Self-adaptive traffic light control method and system based on real-time intersection condition

Abstract

The invention belongs to but not limited to the technical field of traffic management, and particularly relates to a self-adaptive traffic light control method and system based on real-time intersection conditions, and the method comprises the steps: S1, collecting the traffic flow data of each phase in real time; s2, judging whether the duration exceeds the longest green light duration or is less than the shortest green light duration according to the current phase time, and adjusting the phase according to the result; s3, the congestion coefficient of each phase is calculated according to the traffic flow data, and whether the current phase is congested or not is judged according to the congestion coefficient of the current phase and a preset threshold value; s4, if not, switching the phase; if yes, calculating temporary time according to a formula; s5, judging whether the temporary time is exceeded or not according to the time of the current phase, switching the phase if the temporary time is exceeded, and otherwise, continuing the next step; s6, judging whether the congestion coefficient of other phases is greater than 1.5 times of the congestion coefficient of the current phase or not, if so, switching the phase, otherwise, keeping the phase unchanged; and S7, repeatedly executing the steps to realize intelligent traffic signal control.

Description

Technical Field

[0001] The present invention belongs to, but is not limited to, the technical field of traffic management, and particularly relates to an adaptive traffic light control method and system based on real-time intersection conditions. Background Art

[0002] In the field of traffic signal control, traditional traffic light control systems generally adopt a fixed time cycle control strategy. The core of this strategy is to allocate fixed green and red light times to each direction of the intersection according to historical traffic data or preset rules. However, the actual traffic flow often shows a high degree of dynamism and uncertainty, and the fixed time cycle control strategy is difficult to effectively cope with this change, resulting in serious congestion at intersections during peak traffic hours, an increase in the vehicle queue length, and a decrease in traffic efficiency; while during periods of sparse traffic flow, it causes unnecessary waiting for pedestrians and vehicles, affecting the rational utilization of traffic resources.

[0003] Furthermore, intelligent traffic light systems attempt to optimize traffic signal control by presetting phase plans for different time periods. These systems usually switch corresponding signal control plans according to different time periods of the day (such as morning and evening rush hours, off-peak hours, etc.) to better adapt to the changes in traffic flow. However, this improvement is still limited to the preset time periods and phase plans and cannot respond in real time to sudden changes in traffic conditions, such as traffic accidents, special events, or traffic flow surges or drops caused by weather factors.

[0004] In the prior art, although there have been some studies and practices on applying intelligent algorithms to traffic signal control, most of these solutions focus on optimizing traffic flow in specific scenarios, such as control strategies at a single intersection, a specific road section, or a specific traffic pattern, and lack an adaptive control method that can be generally applied to intersections of different shapes and respond in real time to changes in traffic conditions.

[0005] In view of the above analysis, the technical problems that urgently need to be solved in the prior art are as follows:

[0006] Limitations of fixed time cycle control: Unable to respond in real time to the dynamic changes in traffic flow, resulting in serious congestion during peak hours and unnecessary waiting during periods of sparse traffic flow.

[0007] Deficiencies of preset phase plans: Although they can switch phase plans according to different time periods, they cannot cope with real-time changes and diverse traffic conditions.

[0008] Lack of a generally applicable adaptive control method: Most of the existing applications of intelligent algorithms in traffic signal control are targeted at specific scenarios or problems, lacking an adaptive control method that can be generally applied to intersections of different shapes and respond in real time to changes in traffic conditions. Summary of the Invention

[0009] In view of the problems in the existing traffic light control system that the fixed-time cycle control cannot adapt to the changes in real-time traffic flow and the preset phase scheme is difficult to cope with emergencies, the present invention aims to provide an adaptive traffic light control method and system based on real-time intersection conditions. The algorithm realizes the adaptive control of the traffic light duration at intersections of different shapes by collecting traffic data in real time and comprehensively analyzing multiple groups of parameters, so as to optimize the traffic flow, improve the road traffic efficiency, reduce congestion and waiting time, and thus enhance the public travel experience and traffic safety level.

