Traffic control system and traffic control method

Abstract

The invention relates to the technical field of traffic control, in particular to a traffic control system and a traffic control method. The control method comprises the following steps: collecting real-time traffic data of a road network through a multi-source sensing device, and generating a real-time traffic state evaluation result according to the real-time traffic data; acquiring traffic priority data of each intersection, dynamically calculating a traffic signal timing scheme based on a real-time traffic state evaluation result, and outputting the traffic signal timing scheme; executing a traffic signal timing scheme, collecting traffic state data in real time after the scheme is executed, comparing the traffic state data with a preset traffic efficiency evaluation index, and performing closed-loop iterative optimization; the control system comprises a real-time traffic state sensing module, a traffic signal timing dynamic adjustment module and an adjustment execution and iterative optimization module. By means of the mode, the real-time traffic state is sensed, the timing scheme is dynamically matched, and the dynamic change of the traffic flow is responded in real time.

Description

Technical Field

[0001] This invention relates to the field of traffic control technology, and in particular to a traffic control system and a traffic control method. Background Technology

[0002] With the acceleration of urbanization and the rapid growth of motor vehicle ownership, urban road traffic flow continues to rise, and traffic congestion is becoming increasingly prominent. This not only reduces people's travel efficiency but also exacerbates energy consumption and environmental pollution. As a core technical means to regulate traffic flow and improve road traffic efficiency, the effectiveness of traffic signal control directly determines the operational quality of the urban transportation system.

[0003] Existing traffic signal control methods mostly employ fixed or semi-fixed timing schemes. These methods pre-set signal timing schemes based on historical traffic data and cannot respond to dynamic changes in traffic flow in real time. For example, during morning and evening rush hours, fixed timing schemes often cannot meet the surge in traffic demand, leading to continuously increasing queue lengths and exacerbating traffic congestion; while during off-peak hours, green light time may be wasted, reducing the utilization rate of road resources.

[0004] Therefore, it is essential to propose a traffic control system and method that can sense real-time traffic conditions and dynamically match timing schemes. Summary of the Invention

[0005] The purpose of this invention is to provide a traffic control system and a traffic control method, which aims to achieve the effect of sensing real-time traffic conditions and dynamically matching timing schemes to realize real-time response to dynamic changes in traffic flow.

[0006] To achieve the above objectives, the present invention provides a traffic control method comprising the following steps: Real-time traffic data of the road network is collected by multi-source sensing devices, and real-time traffic status assessment results are generated based on the real-time traffic data; the real-time traffic data includes vehicle flow, vehicle speed, queue length and intersection congestion status; Obtain traffic priority data for each intersection, dynamically calculate traffic signal timing schemes based on real-time traffic condition assessment results, and output the traffic signal timing schemes. The system implements traffic signal timing schemes and collects real-time traffic status data after the schemes are implemented. It then compares the data with preset traffic efficiency evaluation indicators and performs closed-loop iterative optimization.

[0007] The process includes collecting real-time traffic data from the road network using multi-source sensing devices and generating real-time traffic status assessment results based on this data. This real-time traffic data includes vehicle flow, speed, queue length, and intersection congestion status. Deploy multi-source sensing devices to key nodes of the road network; key nodes include main road intersections, secondary road intersections, branch road junctions and the middle area of ​​road sections; multi-source sensing devices include video surveillance cameras, microwave radar, geomagnetic sensors and roadside units. The multi-source sensing equipment is activated to collect and summarize data to obtain real-time traffic data including vehicle flow, vehicle speed, queue length and intersection congestion status. Real-time traffic data is preprocessed to remove abnormal data caused by equipment failure, extreme weather, and traffic accidents, and the dimensional differences between different dimensions of data are eliminated through data standardization.

[0008] Among them, in the step of activating the multi-source sensing equipment and collecting and summarizing data to obtain real-time traffic data including vehicle flow, vehicle speed, queue length, and intersection congestion status: Video surveillance cameras collect image data of vehicle queue length and vehicle type at intersections, microwave radar collects vehicle speed and real-time traffic dynamic data, geomagnetic sensors detect the frequency of vehicle passage to assist in the statistics of traffic flow on road sections, and roadside units interact with vehicle on-board units through V2X communication to obtain real-time vehicle location and driving intention information.

