An intelligent traffic light coordinated control method and system for traffic flow optimization

Abstract

The application discloses a kind of intelligent traffic light coordination control method and system of traffic flow optimization, it is related to intelligent control technical field.The method includes: real-time acquisition of the traffic flow data of each direction lane in target area by the heterogeneous sensor network deployed at each intersection;Traffic flow data is identified to obtain tidal flow characteristics;Dynamic intensity evaluation is carried out based on tidal flow characteristics, and tidal direction intensity index is obtained;A multi-agent collaborative network is constructed, and a continuous intersection green phase offset sequence is generated based on multi-agent collaborative network collaborative calculation;When tidal direction intensity index is greater than the preset threshold, the continuous intersection green phase offset sequence is input into the traffic signal control system to start the tidal green wave mode.The technical problems that traffic flow control is not flexible enough in the prior art, and traffic signal lights cannot be intelligently adjusted according to traffic conditions are solved, achieving the technical effects of improving road traffic efficiency and reducing congestion.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to an intelligent traffic light coordination method and system for traffic flow optimization. Background Technology

[0002] With the acceleration of urbanization, urban traffic flow is constantly increasing, and traditional traffic light control methods are proving inadequate in dealing with complex traffic patterns. Especially during peak hours, uneven traffic flow often leads to road congestion and inappropriate signal switching, affecting traffic efficiency and increasing travel time and energy consumption. Traditional traffic light control systems often switch signals based on fixed time intervals, failing to effectively address dynamic changes in traffic flow, particularly the tidal flow phenomenon during morning and evening rush hours—where a large number of vehicles flow from the suburbs into the city center in the morning and then flow in the opposite direction in the evening. This one-way peak flow pattern not only exacerbates traffic congestion but also reduces the efficiency of road resource utilization, increases commuting time, and causes environmental pollution. Summary of the Invention

[0003] This application provides an intelligent traffic light coordination method and system for traffic flow optimization, which solves the technical problems of insufficient flexibility in traffic flow control and the failure of traffic lights to intelligently adjust according to traffic flow conditions in the prior art.

[0004] The first aspect of this application provides a smart traffic light coordination method for traffic flow optimization, the method comprising:

[0005] Traffic flow data in all directions within the target area is collected in real time through a heterogeneous sensor network deployed at each intersection. The traffic flow data is then identified to obtain tidal traffic flow characteristics, including inbound and outbound traffic flow, platoon aggregation, and average platoon speed. Based on the tidal traffic flow characteristics, dynamic intensity assessment is performed to obtain a tidal direction intensity index. A multi-agent collaborative network is constructed, and a continuous intersection green light phase offset sequence is generated based on the collaborative calculation of the multi-agent collaborative network. When the tidal direction intensity index is greater than a preset threshold, the continuous intersection green light phase offset sequence is input into the traffic signal control system to activate the tidal green wave mode.

[0006] A second aspect of this application provides an intelligent traffic light coordination system for traffic flow optimization, the system comprising:

[0007] The system comprises the following modules: a data acquisition module (using a heterogeneous sensor network deployed at each intersection to collect real-time traffic flow data in all directions within the target area); an identification module (identifying the traffic flow data to obtain tidal traffic flow characteristics, including inbound and outbound traffic flow, platoon density, and average platoon speed); an evaluation module (performing dynamic intensity assessment based on the tidal traffic flow characteristics to obtain a tidal direction intensity index); a calculation module (constructing a multi-agent collaborative network to collaboratively calculate and generate a continuous intersection green light phase offset sequence); and a control module (inputting the continuous intersection green light phase offset sequence into the traffic signal control system when the tidal direction intensity index exceeds a preset threshold to activate the tidal green wave mode).

