Road traffic management system based on intelligent street lamps

Abstract

The invention relates to the technical field of intelligent traffic management, in particular to a road traffic management system based on an intelligent street lamp, which comprises a data sensing module, a flow evaluation module, a signal adjustment module, a burst recognition module and a response optimization module. According to the invention, through geomagnetic and camera data fusion, high-frequency accurate perception of vehicle dynamic behaviors is realized, data timeliness and integrity are improved, traffic density and time section joint analysis is realized, saturation determination is more detailed, queuing length is compared with the number of passing vehicles, traffic overload is identified in advance, and signal skewness is determined through signal period proportion linkage. Regional timing coordination, intensive vehicle shadow and speed sudden change joint recognition, anomaly detection precision improvement, response delay comparison, dynamic priority adjustment and key region signal accurate regulation and control enhancement are realized, the crossing from static control to active closed-loop regulation is integrally realized, and traffic operation efficiency and adaptability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent traffic management, and in particular, to a road traffic management system based on intelligent street lights. Background Art

[0002] The technical field of intelligent traffic management includes related technologies such as traffic flow management, road condition monitoring, traffic signal control, road monitoring, and public transportation management. Its aim is to improve the operation efficiency and safety of the traffic system through intelligent means. The core content of this technical field mainly involves aspects such as traffic data collection and analysis, vehicle behavior prediction, real-time monitoring, and dynamic scheduling. With the continuous development of information technology and Internet of Things technology, intelligent traffic management has gradually achieved real-time perception and feedback of traffic conditions, making traffic management more efficient and accurate.

[0003] Among them, the road traffic management system refers to using intelligent street lights as a traffic management platform, and through integrating means such as sensors, cameras, and wireless communication technologies, it can monitor the road traffic conditions in real time, collect traffic data and perform analysis and processing, provide solutions for optimizing traffic signal control and traffic flow management, solve problems such as information silos and response lags existing in road traffic management, deploy intelligent street lights to collect information such as vehicle flow and speed, and realize intelligent adjustment of traffic lights in combination with an intelligent decision-making mechanism.

[0004] The existing technologies mainly rely on basic data collection and static rule execution. The traffic data collection methods are insufficient in reflecting time continuity and dynamic behavior characteristics, unable to accurately track the instantaneous state changes of vehicles, resulting in traffic state recognition deviation. In terms of signal control, the existing systems generally adopt fixed cycle or empirical setting methods, lacking sensitive response to real-time traffic loads. As a result, in situations such as morning and evening rush hours when the instantaneous density surges, the signal timing still remains lagging, causing queue extension and intersection congestion. There is a lack of coordinated linkage in signal timing between adjacent intersections, leading to local adjustments causing regional conduction blockage phenomena. In terms of sudden state recognition, relying on the single dimension of traditional camera recognition, it is unable to accurately locate dense abnormal areas and synchronous speed mutation phenomena, resulting in delayed response to accidents or abnormal congestion. The existing systems lack analysis of reaction time and strategy feedback after signal intervention, resulting in the inability of the original regulation effect to feed back to the current decision-making, and the emergence of repeated and inefficient control behaviors, directly affecting road traffic efficiency and overall traffic operation safety. Summary of the Invention

[0005] The purpose of the present invention is to solve the disadvantages existing in the prior art, and to propose a road traffic management system based on intelligent street lights.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A road traffic management system based on intelligent street lights includes:

[0007] The data perception module measures data based on the geomagnetic sensors of smart street lights and traffic camera components, including the number of lane occupancies, passing vehicle speeds, and the number of vehicles passing through during a signal cycle. By combining the occupancy times within a unit time and the passing time segments, it analyzes the average passing saturation of the intersection to obtain the passing saturation value of the intersection;

[0008] The traffic flow assessment module, based on the passing saturation value of the intersection, identifies the queue length and the total number of vehicles passing through during the green light in the signal control cycle. By comparing the ratio of the two, it determines whether the current traffic flow has reached the adjustment threshold to obtain the signal load prediction index;

[0009] The signal adjustment module, according to the signal load prediction index, determines whether it deviates from the green light priority interval by comparing the cycle ratio of the number of vehicles passing through during the green light and the red light waiting time, and makes a cycle deviation determination for adjacent intersections to obtain the signal control synchronization adjustment instruction;

[0010] The sudden event recognition module calls the signal control synchronization adjustment instruction. By analyzing the vehicle density distribution map and speed mutation points obtained from the front-end image acquisition component, it identifies the concentrated appearance of the dense vehicle shadow area and speed sharp drop points, screens the abnormal sections and associates the adjustment instruction range to obtain the abnormal concentrated response block identifier.

[0011] As a further solution of the present invention, the passing saturation value of the intersection includes the passing density distribution, the vehicle speed change range, and the signal cycle passing rate. The signal load prediction index includes the queue growth ratio, the green light passing saturation, and the cycle utilization efficiency. The signal control synchronization adjustment instruction includes the timing deviation between adjacent intersections, the green light priority interval determination value, and the signal cycle correction factor. The abnormal concentrated response block identifier includes the abnormal density section number, the speed mutation recognition label, and the image aggregation area identifier.

[0012] As a further solution of the present invention, the data perception module includes:

[0013] The occupancy detection sub-module measures data based on the geomagnetic sensors of smart street lights and traffic camera components, extracts vehicle induction data and lane image frames of the camera component, analyzes the matching relationship between vehicle trajectories and induction point signals, counts the number of vehicle occupancies on each lane, and generates a signal cycle lane occupancy sequence table;

[0014] The passing efficiency sub-module, based on the signal cycle lane occupancy sequence table, extracts the start and end coordinates and timestamps of the trajectories in the traffic camera component, identifies the passing distance and time interval of continuous trajectories, summarizes the average passing speed of the lane and the number of vehicles passing through during the signal cycle, and generates an intersection passing speed and traffic flow index set;

[0015] The saturation ratio evaluation sub-module, based on the intersection passing speed and traffic flow index set, identifies the passing time and passing quantity data of each lane, and numerically calculates the lane saturation state according to the ratio of the lane passing demand to the available time period per unit time, so as to obtain the intersection passing saturation value.