[0009] The process involves preprocessing real-time traffic data to remove abnormal data caused by equipment malfunctions, extreme weather, and traffic accidents, and then standardizing the data to eliminate dimensional differences between different data dimensions. Multi-dimensional data fusion analysis is performed on real-time traffic data to generate real-time traffic status assessment results for corresponding intersections and road segments.

[0010] Among the steps, the following steps are involved: acquiring traffic priority data for each intersection, dynamically calculating traffic signal timing schemes based on real-time traffic condition assessment results, and outputting the traffic signal timing schemes: Collect basic information data for each intersection, including intersection type and area control requirements, while monitoring the real-time passage needs of special vehicles and summarizing the data to form the passage priority data for each intersection; Based on real-time traffic condition assessment results, the traffic priority of each intersection is dynamically determined.

[0011] After the step of dynamically determining the traffic priority at each intersection based on real-time traffic condition assessment results: Based on real-time traffic condition assessment results and dynamically determined traffic priorities, the traffic demand at each intersection is obtained.

[0012] After obtaining the traffic demand at each intersection based on real-time traffic condition assessment results and dynamically determined traffic priorities: The green light duration for each traffic phase is dynamically calculated based on traffic demand, and the durations of each phase are integrated to form a complete traffic signal timing scheme and output.

[0013] Among these steps, the process of implementing traffic signal timing schemes, collecting real-time traffic status data after the schemes are implemented, comparing it with preset traffic efficiency evaluation indicators, and performing closed-loop iterative optimization includes: The generated traffic signal timing scheme is sent to the corresponding traffic signal control equipment and executed. Real-time traffic status data is collected after the plan is implemented, including average vehicle speed, vehicle delay time, intersection capacity, and changes in queue length. Retrieve preset traffic efficiency evaluation indicators; the indicators are set according to intersection type, including average vehicle speed, vehicle delay time and intersection capacity standards for main road intersections, secondary road intersections and branch road intersections.

[0014] After retrieving the preset traffic efficiency evaluation indicators: The collected traffic status data after the plan is implemented is compared with the preset evaluation indicators to determine whether all indicators meet the standards, and the judgment result is output.

[0015] This invention also provides a traffic control system, including a real-time traffic status perception module, a traffic signal timing dynamic adjustment module, and an adjustment execution and iterative optimization module; wherein: The real-time traffic status perception module is used to collect real-time traffic data of the road network through multi-source sensing devices and generate real-time traffic status assessment results based on the real-time traffic data; wherein the real-time traffic data includes vehicle flow, vehicle speed, queue length and intersection congestion status. The traffic signal timing dynamic adjustment module is used to acquire traffic priority data at each intersection, dynamically calculate the traffic signal timing scheme based on real-time traffic condition assessment results, and output the traffic signal timing scheme. The adjustment execution and iterative optimization module is used to execute the traffic signal timing scheme, collect traffic status data after the scheme is executed in real time, compare it with the preset traffic efficiency evaluation index, and perform closed-loop iterative optimization.

[0016] The present invention discloses a traffic control system and a traffic control method, comprising the real-time traffic state perception module, the traffic signal timing dynamic adjustment module, and the adjustment execution and iterative optimization module, which perform the following steps: Real-time traffic data of the road network is collected through multi-source sensing devices; a real-time traffic state assessment result is generated based on the real-time traffic data; wherein the real-time traffic data includes vehicle flow, vehicle speed, queue length, and intersection congestion status; traffic priority data for each intersection is obtained; based on the real-time traffic state assessment result, a traffic signal timing scheme is dynamically calculated and output; the traffic signal timing scheme is executed, and traffic state data after the scheme is executed is collected in real time; the data is compared with preset traffic efficiency evaluation indicators, and closed-loop iterative optimization is performed; through the above methods, the real-time traffic state is perceived, and the timing scheme is dynamically matched to achieve real-time response to dynamic changes in traffic flow. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the traffic control method of the present invention.

