Intelligent regulation and control method and device for traffic signal lamp and medium

Abstract

The invention discloses an intelligent regulation and control method and device of a traffic signal lamp and a medium, and relates to the field of traffic signal lamp control, and the method comprises the steps: collecting the traffic data of a target crossroad in real time through a traffic monitoring device, the traffic data comprising a traffic video and traffic behavior information; analyzing the traffic video, and determining the lane traffic flow corresponding to each lane in the current time period; according to the traffic behavior information and the lane traffic flow, predicting the required traffic duration of each lane; according to the current phase information of the traffic lights in the target crossroad and the traffic light timing of the current time period, determining a green light lane and a green light residual duration, and determining whether the green light residual duration satisfies a required passing duration corresponding to the green light lane; and adjusting the current phase duration of the traffic signal lamp according to the judgment result. By monitoring the traffic data of each lane in real time, the time length of the signal lamp of each lane can be dynamically adjusted.

Description

Technical Field

[0001] The present application relates to the technical field of traffic light control, and in particular to an intelligent control method, device and medium for a traffic light. Background Art

[0002] With the acceleration of urbanization, the number of urban residents has increased, making traffic congestion on urban roads increasingly serious, especially during peak hours in the morning and evening, when congestion is extremely likely to occur, affecting people's normal travel. Therefore, traffic management has become an important issue in the modern urban transportation system.

[0003] The development of intelligent transportation systems (ITS) provides a new way to solve the problem of traffic congestion. It responds to changes in traffic conditions at different time periods through real-time monitoring of vehicles on the road. However, existing intelligent transportation systems often pay insufficient attention to pedestrians and non-motor vehicles on the road, ignoring the needs of these road users, thus affecting overall traffic safety and traffic efficiency.

[0004] At the same time, the existing intelligent transportation system lacks an effective immediate warning mechanism, making it difficult to predict and avoid impending traffic incidents in advance, such as intersection overflow or illegal parking. In addition, due to the inflexible switching of signal light phases, there are often situations where there are no vehicles in the green light release phase or the intersection cannot be passed within the remaining time, while there are vehicles waiting in other phases, resulting in a waste of green light time, further reducing traffic efficiency. Summary of the invention

[0005] In order to solve the above problems, the present application proposes an intelligent control method for traffic lights, including:

[0006] Collecting traffic data of the target intersection in real time through traffic monitoring equipment, wherein the traffic data includes traffic video and traffic behavior information;

[0007] Analyze the traffic video to determine the lane traffic flow corresponding to each lane in the current time period, wherein the lane traffic flow includes lane motor vehicle flow, lane non-motor vehicle flow, and lane pedestrian flow;

[0008] Predicting the required travel time of each lane according to the traffic behavior information and the lane traffic volume;

[0009] Determine the green light lane and the remaining green light time according to the current phase information of each traffic light in the target intersection and the signal light timing of the current time period, and judge whether the remaining green light time satisfies the required passage time corresponding to the green light lane;

[0010] According to the determination result, the current phase duration of the traffic light is adjusted.

[0011] On the other hand, the present application also proposes an intelligent control device for a traffic light, comprising:

[0012] at least one processor; and,

[0013] a memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for intelligently controlling a traffic light as described in the above example.

[0015] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as: an intelligent control method for a traffic light as described in the above example.

[0016] The intelligent control method of a traffic light proposed in this application can bring the following beneficial effects:

[0017] By real-time monitoring of traffic flow, vehicle speed and expected traffic volume in each lane, the signal light duration of each lane can be dynamically adjusted to improve traffic efficiency, avoid vehicles being stranded near intersections or junctions, and alleviate traffic congestion.