[0016] As a further solution of the present invention, the traffic flow evaluation module includes:

[0017] The passing saturation value calculation sub-module, based on the intersection passing saturation value, extracts the number of vehicles passing through per unit time and the upper limit of the lane passing capacity, identifies the ratio of the vehicle flow passing volume to the theoretical passing volume in each cycle, and obtains the passing saturation value;

[0018] The queue proportion determination sub-module calls the passing saturation value, counts the number of queuing vehicles and the number of vehicles passing through in real time during the green light period of each cycle, analyzes the ratio difference between the two, and compares it with a fixed passing benchmark ratio to obtain the queue occupancy ratio;

[0019] The signal load identification sub-module, according to the queue occupancy ratio, combines the original queue trend and the total number of vehicles passing through in the current signal cycle, and uses the formula:

[0020] ;

[0021] Identify the signal cycle load degree, and compare it with the set signal load adjustment threshold to obtain the signal load prediction index;

[0022] Among them, represents the signal load prediction index, represents the current number of queuing vehicles, represents the total number of vehicles passing through during the green light, represents the passing saturation value, represents the number of queuing vehicles in the cycle, represents the duration of the green light in the cycle, represents the number of vehicles passing through during the green light in the cycle, represents the queue occupancy ratio, represents the average value of the original queue trend value, represents the selected number of cycles.

[0023] As a further solution of the present invention, the signal adjustment module includes:

[0024] The priority interval judgment sub-module judges whether the signal cycle deviates from the priority control strategy according to the signal load prediction index, combines the number of green-light passing vehicles collected by the intelligent street lamp platform and the cumulative red-light waiting time, and compares with the set green-light priority threshold to obtain the traffic control deviation status.

[0025] The signal timing correction sub-module extracts the green-light passing rate and green-light duration in the traffic direction from the intelligent street lamp regulation library according to the traffic control deviation status, combines the average headway and the queuing vehicle density, aggregates the traffic efficiency and traffic intensity differences in each direction, and uses the formula:

[0026] ;

[0027] Calculate the signal timing deviation level, and adjust the order of the signal timing scheme according to the level range to construct a traffic light control rhythm rearrangement scheme.

[0028] Among them, represents the signal timing deviation level, is the offset of the green-light passing rate, is the number of green-light passing vehicles, is the number of red-light waiting vehicles, is the direction queuing length, is the direction green-light time, is the direction passing vehicle number, is the direction queuing density, is the total number of traffic directions;

[0029] The traffic light control synchronization instruction acquisition sub-module calls the traffic light control rhythm rearrangement scheme, collects the signal start time and synchronization deviation reference value of the current intersection and adjacent intersections in the intelligent street lamp control gateway, and judges whether the rearrangement scheme causes cycle misalignment and overlap to obtain the signal control synchronization adjustment instruction.

[0030] As a further solution of the present invention, the sudden change recognition module includes:

[0031] The vehicle shadow density recognition sub-module calls the signal control synchronization adjustment instruction, extracts the vehicle density distribution map, analyzes the pixel aggregation, boundary overlap and lane passing space parameters of the image frame, and compares the spacing fluctuation between vehicle shadows in the area to obtain the vehicle shadow density coefficient value.

[0032] The speed mutation judgment sub-module identifies the original vehicle speed information according to the vehicle shadow density coefficient value, extracts the speed jump amplitude and frequency, and analyzes the deviation from the median value of the speed fluctuation interval to obtain the speed jump comparison value.

[0033] The abnormal section screening sub-module extracts the block number, response time series, and boundary coverage ratio involved in the signal control adjustment instruction according to the speed jump comparison value, and uses the formula:

[0034] ;

[0035] Identify the abnormal intensity, map it to the signal control number, and obtain the abnormal concentrated response block identifier;

[0036] Among them, represents the abnormal concentrated response block identifier, is the vehicle shadow overlap degree of the sub-block in the area , is the minimum vehicle speed of the sub-block in the area ; is the response time series equilibrium value of the area ; is the boundary coverage ratio of the area ; represents the total number of areas.

[0037] As a further solution of the present invention, the system further includes a response optimization module:

[0038] Based on the abnormal concentrated response block identifier, the response optimization module extracts the time difference between the signal intervention delay and the evacuation result by calling the original signal adjustment record, compares the current block number with the original response duration, adjusts the trigger priority sequence, and obtains the signal regulation instruction for the key traffic area;

[0039] The signal regulation instruction for the key traffic area includes the signal response priority, the original intervention delay data, and the regulation block number matching value.

[0040] As a further solution of the present invention, the response optimization module includes:

[0041] Based on the abnormal concentrated response block identifier, the block identifier extraction sub-module extracts the signal response time and the abnormal event number from the intelligent street lamp log, identifies the response time period corresponding to the number, and maps and combines it with the road section number to generate an intelligent street lamp response block marker set;

[0042] The signal response delay calculation sub-module calls the intelligent street lamp response block marker set, extracts the signal intervention start time and the evacuation end time of the corresponding block, analyzes the time difference between the two and matches it to the block number, identifies the corresponding data between the block number and the response delay, and obtains the road section response delay list;

[0043] The signal regulation instruction generation sub-module rearranges the signal priority sequence according to the delay degree with reference to the original response duration based on the road section response delay list, and allocates the adjusted traffic light cycle parameter values in combination with the road section numbers to obtain the traffic key area signal regulation instructions.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In the present invention, by jointly collecting the number of lane occupancies, passing vehicle speeds, and the number of vehicles passing through the signal cycle by the geomagnetic sensor and the traffic camera component, a high-frequency perception of vehicle dynamic behavior is formed, improving the data accuracy and timeliness, and effectively making up for the limitation that static detection means cannot continuously track. Based on the comprehensive analysis means of traffic density and traffic time period, the determination of the average traffic saturation degree is made more granular, providing a stable basis for subsequent traffic state judgment. By comparing the queue length with the number of vehicles passing through the green light, the traffic flow carrying pressure is accurately characterized, and a forward response under the overload situation is realized. Linking the determination of the traffic deviation interval with the signal cycle ratio realizes the agile identification of signal skewness, and introduces the cycle coordination judgment between adjacent intersections to improve the regional traffic time effect adaptation ability. Jointly extracting the dense vehicle shadow area and speed mutation points in the front-end image improves the accuracy and response granularity of abnormal area identification. By comparing the original signal intervention delay with the current response duration, the trigger priority is dynamically adjusted, optimizing the immediate response of the control signal to local sudden states, avoiding the aggravation of congestion caused by signal control lag, and strengthening the accurate coverage ability of signal regulation for key areas. The overall solution breaks through the limitation of static rule regulation, forms a full-chain closed-loop control logic in aspects such as acquisition dimension, response granularity, regional linkage, and sudden adaptation, enabling the traffic control to leap from passive response to active adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is the system flow chart of the present invention;

