Intelligent signal lamp control device and method for preventing vehicle congestion
Through the intelligent signal light control device, combined with radar speed measurement and video surveillance technology, the signal light timing and right of road allocation are dynamically adjusted to identify emergency vehicles, which solves the problems of inefficiency in traffic during peak hours and emergency vehicle traffic guarantee in traditional traffic management systems, and achieves more Efficient and safe traffic management.
Patent Information
- Application Number
- CN202411085591.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-08
- Publication Date
- 2024-12-20
AI Technical Summary
Traditional traffic light control systems are difficult to cope with complex and changing traffic conditions, especially during peak hours, which cannot effectively manage different types of vehicles and road users, resulting in low traffic efficiency, frequent congestion, and limited priority traffic guarantee capabilities for emergency vehicles.
Using intelligent signal light control device, the logic control unit is combined with radar speed measurement unit and video unit to monitor vehicle flow information in real time, dynamically adjust signal light timing, optimize road rights allocation, identify emergency vehicles and give priority to passing, and combine log module to record historical data for prediction and Analysis: Establish a score system to manage accurately based on the degree of congestion.
The traffic mobility has been optimized, traffic efficiency and road safety have been improved, violations and potential accident risks caused by long-term waiting have been reduced, emergency response capabilities have been improved, and the intelligence and efficiency of urban traffic management have been significantly improved. .
Smart Images
Figure CN119169838A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the field of traffic technology, and in particular, relates to an intelligent signal light control device and method for preventing vehicle congestion. Background Art
[0002] With the acceleration of urbanization, the traffic volume on urban roads is increasing, and traffic congestion is becoming more and more serious, which has brought great inconvenience to people's travel and put forward higher requirements for urban traffic management. Traditional traffic light control systems mostly rely on fixed time control or simple vehicle detection technology, which is difficult to cope with complex and changing traffic conditions, especially during peak hours. It is often impossible to effectively manage different types of vehicles and road users, resulting in low traffic efficiency and frequent congestion.
[0003] In traditional traffic management systems, traffic pressure is mainly relieved by extending the green light time in the congested direction, but this approach often ignores the needs of non-motorized vehicle lanes and sidewalks, causing non-motorized vehicles and pedestrians to wait for a long time, and even violate traffic signals, further exacerbating traffic chaos. In addition, the traditional system has limited ability to ensure priority passage for emergency vehicles, and it is difficult to adjust the traffic lights in time to ensure the rapid passage of these vehicles, which affects the city's emergency response capabilities.
[0004] To solve the above problems, this solution proposes an intelligent traffic light control device and its matching method, which aims to achieve intelligent management of urban traffic flow by integrating multiple advanced detection and control technologies. The device is based on a logic control unit, which connects the radar speed measurement unit and the video unit through a data exchanger to monitor the traffic flow information of multiple intersections in real time, and dynamically adjusts the traffic light timing by analyzing the vehicle density, flow direction and type data, optimizes the road right allocation, and ensures the passage needs of different types of road users.
[0005] In view of this, the present invention is proposed. Summary of the invention
[0006] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:
[0007] An intelligent signal light control device for preventing vehicle congestion comprises a logic control unit, the logic control unit is electrically connected to a plurality of radar speed measurement units and a video unit at intersections through a data exchanger, the video unit comprises a vehicle density detection module and a road recognition module, the road recognition module comprises a public vehicle recognition submodule and a lane recognition submodule;
[0008] The logic control unit is provided with a log module at the lower level, and the logic control unit is provided with a variable lane control module, a tidal lane control module and a signal light control module at the upper level, and the radar speed measurement units and video units of multiple intersections are communicated with the traffic control system, wherein the variable lane control module, the tidal lane control module and the signal light control module are all communicated with the traffic control system, and are used to send control instructions to the traffic control system for effective response after security review.
[0009] Preferably, the radar speed measurement unit measures the speed of vehicles in sequence, and cooperates with the video unit to count the passing vehicles and obtain vehicle type information.
[0010] Preferably, the logic control unit includes a flow direction identification module, a congestion prediction module and a road right allocation module. The flow direction identification module is used to cooperate with the lane identification submodule to obtain the general trend data of the vehicle flow direction. The congestion prediction module predicts the traffic pressure of subsequent road sections based on the general trend data of the vehicle flow direction obtained by the flow direction identification module.
