Traffic management cooperation system based on vehicle-road cooperation

Abstract

The invention relates to the technical field of traffic management, and particularly discloses a traffic management coordination system based on vehicle-road coordination, and the system comprises an acquisition module which collects the position information of a fuel vehicle, generates a coordinate point, and obtains a target ball based on the coordinate point; the analysis module is used for setting time nodes and determining a first area according to the target balls corresponding to the same time node; calculating a tail gas influence value in the first area, and determining a target area; the management module is used for determining a target time period; determining a target intersection; and according to the number of the vehicles in the target area in the target time period, when the fuel vehicles arrive at the target intersection, prompting the fuel vehicles. The situation that the tail gas content in a certain area is too high can be avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and in particular to a traffic management coordination system based on vehicle-road coordination. Background Art

[0002] Vehicle-road collaboration refers to a technology and management model that achieves efficient, safe and sustainable operation of the transportation system through real-time information exchange and intelligent interaction between vehicles and road infrastructure. It can not only optimize traffic flow and reduce congestion, but also significantly improve traffic safety.

[0003] Traditional fuel vehicles release a large amount of harmful gases during operation, such as carbon dioxide, nitrogen monoxide and particulate matter. These exhaust emissions greatly aggravate air pollution. Existing traffic management collaborative methods mainly focus on improving driving safety and improving road use efficiency, and often ignore the impact of vehicle exhaust emissions on the urban environment during driving on urban roads. When the exhaust concentration is too high, it will cause a significant decline in air quality, threaten the health of citizens, and increase the incidence of respiratory diseases. Therefore, how to avoid excessive exhaust concentration and reduce the impact on the environment has become an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to provide a traffic management collaborative system based on vehicle-road collaboration to solve the following technical problems: Existing collaborative methods for traffic management mainly focus on improving driving safety and increasing road use efficiency, but often ignore the impact of vehicle exhaust emissions on the urban environment during driving on urban roads.

[0005] The purpose of the present invention can be achieved by the following technical solutions: A traffic management coordination system based on vehicle-road coordination includes: Acquisition module: Acquisition of location information of fuel vehicles in a preset monitoring area, generation of coordinate points (a1, a2, a3) based on the location information, a1, a2, a3 respectively represent the horizontal axis coordinate, vertical axis coordinate and vertical axis coordinate of the fuel vehicle in a preset coordinate system, clustering the coordinate points to obtain point clusters, obtaining the minimum circumscribed sphere of the point clusters, and using it as the target sphere; Analysis module: setting m monitoring periods, where m is the number of preset monitoring periods, setting a number of time nodes at preset time intervals TJG within the monitoring period, obtaining the target balls at the time nodes, obtaining the overlapping parts of the target balls corresponding to the same time node, and taking them as the first area; Obtain the volume V of the first region and calculate the exhaust gas impact value , is a preset coefficient. When there is a preset type of building in the first area and the corresponding exhaust gas impact value is greater than or equal to a preset exhaust gas impact value threshold Kys, the first area is marked as a target area; Management module: obtain the time node C corresponding to the target area and determine the target period , time difference ; Taking a route that has an intersection with the target area in the monitoring area as a target route, determining a road intersection on the target route that is closest to the intersection, and taking it as the target intersection; Calculate the number of target vehicles , Nys represents the preset theoretical number of vehicles. When the number of vehicles in the target area within the target time period reaches the target number of vehicles NMB, the fuel vehicle is prompted after the fuel vehicle arrives at the target intersection.

