A traffic management collaboration system based on vehicle-road collaboration

Abstract

The present invention relates to the technical field of traffic management, and specifically discloses a traffic management collaboration system based on vehicle-road collaboration, including: a collection module: collecting the position information of fuel vehicles and generating coordinate points, and obtaining target spheres based on the coordinate points; an analysis module: setting time nodes, determining a first area according to the target spheres corresponding to the same time node; calculating the exhaust gas influence value within the first area and determining a target area; a management module: determining a target time period; determining a target intersection; and according to the number of vehicles in the target area during the target time period, giving a prompt to the fuel vehicles when the fuel vehicles reach the target intersection. The present invention can avoid the situation where the exhaust gas content in a certain area is too high.

Description

Technical Field

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

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

[0003] Traditional fuel vehicles will emit a large amount of harmful gases during operation, such as carbon dioxide, nitrogen monoxide and particulate matter. These exhaust emissions have greatly exacerbated air pollution. The existing traffic management collaborative methods mainly focus on improving driving safety and road use efficiency, and often ignore the impact of vehicle exhaust emissions on the urban environment during the driving process on urban roads. When the exhaust concentration is too high, it will lead to a significant decline in air quality, threaten the health of citizens, and increase the incidence of respiratory diseases. Therefore, how to avoid too high 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 cooperation, and solve the following technical problems:

[0005] The existing traffic management collaborative methods mainly focus on improving driving safety and road use efficiency, and often ignore the impact of vehicle exhaust emissions on the urban environment during the driving process on urban roads.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A traffic management collaborative system based on vehicle-road cooperation includes:

[0008] A collection module: collecting the position information of fuel vehicles in a preset monitoring area, generating coordinate points (a1, a2, a3) based on the position information, where a1, a2, and 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 taking it as the target sphere;

[0009] An analysis module: setting m monitoring time periods, where m is the number of preset monitoring time periods, setting a number of time nodes at a preset time interval TJG during the monitoring time periods, obtaining the target sphere at the time nodes, and obtaining the overlapping part of the target spheres corresponding to the same time node, and taking it as the first area;

[0010] Obtain the volume V of the first area and calculate the exhaust gas influence value , is a preset coefficient. When there is a building of a preset type in the first area and the corresponding exhaust gas influence value is greater than or equal to the preset exhaust gas influence value threshold Kys, mark the first area as the target area;

[0011] Management module: Obtain the time node C corresponding to the target area and determine the target time period , time difference ;

[0012] Take the route in the monitoring area that intersects with the target area as the target route, determine the road intersection closest to the intersection on the target route, and use it as the target intersection;

[0013] Calculate the target number of vehicles , Nys represents the preset theoretical number of vehicles. When the number of vehicles in the target area reaches the target number of vehicles NMB during the target time period, after the fuel vehicle arrives at the target intersection, give a prompt to the fuel vehicle.

[0014] As a further solution of the present invention: In the acquisition module, the process of clustering the coordinate points specifically includes:

[0015] Step 1: Taking the coordinate point as the center, calculate the density of the coordinate points within the preset clustering radius r. When the density of the coordinate points 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;

[0016] Obtain the theoretical centroid of the initial point cluster. The theoretical centroid is determined based on the distribution of the coordinate points in the initial point cluster, and group the initial point clusters. The distance between the theoretical centroids corresponding to any two initial point clusters in the grouping is less than the preset distance threshold;

[0017] Step 2: Obtain the theoretical centroid of the coordinate points within the group, use it as the target centroid, determine the distances between the target centroid and all the coordinate points in the group, and obtain the maximum distance D, use it as the target distance. Taking the target centroid as the center, calculate the density of the coordinate points within the target distance, and use it as the pending density;

[0018] Reduce the target distance at preset target distance intervals, and record the corresponding pending density each time the target distance is reduced. Take the pending density that is greater than or equal to the coordinate point density threshold Pys and has the largest corresponding target distance as the first density;

[0019] Step 3: Obtain the target distance Ddy corresponding to the first density. When the target distance When it is, the coordinate point closest to the target center of gravity is used as the new target center of gravity, and the first density and the corresponding target distance are obtained again. μ is a preset second correction coefficient;