[0018] By collecting and analyzing a large amount of real-time traffic data, it is possible to provide accurate traffic flow analysis reports and help traffic management departments make data-supported decisions. This not only improves management efficiency, but also provides a strong basis for traffic planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0020] Figure 1 A schematic diagram of a flow chart of an intelligent control method for a traffic light in an embodiment of the present application;

[0021] Figure 2 This is a schematic diagram of an intelligent control device for a traffic light in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0023] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0024] like Figure 1 As shown, the embodiment of the present application provides an intelligent control method of a traffic light, comprising:

[0025] S101: Collecting traffic data of a target intersection in real time through traffic monitoring equipment, wherein the traffic data includes traffic video and traffic behavior information.

[0026] Specifically, high-definition cameras and radar sensors are installed in all directions of the target intersection to achieve real-time monitoring of traffic in each lane. The target intersection is recorded in real time by the camera equipment to obtain the traffic video of the target intersection, and the traffic behavior information of the target intersection is collected in real time by the radar sensor. The traffic behavior information includes the distance between the traffic participants and the target intersection, the moving speed of the traffic participants, the moving direction of the traffic participants and other information.

[0027] Furthermore, the traffic data of the target intersection is uploaded to the cloud and the central server for storage.

[0028] It should be noted that the target intersection is not limited to the configuration of high-definition cameras and radar sensors, but also includes the deployment of integrated edge computing modules, which are directly deployed near the monitoring equipment to instantly process traffic data from cameras and radars, reducing the amount of data transmitted to the cloud and central servers, thereby reducing latency and improving response speed. The edge computing module uses built-in high-performance processors and intelligent algorithms to detect the number of vehicles in each lane, speed, and distance to the intersection through a deep learning model, and judge traffic events such as overflow, intersection overflow, and illegal parking through regional vehicle occupancy and vehicle speed.

[0029] It should be noted that the radar sensor is a radar that calculates the distance, relative speed, angle and other information of an object by emitting electromagnetic waves and receiving echoes. It is not affected by external environment such as light and weather. Therefore, it can work stably at night or in bad weather conditions (such as haze, heavy rain, etc.).

[0030] S102: Analyze the traffic video to determine the lane traffic flow corresponding to each lane in the current time period, where the lane traffic flow includes lane motor vehicle flow, lane non-motor vehicle flow, and lane pedestrian flow.

[0031] Specifically, the traffic video in the current time period is split into frame images, and each frame image is preprocessed before target detection, including image enhancement, denoising, scaling, normalization and other operations. In an embodiment of the present application, the traffic video is decomposed into 10 frames of images in 1s for analysis, and the analysis results are processed into data with a granularity of seconds.

[0032] Furthermore, each frame of the preprocessed image is analyzed by a target detection algorithm (such as YOLO, Faster R-CNN, etc.), which can identify different objects in the image and generate a target frame for each object. Therefore, the target detection algorithm can identify the traffic participants in each lane and frame the identified participants with a target frame.

[0033] Each target frame will not only contain location data, but also add corresponding category labels to the target frame based on the type of traffic participants. Category labels include cars, buses, pedestrians, bicycles, motorcycles, etc. Each target frame will be classified according to the lane, and the number of different types of traffic participants in each lane will be counted to obtain the lane traffic flow corresponding to each lane. Among them, each lane includes a motor vehicle lane, a non-motor vehicle lane, and a sidewalk. The types of traffic participants include motor vehicles, non-motor vehicles, and pedestrians. The lane traffic flow includes lane motor vehicle flow, lane non-motor vehicle flow, and lane pedestrian flow.

[0034] It should be noted that through high-definition cameras and radar sensors, the number of vehicles in each lane, their speed, distance to the intersection, and vehicle flow are detected at the edge. The regional vehicle occupancy rate and vehicle speed are used to judge traffic incidents such as overflow, intersection overflow, and illegal parking, as well as information on pedestrians and non-motor vehicles at zebra crossings and waiting areas, and the analysis results are returned to the control center.

[0035] S103: Predicting the required travel time of each lane according to the traffic behavior information and the lane traffic volume.