[0010] The present invention is implemented as follows. An adaptive traffic light control method based on real-time intersection conditions includes:

[0011] S1: Collect the traffic flow data of each phase in real time.

[0012] S2: Judge whether the current phase time exceeds the longest green light duration or is less than the shortest green light duration, and adjust the phase according to the result.

[0013] S3: Calculate the congestion coefficient of each phase according to the traffic flow data, and judge whether the current phase is congested by comparing the congestion coefficient of the current phase with the preset threshold.

[0014] S4: If it is not congested, switch the phase. If it is congested, calculate the temporary time according to the formula.

[0015] S5: Judge whether the current phase time exceeds the temporary time. If it exceeds, switch the phase; otherwise, continue to the next step.

[0016] S6: Judge whether the congestion coefficient of other phases is greater than 1.5 times the congestion coefficient of the current phase. If so, switch the phase; otherwise, the phase remains unchanged.

[0017] S7: Repeat the above steps to realize intelligent traffic signal control.

[0018] Another object of the present invention is to provide an adaptive traffic light control system based on real-time intersection conditions for implementing the above-mentioned adaptive traffic light control method based on real-time intersection conditions, including:

[0019] Real-time traffic flow monitoring module: Use intersection cameras or other sensors to monitor the traffic flow of each phase in real time.

[0020] Phase congestion judgment module: Compare the real-time monitored traffic flow with the preset congestion coefficient threshold to judge whether the current phase is congested.

[0021] Intelligent switching module: Determine whether the phase time exceeds the longest green light time and is less than the shortest green light duration, and make adjustments according to the results. Calculate the congestion coefficient of the current phase. When the congestion coefficient of the current phase is less than the threshold, quickly switch the phase to reduce the pedestrian waiting time. When the traffic flow is at a medium level, determine whether to switch the phase based on the passing duration T and the calculated temporary time. If the congestion coefficient of other phases exceeds 1.5 times that of the current phase T, give priority to switching to that intersection to relieve more serious congestion.

[0022] Parameter adjustment module: Introduce the parameter C (1 - 2, default is 1) to adjust the sensitivity of the traffic flow to the temporary duration T temp to meet the control requirements under different traffic conditions.

[0023] Data acquisition module, used to collect traffic data such as traffic flow, vehicle speed, and queue length of each phase in real time.

[0024] Congestion coefficient calculation module, used to calculate the congestion coefficient of each phase according to the traffic data and determine whether the current phase is congested.

[0025] Time control module, used to determine whether the current green light duration exceeds the preset longest green light duration or is less than the shortest green light duration, and dynamically adjust the green light duration.

[0026] Temporary time calculation module, used to calculate the temporary time according to a preset formula and allocate additional green light time when the current phase is congested.

[0027] Priority switching module, used to determine whether the congestion coefficient of other phases exceeds 1.5 times that of the current phase congestion coefficient, and give priority to switching to the more congested phase.

[0028] Signal control module, used to execute the dynamic adjustment of traffic signals and phase switching according to the judgment results of each module.

[0029] Loop execution module, used to repeat the above steps to achieve intelligent traffic signal control.

[0030] Furthermore, the data acquisition module includes a traffic flow detection sensor and a queue length detection sensor, used to obtain the dynamic traffic data of each phase in real time.

[0031] Furthermore, the congestion coefficient calculation module calculates the congestion coefficient according to the following formula:

[0032]

[0033] And compare the congestion coefficient with a preset threshold to determine whether there is congestion.

[0034] Further, the time control module sets the longest green light duration to 120 seconds and the shortest green light duration to 10 seconds to ensure basic traffic fairness.

[0035] Further, the temporary time calculation module calculates the temporary time according to the following formula:

[0036] Temporary time = shortest time + adjustment coefficient x (longest time - shortest time).

[0037] It is used to calculate the communication duration of the temporary green light.

[0038] Further, when the priority switching module determines that the congestion coefficient of other phases exceeds 1.5 times that of the current phase, it switches to that phase and gives priority to dredging.

[0039] Another object of the present invention is to provide a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the adaptive traffic light control method based on the real-time intersection conditions.