[0047] Figure 2 is the flow chart of the data perception module in the present invention;

[0048] Figure 3 is the flow chart of the traffic flow evaluation module in the present invention;

[0049] Figure 4 is the flow chart of the signal adjustment module in the present invention;

[0050] Figure 5 is the flow chart of the sudden identification module in the present invention;

[0051] Figure 6 is the flow chart of the response optimization module in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0052] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0053] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0054] Please refer to Figure 1 , a road traffic management system based on intelligent street lights includes:

[0055] The data perception module measures data based on the geomagnetic sensors and traffic camera components of the intelligent street lights, including the number of lane occupancies, passing vehicle speeds, and the number of vehicles passing through within the signal cycle. By combining the number of occupancies per unit time and the passing time period, it analyzes the average traffic saturation of the intersection to obtain the traffic saturation value of the intersection.

[0056] The traffic flow assessment module, based on the traffic saturation value of the intersection, identifies the queue length and the total number of vehicles passing through the green light during the signal control cycle. By comparing the ratio of the two, it determines whether the current traffic flow has reached the adjustment threshold to obtain the signal load prediction index.

[0057] The signal adjustment module, according to the signal load prediction index, determines whether it deviates from the green light priority interval by comparing the cycle ratio of the number of vehicles passing through the green light and the red light waiting time. If it deviates, it adjusts the signal timing sequence and determines the cycle deviation of adjacent intersections to obtain the signal control synchronous adjustment instruction.

[0058] The sudden event identification module calls the signal control synchronous adjustment instruction. By analyzing the vehicle density distribution map and speed mutation points obtained from the front-end image acquisition component, it identifies the concentrated appearance of the dense vehicle shadow area and the speed sudden drop points, screens the abnormal sections and associates the adjustment instruction range to obtain the abnormal concentration response block identifier.

[0059] The response optimization module, based on the abnormal concentration response block identifier, extracts the time difference between the signal intervention delay and the evacuation result by calling the original signal adjustment record, compares the current block number with the original response duration, and adjusts the trigger priority sequence to obtain the signal regulation instruction for key traffic areas.

[0060] The intersection traffic saturation value includes traffic density distribution, vehicle speed change range, and signal cycle passing rate. The signal load prediction indicators include queue growth ratio, green light passing saturation, and cycle utilization efficiency. The signal control synchronization adjustment instructions include the timing deviation between adjacent intersections, the determination value of the green light priority interval, and the signal cycle correction factor. The abnormal concentrated response block identifier includes the abnormal density section number, the speed mutation identification label, and the image aggregation area identifier. The traffic key area signal regulation instructions include the signal response priority, the original intervention delay data, and the regulation block number matching value.

[0061] Please refer to Figure 2 , the data perception module includes:

[0062] The occupancy detection sub-module extracts vehicle induction data and lane image frames of the traffic camera component based on the measurement data of the intelligent street lamp geomagnetic sensor and the traffic camera component, analyzes the matching relationship between the vehicle trajectory and the induction point signal, counts the vehicle occupancy times of each lane, and generates a signal cycle lane occupancy sequence table;

[0063] First, extract the vehicle induction data and obtain the lane image frames of the traffic camera component. The geomagnetic sensor converts the signal of the vehicle passing by into induction data, which includes the timestamp of the vehicle passing by and the position of the induction point. The traffic camera component captures the image frames of each lane, and the image includes the moving trajectory of the vehicle. After extracting the data, through the matching analysis of the vehicle trajectory and the induction point signal, identify the moment when the vehicle passes through a specific position and its travel path in the lane. The matching process is achieved through the relative relationship between the timestamp and the image frame, involving setting a time window to ensure that the vehicle passes through a certain induction point within this time. If the vehicle passes through a certain induction point within this window, it is determined that the vehicle passes through this induction point. According to the data of multiple induction points, count the vehicle occupancy times of each lane for subsequent traffic flow and efficiency analysis. For example, if the induction point signal of lane 1 shows that the vehicle occupancy time is 10 seconds and the vehicle passes through this area within 10 seconds, record the occupancy situation of this lane as one occupancy, and generate a signal cycle lane occupancy sequence table to show the occupancy situation of each lane at different time periods.

[0064] The traffic efficiency sub-module extracts the start and end coordinates and timestamps of the trajectories in the traffic camera component based on the signal cycle lane occupancy sequence table, identifies the passing distance and time interval of the continuous trajectories, summarizes the average passing speed of the lane and the number of vehicles passing through within the signal cycle, and generates an intersection passing speed and traffic flow index set;

[0065] Extract the start and end coordinates and timestamps of vehicle trajectories in the traffic camera component. Extract the trajectories of each vehicle from the image frames, determine the coordinates of their starting and ending points, and combine the timestamps of each point to calculate the passing time and the distance traveled by the vehicle. The distance traveled by the vehicle is achieved by calculating the physical distance between the start and end coordinates, using a geographic coordinate conversion algorithm or directly converting the pixel coordinates in the image to the actual distance. The time interval is obtained by calculating the difference between the start and end timestamps. By analyzing the data, identify the passing distance and time interval of continuous trajectories. Calculate the average passing speed of the vehicle within the signal cycle, which can be calculated by the formula: speed = distance / time, to obtain the passing speed of each vehicle. Then, according to the statistical results, summarize the lane average passing speed and the number of vehicles passing through within the signal cycle for each lane, so as to obtain the passing capacity of each lane. For example, if a vehicle passes through a 200-meter lane in 20 seconds, the passing speed of the vehicle is 10 meters per second. Summarize the data to generate the intersection passing speed and flow index set.

