Intelligent traffic signal control method and system based on vehicle-road cooperation

Abstract

The invention discloses an intelligent traffic signal control method and system based on vehicle-road cooperation, and belongs to the technical field of traffic control, and the method specifically comprises the steps: setting the initial time length of a collection period as T when the initial vehicle flow in the driving direction is in a set interval [M, N]; every acquisition period T, the system obtains traffic flow Q in all driving directions of the intersection and generates a traffic flow sequence; calculating the mean value of the traffic flow in all the driving directions every m acquisition periods, and dynamically adjusting the green light signal time distribution in each direction in unit time according to the traffic flow proportion in different driving directions; meanwhile, analyzing the proportions q and t which exceed and are lower than the interval value in the sequence and the linear fitting slope k after the scatter diagram is generated, and calculating the adjustment coefficient beta of the collection period according to the proportions q and t; according to the method, the traffic flow collection period is dynamically adjusted, so that the method adapts to the actual traffic flow change, and the traffic management efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of traffic control technology, and in particular to an intelligent traffic signal control method and system based on vehicle-road collaboration. Background Art

[0002] With the acceleration of urbanization, traffic congestion is becoming more and more serious, especially during peak hours and at busy intersections. Traditional traffic signal control methods mainly rely on the switching of traffic lights at fixed time intervals, which cannot flexibly respond to real-time changes in traffic volume. This fixed mode not only leads to low traffic efficiency, but also may aggravate traffic congestion. Therefore, it is particularly important to develop a method that can dynamically adjust the timing of traffic lights according to actual road conditions.

[0003] In recent years, with the development of Internet of Things technology and big data analysis technology, intelligent transportation systems based on vehicle-to-infrastructure (V2I) have gradually become a research hotspot. Through the communication between sensors installed on vehicles and roadside infrastructure, real-time monitoring of road traffic conditions can be achieved, and this information can be used to optimize traffic management strategies. Among them, using traffic flow data to guide traffic signal control is one of the effective means.

[0004] However, the existing technology often adopts fixed locations or fixed time intervals to collect data, which is difficult to accurately reflect the complex and changeable road conditions. When the traffic flow changes rapidly, the traffic lights cannot be adjusted in time according to the real-time traffic flow. Based on this, the present invention proposes an intelligent traffic signal control method based on vehicle-road collaboration. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent traffic signal control method and system based on vehicle-road collaboration to solve the following technical problems:

[0006] Existing technologies often use fixed locations or fixed time intervals to collect data, which makes it difficult to accurately reflect complex and changeable road conditions. When the traffic volume changes rapidly, the traffic lights cannot be adjusted in time according to the real-time traffic volume.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] The intelligent traffic signal control method based on vehicle-road collaboration includes the following steps:

[0009] Set the traffic flow collection cycle. When the initial traffic flow in the driving direction is within the set interval [M, N], set the initial duration of the collection cycle to T, and obtain the traffic flow Q of all driving directions at the intersection every collection cycle T;

[0010] Calculate the mean traffic flow Q of all driving directions every m collection cycles a , m is a positive integer, and the green light signal time allocation ratio of different driving directions in unit time is set according to the traffic volume ratio of different driving directions;

[0011] Generate m traffic flow sequences Q1, Q2, ..., Q m , and obtain the proportion q of the values ​​in the sequence that exceed the interval value N, and the proportion t of the values ​​that are lower than the interval value M; add the weighted traffic flow value Q0 of the first m collection cycles to the sequence, generate a scatter plot of the traffic flow changing with the number of collections based on the added sequence, perform linear fitting on the scatter plot, calculate the slope k of the fitting line, and calculate the adjustment coefficient β of the collection cycle according to the following formula:

[0012]

[0013] Among them, Q max Represents the maximum value in the traffic flow sequence, n = m + 1;

[0014] The adjustment coefficient β is calculated according to the traffic flow index every m collection cycles. The traffic flow index includes the traffic flow mean Qa, proportion q, proportion t and slope k. According to the adjustment coefficient β obtained in the current m collection cycles, the duration T' of a single collection cycle of the next m collection cycles is calculated, T'=T / β.

