Smart city traffic management method, system and equipment based on ramp control

Abstract

The invention provides a smart city traffic management method, system and device based on ramp control, and the method specifically comprises the steps: generating a data model according to the historical vehicle number and historical vehicle speed of each lane; using the data model, the real-time vehicle number of each lane and the real-time vehicle speed to control the frequency of merging the vehicles from the ramp; and adaptively learning the optimized data model according to the road traffic result. Wherein a lane merging section is arranged at the exit end of the ramp, and the length of the lane merging section is greater than or equal to a lane merging threshold value; a vehicle waiting area is arranged on the ramp and used for temporary parking in the traffic peak period. Therefore, according to the number of the vehicles on the lane and the vehicle speed, the frequency of the vehicles entering the main lane from the ramp is controlled, so that the merging amount of the vehicles from the ramp is matched with the real-time vehicle condition on the road, the high-traffic-volume operation of the main lane is ensured, meanwhile, the vehicles on the ramp are maintained to stably and continuously pass through, and traffic jam is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of smart city road traffic, and particularly to a smart city traffic management method, system and device based on ramp control. Background Art

[0002] Existing urban road intersections are usually connected by simple traffic lights or uncontrolled ramps, and generally single-lane ramps. When vehicles traveling in one direction enter the road in another direction through the ramp, the speed of the vehicles on the main road is reduced due to the merging of vehicles from the secondary road. Moreover, during peak hours, due to the increase in traffic flow, the impact of ramp vehicle merging on vehicle speed is also significantly amplified, resulting in vehicles that need to enter the other direction unable to enter, and vehicles that cannot enter the other direction gathering and congesting on the original driving lane. The most obvious example is the embarrassing situation where vehicles on the urban expressway main road or the ring expressway cannot go up or down during congested hours.

[0003] Precisely because the existing ramps connecting road intersections simply provide the connection of traffic in different directions and usually have no traffic flow control, when a large number of vehicles enter another direction from one driving direction through the ramp, the speed of the vehicles driving on the main lane will be greatly reduced, and the road throughput will be significantly reduced, thus forming congestion. Summary of the Invention

[0004] In view of the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a smart city traffic management method, system and device based on ramp control, which is used to solve the problem that the traffic flow of vehicles merging from the ramp is not controlled in the prior art, resulting in easy formation of road congestion.

[0005] To achieve the above object and other related objects, the present invention provides a smart city traffic management method based on ramp control, including:

[0006] Step S1, generating a data model according to the historical vehicle quantity and historical vehicle speed of each lane;

[0007] Step S2, using the data model, the real-time vehicle quantity and real-time vehicle speed of each lane to control the frequency of vehicles merging from the ramp;

[0008] Step S3, adaptively learning and optimizing the data model according to the road traffic result;

[0009] Wherein, a merging section is provided at the ramp exit end, and the length of the merging section is greater than or equal to the merging threshold; a vehicle waiting area is provided on the ramp for temporary parking during peak traffic hours.

[0010] Furthermore, in some embodiments of the present application, it further includes:

[0011] When the distance between the on-ramp and the next off-ramp is less than a preset distance threshold, resulting in the mutual influence between the incoming vehicles and the outgoing vehicles on the lane, a cloverleaf lane is set and the vehicle merging area of the on-ramp is extended behind the off-ramp.

[0012] Further, in some embodiments of the present application, the step S2 includes:

[0013] Step S21, obtaining the images captured by the road surface monitoring probes, obtaining the real-time vehicle numbers of each lane based on image analysis, and storing the data;

[0014] Step S22, obtaining the data of the road surface speed measurement probes, obtaining the real-time vehicle speeds of each vehicle on each lane, and storing the data;

[0015] Step S23, processing the data obtained in step S21 and step S22, and importing the data into the data model.