[0040] Another object of the present invention is to provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by the processor, the processor executes the steps of the adaptive traffic light control method based on the real-time intersection conditions.

[0041] Another object of the present invention is to provide an information data processing terminal, which includes the adaptive traffic light control system based on the real-time intersection conditions.

[0042] Combined with the above technical solutions and the solved technical problems, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0043] The advantages of the present invention include:

[0044] 1. Significantly improve road traffic efficiency and reduce congestion.

[0045] 2. Optimize the pedestrian waiting time and improve public satisfaction.

[0046] 3. Strong adaptability, capable of adapting to the management requirements under different traffic flow conditions.

[0047] 4. Have the ability to cope with information loss and ensure the stability of the traffic management system.

[0048] 5. Through the sensitivity adjustment parameter C, flexible traffic management strategy adjustment can be realized.

[0049] As the creative auxiliary evidence of the claims of the present invention, it is also reflected in the following important aspects:

[0050] After the technical solution of the present invention is transformed, it will significantly improve the road traffic efficiency, reduce traffic congestion, and thus optimize the public travel experience. This intelligent traffic signal control system can respond to the changes in traffic flow in real time and adaptively adjust the duration of traffic lights, enabling more reasonable utilization of traffic resources. This will bring significant social benefits, such as reducing traffic accidents and environmental pollution. At the same time, this technical solution also has high commercial value and can be applied to fields such as urban traffic management and the construction of intelligent transportation systems, bringing considerable economic benefits to relevant enterprises and institutions.

[0051] Currently, traditional traffic light control systems generally adopt a fixed-time cycle control strategy and cannot respond to the dynamic changes in traffic flow in real time. Although there are already some intelligent traffic light systems that attempt to optimize traffic signal control by pre-setting phase schemes for different time periods, these systems are still limited to the pre-set time periods and phase schemes and cannot respond to sudden changes in traffic conditions in real time. The technical solution of the present invention proposes an adaptive traffic light control method and system based on real-time intersection conditions, filling the technical gap in this field at home and abroad. This technology can collect traffic data in real time, conduct comprehensive analysis by combining multiple groups of parameters, and achieve adaptive control of the traffic light duration, thereby optimizing traffic flow and improving road traffic efficiency.

[0052] In the past, it was generally believed that the design of traffic light control systems could only rely on fixed-time cycle control strategies or pre-set phase schemes. However, this concept can no longer meet the needs of modern urban traffic management. The technical solution of the present invention breaks this technical prejudice and proposes a new adaptive traffic light control method and system based on real-time intersection conditions. This technology can respond to the changes in traffic flow in real time and adaptively adjust the duration of traffic lights, thus realizing more intelligent and efficient traffic signal control. This not only improves road traffic efficiency but also optimizes the public travel experience, bringing new ideas and methods to urban traffic management. Brief Description of the Drawings

[0053] Figure 1 is a flowchart of the adaptive traffic light control method based on real-time intersection conditions provided by an embodiment of the present invention.

[0054] Figure 2 is a structural diagram of the adaptive traffic light control system based on real-time intersection conditions provided by an embodiment of the present invention.

[0055] Figure 3 is a logic diagram of the control algorithm provided by an embodiment of the present invention. Detailed Embodiments

[0056] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] Embodiment 1: Adaptive control of traffic lights at intersections of urban arterial roads during peak hours

[0058] At an intersection of an urban arterial road, during the morning peak period, the traffic flow in the east-west direction is much larger than that in the north-south direction, and the dynamic change of vehicle flow is obvious.

[0059] 1) Real-time data collection (corresponding to S1): The traffic flow, vehicle speed and queue length in each direction are collected through traffic flow detectors at the intersection. During the morning peak period, the traffic flow in the east-west direction is about twice that in the north-south direction, and the initial congestion coefficients are 1.8 and 0.8 respectively.

[0060] 2) Preliminary judgment (corresponding to S2): The current phase is a green light in the east-west direction. The system determines that the current green light time is close to the set maximum duration, and adjusts the green light duration according to the algorithm to extend the green light time to relieve the traffic flow in the east-west direction.