[0011] Preferably, the public vehicle identification module is used to identify the appearance features and logos of buses, fire trucks, medical vehicles and police vehicles, and to identify whether a dedicated siren sound is emitted.
[0012] Preferably, the log module includes traffic flow data, congestion duration and accident information records for each period of time with large traffic flow.
[0013] Preferably, the lane recognition submodule includes recognition of motor vehicle lanes, non-motor vehicle lanes and sidewalks, and the motor vehicle lanes include recognition of straight lanes, left turn lanes and right turn lanes.
[0014] Preferably, an intelligent traffic light control method for preventing vehicle congestion includes the following methods:
[0015] Taking the congested intersection L as the center, a radar speed measurement unit and a video unit are installed at the intersection L1 entering the intersection L, and a radar speed measurement unit and a video unit are selectively installed at the subsequent intersection L2 of L1, until they are installed at Ln intersections related to the flow direction of the congested vehicles as needed;
[0016] The video data and vehicle speed data of the congested intersection and the Ln intersections are recorded respectively, and integrated into a data packet and sent to the traffic control system;
[0017] The data packet is intercepted and read by the data switch during transmission, and the data is decompressed and sent to the logic control unit;
[0018] The logic control unit recognizes and reads the video, and obtains the traffic density and traffic volume data of each lane of each intersection L according to the video content recognition and matches it with the vehicle speed data;
[0019] Based on the historical average traffic flow data of each lane and combined with the current traffic density data, a comparative analysis is performed to obtain the percentage change P of the current traffic density;
[0020] And set a preset threshold ΔD based on experience or historical data, and determine the congestion state in combination with the percentage change P exceeding the limit;
[0021] The road right allocation logic is established through the road right allocation module according to the congestion status.
[0022] Preferably, the video content of the logic control unit identifies each lane of each intersection L, including vehicle density and volume data of motor vehicle lanes, non-motor vehicle lanes and sidewalks, and the motor vehicle lane includes vehicle density data for identifying left turns, right turns and straight driving.
[0023] Preferably, the right-of-way allocation logic includes establishing a scoring system, the scoring system for the same intersection establishes a priority for right-of-way allocation, and a scoring system for primary and secondary intersections is established based on the degree of congestion at different intersections.
[0024] Beneficial effects:
[0025] The intelligent traffic light control device optimizes traffic flow and improves traffic efficiency through real-time monitoring and comprehensive analysis of multiple data sources. Its core logic control unit effectively integrates the data of the radar speed measurement unit and the video unit while ensuring data security, and only reads necessary information packets to avoid intrusion into other data in the traffic control system. In addition, through the comprehensive management of motor vehicle lanes, non-motor vehicle lanes and sidewalks, it not only improves the overall traffic order, but also significantly improves road safety and reduces violations and potential accident risks caused by long waiting times.
[0026] The solution has achieved many innovations in technology and methods. First, by integrating radar speed measurement and video surveillance technology, it can comprehensively capture vehicle speed, density and type information to provide more accurate traffic data support. Secondly, the public vehicle identification submodule and lane identification submodule in the video unit can identify the appearance characteristics and signs of special vehicles (such as buses, fire trucks, 120 and police cars, etc.), and give priority to them in combination with exclusive sirens to ensure the rapid passage of emergency vehicles. Furthermore, the log module records and analyzes historical traffic data, compares it with current data, and uses big data and machine learning algorithms to accurately predict future traffic pressure and provide data-driven intelligent decision support. Finally, a refined scoring system is established to accurately manage the congestion levels of different intersections and lanes to ensure the optimal allocation of road resources.
[0027] Compared with traditional traffic management methods, the intelligent traffic light control device realizes a comprehensive traffic management and dynamic adjustment mechanism. Its ability to comprehensively manage motor vehicle lanes, non-motor vehicle lanes and sidewalks makes traffic control more comprehensive and three-dimensional. Through the flow recognition module and congestion prediction module, the system can identify and predict possible future traffic congestion in real time, take effective measures in advance, and reduce the risk of traffic congestion. In addition, the recognition and priority handling mechanism for emergency vehicles ensures a rapid response to emergencies and improves the efficiency and level of public safety services. Overall, this solution significantly improves the intelligence, modernization and efficiency of urban traffic management, and provides a new solution to modern urban traffic problems.