[0020] Repeat the above steps. When a new target center of gravity is obtained for the bth time, if the corresponding target distance is still less than or equal to Execute Step Four. b is a preset quantity;

[0021] Among them, when the target distance Or after a new target center of gravity is obtained for a certain time, if the corresponding target distance is greater than Execute Step Six;

[0022] Step Four: Remove the coordinate point corresponding to the maximum distance D, obtain a new maximum distance Dx, and execute Step One - Step Three. If there is still no target distance corresponding to the first density greater than , execute Step Five;

[0023] When a new maximum distance is obtained for a certain time and the corresponding target distance is greater than Execute Step Six;

[0024] Step Five: Let the new coordinate point density threshold , where ΔPys is a preset coordinate point density threshold correction value, and execute Step One - Step Four again;

[0025] Step Six: Generate a new initial point cluster with the current target center of gravity and target distance to replace all the initial point clusters in the grouping;

[0026] Take all the initial point clusters as point clusters.

[0027] As a further solution of the present invention: In Step One, calculate the coordinate point density , where F represents the number of coordinate points within the clustering radius r.

[0028] As a further solution of the present invention: In the analysis module, when there is no overlapping part in the target sphere corresponding to the time node i, execute the following steps:

[0029] Remove the target sphere corresponding to the monitoring period x1, and determine whether there is an overlapping part in the remaining target spheres corresponding to the time node i;

[0030] When there is no overlapping part in the remaining target spheres corresponding to the time node i, retain the target sphere corresponding to the monitoring period x1, remove the target sphere corresponding to the monitoring period x2, and determine again whether there is an overlapping part;

[0031] Repeat the above steps until, when removing the target balls corresponding to a certain monitoring period, there is an overlapping part among the remaining target balls corresponding to time node i, and take this overlapping part as the first area corresponding to time node i.

[0032] As a further solution of the present invention: in the acquisition module, the position information of fuel vehicles is acquired based on Beidou positioning and / or GPS positioning.

[0033] As a further solution of the present invention: in the acquisition module, the target balls in the monitoring area are different on rest days and working days.

[0034] As a further solution of the present invention: in the management module, the ways to give prompts to fuel vehicles include sound prompts and vibration prompts.

[0035] The beneficial effects of the present invention: In this solution, target balls are obtained through the acquisition and clustering analysis of position information. It should be noted that the spatial distribution data of vehicles are obtained by standardized and systematic methods to ensure the accuracy and consistency of the data, so as to clarify how to effectively identify the areas where vehicles are concentrated, laying a solid foundation for subsequent environmental impact assessment and the determination of target areas, and ensuring that the subsequent steps can make scientific decisions based on accurate data. Then, the first area, that is, the representative area, is determined through the overlapping part of the target balls at the same time node in multiple monitoring periods. It should be noted that through the overlapping analysis of multi-period data, the consistent areas where vehicles appear concentrated at different time periods can be identified, enhancing the representativeness and reliability of area identification, and extracting representative areas from multiple monitoring periods to ensure that these areas still have the potential of high exhaust emissions (i.e., the areas that need to be monitored and managed) under long-term and diverse traffic conditions. It should be noted 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 allowable number of target vehicles NMB. When the actual number of vehicles reaches NMB, the system will give prompts to fuel vehicles at the target intersection, and the prompt content includes but is not limited to that the exhaust content ahead is too high and it is recommended to take a detour, etc., so as to achieve dynamic management and intelligent intervention and avoid the situation of high exhaust emissions in the area. The present invention can avoid the situation of too high exhaust content in a certain area. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below with reference to the drawings.

