Queuing overflow control method and system in second-level adaptive control system

Abstract

A queuing overflow control method and system in a second-level adaptive control system relates to the field of electrical digital data processing, and the method comprises the following steps: collecting real-time traffic data of each entrance lane, calculating a queuing proportion and a vehicle accumulation rate of each entrance lane according to the real-time traffic data, and calculating a queuing overflow value of each entrance lane; the traffic state of each entrance lane is divided into a normal state, an early warning state and a dangerous state according to a preset state threshold value; when the traffic state is in an early warning state, generating a traffic flow time sequence curve according to the real-time traffic data; extracting traffic flow characteristic parameters from the traffic flow time sequence curve, arranging the traffic flow characteristic parameters according to the numbers of the entrance lanes to construct a traffic flow characteristic matrix, analyzing a traffic flow complementary relation between the entrance lanes based on the traffic flow characteristic matrix, and calculating phase compensation parameters according to the traffic flow complementary relation; and adjusting the signal timing sequence according to the phase compensation parameter. By implementing the method, the balance of resource utilization in the traffic entrance can be improved.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to a queue overflow control method and system in a second-level adaptive control system. Background Art

[0002] With the acceleration of urbanization and the continuous growth of motor vehicle ownership, urban traffic congestion has become increasingly prominent. As an important means to alleviate traffic congestion, the control effect of urban traffic signal control system directly affects the efficiency of urban road traffic and the quality of traffic operation.

[0003] In related technologies, fixed timing schemes or adaptive control schemes based on historical data can be used. These control schemes adjust the traffic flow of vehicles at intersections through pre-set parameters such as cycle and green signal ratio, or perform periodic signal timing optimization based on traffic flow data in historical databases, thereby achieving traffic flow diversion.

[0004] However, due to the short-term sudden fluctuations in traffic flow, the signal timing scheme cannot respond to the instantaneous changes in traffic flow in a timely manner. At the same time, the dynamic changes in traffic flow between the entrances have not been fully utilized, resulting in queue overflows at some entrances while other entrances waste green light resources. Summary of the invention

[0005] The present application provides a queue overflow control method and system in a second-level adaptive control system, which are used to improve the balance of resource utilization at a traffic entrance.

[0006] In a first aspect, the present application provides a queue overflow control method in a second-level adaptive control system, which is applied to a second-level adaptive control system, and the method includes: collecting real-time traffic data of each entrance road, the real-time traffic data including vehicle queue length, vehicle arrival rate and vehicle departure rate; calculating the queue ratio and vehicle accumulation rate of each entrance road according to the real-time traffic data, and dividing the traffic state of each entrance road into normal state, warning state and dangerous state according to a preset state threshold; when the traffic state is in the warning state, generating a traffic flow timing curve according to the real-time traffic data; extracting traffic flow characteristic parameters from the traffic flow timing curve, the traffic flow characteristic parameters including maximum traffic flow, minimum traffic flow, maximum occurrence time and minimum occurrence time; arranging the traffic flow characteristic parameters according to the number of the entrance road to construct a traffic flow characteristic matrix; analyzing the traffic flow complementary relationship between the entrance roads based on the traffic flow characteristic matrix, and calculating the phase compensation parameter according to the traffic flow complementary relationship; adjusting the signal timing sequence according to the phase compensation parameter.

[0007] By adopting the above technical solution, the traffic state is divided by collecting traffic data in real time and calculating the queue ratio and vehicle accumulation rate, generating a traffic flow time series curve in the early warning state and extracting characteristic parameters to construct a characteristic matrix. This characteristic matrix effectively captures the short-term sudden fluctuation characteristics of traffic flow, and by analyzing the complementary relationship between the traffic flow of the entrance lanes, the phase compensation parameters are calculated to dynamically adjust the signal timing, making full use of the dynamic change law of the traffic flow between the entrance lanes, avoiding the problem of queue overflow in some entrance lanes and waste of green light resources in other entrance lanes.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of analyzing the complementary relationship of traffic flow between entrance lanes based on the traffic flow characteristic matrix and calculating the phase compensation parameter according to the complementary relationship of traffic flow specifically includes: arranging the traffic flow characteristic matrix in chronological order to form a time series matrix; calculating the traffic flow correlation coefficient between any two entrance lanes according to the time series matrix; determining the entrance lane combination whose traffic flow correlation coefficient is less than a preset threshold as the entrance lane combination with a complementary relationship; calculating the time interval between the moment of maximum traffic flow of the first entrance lane and the moment of minimum traffic flow of the second entrance lane in the entrance lane combination with a complementary relationship; using the time interval as the initial value of the phase compensation parameter; and weightedly correcting the initial value of the phase compensation parameter according to the ratio of the maximum traffic flow of the first entrance lane to the minimum traffic flow of the second entrance lane to obtain the phase compensation parameter.

[0009] By adopting the above technical solution, the traffic feature matrix is ​​arranged in chronological order to form a time series matrix, the traffic flow correlation coefficient between the entrance lanes is calculated, and the complementary relationship entrance lane combination is determined. Based on the time interval between the maximum and minimum traffic flow moments as the initial value, combined with the traffic flow ratio, weighted correction is performed to obtain more accurate phase compensation parameters, optimize the timing adjustment accuracy of signal timing, and make traffic flow conversion smoother.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the step of adjusting the signal timing sequence according to the phase compensation parameter specifically includes: subtracting the phase compensation parameter from the current phase difference to obtain a target phase difference; adjusting the difference between the phase start time of the first entrance road and the phase start time of the second entrance road in the entrance road combination with a complementary relationship to the target phase difference; setting the first green light phase duration according to the maximum vehicle flow rate of the first entrance road, and setting the second green light phase duration according to the ratio of the maximum vehicle flow rate to the minimum vehicle flow rate of the second entrance road; allocating the remaining duration to the green light phases of the third entrance road and the fourth entrance road according to the proportion of the maximum vehicle flow rate; determining the red light phase duration of each entrance road according to the difference in the phase start time, and the sum of the green light phase duration and the red light phase duration of each entrance road is equal to the total cycle duration.

[0011] By adopting the above technical solution, the phase start time of the complementary entrance lane is adjusted according to the target phase difference, the first green light phase duration is set according to the maximum traffic flow, and the second green light phase duration is set according to the ratio of the maximum and minimum traffic flow. The remaining duration is allocated to other entrance lanes in proportion, realizing the dynamic optimization allocation of the green light phase duration and improving the scientificity and rationality of the signal timing plan.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of calculating the queue ratio and vehicle accumulation rate of each entrance road based on the real-time traffic data, and dividing the traffic state of each entrance road into normal state, warning state and dangerous state according to a preset state threshold, the method also includes: when the traffic state is in the normal state, obtaining the vehicle gap acceptance rate of the entrance road; when the vehicle gap acceptance rate is greater than the preset acceptance rate threshold, shortening the signal timing sequence by a preset time length; when the vehicle gap acceptance rate is not greater than the preset acceptance rate threshold, extending the signal timing sequence by a preset time length.