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] First, a heterogeneous sensor network deployed at each intersection collects real-time traffic flow data for lanes in all directions within the target area. Next, the traffic flow data is identified to obtain tidal traffic flow characteristics, including inbound and outbound traffic volume, platoon density, and average platoon speed. Further, dynamic intensity assessment is performed based on these tidal traffic flow characteristics to obtain a tidal direction intensity index. Then, a multi-agent collaborative network is constructed to collaboratively calculate and generate a continuous sequence of green light phase offsets at intersections. Finally, when the tidal direction intensity index exceeds a preset threshold, the continuous green light phase offset sequence is input into the traffic signal control system to activate the tidal green wave mode. This solves the technical problems of insufficient flexibility in traffic flow control and the failure of traffic lights to intelligently adjust according to traffic flow conditions in existing technologies, achieving the technical effects of improving road traffic efficiency and reducing congestion. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0011] Figure 1 A schematic flowchart of an intelligent traffic light coordination method for traffic flow optimization provided in this application embodiment;

[0012] Figure 2 This is a schematic diagram of a traffic flow optimization intelligent traffic light coordination system provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached diagram: Data acquisition module 11, identification module 12, evaluation module 13, calculation module 14, control module 15. Detailed Implementation

[0014] This application provides an intelligent traffic light coordination method and system for traffic flow optimization, which solves the technical problems of insufficient flexibility in traffic flow control and the failure of traffic lights to intelligently adjust according to traffic flow conditions in the prior art.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, this application provides an intelligent traffic light coordination method for traffic flow optimization, wherein the method includes:

[0018] Traffic flow data in all directions within the target area is collected in real time through a heterogeneous sensor network deployed at each intersection.

[0019] By deploying a heterogeneous sensor network at various intersections within the target area, real-time traffic flow data from lanes in all directions is collected. This heterogeneous sensor network comprises various types of sensors, including but not limited to geomagnetic sensors, video surveillance equipment, millimeter-wave radar, and infrared sensors. These sensors provide comprehensive monitoring and data collection of traffic flow from different dimensions. Specifically, geomagnetic sensors detect changes in the magnetic field generated by passing vehicles to perceive traffic flow; video surveillance systems use image recognition technology to acquire real-time information such as traffic volume, vehicle speed, and platoon density; millimeter-wave radar accurately measures vehicle speed and operates stably in adverse weather conditions; and infrared sensors detect vehicle presence and speed, particularly suitable for nighttime or low-visibility environments. Through the collaborative work of these multiple sensors, the system can collect various traffic data in real-time across different road sections and lanes, including traffic volume, vehicle speed, platoon density, and traffic flow status. This data provides accurate and real-time foundational information for subsequent traffic flow analysis and traffic light control, ensuring that dynamic adjustments to traffic light timings reflect current traffic conditions in real time, thereby achieving more intelligent and efficient traffic flow optimization control.

[0020] Furthermore, the method involves collecting real-time traffic flow data for lanes in all directions within the target area using a heterogeneous sensor network deployed at each intersection.

[0021] The heterogeneous sensor network includes a geomagnetic detector, a video recognition unit, and a radar speed measurement device. The geomagnetic detector collects vehicle presence signals at a frequency of 10Hz, the video recognition unit extracts license plate features at a frequency of 1Hz, and the radar speed measurement device acquires instantaneous vehicle speed at a frequency of 5Hz. The vehicle presence signals, license plate features, and instantaneous vehicle speed are fused to generate traffic flow data.

[0022] Preferably, the heterogeneous sensor network consists of various types of sensors, mainly including geomagnetic detectors, video recognition units, and radar speed measuring devices. Each sensor collects data at different frequencies and in different ways to comprehensively and accurately acquire traffic flow information. Specifically, the geomagnetic detector collects vehicle presence signals at a frequency of 10Hz, that is, by detecting changes in the magnetic field generated when vehicles pass under the road surface, it determines in real time whether a vehicle has passed through the lane; the video recognition unit extracts license plate features at a frequency of 1Hz, and through high-definition cameras and image processing algorithms, it performs real-time video monitoring of all vehicles passing through the lane, identifies license plate information, and tracks the driving trajectory of each vehicle; the radar speed measuring device acquires instantaneous vehicle speed at a frequency of 5Hz, and accurately measures the real-time speed of each vehicle through radar technology. The vehicle presence signals, license plate features, and instantaneous vehicle speeds collected by the above sensors are fused to generate comprehensive traffic flow data. This data can comprehensively reflect the traffic status of each lane in the target area, including multi-dimensional information such as traffic volume, vehicle speed, vehicle type, and lane occupancy, thereby providing accurate basic data support for subsequent traffic flow analysis, traffic light optimization and control, and traffic flow management.

