Signal lamp intelligent control system for road traffic flow regulation
By combining data acquisition, analysis, and intelligent control modules, the traffic light scheme is dynamically adjusted, which solves the shortcomings of the traffic light control system under sudden traffic accidents and realizes precise guidance of accident roads and efficient utilization of non-accident roads.
Patent Information
- Application Number
- CN202511859925.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-10
- Publication Date
- 2026-02-27
AI Technical Summary
Existing traffic light control systems cannot adjust in real time according to changes in traffic flow during sudden traffic accidents, leading to increased vehicle queues on accident-affected roads or wasted resources on non-accident-affected roads, making it difficult to meet the precise management needs in complex traffic scenarios.
The data acquisition module acquires traffic flow data and accident information, the data analysis module assesses the impact of accidents and the road carrying capacity of non-accident roads, the preset adjustment module formulates traffic light adjustment plans, and the intelligent control module performs real-time dynamic adjustments.
It enables precise traffic management of accident-affected roads in the event of a sudden traffic accident, avoiding congestion on non-accident roads and improving traffic efficiency.
Smart Images
Figure CN121583129A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of traffic signal control, in particular to a signal lamp intelligent control system for road traffic flow regulation. BACKGROUND
[0002] The signal lamp control system is a core equipment system for regulating different direction traffic flow and ensuring the order of intersection traffic in road traffic management, and is widely used in various traffic scenes such as urban trunk road intersections, scenic area surrounding roads, mountainous road intersections, etc., especially in areas where traffic converges densely or traffic conditions are complex, and is a key infrastructure for maintaining orderly traffic operation. With the gradual maturity of intelligent transportation Internet of Things application services, signal lamp timing is gradually changed according to traffic flow. In the prior art, when traffic flow regulation is needed, signal lamp regulation can be achieved by adjusting signal lamp timing. Only a simple controller is needed to run according to a preset program, which is suitable for large-scale popularization and application. At the same time, early traffic flow changes are relatively stable, and historical data can better reflect traffic rules at different times, so that the fixed timing scheme can meet the daily traffic management needs. However, with the increasing growth and intensification of traffic flow, the shortcomings of the fixed timing mode become more and more obvious. In the scene of sudden traffic accidents, the fixed timing cannot adjust the time length according to the traffic congestion of the accident road, which easily leads to continuous increase of the queuing vehicles on the accident road, while the non-accident road may be idle or excessively congested due to fixed timing, causing waste of road resources or low traffic efficiency, and even causing chain congestion, which is difficult to meet the precise management needs in complex traffic scenes.
[0003] Therefore, the present application provides a signal lamp intelligent control system for road traffic flow regulation to solve the above problems. SUMMARY
[0004] The purpose of the present application is to provide a signal lamp intelligent control system for road traffic flow regulation to solve the problems raised in the background art.
[0005] The purpose of the present application can be achieved by the following technical solutions: A signal lamp intelligent control system for road traffic flow regulation, characterized by comprising the following modules: A data acquisition module acquires traffic flow data of an accident road and vehicle types and accident severity of vehicles involved in an accident, and acquires traffic flow data of a non-accident road; A data analysis module pre-processes the acquired data, evaluates an accident influence value of the traffic accident according to the pre-processed data, and evaluates an accident bearing value of the non-accident road; The preset adjustment module adjusts the signal lights corresponding to the accident road and the non-accident road according to the accident influence value of the accident road and the accident bearing value of the non-accident road. The intelligent control module dynamically adjusts the signal lights in real time according to the real-time traffic flow data of the accident road and the non-accident road.
[0006] Preferably, the working method of the data acquisition module is as follows: The video stream at the moment of the accident is dynamically analyzed through the road monitoring of the accident road to obtain the type of the accident vehicle and the severity of the accident; The road within a preset distance range before and after the location of the traffic accident is divided into multiple monitoring sections, the traffic flow data of each monitoring section is collected in real time, and the vehicle queue length and the passing speed of the accident road are obtained through the traffic flow data of the multiple monitoring sections; The traffic flow source and destination data of the multiple non-accident roads are obtained according to the traffic flow data of the multiple non-accident roads, and the traffic distribution law is analyzed.