[0066] The saturation ratio evaluation sub-module, based on the intersection passing speed and flow index set, identifies the passing time and passing quantity data of each lane, and performs a numerical operation on the lane saturation state according to the ratio of the lane passing demand to the available time period per unit time to obtain the intersection passing saturation value;

[0067] Identify the passing time and passing quantity data of each lane, and calculate the saturation of the lane. By analyzing the passing speed and flow, obtain the number of passing vehicles and the time required for passing in each lane within one signal cycle. The passing time of the lane can be obtained by summing up the passing time of each vehicle, and the passing quantity is the total number of passing vehicles. According to the data, calculate the passing demand per unit time of each lane, that is, the number of vehicles expected to pass through each lane per unit time. By comparing the passing demand per unit time with the available time period of the lane, calculate the saturation ratio of the lane. For example, if the passing demand of the lane is 10 vehicles per minute, and the signal cycle of the lane is 60 seconds (i.e., 1 minute), then its saturation ratio is 10 / 10 = 1, indicating that the lane reaches the full-load state. In this way, obtain the intersection passing saturation value to evaluate the passing pressure of the intersection.

[0068] Please refer to Figure 3 , the flow evaluation module includes:

[0069] The passing saturation value calculation sub-module, based on the intersection passing saturation value, extracts the number of passing vehicles per unit time and the upper limit of the lane passing capacity, and identifies the ratio of the traffic flow passing volume to the theoretical passing volume per cycle to obtain the passing saturation value;

[0070] In intelligent transportation, for example, the traffic management at an intersection calculates the traffic flow volume and lane passing capacity within each signal cycle in real time, and computes the traffic flow volume and theoretical passing volume for each signal cycle. This is based on data collected by road sensors and traffic monitoring cameras. For instance, the number of vehicles passing through in one cycle is 1000, while the theoretical passing capacity of the road design is 1200 vehicles per hour. Based on this data, the passing saturation value for each cycle is calculated. The passing saturation value is calculated by dividing the actual number of vehicles passing through each cycle by the theoretical maximum passing volume. For example, 1000 / 1200 = 0.83, which indicates that the passing saturation value for this cycle is 83%. This value can intuitively reflect the passing efficiency of the traffic flow and further provide data support for traffic signal adjustment to obtain the passing saturation value.

[0071] The queuing ratio determination sub-module calls the passing saturation value, counts the number of queuing vehicles and the number of vehicles passing through in real time during the green light period of each cycle, analyzes the ratio difference between the two, and compares it with a fixed passing benchmark ratio to obtain the queuing occupancy ratio.

[0072] In intelligent traffic signal control, using the passing saturation value, the queuing length formed during the red light period and the number of vehicles actually passing through during the green light period in each signal cycle are further obtained. For example, during a busy traffic signal cycle, the number of queuing vehicles reaches 150, while only 100 vehicles actually pass through during the green light period. By calculating the ratio of these two values, such as 150 / 100 = 1.5, it means that the queuing length is 1.5 times that of the passing vehicles. Comparing it with the set passing ratio benchmark, if the set benchmark is 1.2 times, it indicates that the current queuing ratio exceeds the ideal state and the signal settings need to be adjusted. In this way, the efficiency of traffic signals is evaluated and adjusted in real time to reduce queuing, thereby improving the passing speed of the traffic flow to obtain the queuing occupancy ratio.

[0073] The signal load identification sub-module, based on the queuing occupancy ratio, combines the original queuing trend and the total number of vehicles passing through in the current signal cycle, and uses the formula:

[0074] ;

[0075] Identify the signal cycle load degree and compare it with the set signal load adjustment threshold to obtain the signal load prediction index.

[0076] Among them, represents the signal load prediction index, represents the current number of queuing vehicles, represents the total number of vehicles passing through during the green light, represents the passing saturation value, represents the number of queuing vehicles in the cycle, represents the Duration of the green light in a cycle, represents the number of vehicles passing during the green light in a cycle, represents the queuing occupancy ratio, represents the average value of the original queuing trend value, represents the number of selected cycles;

[0077] Combining the queuing occupancy ratio and the passing saturation value, further identify the signal cycle load situation, determine whether the signal flow reaches the adjustment threshold through numerical calculation, and obtain the load metric level under the current signal control state. First, obtain the numerical values of each parameter in the formula, where the current number of queuing vehicles is obtained from the measurement of the queuing length at 1 second before the end of the red light in a certain approach of the intersection during the current signal cycle. For example, if it is detected by a radar sensor that there are 75 vehicles queuing in this approach, then , and the total number of vehicles passing during the green light can be detected by a loop induction coil to obtain the number of vehicles leaving during the green light period, and the detection result is vehicles, and the passing saturation value is the result calculated previously, set as , and the original queuing trend value is the average value of the queuing vehicles in 3 consecutive signal cycles. For example, if the 3 cycles are 78, 72, and 81 respectively, then , and the queuing occupancy ratio is the result obtained in the previous sub-module, set as , when calculating the summation term , it is assumed that original cycle data are taken for judgment, and the data for each cycle are respectively:

[0078] The 1st cycle: vehicles, the green light time seconds, the passing vehicles ;

[0079] The 2nd cycle: vehicles, seconds, ;

[0080] The 3rd cycle: vehicles, seconds, ;

[0081] Substitute them into the calculation respectively to get:

[0082] ;

[0083] ;

[0084] ;

[0085] Sum the above values to obtain ;

[0086] Then, calculate the first term of the numerator part: ;

[0087] The final value of the numerator is: ;

[0088] The denominator part is: ;

[0089] Finally, substitute into the formula to obtain: ;

[0090] This result indicates that the signal load prediction index is 4.53, which is significantly higher than the standing adjustment threshold (set to 1.0), indicating that there is a serious load pressure in the current signal cycle and it is necessary to trigger the dynamic adjustment mechanism of the signal cycle. In terms of dimension, it is unified in the form of number of vehicles / dimensionless coefficient, enabling the index to have standardization ability and facilitating subsequent horizontal comparison among multiple signal intersections;

[0091] Among them, represents the number of queuing vehicles (vehicles) at the end of the red light in the current cycle, is the total number of vehicles passing through during the green light (vehicles), is the traffic saturation value (dimensionless, value range 0 - 1), is the number of queuing vehicles (vehicles) in the th original cycle, is the duration of the green light (seconds) in the th cycle, is the actual number of vehicles passing through during the green light (vehicles) in the th cycle, is the number of original cycles for value taking, is the current queuing occupancy ratio (dimensionless), is the average value of the original queuing vehicles (vehicles), is the finally calculated signal load prediction index (dimensionless);

[0092] By introducing multiple dynamic elements such as the comparison of the current and original queuing quantities, the deviation of the green light passing efficiency, and the weighting of the original saturation value, a complex determination mechanism for the signal cycle load is realized, which has scalability and real-time performance in the system-level traffic flow regulation and effectively enhances the basis of traffic signal dynamic regulation.