[0028] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] In the attached picture:
[0030] Figure 1 It is a schematic diagram of the system architecture of the present invention;
[0031] Figure 2 It is a flowchart of the method of the present invention;
[0032] Figure 3 This is a schematic diagram of a road recognition module of the present invention;
[0033] Figure 4 It is a schematic diagram of the system structure in a single intersection state of the present invention. DETAILED DESCRIPTION
[0034] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The following embodiments are used to illustrate the present invention.
[0035] like Figures 1 to 4 As shown, an intelligent traffic light control device for preventing vehicle congestion includes a logic control unit, the logic control unit is electrically connected to a plurality of radar speed measurement units and a video unit at intersections through a data exchanger, the video unit includes a vehicle density detection module and a road recognition module, and the road recognition module includes a public vehicle recognition submodule and a lane recognition submodule;
[0036] A log module is provided at the lower level of the logic control unit, and a variable lane control module, a tidal lane control module and a signal light control module are provided at the upper level of the logic control unit. The radar speed measurement units and video units at multiple intersections are communicated with the traffic control system, among which the variable lane control module, the tidal lane control module and the signal light control module are all communicated with the traffic control system, and are used to send control instructions to the traffic control system for effective response after security review.
[0037] By setting up an intelligent traffic light control device with a logic control unit as the core, the installed radar speed measurement unit and video unit are directly integrated into the supervision of the traffic control system, and only the data packets of these two units are read through the data exchanger, and other data of the traffic control system cannot be read. Targeted data capture is carried out through the data exchanger to ensure the security of the traffic control system data.
[0038] The radar speed measurement unit measures the speed of vehicles one by one, and cooperates with the video unit to count the passing vehicles and obtain vehicle type information.
[0039] The speed of vehicles is measured in sequence by the radar speed measurement unit, and the type information of the vehicles is matched with the video unit to obtain the vehicle type information. The obtained vehicle type information is sent to the public vehicle identification submodule to identify the appearance characteristics and logos of buses, fire trucks, medical vehicles and police cars, and to identify whether there is a unique siren sound. If the appearance characteristics and the unique siren sound are identified, the road section will be subject to special control, and the right of way of the flow lane will be adjusted to the maximum. The maximum right of way is not directly adjusted for buses, but the more buses there are on the road section, the greater the weight. The additional score for each bus is calculated at 0.1-0.2 points (specific settings are based on experience).
[0040] The logic control unit includes a flow direction recognition module, a congestion prediction module and a road right allocation module. The flow direction recognition module is used to cooperate with the lane recognition submodule to obtain the general trend data of the vehicle flow direction. The congestion prediction module predicts the traffic pressure of the subsequent road section based on the general trend data of the vehicle flow direction obtained by the flow direction recognition module.
[0041] The flow direction recognition module is used to cooperate with the lane recognition submodule to recognize the traffic flow data of the straight lane, left turn lane and right turn lane of the motor vehicle lane.
[0042] The log module includes traffic data, congestion duration and accident information records for each period with large traffic volume, and forms historical data. The traffic data, congestion duration and accident information records of the historical data are marked with timestamps to generate a data set. When calculating the historical average traffic density data later, it is convenient to match the timestamp of the access data set to a specific time period.
[0043] The lane recognition submodule includes the recognition of motor vehicle lanes, non-motor vehicle lanes and sidewalks. The motor vehicle lanes include the recognition of straight lanes, left turn lanes and right turn lanes.