[0007] Preferably, the dynamic analysis process is as follows: Noise filtering and frame rate optimization are performed on the original video stream transmitted by the accident road monitoring equipment to capture the video frame at the moment of the accident; The pixel change rate of adjacent frames in the video stream is compared, the initial trigger frame of the accident occurrence is located, and the trigger frame is taken as the center, a preset frame is traced back and extended forward, and the accident key frame sequence is marked; The contour features of the accident vehicle in each frame are extracted, and the vehicle type identifier in the license plate is identified; The identified vehicle contour and license plate information are compared with the vehicle parameters in the database to determine the type of the accident vehicle; Three-dimensional modeling and deformation calculation are performed on the contour of the accident vehicle in the key frame sequence, the standard model of the vehicle before the accident is compared, the deformation volume ratio of the vehicle body is calculated, and the deformation of the vehicle cab area is obtained; The personnel dynamics in the key frame sequence are identified, if it is detected that there are personnel standing around the accident vehicle and the body movements are normal, it is determined that there is no personnel retention risk; if it is detected that the personnel are lying down, the body movements are not obvious, or the personnel are trapped in the vehicle, it is determined that there is a personnel retention risk.
[0008] Preferably, the working method of the data analysis module is as follows: The data acquisition module obtains various types of data, and the abnormal values are removed through a preset rule, the data in different formats and units are standardized, the time of the accident is taken as the reference point, and the traffic flow data of the accident road and the non-accident road within a fixed time interval before and after the reference point are obtained. Obtain the space influence coefficient, time influence coefficient and traffic flow interference coefficient of the accident road, and obtain the accident influence value of the accident road.
[0009] Obtain the basic bearing capacity, transferable bearing capacity and transfer bearing rate of the non-accident road, and obtain the accident bearing value.
[0010] Preferably, the accident influence value acquisition method is: Calculate the road congestion proportion of the vehicle queue length of each monitoring section and the design passing length of the section, set a weight coefficient according to the distance between each monitoring section and the accident point, and calculate the accident space influence coefficient by weighted summation; Obtain the time influence coefficient according to the accident severity and the estimated processing time, obtain the time correction factor according to the time of the accident, and obtain the accident time influence coefficient according to the time influence coefficient and the time correction factor; Compare the average passing speed of the accident road before and after the accident with the design passing speed of the accident road, calculate the speed attenuation rate, set the vehicle influence weight according to the vehicle type involved in the accident, and obtain the accident traffic flow interference coefficient according to the speed attenuation rate and the vehicle influence weight; Comprehensive accident space influence coefficient, accident time influence coefficient, accident traffic flow interference coefficient to obtain accident influence value.
[0011] Preferably, the accident bearing value acquisition method is: For the non-accident road, calculate the real-time traffic flow and the maximum passing flow designed for the non-accident road to obtain the basic bearing rate; Obtain the lane correction coefficient according to the number of lanes of the non-accident road, and obtain the basic bearing capacity of the non-accident road according to the obtained basic bearing rate and the lane correction coefficient; According to the traffic flow source and destination data of the non-accident road, estimate the proportion of traffic flow moving from the accident road to the non-accident road, and obtain the transferable bearing rate of the non-accident road according to the traffic flow proportion and the remaining passing capacity Obtain the transfer bearing rate according to the transferable bearing flow and the remaining passing capacity, and obtain the accident bearing value through the basic bearing capacity and the transfer bearing rate.
[0012] Preferably, the method for the preset adjustment module to work is: According to the historical accident handling data and traffic flow characteristics, a preset scheme library is constructed; According to the accident influence value and the accident bearing value, obtain the accident influence level and the accident bearing level, and according to the current time period, match the signal light basic adjustment direction corresponding to the current accident influence level and the accident bearing level from the preset scheme library. According to the matched basic adjustment direction, the signal lamp adjustment parameter is set according to the accident influence value for the accident road: If the accident influence value is at a higher level, the red light duration of the accident road needs to be extended; If the accident influence value is at a lower level, the red light and green light durations need to be slightly adjusted.