[0061] 3) Congestion analysis (corresponding to S3-S4): The system calculates the congestion coefficient of each phase, and determines that the current congestion coefficient in the east-west direction is higher than the preset threshold, and the traffic flow in the north-south direction is low, and continues to allocate temporary time to the east-west direction.

[0062] 4) Priority phase switching (corresponding to S5-S6): After the temporary time, the system compares the congestion coefficients in the north-south direction and the east-west direction, and finds that the congestion coefficient in the north-south direction is 1.2, which is not higher than 1.5 times that in the east-west direction. Therefore, the current phase is maintained to give priority to dredging the east-west direction.

[0063] 5) Loop optimization (corresponding to S7): The system repeats the above steps, and effectively relieves the congestion of the arterial road during the morning peak period by adjusting the green light duration in the east-west and north-south directions in real time.

[0064] Embodiment 2: Dynamic regulation of vehicle flow at the intersection of a seaside scenic area during holidays

[0065] During holidays, the number of tourists in a seaside scenic area surges, the traffic flow distribution at the entrance and exit is uneven, the queue at the entrance direction is serious, and the traffic flow at the exit direction is small.

[0066] 1) Real-time data collection (corresponding to S1): The vehicle queue length in the entrance direction reaches 50 vehicles, the traffic flow is 80 vehicles per minute, and the traffic flow in the exit direction is only 30 vehicles per minute. The system records the traffic flow and queue data through sensors.

[0067] 2) Preliminary judgment (corresponding to S2): The current phase is a green light in the entrance direction. The system determines that the green light time has not exceeded the maximum green light duration but is approaching the minimum green light duration. Therefore, the green light time is continued to be extended.

[0068] 3) Congestion analysis and temporary time allocation (corresponding to S3 - S4): The system calculates that the congestion coefficient in the entrance direction is 2.0 and in the exit direction is 0.6. The congestion coefficient in the entrance direction far exceeds the preset threshold. Therefore, a temporary time of 30 seconds is allocated, and the green light status is continued for the entrance direction.

[0069] 4) Phase - switching judgment (corresponding to S5 - S6): After the temporary time ends, the system checks whether the congestion coefficient in the exit direction exceeds 1.5 times that in the entrance direction. It is found that the congestion coefficient in the exit direction is still 0.6, which is lower than the switching condition. Therefore, the green light status in the entrance direction is continued.

[0070] 5) Loop adjustment (corresponding to S7): The system continuously extends the green light time in the entrance direction according to the real - time traffic flow changes until the queue in the entrance direction is relieved, and then switches to the green light in the exit direction. Finally, the dynamic optimization of the scenic area traffic during the peak tourist period is achieved.

[0071] These two embodiments respectively target different scenarios (the peak of urban arterial roads and the holiday traffic flow in scenic areas). By collecting data in real - time, dynamically adjusting the green light duration and phase - switching strategies, the congestion problem is effectively alleviated, and the overall traffic flow passing efficiency is improved.

[0072] As Figure 1 shown, the implementation steps of the adaptive traffic light control based on real - time intersection conditions of the present invention are as follows:

[0073] S1: Collect the traffic flow data of each phase in real - time.

[0074] S2: Judge whether the current phase time exceeds the maximum green light duration or is less than the minimum green light duration according to the current phase time, and adjust the phase according to the result.

[0075] S3: Calculate the congestion coefficient of each phase according to the traffic flow data, and judge whether the current phase is congested by comparing the congestion coefficient of the current phase with the preset threshold.

[0076] S4: If not congested, switch the phase. If congested, calculate the temporary time according to the formula.

[0077] S5: Judge whether the current phase time exceeds the temporary time. If it exceeds, switch the phase; otherwise, continue to the next step.

[0078] S6: Judge whether the congestion coefficient of any other phase is greater than 1.5 times the congestion coefficient of the current phase. If so, switch the phase; otherwise, the phase remains unchanged.

[0079] S7: Repeat the above steps to achieve intelligent traffic signal control.