[0093] Please refer to Figure 4 , the signal adjustment module includes:

[0094] The priority interval judgment sub-module judges whether the signal cycle deviates from the priority control strategy according to the signal load prediction index, combines the number of vehicles passing through the green light and the cumulative waiting time of the red light collected by the intelligent street lamp platform, and compares with the set green light priority threshold to obtain the traffic control deviation status;

[0095] In the management of intelligent street lamps, through real-time data analysis, the vehicle passing data during green and red lights is monitored. This data is obtained from the sensors of intelligent street lamps, including vehicle counters and time recorders. The data of the number of vehicles passing through during the green light and the waiting time of vehicles during the red light quantifies the traffic flow. Based on this data, the actual passing ratio is compared with the preset priority passing threshold. For example, within a traffic cycle, 100 vehicles are allowed to pass through the green light and the red light waiting time is 120 seconds. The passing ratio is calculated as 100 vehicles / 120 seconds and compared with the set upper limit of the green light priority interval of 1.0 vehicle / second to judge whether traffic control needs to be adjusted to adapt to traffic flow changes. In this way, it is confirmed whether the optimal traffic control strategy is deviated. If the current passing ratio is lower than the preset threshold, it means that the green light period is not fully utilized, and the green light duration needs to be increased or the traffic signal control strategy needs to be adjusted to optimize the traffic flow and achieve a more efficient intersection passing capacity. Finally, the traffic control deviation status is obtained, which indicates the deviation between the current signal setting and the optimal flow control.

[0096] The signal timing correction sub-module extracts the green light passing rate and green light duration in the passing direction from the intelligent street lamp regulation library according to the traffic control deviation status, combines the mean headway and the density of queuing vehicles, aggregates the passing efficiency and passing intensity differences in each direction, and uses the formula:

[0097] ;

[0098] Calculate the signal timing deviation level, and adjust the order of the signal timing plan according to the level range to construct a reorder plan for the lamp control rhythm;

[0099] Among them, represents the signal timing deviation level, is the offset of the green light passing rate, is the number of vehicles passing through the green light, is the number of vehicles waiting for the red light, is the direction queuing length, is the direction green light time, is the direction passing vehicle number, is the direction queuing density, is the total number of passing directions;

[0100] In the intelligent street lamp road traffic management, the signal timing correction is based on the precondition of the traffic control offset state. Its core objective is to generate adjustable timing structure adjustment suggestions through quantitative analysis of green light efficiency, queue density, and directional traffic intensity, and obtain the number of vehicles passing through the green light and the number of vehicles waiting at the red light , which are recorded in real time by the embedded vehicle detector and the lane coil, and obtained through periodic cumulative sampling. Suppose the number of vehicles passing through the green light recorded by the counter during this period is 60, that is , and the number of vehicles queuing up before the end of the red light is 90, that is . The equipment uniformly uses a monitoring section with a cycle length of 90 seconds to capture parameters. The offset of the green light passing rate comes from the comparison of the number of vehicles passing through this period with the average value of the number of vehicles passing through the previous 5 periods. The original average value is 75 vehicles, then , reflecting the decrease in efficiency during this period;

[0101] Next, collect the traffic direction data, including the queue length , green light time , number of passing vehicles , density of each traffic direction, which are jointly collected by the video analysis module, traffic signal timing controller, traffic flow counter, radar probe or laser distance sensor in the intelligent street lamp system;

[0102] In direction 1, the camera detects the head position sequence of the vehicles in the queuing area and calculates that the current total number of vehicles is 35 , the traffic signal control records the green light time as 30 seconds , the vehicle passing counter records 40 vehicles , the density is calculated in segments by the ranging module, and the total queue length is 32 meters, and the average density is approximately 1.1 vehicles per meter;

[0103] The data collection process for direction 2 and direction 3 is the same, and the following results are obtained respectively: , , , the queue length is 35 meters, then ; , , , the queue length is 37 meters, then ;

[0104] Traffic efficiency offset item: ;

[0105] Offset items for each direction:

[0106] Direction 1: ;

[0107] Direction 2: ;

[0108] Direction 3: ;

[0109] The summation term is: ;

[0110] Total formula calculation: ;

[0111] Here, all parameters have been automatically collected and pre - processed by the embedded sensors or edge computing nodes in the intelligent street lights, without relying on external data sources, forming a closed - loop monitoring link. The unit is uniformly processed into dimensionless, and each participating item is normalized in the following way: time, the number of vehicles, density, etc. form a ratio for unit elimination, the seconds, vehicles, and meters in it are resolved into proportional terms. Finally, all parameters are combined and calculated into the signal timing deviation level. The result value is 23.031, which belongs to the middle interval of the deviation level (the preset reference interval is 0 - 15 for stability, 15 - 30 for the adjustment transition zone, > 30 for the reconstruction trigger zone). Therefore, it should enter the rhythm structure optimization process, and thus obtain the light control rhythm rearrangement plan.

