the laws and characteristics of the target traffic system.

[0127] In addition, in the above embodiments, since various original road network data of different formats and sources are processed through association and fusion to form standardized target road network data, during the simulation process, not only can the data required for different simulation levels be obtained from the target road network data, but the uniformity between these data can also be guaranteed. Thus, the simulation results corresponding to different simulation levels can be seamlessly and smoothly switched without any sense of disconnect, meeting the needs of multi-level traffic simulation and ensuring user experience.

[0128] In some optional embodiments of this disclosure, the traffic simulation method described above may further include the following steps:

[0129] Obtain interactive operation information; and,

[0130] Respond to the above interactive operation information.

[0131] In the above embodiments, the responses executed are different depending on the interactive operation information obtained.

[0132] In some optional embodiments, the aforementioned interactive operation information may include data editing operation information. Accordingly, the response executed based on the data editing operation information is to edit the target road network data.

[0133] For example, an interactive panel 70 can be set in the display interface of the relevant simulation system to receive user-inputted data editing operation information, thereby enabling the editing of corresponding data in the target road network data; such as Figure 7As shown, users can edit the number of lanes (Lane Count) to "3", the lane width (LaneWidth) to "4", the sidewalk width (Side Walk) to "4m", and the bicycle lane width (Bicycle Lane) to "4m" through the interactive interface or interactive window 70.

[0134] In addition, such as Figure 7 As shown, other interactive operation controls can also be set in the interactive panel 70, such as building editing control 71, road editing control 72, input import control 73, etc., to realize related interactive functions.

[0135] In some optional embodiments, the aforementioned interactive operation information may include simulation requirement configuration information; this simulation requirement configuration information is used to define the simulation requirements such as the region and simulation level for this simulation. Accordingly, the response executed based on the simulation requirement configuration information is to determine the simulation requirements for this simulation so that, in step 30, the simulation is performed according to the determined simulation requirements.

[0136] For example, before each simulation starts, the simulation requirement configuration information input by the user can be received through the above-mentioned exchange panel to determine the simulation requirements such as the simulation area and the simulation level. Then, in step 30, the simulation data can be accurately and concisely extracted from the target road network data according to the simulation requirements such as the simulation area and the simulation level, and the simulation results that meet the user's needs can be quickly rendered to improve the user experience.

[0137] In the above embodiments, by acquiring interactive operation information, it is possible to manage and control the relevant data and simulation results of the target traffic system according to user needs, thereby meeting diverse traffic simulation requirements and improving user experience.

[0138] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0139] Based on the same concept, this invention also provides a traffic simulation device. Since the principle by which this device solves the problem is similar to the aforementioned traffic simulation method, the implementation of this device can refer to the implementation of the aforementioned traffic simulation method, and repeated details will not be elaborated further.

[0140] Figure 8 This is a schematic diagram of the traffic simulation device. (See attached diagram) Figure 8 The traffic simulation device 800 may include:

[0141] The data collection unit 801 is used to acquire various raw road network data of the target traffic system based on different data sources;

[0142] The data fusion unit 802 is used to correlate and fuse various raw road network data collected by the data collection unit 801 to obtain target road network data;

[0143] The simulation rendering unit 803 is used to simulate the target traffic system based on the target road network data processed by the data fusion unit 802.

[0144] In an optional embodiment of this disclosure, the traffic simulation device 800 may further include:

[0145] The interaction unit is used to acquire and respond to interactive operation information to enable editing of target road network data or configuration of simulation requirements.

[0146] It should be noted that the specific data processing methods of each unit in the traffic simulation device 800 described above can be referred to the method embodiments described above, and will not be repeated here.

[0147] This disclosure also provides a computing device. Figure 9 This is a structural block diagram of a computing device provided in an embodiment of this disclosure. Figure 9 As shown, the computing device 900 may include a processor 901 and a memory 902; the memory 902 may be coupled to the processor 901.

[0148] It is worth noting that Figure 9 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions. Optionally, the computing device 900 can be a server or a local computing device, thereby enabling traffic simulation in the cloud or locally.

[0149] In one possible implementation, the functionality of the traffic simulation device 800 can be integrated into the processor 901. The processor 901 can be configured to perform the following operations:

[0150] Acquire multiple raw road network data of the target traffic system based on different data sources;

[0151] Multiple raw road network data are correlated and fused to obtain the target road network data;

[0152] The target traffic system is simulated based on the target road network data.

[0153] In another possible implementation, the traffic simulation device 800 can be configured separately from the processor 901. For example, the traffic simulation device 800 can be configured as a chip connected to the processor 901, and the traffic simulation method described in the previous embodiment can be implemented through the control of the processor 901.