[0080] Adaptive traffic light control working principle based on real-time intersection conditions:

[0081] 1. Data collection and basic judgment

[0082] The system collects real-time traffic flow data of each phase (different directions in the traffic light cycle) through sensors (S1). These data include traffic flow, vehicle speed, queue length, etc. First, the system determines whether the duration of the green light in the current phase exceeds the maximum green light duration or is less than the minimum green light duration (S2). This is to ensure basic traffic fairness and safety. When it exceeds or is less than these durations, the system adjusts the current phase time to optimize traffic efficiency.

[0083] 2. Congestion status judgment and dynamic adjustment

[0084] Through the real-time collected traffic flow data, the system calculates the congestion coefficient of each phase (S3). The congestion coefficient is a key indicator reflecting the current traffic condition, usually calculated comprehensively by parameters such as traffic flow, road capacity ratio, and queue length. The congestion coefficient of the current phase is compared with a preset threshold. If it is lower than the threshold, it means there is no congestion, and at this time, the system will switch to the next phase (S4). If there is congestion, the system calculates the temporary time through a preset formula and allocates additional green light time for the current phase to relieve congestion.

[0085] 3. Temporary time judgment and priority phase switching

[0086] After allocating the temporary time, the system continuously monitors the green light time of the current phase (S5). If the green light time has exceeded the temporary time, the system immediately switches to the next phase. If it has not exceeded the temporary time, the system further determines whether the congestion coefficient of other phases is greater than 1.5 times the congestion coefficient of the current phase (S6).

[0087] If there is such a phase, it will preferentially switch to the more congested phase to give priority to dredging the seriously congested direction; otherwise, the current phase remains unchanged and continues to provide passing time for vehicles.

[0088] 4. Periodic cycle and intelligent optimization

[0089] The above process (S1 to S6) is cyclically executed in the system to achieve real-time dynamic adjustment of traffic signals (S7). By combining real-time data collection, congestion coefficient calculation, and phase priority judgment, the system continuously adjusts the switching time and sequence of traffic lights to dynamically adapt to changes in traffic flow. This adaptive control strategy effectively improves the traffic efficiency at intersections, reduces the vehicle queue length and average waiting time, thus realizing intelligent traffic signal control and optimization.

[0090] Real-time collection: The system collects vehicle flow, vehicle speed, and queue length as the control basis.

[0091] Congestion judgment: Calculate the congestion coefficient to evaluate the traffic condition.

[0092] Dynamic adjustment: Through temporary time and priority judgment, realize intelligent phase switching.

[0093] Cyclic optimization: Continuously iterate the above steps and dynamically adjust the traffic light control strategy according to the road conditions.

[0094] This design ensures the intelligent response ability of traffic signals, maximizes the traffic efficiency at intersections, and reduces congestion and unnecessary waiting time at the same time.

[0095] Such as Figure 2 shown, the adaptive traffic light control system based on real-time intersection conditions includes:

[0096] Real-time vehicle flow monitoring module: Use intersection cameras or other sensors to monitor the vehicle flow of each phase in real time.

[0097] Phase congestion judgment module: Compare the real-time monitored vehicle flow with the preset congestion coefficient threshold to judge whether the current phase is congested.

[0098] Intelligent switching module: Judge whether the phase time exceeds the longest green light time and is less than the shortest green light duration, and adjust according to the results. Calculate the congestion coefficient of the current phase. When the congestion coefficient of the current phase is less than the threshold, quickly switch the phase to reduce the pedestrian waiting time. When the vehicle flow is at a medium level, judge whether to switch the phase according to the passing duration T and the calculated temporary time. If the congestion coefficient of other phases exceeds 1.5 times of the current phase T, give priority to switching to that intersection to relieve more serious congestion.

[0099] Parameter adjustment module: Introduce the parameter C (1-2, default is 1) to adjust the sensitivity of vehicle flow to the passing temporary duration T temp to meet the control requirements under different traffic conditions.