[0023] The traffic flow data is identified to obtain tidal traffic flow characteristics, which include inbound and outbound traffic flow, platoon concentration, and average platoon speed.

[0024] The collected traffic flow data is further identified and analyzed to extract tidal traffic flow characteristics, specifically including key indicators such as inbound traffic volume, outbound traffic volume, platoon density, and average platoon speed. Tidal traffic flow characteristics reflect the fluctuation patterns of traffic flow at different times and in different directions, effectively revealing the dynamic changes in urban traffic.

[0025] Inbound traffic flow refers to the number of vehicles entering the city area within a specific time period. This indicator typically increases significantly during the morning rush hour, reflecting the inflow pressure in the city center. By analyzing traffic flow data, the system can accurately identify the number of inbound vehicles and quantify changes in this flow.

[0026] Outbound traffic volume refers to the number of vehicles leaving the city within a certain time period. Outbound traffic volume typically increases during the evening rush hour. This indicator helps understand traffic flow trends and predict potential congestion or traffic bottlenecks. By identifying and extracting outbound traffic volume data, the system can monitor traffic conditions in outbound directions in real time.

[0027] Fleet density reflects the concentration of vehicles on a particular road segment or lane. When traffic density is too high, fleet density increases, which may lead to traffic congestion or slow movement. By monitoring traffic density in real time, the system can calculate fleet density, assess the traffic load on a certain route or lane, and predict potential congestion risks in advance.

[0028] Fleet average speed refers to the average speed of all vehicles over a given period of time. Lower speeds typically indicate poor traffic flow, possibly due to traffic congestion or traffic restrictions. By analyzing vehicle speed data in real time, the system can monitor fleet speeds and promptly detect and respond to slow-moving or congested situations.

[0029] Furthermore, the method for identifying the traffic flow data to obtain tidal traffic flow characteristics includes:

[0030] The traffic flow data includes vehicle identification information, vehicle speed information, and vehicle queue information. Based on the vehicle identification information, the number of vehicles passing through each direction per unit time is counted to obtain the inbound and outbound traffic flow. Based on the vehicle speed information, the average speed over a predetermined time period is calculated to obtain the average speed of the convoy. Based on the vehicle queue information, the number of vehicles per unit distance is calculated to obtain the convoy concentration. The inbound traffic flow, outbound traffic flow, convoy concentration, and average convoy speed are then incorporated into the tidal traffic flow characteristics.

[0031] Based on vehicle identification information, the system counts the number of vehicles passing through each direction per unit time. Through license plate recognition or other vehicle detection technologies, the system accurately identifies the passage of each vehicle and calculates traffic flow in each direction based on vehicle timestamp information. This yields data on inbound and outbound traffic flow, revealing the number of vehicles entering and leaving the city and their fluctuations over different time periods, reflecting typical tidal traffic flow phenomena. Based on vehicle speed information, the system calculates the average vehicle speed over a predetermined time period. This information reflects the overall speed of the convoy; lower speeds may indicate traffic congestion. By calculating speed changes within a unit of time, the system obtains the average convoy speed, an indicator that helps further assess traffic flow and congestion, allowing for real-time monitoring of traffic flow. Based on vehicle queue information, the system calculates the number of vehicles per unit distance to determine the traffic density on the lanes, i.e., the convoy concentration. Congestion concentration reflects the degree of vehicle concentration on the lanes; higher concentrations typically indicate poor traffic flow on that road segment and potential congestion. Fleet aggregation, as one of the characteristics of tidal traffic flow, can help traffic management systems identify traffic bottlenecks or congestion points in a timely manner. By combining key data such as inbound traffic flow, outbound traffic flow, fleet aggregation, and average fleet speed, a complete tidal traffic flow characteristic can be formed.

[0032] Dynamic intensity assessment is performed based on tidal traffic flow characteristics to obtain the tidal direction intensity index.

[0033] Based on the extracted tidal traffic flow characteristics, a dynamic intensity assessment is further conducted to obtain the tidal direction intensity index. The tidal direction intensity index is used to measure the traffic flow intensity in each direction within the target area at different time periods, and provides data support for subsequent traffic signal optimization and flow scheduling.