[0015] As a further solution of the present invention: the vehicle flow rate is the number of vehicles passing through the intersection from any driving direction within a set time t.

[0016] As a further solution of the present invention: the calculation process of the weighted vehicle flow value Q0 of the first m acquisition cycles is:

[0017] Get the traffic flow sequence QX1, QX2, ..., QX of the first m acquisition cycles m , and assign a corresponding weight to each traffic flow value, the weight gradually increases according to the time sequence, then the calculation formula of Q0 is:

[0018]

[0019] where w i Represents weight.

[0020] As a further solution of the present invention, according to the traffic volume ratio of different driving directions, the specific process of setting the green light signal time distribution ratio of different driving directions within a unit time is as follows:

[0021] The unit time represents the time it takes for a traffic light to change for one round, marked as T0. Each road has two driving directions. The road with the larger average traffic flow is taken as the representative of the road. The traffic flow ratio of the two intersecting roads is calculated, that is, A:B. For road A, the corresponding green light signal time is T0A / A+B.

[0022] As a further solution of the present invention: for different driving directions at the intersection, a minimum green light time limit and a maximum green light time limit are set.

[0023] As a further solution of the present invention: each time period corresponds to a different unit time, and the time period includes morning and evening peak periods, daytime regular period and nighttime trough period, the unit time of morning and evening peak periods is greater than that of daytime regular period, and the unit time of daytime regular period is greater than that of nighttime trough period.

[0024] As a further solution of the present invention: the collection period is set with a shortest upper limit. When the collection period reaches the shortest upper limit, the collection period will not be further shortened even if the adjustment coefficient is greater than 1.

[0025] The present invention also includes an intelligent traffic signal control system based on vehicle-road collaboration, which is used to implement the above-mentioned intelligent traffic signal control method based on vehicle-road collaboration, including:

[0026] The data collection module is used to set the traffic flow collection cycle. When the initial traffic flow in the driving direction is within the set interval [M, N], the initial duration of the collection cycle is set to T, and the traffic flow Q of all driving directions at the intersection is obtained every collection cycle T;

[0027] The signal distribution module is used to calculate the mean traffic flow Q of all driving directions every m acquisition cycles. a , m is a positive integer, and the green light signal time allocation ratio of different driving directions in unit time is set according to the traffic volume ratio of different driving directions;

[0028] The data processing module is used to generate the traffic flow sequence Q1, Q2, ..., Q m , and obtain the proportion q of the values ​​in the sequence that exceed the interval value N, and the proportion t of the values ​​that are lower than the interval value M; add the weighted traffic flow value Q0 of the first m collection cycles to the sequence, generate a scatter plot of the traffic flow changing with the number of collections based on the added sequence, perform linear fitting on the scatter plot, calculate the slope k of the fitting line, and calculate the adjustment coefficient β of the collection cycle according to the following formula:

[0029]

[0030] Among them, Q max Represents the maximum value in the traffic flow sequence, n = m + 1;

[0031] The cycle adjustment module is used to calculate the adjustment coefficient β according to the vehicle flow index every m collection cycles. The vehicle flow index includes the vehicle flow mean Qa, proportion q, proportion t and slope k. According to the adjustment coefficient β obtained in the current m collection cycles, the duration T' of a single collection cycle of the next m collection cycles is calculated, T'=T / β.

[0032] Beneficial effects of the present invention:

[0033] The frequency of traffic flow data collection is dynamically adjusted according to the changing trend of actual traffic flow, so as to achieve more accurate traffic signal control. By setting the traffic flow collection cycle, analyzing the traffic flow sequence and calculating the adjustment coefficient, the duration of the collection cycle is dynamically adjusted according to the changing trend of traffic flow. This method can further optimize the collection cycle, make data collection more efficient, reduce unnecessary data collection, and maintain real-time monitoring of traffic conditions. This method can flexibly respond to changes in traffic flow. The average traffic flow is calculated every several collection cycles, and the green light signal time is allocated according to the traffic flow ratio, so that the control of traffic lights is more in line with actual traffic needs and improves traffic operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 As shown, the present invention is an intelligent traffic signal control method based on vehicle-road collaboration, comprising the following steps:

[0038] 1. Initial Setup:

[0039] Traffic flow is defined as the number of vehicles passing through an intersection from any direction within a set time t. This time t can be a few minutes, a few hours, or even a day, depending on the needs and goals of traffic management. For example, if the goal is to monitor traffic conditions in real time, t may be set to a few minutes.