[0044] Based on the above-mentioned intelligent traffic light control device, the following intelligent traffic light control method for preventing vehicle congestion is adopted:
[0045] Taking the congested intersection L as the center, a radar speed measurement unit and a video unit are installed at the intersection L1 entering the intersection L, and a radar speed measurement unit and a video unit are selectively installed at the subsequent intersection L2 of L1, until they are installed at Ln intersections related to the flow direction of the congested vehicles as needed;
[0046] The video data and vehicle speed data of the congested intersection and the Ln intersections are recorded respectively, and integrated into a data packet and sent to the traffic control system;
[0047] The data packet is intercepted and read by the data switch during transmission, and the data is decompressed and sent to the logic control unit;
[0048] The logic control unit recognizes and reads the video, and obtains the traffic density and traffic volume data of each lane of each intersection L according to the video content recognition and matches it with the vehicle speed data;
[0049] Based on the historical average traffic flow data of each lane and combined with the current traffic density data, a comparative analysis is performed to obtain the percentage change P of the current traffic density;
[0050] And set a preset threshold ΔD based on experience or historical data, and determine the congestion state in combination with the percentage change P exceeding the limit;
[0051] The road right allocation logic is established through the road right allocation module according to the congestion status.
[0052] The logic control unit video content identifies each lane of each intersection L, including the vehicle density and traffic volume data of the motor vehicle lane, non-motor vehicle lane and sidewalk, and the motor vehicle lane includes the vehicle density data for identifying left turns, right turns and straight driving.
[0053] The logic of right-of-way allocation includes establishing a scoring system. The scoring system for the same intersection establishes the priority of right-of-way allocation, and establishes a scoring system for primary and secondary intersections based on the degree of congestion at different intersections.
[0054] More specifically, the scheme is as follows:
[0055] Traditional traffic congestion is solved by extending the time it takes for congested vehicles to reach the green light. The continued congestion will cause non-motor vehicles and pedestrians in another lane to gather. If the waiting time is too long, some non-motor vehicles and pedestrians will violate traffic lights. Therefore, the traditional traffic diversion method has drawbacks in the allocation of road rights.
[0056] This solution uses a video unit to monitor the traffic density data of each lane (motor vehicle lane, non-motor vehicle lane, and sidewalk), and uses the following formula:
[0057] D = L / N;
[0058] Where D is the traffic density (vehicles / km), N is the total number of vehicles on a road of a specific length F at a certain point in time, and F is the length of the road (km).
[0059] Calculate the density of vehicles or people in different lanes, and calculate the historical average traffic density data of different lanes in the (specific) period according to the following formula:
[0060]
[0061] Among them, D average is the historical average traffic density (vehicles / km) of the period, and the data set of the historical average traffic density includes data sets within one year or more than one year; n is the time range of the historical data, and Di is the traffic density in the i-th year.
[0062] The historical average traffic flow data of the lane is calculated respectively, and compared and analyzed with the current traffic density data.
[0063] By calculating the current traffic density D current The percentage change relative to the historical average traffic density includes the following formula:
[0064] P={(D current -D average ) / D average}*100%;
[0065] The percentage change P of the current traffic density can be obtained;
[0066] And set a preset threshold ΔD based on experience or historical data, such as:
[0067] Normal: ΔD is within the range [-x, x] (i.e., the difference is within the normal fluctuation range).
[0068] Mild congestion: ΔD percentage change P exceeds y%.
[0069] Moderate congestion: ΔD percentage change P exceeds 2y%.
[0070] Severe congestion: ΔD percentage change P exceeds 3y%.
[0071] The y here can be adjusted according to specific traffic characteristics and historical data.
[0072] In summary, the data of congestion status of different lanes is realized, and the right of way is improved according to the congestion status of different lanes, that is, the green light time is increased and the red light time is shortened;
[0073] The above is the right-of-way allocation for vehicles in different driving directions under ideal conditions. Based on the above underlying right-of-way allocation logic, the following right-of-way allocation logic is also included:
[0074] The congestion status of the motor vehicle lane A1, non-motor vehicle lane A2 and pedestrian lane A3 in the direction of travel A may be different, including the following congestion situations from large to small:
[0075] That is, A1>A2>A3; A3>A2>A1; A2>A1>A3; A2>A3>A1; A1>A3>A2;
[0076] The traffic in different lanes in directions B, C, and D all have the above situations;
[0077] A scoring system for the same intersection is established: A, B, C, and D directions are prioritized by comparing the degree of congestion ΔD of vehicles, and then comparing the number of congested lanes (mild congestion, moderate congestion, and severe congestion are 1 point, 2 points, and 3 points, respectively; the number of congested lanes is 3 points for motor vehicle lanes, 2 points for non-motor vehicle lanes, and 1 point for sidewalks). After comparing the scores, a ranking is obtained. The higher the ranking, the more right of way the traffic in that direction has, i.e., the green light time is increased and the red light time is shortened.