[0006] As a further solution of the present invention: in the acquisition module, the process of clustering the coordinate points specifically includes: Step 1: Taking the coordinate point as the center, calculate the coordinate point density within the preset clustering radius r, and when the coordinate point density is greater than or equal to the preset coordinate point density threshold Pys, generate an initial point cluster with the coordinate point as the center; Obtaining the theoretical center of gravity of the initial point cluster, the theoretical center of gravity is determined based on the distribution of coordinate points in the initial point cluster, grouping the initial point clusters, and the distance between the theoretical centers of gravity corresponding to any two initial point clusters in the group is less than a preset distance threshold; Step 2: Obtain the theoretical centroid of the coordinate points in the group, use it as the target centroid, determine the distance between the target centroid and all the coordinate points in the group, and obtain the maximum distance D, use it as the target distance, and calculate the density of the coordinate points within the target distance with the target centroid as the center, and use it as the to-be-determined density; The target distance is reduced at a preset target distance interval, and after each reduction of the target distance, the corresponding undetermined density is recorded, and the undetermined density that is greater than or equal to the coordinate point density threshold Pys and has the largest corresponding target distance is taken as the first density; Step 3: Obtain the target distance Ddy corresponding to the first density. , taking the coordinate point closest to the target gravity center as the new target gravity center, obtaining the first density and the corresponding target distance again, and μ is the preset second correction coefficient; Repeat the above steps. When the new target center of gravity is obtained for the bth time, the corresponding target distance is still less than or equal to When , execute step 4, b is the preset number; Wherein, when the target distance Or after obtaining a new target center of gravity, the corresponding target distance is greater than When , execute step 6; Step 4: Remove the coordinate point corresponding to the maximum distance D, obtain a new maximum distance Dx, and execute steps 1 to 3. If there is still no target distance greater than the first density corresponding to the target , execute step five; When a new maximum distance is obtained, the corresponding target distance is greater than When , execute step 6; Step 5: Set the new coordinate point density threshold , where ΔPys is the preset coordinate point density threshold correction value, and step 1 to step 4 are executed again; Step 6: Generate a new initial point cluster using the target center of gravity and target distance at this time to replace all the initial point clusters in the group; All initial point clusters are taken as point clusters.

[0007] As a further solution of the present invention: in the step 1, the coordinate point density is calculated , F represents the number of coordinate points within the clustering radius r.

[0008] As a further solution of the present invention: in the analysis module, when there is no overlapping part of the target balls corresponding to the time node i, the following steps are performed: Remove the target ball corresponding to the monitoring period x1, and determine whether there is any overlap between the remaining target balls corresponding to the time node i; When the remaining target balls corresponding to the time node i do not have overlapping parts, the target ball corresponding to the monitoring period x1 is retained, and the target ball corresponding to the monitoring period x2 is removed, and it is determined again whether there is an overlapping part; Repeat the above steps until the target ball corresponding to a certain monitoring period is removed and there is an overlapping part between the remaining target balls corresponding to the time node i, and the overlapping part at this time is used as the first area corresponding to the time node i.

[0009] As a further solution of the present invention: in the acquisition module, the location information of the fuel vehicle is acquired based on Beidou positioning and / or GPS positioning.

[0010] As a further solution of the present invention: in the acquisition module, the target ball in the monitoring area is different on weekends and weekdays.

[0011] As a further solution of the present invention: in the management module, the method of prompting fuel vehicles includes sound prompting and vibration prompting.

[0012] Beneficial effects of the present invention: In this solution, the target ball is obtained through the collection and cluster analysis of position information; it is worth noting that the spatial distribution data of vehicles is obtained through a standardized and systematic method to ensure the accuracy and consistency of the data, so as to clarify how to effectively identify areas where vehicles are concentrated, which lays a solid foundation for subsequent environmental impact assessment and determination of target areas, and ensures that subsequent steps can make scientific decisions based on accurate data; then, the first area, that is, the representative area, is determined by the overlapping parts of the target balls at the same time node in multiple monitoring periods; it is worth noting that through the overlapping analysis of multi-time period data, the consistent areas where vehicles appear in different time periods can be identified, which enhances the representativeness of regional identification and reliability, extract representative areas from multiple monitoring periods to ensure that these areas still have the potential for high tail gas emissions under long-term and diverse traffic conditions (i.e., areas that need to be monitored and managed); it is worth noting that the length of a single monitoring period is one day, and the preset types of buildings include but are not limited to schools, hospitals, and residential buildings; finally, the target period is determined based on the time nodes of these target areas, and the number of vehicles is controlled by calculating the allowed target number of vehicles NMB. When the actual number of vehicles reaches NMB, the system will prompt fuel vehicles at the target intersection, and the prompt content includes but is not limited to the fact that the exhaust gas content ahead is too high, and it is recommended to detour, etc., so as to achieve dynamic management and intelligent intervention to avoid high exhaust gas emissions in the area. The present invention can avoid the situation where the exhaust gas content is too high in a certain area. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention will be further described below in conjunction with the accompanying drawings.