[0112] The light control synchronization instruction acquisition sub - module calls the light control rhythm rearrangement plan, collects the signal start time and the synchronization deviation reference value of the current intersection and adjacent intersections in the intelligent street light control gateway, judges whether the rearrangement plan causes cycle misalignment and overlap, and obtains the signal control synchronization adjustment instruction;

[0113] Controlling signal synchronization is the key to ensuring smooth road traffic. By real - time monitoring the signal timing of adjacent intersections and the signal timing of the current intersection. For example, if the signal timing of an adjacent intersection is advanced by 30 seconds due to a special event, and the current intersection is delayed by 20 seconds due to the light control rhythm rearrangement plan, this deviation causes the traffic signals at the two intersections to be out of sync, thus affecting the coherence and efficiency of the traffic flow. By comparing the adjusted time with the synchronization deviation reference value, it can be judged whether further adjustment is needed to maintain signal synchronization. If it is detected that the time deviation caused by the rearrangement plan exceeds the allowable error range, the intelligent street light control will automatically generate a calibration instruction to adjust the signal start time to ensure that the signal lights at all intersections operate synchronously, thereby maintaining the smoothness of the traffic flow. Finally, a signal synchronization control instruction is generated, which ensures the coordination of signal timing among intersections and optimizes the traffic flow in the entire area.

[0114] Please refer to Figure 5 , the sudden event recognition module includes:

[0115] The vehicle shadow density recognition sub-module calls the signal control synchronization adjustment instruction, extracts the vehicle density distribution map, analyzes the pixel aggregation, boundary overlap and lane passing space parameters of the image frame, compares the spacing fluctuations between vehicle shadows in the area, and obtains the vehicle shadow density coefficient value;

[0116] Through the high-resolution cameras deployed by the roadside, the real-time images of the road are collected in real time. Through image processing technology, the distribution density of vehicles can be analyzed. For example, in one instance, the vehicle density area of a certain main road during the evening rush hour is monitored. Then, the pixel aggregation parameters of the image frame are extracted from the vehicle density map, and the vehicle density is judged by analyzing the color depth of each image pixel point. The area with a higher vehicle density is darker in color. The vehicle shadow boundary overlap parameter is calculated by detecting the overlap degree of the vehicle contours. The data helps to judge the continuity of the traffic flow and the congestion points. The lane passing space parameter is measured by measuring the remaining visible space in the lane, which can reflect the usage efficiency of the lane. If the space is less than a certain threshold, it indicates an upcoming or existing traffic congestion. The minimum spacing fluctuation between vehicle shadows in the area is compared, which is achieved by calculating the position changes of the same vehicle or different vehicles in consecutive frames. If it is detected that the smaller spacing continues to decrease, an early warning will be triggered to prevent potential traffic accidents. The vehicle shadow density coefficient value is obtained. This coefficient value is a comprehensive index calculated through the above parameters, reflecting the density of vehicles in a certain area. Through real-time data display, such as during the evening rush hour, this index increases significantly, and the traffic management center will adjust the signal timing or issue traffic control information accordingly.

[0117] The speed mutation judgment sub-module identifies the original vehicle speed information according to the vehicle shadow density coefficient value, extracts the speed jump amplitude and frequency, analyzes the deviation from the median value of the speed fluctuation range, and obtains the speed jump comparison value;

[0118] The original vehicle speed information is called. In intelligent transportation, based on the comparative analysis of the original data and real-time monitoring data, such as recording the vehicle speed data in the same period in the past, it can be used to predict and compare the current vehicle speed trend. The speed jump amplitude and minimum frequency are extracted, and the speed change of each vehicle is analyzed through an algorithm to identify the vehicles whose speed reduction exceeds the set threshold. The threshold is set based on long-term data statistics and traffic flow theory. For example, if the vehicle speed drops suddenly by more than 20% within three consecutive image frames, it is considered that a speed mutation has occurred. Calculate its deviation from the median value of the speed fluctuation range. The calculation is achieved by comparing the real-time vehicle speed with the median speed of the range. If the deviation exceeds the preset safety range, an alarm will be generated or the working mode of the signal light will be adjusted to relieve the traffic congestion or accident risk. The speed jump comparison value is obtained. This comparison value is a numerical value that can intuitively reflect the stability of the vehicle speed during a certain period of time. If the value is large, it indicates that the speed changes violently and measures need to be taken.

[0119] The abnormal section screening sub-module extracts the block number, response time series, and boundary coverage ratio involved in the signal control adjustment instruction according to the speed jump comparison value, using the formula:

[0120] ;

[0121] Identify the abnormal intensity and map it to the signal control number to obtain the abnormal concentrated response block identifier;

[0122] Among them, represents the abnormal concentrated response block identifier, is the vehicle shadow overlap degree of the sub-block within the area , is the minimum vehicle speed of the sub-block within the area , is the response time series equilibrium value of the area , is the boundary coverage ratio of the area , represents the total number of areas;

[0123] Through the real-time records deployed in the signal control cabinets at each intersection in traffic control, within blocks with different numbers, record their signal light control cycles and their changes respectively. The block number is the logical index, and the response time series refers to the control cycle of the signal lights in each round of traffic state changes. For example, during the period from 8:00 to 9:00 in the morning in the block numbered B1, its response time series is [30s, 28s, 32s, 30s], reflecting the real-time fluctuations of the signal light intervals in this section. The boundary coverage ratio is identified by the image edge detection method to calculate the proportion of the vehicle shadow boundary pixels, and extract the proportion of the edge pixels corresponding to the effective width of the lane in this area from the image frame. For example, the vehicle shadow boundary coverage ratio in the B1 section is 88.6%;

[0124] In terms of the vehicle shadow overlap degree , it is calculated by the proportion of the cumulative pixel area of the vehicle occlusion area in the image sequence, using the following method: For the a-th sub-block within the area b, count all the pixel points with vehicle shadows in the frame graph within 5 seconds and multiply by the ratio of the total effective pixels of this sub-block. For example, if the cumulative vehicle-covered pixels in the 5-second image of a certain sub-block are 42000 and the total effective pixels are 60000, then ;

[0125] The minimum vehicle speed is obtained from the speed trajectory data generated by the license plate tracking module synchronized with the image frame, and select the minimum vehicle speed among all the vehicles recorded within the sampling period in the corresponding sub-block. For example, if the speed values within a certain sub-block are [24, 28, 20, 31] km / h, then km / h, the vehicle speed needs to be converted to , that is ;

[0126] Response time series equilibrium value is the average signal cycle corresponding to the current block number. For example, taking the average of the time series in this area [30s, 28s, 32s, 30s] gives ;

[0127] Boundary coverage rate is the average boundary pixel coverage rate of the area directly calculated by the image processing module, standardized to a decimal value. For example, 88.6% is ;