[0016] Further, in some embodiments of the present application, the data relationships in the data model are H=T(2+m / 10)·R(1+n / 10)·F·h;

[0017] Wherein, H represents the vehicle frequency for controlling the vehicle to enter the main lane from the secondary lane, which is the number of vehicles allowed to enter the main lane from the secondary lane per unit time;

[0018] T represents the vehicle dredging degree of the main lane, which is the ratio of the real-time vehicle number of the main lane to the maximum allowable vehicle number in the main lane determined based on the historical vehicle number;

[0019] R represents the vehicle speed reduction ratio, which is the ratio of the real-time average driving speed of the vehicle to the fastest vehicle speed determined based on the historical vehicle speed, and the real-time average driving speed of the vehicle is determined based on the real-time vehicle speed;

[0020] h represents the average frequency of the vehicle entering the ramp from the secondary lane, which is the number of vehicles entering the ramp from the secondary lane per unit time, and is determined based on the real-time vehicle number;

[0021] F represents the fullness of the vehicles waiting on the ramp, which is the ratio of the real-time number of waiting vehicles to the total number of ramp parking spaces;

[0022] m is the first exponential coefficient, n is the second exponential coefficient, and both m and n are positive constants less than 10, and are continuously adjusted when the data model is adaptively learned and optimized;

[0023] Wherein, the main lane is the lane that the vehicle is to merge into; the secondary lane is the lane that the vehicle is to leave.

[0024] Further, in some embodiments of the present application, it further includes calculating the waiting time of a vehicle in the vehicle waiting area of the ramp based on the following formula:

[0025] t = N / (H - h);

[0026] Wherein, t represents the waiting time of the vehicle in the vehicle waiting area of the ramp; N represents the number of vehicle positions in the vehicle waiting area of the ramp.

[0027] Further, in some embodiments of the present application, the maximum waiting time in the vehicle waiting area is 5 minutes.

[0028] Further, in some embodiments of the present application, the lane-changing threshold is greater than the driving distance traveled at the lane speed limit for 5 seconds.

[0029] Further, in some embodiments of the present application, the vehicle waiting area of the ramp adopts one or more of a three-dimensional ramp, a multi-layer ramp, and an extended ramp.

[0030] The present invention also provides a smart city traffic management system based on ramp control, including:

[0031] A model generation module, configured to generate a data model according to the historical vehicle quantity and historical vehicle speed of each lane;

[0032] A control optimization module, configured to use the data model, the real-time vehicle quantity and real-time vehicle speed of each lane to control the frequency of vehicles merging from the ramp, and adaptively learn and optimize the data model according to the road traffic result;

[0033] Wherein, a lane-changing section is provided at the ramp exit end, and the length of the lane-changing section is greater than or equal to the lane-changing threshold; a vehicle waiting area is provided on the ramp for temporary parking during peak traffic hours.

[0034] The present invention also provides a smart city traffic management device based on ramp control, including:

[0035] It includes a processor and a memory, and the processor is connected to the memory:

[0036] Wherein, the processor is configured to call and execute the program stored in the memory;

[0037] The memory is configured to store the program, and the program is at least used to execute the above-mentioned smart city traffic management method based on ramp control

[0038] As described above, the smart city traffic management method, system, and device based on ramp control of the present invention have the following beneficial effects:

[0039] 1. The historical number of vehicles and the historical speed of vehicles in each lane obtained by historical statistics are used to form a data model, and then the real-time number of vehicles and the real-time speed of vehicles in each lane obtained in real time are imported to obtain control information. On the one hand, the frequency of vehicles merging from the ramp into the main lane is controlled according to the number of vehicles and the speed of vehicles on the lane, rather than leaving the vehicles in an uncontrolled state or only using the intermittent method of traffic lights for control, so that the amount of vehicles merging from the ramp matches the real-time vehicle conditions on the road, ensuring high-volume operation of the main lane, while maintaining the stable and continuous passage of vehicles on the ramp, avoiding traffic congestion, and on the other hand, improving the intelligence of the control of vehicles merging into the ramp.