[0014] Figure 1 It is a flow chart of a traffic management collaborative system based on vehicle-road collaboration of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] See also Figure 1 As shown, the present invention is a traffic management coordination system based on vehicle-road coordination, comprising: Acquisition module: Acquisition of location information of fuel vehicles in a preset monitoring area, generation of coordinate points (a1, a2, a3) based on the location information, a1, a2, a3 respectively represent the horizontal axis coordinate, vertical axis coordinate and vertical axis coordinate of the fuel vehicle in a preset coordinate system, clustering the coordinate points to obtain point clusters, obtaining the minimum circumscribed sphere of the point clusters, and using it as the target sphere; Analysis module: setting m monitoring periods, where m is the number of preset monitoring periods, setting a number of time nodes at preset time intervals TJG within the monitoring period, obtaining the target balls at the time nodes, obtaining the overlapping parts of the target balls corresponding to the same time node, and taking them as the first area; Obtain the volume V of the first region and calculate the exhaust gas impact value , is a preset coefficient. When there is a preset type of building in the first area and the corresponding exhaust gas impact value is greater than or equal to a preset exhaust gas impact value threshold Kys, the first area is marked as a target area; Management module: obtain the time node C corresponding to the target area and determine the target period , time difference ; Taking a route that has an intersection with the target area in the monitoring area as a target route, determining a road intersection on the target route that is closest to the intersection, and taking it as the target intersection; Calculate the number of target vehicles , Nys represents the preset theoretical number of vehicles. When the number of vehicles in the target area within the target time period reaches the target number of vehicles NMB, the fuel vehicle is prompted after the fuel vehicle arrives at the target intersection.

[0017] It should be noted that the target ball is obtained through the collection and cluster analysis of location information; it is worth noting that the spatial distribution data of vehicles is obtained through standardized and systematic methods to ensure the accuracy and consistency of the data, so as to clarify how to effectively identify areas where vehicles are concentrated, which lays a solid foundation for subsequent environmental impact assessment and determination of target areas, and ensures that subsequent steps can make scientific decisions based on accurate data; then, the first area, that is, the representative area, is determined by the overlapping parts of the target balls at the same time node in multiple monitoring periods; it is worth noting that through the overlapping analysis of multi-time period data, the consistent areas where vehicles appear in different time periods can be identified, which enhances the representativeness and reliability of regional identification. , extract representative areas from multiple monitoring periods to ensure that these areas still have the potential for high exhaust emissions under long-term and diverse traffic conditions (i.e., areas that need to be monitored and managed); it is worth noting that the length of a single monitoring period is one day, and the preset types of buildings include but are not limited to schools, hospitals, and residential buildings; finally, determine the target period based on the time nodes of these target areas, and control the number of vehicles by calculating the allowed target number of vehicles NMB. When the actual number of vehicles reaches NMB, the system will prompt fuel vehicles at the target intersection, and the prompt content may include but is not limited to excessive exhaust gas content ahead and detour suggestions, thereby achieving dynamic management and intelligent intervention to avoid high exhaust emissions in the area.

[0018] In another preferred embodiment of the present invention, in the acquisition module, the process of clustering the coordinate points specifically includes: Step 1: Taking the coordinate point as the center, calculate the coordinate point density within the preset clustering radius r, and when the coordinate point density is greater than or equal to the preset coordinate point density threshold Pys, generate an initial point cluster with the coordinate point as the center; Obtaining the theoretical center of gravity of the initial point cluster, the theoretical center of gravity is determined based on the distribution of coordinate points in the initial point cluster, grouping the initial point clusters, and the distance between the theoretical centers of gravity corresponding to any two initial point clusters in the group is less than a preset distance threshold; Step 2: Obtain the theoretical centroid of the coordinate points in the group, use it as the target centroid, determine the distance between the target centroid and all the coordinate points in the group, and obtain the maximum distance D, use it as the target distance, and calculate the density of the coordinate points within the target distance with the target centroid as the center, and use it as the to-be-determined density; The target distance is reduced at a preset target distance interval, and after each reduction of the target distance, the corresponding undetermined density is recorded, and the undetermined density that is greater than or equal to the coordinate point density threshold Pys and has the largest corresponding target distance is taken as the first density; Step 3: Obtain the target distance Ddy corresponding to the first density. , taking the coordinate point closest to the target gravity center as the new target gravity center, and obtaining the first density and the corresponding target distance again, is the preset second correction coefficient; Repeat the above steps. When the new target center of gravity is obtained for the bth time, the corresponding target distance is still less than or equal to When , execute step 4, b is the preset number; Wherein, when the target distance Or after obtaining a new target center of gravity, the corresponding target distance is greater than When , execute step 6; Step 4: Remove the coordinate point corresponding to the maximum distance D, obtain a new maximum distance Dx, and execute steps 1 to 3. If there is still no target distance greater than the first density corresponding to the target , execute step five; When a new maximum distance is obtained, the corresponding target distance is greater than When , execute step 6; Step 5: Set the new coordinate point density threshold ,in The preset coordinate point density threshold correction value is used, and steps 1 to 4 are executed again; Step 6: Generate a new initial point cluster using the target center of gravity and target distance at this time to replace all the initial point clusters in the group; All initial point clusters are taken as point clusters.