[0013] By adopting the above technical solution, the vehicle gap acceptance rate of the entrance lane is obtained under normal conditions, and the length of the signal timing sequence is dynamically adjusted according to the comparison result with the preset acceptance rate threshold. When the acceptance rate is greater than the threshold, the timing sequence is shortened, and when it is less than the threshold, the timing sequence is extended, realizing the refined adaptive adjustment of signal timing, improving traffic efficiency and reducing vehicle waiting time.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of extending the signal timing sequence for a preset time length when the vehicle gap acceptance rate is not greater than the preset acceptance rate threshold, the method also includes: real-time monitoring of the changing trends of the queuing ratio and the vehicle accumulation rate; when it is detected that the queuing ratio continues to rise in multiple consecutive signal cycles and exceeds the first warning threshold, switching the traffic state to the warning state.

[0015] By adopting the above technical solution, the changing trends of the queue ratio and vehicle accumulation rate are monitored in real time. When the queue ratio continues to rise in multiple consecutive signal cycles and exceeds the first warning threshold, the traffic state is switched to the warning state in a timely manner. A dynamic state switching mechanism is established to accurately grasp the evolution of traffic state and ensure the timely responsiveness of the traffic control system.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of calculating the queue ratio and vehicle accumulation rate of each entrance road based on the real-time traffic data, and dividing the traffic state of each entrance road into a normal state, a warning state and a dangerous state according to a preset state threshold, the method also includes: when the traffic state is in the dangerous state, calculating the vehicle saturation of each entrance road, the vehicle saturation being equal to the product of the vehicle arrival rate and the vehicle gap acceptance rate; prioritizing the entrance roads according to the vehicle saturation; and extending the green light period of the highest priority entrance road to a preset time.

[0017] By adopting the above technical solution, the vehicle saturation of each entrance lane is calculated in a dangerous state, and the priority is sorted based on the product of the vehicle arrival rate and the gap acceptance rate, and the green light period of the highest priority entrance lane is extended to the preset time. A dynamic priority allocation mechanism based on saturation is constructed, which effectively alleviates the traffic pressure on the entrance lanes with high traffic congestion.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of extending the green light period of the highest priority entrance lane to a preset time, the method further includes: when the queuing ratio decreases below a second warning threshold and the vehicle accumulation rate shows a downward trend, and the vehicle gap acceptance rate is within the preset normal range, switching the traffic state to the warning state.

[0019] By adopting the above technical solution, the monitoring queue ratio is reduced to below the second warning threshold and the vehicle accumulation rate is on a downward trend. At the same time, combined with whether the vehicle gap acceptance rate is within the normal range, the traffic state is automatically switched from a dangerous state to a warning state. A joint judgment mechanism of multi-dimensional indicators is established to accurately identify the turning point of traffic condition improvement, achieve a smooth transition of traffic control status, and avoid fluctuations in traffic efficiency caused by frequent switching of control status.

[0020] In a second aspect, an embodiment of the present application provides a second-level adaptive control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the second-level adaptive control system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when the computer program product runs on a second-level adaptive control system, enables the second-level adaptive control system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the instructions are executed on a second-level adaptive control system, the second-level adaptive control system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It can be understood that the second-level adaptive control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application divides traffic states by collecting traffic data in real time and calculating queue ratios and vehicle accumulation rates, generates traffic flow time series curves in the early warning state, and extracts characteristic parameters to construct a characteristic matrix. This characteristic matrix effectively captures the short-term sudden fluctuation characteristics of traffic flow, and by analyzing the complementary relationship between traffic flows in the entrance lanes, calculates phase compensation parameters to dynamically adjust signal timing, and fully utilizes the dynamic change law of traffic flows between the entrance lanes, avoiding the coexistence of queue overflows in some entrance lanes and waste of green light resources in other entrance lanes.

[0025] 2. This application arranges the traffic feature matrix in chronological order to form a time series matrix, calculates the traffic flow correlation coefficient between the entrance lanes, and determines the complementary relationship entrance lane combination. Based on the time interval between the maximum and minimum traffic flow moments as the initial value, combined with the traffic flow ratio, weighted correction is performed to obtain more accurate phase compensation parameters, optimize the timing adjustment accuracy of signal timing, and make traffic flow conversion smoother.

[0026] 3. This application adjusts the phase start time of the complementary entrance lane according to the target phase difference, sets the first green light phase duration according to the maximum traffic flow, and sets the second green light phase duration according to the ratio of the maximum and minimum traffic flow. The remaining duration is allocated to other entrance lanes in proportion, realizing the dynamic optimization allocation of the green light phase duration and improving the scientificity and rationality of the signal timing plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a queue overflow control method in a second-level adaptive control system in an embodiment of the present application; Figure 2 It is another flow chart of the queue overflow control method in the second-level adaptive control system in the embodiment of the present application; Figure 3It is a schematic diagram of the structure of a physical device of a second-level adaptive control system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] At a busy intersection, severe traffic congestion often occurs during peak hours. The east-west main road and the north-south secondary road intersect at this intersection, and the traffic flow distribution during peak hours is extremely uneven. During the morning peak hours, the traffic flow on the eastbound entrance suddenly increases, and the queue length quickly grows to 300 meters. At the same time, the traffic flow on the southbound entrance is sparse, and a large amount of green light time is not fully utilized. During the noon period, the situation is reversed, and the traffic flow on the southbound entrance increases sharply, while the traffic flow on the eastbound entrance drops significantly. This short-term and drastic fluctuation in traffic flow makes it difficult for the fixed timing plan to respond in time, causing some entrances to overflow and other entrances to waste green light resources. In particular, when the queue length of a certain entrance exceeds 300 meters, it will also cause traffic obstruction at the upstream intersection, triggering a chain reaction. In this case, there is an urgent need for an adaptive control method that can quickly respond to traffic flow mutations and make full use of the green light resources of each entrance.

[0032] In the current traffic signal control system, two solutions are usually used to deal with the above problems. The first is to use a fixed timing scheme, which pre-sets the signal timing scheme for the morning, midday and evening periods. For example, during the morning peak period, the green light duration of the eastbound entrance lane is 60 seconds, and that of the southbound lane is 30 seconds; during the noon period, the eastbound lane is 40 seconds, and the southbound lane is 50 seconds. However, this solution cannot cope with sudden traffic fluctuations outside the preset period. The second is an adaptive control scheme based on historical data. The system counts traffic flow data every 5 minutes, predicts the traffic flow trend for the next 5 minutes based on historical data of the same period, and dynamically adjusts the signal timing. However, when sudden traffic fluctuations occur, such as an emergency causing a sharp increase of 200% in traffic flow on a certain entrance lane within 1 minute, the 5-minute response cycle is significantly delayed, and the signal timing cannot be adjusted in time, resulting in queue overflow. At the same time, both solutions fail to fully utilize the complementary characteristics of traffic flow between entrance lanes, resulting in low overall system efficiency.