[0040] When it is detected that the initial traffic flow in a certain driving direction falls within the preset interval [M, N], it means that the current traffic condition is neither particularly congested nor particularly smooth, and is in a relatively balanced state. In this case, the initial duration of the collection cycle is set to T. This duration T is a benchmark value, which is used to dynamically adjust the collection cycle according to the actual changes in traffic flow.

[0041] 2. Periodic data collection:

[0042] During the periodic data collection phase, the system automatically collects traffic flow data according to the preset collection period T. At the end of each collection period T, the system triggers the data collection mechanism to detect and record the traffic flow Q of all driving directions at the current moment through various sensor devices installed at the intersection (such as geomagnetic sensors, infrared sensors, video surveillance cameras, etc.). These sensors can accurately count every vehicle passing through the intersection, regardless of which direction the vehicle comes from.

[0043] The collected data will be immediately transmitted to the central processing system or database for storage and further analysis. After each collection cycle, the system will generate a new traffic flow value Q, which represents the total traffic flow in all driving directions during the cycle or the traffic flow in each direction. As time goes by, these continuously collected traffic flow data will form a time series, namely Q1, Q2, ..., Q m , where m represents the number of acquisition cycles that have passed.

[0044] This sequence not only contains the traffic flow information of each cycle, but also implies the changing trends and patterns of traffic flow.

[0045] 3. Calculate the mean traffic volume:

[0046] Every m collection cycles (m is a positive integer), calculate the mean traffic flow Q of all driving directions in these cycles a .

[0047] 4. Green light signal time allocation:

[0048] In intelligent traffic signal control systems, the dynamic allocation of green light signal time is one of the key measures to improve traffic efficiency at intersections and reduce congestion. This process involves flexibly adjusting the duration of green lights in each direction based on real-time traffic flow data to ensure smooth and balanced traffic flow. The following is a detailed expansion of the above process:

[0049] Definition of unit time and calculation of traffic flow ratio:

[0050] First, let's clarify the concept of "unit time" T0, which refers to the time required for a traffic light to complete a full cycle, including the sum of the green, yellow and red light times in all directions. In each unit time, the system will recalculate and allocate the green light time based on the current traffic flow in all directions at the intersection.

[0051] For each road, considering that it has two opposite driving directions, the system will select the direction with larger traffic volume as the representative of the road for calculation. Then, by comparing the representative traffic volume on two intersecting roads (assuming they are Road A and Road B), the ratio between them is obtained, which is recorded as A:B. This ratio directly reflects the difference in traffic demand between different roads.

[0052] Dynamic allocation of green light time:

[0053] Based on the above traffic volume ratio, the system will allocate green light time for each road according to the following formula:

[0054] For road A, its green light time is T0A / A+B;

[0055] For road B, its green light time is T0B / A+B;

[0056] Minimum and maximum green time limits:

[0057] In order to ensure traffic safety and pedestrian crossing needs, the system will also set the minimum and maximum green light time limits for each direction. The minimum green light time ensures that even in extremely low traffic conditions, there is enough time for vehicles and pedestrians to pass through the intersection safely; while the maximum green light time prevents one direction from occupying the green light for a long time, affecting the traffic flow in other directions.

[0058] Differentiated management of time periods:

[0059] Taking into account the traffic characteristics of different time periods in a day, the system divides a day into three stages: morning and evening peak periods, daytime regular period, and nighttime trough period, and sets a different unit time T0 for each stage:

[0060] Morning and evening rush hours: Due to heavy traffic, the unit time is set longer to reduce the number of signal light cycles and avoid traffic congestion caused by frequent switching.