[0019] It is understandable that the theoretical center of gravity can be obtained by referring to the method of obtaining the center of gravity in the prior art, which will not be elaborated here.

[0020] In another preferred embodiment of the present invention, in the step 1, the coordinate point density is calculated. , F represents the number of coordinate points within the clustering radius r.

[0021] In another preferred embodiment of the present invention, in the analysis module, when there is no overlapping part of the target balls corresponding to the time node i, the following steps are performed: Remove the target ball corresponding to the monitoring period x1, and determine whether there is any overlap between the remaining target balls corresponding to the time node i; When the remaining target balls corresponding to the time node i do not have overlapping parts, the target ball corresponding to the monitoring period x1 is retained, and the target ball corresponding to the monitoring period x2 is removed, and it is determined again whether there is an overlapping part; Repeat the above steps until the target ball corresponding to a certain monitoring period is removed and there is an overlapping part between the remaining target balls corresponding to the time node i, and the overlapping part at this time is used as the first area corresponding to the time node i.

[0022] It should be noted that, when the target balls corresponding to any of the monitoring periods are removed and the remaining target balls corresponding to the time node i do not have overlapping parts, the following steps are performed: Remove the target balls corresponding to the monitoring period x3 and the monitoring period x4, and determine whether there are any overlapping parts of the remaining target balls corresponding to the time node i; When the remaining target balls corresponding to the time node i do not have overlapping parts, the target balls corresponding to the monitoring periods x3 and x4 are retained, and the target balls corresponding to the monitoring periods x5 and x6 are removed, and it is determined whether the remaining target balls have overlapping parts; Repeat the above steps until the target balls corresponding to the two monitoring periods are removed and the remaining target balls corresponding to the time node i have overlapping parts, and the overlapping parts at this time are taken as the first area corresponding to the time node i; If there is no overlap after removing two, remove three and repeat the above steps, and so on, until an overlap appears. Determine the number of target balls that have not been removed at this time. If it is less than the preset number, set a new monitoring period and execute the above steps again.

[0023] In another preferred embodiment of the present invention, in the acquisition module, the location information of the fuel vehicle is acquired based on Beidou positioning and / or GPS positioning.

[0024] In another preferred embodiment of the present invention, in the acquisition module, the target ball in the monitoring area is different on weekends and weekdays.

[0025] In another preferred embodiment of the present invention, in the management module, the prompting method for fuel vehicles includes sound prompting and vibration prompting.

[0026] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A traffic management coordination system based on vehicle-road coordination, characterized in that: include: Acquisition module: Acquisition of location information of fuel vehicles in a preset monitoring area, generation of coordinate points (a1, a2, a3) based on the location information, a1, a2, a3 respectively represent the horizontal axis coordinate, vertical axis coordinate and vertical axis coordinate of the fuel vehicle in a preset coordinate system, clustering the coordinate points to obtain point clusters, obtaining the minimum circumscribed sphere of the point clusters, and using it as the target sphere; Analysis module: setting m monitoring periods, where m is the number of preset monitoring periods, setting a number of time nodes at preset time intervals TJG within the monitoring period, obtaining the target balls at the time nodes, obtaining the overlapping parts of the target balls corresponding to the same time node, and taking them as the first area; Obtain the volume V of the first region and calculate the exhaust gas impact value , is a preset coefficient. When there is a preset type of building in the first area and the corresponding exhaust gas impact value is greater than or equal to a preset exhaust gas impact value threshold Kys, the first area is marked as a target area; Management module: obtain the time node C corresponding to the target area and determine the target period , time difference ; Take the route in the monitoring area that has an intersection with the target area as the target route, determine the road intersection on the target route that is closest to the intersection, and take it as the target intersection; Calculate the number of target vehicles , Nys represents the preset theoretical number of vehicles. When the number of vehicles in the target area within the target time period reaches the target number of vehicles NMB, the fuel vehicle is prompted after the fuel vehicle arrives at the target intersection.