[0100] Embodiment 1:

[0101] The present invention provides a specific implementation method for optimizing the intelligent traffic signal control algorithm. This solution mainly includes the following steps and structures:

[0102] I. System structure

[0103] 1. Data collection module: Use intersection cameras or other sensors to monitor the vehicle flow data of each phase in real time and transmit the data to the control unit.

[0104] 2. Control Unit: Receives the traffic flow data transmitted by the data acquisition module, makes judgments and decisions according to the preset algorithm, and outputs control signals to the traffic lights.

[0105] 3. Traffic Lights: Receives the control signals from the control unit and changes the current phase.

[0106] II. Algorithm Implementation Steps

[0107] 1. Real-time collection of traffic flow data: Through the data acquisition module, the traffic flow data of each phase is obtained in real time.

[0108] 2. Judgment of phase time: The control unit receives the time information of the current phase and judges whether it exceeds the longest green light duration or is less than the shortest green light duration. If it exceeds the longest green light duration, the phase is directly switched; if it is less than the shortest green light duration, the current phase remains unchanged.

[0109] 3. Calculation of congestion coefficient: According to the traffic flow data, the congestion coefficient of each phase is calculated. Different calculation methods can be selected for the congestion coefficient according to the actual situation, such as calculating according to the ratio of the number of vehicles to the number of congested vehicles in the preset lane.

[0110] 4. Judgment of whether the current phase is congested: Compare the congestion coefficient of the current phase with the preset threshold. If the congestion coefficient is greater than the threshold, it is judged as congested; otherwise, it is judged as not congested.

[0111] 5. Handling in case of congestion:

[0112] If the current phase is congested and the time has not reached the temporary time threshold calculated by the time adjustment parameters K and C, the current phase remains unchanged.

[0113] If the current phase is congested and the time has exceeded the temporary time threshold, the phase is switched.

[0114] When switching phases, consider the congestion situation of other phases. If the congestion coefficient of other phases exceeds 1.5 times that of the current phase, switch to that phase preferentially to relieve more serious congestion.

[0115] 6. Handling in case of non-congestion: If the current phase is not congested and the relative number of vehicles is less than that of other phases, quickly switch the phase to reduce the waiting time of pedestrians.

[0116] 7. Repeated execution: The control unit repeats the above steps to achieve intelligent control of the traffic lights.

[0117] III. Parameter Adjustment

[0118] To meet the control requirements under different traffic conditions, the present invention introduces a parameter C to adjust the sensitivity of traffic flow to passing time. The value range of parameter C is 1 to 2, and the default value is 1. By adjusting parameter C, flexible control of different traffic flows can be achieved.

[0119] Embodiment 2:

[0120] As an alternative implementation of Embodiment 1, this embodiment optimizes some steps in the algorithm implementation.

[0121] 1. Real-time collection of traffic flow data: The same as Embodiment 1, the data collection module is used to obtain the traffic flow data of each phase in real time.

[0122] 2. Judgment of phase time: Similarly, the control unit receives the time information of the current phase, judges whether it exceeds the longest green light duration or is less than the shortest green light duration, and makes adjustments according to the results.

[0123] 3. Calculation of congestion coefficient and judgment of congestion: The same as Embodiment 1, calculate the congestion coefficient of each phase and compare it with the preset threshold to judge whether the current phase is congested.

[0124] 4. Optimization processing in case of congestion:

[0125] If the current phase is congested and the time has not reached the temporary time threshold, consider whether there is serious congestion in other phases (the congestion coefficient exceeds 1.5 times that of the current phase). If so, give priority to switching to that phase; otherwise, keep the current phase unchanged.

[0126] If the current phase is congested and the time has exceeded the temporary time threshold, directly switch the phase. When switching the phase, consider the comprehensive influence of traffic flow and the number of vehicles, and select the optimal phase for switching.

[0127] 5. Processing in case of non-congestion: If the current phase is not congested, quickly switch the phase according to the changes in traffic flow and the number of vehicles to optimize traffic fluency.

[0128] 6. Introduction of green wave control: In specific situations (such as when the traffic light phases in the connected directions of two intersections meet specific conditions), introduce the green wave control algorithm to further improve traffic efficiency.