[0037] Figure 1 is a schematic flowchart of a traffic management cooperation system based on vehicle-road cooperation of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Please refer to Figure 1 As shown, the present invention is a traffic management collaboration system based on vehicle-road collaboration, including:

[0040] Collection module: Collect the position information of fuel vehicles in a preset monitoring area, generate coordinate points (a1, a2, a3) based on the position information, where a1, a2, and a3 respectively represent the horizontal axis coordinate, vertical axis coordinate, and vertical axis coordinate of the fuel vehicle in a preset coordinate system, cluster the coordinate points to obtain point clusters, obtain the minimum circumscribed sphere of the point clusters, and use it as the target sphere;

[0041] Analysis module: Set m monitoring time periods, where m is the number of preset monitoring time periods, set several time nodes at a preset time interval TJG within the monitoring time periods, obtain the target sphere at the time nodes, obtain the overlapping part of the target spheres corresponding to the same time node, and use it as the first area;

[0042] Obtain the volume V of the first area, calculate the tail gas influence value , is a preset coefficient. When there is a building of a preset type in the first area and the corresponding tail gas influence value is greater than or equal to a preset tail gas influence value threshold Kys, mark the first area as the target area;

[0043] Management module: Obtain the time node C corresponding to the target area and determine the target time period , time difference ;

[0044] Use the route in the monitoring area that intersects with the target area as the target route, determine the road intersection closest to the intersection on the target route, and use it as the target intersection;

[0045] Calculate the target vehicle number , where Nys represents the preset theoretical vehicle number. When the number of vehicles in the target area reaches the target vehicle number NMB during the target time period, after the fuel vehicle arrives at the target intersection, prompt the fuel vehicle.

[0046] It should be noted that the target ball is obtained through the collection and clustering analysis of position 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, thereby clarifying how to effectively identify the areas where vehicles are concentrated, laying a solid foundation for subsequent environmental impact assessment and the determination of target areas, and ensuring that the subsequent steps can make scientific decisions based on accurate data. After that, the first area, that is, the representative area, is determined through the overlapping part of the target balls at the same time node in multiple monitoring periods. It is worth noting that through the overlapping analysis of multi-period data, the consistent areas where vehicles appear concentrated at different time periods can be identified, enhancing the representativeness and reliability of area identification, and extracting representative areas from multiple monitoring periods to ensure that these areas still have the potential for high exhaust emissions (i.e., the areas that need to be monitored and managed) under long-term and diverse traffic conditions. 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 allowable number of target 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 that the exhaust content ahead is too high, it is recommended to take a detour, etc., so as to achieve dynamic management and intelligent intervention and avoid the situation of high exhaust emissions in the area.

[0047] In another preferred embodiment of the present invention, in the acquisition module, the process of clustering the coordinate points specifically includes:

[0048] Step 1: Taking the coordinate point as the center, calculate the density of the coordinate points within the preset clustering radius r. When the density of the coordinate points is greater than or equal to the preset coordinate point density threshold Pys, an initial point cluster is generated with the coordinate point as the center.

[0049] Obtain the theoretical centroid of the initial point cluster, which is determined based on the distribution of the coordinate points in the initial point cluster, and group the initial point cluster. The distance between the theoretical centroids corresponding to any two initial point clusters in the grouping is less than the preset distance threshold.

[0050] Step 2: Obtain the theoretical centroid of the coordinate points within the grouping and use it as the target centroid. Determine the distances between the target centroid and all the coordinate points in the grouping, and obtain the maximum distance D, which is used as the target distance. Taking the target centroid as the center, calculate the density of the coordinate points within the target distance and use it as the pending density.

[0051] 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;

[0052] 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;

[0053] 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;

[0054] 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;

[0055] 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;

[0056] When a new maximum distance is obtained, the corresponding target distance is greater than When , execute step 6;

[0057] 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;

[0058] 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;

[0059] All initial point clusters are taken as point clusters.

[0060] 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.

[0061] 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.

[0062] In another preferred embodiment of the present invention, in the analysis module, when there is no overlapping part for the target balls corresponding to time node i, the following steps are executed:

[0063] Remove the target balls corresponding to monitoring period x1, and determine whether there is an overlapping part for the remaining target balls corresponding to time node i;

[0064] When there is no overlapping part for the remaining target balls corresponding to time node i, retain the target balls corresponding to monitoring period x1, remove the target balls corresponding to monitoring period x2, and determine again whether there is an overlapping part;

[0065] Repeat the above steps until, when removing the target balls corresponding to a certain monitoring period, there is an overlapping part for the remaining target balls corresponding to time node i, and take the overlapping part at this time as the first region corresponding to time node i.