[0034] Furthermore, based on the characteristics of tidal traffic flow, a dynamic intensity assessment is performed to obtain the tidal direction intensity index. The method includes:

[0035] Tidal direction intensity index: I(t)=ω1I1(t)+ω2I2(t)+ω3I3(t), where I(t) is the tidal direction intensity index at time t, I1(t) is the traffic flow intensity at time t, I2(t) is the platoon concentration intensity at time t, I3(t) is the vehicle speed intensity at time t, and ω1, ω2, ω3 are the weighting factors of traffic flow intensity, platoon concentration intensity, and vehicle speed intensity.

[0036] The tidal direction intensity index is calculated using a formula that weights and combines multiple traffic flow characteristics to obtain the index, reflecting the traffic flow intensity in a specific direction at a given time. Here, I(t) is the tidal direction intensity index at time t, I1(t) is the traffic volume intensity at time t, I2(t) is the fleet concentration intensity at time t, I3(t) is the vehicle speed intensity at time t, and ω1, ω2, and ω3 are the weighting factors for traffic volume intensity, fleet concentration intensity, and vehicle speed intensity, respectively. Through this formula, the system can comprehensively evaluate the tidal direction intensity at a given time, reflecting the traffic pressure and flow conditions in that direction. The tidal direction intensity index is a dynamically changing quantity, updated in real time as traffic flow changes.

[0037] Furthermore, based on the characteristics of tidal traffic flow, a dynamic intensity assessment is performed to obtain the tidal direction intensity index. The method includes:

[0038] Q1(t) represents the inbound traffic flow at time t, and Q2(t) represents the outbound traffic flow at time t. Where D(t) is the convoy aggregation degree at time t, D max Maximum fleet density; Where V(t) is the average speed of the convoy at time t, V max This is the maximum speed.

[0039] Traffic flow intensity measures the relative intensity of traffic flow entering and leaving the city, reflecting the degree of imbalance in traffic flow. Q1(t) represents the inbound traffic flow at time t, and Q2(t) represents the outbound traffic flow at time t. Fleet aggregation intensity reflects the density of vehicles at a specific time, representing the traffic density on a road segment or lane. D(t) represents the fleet aggregation at time t. max This represents the maximum platoon density. Speed ​​intensity measures the speed of the platoon and reflects traffic flow. V(t) is the average platoon speed at time t. max This is the maximum speed.

[0040] A multi-agent collaborative network is constructed, and a continuous sequence of green light phase offsets at intersections is generated based on the collaborative calculation of the multi-agent collaborative network.

[0041] By constructing a multi-agent collaborative network and utilizing its collaborative computing capabilities, a continuous sequence of green light phase offsets at intersections can be generated, thereby achieving intelligent traffic flow optimization.

[0042] Furthermore, the method for constructing a multi-agent cooperative network includes:

[0043] The traffic control unit at each intersection within the target area is designated as an intelligent agent node, which is used to receive real-time traffic data; multiple intelligent agent nodes are added to a multi-agent cooperative network.

[0044] Each traffic control unit at each intersection within the target area is considered an independent intelligent agent node. Each intelligent agent node corresponds to the traffic light control system at a specific intersection, responsible for receiving real-time traffic data from various sensors (such as geomagnetic detectors, video recognition units, and radar speed measuring devices), including information on traffic flow, vehicle speed, and vehicle clustering. By acquiring and processing this traffic data in real time, the intelligent agent node can dynamically assess the traffic conditions at its intersection and respond according to changes in traffic flow. Next, multiple intelligent agent nodes are added to a multi-agent cooperative network, a network structure formed by multiple intelligent agent nodes through communication and collaboration. In this network, intelligent agent nodes can share traffic flow data, exchange information, and make coordinated decisions based on shared data. Each intelligent agent node not only makes decisions based on the traffic conditions at its own intersection but also receives real-time information from intelligent agents at other intersections, performing global optimization through information exchange and collaboration. Each intersection's intelligent agent node can make decisions based on local traffic data while simultaneously leveraging information from other intelligent agent nodes in the network to achieve cross-intersection coordinated control. Through collaborative computing, the entire system can dynamically adjust the timing of traffic lights based on real-time data such as tidal traffic flow characteristics and traffic flow changes at each intersection, optimize traffic flow, avoid traffic bottlenecks, and improve the overall efficiency of traffic in the region.