[0019] Figure 2 This is a flowchart of steps S100 of the present invention.

[0020] Figure 3 This is a flowchart of steps S200 of the present invention.

[0021] Figure 4 This is a flowchart of steps S300 of the present invention.

[0022] Figure 5 This is a schematic diagram of the traffic control system of the present invention.

[0023] Figure 6 This is a schematic diagram of the electronic device of the present invention.

[0024] 401 - Real-time traffic status perception module, 402 - Traffic signal timing dynamic adjustment module, 403 - Adjustment execution and iterative optimization module. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0026] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0027] It should be understood that although the terms first, second, third, etc., may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to determination."

[0028] Please see Figures 1-4 The present invention provides a traffic control method, comprising the following steps: S100: Collects real-time traffic data of the road network through multi-source sensing devices, and generates real-time traffic status assessment results based on the real-time traffic data; the real-time traffic data includes vehicle flow, vehicle speed, queue length and intersection congestion status.

[0029] In this embodiment, real-time traffic data of the road network is collected through multi-source sensing devices, and a real-time traffic condition assessment result is generated based on the real-time traffic data. The specific process is as follows: S101: Deploy multi-source sensing devices to key nodes of the road network; among which key nodes include main road intersections, secondary road intersections, branch road junctions and the middle area of ​​road sections, and multi-source sensing devices include video surveillance cameras, microwave radar, geomagnetic sensors and roadside units; S102: Activate the operation of multi-source sensing equipment to collect and summarize real-time traffic data including vehicle flow, vehicle speed, queue length, and intersection congestion status; among them, video surveillance cameras collect image data of vehicle queue length and vehicle type at intersections, microwave radar collects vehicle speed and real-time traffic flow dynamic data, geomagnetic sensors detect the frequency of vehicle passage to assist in the statistics of road segment traffic flow, and roadside units interact with vehicle on-board units through V2X communication to obtain real-time vehicle location and driving intention information. S103: Preprocess real-time traffic data to remove abnormal data caused by equipment failure, extreme weather, and traffic accidents, and eliminate the dimensional differences of data from different dimensions through data standardization. S104: Perform multi-dimensional data fusion analysis on real-time traffic data to generate real-time traffic status assessment results for corresponding intersections and road segments.

[0030] In the above process, multi-source sensing devices are deployed to key nodes of the road network. These key nodes include main road intersections, secondary road intersections, branch road junctions, and the middle area of ​​road sections. The multi-source sensing devices include video surveillance cameras, microwave radars, geomagnetic sensors, and roadside units. Specifically, at main road intersections, a standard configuration of at least one video surveillance camera and one microwave radar is deployed for each direction of traffic. At secondary road intersections and branch road junctions, the number of devices is reasonably reduced according to traffic density. In the middle area of ​​the road section, one geomagnetic sensor is deployed every 500 meters. Additional roadside units are added at intersections in core areas to ensure V2X communication coverage.

[0031] The system activates multi-source sensing devices to collect and aggregate real-time traffic data, including vehicle flow, speed, queue length, and intersection congestion status. Video surveillance cameras collect image data of queue length and vehicle type at intersections at a frequency of one frame every 500 milliseconds. Image recognition technology extracts the coordinates of the first and last vehicles in the queue to calculate the queue length and identifies vehicle outline features to distinguish between small and large vehicles. Microwave radar collects vehicle speed and real-time traffic flow dynamics at a sampling frequency of 10Hz. Vehicle speed is calculated using the radar wave reflection time difference, and the number of vehicles passing through the monitored area per unit time is counted to obtain real-time traffic flow. Geomagnetic sensors record the frequency of vehicle passage by detecting changes in the magnetic field generated when vehicles pass, and this data, combined with road segment length and detection time windows, assists in calculating traffic flow for road segments. Roadside units establish real-time data links with the onboard units of moving vehicles via V2X communication technology, exchanging information such as real-time vehicle location and driving intention (left turn, right turn, straight ahead) at a communication frequency of 20Hz. The control center aggregates and integrates the data collected by each device, removing duplicate data to form a complete real-time traffic dataset.