[0066] It should be noted that when removing the target balls corresponding to any one of the monitoring periods and there is no overlapping part for the remaining target balls corresponding to time node i, the following steps are executed:

[0067] Remove the target balls corresponding to monitoring period x3 and monitoring period x4, and determine whether there is an overlapping part for the remaining target balls corresponding to time node i;

[0068] When there is no overlapping part for the remaining target balls corresponding to time node i, retain the target balls corresponding to monitoring period x3 and monitoring period x4, remove the target balls corresponding to monitoring period x5 and monitoring period x6, and determine whether there is an overlapping part for the remaining target balls;

[0069] Repeat the above steps until, when removing the target balls corresponding to two monitoring periods, there is an overlapping part for the remaining target balls corresponding to time node i, and take the overlapping part at this time as the first region corresponding to time node i;

[0070] If there is no overlapping part even after removing two, then remove three and repeat the above steps, and so on, until there is an overlapping part. 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.

[0071] In another preferred embodiment of the present invention, in the acquisition module, the position information of the fuel vehicle is acquired based on Beidou positioning and / or GPS positioning.

[0072] In another preferred embodiment of the present invention, in the acquisition module, the target balls in the monitoring area are different on rest days and working days.

[0073] In another preferred embodiment of the present invention, in the management module, the ways of prompting the fuel vehicle include sound prompting and vibration prompting.

[0074] The above has described an embodiment of the present invention in detail, but the content described is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. Any equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A traffic management collaboration system based on vehicle-road collaboration, characterized in that Including: Collection module: Collect the position information of fuel vehicles in a preset monitoring area, generate coordinate points (a1, a2, a3) based on the position information, where a1, a2, and a3 respectively represent the horizontal axis coordinate, vertical axis coordinate, and vertical axis coordinate of the fuel vehicle in a preset coordinate system, cluster the coordinate points to obtain point clusters, obtain the minimum circumscribed sphere of the point clusters, and use it as the target sphere; Analysis module: Set m monitoring time periods, where m is the preset number of monitoring time periods, set a number of time nodes at a preset time interval TJG within the monitoring time periods, obtain the target sphere at the time nodes, and obtain the overlapping part of the target spheres corresponding to the same time node, and use it as the first area; Obtain the volume V of the first region and calculate the tail gas influence value , is a preset coefficient. When there is a building of a preset type in the first region and the corresponding tail gas influence value is greater than or equal to the preset tail gas influence value threshold Kys, mark the first region as the target region; Management module: Obtain the time node C corresponding to the target area and determine the target time period , time difference ; Take the route in the monitoring area that has an intersection with the target area as the target route, and determine the road intersection closest to the intersection on the target route, and use it as the target intersection; Calculate the number of target vehicles It represents the preset theoretical number of vehicles. When the number of vehicles in the target area reaches the target number of vehicles NMB during the target time period, after the fuel vehicle arrives at the target intersection, a prompt is given to the fuel vehicle.

2. The traffic management collaboration system based on vehicle-road collaboration according to claim 1, wherein In the analysis module, when there is no overlapping part of the target sphere corresponding to time node i, perform the following steps: Remove the target sphere corresponding to monitoring time period x1, and determine whether there is an overlapping part of the remaining target spheres corresponding to time node i; When there is no overlapping part of the remaining target spheres corresponding to time node i, retain the target sphere corresponding to monitoring time period x1, remove the target sphere corresponding to monitoring time period x2, and determine again whether there is an overlapping part; Repeat the above steps until when removing the target sphere corresponding to a certain monitoring time period, there is an overlapping part of the remaining target spheres corresponding to time node i, and use the overlapping part at this time as the first area corresponding to time node i.

3. A traffic management collaboration system based on vehicle-road collaboration according to claim 1, characterized in that, In the collection module, collect the position information of fuel vehicles based on Beidou positioning and / or GPS positioning.

4. A traffic management collaboration system based on vehicle-road collaboration according to claim 1, characterized in that, In the collection module, the target spheres in the monitoring area are different on rest days and working days.

5. A traffic management collaboration system based on vehicle-road collaboration according to claim 1, characterized in that, In the management module, the ways to prompt fuel vehicles include sound prompts and vibration prompts.

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