[0033] After adopting the second-level adaptive control system of this application, the operation effect of the intersection has been significantly improved. The system collects traffic data every 20 seconds through a video detector to quickly perceive changes in traffic conditions. When the traffic volume on the eastbound entrance suddenly increases, the system immediately calculates that the queuing ratio exceeds 0.6, triggering the early warning state. Subsequently, the system analysis found that the traffic volume on the southbound entrance was at a low point, and the correlation coefficient between the two entrances was -0.8, confirming that there was an obvious complementary relationship. The system calculated the optimal phase compensation parameter of 15 seconds, advanced the start time of the eastbound green light, and adjusted the eastbound green light duration to 55 seconds and the southbound green light to 35 seconds according to the traffic ratio. This second-level adjustment based on the complementary relationship between traffic flow allows the eastbound queuing vehicles to dissipate quickly within 2-3 cycles, avoiding queue overflow, and at the same time the green light resources on the southbound entrance are reasonably utilized. Practice has shown that this solution can reduce the average delay at the intersection by 25% and improve traffic efficiency by 30%.

[0034] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a queue overflow control method in a second-level adaptive control system in an embodiment of the present application.

[0035] S101. Collect real-time traffic data of each entrance, where the real-time traffic data includes vehicle queue length, vehicle arrival rate, and vehicle departure rate.

[0036] Among them, the entrance road refers to the vehicle traffic section connected to the intersection. Real-time traffic data refers to the data set reflecting the current traffic operation status obtained by the detection equipment. The vehicle queue length refers to the total length of the vehicle queue waiting to pass through the intersection, measured in meters. The vehicle arrival rate refers to the number of vehicles arriving at the intersection per unit time, measured in vehicles per second. The vehicle departure rate refers to the number of vehicles leaving the intersection per unit time, measured in vehicles per second. The detection equipment includes coil detectors, video detectors, radar detectors, etc. The traffic data collection cycle is usually set to 20 seconds.

[0037] This step is executed after the system is started and the initial configuration is completed. It is used to continuously obtain traffic operation status data. Specifically, the system first checks the working status of each detection device to confirm that the data acquisition channel is normal. Then, according to the preset sampling period, the detection data of each entrance is synchronously collected. For the length of the vehicle queue, it is calculated by video image analysis or coil occupancy time. For the vehicle arrival rate, the upstream detector records the number of passing vehicles and divides it by the time interval. For the vehicle departure rate, the downstream detector records the number of passing vehicles and divides it by the time interval.

[0038] In some embodiments, the collection and processing of traffic data can be achieved in a variety of ways: Optionally, a video detection solution is used: deploy high-definition cameras to collect vehicle images, use deep learning algorithms to identify and track vehicles, extract vehicle location and speed information, calculate queue length and flow parameters, and transmit the processing results to the controller; Optionally, a coil detection solution is used: bury induction coils in the road surface, collect coil impedance change signals through detectors, calculate vehicle occupancy time and passing time, combine with pre-calibrated parameter models, convert queue length and flow data, and upload the data to the controller. It is understandable that other methods such as radar detection and ultrasonic detection can also be used to collect traffic data, which are not limited here.

[0039] S102: Calculate the queue ratio and vehicle accumulation rate of each entrance road according to the real-time traffic data, and classify the traffic state of each entrance road into a normal state, a warning state and a dangerous state according to a preset state threshold.

[0040] Among them, the queue ratio refers to the ratio of the length of the vehicle queue to the length of the road, which is used to measure the degree of road congestion. The vehicle accumulation rate refers to the ratio of the vehicle arrival rate to the departure rate, which is used to measure the degree of traffic backlog. The preset state thresholds include the first warning threshold and the second warning threshold, which are used to classify the traffic state level. The normal state indicates that the traffic flow is running smoothly. The warning state indicates that the traffic flow is congested. The dangerous state indicates that traffic congestion has formed. The state classification cycle is usually set to 60 seconds.

[0041] This step is performed after the completion of traffic data collection and is used to evaluate the traffic operation status. Specifically, the system first obtains the total road length L0 and the current queue length L1 of each entrance road, and calculates the queue ratio r1=L1 / L0. Then obtain the vehicle arrival rate f1 and the departure rate f2, and calculate the vehicle accumulation rate r2=f1 / f2. Compare the calculated r1 with the first warning threshold 0.6 and the second warning threshold 0.8, and compare r2 with the first accumulation rate threshold 1.2 and the second accumulation rate threshold 1.5. When r1<0.6 and r2<1.2, it is judged to be in a normal state, when 0.6≤r1<0.8 or 1.2≤r2<1.5, it is judged to be in a warning state, and when r1≥0.8 or r2≥1.5, it is judged to be in a dangerous state.

[0042] In some embodiments, the calculation and division of traffic status can be achieved in a variety of ways: optionally, using a fuzzy evaluation scheme: establishing a fuzzy set of queuing ratios and cumulative rates, designing a membership function, calculating the membership value of each state, and determining the traffic status according to the maximum membership principle; optionally, using a comprehensive index scheme: introducing supplementary indicators such as vehicle speed and occupancy rate, obtaining a comprehensive score through weighted summation, setting the score interval to correspond to different traffic states, and achieving state division. It is understandable that other methods such as neural network evaluation and expert system can also be used to determine the traffic status, which is not limited here.

[0043] S103: When the traffic state is in the warning state, a vehicle flow time series curve is generated according to the real-time traffic data.

[0044] Among them, the warning state refers to the traffic operation state in which the queue ratio exceeds the first warning threshold but does not reach the second warning threshold. Real-time traffic data refers to the data set obtained by the detection equipment that reflects the current traffic operation state. The traffic flow time series curve refers to a two-dimensional curve chart with time as the horizontal axis and traffic flow as the vertical axis, which is used to represent the change trend of traffic flow over time. Time series refers to a sequence of data arranged in chronological order. Generation refers to obtaining a continuous change curve through data processing and curve fitting. The data sampling period is usually set to 20 seconds, and the data storage period is a complete signal cycle.

[0045] This step is executed when the system determines that the traffic state is in the warning state, and is used to analyze the traffic flow change pattern. Specifically, the system first confirms that the entrance road is in the warning state and reads the traffic flow data in the most recent complete signal cycle. For each sampling moment, the number of vehicles passing through the detector per unit time is calculated to obtain discrete traffic data points. The cubic spline interpolation method is used to fit these discrete data points to generate a smooth traffic flow time series curve. The horizontal axis range of the curve is 0 to the signal cycle length, and the vertical axis range is 0 to the upper limit of the road capacity.

[0046] In some embodiments, the generation of the traffic flow time series curve can be achieved in a variety of ways: optionally, a direct drawing scheme is used: the traffic flow data at each sampling moment is collected, and adjacent sampling points are connected with straight line segments to form a piecewise linear time series curve, and the mean and standard deviation of the curve are calculated to evaluate the degree of discreteness of the data; optionally, a curve fitting scheme is used: the sampled data is preprocessed, outliers are removed, the polynomial fitting coefficients are calculated using the least squares method, a smooth continuous curve is generated, and a goodness of fit evaluation index is calculated. It is understandable that other methods such as wavelet analysis and Fourier transform can also be used to achieve the generation of the traffic flow time series curve, which is not limited here.

[0047] S104. Extracting traffic flow characteristic parameters from the traffic flow time series curve, wherein the traffic flow characteristic parameters include a maximum traffic flow value, a minimum traffic flow value, a maximum value occurrence time, and a minimum value occurrence time.