[0032] Real-time traffic data is preprocessed to remove abnormal data caused by equipment failure, extreme weather, and traffic accidents. Data standardization is used to eliminate dimensional differences between different data dimensions. Specifically, an anomaly detection method based on the 3σ criterion is adopted to calculate the mean and standard deviation of each data dimension. Data exceeding the mean ± 3 times the standard deviation is identified as abnormal data and removed. Examples include data with vehicle speeds of 0 or far exceeding normal driving speeds due to equipment failure, and false traffic flow data mistakenly collected during extreme rainstorms. Data standardization adopts the min-max standardization method to map data such as vehicle flow, vehicle speed, and queue length with different dimensions to the same numerical range, eliminating the impact of data magnitude differences on subsequent analysis.

[0033] The preprocessed real-time traffic data is subjected to multi-dimensional data fusion analysis to generate real-time traffic status assessment results for corresponding intersections and road segments. During the fusion analysis, an assessment index system is constructed by combining multi-dimensional data such as vehicle flow, vehicle speed, and queue length. The comprehensive traffic status score is calculated by weighted summation, and the traffic status level is divided according to the score range. For example, a comprehensive score of 0~0.3 corresponds to smooth flow, 0.3~0.7 corresponds to slow flow, and 0.7~1.0 corresponds to congestion. Finally, the real-time traffic status assessment results containing the traffic status level and key traffic parameters of each intersection and road segment are output.

[0034] S200: Obtain traffic priority data for each intersection, dynamically calculate traffic signal timing schemes based on real-time traffic condition assessment results, and output the traffic signal timing schemes.

[0035] In this implementation, traffic priority data for each intersection is acquired, and a traffic signal timing scheme is dynamically calculated based on real-time traffic condition assessment results, and then output. The specific process is as follows: S201: Collect basic information data of each intersection, including intersection type and area control requirements, while monitoring the real-time passage needs of special vehicles and summarizing the data to form the passage priority data of each intersection; S202: Based on real-time traffic condition assessment results, the traffic priority of each intersection is dynamically determined; S203: Based on real-time traffic condition assessment results and dynamically determined traffic priorities, obtain traffic demand at each intersection; S204: Dynamically calculate the green light duration for each traffic phase based on traffic demand, integrate the durations of each phase to form a complete traffic signal timing scheme, and output it.

[0036] In the above process, basic information data of each intersection is collected, including intersection type and area control requirements. At the same time, the real-time passage demand of special vehicles is monitored, and the data is summarized to form the passage priority data of each intersection. The intersection type is divided into main road intersection, secondary road intersection, and branch road intersection. The area control requirements include the passage priority regulations for special control areas such as core business districts, areas around hospitals, and areas around schools. The real-time passage demand of special vehicles such as ambulances, fire trucks, and police cars is monitored through multiple channels such as roadside units and traffic command platforms. The above basic information data and special vehicle passage demand data are summarized to establish the initial priority dataset of each intersection.

[0037] Based on real-time traffic condition assessment results, the priority of each intersection is dynamically determined. During the determination process, the priority level is adjusted according to the real-time traffic condition assessment results based on the initial priority dataset. For example, the priority of a main road intersection in a congested state is increased by 1 level compared to a smooth state, and the priority of a secondary road intersection in a slow-moving state is increased by 0.5 levels compared to a smooth state. If a special vehicle is detected to be about to pass through an intersection, the priority of that intersection is directly set to the highest level to ensure the rapid passage of special vehicles.

[0038] Based on real-time traffic condition assessment results and dynamically determined traffic priorities, traffic demand at each intersection is obtained. By analyzing data such as vehicle flow and queue length in the real-time traffic condition assessment results, the traffic flow demand of different traffic directions (left turn, right turn, straight) and pedestrians at each intersection is determined. Combined with traffic priority weights, the urgency of traffic demand in each traffic direction is quantified. For example, the straight traffic flow demand weight of the highest priority intersection is higher than that of other traffic directions.