[0154] Furthermore, in some alternative embodiments, the computing device 900 may also include: a communication module, an input unit, an audio processor, a display, a power supply, etc. It is worth noting that the computing device 900 is not necessarily required to include these components. Figure 5 All components shown; in addition, the computing device 900 may also include Figure 5 For components not shown, please refer to existing technologies.

[0155] In some alternative implementations, the processor 901, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives input and controls the operation of various components of the computing device 900.

[0156] The memory 902 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned information related to the traffic simulation device 800, and may also store programs for executing that information. The processor 901 can execute the program stored in the memory 902 to perform information storage or processing, etc.

[0157] The input unit can provide input to the processor 901, such as inputting the interactive operation information described above. This input unit can be, for example, a button or touch input device. A power supply can be used to provide power to the computing device 900. The display can be used to display images and text, such as displaying traffic simulation results. This display can be, for example, an LCD display, but is not limited to this.

[0158] This disclosure also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it can implement the various processes of the above-described traffic simulation method embodiments and achieve the same technical effects. To avoid repetition, it will not be described again here.

[0159] The aforementioned readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and system embodiments are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0161] While one or more embodiments of this specification provide the operational steps of the methods described in the embodiments or flowcharts, more or fewer operational steps may be included based on conventional or non-inventive labor. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual device or client product execution, the methods shown in the embodiments or drawings may be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0162] In the description of this application, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "left", "right", etc., indicate the orientation or positional relationship based on the orientation or positional relationship in the working state of this application. They are only for the convenience of describing this application 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. Therefore, they should not be construed as limitations on this application.

[0163] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. Those skilled in the art can understand the specific meaning of these terms in this application based on the specific circumstances.

[0164] The present application has been described above with reference to preferred embodiments; however, these embodiments are merely exemplary and illustrative. Various substitutions and modifications can be made to the present application based on these embodiments, all of which fall within the protection scope of the present application.

Claims

1. A traffic simulation method, characterized in that, include: Acquire multiple raw road network data of the target traffic system based on different data sources; The various original road network data are correlated and fused to obtain the target road network data; The target traffic system is simulated based on the target road network data.

2. The method according to claim 1, characterized in that, The various original road network data include at least one of micro road network data, meso road network data, and macro road network data corresponding to different simulation levels.

3. The method according to claim 1, characterized in that, The process of linking and fusing the various original road network data includes at least one of the following: The original location data from the various original road network data are transformed into coordinates to obtain target location data based on a preset coordinate system; Based on the successive relationship data of road segments in the various original road network data, adjacent road segments in the target traffic system are associated. Associate multiple sets of road segment data corresponding to the same road segment from the various original road network data; The multiple sets of road segment data corresponding to the same road segment are classified and labeled according to different simulation levels.

4. The method according to claim 1, characterized in that, The simulation of the target traffic system based on the target road network data includes: Obtain the simulation data corresponding to the simulation area from the target road network data; The simulation image is rendered based on the simulation data.

5. The method according to claim 4, characterized in that, The simulation data includes at least one of the following: microscopic simulation data, mesoscopic simulation data, and macroscopic simulation data corresponding to different simulation levels. The microscopic simulation data includes at least one of the following: location, shape, width, number of lanes, and vehicle position for each road segment within the simulation area. The mesoscopic simulation data includes at least one of the following: location, shape, and vehicle speed of each road segment within the simulation area. The macroscopic simulation data includes at least one of the following: location, shape, and vehicle density of each road segment within the simulation area.

6. The method according to claim 5, characterized in that, The process of rendering the simulation image based on the simulation data includes at least one of the following: Based on the microscopic simulation data, each lane and each vehicle in each road segment within the simulation area are rendered to obtain the microscopic simulation image. Based on the mesoscopic simulation data, the vehicle queues on each road segment within the simulation area are rendered with different colors according to the different vehicle speed levels to obtain the mesoscopic simulation image. Based on the macroscopic simulation data, each road segment within the simulation area is rendered with different colors according to the different vehicle density levels, thus obtaining the macroscopic simulation image.

7. The method according to claim 1, characterized in that, Also includes: Obtain interactive operation information; The target road network data can be edited based on the interactive operation information, or at least one of the simulation area and simulation level can be determined based on the interactive operation information.

8. A traffic simulation device, characterized in that, include: The data collection unit is used to acquire various raw road network data of the target traffic system based on different data sources; The data fusion unit is used to correlate and fuse the various original road network data to obtain target road network data; The simulation rendering unit is used to simulate the target traffic system based on the target road network data.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method of any one of claims 1 to 7.

10. A computing device, characterized in that, include: Memory, used to store computer program products; A processor is configured to execute a computer program product stored in the memory, wherein when the computer program product is executed, it implements the method described in any one of claims 1 to 7.

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