[0013] According to the signal lamp adjustment direction, the signal lamp corresponding to the non-accident road is adjusted in combination with the accident bearing value of the non-accident road: If the accident bearing value of the non-accident road is at a bearable level, the green light duration is appropriately extended to improve the traffic efficiency; If the bearing value is at a full bearing level, the original green light duration is maintained or slightly adjusted.
[0014] Preferably, the method for the intelligent control module to work is: The real-time traffic flow data of the accident road and the non-accident road is continuously acquired; The acquired real-time traffic flow data of the accident road and the non-accident road is compared with the expected traffic flow corresponding to the preset initial signal lamp timing scheme: If the traffic flow of the accident road is congested, the red light duration is extended; If the traffic flow of the accident road gradually decreases, the green light duration is extended; If the traffic flow of the non-accident road increases, the green light duration needs to be shortened; If the traffic flow of the non-accident road is less, the green light duration is extended.
[0015] The present application has the following advantages: 1. The present application acquires the traffic flow data of the accident road and the non-accident road and the accident related information, and then evaluates the accident influence value and the non-accident road bearing value after data preprocessing by the data analysis module, formulates the initial signal lamp timing scheme by the preset adjustment module in combination with the above evaluation results, and finally dynamically adjusts the signal lamp according to the real-time traffic flow data by the intelligent control module, effectively solving the problem that the signal lamp timing in the prior art is mostly fixed mode, which easily leads to congestion of the non-accident road or untimely relief of the accident road, and realizing accurate relief of the traffic flow of the accident road in the accident scene.
[0016] Of course, any product implementing the present application does not necessarily need to achieve all the advantages described above. BRIEF DESCRIPTION OF DRAWINGS
[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments, obviously, the drawings in the following description are only some of the embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.
[0018] Figure 1 A perspective view of a signal lamp intelligent control system for road traffic flow regulation according to the present application. DETAILED DESCRIPTION
[0019] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application, obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0020] Please refer to Figure 1 The present application is a signal lamp intelligent control system for road traffic flow regulation, which comprises the following modules: A data acquisition module acquires traffic flow data of an accident road and vehicle types and accident severity of vehicles involved in the accident, and acquires traffic flow data of non-accident roads. The data acquisition module is mainly used for dynamic analysis of video streams at the moment of accident occurrence through road monitoring, acquisition of vehicle types involved in the accident and accident severity, division of the road within a certain distance before and after the location of the traffic accident into multiple monitoring sections, collection of traffic flow data of each monitoring section, further acquisition of the length of vehicle queue and the speed of vehicle passing on the accident road according to the traffic flow data of each monitoring section, and comprehensive grasp of the traffic operation state of the accident road; at the same time, continuous collection of traffic flow data of each non-accident road, and analysis of the source direction and the destination direction of vehicle flow on the non-accident road according to the traffic flow data of the non-accident road.
[0021] Specifically, the original video stream transmitted by the road monitoring device is subjected to noise filtering and frame rate optimization, video interference caused by rain, fog or lens stains is removed, and the video frame rate is improved to capture the video frames at the moment of accident occurrence more clearly. The pixel change rate of adjacent frames in the video stream is compared, because the pixel change rate is relatively stable under normal driving state, and the pixel change rate will obviously rise sharply when the accident occurs, so that the initial trigger frame of accident occurrence can be located. Taking the trigger frame as the center, ten video frames are traced back and twenty-one video frames are extended backward, and these frames are marked as the accident key frame sequence, which completely covers the vehicle driving state before the accident and the scene after the accident.
[0022] Extract the contour features of the accident vehicle in each frame from the accident key frame sequence, including the length, width, height ratio of the vehicle body, etc., while identifying the vehicle type identification in the license plate, such as the color features or specific letter marks of different types of vehicle license plates, and then comparing the identified vehicle contour with the license plate information and the vehicle parameters stored in the database, which contains detailed information such as the brand, model, body size and rated load of various vehicles. Through comparison, possible recognition bias can be corrected, such as when the contour of a partially modified vehicle is abnormal, the real vehicle type can be accurately determined with the help of database parameters to determine the type of vehicle involved in the accident.