[0039] The green light duration for each traffic phase is dynamically calculated based on traffic demand. The durations of each phase are then integrated to form a complete traffic signal timing scheme, which is then output. During the calculation process, the urgency of traffic demand in each direction is considered, and longer green light durations are allocated to directions with high demand. At the same time, it is ensured that the green light duration for each traffic phase is not lower than a preset safety threshold (e.g., the green light duration for pedestrian traffic phases is not less than 15 seconds). The green light duration, yellow light duration, and red light duration for each traffic phase are then integrated to form a traffic signal timing scheme that includes the complete signal cycle, which is then transmitted to the traffic signal control equipment via a communication network.

[0040] S300: Implements traffic signal timing schemes and collects traffic status data in real time after the schemes are implemented, compares it with preset traffic efficiency evaluation indicators, and performs closed-loop iterative optimization.

[0041] In this embodiment, a traffic signal timing scheme is implemented, and traffic status data is collected in real time after the scheme is implemented. This data is then compared with preset traffic efficiency evaluation indicators to perform closed-loop iterative optimization. The specific process is as follows: S301: Send the generated traffic signal timing scheme to the corresponding traffic signal control equipment and execute the traffic signal timing scheme; S302: Real-time collection of traffic status data after the implementation of the plan, including average vehicle speed, vehicle delay time, intersection capacity and queue length changes. S303: Retrieve preset traffic efficiency evaluation indicators; the indicators are set according to the type of intersection, including the average vehicle speed, vehicle delay time and intersection capacity standards for main road intersections, secondary road intersections and branch road intersections. S304: Compare the collected traffic status data after the plan is implemented with the preset evaluation indicators to determine whether all indicators meet the standards, and output the judgment result.

[0042] During the above process, the generated traffic signal timing scheme is sent to the corresponding traffic signal control equipment and executed. The timing scheme is encrypted and sent to the traffic signal control terminals at each intersection using 5G or fiber optic communication. After receiving the scheme, the control terminal decrypts and verifies it. Once the verification is successful, it immediately switches to the new timing scheme and starts execution, while recording the start time and initial state of the scheme execution.

[0043] Real-time traffic status data is collected after the implementation of the plan, including average vehicle speed, vehicle delay time, intersection capacity, and queue length changes. The multi-source sensing devices deployed in step S100 are used to continuously collect traffic data during the implementation of the plan. Through data processing, the average vehicle speed (the average speed of vehicles passing through the intersection per unit time), vehicle delay time (the time difference between a vehicle entering the queuing area of ​​the intersection and exiting the intersection), intersection capacity (the maximum number of vehicles that can pass through the intersection per unit time), and queue length changes (the difference in queue length at different time points) of each intersection are calculated to form a dataset of the plan implementation effect.

[0044] The system retrieves preset traffic efficiency evaluation indicators. These indicators are categorized by intersection type, including average vehicle speed, vehicle delay time, and intersection capacity standards for arterial road intersections, secondary arterial road intersections, and local road intersections. The preset indicators are determined by analyzing historical traffic data and regional traffic planning requirements. For example, the preset average vehicle speed for arterial road intersections is no less than 40 km / h, vehicle delay time is no more than 30 seconds per vehicle, and intersection capacity is no less than 1200 vehicles per hour; the preset average vehicle speed for secondary arterial road intersections is no less than 35 km / h, vehicle delay time is no more than 35 seconds per vehicle, and intersection capacity is no less than 1000 vehicles per hour; and the preset average vehicle speed for local road intersections is no less than 30 km / h, vehicle delay time is no more than 40 seconds per vehicle, and intersection capacity is no less than 800 vehicles per hour.

[0045] The collected traffic status data after the scheme is implemented is compared with the preset evaluation indicators to determine whether all indicators meet the standards, and the judgment result is output. During the comparison process, the corresponding preset indicators are matched according to the intersection type, and the differences between the data such as average vehicle speed, vehicle delay time, intersection capacity, and queue length changes and the preset indicators are compared one by one. If all data meet the preset indicator requirements, it is judged as compliant and the compliance judgment result is output. If any data does not meet the preset indicator requirements, it is judged as non-compliant and the non-compliant judgment result is output, and the non-compliant indicator and the corresponding difference value are marked.