[0048] Among them, the traffic flow time series curve represents a two-dimensional graph of traffic flow changes over time. Traffic flow characteristic parameters refer to a set of key indicators that describe the characteristics of traffic flow operation. The maximum traffic flow refers to the flow value corresponding to the highest point on the curve. The minimum traffic flow refers to the flow value corresponding to the lowest point on the curve. The maximum value time refers to the time point corresponding to the maximum value of the traffic flow. The minimum value time refers to the time point corresponding to the minimum value of the traffic flow. Extraction refers to the process of obtaining characteristic parameters through numerical calculation. The parameter storage period is usually set to a complete signal period.

[0049] This step is executed after the vehicle flow time series curve is generated, and is used to obtain the key characteristics of the traffic flow. Specifically, the system first performs a numerical analysis on the vehicle flow time series curve, and determines the extreme points of the curve by derivative calculation. Calculate the first-order derivative of the curve in the entire cycle. When the derivative changes from positive to negative, it is the maximum point, and when the derivative changes from negative to positive, it is the minimum point. Record the flow values ​​and corresponding times of all extreme points, compare the flow values ​​of all maximum points, select the largest one as the maximum value of the vehicle flow, and the corresponding time as the time when the maximum value occurs. Compare the flow values ​​of all minimum points, select the smallest one as the minimum value of the vehicle flow, and the corresponding time as the time when the minimum value occurs.

[0050] In some embodiments, the extraction of traffic flow characteristic parameters can be achieved in a variety of ways: optionally, using a numerical differentiation scheme: calculating the left and right derivatives of each point on the curve, judging the extreme point by the change in the sign of the derivative, confirming the extreme type in combination with the second-order derivative, recording the position and value of the extreme point, and screening the maximum and minimum values; optionally, using a sliding window scheme: setting an appropriate window length, sliding on the curve to search for local maximum and minimum values, comparing all local extreme values ​​to obtain the global maximum and minimum values, and recording the corresponding time information. It is understandable that other methods such as statistical analysis and peak detection can also be used to extract traffic flow characteristic parameters, which are not limited here.

[0051] S105: Arrange the traffic flow characteristic parameters according to the numbers of the entrance lanes to construct a traffic flow characteristic matrix.

[0052] Among them, traffic characteristic parameters refer to a set of key indicators that describe the characteristics of traffic flow operation, including the maximum and minimum traffic flow and their corresponding times. The entrance lane number refers to the unique identifier of each entrance lane at the intersection, usually numbered clockwise according to the geographical location. The traffic characteristic matrix refers to a two-dimensional data table arranged in the order of the entrance lane numbers, which is used to store the traffic characteristic parameters of each entrance lane. Matrix construction refers to the process of organizing discrete characteristic parameters into structured data according to specific rules. Arrangement refers to the data sorting in ascending or descending order of the entrance lane numbers. The number of rows of the matrix is ​​equal to the number of entrance lanes, and the number of columns is equal to the number of characteristic parameters.

[0053] This step is performed after the extraction of traffic characteristic parameters is completed, and is used to build a basis for data analysis. Specifically, the system first determines the dimension of the matrix, with the number of rows equal to the number of entrance lanes n, and the number of columns equal to the number of characteristic parameters 4. Create an n×4 empty matrix, with each row of the matrix corresponding to an entrance lane, and each column corresponding to the maximum traffic flow, the maximum moment, the minimum traffic flow, and the minimum moment. Fill the traffic characteristic parameters of each entrance lane into the corresponding position of the matrix in ascending order of the entrance lane number. Normalize the matrix, divide the traffic flow value by the upper limit of the road capacity, and divide the moment value by the length of the signal cycle.

[0054] In some embodiments, the construction of the traffic feature matrix can be achieved in a variety of ways: optionally, using a sequential filling scheme: creating a two-dimensional array storage matrix, reading the feature parameters in the order of the entrance lane number, filling the parameters into the corresponding positions of the array in sequence, and performing data verification on the filled matrix to ensure data integrity and correctness; optionally, using a parallel processing scheme: processing the feature parameters of multiple entrance lanes at the same time, creating a temporary data cache, determining the position of the data in the matrix according to the entrance lane number, writing the data concurrently, and finally performing data synchronization and verification. It is understandable that the construction of the traffic feature matrix can also be achieved by other methods such as sparse matrix storage and database table storage, which are not limited here.

[0055] S106: Analyze the complementary relationship of vehicle flows between entrance lanes based on the vehicle flow characteristic matrix, and calculate phase compensation parameters according to the complementary relationship of vehicle flows.

[0056] Among them, the traffic characteristic matrix refers to a two-dimensional data table that stores the traffic characteristic parameters of each entrance lane. The complementary relationship of traffic flow refers to the increase and decrease characteristics of traffic flow changes between different entrance lanes. The phase compensation parameter refers to the time correction used to adjust the signal phase difference. Analysis refers to the study of the correlation between data through mathematical methods. Calculation refers to the process of obtaining numerical results through a specific algorithm. The complementary relationship is usually quantified by the correlation coefficient, and the value range of the correlation coefficient is [-1, 1].

[0057] This step is executed after the traffic feature matrix is ​​constructed and is used to determine the entrance lane timing optimization strategy. Specifically, the system first calculates the traffic flow correlation coefficient between any two entrance lanes. For entrance lanes i and j, their traffic flow time series data are extracted and the Pearson correlation coefficient rij is calculated. When rij<-0.6, the two entrance lanes are considered to have a significant complementary relationship. After determining the complementary relationship of the entrance lane combination, calculate the difference between the maximum traffic flow moment t1 of the first entrance lane and the minimum traffic flow moment t2 of the second entrance lane, and use the time difference as the basic value of the phase compensation parameter. The basic value is weighted and corrected according to the ratio of the maximum traffic flow of the two entrance lanes to obtain the final phase compensation parameter.

[0058] In some embodiments, complementary relationship analysis and phase compensation parameter calculation can be implemented in a variety of ways: Optionally, a correlation analysis scheme is used: data preprocessing is performed on the traffic feature matrix, the correlation coefficient matrix between the entrance lanes is calculated, and a correlation threshold is set to screen the entrance lane pairs with significant complementary relationships, and the basic compensation value is calculated according to the time series characteristics; Optionally, a clustering analysis scheme is used: spatiotemporal clustering of traffic flow data is performed, and the entrance lane groups with complementary change patterns are identified, and the distance between clusters is calculated as a measure of the degree of complementarity, and the compensation parameters are determined in combination with the traffic ratio. It is understandable that other methods such as time series analysis and neural networks can also be used to implement complementary relationship analysis and phase compensation parameter calculation, which are not limited here.

[0059] S107: Adjust the signal timing sequence according to the phase compensation parameter.

[0060] Among them, the phase compensation parameter refers to the time correction used to adjust the signal phase difference, expressed in seconds. The signal timing sequence refers to the time schedule of each phase in a complete cycle, including parameters such as phase difference and green light duration. Adjustment refers to the process of modifying the original timing plan according to the phase compensation parameters. The signal cycle refers to the time required for a complete set of signal displays to be completed. The phase difference refers to the time difference between the start times of the green lights of adjacent entrance lanes. The phase green light duration refers to the duration of the signal light showing the green light. The timing parameter adjustment accuracy is usually set to 1 second. The cycle length is usually between 60 seconds and 180 seconds.