[0036] Specifically, the maximum capacity of each lane is determined according to the capacity of the intersection, and the required travel time of each lane is predicted based on the traffic flow and the maximum capacity.

[0037] Before predicting the travel time of each lane, the probability of occurrence of irregular traffic behavior is calculated based on traffic behavior information and the amount of traffic to be passed, and a judgment is made as to whether the probability of occurrence is higher than a preset threshold. Irregular traffic behavior includes impending overflow events, intersection overflow events, and illegal parking events. If so, the early warning mechanism is triggered, and an alarm message is sent to the traffic manager based on the corresponding irregular traffic behavior.

[0038] Furthermore, according to the distance and moving speed, the arrival time of each traffic participant at the target intersection is determined, based on the arrival time, the total arrival volume of traffic participants accumulated within the preset time period is calculated, and the capacity ratio of the total arrival volume and the total capacity of the intersection is obtained. Based on the capacity ratio, a first probability of an overflow event is determined, and according to the waiting traffic volume and the arrival time, the traffic flow reaching the target intersection per unit time is determined, and the flow ratio of the traffic flow and the intersection capacity is obtained. Based on the flow ratio, a second probability of an overflow event is determined, and through a behavior analysis model, the parking behavior of traffic participants is predicted, and according to the prediction result, a third probability of an illegal parking event is determined.

[0039] S104: Determine the green light lane and the remaining green light time according to the current phase information of each traffic light in the target intersection and the signal light timing of the current time period, and judge whether the remaining green light time satisfies the required passage time corresponding to the green light lane.

[0040] Specifically, based on a preset cycle, the signal light timing information of the traffic lights in the target intersection is read at regular intervals to obtain the signal light timing for the current time period. The current phase information of each traffic light in the target intersection is obtained in real time, and the green light phase of the traffic light is determined based on the current phase information. Based on the green light phase and the signal light timing, the green light lane for the current time period and the remaining green light duration are determined.

[0041] Among them, the current phase information includes the basic status information, phase operation information, associated traffic flow information and special status information of the traffic light. Among them, the basic status information includes the light color of the current traffic light, the duration of the current light color, and the remaining duration of the current light color; the phase operation information includes the phase sequence of the traffic light; the associated traffic flow information includes the corresponding lane direction (such as straight lane, left turn lane or right turn lane), the corresponding traffic participant type; the special status information includes the fault status (such as the light color is not on, the flashing is abnormal, etc.), fault control, etc.

[0042] Further, it is determined whether the remaining time is not less than the required time corresponding to the green light lane. When the remaining time is less than the required time corresponding to the green light lane, the non-green light traffic volume corresponding to the non-green light lane is obtained, and it is determined whether the non-green light traffic volume is higher than a preset traffic volume threshold. When the remaining time is not less than the required time corresponding to the green light lane, the current phase duration of the traffic light is not adjusted.

[0043] It should be noted that the timing information of the traffic lights is read periodically, the light state information of the traffic lights is read in real time, and the green light direction of the current phase is found according to the light state information read, so as to obtain the phase information of the current moment. According to the green light direction of the current phase, the next phase and other phases, the corresponding intersection state information, vehicle information of the passing lane, and pedestrian and non-motor vehicle information of the sidewalk and waiting area that will be affected by the change of phase are obtained in the intersection data transmitted back from the edge.

[0044] S105: According to the determination result, the current phase duration of the traffic light is adjusted.

[0045] Specifically, the time difference between the remaining green light duration and the required passing time corresponding to the green light lane is determined, and the phase duration of the green light lane is adjusted based on the time difference.