[0046] Corresponding to the aforementioned embodiments of traffic control methods, this application also provides embodiments of traffic control systems.

[0047] Figure 5 This is a block diagram illustrating a traffic control system according to an exemplary embodiment. (Refer to...) Figure 5 The system may include: a real-time traffic condition perception module 401, a traffic signal timing dynamic adjustment module 402, and an adjustment execution and iterative optimization module 403; wherein: The real-time traffic status perception module 401 is used to collect real-time traffic data of the road network through multi-source perception devices and generate real-time traffic status assessment results based on the real-time traffic data; wherein the real-time traffic data includes vehicle flow, vehicle speed, queue length and intersection congestion status. The traffic signal timing dynamic adjustment module 402 is used to acquire traffic priority data at each intersection, dynamically calculate the traffic signal timing scheme based on the real-time traffic condition assessment results, and output the traffic signal timing scheme. The adjustment execution and iterative optimization module 403 is used to execute the traffic signal timing scheme, collect traffic status data after the scheme is executed in real time, compare it with the preset traffic efficiency evaluation index, and perform closed-loop iterative optimization.

[0048] In this embodiment, the real-time traffic status perception module 401 collects real-time traffic data of the road network through multi-source sensing devices and generates a real-time traffic status assessment result based on the real-time traffic data; wherein the real-time traffic data includes vehicle flow, vehicle speed, queue length, and intersection congestion status; the traffic signal timing dynamic adjustment module 402 obtains the traffic priority data of each intersection, dynamically calculates the traffic signal timing scheme based on the real-time traffic status assessment result, and outputs the traffic signal timing scheme; the adjustment execution and iterative optimization module 403 executes the traffic signal timing scheme, collects traffic status data after the scheme is executed in real time, compares it with the preset traffic efficiency evaluation index, and performs closed-loop iterative optimization; through the above methods, the real-time traffic status is perceived, and the timing scheme is dynamically matched to achieve real-time response to dynamic changes in traffic flow.

[0049] Regarding the system in the above embodiments, the specific ways in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0050] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this application according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0051] Accordingly, this application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the traffic control method described above. Figure 6 The diagram shown is a hardware structure diagram of any data processing device within a traffic control system according to an embodiment of the present invention, except... Figure 6 In addition to the processor, memory, and network interface shown, any data processing device in the embodiment may also include other hardware depending on the actual function of the data processing device, which will not be described in detail here.

[0052] Accordingly, this application also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the traffic control method described above. The computer-readable storage medium can be an internal storage unit of any data-processing device as described in any of the foregoing embodiments, such as a hard disk or memory. The computer-readable storage medium can also be an external storage device, such as a plug-in hard disk, smart media card (SMC), SD card, flash card, etc., equipped on the device. Furthermore, the computer-readable storage medium can include both internal storage units of any data-processing device and external storage devices. The computer-readable storage medium is used to store the computer program and other programs and data required by the data-processing device, and can also be used to temporarily store data that has been output or will be output.

[0053] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0054] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A traffic control method, characterized in that, Includes the following steps: Real-time traffic data of the road network is collected by multi-source sensing devices, and real-time traffic status assessment results are generated based on the real-time traffic data; the real-time traffic data includes vehicle flow, vehicle speed, queue length and intersection congestion status; Obtain traffic priority data for each intersection, dynamically calculate traffic signal timing schemes based on real-time traffic condition assessment results, and output the traffic signal timing schemes. The system implements traffic signal timing schemes and collects real-time traffic status data after the schemes are implemented. It then compares the data with preset traffic efficiency evaluation indicators and performs closed-loop iterative optimization.

2. The traffic control method as described in claim 1, characterized in that, The process involves collecting real-time traffic data from the road network using multi-source sensing devices, and generating real-time traffic condition assessment results based on this data. This real-time traffic data includes vehicle flow, speed, queue length, and intersection congestion status. Deploy multi-source sensing devices to key nodes of the road network; key nodes include main road intersections, secondary road intersections, branch road junctions and the middle area of ​​road sections; multi-source sensing devices include video surveillance cameras, microwave radar, geomagnetic sensors and roadside units. The multi-source sensing equipment is activated to collect and summarize data to obtain real-time traffic data including vehicle flow, vehicle speed, queue length and intersection congestion status. Real-time traffic data is preprocessed to remove abnormal data caused by equipment failure, extreme weather, and traffic accidents, and the dimensional differences between different dimensions of data are eliminated through data standardization.