[0045] Furthermore, the multi-agent cooperative network, based on shared real-time traffic data, collaboratively calculates and generates a sequence of consecutive intersection green light phase offsets. The method includes:

[0046] Real-time traffic data of each intersection within the target area is acquired through a multi-agent collaborative network; based on the real-time traffic data, the green light phase offset of each intersection is calculated; and a green light phase sequence of consecutive intersections is generated based on the green light phase offset.

[0047] First, through a multi-agent collaborative network, the traffic control unit at each intersection within the target area acts as an agent node, receiving and uploading local traffic data in real time. Each agent node acquires real-time vehicle information via sensors, including traffic flow, vehicle speed, and platoon concentration, and shares this data with agent nodes at other intersections through the collaborative network. Each agent node can not only obtain traffic conditions at its local intersection but also receive real-time traffic information from surrounding intersections, forming a dynamic information flow network to help achieve global optimization. Next, based on the aforementioned real-time traffic data, each agent node calculates the green light phase offset at its intersection, which represents the degree of deviation between the current green light phase and the preset signal timing. The agent nodes dynamically adjust the green light duration by analyzing tidal traffic flow characteristics such as traffic flow, vehicle speed, and platoon concentration. When traffic flow at an intersection is high and congested, the system may extend the green light duration at that intersection; conversely, if traffic flow at an intersection is low, the system will shorten the green light time, thereby optimizing the traffic signal cycle and avoiding unnecessary waiting time. After calculating the green light phase offset at each intersection, the system further generates a green light phase sequence for consecutive intersections. This sequence coordinates the green light signals at each intersection in chronological order, enabling synchronized adjustments between adjacent intersections to create a green wave effect, ensuring smooth passage of vehicles between a series of consecutive intersections. Precise green light timing reduces waiting times caused by asynchronous traffic signals, effectively improving overall traffic flow efficiency and reducing congestion. Finally, the generated green light phase offset sequence is input into the traffic signal control system in real time to adjust the timing of traffic lights.

[0048] When the tidal direction intensity index is greater than a preset threshold, the continuous intersection green light phase offset sequence is input into the traffic signal control system to activate the tidal green wave mode.

[0049] When the tidal direction intensity index is greater than the preset threshold, it indicates that the traffic flow in that direction is too large, and there is a potential traffic bottleneck or congestion problem. It is necessary to optimize the flow by adjusting the traffic signal timing.

[0050] When the tidal direction intensity index exceeds a preset threshold, the system triggers the tidal green wave mode activation process. Specifically, the system, through a multi-agent collaborative network, calculates the green light phase offset sequence for consecutive intersections based on the current traffic flow status and tidal traffic flow characteristics. This sequence adjusts the signal light phases at adjacent intersections to ensure vehicles can pass through multiple intersections continuously without frequent stops and waits, thus achieving a green wave effect, reducing traffic pressure, and improving road efficiency. After activating the tidal green wave mode, the green light signals at all intersections are adjusted according to the optimized phase sequence, ensuring smooth traffic flow between multiple intersections and avoiding traffic bottlenecks. Under the tidal green wave mode, vehicles can pass through multiple intersections continuously within a certain speed range, thereby maximizing road capacity, reducing traffic congestion, and improving the intelligence level of traffic management.

[0051] In summary, the embodiments of this application have at least the following technical effects:

[0052] First, a heterogeneous sensor network deployed at each intersection collects real-time traffic flow data for lanes in all directions within the target area. Next, the traffic flow data is identified to obtain tidal traffic flow characteristics, including inbound and outbound traffic volume, platoon density, and average platoon speed. Further, dynamic intensity assessment is performed based on these tidal traffic flow characteristics to obtain a tidal direction intensity index. Then, a multi-agent collaborative network is constructed to collaboratively calculate and generate a continuous sequence of green light phase offsets at intersections. Finally, when the tidal direction intensity index exceeds a preset threshold, the continuous green light phase offset sequence is input into the traffic signal control system to activate the tidal green wave mode. This solves the technical problems of insufficient flexibility in traffic flow control and the failure of traffic lights to intelligently adjust according to traffic flow conditions in existing technologies, achieving the technical effects of improving road traffic efficiency and reducing congestion.