[0061] This step is performed after the phase compensation parameters are calculated and is used to optimize the signal timing scheme. Specifically, the system first reads the current signal timing sequence, including the cycle length T, the phase difference Di of each entrance lane, and the green light duration Gi. For the entrance lane pair (i, j) with a complementary relationship, the phase difference of the entrance lane i is subtracted from the phase compensation parameter P to obtain a new phase difference Di_new=Di-P. If Di_new is less than 0, add the cycle length T; if Di_new is greater than T, subtract the cycle length T to ensure that the new phase difference is within the range of [0, T]. Keep the green light duration of each entrance lane unchanged and update the phase difference parameters in the timing sequence. The modified timing sequence is sent to the signal controller for execution.

[0062] In some embodiments, the adjustment of the signal timing sequence can be achieved in a variety of ways: optionally, a direct adjustment scheme is used: read the original timing sequence, obtain the phase compensation parameters, directly modify the phase difference of the complementary entrance channel, perform period normalization processing, verify whether the adjusted timing scheme meets the minimum green light duration constraint, and generate a new timing sequence; optionally, an optimization adjustment scheme is used: establish a timing optimization model, use the phase compensation parameters as constraints, consider the delay minimization goal, use a genetic algorithm to solve the optimal timing scheme, and perform a feasibility test on the solution result. It is understandable that other methods such as fuzzy control and dynamic programming can also be used to adjust the signal timing sequence, which is not limited here.

[0063] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the queue overflow control method in the second-level adaptive control system in an embodiment of the present application.

[0064] S201. Arrange the traffic flow feature matrix in chronological order to form a time series matrix.

[0065] The traffic flow feature matrix is ​​a 4×4 matrix, and each row contains the maximum flow value, minimum flow value, maximum time and minimum time of an entrance lane. The time series matrix is ​​a matrix that rearranges the traffic flow features by time, reflecting the change pattern of the traffic flow of each entrance lane over time. Time order refers to the order of occurrence in time. The system reorganizes the traffic flow feature matrix into a time series matrix. First, the maximum and minimum time of each entrance lane are extracted, and all the time are sorted from small to large. According to the sorted time series, the system constructs an n×4 matrix (n is the total number of time points), each row of the matrix corresponds to a time point, and contains four columns of data representing the traffic flow of the four entrance lanes at that time. For the maximum value moment, fill in the corresponding maximum flow value; for the minimum value moment, fill in the corresponding minimum flow value; for other time points, the corresponding flow value is calculated by linear interpolation. The time series matrix obtained in this way fully records the change process of the traffic flow of each entrance lane over time.

[0066] S202: Calculate the traffic flow correlation coefficient between any two entrance lanes according to the time series matrix.

[0067] The traffic flow correlation coefficient is a statistical indicator to measure the degree of correlation between traffic flow changes between two entrance lanes, and its value range is [-1, 1]. A positive correlation coefficient indicates positive correlation, and a negative correlation indicates negative correlation. The larger the absolute value, the stronger the correlation. The time series matrix contains the traffic flow data of each entrance lane at different times. The system uses the Pearson correlation coefficient method to calculate the correlation between any two entrance lanes. For entrance lanes i and j, extract the two columns of data corresponding to them from the time series matrix, recorded as Xi and Xj. Calculate the covariance cov(Xi, Xj) and standard deviation σi, σj of these two columns of data, and then calculate the correlation coefficient according to the formula ρij=cov(Xi, Xj) / (σi×σj). Specifically, the covariance calculation formula is cov(Xi, Xj)=Σ[(Xi-μi)(Xj-μj)] / n, where μi and μj are the average values ​​of Xi and Xj respectively, and n is the number of samples. The standard deviation calculation formula is σ=√[Σ(X-μ)² / n]. The system calculates the correlation coefficients for all possible inlet channel combinations to form a 4×4 correlation coefficient matrix.

[0068] S203: Determine the inlet lane combination whose vehicle flow correlation coefficient is less than a preset threshold as an inlet lane combination having a complementary relationship.

[0069] The traffic flow correlation coefficient reflects the synchronization of traffic flow changes in the two entrance lanes. The preset threshold is the standard value for judging the complementary relationship, which is usually set to a negative value. The complementary relationship refers to the fact that the traffic flow of the two entrance lanes presents a change characteristic of one increasing while the other decreasing, that is, when the traffic flow of one entrance lane increases, the traffic flow of the other entrance lane decreases. The system sets the preset threshold to -0.6, which means that when the correlation coefficient between the two entrance lanes is less than -0.6, they are considered to have a significant complementary relationship. The system traverses each non-diagonal element in the correlation coefficient matrix and records the pairs of entrance lanes that meet the conditions. For each complementary relationship of entrance lane combination, the system records their number pairs, for example (1, 3) means that entrance lane No. 1 and entrance lane No. 3 have a complementary relationship. If there are multiple combinations that meet the conditions, the system stores them in a complementary relationship list. These complementary relationship of entrance lane combinations will be used for subsequent phase compensation parameter calculations.

[0070] S204, calculating the time interval between the maximum vehicle flow rate of the first entrance road and the minimum vehicle flow rate of the second entrance road in the entrance road combination having a complementary relationship.

[0071] The time interval refers to the difference in duration between two time points. The maximum traffic flow moment refers to the time point when the traffic flow of a certain entrance lane reaches its peak value during the traffic light cycle. The minimum traffic flow moment refers to the time point when the traffic flow of a certain entrance lane reaches its valley value during the traffic light cycle. A combination of entrance lanes with a complementary relationship refers to two entrance lanes whose correlation coefficient is less than a preset threshold, where the one with a smaller number is the first entrance lane and the one with a larger number is the second entrance lane. The calculation reference point of the moment is the start time of a complete signal cycle, that is, time 0. The system extracts the maximum traffic flow moment of the first entrance lane from the time series matrix as T1, and the minimum traffic flow moment of the second entrance lane as T2, and performs a subtraction operation T1-T2 to obtain the time interval ΔT. This time interval reflects the time relationship between the peaks and valleys of traffic flow between complementary entrance lanes, providing a basis for subsequent phase difference optimization.

[0072] S205, the time interval is used as the initial value of the phase compensation parameter; the initial value of the phase compensation parameter is weightedly corrected according to the ratio of the maximum vehicle flow of the first entrance road to the minimum vehicle flow of the second entrance road to obtain the phase compensation parameter.