[0040] 2. By setting up a vehicle waiting area on the ramp and providing an area for temporary parking when vehicles on the ramp are not allowed to merge due to data model control, it is prevented that vehicles changing direction will not affect the normal traffic of the road while waiting, which further ensures the high traffic volume of the main lane. In addition, the waiting time of vehicles on the ramp can be adjusted according to the congestion of the lane, so that the waiting time of vehicles in the vehicle waiting area matches the real-time vehicle conditions on the road, further improving the intelligence of traffic control.

[0041] 3. Since the parking space occupancy rate of the ramp waiting area affects the frequency of vehicles entering the main lane, the data model can be used to link all ramps entering the same lane, thereby ensuring the smooth operation of the entire urban traffic. The system can achieve independent and coordinated regulation during real-time control on site, effectively avoiding the shortcomings of large-scale integrated control systems such as complexity and high risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of a smart city traffic management method based on ramp control provided in an embodiment of the present invention.

[0043] Figure 2 A schematic structural diagram of a smart city traffic management system based on ramp control provided in an embodiment of the present invention.

[0044] Figure 3 A schematic structural diagram of a smart city traffic management device based on ramp control provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following is a description of the implementation of the present invention by means of specific embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification.

[0046] See also Figure 1It should be noted that the structures, proportions, sizes, etc. shown in the drawings of this specification are only used to cooperate with the content disclosed in the specification for those familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have any substantial technical significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention. At the same time, the terms such as "upper", "lower", "left", "right", "middle", and "one" cited in this specification are only for the convenience of clear narration and are not used to limit the scope for the implementation of the present invention. The change or adjustment of their relative relationships, without substantial change in the technical content, should also be regarded as the scope within which the present invention can be implemented.

[0047] Method Embodiment:

[0048] Figure 1 The following is a schematic flowchart of a smart city traffic management method based on ramp control provided by an embodiment of the present invention. Please refer to Figure 1 The smart city traffic management method based on ramp control provided by the present invention at least includes the following steps:

[0049] Step S1: Generate a data model according to the historical vehicle quantity and historical vehicle speed of each lane.

[0050] Specifically, obtain the historical vehicle quantity and historical vehicle speed of each lane through historical statistics, so as to generate a data model.

[0051] Step S2: Use the data model, the real-time vehicle quantity and real-time vehicle speed of each lane to control the frequency of vehicles merging from the ramp.

[0052] Step S3: Adaptively learn and optimize the data model according to the road traffic result.

[0053] In addition, it also includes setting a merging section at the ramp exit end, and the length of the merging section is not less than the specified merging threshold; and setting a vehicle waiting area on the ramp for temporary parking during peak traffic hours.

[0054] Among them, the data model itself (in some other embodiments, it can also be through other relevant modules) can be used to adaptively learn and optimize the data model according to the road traffic result.

[0055] Through the above technical solutions of the present application, the quantity of vehicles merging from the ramp is matched with the real-time traffic conditions on the road, ensuring the high throughput operation of the main lane, while maintaining the stable and continuous passing of vehicles on the ramp, and avoiding the occurrence of traffic congestion.

[0056] On this basis, in some embodiments of the present application, it further includes: when the distance between the entrance ramp (specifically, the ramp entrance) and the next exit ramp is too close, such as less than a preset distance threshold, resulting in mutual influence between the incoming vehicles and the outgoing vehicles on the lane, a cloverleaf interchange lane is set and the vehicle merging area of the entrance ramp is extended after the exit ramp, so as to avoid the mutual influence between the incoming vehicles and the outgoing vehicles.

[0057] On this basis, in some embodiments of the present application, the above step S2 may specifically include:

[0058] Step S21, obtain the images of the road surface monitoring probes, and based on visual analysis, obtain the real-time vehicle numbers of each lane, and store the data.