[0061] During normal daytime: the traffic volume is moderate, the unit time is moderate, and the traffic lights maintain a normal cycle frequency.

[0062] Nighttime low period: Traffic volume is low, so the unit time can be shortened appropriately to speed up the traffic light cycle, while ensuring the minimum green light time to ensure nighttime driving safety.

[0063] Through such refined management strategies, the intelligent traffic signal control system can flexibly adjust the green light time according to actual conditions, effectively alleviate traffic pressure and improve overall traffic operation efficiency.

[0064] 5. Data Analysis:

[0065] The proportion q of the total number of values ​​in the sequence that exceed the interval value N is counted.

[0066] At the same time, the proportion t of the values ​​below the interval value M to the total number is counted.

[0067] The calculation process of the weighted traffic flow value Q0 of the first m collection cycles is:

[0068] Get the traffic flow sequence QX1, QX2, ..., QX of the first m acquisition cycles m , and assign a corresponding weight to each traffic flow value, the weight gradually increases according to the time sequence, then the calculation formula of Q0 is:

[0069]

[0070] where w i Represents the weight. Add the weighted traffic flow value Q0 to the sequence.

[0071] 6. Scatter plot and linear fitting:

[0072] A scatter plot of traffic flow versus collection times is generated based on the added sequence.

[0073] Perform a linear fit on the scatter plot and calculate the slope k of the fitted line.

[0074] 7. Calculate the adjustment coefficient β:

[0075] The adjustment coefficient β of the acquisition period is calculated using the formula:

[0076]

[0077] Where Q max Represents the maximum value in the traffic flow sequence, n=m+1.

[0078] 8. Adjust the collection cycle:

[0079] The adjustment coefficient β is calculated based on the traffic flow data of the past m collection cycles. It reflects the magnitude and trend of traffic flow changes. When the traffic flow increases, the β value will increase; when the traffic flow decreases, the β value will decrease. In this way, the system can predict the traffic conditions in the future and adjust the collection cycle accordingly.

[0080] According to the adjustment coefficient β, the system can calculate the new collection cycle length T', T' = T / β. If β is greater than 1, it means that the traffic volume has increased, and the system will shorten the collection cycle to collect data more frequently; conversely, if β is less than 1, it means that the traffic volume has decreased, and the system will extend the collection cycle to reduce unnecessary data collection. This dynamic adjustment mechanism ensures that the system can always monitor traffic conditions at the most appropriate frequency.

[0081] In order to avoid data redundancy and increased processing pressure caused by too short a collection cycle, the system sets an upper limit for the shortest collection cycle. Even if the adjustment coefficient β is greater than 1, indicating that the collection cycle needs to be further shortened, the system will limit the collection cycle to the shortest upper limit. This measure ensures the stable operation of the system and avoids the negative impact caused by excessive adjustment.

[0082] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0083] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0084] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0085] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0086] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0087] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0089] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent traffic signal control method based on vehicle-road collaboration is characterized in that: The following steps are involved: Set the traffic flow collection cycle. When the initial traffic flow in the driving direction is within the set interval [M, N], set the initial duration of the collection cycle to T, and obtain the traffic flow Q of all driving directions at the intersection every collection cycle T; Calculate the mean traffic flow Q of all driving directions every m collection cycles a , m is a positive integer, and the green light signal time allocation ratio of different driving directions in unit time is set according to the traffic volume ratio of different driving directions; Generate m traffic flow sequences Q1, Q2, ..., Q m , and obtain the proportion q of the values ​​in the sequence that exceed the interval value N, and the proportion t of the values ​​that are lower than the interval value M; add the weighted traffic flow value Q0 of the first m collection cycles to the sequence, generate a scatter plot of the traffic flow changing with the number of collections based on the added sequence, perform linear fitting on the scatter plot, calculate the slope k of the fitting line, and calculate the adjustment coefficient β of the collection cycle according to the following formula: Among them, Q max Represents the maximum value in the traffic flow sequence, n = m + 1; The adjustment coefficient β is calculated according to the traffic flow index every m collection cycles. The traffic flow index includes the traffic flow mean Qa, proportion q, proportion t and slope k. According to the adjustment coefficient β obtained in the current m collection cycles, the duration T' of a single collection cycle of the next m collection cycles is calculated, T'=T / β.