[0128] Substitute the example data. Assume that the corresponding values of sub-blocks to are:

[0129] , ;

[0130] , ;

[0131] , ;

[0132] , ;

[0133] Calculate the numerator part of the above formula:

[0134] ;

[0135] ;

[0136] ;

[0137] ;

[0138] Numerator:

[0139] Calculate the denominator part:

[0140] ;

[0141] ;

[0142] ;

[0143] Final calculation: ;

[0144] The result shows that the response section numbered B1 presents an abnormally concentrated response block with an identification value of 191.24 during the current monitoring period, which is significantly higher than the empirical benchmark value of 120, indicating that this area needs to be included in the priority of synchronous signal adjustment;

[0145] Representative area of the abnormal response intensity value, is the vehicle shadow overlap degree (unitless, range 0 - 1) in the sub-block is the minimum vehicle speed (unit: m / s) in the sub-block is the mean signal response period (unit: s) in the area is the boundary pixel coverage rate (unitless); is the boundary pixel coverage rate (unitless); is the mean signal response period (unit: s) in the area is the boundary pixel coverage rate (unitless);

[0146] Quantify the congestion degree by multiplying the vehicle shadow overlap degree and the minimum vehicle speed, perform square amplification processing on the response time deviation, and add a boundary interference term. Compress the abnormal interference data fluctuation through the square difference and square root structure, effectively revealing sudden congestion or traffic abnormal areas, and ensuring the accuracy and sensitivity of subsequent synchronous instruction adjustment.

[0147] Please refer to Figure 6 The response optimization module includes:

[0148] The block identification extraction sub-module extracts the signal response time and abnormal event number from the intelligent street lamp log based on the abnormally concentrated response block identification, identifies the corresponding response time period of the number, and maps and combines it with the road section number to generate an intelligent street lamp response block tag set;

[0149] Extract the signal response time and abnormal event number, obtain the response data of the abnormal event from the intelligent street lamp log, distinguish it through the abnormally concentrated response block identification, then determine the signal response time period corresponding to the number, map each time period to the road section number, extract each abnormal event record through the abnormally concentrated response block identification, obtain the corresponding signal response time and event number from it, judge the start and end time of the event, divide each time period based on the response time range, and at the same time, according to the road section number, correspond each time period to the specific road section number one by one, and finally combine it into a block tag set. For example, assume that the response time of an intelligent street lamp is 20 seconds, the event number is 123, this event occurs in the area with road section number 5, the start point of the response time is 9 am, and the end point is 9:20 am, then the road section number and time period are mapped in the tag set, and finally an intelligent street lamp response block tag set is obtained. This tag set can provide data support for signal response delay calculation and instruction generation in subsequent processing.

[0150] The signal response delay calculation sub-module calls the intelligent street lamp response block marker set, extracts the signal intervention start time and the evacuation end time of the corresponding block, analyzes the time difference between the two, matches it to the block number, identifies the corresponding data between the block number and the response delay, and obtains the road section response delay list;

[0151] Extract the data in the intelligent street lamp response block marker set, extract the signal intervention start time and the evacuation end time, calculate the time difference between the two, that is, the response delay, and correspond this result to the block number. By traversing the intelligent street lamp response block marker set, obtain the signal intervention start time and the evacuation end time of each block therein, calculate the difference between these two time points. If the intervention start time is 9 o'clock and the evacuation end time is 9:15, then the delay is 15 minutes. Then associate the delay value with the block number to form a delay data. Perform the same processing for each block, and finally summarize and generate the road section response delay list, which records the signal response delay situation of each road section. The list provides data support for the generation of subsequent signal control instructions.

[0152] The signal control instruction generation sub-module, according to the road section response delay list, compares with the original response duration, rearranges the signal priority sequence according to the delay degree, and combines with the road section number to allocate the adjusted traffic light cycle parameter value to obtain the signal control instruction for the key traffic area;

[0153] Rearrange the signal priority according to the delay degree. The operations in this process include sorting the delay data, sorting the signal priority from long to short according to the time delay, and then combining with the road section number to allocate the adjusted traffic light cycle parameter value to each signal. For example, assume that the response delay of a certain road section is 30 seconds, while the delay of another road section is 10 seconds. Then give priority to adjusting the signal cycle of the road section with a longer delay to ensure smooth traffic. The ultimate goal of the control instruction is to optimize the traffic flow by adjusting the signal light cycle and alleviate the traffic congestion caused by the delay. Through the comparative analysis of the delay data, the control priority of the signal lights can be effectively allocated to obtain the signal control instruction for the key traffic area and ensure that the key area can be more effectively signal-controlled.

[0154] The above is only the preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical content of the technical solution of the present invention, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A road traffic management system based on smart street lamps, characterized in that: The system comprises: The data perception module measures data based on the geomagnetic sensors of smart street lamps and traffic camera components, including lane occupancy times, vehicle speeds, and the number of vehicles passing through a signal cycle. It combines the occupancy times per unit time with the travel time segment to analyze the average traffic saturation of the intersection and obtain the intersection traffic saturation value. The flow evaluation module identifies the queue length and the total number of vehicles passing through the green light in the signal control cycle based on the traffic saturation value of the intersection, and determines whether the current flow reaches the adjustment threshold by comparing the ratio of the two, thereby obtaining a signal load prediction index; The signal adjustment module determines whether the signal has deviated from the green light priority interval by comparing the cycle ratio of the number of vehicles passing the green light and the waiting time of the red light according to the signal load prediction index, and determines the cycle deviation of adjacent intersections to obtain a signal control synchronization adjustment instruction; The burst recognition module calls the signal control synchronization adjustment instruction, identifies the concentrated appearance of dense vehicle shadow areas and speed drop points by analyzing the vehicle dense distribution map and speed mutation points obtained by the front-end image acquisition component, screens abnormal sections and associates them with the adjustment instruction range, and obtains the abnormal concentrated response block identifier.

2. The road traffic management system based on smart street lamps according to claim 1 is characterized in that: The intersection traffic saturation value includes traffic density distribution, vehicle speed change amplitude, and signal cycle traffic rate; the signal load prediction indicators include queue growth ratio, green light traffic saturation, and cycle utilization efficiency; the signal control synchronization adjustment instructions include adjacent intersection timing deviation, green light priority interval judgment value, and signal cycle correction factor; the abnormal concentrated response block identifier includes abnormal density section number, speed mutation identification label, and image aggregation area identifier.