[0059] Step S22, obtain the data of the road surface speed measurement probes, obtain the real-time vehicle speeds of each vehicle on each lane, and store the data;

[0060] Step S23, process the data obtained in step S21 and step S22, and import it into the data model.

[0061] It should be noted that in the present application, the historical vehicle numbers and historical vehicle speeds used to generate the data model can also be obtained through the above principle.

[0062] In this way, the intelligent city traffic management method based on ramp control of the present application can be realized by using the existing devices and equipment, which improves the implementation efficiency and reduces the cost at the same time.

[0063] On this basis, in some embodiments of the present application, the frequency for controlling the vehicles merging from the ramp is determined through the data relationship formulas in the data model. The specific formula is as follows:

[0064] H = T (2+m / 10) ·R (1+n / 10) ·F·h

[0065] Among them, H represents the vehicle frequency for controlling vehicles to enter the main lane from the secondary lane, that is, the number of vehicles allowed to enter the main lane from the secondary lane per unit time; T represents the vehicle dredging degree of the main lane, that is, the ratio of the real-time vehicle number in the main lane to the maximum allowable vehicle number in the main lane obtained based on the historical vehicle number; R represents the vehicle speed reduction ratio, that is, the ratio of the real-time average driving speed of the vehicle determined based on the real-time vehicle speed to the fastest speed of the vehicle determined based on the historical vehicle speed; h represents the average frequency of vehicles entering the ramp from the secondary lane, that is, the number of vehicles entering the ramp from the secondary lane per unit time (determined based on the real-time vehicle number); F represents the occupancy rate of the waiting vehicles in the ramp, that is, the ratio of the real-time waiting vehicle number to the total number of ramp parking spaces; m is the first exponential coefficient, n is the second exponential coefficient, and both m and n are positive constants less than 10, which are continuously adjusted during the adaptive learning of the data model; among them, the main lane is the lane where vehicles merge (that is, the lane where vehicles are to merge); the secondary lane is the lane where vehicles leave (that is, the lane where vehicles are to leave).

[0066] In this way, a data model is formed using the vehicle numbers and vehicle speeds of each lane obtained from historical statistics (i.e., historical vehicle numbers and historical vehicle speeds), and then the control information for controlling the vehicles merging from the ramp can be obtained by importing the real-time vehicle numbers and vehicle speeds of each lane (i.e., real-time vehicle numbers and real-time vehicle speeds), improving the intelligence level of the control of vehicles merging into the ramp.

[0067] And in some embodiments of the present application, the waiting time of the vehicle in the vehicle waiting area of the ramp can be determined by the following formula:

[0068] t = N / (H - h)

[0069] Among them, t represents the waiting time of the vehicle in the vehicle waiting area of the ramp; N represents the number of parking spaces in the vehicle waiting area of the ramp, and a maximum value can be set for N, such as the maximum value being the actual statistical value of the maximum vehicle traffic volume in the ramp within a preset time period such as 2 minutes.

[0070] In this way, the waiting time of the vehicle on the ramp is adjusted according to the lane congestion situation, so that the waiting time of the vehicle in the vehicle waiting area matches the real-time traffic conditions on the road, further improving the intelligence level of traffic control.

[0071] And a maximum waiting time threshold such as 5 minutes can also be set for the vehicle waiting area, so as to prevent the driver's mood from fluctuating due to too long waiting time, thus avoiding road accidents that may be caused by road rage driving.

[0072] It can be understood that in the above scheme, on the one hand, the information such as H calculated by the data model can be used to calculate the waiting time of the vehicle waiting area of ​​the above ramp for actual control; on the other hand, the threshold set for the maximum waiting time can also be used to optimize the data model, so as to avoid emotional fluctuations of the driver caused by too long waiting time, and further avoid road accidents that may be caused by road rage driving.

[0073] In some embodiments of the present application, the merging threshold may be set to be greater than a driving distance that is greater than a preset time period, such as 5 seconds, at the lane speed limit.