2. A traffic management coordination system based on vehicle-road coordination according to claim 1, characterized in that: In the acquisition module, the process of clustering the coordinate points specifically includes: Step 1: Taking the coordinate point as the center, calculate the coordinate point density within the preset clustering radius r, and when the coordinate point density is greater than or equal to the preset coordinate point density threshold Pys, generate an initial point cluster with the coordinate point as the center; Obtaining the theoretical center of gravity of the initial point cluster, the theoretical center of gravity is determined based on the distribution of coordinate points in the initial point cluster, grouping the initial point clusters, and the distance between the theoretical centers of gravity corresponding to any two initial point clusters in the group is less than a preset distance threshold; Step 2: Obtain the theoretical centroid of the coordinate points in the group, use it as the target centroid, determine the distance between the target centroid and all the coordinate points in the group, and obtain the maximum distance D, use it as the target distance, and calculate the density of the coordinate points within the target distance with the target centroid as the center, and use it as the to-be-determined density; The target distance is reduced at a preset target distance interval, and after each reduction of the target distance, the corresponding undetermined density is recorded, and the undetermined density that is greater than or equal to the coordinate point density threshold Pys and has the largest corresponding target distance is taken as the first density; Step 3: Obtain the target distance Ddy corresponding to the first density. , taking the coordinate point closest to the target gravity center as the new target gravity center, and obtaining the first density and the corresponding target distance again, is the preset second correction coefficient; Repeat the above steps. When the new target center of gravity is obtained for the bth time, the corresponding target distance is still less than or equal to When , execute step 4, b is the preset number; Wherein, when the target distance Or after obtaining a new target center of gravity, the corresponding target distance is greater than When , execute step 6; Step 4: Remove the coordinate point corresponding to the maximum distance D, obtain a new maximum distance Dx, and execute steps 1 to 3. If there is still no target distance corresponding to the first density greater than , execute step five; When a new maximum distance is obtained, the corresponding target distance is greater than When , execute step 6; Step 5: Set the new coordinate point density threshold ,in The preset coordinate point density threshold correction value is used, and steps 1 to 4 are executed again; Step 6: Generate a new initial point cluster using the target center of gravity and target distance at this time to replace all the initial point clusters in the group; All initial point clusters are taken as point clusters.

3. A traffic management coordination system based on vehicle-road coordination according to claim 2, characterized in that: In the step 1, the coordinate point density is calculated , F represents the number of coordinate points within the clustering radius r.

4. The traffic management coordination system based on vehicle-road coordination according to claim 1 is characterized in that: In the analysis module, when there is no overlapping part of the target balls corresponding to the time node i, the following steps are performed: Remove the target ball corresponding to the monitoring period x1, and determine whether there is any overlapping part among the remaining target balls corresponding to the time node i; When the remaining target balls corresponding to the time node i do not have overlapping parts, the target ball corresponding to the monitoring period x1 is retained, and the target ball corresponding to the monitoring period x2 is removed, and it is determined again whether there is an overlapping part; Repeat the above steps until the target ball corresponding to a certain monitoring period is removed and there is an overlapping part between the remaining target balls corresponding to the time node i, and the overlapping part at this time is used as the first area corresponding to the time node i.

5. The traffic management coordination system based on vehicle-road coordination according to claim 1 is characterized in that: In the acquisition module, the location information of the fuel vehicle is acquired based on Beidou positioning and / or GPS positioning.

6. The traffic management coordination system based on vehicle-road coordination according to claim 1 is characterized in that: In the acquisition module, the target ball in the monitoring area is different on weekends and weekdays.

7. The traffic management coordination system based on vehicle-road coordination according to claim 1 is characterized in that: In the management module, the prompting methods for fuel vehicles include sound prompting and vibration prompting.

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