[0073] The phase compensation parameter is a time parameter used to adjust the phase difference in traffic signal timing, and its initial value is derived from the time interval between peak and valley traffic flow. The ratio of the maximum and minimum traffic flow reflects the relative degree of change in traffic flow intensity. Weighted correction is the process of adjusting the initial compensation parameter according to the traffic flow ratio. In a complete signal cycle, the phase corresponds to the conversion timing of the signal light, and the phase compensation parameter directly affects the adjustment amount of the green light start time. The system first sets the time interval ΔT as the initial value P0 of the phase compensation parameter, and then calculates the ratio r=M1 / M2 of the maximum traffic flow M1 of the first entrance lane and the minimum traffic flow M2 of the second entrance lane. When r>2, the system multiplies the initial value by 1.2 to obtain the final compensation parameter; when 1≤r≤2, the initial value is directly used; when r<1, the initial value is divided by 1.2. This weighted correction method comprehensively considers the influence of time difference and traffic ratio.

[0074] S206: Subtract the phase compensation parameter from the current phase difference to obtain a target phase difference.

[0075] The current phase difference refers to the time difference between the green light start times of two complementary entrance lanes in the existing signal timing scheme. The target phase difference is the time difference between the green light start times of the two entrance lanes after optimization. The phase compensation parameter is the amount of time that needs to be adjusted. In traffic signal timing, the correspondence between the phase and the traffic light time is: the phase starting point corresponds to the green light start time, the phase ending point corresponds to the green light end time, and the phase length is equal to the green light duration. The system reads the phase difference D from the current timing scheme, subtracts the calculated phase compensation parameter P from it, and obtains the target phase difference Dnew=DP. If the calculated result exceeds the range of the signal cycle length T, cycle normalization is required: when Dnew<0, add the cycle length T; when Dnew>T, subtract the cycle length T to ensure that the target phase difference is within the valid range.

[0076] S207: Adjust the difference between the phase start time of the first inlet channel and the phase start time of the second inlet channel in the inlet channel combination having a complementary relationship to the target phase difference.

[0077] The phase start time refers to the time when the traffic lights of each entrance lane change from red to green. The phase difference refers to the time difference between the phase start times of two entrance lanes. The target phase difference is the optimized phase difference value calculated by the complementary relationship of vehicle flow. The relationship between phase time and traffic light time is as follows: the phase start time corresponds to the green light start time, the phase end time corresponds to the green light end time, and the phase duration is the green light duration. When adjusting the phase difference, the system keeps the signal cycle length unchanged and adjusts the phase difference by changing the green light start time.

[0078] The system performs phase adjustment on the inlet channel combination with a complementary relationship. First, read the current phase start time t1 and t2 of the first inlet channel and the second inlet channel, and calculate the current phase difference dc=t1-t2. Then adjust the phase difference to the target phase difference dt. The adjustment method is: fix the phase start time t1 of the first inlet channel unchanged, and adjust the phase start time of the second inlet channel to t1-dt. If the adjusted time exceeds the cycle length T, the cycle normalization is performed: when the time value is greater than T, subtract T; when the time value is less than 0, add T. This ensures that the adjusted phase start time is within the range of [0, T].

[0079] S208. Set a first green light phase duration according to the maximum value of the vehicle flow at the first entrance, and set a second green light phase duration according to the ratio of the maximum value to the minimum value of the vehicle flow at the second entrance.

[0080] The green light phase duration refers to the duration of the signal light showing a green light. The maximum traffic flow reflects the maximum traffic demand of the entrance lane. The ratio of the maximum and minimum traffic flow indicates the degree of fluctuation of traffic demand. The first green light phase duration and the second green light phase duration correspond to the green light duration of the first entrance lane and the second entrance lane respectively. The setting of the phase duration directly affects the traffic capacity of each entrance lane.

[0081] The system sets the green light phase duration based on the traffic flow data. For the first entrance, substitute its maximum traffic flow Q1max into the formula t1=20+Q1max / 50 (unit: seconds) to calculate the first green light phase duration. For the second entrance, first calculate the ratio of its maximum and minimum traffic flow r=Q2max / Q2min, then substitute it into the formula t2=15+Q2max / 50+5r (unit: seconds) to calculate the second green light phase duration. This calculation method takes into account both the absolute value of traffic flow and the fluctuation of traffic flow.

[0082] S209: Allocate the remaining time to the green light phases of the third entrance lane and the fourth entrance lane according to the ratio of the maximum traffic volume.

[0083] The remaining time refers to the remaining time after deducting the green light phase time of the first and second entrance lanes in the signal cycle. The third and fourth entrance lanes refer to the other two entrance lanes not included in the complementary relationship combination. Phase allocation refers to allocating the available time to each entrance lane as its green light display time. The proportion of the maximum traffic volume reflects the relative traffic demand of each entrance lane.

[0084] The system calculates the remaining time tr=T-t1-t2-ty, where T is the cycle length, t1 and t2 are the set green light phase durations, and ty is the yellow light duration. Then the maximum traffic volume Q3max and Q4max of the third and fourth entrances are obtained, and the proportional coefficients p3=Q3max / (Q3max+Q4max) and p4=Q4max / (Q3max+Q4max) are calculated. The remaining time is allocated in proportion: the green light phase duration of the third entrance is t3=tr×p3, and the green light phase duration of the fourth entrance is t4=tr×p4. This allocation method ensures that each entrance obtains a green light time that is suitable for its traffic needs.

[0085] S210, determining the red light phase duration of each entrance lane according to the difference between the phase start times, the sum of the green light phase duration and the red light phase duration of each entrance lane equals the total cycle duration.

[0086] The phase start time difference refers to the time difference between the green light start times of adjacent entrance lanes. The red light phase duration refers to the duration that the signal light displays red light. The green light phase duration refers to the duration that the signal light displays green light. The total cycle duration refers to the time required for a complete signal cycle, including all red light durations, green light durations and yellow light durations. In the phase sequence, the red light phase duration and the green light phase duration are complementary sets, and their sum plus the yellow light duration equals the total cycle duration. The system calculates the red light phase duration of each entrance lane based on the phase start time difference determined in the previous steps. Suppose the green light phase duration of entrance lane i is Gi, and the phase start time difference of the next entrance lane is Di, then the red light phase duration of entrance lane i is Ri=T-Gi-Y, where T is the total cycle duration and Y is the yellow light duration. This calculation method ensures the continuity of the signal timing of adjacent entrance lanes and the integrity of the cycle.

[0087] S211. When the traffic state is in the normal state, obtain the vehicle clearance acceptability rate of the entrance road.

[0088] The vehicle gap refers to the time interval between two consecutive vehicles. The gap acceptability rate refers to the proportion of all vehicle gaps that are greater than the critical gap. The normal state refers to the traffic state in which the queuing ratio and the vehicle accumulation rate do not exceed the first-level threshold. The critical gap is usually set at 2.5 seconds, which represents the minimum headway required for safe and effective passage. The system obtains the time interval data of consecutive vehicles passing through each entrance through the vehicle detector. For n consecutive vehicles passing, record n-1 vehicle gap values, count the number m greater than the critical gap, and calculate the gap acceptability rate r=m / (n-1). In this way, the system obtains a quantitative indicator reflecting the density of traffic flow.

[0089] S212: When the vehicle gap acceptability rate is greater than a preset acceptance rate threshold, shorten the signal timing sequence by a preset time length.