[0074] By adopting the above technical solution, a longer merging distance can be maintained. On the one hand, it can prevent rear-end collisions caused by sudden lane changes in a short distance. On the other hand, it can prevent traffic congestion caused by vehicles stagnating and changing lanes, which makes it impossible for the following vehicles to pass, further ensuring high-throughput operation of the main lane.

[0075] On this basis, in some embodiments of the present application, the vehicle waiting area of ​​the ramp can adopt one or more of a three-dimensional ramp, a multi-layer ramp and an extended ramp, so as to increase the number of vehicles that the vehicle waiting area can accommodate.

[0076] On this basis, due to the influence of the parking space fullness of the ramp waiting area on the frequency of vehicles entering the main lane, the data model of this application can be used to link all ramps entering the same lane, thereby ensuring the smooth operation of the entire urban traffic, so that the system can achieve independent and coordinated regulation during real-time control on site, effectively avoiding the shortcomings of complexity and high risk of large-scale integrated control systems.

[0077] System Example:

[0078] Based on the same inventive concept, the present invention also provides a smart city traffic management system based on ramp control. Figure 2 A schematic diagram of a smart city traffic management system based on ramp control provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the device at least includes:

[0079] The model generation module 11 is used to generate a data model according to the historical number of vehicles and the historical vehicle speeds of each lane.

[0080] A control optimization module 12, for controlling the frequency of vehicles merging from ramps by using a data model, the real-time number of vehicles in each lane, and the real-time vehicle speed; and adaptively learning and optimizing the data model according to road traffic results;

[0081] Among them, a merging section is set at the exit end of the ramp, and the length of the merging section is greater than or equal to the merging threshold; a vehicle waiting area is set on the ramp for temporary parking during peak traffic hours.

[0082] It should be noted that the system can also connect the vehicle quantity measurement module to the road monitoring probe to obtain the picture of the road monitoring probe, and visually analyze to obtain the vehicle information of each lane; and connect the vehicle speed measurement module to the road speed measurement probe to obtain the data of the road speed measurement probe and obtain the vehicle speed information of each vehicle on each lane. Of course, in some other embodiments, the above data can also be directly obtained by connecting to relevant existing systems.

[0083] Regarding the device in the above embodiments, the specific ways for each module to execute operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0084] Device embodiments:

[0085] Based on the same inventive concept, the present invention also provides a smart city traffic management device based on ramp control. Figure 3 FIG. is a schematic structural diagram of a smart city traffic management device based on ramp control provided by an embodiment of the present invention. As Figure 3 shown, the smart city traffic management device based on ramp control in this embodiment includes a processor 21 and a memory 22, and the processor 21 is connected to the memory 22. Among them, the processor 21 is used to call and execute the program stored in the memory 22; the memory 22 is used to store the program, and this program is at least used to execute the smart city traffic management method based on ramp control in the above embodiments.

[0086] The specific implementation scheme of the smart city traffic management device based on ramp control provided by the embodiments of the present application can refer to the implementation manners of the smart city traffic management method based on ramp control in any of the above embodiments, and will not be elaborated here.

[0087] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be seen in the same or similar content in other embodiments.

[0088] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be seen in the same or similar content in other embodiments.

[0089] It should be noted that in the description of the present invention, terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "a plurality of" refers to at least two.

[0090] Any process or method description depicted in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code that includes one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations where functions may be executed not in the order shown or discussed, including in a substantially simultaneous manner according to the involved functions or in a reverse order, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0091] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0092] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing relevant hardware through a program. The said program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0093] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0094] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc.

[0095] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0096] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A smart city traffic management method based on ramp control, characterized in that: include: Step S1, generating a data model according to the historical number of vehicles and historical vehicle speeds of each lane; Step S2, controlling the frequency of vehicles merging from the ramp using the data model, the real-time number of vehicles in each lane, and the real-time vehicle speed; Step S3, adaptively learning and optimizing the data model according to the road traffic results; Among them, a merging section is set at the exit end of the ramp, and the length of the merging section is greater than or equal to the merging threshold; a vehicle waiting area is set on the ramp for temporary parking during peak traffic hours.