[0023] Three-dimensional modeling and deformation calculation are performed on the contour of the accident vehicle in the key frame sequence, and the monitoring screen is converted into a three-dimensional vehicle model. The standard three-dimensional model of the accident vehicle before the accident is compared, which can be retrieved from the vehicle registration database. The deformation volume ratio of the vehicle body is calculated, and the damage degree of the vehicle is reflected by the size of the deformation volume ratio. According to the deformation of the vehicle cab area, if the cab deformation ratio is too high, it may indicate a risk of personnel injury. The dynamic of the key frame sequence is identified to determine the state of the personnel around the accident vehicle. If a person is standing and the limbs are moving normally, such as being able to make a phone call or wave for help, it is determined that there is no risk of personnel remaining. If a person is lying down and the limbs are not moving significantly, or if a person is trapped in the vehicle and cannot leave on their own through the window glass reflection, it is determined that there is a risk of personnel remaining. The severity of the accident is determined by combining the deformation of the vehicle and the dynamic state of the personnel. If the vehicle body deformation volume is large, the cab deformation ratio is too high, and there is a risk of personnel remaining, it is determined to be extremely serious. If the vehicle body deformation volume is small, the cab deformation ratio is too high, and there is a risk of personnel remaining, it is determined to be relatively serious. If the vehicle body deformation volume is small, the cab deformation ratio is small, and there is no risk of personnel remaining, it is determined to be generally serious. The type of accident vehicle and the severity of the accident are obtained. The road within a certain distance before and after the location of the accident is divided into multiple monitoring sections. Real-time traffic flow data of each monitoring section is collected, including the number of vehicles passing through the section per unit time, etc. The length of the vehicle queue on the accident road and the speed of the vehicle passing through are obtained according to the traffic flow data of multiple monitoring sections. The queue length is used to reflect the congestion range caused by the accident, and the passing speed is used to reflect the impact of the accident on the road passing efficiency. For non-accident roads, traffic flow data of each non-accident road is continuously collected, including the number of vehicles passing through and the speed of vehicle travel at different times, etc. Then, the traffic flow source direction and destination direction of the non-accident road are analyzed in depth according to these flow data to determine whether the non-accident road has the ability to accommodate the traffic transferred from the accident road.
[0024] The data analysis module pre-processes the obtained data, and evaluates the accident influence value of the traffic accident and the accident bearing value of the non-accident road according to the pre-processed data; The data analysis module pre-processes the obtained data, and evaluates the accident influence value of the traffic accident and the accident bearing value of the non-accident road according to the pre-processed data; The data analysis module pre-processes the obtained data, and evaluates the accident influence value of the traffic accident and the accident bearing value of the non-accident road according to the pre-processed data;
[0025] The data analysis module pre-processes the obtained data, and evaluates the accident influence value of the traffic accident and the accident bearing value of the non-accident road according to the pre-processed data; The speed decay rate is calculated by comparing the average traffic speed of the accident road before and after the accident with the design traffic speed of the road. The higher the speed decay rate, the greater the impact of the accident on vehicle traffic speed, and the slower the traffic flow. According to the type of vehicle involved in the accident, a corresponding vehicle impact weight is set. Different types of vehicles have different levels of interference with traffic flow. For example, large trucks have a larger volume, slower turning and starting speed, and therefore have a higher weight than small cars. The speed decay rate is multiplied by the vehicle impact weight to obtain the accident traffic interference coefficient, which reflects the interference of the accident vehicle with the surrounding normal traffic flow. The accident impact value is calculated by weighting and summing the accident spatial influence coefficient, the accident time influence coefficient and the accident traffic interference coefficient.