[0078] Based on the above logic, if the density of non-motorized vehicle lanes and pedestrians in a single direction reaches the preset value, the right of way in that direction will be maximized and priority will be given to avoid chaos or traffic congestion in that direction, thereby improving road safety.
[0079] The scoring system for major and minor intersections is established:
[0080] Based on the above-mentioned scores of the same intersection, the traffic flow data of each lane is understood through the flow direction recognition module, the flow trend of the traffic flow is understood, and the traffic pressure on the next intersection is predicted based on the traffic density. The traffic pressure is calculated using the following formula:
[0081] M = Q*Y*(1 / V);
[0082] Where M is the traffic pressure, Q is the traffic volume (vehicles / hour), Y is the traffic density (vehicles / km), and V is the average speed (km / hour);
[0083] The traffic pressure at the next intersection is predicted as follows:
[0084] M = Q*(Y1+Y2)*(1 / V);
[0085] Where M is the traffic pressure, Q is the traffic volume (vehicles / hour), Y1 is the traffic density of the current road (vehicles / km), Y2 is the density of the incoming traffic (i.e., the congested intersection L corresponds to the connected Ln intersection), and V is the average speed (km / hour);
[0086] More specifically, Y2 is the traffic density of straight-moving vehicles heading toward the congested intersection L in the L1 intersection;
[0087] This can be used to predict the impact of the opening of one intersection on the traffic pressure at the next intersection. If it causes greater traffic pressure, the score of the intersection will be -1, and the road right scores of adjacent intersections of the congested intersection can be determined based on this.
[0088] In summary, compared with the traditional method, this solution not only considers the right of way of motor vehicle lanes, but also extends the right of way balance of non-motor vehicle lanes and sidewalks, which greatly avoids the situation where non-motor vehicles and pedestrians wait for a long time and violate traffic lights. It not only avoids continuous traffic congestion, but also significantly improves the safety of everyone on the road, and maximizes the tolerance of non-motor vehicles and pedestrians during modern peak hours.
[0089] This solution uses radar speed measurement units and video units to monitor the traffic volume, speed and vehicle type data of each intersection in real time, and can respond quickly to traffic changes. Through the flow recognition module, congestion prediction module and road right allocation module of the logic control unit, it can quickly analyze the current traffic conditions and make corresponding road right allocation decisions to improve traffic fluidity. Using the data exchanger, the device only reads the necessary traffic data packets and will not access other data of the traffic control system, ensuring the security of the traffic control system data. Based on the comparative analysis of real-time and historical traffic data, the road right allocation is optimized by adjusting the green and red light times, reducing traffic congestion and improving road traffic efficiency. It not only manages motor vehicle lanes, but also includes the monitoring and allocation of non-motor vehicle lanes and sidewalks to avoid violations caused by long waiting times and improve the overall traffic order.
[0090] Compared with the traditional management method that only targets motor vehicle lanes, this solution extends to the comprehensive management of non-motor vehicle lanes and sidewalks, significantly improving road safety and traffic order. Through the combination of real-time data and historical data, it can dynamically adjust the allocation of road rights to adapt to the ever-changing traffic conditions, avoiding the lag and limitations of traditional methods. Combined with the recognition and priority processing mechanism of emergency vehicles, it ensures a rapid response to emergencies and improves the efficiency of public safety services. The application of congestion prediction modules and flow recognition modules enables traffic management to not only solve current problems, but also predict future congestion, take measures in advance, and reduce congestion risks.
[0091] This solution combines radar speed measurement with video surveillance technology to comprehensively collect information on vehicle speed, density and type, and provide more accurate traffic data support. The public vehicle identification submodule and lane identification submodule of the video unit can identify special vehicles (such as buses, fire trucks, etc.) and make special treatments to ensure priority passage for emergency vehicles. The log module records and analyzes historical traffic data, compares it with current data, accurately predicts future traffic pressure, and provides data-driven intelligent decision support. At the same time, a scoring system is established to conduct refined management according to the congestion level of different intersections and lanes to ensure the optimal allocation of road resources.