3. The road traffic management system based on smart street lamps according to claim 1 is characterized in that: The data perception module includes: The occupancy detection submodule extracts vehicle sensing data and camera lane image frames based on the measurement data of the smart street lamp geomagnetic sensor and traffic camera assembly, analyzes the matching relationship between vehicle trajectory and sensing point signal, counts the number of vehicle occupancy times in each lane, and generates a signal cycle lane occupancy sequence table; The traffic efficiency submodule extracts the start and end coordinates and timestamps of the tracks in the traffic camera assembly based on the signal cycle lane occupancy sequence table, identifies the travel distance and time interval of the continuous tracks, summarizes the average lane speed and the number of vehicles passing through the signal cycle, and generates a set of intersection speed and flow indicators; The saturation ratio evaluation submodule identifies the travel time and traffic volume data of each lane based on the intersection traffic speed and flow index set, and performs numerical calculations on the lane saturation state according to the ratio of lane traffic demand to available time segment per unit time to obtain the intersection traffic saturation value.

4. The road traffic management system based on smart street lamps according to claim 3 is characterized in that: The flow evaluation module comprises: The traffic saturation value calculation submodule extracts the number of vehicles passing through the intersection and the upper limit of the lane capacity per unit time based on the traffic saturation value of the intersection, identifies the ratio of the traffic flow volume per cycle to the theoretical traffic flow volume, and obtains the traffic saturation value; The queue ratio determination submodule calls the traffic saturation value, counts the number of queued vehicles and the number of real-time passing vehicles during the green light period of the traffic light in each cycle, analyzes the difference between the two ratios, and compares them with the fixed traffic benchmark ratio to obtain the queue occupancy ratio; The signal load identification submodule uses the formula: ； Identify the signal cycle load degree and compare it with the set signal load adjustment threshold to obtain the signal load prediction index; in, Represents the signal load prediction indicator, Represents the number of vehicles currently in the queue, Represents the total number of vehicles passing through the green light. represents the traffic saturation value, Representative The number of vehicles in the periodic queue, Representative The duration of the green light cycle, Representative The number of vehicles passing during the green light period, represents the queue occupancy ratio, Represents the average value of the original queue trend value, Represents the number of selected periods.

5. The road traffic management system based on smart street lamps according to claim 4 is characterized in that: The signal adjustment module comprises: The priority interval judgment submodule determines whether the signal cycle deviates from the priority control strategy and obtains the traffic control deviation status based on the signal load prediction index, the number of green light vehicles and the accumulated waiting time of the red light collected by the smart street light platform, and compares them with the set green light priority threshold; The signal timing correction submodule extracts the green light rate and green light duration of the traffic direction in the smart street light control library according to the traffic control offset state, combines the mean headway time and the density of queued vehicles, aggregates the traffic efficiency and traffic intensity differences in each direction, and uses the formula: ； Calculate the signal timing deviation level, adjust the signal timing plan sequence according to the level range, and build a lighting control rhythm rearrangement plan; in, Indicates the signal timing deviation level. is the green light rate offset, The number of vehicles passing through the green light, is the number of vehicles waiting at the red light, For the Direction queue length, For the Direction green light time, For the Number of vehicles passing in the direction, For the Direction queue density, is the total number of traffic directions; The lighting control synchronization instruction acquisition submodule calls the lighting control rhythm rearrangement scheme, collects the signal start time and synchronization deviation reference value of the current intersection and the adjacent intersection in the smart street light control gateway, determines whether the rearrangement scheme causes cycle dislocation and overlap, and obtains the signal control synchronization adjustment instruction.

6. The road traffic management system based on smart street lamps according to claim 5 is characterized in that: The burst identification module comprises: The vehicle shadow density recognition submodule calls the signal control synchronization adjustment instruction, extracts the vehicle density distribution map, analyzes the pixel aggregation, boundary overlap and lane passage space parameters of the image frame, compares the spacing fluctuations between vehicle shadows in the area, and obtains the vehicle shadow density coefficient value; The speed mutation judgment submodule identifies the original speed information of the vehicle according to the vehicle shadow density coefficient value, extracts the speed jump amplitude and frequency, analyzes the deviation from the median value of the speed fluctuation interval, and obtains the speed jump comparison value; The abnormal section screening submodule extracts the block number, response time sequence and boundary coverage rate involved in the signal control adjustment instruction according to the speed jump comparison value, using the formula: ； Identify the abnormal intensity and map it to the signal control number to obtain the abnormal concentrated response block identifier; in, Represents the abnormal centralized response block identifier, For sub-block In the area The overlap of the car shadows inside For sub-block In the area The minimum speed within For Region The equilibrium value of the response time series, For Region The boundary coverage of Indicates the total number of regions.

7. The road traffic management system based on smart street lamps according to claim 1 is characterized in that: The system also includes a response optimization module: The response optimization module, based on the abnormal concentrated response block identifier, extracts the time difference between the signal intervention delay and the relief result by calling the original signal adjustment record, compares the current block number with the original response time, adjusts the trigger priority sequence, and obtains the signal control instruction for the key traffic area; The traffic key area signal control instruction includes signal response priority, original intervention delay data, and control block number matching value.

8. The road traffic management system based on smart street lamps according to claim 7 is characterized in that: The response optimization module includes: The block identifier extraction submodule extracts the signal response time and the abnormal event number in the smart street light log based on the response block identifier in the abnormal set, identifies the response time period corresponding to the number, and maps and combines it with the road section number to generate a smart street light response block tag set; The signal response delay calculation submodule calls the smart street light response block tag set, extracts the signal intervention start time and relief end time of the corresponding block, analyzes the time difference between the two and matches them to the block number, identifies the corresponding data of the block number and the response delay, and obtains the road section response delay list; The signal control instruction generation submodule rearranges the signal priority sequence according to the delay degree based on the section response delay list and the original reaction time, and obtains the signal control instruction for the key traffic area by combining the adjusted traffic light cycle parameter value with the section number allocation.

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