[0129] 7. Repeat execution: The control unit repeats the above steps to achieve intelligent control of traffic lights.

[0130] From the description of the above embodiments, it can be seen that the intelligent traffic signal control algorithm optimization scheme provided by the present invention can effectively perform intelligent control according to real-time traffic flow and phase congestion conditions, improve traffic efficiency and reduce pedestrian waiting time.

[0131] As Figure 3 shown in the algorithm logic diagram of the present invention, the meanings, ranges, and calculation methods of the parameters are as follows:

[0132] K is the congestion coefficient of the current phase.

[0133] Thresh is the congestion coefficient threshold of the current phase.

[0134] T max The longest green light duration.

[0135] T min The shortest green light duration.

[0136] T temp The calculated temporary green light duration.

[0137] T is the current phase time.

[0138] w is the weight of each phase.

[0139] i is the different phase number.

[0140] C is a coefficient that controls the length of the temporary green light duration;

[0141] T temp = T min + K C *(T max - T min )

[0142] 0 <= K <= 1

[0143] 1 <= C <= 2

[0144] T max > T temp > T min 。

[0145] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. When the computer program stored in the memory is executed by the processor, the processor executes the steps of an adaptive traffic light control method based on the real-time intersection conditions.

[0146] An application embodiment of the present invention provides a computer-readable storage medium, storing a computer program. When the computer program is executed by the processor, the processor executes the steps of an adaptive traffic light control method based on the real-time intersection conditions.

[0147] An application embodiment of the present invention provides an information data processing terminal, which includes an adaptive traffic light control system based on the real-time intersection conditions.

[0148] Evidence related to the technical effects obtained in the embodiments of the present invention.

[0149] Experimental site: The same actual traffic road is used, with the adaptive traffic light control method designed being adopted and not adopted as the experimental group and the control group respectively.

[0150] Experimental equipment: In the experimental group, the adaptive traffic light control system of the present invention is adopted at the intersection, while in the control group, the traditional fixed-time cycle control strategy is adopted.

[0151] Monitoring equipment: Key indicators such as traffic flow data, congestion coefficient, and vehicle waiting time are collected in real time through monitoring videos.

[0152] Experimental steps:

[0153] Data collection: During the experiment, traffic data at the intersection at the same time period on different days is continuously collected, including traffic flow, congestion coefficient, traffic light switching time, vehicle waiting time, etc. for each phase.

[0154] Data analysis: The collected data is sorted and analyzed to compare the differences between the experimental group and the control group in terms of traffic efficiency, congestion status, waiting time, etc.

[0155] Experimental data

[0156] Average traffic flow (vehicles / h) Average congestion coefficient Improvement in traffic efficiency (%) Experimental group (adaptive system) 0.6 25％ Control group (traditional system) 0.95 -

[0157] Analysis of experimental results. Improvement in traffic efficiency: The experimental data shows that the average traffic efficiency at the intersection of the experimental group has increased by 25% compared to the control group. This is mainly due to the fact that the adaptive traffic light control system can adjust the traffic light duration in real time according to the traffic flow, effectively reducing congestion.

[0158] Improvement in congestion status: The average congestion coefficient at the intersection of the experimental group is significantly lower than that of the control group, indicating that the adaptive system can relieve traffic congestion more effectively.

[0159] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated designed hardware. Those of ordinary skill in the art can understand that the above-described devices and methods can be implemented using computer-executable instructions and / or included in processor control code, such as provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and their modules of the present invention can be implemented by hardware circuits of programmable hardware devices such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above hardware circuits and software such as firmware.

[0160] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be covered by the protection scope of the present invention.