[0053] Example 2, based on the same inventive concept as the intelligent traffic light coordination method for traffic flow optimization in the foregoing examples, such as... Figure 2 As shown, this application provides an intelligent traffic light coordination system for traffic flow optimization, wherein the system includes:

[0054] Data acquisition module 11: Collects traffic flow data of lanes in each direction within the target area in real time through a heterogeneous sensor network deployed at each intersection; Identification module 12: Identifies the traffic flow data to obtain tidal traffic flow characteristics, including inbound and outbound traffic flow, platoon aggregation, and average platoon speed; Evaluation module 13: Performs dynamic intensity evaluation based on tidal traffic flow characteristics to obtain a tidal direction intensity index; Calculation module 14: Constructs a multi-agent collaborative network and generates a continuous intersection green light phase offset sequence based on the multi-agent collaborative network; Control module 15: When the tidal direction intensity index is greater than a preset threshold, inputs the continuous intersection green light phase offset sequence into the traffic signal control system to activate the tidal green wave mode.

[0055] Furthermore, the data acquisition module 11 is used to perform the following methods:

[0056] The heterogeneous sensor network includes a geomagnetic detector, a video recognition unit, and a radar speed measurement device. The geomagnetic detector collects vehicle presence signals at a frequency of 10Hz, the video recognition unit extracts license plate features at a frequency of 1Hz, and the radar speed measurement device acquires instantaneous vehicle speed at a frequency of 5Hz. The vehicle presence signals, license plate features, and instantaneous vehicle speed are fused to generate traffic flow data.

[0057] Furthermore, the identification module 12 is used to perform the following method:

[0058] The traffic flow data includes vehicle identification information, vehicle speed information, and vehicle queue information. Based on the vehicle identification information, the number of vehicles passing through each direction per unit time is counted to obtain the inbound and outbound traffic flow. Based on the vehicle speed information, the average speed over a predetermined time period is calculated to obtain the average speed of the convoy. Based on the vehicle queue information, the number of vehicles per unit distance is calculated to obtain the convoy concentration. The inbound traffic flow, outbound traffic flow, convoy concentration, and average convoy speed are then incorporated into the tidal traffic flow characteristics.

[0059] Furthermore, the evaluation module 13 is used to perform the following method:

[0060] Tidal direction intensity index: I(t)=ω1I1(t)+ω2I2(t)+ω3I3(t), where I(t) is the tidal direction intensity index at time t, I1(t) is the traffic flow intensity at time t, I2(t) is the platoon concentration intensity at time t, I3(t) is the vehicle speed intensity at time t, and ω1, ω2, ω3 are the weighting factors of traffic flow intensity, platoon concentration intensity, and vehicle speed intensity.

[0061] Furthermore, the evaluation module 13 is used to perform the following method:

[0062] Q1(t) represents the inbound traffic flow at time t, and Q2(t) represents the outbound traffic flow at time t. Where D(t) is the convoy aggregation degree at time t, D max Maximum fleet density; Where V(t) is the average speed of the convoy at time t, V max This is the maximum speed.

[0063] Furthermore, the calculation module 14 is used to perform the following method:

[0064] The traffic control unit at each intersection within the target area is designated as an intelligent agent node, which is used to receive real-time traffic data; multiple intelligent agent nodes are added to a multi-agent cooperative network.

[0065] Furthermore, the calculation module 14 is used to perform the following method:

[0066] Real-time traffic data of each intersection within the target area is acquired through a multi-agent collaborative network; based on the real-time traffic data, the green light phase offset of each intersection is calculated; and a green light phase sequence of consecutive intersections is generated based on the green light phase offset.

[0067] It should be noted that the order of the embodiments described above is for descriptive purposes only and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0068] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0069] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for intelligent traffic light coordination and control to optimize traffic flow, characterized in that, The method includes: Traffic flow data in all directions within the target area is collected in real time through a heterogeneous sensor network deployed at each intersection. The traffic flow data is identified to obtain tidal traffic flow characteristics, which include inbound and outbound traffic flow, platoon concentration, and average platoon speed. Dynamic intensity assessment is performed based on tidal traffic flow characteristics to obtain the tidal direction intensity index; Construct a multi-agent cooperative network, and generate a continuous sequence of green light phase offsets at intersections based on the collaborative calculation of the multi-agent cooperative network. When the tidal direction intensity index is greater than a preset threshold, the continuous intersection green light phase offset sequence is input into the traffic signal control system to activate the tidal green wave mode. The method for dynamically assessing the intensity of traffic flow based on tidal traffic characteristics to obtain a tidal direction intensity index includes: Tidal direction intensity index: ,in, Let be the tidal direction intensity index at time t. Let be the traffic flow intensity at time t. Let be the convoy aggregation intensity at time t. Let the vehicle speed intensity be at time t. , , where are the weighting factors for traffic flow intensity, fleet concentration intensity, and vehicle speed intensity.