2. The intelligent traffic signal control method based on vehicle-road collaboration according to claim 1 is characterized in that: The traffic volume is the number of vehicles passing through the intersection from any driving direction within a set time t.

3. The intelligent traffic signal control method based on vehicle-road collaboration according to claim 1 is characterized in that: The calculation process of the weighted traffic flow value Q0 of the first m collection cycles is: Get the traffic flow sequence QX1, QX2, ..., QX of the first m acquisition cycles m , and assign a corresponding weight to each traffic flow value, the weight gradually increases according to the time sequence, then the calculation formula of Q0 is: where w i Represents weight.

4. The intelligent traffic signal control method based on vehicle-road collaboration according to claim 1 is characterized in that: According to the traffic volume ratio of different driving directions, the specific process of setting the green light signal time distribution ratio of different driving directions within a unit time is as follows: The unit time represents the time it takes for a traffic light to change for one round, marked as T0. Each road has two driving directions. The road with the larger average traffic flow is taken as the representative of the road. The traffic flow ratio of the two intersecting roads is calculated, that is, A:B. For road A, the corresponding green light signal time is T0A / A+B.

5. The intelligent traffic signal control method based on vehicle-road collaboration according to claim 1 is characterized in that: For different driving directions at the intersection, set minimum green light time limit and maximum green light time limit.

6. The intelligent traffic signal control method based on vehicle-road collaboration according to claim 4 is characterized in that: Each time period corresponds to a different unit time, and the time period includes morning and evening peak periods, daytime regular periods and nighttime trough periods. The unit time of the morning and evening peak periods is greater than that of the daytime regular period, and the unit time of the daytime regular period is greater than that of the nighttime trough period.

7. The intelligent traffic signal control method based on vehicle-road collaboration according to claim 1 is characterized in that: The collection period is set with a minimum upper limit. When the collection period reaches the minimum upper limit, the collection period will not be further shortened even if the adjustment coefficient is greater than 1.

8. An intelligent traffic signal control system based on vehicle-road collaboration, used to implement the intelligent traffic signal control method based on vehicle-road collaboration according to any one of claims 1 to 7, characterized in that: include: The data collection module is used to set the traffic flow collection cycle. When the initial traffic flow in the driving direction is within the set interval [M, N], the initial duration of the collection cycle is set to T, and the traffic flow Q of all driving directions at the intersection is obtained every collection cycle T; The signal distribution module is used to calculate the mean traffic flow Q of all driving directions every m acquisition cycles. a , m is a positive integer, and the green light signal time allocation ratio of different driving directions in unit time is set according to the traffic volume ratio of different driving directions; The data processing module is used to generate the traffic flow sequence Q1, Q2, ..., Q m , and obtain the proportion q of the values ​​in the sequence that exceed the interval value N, and the proportion t of the values ​​that are lower than the interval value M; add the weighted traffic flow value Q0 of the first m collection cycles to the sequence, generate a scatter plot of the traffic flow changing with the number of collections based on the added sequence, perform linear fitting on the scatter plot, calculate the slope k of the fitting line, and calculate the adjustment coefficient β of the collection cycle according to the following formula: Among them, Q max Represents the maximum value in the traffic flow sequence, n = m + 1; The cycle adjustment module is used to calculate the adjustment coefficient β according to the vehicle flow index every m collection cycles. The vehicle flow index includes the vehicle flow mean Qa, proportion q, proportion t and slope k. According to the adjustment coefficient β obtained in the current m collection cycles, the duration T' of a single collection cycle of the next m collection cycles is calculated, T'=T / β.

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