Claims

1. An adaptive traffic light control method based on real-time intersection conditions, characterized in that, Including: S1: Real-time collect the traffic flow data of each phase; S2: Determine whether it exceeds the longest green light duration or is less than the shortest green light duration according to the current phase time, and adjust the phase according to the result; S3: Calculate the congestion coefficient of each phase according to the traffic flow data, and judge whether the current phase is congested by comparing the congestion coefficient of the current phase with the preset threshold; S4: If it is not congested, switch the phase. If it is congested, calculate the temporary time according to the formula; S5: Judge whether the time of the current phase exceeds the temporary time according to the time of the current phase. If it exceeds, switch the phase, otherwise continue to the next step; S6: Judge whether the congestion coefficient of other phases is greater than 1.5 times the congestion coefficient of the current phase. If so, switch the phase, otherwise the phase remains unchanged; S7: Repeat the above steps to achieve intelligent traffic signal control; Data acquisition module, used to collect traffic data such as traffic flow, vehicle speed and queue length of each phase in real time; Congestion coefficient calculation module, used to calculate the congestion coefficient of each phase according to the traffic data and judge whether the current phase is congested; Time control module, used to judge whether the current green light duration exceeds the preset longest green light duration or is less than the shortest green light duration, and dynamically adjust the green light duration; Temporary time calculation module, used to calculate the temporary time according to the preset formula and allocate additional green light time when the current phase is congested; Priority switching module, used to judge whether the congestion coefficient of other phases exceeds 1.5 times the congestion coefficient of the current phase, and give priority to switching to the more congested phase; Signal control module, used to execute the dynamic adjustment and phase switching of traffic signals according to the judgment results of each module; Loop execution module, used to repeat the above steps to achieve intelligent traffic signal control.

2. The adaptive traffic light control method based on real-time intersection conditions according to claim 1, wherein The data acquisition module includes a traffic flow detection sensor and a queue length detection sensor, used to obtain the dynamic traffic data of each phase in real time.

3. The adaptive traffic light control method based on real-time intersection conditions according to claim 1, wherein, The congestion coefficient calculation module calculates the congestion coefficient according to the following formula: And compare the congestion coefficient with the preset threshold to judge whether there is congestion.

4. The adaptive traffic light control method based on real-time intersection conditions according to claim 1, characterized in that The time control module sets the longest green light duration to 120 seconds and the shortest green light duration to 10 seconds.

5. The adaptive traffic light control method based on real-time intersection conditions according to claim 1, characterized in that, The temporary time calculation module calculates the temporary time according to the following formula: Temporary time = shortest time + adjustment coefficient x (longest time - shortest time) Used to calculate the communication duration of the temporary green light.

6. The adaptive traffic light control method based on real-time intersection conditions according to claim 1, wherein, When the priority switching module judges that the congestion coefficient of other phases exceeds 1.5 times the congestion coefficient of the current phase, it switches to that phase and gives priority to dredging.

7. An adaptive traffic light control system based on real-time intersection conditions for implementing the adaptive traffic light control method based on real-time intersection conditions as claimed in claim 1, characterized in that, Including: Real-time traffic flow monitoring module: Use intersection cameras or other sensors to monitor the traffic flow of each phase in real time; Phase congestion judgment module: Compare the real-time monitored traffic flow with the preset congestion coefficient threshold to judge whether the current phase is congested; Intelligent switching module: Judge whether the phase time exceeds the longest green light time and whether it is less than the shortest green light duration, and adjust according to the result; Calculate the congestion coefficient of the current phase. When the congestion coefficient of the current phase is less than the threshold, quickly switch the phase to reduce the waiting time of pedestrians; When the traffic flow is at a medium level, judge whether it is necessary to switch the phase according to the passing duration T and the calculated temporary time; If the congestion coefficient of other phases exceeds 1.5 times that of the current phase T, it is preferred to switch to that intersection first to relieve more serious congestion; Parameter adjustment module: Introduce parameter C to adjust the sensitivity of traffic flow to passing time T temp to meet the control requirements under different traffic conditions.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor is caused to execute the steps of the adaptive traffic light control method based on real-time intersection conditions as described in claim 1.

9. A computer-readable storage medium, characterized in that, Stores a computer program. When the computer program is executed by the processor, the processor is caused to execute the steps of the adaptive traffic light control method based on real-time intersection conditions as described in claim 1.

10. An information data processing terminal, characterized in that, The information data processing terminal includes the adaptive traffic light control system based on real-time intersection conditions as described in claim 7.

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