[0026] For each non-accident road, the ratio of the real-time traffic flow to the maximum traffic flow designed for the road is calculated to obtain the basic bearing rate. The higher the basic bearing rate, the closer the current road traffic is to the saturation state. According to the number of lanes of the non-accident road, the corresponding lane correction coefficient is obtained. The more lanes a road has, the stronger its traffic capacity, and the higher the correction coefficient. The basic bearing rate is multiplied by the lane correction coefficient to obtain the basic bearing capacity of the non-accident road. The basic bearing capacity reflects the basic traffic bearing level of the non-accident road under the current state. According to the traffic source and destination data of the non-accident road, the proportion of traffic moving from the accident road to the non-accident road is analyzed and estimated. The remaining traffic capacity of the non-accident road is calculated by subtracting the real-time traffic flow from the maximum traffic flow designed for the road. The remaining traffic capacity is the additional traffic space that the road can accommodate. According to the estimated traffic transfer proportion and the remaining traffic capacity, the transferable traffic of the non-accident road can be calculated. By comparing the transferable traffic with the remaining traffic capacity, the transfer bearing rate is obtained, which reflects the ability of the non-accident road to additionally accommodate accident transfer traffic under the current state.
[0027] The accident bearing value of each non-accident road is obtained by multiplying the basic bearing capacity and the transfer bearing rate of the non-accident road. The accident bearing value reflects the overall bearing capacity of the non-accident road under the accident scenario. According to the calculation result, the bearing level is divided. The accident bearing value is low, the accident bearing value is medium, and the accident bearing value is high. After calculating the bearing degree of all non-accident roads, the maximum value is selected as the overall accident bearing degree of the non-accident road.
[0028] A preset adjustment module adjusts the signal lights corresponding to the accident road and the non-accident road according to the accident impact value of the accident road and the accident bearing value of the non-accident road. The preset adjustment module is used to preset adjust the traffic lights corresponding to accident-affected and non-accident-affected roads based on the accident impact value and accident capacity value. Based on historical accident handling data and traffic flow characteristics at different times, a preset scheme library is constructed, containing traffic light adjustment schemes for various scenarios. The scheme library covers traffic light adjustment strategies corresponding to different accident impact levels, different accident capacity levels, and different time periods. The accident impact level is determined based on the accident impact value, and the accident capacity level is determined based on the accident capacity value. Considering the current time period, the module matches the basic traffic light adjustment direction corresponding to the current level and time period from the preset scheme library. For the accident-affected road, the traffic light adjustment parameters are set according to the specific level of the accident impact value. If the accident impact value is at a high level, it indicates that the accident has a significant impact on the accident-affected road. Traffic flow is significantly affected, requiring extended red light durations on the affected roads to reduce the number of vehicles entering the accident area and prevent further queuing. If the accident impact is at a low level, only minor adjustments to red and green light durations are needed to manage traffic flow while minimizing disruption to normal traffic. For non-accident roads, the signal light adjustments should be made based on the matched baseline adjustment direction and the accident load factor of the non-accident roads. If the accident load factor of the non-accident roads is within the acceptable range, it means that the road can handle some of the traffic diverted from the accident roads, and the green light duration can be appropriately extended to improve traffic efficiency and facilitate diversion. If the load factor is at the full load level, it indicates that the current traffic flow on the road is close to saturation, and the original green light duration should be maintained or only slightly adjusted to prevent excessive increase in traffic flow and congestion.
[0029] The intelligent control module dynamically adjusts the traffic lights in real time based on real-time traffic flow data for accident-prone and accident-free roads.
[0030] By continuously receiving real-time traffic flow data from the data acquisition module for accident-affected and non-accident-affected roads, including vehicle speed, queue conditions, and the number of vehicles passing through per unit time, this data is dynamically monitored to track the traffic flow trends on both roads in real time. The focus is on whether the queue length on the accident-affected road continues to increase or whether the speed further decreases, and whether there is a sudden increase in traffic flow on the non-accident-affected road that reduces traffic efficiency. This provides real-time data support for subsequent judgments on whether traffic lights need to be adjusted.