[0092] The above are only preferred specific implementation modes of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.
Claims
1. An intelligent signal light control device for preventing vehicle congestion, characterized in that: It includes a logic control unit, the logic control unit is electrically connected to a plurality of radar speed measurement units and a video unit at intersections through a data exchanger, the video unit includes a vehicle density detection module and a road recognition module, and the road recognition module includes a public vehicle recognition submodule and a lane recognition submodule; The logic control unit is provided with a log module at the lower level, and the logic control unit is provided with a variable lane control module, a tidal lane control module and a signal light control module at the upper level, and the radar speed measurement units and video units of multiple intersections are communicated with the traffic control system, wherein the variable lane control module, the tidal lane control module and the signal light control module are all communicated with the traffic control system, and are used to send control instructions to the traffic control system for effective response after security review.
2. The intelligent traffic light control device for preventing vehicle congestion according to claim 1, characterized in that: The radar speed measurement unit measures the speed of vehicles in sequence, and cooperates with the video unit to count the passing vehicles and obtain vehicle type information.
3. The intelligent traffic light control device for preventing vehicle congestion according to claim 2, characterized in that: The logic control unit includes a flow direction identification module, a congestion prediction module and a road right allocation module. The flow direction identification module is used to cooperate with the lane identification submodule to obtain the general trend data of the vehicle flow direction. The congestion prediction module predicts the traffic pressure of the subsequent road section based on the general trend data of the vehicle flow direction obtained by the flow direction identification module.
4. The intelligent traffic light control device for preventing vehicle congestion according to claim 3, characterized in that: The public vehicle identification module is used to identify the appearance features and logos of buses, fire trucks, medical vehicles and police vehicles, and to identify whether a dedicated siren sound is emitted.
5. The intelligent traffic light control device for preventing vehicle congestion according to claim 1, characterized in that: The log module includes traffic flow data, congestion duration and accident information records for each period with large traffic flow.
6. The intelligent traffic light control device for preventing vehicle congestion according to claim 4, characterized in that: The lane recognition submodule includes the recognition of motor vehicle lanes, non-motor vehicle lanes and sidewalks, wherein the motor vehicle lanes include the recognition of straight lanes, left turn lanes and right turn lanes.
7. An intelligent traffic light control method for preventing vehicle congestion, using an intelligent traffic light control device for preventing vehicle congestion according to any one of claims 1 to 6, characterized in that: Includes the following methods: Taking the congested intersection L as the center, a radar speed measurement unit and a video unit are installed at the intersection L1 entering the intersection L, and a radar speed measurement unit and a video unit are selectively installed at the subsequent intersection L2 of L1, until they are installed at Ln intersections related to the flow direction of the congested vehicles as needed; Record the video data and vehicle speed data of the congested intersection and the Ln intersections, and integrate them into a data packet and send them to the traffic control system; The data packet is intercepted and read by the data switch during transmission, and the data is decompressed and sent to the logic control unit; The logic control unit recognizes and reads the video, and obtains the traffic density and traffic volume data of each lane of each intersection L according to the video content recognition and matches it with the vehicle speed data; Based on the historical average traffic flow data of each lane and combined with the current traffic density data, a comparative analysis is performed to obtain the percentage change P of the current traffic density; A preset threshold ΔD is set based on experience or historical data, and the congestion state is determined based on the percentage change P exceeding the limit; The road right allocation logic is established through the road right allocation module based on the congestion status.
8. The intelligent traffic light control method for preventing vehicle congestion according to claim 7, characterized in that: The logic control unit video content identifies the traffic density and traffic volume data of each lane of each intersection L, including motor vehicle lanes, non-motor vehicle lanes and sidewalks, and the motor vehicle lanes include traffic density data for identifying left turns, right turns and straight driving.
9. The intelligent traffic light control method for preventing vehicle congestion according to claim 7, characterized in that: The logic of road right allocation includes establishing a scoring system, the scoring system at the same intersection establishes the priority of road right allocation, and the scoring system of primary and secondary intersections is established according to the congestion degree of different intersections.