2. The intelligent traffic light coordination method for traffic flow optimization as described in claim 1, characterized in that, The method involves collecting real-time traffic flow data for each direction within a target area using a heterogeneous sensor network deployed at various intersections. The heterogeneous sensor network includes a geomagnetic detector, a video recognition unit, and a radar speed measurement device; The geomagnetic detector collects vehicle presence signals at a frequency of 10Hz, the video recognition unit extracts license plate features at a frequency of 1Hz, and the radar speed measuring device obtains instantaneous vehicle speed at a frequency of 5Hz. The vehicle presence signal, license plate features, and instantaneous vehicle speed are fused together to generate traffic flow data.

3. The intelligent traffic light coordination method for traffic flow optimization as described in claim 1, characterized in that, The method for identifying the traffic flow data to obtain tidal traffic flow characteristics includes: The traffic flow data includes vehicle identification information, vehicle speed information, and vehicle queue information; Based on the vehicle identification information, the number of vehicles passing through each direction per unit time is counted to obtain the inbound and outbound traffic flow. Based on the vehicle speed information, the average vehicle speed over a predetermined time period is calculated to obtain the average vehicle speed of the fleet. Based on the vehicle queue information, the number of vehicles per unit distance is calculated to obtain the fleet aggregation degree. The inbound traffic flow, outbound traffic flow, platoon concentration, and average platoon speed are incorporated into the tidal traffic flow characteristics.

4. The intelligent traffic light coordination method for traffic flow optimization as described in claim 1, characterized in that, The method for dynamically assessing the intensity of traffic flow based on tidal traffic characteristics to obtain a tidal direction intensity index includes: , For the inbound traffic flow at time t, Let t be the outbound traffic volume. ,in, Let be the convoy concentration at time t. Maximum fleet density; ,in, Let be the average speed of the convoy at time t. This is the maximum speed.

5. The intelligent traffic light coordination method for traffic flow optimization as described in claim 1, characterized in that, The method for constructing a multi-agent cooperative network includes: The traffic control unit at each intersection within the target area is used as an intelligent agent node, which is used to receive real-time traffic data. Add multiple agent nodes to a multi-agent collaborative network.

6. The intelligent traffic light coordination method for traffic flow optimization as described in claim 5, characterized in that, A multi-agent cooperative network, based on shared real-time traffic data, collaboratively calculates and generates a sequence of consecutive intersection green light phase offsets. The method includes: Real-time traffic data of each intersection within the target area is obtained through a multi-agent collaborative network; Based on the real-time traffic data, the green light phase offset at each intersection is calculated; A green light phase sequence for consecutive intersections is generated based on the green light phase offset.

7. A smart traffic light coordination system for traffic flow optimization, characterized in that, The system is used to implement the intelligent traffic light coordination method for traffic flow optimization according to any one of claims 1-6, the system comprising: Data acquisition module: Collects traffic flow data of lanes in all directions within the target area in real time through a heterogeneous sensor network deployed at each intersection; Identification module: Identifies the traffic flow data to obtain tidal traffic flow characteristics, which include inbound and outbound traffic flow, platoon concentration, and average platoon speed. Evaluation module: Based on the characteristics of tidal traffic flow, dynamic intensity is evaluated to obtain the intensity index in the tidal direction; Computation module: Constructs a multi-agent collaborative network, and generates a continuous sequence of green light phase offsets at intersections based on the collaborative calculation of the multi-agent collaborative network; Control module: When the tidal direction intensity index is greater than the preset threshold, the continuous intersection green light phase offset sequence is input into the traffic signal control system to start the tidal green wave mode.

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