3. The traffic control method as described in claim 2, characterized in that, In the steps of activating multi-source sensing devices and collecting and summarizing data to obtain real-time traffic data including vehicle flow, vehicle speed, queue length, and intersection congestion status: Video surveillance cameras collect image data of vehicle queue length and vehicle type at intersections, microwave radar collects vehicle speed and real-time traffic dynamic data, geomagnetic sensors detect the frequency of vehicle passage to assist in the statistics of traffic flow on road sections, and roadside units interact with vehicle on-board units through V2X communication to obtain real-time vehicle location and driving intention information.

4. The traffic control method as described in claim 2, characterized in that, After preprocessing real-time traffic data to remove abnormal data caused by equipment failures, extreme weather, and traffic accidents, and eliminating dimensional differences between different dimensions of data through data standardization: Multi-dimensional data fusion analysis is performed on real-time traffic data to generate real-time traffic status assessment results for corresponding intersections and road segments.

5. The traffic control method as described in claim 1, characterized in that, In the steps of acquiring traffic priority data at each intersection, dynamically calculating traffic signal timing schemes based on real-time traffic condition assessment results, and outputting the traffic signal timing schemes: Collect basic information data for each intersection, including intersection type and area control requirements, while monitoring the real-time passage needs of special vehicles and summarizing the data to form the passage priority data for each intersection; Based on real-time traffic condition assessment results, the traffic priority of each intersection is dynamically determined.

6. The traffic control method as described in claim 5, characterized in that, After combining real-time traffic condition assessment results to dynamically determine the traffic priority of each intersection: Based on real-time traffic condition assessment results and dynamically determined traffic priorities, the traffic demand at each intersection is obtained.

7. The traffic control method as described in claim 6, characterized in that, After obtaining the traffic demand at each intersection based on real-time traffic condition assessment results and dynamically determined traffic priorities: The green light duration for each traffic phase is dynamically calculated based on traffic demand, and the durations of each phase are integrated to form a complete traffic signal timing scheme and output.

8. The traffic control method as described in claim 1, characterized in that, In the process of implementing traffic signal timing schemes, collecting real-time traffic status data after the schemes are implemented, comparing it with preset traffic efficiency evaluation indicators, and performing closed-loop iterative optimization: The generated traffic signal timing scheme is sent to the corresponding traffic signal control equipment and executed. Real-time traffic status data is collected after the plan is implemented, including average vehicle speed, vehicle delay time, intersection capacity, and changes in queue length. Retrieve preset traffic efficiency evaluation indicators; the indicators are set according to intersection type, including average vehicle speed, vehicle delay time and intersection capacity standards for main road intersections, secondary road intersections and branch road intersections.

9. The traffic control method as described in claim 8, characterized in that, After retrieving the preset traffic efficiency evaluation indicators: The collected traffic status data after the plan is implemented is compared with the preset evaluation indicators to determine whether all indicators meet the standards, and the judgment result is output.

10. A traffic control system employing the traffic control method as described in claim 1, characterized in that, It includes a real-time traffic condition perception module, a traffic signal timing dynamic adjustment module, and an adjustment execution and iterative optimization module; among which: The real-time traffic status perception module is used to collect real-time traffic data of the road network through multi-source sensing devices and generate real-time traffic status assessment results based on the real-time traffic data; wherein the real-time traffic data includes vehicle flow, vehicle speed, queue length and intersection congestion status. The traffic signal timing dynamic adjustment module is used to acquire traffic priority data at each intersection, dynamically calculate the traffic signal timing scheme based on real-time traffic condition assessment results, and output the traffic signal timing scheme. The adjustment execution and iterative optimization module is used to execute the traffic signal timing scheme, collect traffic status data after the scheme is executed in real time, compare it with the preset traffic efficiency evaluation index, and perform closed-loop iterative optimization.

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