[0031] By combining the monitored real-time traffic flow data and comparing it with the expected traffic flow corresponding to the initial signal timing scheme output by the preset adjustment module, if the real-time traffic flow on the accident road exceeds expectations, the queue length continues to increase, or the passage speed decreases significantly, it indicates that the current signal timing cannot effectively manage the traffic flow on the accident road and there is a need for adjustment. If the real-time traffic flow on non-accident roads exceeds expectations, resulting in slow traffic or queues, it indicates that the current timing may cause congestion on non-accident roads and needs to be adjusted accordingly. If the traffic flow of the two roads is in the expected range and the traffic condition is stable, it is determined that the signal light does not need to be adjusted temporarily.
[0032] If the traffic flow of the accident road is congested, the red light duration is appropriately prolonged to reduce the number of vehicles entering the accident area, and the green light duration is appropriately shortened according to the reduction of the traffic flow to avoid excessive queuing of vehicles in the accident area. If the traffic flow of the accident road gradually reduces and the traffic condition improves, the red light duration can be gradually shortened, the green light duration is prolonged, and the normal traffic rhythm is gradually restored. If the traffic flow of the non-accident road increases and the bearing pressure increases, the green light duration needs to be shortened to control the traffic flow to prevent congestion. If the traffic flow of the non-accident road is small and there is still bearing space, the green light duration can be appropriately prolonged to improve the traffic efficiency and help to divert the traffic flow of the accident road. The above is only an example and description of the concept of the present application. Those skilled in the art can make various modifications or supplements to the described specific embodiments or use similar ways to replace them, as long as they do not deviate from the concept of the present application or exceed the scope defined by the present claims.
Claims
1. A signal light intelligent control system for road traffic flow regulation, characterized in that, The application comprises the following modules: a data acquisition module, which acquires traffic flow data of an accident road and vehicle types and accident severity of a vehicle involved in an accident, and acquires traffic flow data of a non-accident road; a data analysis module, which pre-processes various data acquired, and evaluates an accident influence value of a traffic accident and an accident bearing value of a non-accident road according to the pre-processed data; a preset adjustment module, which pre-sets adjustment of a signal lamp corresponding to the accident road and the non-accident road according to the accident influence value of the accident road and the accident bearing value of the non-accident road; an intelligent control module, which dynamically adjusts the signal lamp in real time according to real-time traffic flow data of the accident road and the non-accident road.
2. The intelligent signal control system for road traffic flow regulation as claimed in claim 1 wherein, The method for the data acquisition module to work is as follows: dynamically analyzing a video stream at an accident occurrence moment through road monitoring of the accident road, and acquiring vehicle types and accident severity of the accident road; dividing a road within a preset distance range before and after a traffic accident occurrence position into multiple monitoring road sections, and acquiring traffic flow data of each monitoring road section in real time, and acquiring vehicle queue length and passing speed of the accident road through the traffic flow data of the multiple monitoring road sections; acquiring vehicle source and destination data of multiple non-accident roads according to traffic flow data of the multiple non-accident roads, and analyzing vehicle flow distribution rules.
3. The signal light intelligent control system for road traffic flow regulation according to claim 2, characterized in that, The process of the dynamic analysis is as follows: performing noise filtering and frame rate optimization on original video streams transmitted by accident road monitoring equipment, and capturing video frames at the accident occurrence moment; comparing pixel change rates of adjacent frames in the video stream, locating an initial trigger frame of the accident occurrence, and marking a sequence of key frames as a center of the trigger frame, preset frames are traced back and extended forward; extracting contour features of the accident vehicle in each frame, and identifying vehicle type identifiers in license plates; comparing the identified vehicle contour and license plate information with vehicle parameters in a database, and determining the vehicle type; performing three-dimensional modeling and deformation calculation on the contour of the accident vehicle in the key frame sequence, comparing a standard model of the vehicle before the accident, calculating a body deformation volume ratio, and acquiring deformation of a vehicle cab area; identifying personnel dynamics in the key frame sequence, and determining no personnel retention risk if it is detected that personnel stand around the accident vehicle and have normal limb movements, and determining personnel retention risk if it is detected that personnel fall down, have no obvious limb movements, or are trapped in the vehicle.