2. A smart city traffic management method based on ramp control according to claim 1, characterized in that: Also includes: When the distance between an entrance ramp and the next exit ramp is less than a preset distance threshold, causing incoming vehicles and outgoing vehicles on the lane to affect each other, a grade separation lane is set and the vehicle merging area of ​​the entrance ramp is extended to behind the exit ramp.

3. A smart city traffic management method based on ramp control according to claim 1, characterized in that: The step S2 comprises: Step S21, obtaining the road monitoring probe image, and obtaining the real-time number of vehicles in each lane based on image analysis, and storing the data; Step S22, acquiring road speed measuring probe data, obtaining the real-time vehicle speed of each vehicle on each lane, and storing the data; Step S23, processing the data obtained in step S21 and step S22, and importing the data into the data model.

4. A smart city traffic management method based on ramp control according to claim 3, characterized in that: The data relationship in the data model is H=T (2+m / 10) ·R (1+n / 10) ·F·h; Among them, H represents the frequency of controlling vehicles to enter the main lane from the secondary lane, which is the number of vehicles allowed to enter the main lane from the secondary lane per unit time; T represents the degree of vehicle unblocking in the main lane, which is the ratio of the real-time number of vehicles in the main lane to the maximum number of vehicles allowed in the main lane determined based on the historical number of vehicles; R represents a vehicle speed reduction ratio, which is a ratio of a real-time average vehicle speed to a maximum vehicle speed determined based on the historical vehicle speed, wherein the real-time average vehicle speed is determined based on the real-time vehicle speed; h represents the average frequency of vehicles entering the ramp from the secondary lane, which is the number of vehicles entering the ramp from the secondary lane per unit time and is determined based on the real-time number of vehicles; F represents the fullness of the ramp waiting vehicles, which is the ratio of the real-time number of waiting vehicles to the total number of ramp parking spaces; m is the first exponential coefficient, n is the second exponential coefficient, both m and n are positive constants less than 10, and are continuously adjusted during the adaptive learning optimization of the data model; The main lane is the lane that the vehicle is going to merge into, and the secondary lane is the lane that the vehicle is going to leave.

5. A smart city traffic management method based on ramp control according to claim 4, characterized in that: It also includes calculating the waiting time of the vehicle in the vehicle waiting area of ​​the ramp based on the following formula: t = N / (Hh); Wherein, t represents the waiting time of the vehicle in the vehicle waiting area of ​​the ramp; N represents the number of parking spaces in the vehicle waiting area of ​​the ramp.

6. A smart city traffic management method based on ramp control according to claim 5, characterized in that: The maximum waiting time in the vehicle waiting area is 5 minutes.

7. A smart city traffic management method based on ramp control according to claim 5, characterized in that: The merging threshold is greater than a driving distance of 5 seconds at the lane speed limit.

8. The smart city traffic management method based on ramp control according to claim 1 is characterized by: The vehicle waiting area of ​​the ramp adopts one or more of a three-dimensional ramp, a multi-layer ramp and an extended ramp.

9. A smart city traffic management system based on ramp control, characterized in that: include: A model generation module, used to generate a data model according to the historical number of vehicles and the historical vehicle speeds of each lane; A control optimization module, for controlling the frequency of vehicles merging from ramps using the data model, the real-time number of vehicles in each lane, and the real-time vehicle speed; and adaptively learning and optimizing the data model according to road traffic results; Among them, a merging section is set at the exit end of the ramp, and the length of the merging section is greater than or equal to the merging threshold; a vehicle waiting area is set on the ramp for temporary parking during peak traffic hours.

10. A smart city traffic management device based on ramp control, characterized in that: include: The invention comprises a processor and a memory, wherein the processor is connected to the memory: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the smart city traffic management method based on ramp control as described in any one of claims 1-7.

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