[0046] It should be noted that when the detected intersection is about to overflow, the system will analyze the number of vehicles in each phase that will merge into the lane of the overflow area, and will position the phase as the nearest phase that will not exceed the carrying capacity of the overflow direction. When it is detected that there is no vehicle in the release phase of the intersection, or the current vehicle at the intersection cannot pass through the intersection within the remaining time, and there is a demand for passage in the next phase, or there is a large demand for passage in other phases, then the phase will be skipped directly under the condition that safety restrictions are met. When it is detected that there is a vehicle in the release phase of the intersection and it cannot pass through the intersection within the remaining time, there is no demand for passage in the next phase, and the demand for passage in other phases is less than a certain threshold, the phase is extended to allow vehicles far away in this phase to pass through the intersection. Among them, within each preset cycle, there is a limit on the number of times the phase can be extended.

[0047] It should be noted that if there is no vehicle in the green light release phase at the intersection or the vehicle cannot pass through the intersection within the remaining time, and there are vehicles in the other phases, a step command is sent to the signal machine to change the current traffic phase. If the vehicle flow cannot pass through the intersection within the remaining time of the green light release flow and there is no vehicle traffic demand in other phases, an extension command is sent to the signal machine to extend the current phase to ensure that vehicles in the current phase pass. When an exit of the intersection is about to overflow, the number of vehicles entering the intersection from all directions is immediately determined, and the phase is adjusted to the phase with fewer vehicles to prevent overflow at the intersection.

[0048] The safety restrictions that need to be met for the above-mentioned phase jumping include the minimum green restriction, pedestrian safety restrictions and non-motor vehicle safety restrictions, that is, if there are pedestrians or non-motor vehicles on the sidewalk that conflicts with the motor vehicle traffic in the next phase, phase jumping is not allowed.

[0049] After the traffic lights are regulated, the regulation log is saved, and the Internet data before and after the regulation and the analysis data at the edge of the intersection are obtained. Based on the formula: idle time ratio = phase green light idle time / phase cycle length, green light utilization rate = phase actual flow / phase saturation flow, according to the relevant data before and after the issuance, the idle time ratio and green light utilization rate are calculated, and the calculated results are compared with the indicators of the simulated unissued strategy to evaluate and optimize the regulation in real time. After collecting data for a period of time, according to the indicators, the system effect is evaluated and optimized by comparing the indicators before and after the use of the system.

[0050] By real-time monitoring of traffic flow, vehicle speed and expected traffic volume in each lane, the signal light duration of each lane can be dynamically adjusted to improve traffic efficiency, avoid vehicles being stranded near intersections or junctions, and alleviate traffic congestion.

[0051] By collecting and analyzing a large amount of real-time traffic data, it is possible to provide accurate traffic flow analysis reports and help traffic management departments make data-supported decisions. This not only improves management efficiency, but also provides a strong basis for traffic planning.

[0052] like Figure 2 As shown, the embodiment of the present application also proposes an intelligent control device for a traffic light, including:

[0053] at least one processor; and,

[0054] a memory communicatively connected to the at least one processor; wherein,

[0055] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for intelligently controlling a traffic light as described in any of the above embodiments.

[0056] An embodiment of the present application further provides a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured as: an intelligent control method for a traffic light as described in any of the above embodiments.

[0057] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0058] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0059] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0061] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0062] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0063] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0064] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0065] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0066] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0067] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for intelligently controlling a traffic light, characterized in that: include: Collecting traffic data of the target intersection in real time through traffic monitoring equipment, wherein the traffic data includes traffic video and traffic behavior information; Analyze the traffic video to determine the lane traffic flow corresponding to each lane in the current time period, wherein the lane traffic flow includes lane motor vehicle flow, lane non-motor vehicle flow, and lane pedestrian flow; Predicting the required travel time of each lane according to the traffic behavior information and the lane traffic volume; Determine the green light lane and the remaining green light time according to the current phase information of the traffic light in the target intersection and the signal light timing of the current time period, and judge whether the remaining green light time satisfies the required passage time corresponding to the green light lane; According to the determination result, the current phase duration of the traffic light is adjusted.