[0090] The preset acceptance rate threshold is the standard value for judging whether the traffic flow is too sparse, usually set to 0.3. The preset time length is the basic unit of signal timing adjustment, usually set to 5 seconds. The signal timing sequence refers to the time arrangement of each phase within a complete signal cycle. Shortening the timing sequence means reducing the duration of each phase in equal proportion, thereby reducing the total cycle duration. The system first compares the vehicle gap acceptance rate with the preset threshold of 0.3. When the acceptance rate is greater than 0.3, it indicates that the current traffic flow is relatively sparse. At this time, the current cycle duration T is subtracted from the preset time length of 5 seconds to obtain a new cycle duration Tnew=T-5. The duration of each phase is reduced according to the original proportion, that is, the new phase duration is equal to the original phase duration multiplied by (Tnew / T). This adjustment method maintains the proportional relationship between the durations of each phase, while improving the efficiency of signal control.

[0091] S213: When the vehicle gap acceptability rate is not greater than the preset acceptance rate threshold, extend the signal timing sequence by a preset time length.

[0092] The vehicle gap acceptability rate refers to the proportion of time when the vehicle gap is greater than the critical gap. The preset acceptance rate threshold is the standard value for judging the density of traffic flow, which is set to 0.3. The preset time length is the basic unit of signal timing adjustment, which is set to 5 seconds. The signal timing sequence contains the start and end times and duration of each phase in a complete cycle. Extending the timing sequence means increasing the duration of each phase in equal proportion and increasing the total cycle duration. When the vehicle gap acceptability rate is not greater than 0.3, the system increases the existing cycle duration T by 5 seconds to obtain a new cycle duration Tnew=T+5. The duration of each phase is increased according to the original proportion, and the new phase duration is equal to the original phase duration multiplied by (Tnew / T). This adjustment keeps the duration ratio of each phase unchanged, and adapts to the more dense traffic flow state by increasing the total cycle duration.

[0093] S214: monitor the changing trends of the queuing ratio and the vehicle accumulation rate in real time.

[0094] The queue ratio refers to the ratio of the queue length at the entrance to the road length. The vehicle accumulation rate refers to the ratio of the number of vehicles entering the intersection to the number of vehicles leaving the intersection per unit time. The change trend refers to the direction and rate of change of these two parameters over time. The monitoring cycle refers to the time interval for the system to collect and update data, which is set to 20 seconds. The system collects data through the vehicle detector every 20 seconds and calculates the latest queue ratio and vehicle accumulation rate. The queue ratio is calculated by dividing the current queue length by the total length of the road. The vehicle accumulation rate is calculated by dividing the number of vehicles passing the upstream detector by the number of vehicles passing the downstream detector within 20 seconds. The system stores this data in chronological order for subsequent trend analysis.

[0095] S215: When it is detected that the queuing ratio continues to increase in a plurality of consecutive signal cycles and exceeds the first warning threshold, the traffic state is switched to the warning state.

[0096] The first warning threshold is the standard value of the queue ratio that triggers the warning state, which is set to 0.6. The warning state refers to the state where traffic congestion begins to worsen but has not yet reached a serious level. Multiple consecutive signal cycles usually refer to 3 or more signal cycles. State switching refers to the change of the system operation mode from normal state to warning state. The system checks the value of the queue ratio at the end of each signal cycle. When it is detected that the queue ratio has been increasing for 3 consecutive cycles and the latest value exceeds 0.6, the system will change the operating state indicator from 1 (normal state) to 2 (warning state). In the warning state, the system will initiate corresponding traffic diversion strategies, including measures such as adjusting the phase difference and green light duration.

[0097] S216. When the traffic state is in the dangerous state, the vehicle saturation of each entrance lane is calculated, and the vehicle saturation is equal to the product of the vehicle arrival rate and the vehicle gap acceptance rate.

[0098] Vehicle saturation is an indicator to measure the degree of matching between traffic demand and traffic capacity. The vehicle arrival rate is the ratio of the number of vehicles arriving at the intersection per unit time to the maximum number of vehicles that the road can accommodate. The vehicle gap acceptance rate is the proportion of time greater than the critical gap. The dangerous state refers to the traffic state where the queuing ratio or vehicle accumulation rate exceeds the second-level threshold. The system calculates the vehicle saturation by first obtaining the number of vehicles N1 passing through the upstream detector within 20 seconds on each entrance, dividing N1 by the maximum number of vehicles N0 that can be accommodated on the entrance to obtain the vehicle arrival rate r1=N1 / N0; then counting the total length of the time period t1 with a vehicle interval greater than 2.5 seconds, dividing it by the observation time of 20 seconds to obtain the vehicle gap acceptance rate r2=t1 / 20; finally, multiplying r1 and r2 to obtain the vehicle saturation S=r1×r2. This calculation method takes into account both traffic demand and traffic capacity.

[0099] S217. Prioritize the entrance lanes according to the vehicle saturation.

[0100] Priority is the service order level of the entrance lanes determined by vehicle saturation. Priority sorting is to sort the entrance lanes from high to low according to vehicle saturation. The sorting result directly determines the order in which each entrance lane gets extra green light time. The system first establishes a two-dimensional array containing the entrance lane number and the corresponding vehicle saturation. Each row of the array contains two elements: the entrance lane number and the vehicle saturation. The array is sorted in descending order according to the vehicle saturation using the quick sort algorithm to obtain the priority order. The first row of the sorted array corresponds to the highest priority entrance lane, and the last row corresponds to the lowest priority entrance lane. This sorting method ensures that the entrance lane with the greatest traffic pressure is given priority.

[0101] S218. Extend the green light period of the highest priority entrance to a preset time.

[0102] The highest priority entrance lane refers to the entrance lane with the highest vehicle saturation. The green light period refers to the time period when the signal light shows green. The preset time is the fixed length of time to extend the green light, which is set to 15 seconds. The extended period is the time added to the original green light duration. The system first reads the first entrance lane number in the priority sorting result and obtains the current green light duration t1 of the entrance lane. Increase t1 by 15 seconds to obtain the new green light duration t2=t1+15. In order to keep the total length of the cycle unchanged, the green light duration of other entrance lanes is reduced proportionally, and the sum of the reductions is equal to 15 seconds. The specific calculation method is: the new green light duration of other entrance lanes is equal to the original green light duration multiplied by [(T-t2) / (T-t1)], where T is the total length of the cycle. This adjustment method gives priority to the traffic needs of the entrance lanes with the greatest traffic pressure.

[0103] S219: When the queuing ratio drops below the second warning threshold and the vehicle accumulation rate shows a downward trend, and the vehicle gap acceptance rate is within a preset normal range, the traffic state is switched to the warning state.

[0104] The queue ratio refers to the ratio of the queue length of the entrance road to the road length, which is calculated in real time by the vehicle detector. The second warning threshold is the standard value of the queue ratio for judging the traffic state from dangerous to warning, which is set to 0.8. The vehicle accumulation rate is the ratio of the number of vehicles entering the intersection to the number of vehicles leaving. The downward trend means that the value continues to decrease for three consecutive cycles. The vehicle gap acceptability rate is the proportion of time greater than the critical gap, and the normal range is between 0.2 and 0.4. The warning state is the intermediate state of traffic operation, between the normal state and the dangerous state. State switching refers to the change of the system operation mode from the dangerous state to the warning state.