4. The intelligent signal control system for road traffic flow regulation as claimed in claim 1 wherein, The method for the data analysis module to work is as follows: performing outlier rejection on various data acquired by the data acquisition module through a preset rule, performing standardized conversion on data in different formats and units, taking a time of the accident occurrence as a reference point, and acquiring traffic flow data of the accident road and the non-accident road within a fixed time interval before and after the reference point; acquiring a space influence coefficient, a time influence coefficient, and a vehicle flow interference coefficient of the accident road, and acquiring the accident influence value of the accident road; acquiring a basic bearing capacity, a transferable traffic flow, and a transfer bearing rate of the non-accident road, and acquiring the accident bearing value.
5. The intelligent signal control system for road traffic flow regulation as claimed in claim 1 wherein, The method for acquiring the accident influence value is as follows: The road congestion proportion of each monitoring road section is calculated by obtaining the vehicle queue length of the road section and the designed passing length of the road section, a weight coefficient is set according to the distance between each monitoring road section and the accident point, and the accident space influence coefficient is calculated by weighted summation; The time influence coefficient is obtained according to the accident severity and the estimated processing time, the time correction factor is obtained according to the time of the accident, and the accident time influence coefficient is obtained according to the time influence coefficient and the time correction factor; The speed attenuation rate is calculated by comparing the average passing speed of the accident road before and after the accident with the designed passing speed of the accident road, the vehicle influence weight is set according to the vehicle type involved in the accident, and the accident vehicle flow interference coefficient is obtained according to the speed attenuation rate and the vehicle influence weight; The accident influence value is obtained by comprehensively considering the accident space influence coefficient, the accident time influence coefficient and the accident vehicle flow interference coefficient.
6. The intelligent signal control system for road traffic flow regulation as claimed in claim 2 wherein, The accident bearing value acquisition method is: For the non-accident road, the basic bearing rate is calculated by obtaining the real-time traffic flow and the designed maximum passing flow of the non-accident road; The lane correction coefficient is obtained according to the number of lanes of the non-accident road, and the basic bearing capacity of the non-accident road is obtained according to the obtained basic bearing rate and the lane correction coefficient; According to the traffic flow source and destination data of the non-accident road, the proportion of traffic flow moving from the accident road to the non-accident road is estimated, and the transferable bearing rate of the non-accident road is obtained according to the traffic flow proportion and the residual passing capacity The transferable bearing rate is obtained according to the transferable flow and the residual passing capacity, and the accident bearing value is obtained by the basic bearing capacity and the transferable bearing rate.
7. The intelligent signal control system for road traffic flow regulation as claimed in claim 6 wherein, The method for the preset adjustment module to work is: According to the historical accident handling data and traffic flow characteristics, a preset scheme library is constructed; According to the accident influence value and the accident bearing value, the accident influence level and the accident bearing level are obtained, and according to the current time period, the signal light basic adjustment direction corresponding to the current accident influence level and the accident bearing level is matched from the preset scheme library; According to the matched basic adjustment direction, for the accident road, the signal light adjustment parameter is set according to the accident influence value: If the accident influence value is at a high level, the red light time of the accident road needs to be extended; If the accident influence value is at a low level, the red light and green light time needs to be slightly adjusted; According to the signal light adjustment direction as a reference, the signal light corresponding to the non-accident road is adjusted in combination with the accident bearing value of the non-accident road: If the accident bearing value of the non-accident road is at a bearable level, the green light time is appropriately extended to improve the passing efficiency; If the bearing value is at a full bearing level, the original green light time is maintained or slightly adjusted.
8. The intelligent signal control system for road traffic flow regulation as claimed in claim 1 wherein, The method for the intelligent control module to work is: The real-time traffic flow data of the accident road and the non-accident road is continuously obtained; The obtained real-time traffic flow data of the accident road and the non-accident road is compared with the expected traffic flow corresponding to the preset initial signal light timing scheme: If the traffic congestion of the accident road is aggravated, the red light time is extended; If the traffic flow of the accident road gradually decreases, the green light time is extended; If the traffic flow of the non-accident road increases, the green light time needs to be shortened; If the non-accident road traffic is light, extend the green light duration.