2. The intelligent control method of a traffic light according to claim 1, characterized in that: Before predicting the travel time of each lane according to the traffic behavior information and the amount of traffic to be passed, the method further includes: Calculate the probability of occurrence of irregular traffic behavior according to the traffic behavior information and the amount of traffic to be passed, and determine whether the probability of occurrence is higher than a preset threshold; the irregular traffic behavior includes overflow events, intersection overflow events, and illegal parking events; If so, a corresponding early warning mechanism is triggered based on the irregular traffic behavior.

3. The intelligent control method of a traffic light according to claim 2, characterized in that: The traffic behavior information includes the remaining distance between the traffic participant and the target intersection, the moving speed of the traffic participant, and the moving direction of the traffic participant; The calculating the probability of occurrence of irregular traffic behavior according to the traffic behavior information and the amount of traffic to be passed specifically includes: Determine the arrival time of the traffic participant at the target intersection according to the remaining distance and the moving speed, and accumulate the total arrival amount of the traffic participants within a preset time period based on the arrival time; Obtaining a capacity ratio of the total arrival volume to the total volume accommodated by the intersection, and determining a first probability of occurrence of the impending overflow event based on the capacity ratio; Determine the traffic flow reaching the target intersection within a unit time according to the waiting traffic volume and the arrival time; Obtaining a flow ratio of the traffic flow and the capacity of the intersection, and determining a second probability of occurrence of the intersection overflow event based on the flow ratio; The parking behavior of the traffic participant is predicted through a behavior analysis model, and the third occurrence probability of the illegal parking event is determined according to the prediction result.

4. The intelligent control method of a traffic light according to claim 3, characterized in that: The predicting the required travel time of each lane according to the traffic behavior information and the lane traffic flow specifically includes: Determining the maximum traffic capacity of each lane according to the capacity of the intersection; Based on the traffic flow and the maximum traffic capacity, the required travel time of each lane is predicted.

5. The intelligent control method of a traffic light according to claim 1, characterized in that: The analyzing the traffic video to determine the lane traffic flow corresponding to each lane in the current time period specifically includes: Splitting the traffic video in the current time period into a plurality of frame images, and preprocessing the frame images; Performing target recognition on the preprocessed frame image by using a target detection algorithm to obtain a recognition target frame in each lane, and adding a corresponding category label to the target frame based on the type of the traffic participant; According to the category labels, the numbers of different types of traffic participants in each lane are counted respectively to obtain the lane traffic flow corresponding to each lane.

6. The intelligent control method of a traffic light according to claim 1, characterized in that: The step of determining the green light lane and the remaining green light time according to the current phase information of the traffic light at the target intersection and the traffic light timing of the current time period specifically includes: Based on a preset cycle, periodically read the signal light timing information of the traffic light at the target intersection to obtain the signal light timing of the current time period; Acquire the current phase information of the traffic light in real time, and determine the green light phase of the traffic light according to the current phase information; Based on the green light phase and the signal light timing, the green light lane and the remaining green light duration in the current time period are determined.

7. The intelligent control method of a traffic light according to claim 1, characterized in that: The determining whether the remaining green light duration satisfies the required passing time corresponding to the green light lane specifically includes: Determine whether the remaining time is not less than the required passing time corresponding to the green light lane; When the remaining time is less than the required travel time corresponding to the green light lane, the non-green light traffic volume corresponding to the non-green light lane is obtained to determine whether the non-green light traffic volume is higher than a preset traffic volume threshold; When the remaining duration is not less than the required passing time corresponding to the green light lane, the current phase duration of the traffic light is not adjusted.

8. The intelligent control method of a traffic light according to claim 7, characterized in that: The adjusting the current phase duration of the traffic light according to the judgment result specifically includes: The time difference between the remaining green light duration and the required passing time corresponding to the green light lane is determined, and the phase duration of the green light lane is adjusted based on the time difference.

9. An intelligent control device for traffic lights, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a method for intelligently controlling a traffic light as described in any one of claims 1 to 8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured as: an intelligent control method for a traffic light as described in any one of claims 1 to 8.

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