[0105] The system performs a status check every 20 seconds. First, it calculates the current queue length L1 through the upstream detector, divides L1 by the total road length L0 to obtain the queue ratio r1 = L1 / L0, and checks if r1 is less than 0.8. Then it calculates the vehicle accumulation rates for the last three cycles, denoted as c1, c2, and c3 respectively, and checks if c3 < c2 < c1. Next, it calculates the total length t1 of time intervals when the vehicle gap is greater than 2.5 seconds, divides t1 by the observation duration of 20 seconds to obtain the vehicle gap acceptance rate r2 = t1 / 20, and checks if r2 is between 0.2 and 0.4. When all three conditions are met simultaneously, the system changes the operation status flag from 3 (hazardous state) to 2 (warning state). Along with the status switch, the system resumes the traffic control strategies in the warning state, including measures such as phase difference optimization and signal timing adjustment. This multi-condition judgment mechanism ensures the stability and reliability of the status switch.

[0106] The following describes the second-level adaptive control system in the embodiments of the present invention application from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic structural diagram of an entity device of the second-level adaptive control system in the embodiments of the present application.

[0107] It should be noted that Figure 3 the structure of the second-level adaptive control system shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present invention.

[0108] As Figure 3 shown, the second-level adaptive control system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the Read-Only Memory (ROM) 302 or the program loaded from the storage section 308 into the Random Access Memory (RAM) 303, such as executing the methods described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other through a bus 304. The Input / Output (I / O) interface 305 is also connected to the bus 304.

[0109] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0110] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0111] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0113] Specifically, the second-level adaptive control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the queue overflow control method in the second-level adaptive control system provided by the above embodiment is implemented.

[0114] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the second-level adaptive control system described in the above embodiment; or may exist independently without being assembled into the second-level adaptive control system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the second-level adaptive control system, the second-level adaptive control system implements the queue overflow control method in the second-level adaptive control system provided in the above embodiment.

[0115] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0116] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0117] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A queue overflow control method in a second-level adaptive control system, characterized in that: Applied to a second-level adaptive control system, the method comprises: Collecting real-time traffic data of each entrance road, wherein the real-time traffic data includes vehicle queue length, vehicle arrival rate and vehicle departure rate; Calculating the queue ratio and vehicle accumulation rate of each of the entrance lanes according to the real-time traffic data, and dividing the traffic state of each of the entrance lanes into a normal state, a warning state, and a dangerous state according to a preset state threshold; When the traffic state is in the warning state, generating a traffic flow time series curve according to the real-time traffic data; Extracting traffic flow characteristic parameters from the traffic flow time series curve, wherein the traffic flow characteristic parameters include a maximum traffic flow value, a minimum traffic flow value, a maximum value occurrence time, and a minimum value occurrence time; Arranging the traffic flow characteristic parameters according to the numbers of the entrance lanes to construct a traffic flow characteristic matrix; Analyzing the complementary relationship of vehicle flows between entrance lanes based on the vehicle flow characteristic matrix, and calculating a phase compensation parameter according to the complementary relationship of vehicle flows; The signal timing sequence is adjusted according to the phase compensation parameter.

2. The method according to claim 1, characterized in that The step of analyzing the complementary relationship of vehicle flow between entrance lanes based on the vehicle flow characteristic matrix and calculating the phase compensation parameter according to the complementary relationship of vehicle flow specifically includes: Arranging the traffic flow feature matrix in chronological order to form a time series matrix; Calculate the traffic flow correlation coefficient between any two entrance lanes according to the time series matrix; Determining the inlet lane combination whose vehicle flow correlation coefficient is less than a preset threshold as an inlet lane combination having a complementary relationship; Calculating the time interval between the maximum value of the traffic flow of the first entrance road and the minimum value of the traffic flow of the second entrance road in the entrance road combination having a complementary relationship; The time interval is used as the initial value of the phase compensation parameter; the initial value of the phase compensation parameter is weightedly corrected according to the ratio of the maximum vehicle flow of the first entrance road to the minimum vehicle flow of the second entrance road to obtain the phase compensation parameter.

3. The method according to claim 1, characterized in that The step of adjusting the signal timing sequence according to the phase compensation parameter specifically includes: Subtract the phase compensation parameter from the current phase difference to obtain a target phase difference; Adjusting the difference between the phase start time of the first inlet and the phase start time of the second inlet in the inlet combination having a complementary relationship to the target phase difference; The first green light phase duration is set according to the maximum value of the traffic flow of the first entrance road, and the second green light phase duration is set according to the ratio of the maximum value to the minimum value of the traffic flow of the second entrance road; the remaining duration is allocated to the green light phases of the third entrance road and the fourth entrance road according to the proportion of the maximum value of the traffic flow; The red light phase duration of each entrance lane is determined according to the difference between the phase start times, and the sum of the green light phase duration and the red light phase duration of each entrance lane is equal to the total cycle duration.

4. The method according to claim 1, characterized in that: After the step of calculating the queue ratio and vehicle accumulation rate of each entrance road according to the real-time traffic data, and dividing the traffic state of each entrance road into a normal state, a warning state and a dangerous state according to a preset state threshold, the method further includes: When the traffic state is in the normal state, obtaining the vehicle clearance acceptability rate of the entrance road; When the vehicle gap acceptability rate is greater than a preset acceptance rate threshold, shortening the signal timing sequence by a preset time length; When the vehicle gap acceptability rate is not greater than the preset acceptance rate threshold, the signal timing sequence is extended by a preset time length.

5. The method according to claim 4, characterized in that After the step of extending the signal timing sequence for a preset time length when the vehicle gap acceptability rate is not greater than the preset acceptance rate threshold, the method further includes: Real-time monitoring of the changing trends of the queuing ratio and the vehicle accumulation rate; When it is detected that the queuing ratio continues to increase in a plurality of consecutive signal cycles and exceeds a first warning threshold, the traffic state is switched to the warning state.

6. The method according to claim 4, characterized in that After the step of calculating the queue ratio and vehicle accumulation rate of each entrance road according to the real-time traffic data, and dividing the traffic state of each entrance road into a normal state, a warning state and a dangerous state according to a preset state threshold, the method further includes: When the traffic state is in the dangerous state, calculating the vehicle saturation of each of the entrance lanes, the vehicle saturation being equal to the product of the vehicle arrival rate and the vehicle gap acceptability rate; Prioritizing the entry lanes according to the vehicle saturation; Extend the green light period for the highest priority entrance lane to a preset time.

7. The method according to claim 6, characterized in that After the step of extending the green light period of the highest priority entrance lane to a preset time, the method further includes: When the queuing ratio decreases below a second warning threshold and the vehicle accumulation rate shows a downward trend, and the vehicle gap acceptance rate is within a preset normal range, the traffic state is switched to the warning state.

8. A second-level adaptive control system, characterized in that: The second-level adaptive control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the second-level adaptive control system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction runs on the second-level adaptive control system, the second-level adaptive control system executes the method as claimed in any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on a second-level adaptive control system, the second-level adaptive control system is enabled to execute the method according to any one of claims 1 to 7.

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