Intelligent traffic regulation and control method and system based on 5G cloud computing terminal technology

Abstract

The invention discloses an intelligent traffic regulation and control method based on a 5G cloud computing terminal technology. The method comprises the steps that data such as traffic flow parameters, weather condition parameters and emergency parameters are collected in real time through a 5G network; performing fusion processing on the data of the traffic flow parameters, the weather condition parameters and the emergency parameters, and judging a comprehensive traffic state based on a fusion algorithm; a regulation and control strategy is generated and applied according to the traffic state, a signal lamp and an electronic signboard are controlled through a 5G terminal, the traffic flow is dynamically adjusted, and vehicles are guided to avoid a congested area; synchronously, predicting future traffic flow by using a traffic flow prediction algorithm; and when it is predicted that the future traffic flow possibly exceeds the road section bearing capacity, dynamic speed limiting adjustment is performed.

Description

Technical Field

[0001] The present application relates to the field of smart transportation technology, and specifically to a smart transportation control method and system based on 5G cloud computing terminal technology. Background Art

[0002] With the development of urbanization, traffic pressure is increasing. Traditional traffic control methods cannot adapt to the rapidly changing traffic conditions in modern cities, and cannot quickly respond to dynamically changing traffic flows. Traditional traffic management systems usually rely on local sensors and centralized data processing centers, and there are certain delays in signal light adjustment and traffic flow guidance. For complex and rapidly changing traffic conditions, traditional systems cannot make corresponding decisions in a short enough time. Most of the existing traffic management is based on predetermined rules or static models, such as fixed signal light cycles or simple traffic management models, which cannot respond to dynamic factors such as weather and emergencies in real time, resulting in a lack of flexibility in traffic control, and prone to traffic congestion or inefficient traffic flow management.

[0003] Therefore, the emergence of 5G technology, through its high bandwidth, low latency and wide coverage, combined with the super processing power of cloud computing, provides technical support for the implementation of smart transportation. Summary of the invention

[0004] The purpose of this application is to provide a smart traffic control method and system based on 5G cloud computing terminal technology, through the high-speed transmission of 5G and the distributed data processing architecture of cloud computing terminals, to achieve dynamic monitoring and intelligent control of complex traffic conditions, improve traffic management efficiency, and reduce traffic congestion.

[0005] To achieve the above objectives, this application document provides a smart traffic control method based on 5G cloud computing terminal technology, including: using the 5G network to collect data such as traffic flow parameters, weather condition parameters, and emergency parameters in real time; fusing the data of traffic flow parameters, weather condition parameters, and emergency parameters, and judging the comprehensive traffic status based on the fusion algorithm; and generating and applying control strategies based on the traffic status, controlling traffic lights and electronic signs through 5G terminals, dynamically adjusting traffic flow, and guiding vehicles to avoid congested areas; synchronously, using traffic flow prediction algorithms to predict future traffic flow; when it is predicted that future traffic flow may exceed the carrying capacity of the road section, dynamic speed limit adjustment is implemented.

[0006] Furthermore, the traffic flow prediction algorithm is implemented using the following formula: T2=λ·T1+(1-λ)·T3; wherein T1 is the real-time traffic flow parameter, T2 is the predicted traffic flow value, T3 is the historical traffic flow parameter, and λ is the balancing weight parameter.

[0007] Furthermore, the balance weight parameter is set to balance the influence of the current traffic flow parameter and the historical traffic flow parameter, and the value of the balance weight parameter is adjusted in real time to adapt to the changes in the traffic environment.

[0008] Preferably, the weight parameters α, β, and γ in the fusion algorithm are dynamically adjusted according to the current traffic environment, the α weight is higher to reflect the importance of real-time traffic flow, the β weight changes according to weather conditions, and the γ weight is set according to the severity of the emergency.

[0009] Preferably, when the system predicts that future traffic conditions will reach a critical value for congestion, route optimization suggestions and traffic warning notifications are sent to vehicle drivers to guide vehicles to detour and alleviate traffic pressure.

[0010] The present application document also provides a smart traffic control system based on 5G cloud computing terminal technology. The system is applicable to any of the above-mentioned smart traffic control methods based on 5G cloud computing terminal technology. The system includes: a distributed traffic data acquisition module and a multi-source data fusion and analysis module; wherein the distributed traffic data acquisition module collects traffic flow parameters, weather condition parameters and emergency parameters of the road section in real time; the multi-source data fusion and analysis module performs real-time fusion processing on traffic flow, weather and emergency data based on a fusion algorithm to generate an evaluation result of the comprehensive traffic status.

[0011] Further preferably, the system also includes: an intelligent prediction module and a path guidance module. The intelligent prediction module generates a control plan based on the traffic flow prediction results, controls the traffic light timing and dynamically adjusts the traffic light timing through the 5G terminal, and the path guidance module realizes traffic sign guidance information and completes the speed limit and diversion plan.

[0012] Further preferably, the system also includes: an end-edge-cloud collaborative computing module, which dynamically allocates complex computing tasks to cloud or edge nodes through distributed cloud computing terminals, thereby improving the computing efficiency of the system under high traffic load.

[0013] Preferably, the distributed traffic data collection module collects data from multiple road sections in real time through a variety of collection devices, and uses the high-speed transmission function of the 5G network to ensure low-latency transmission of the data to the cloud for analysis and processing.

[0014] Compared with the prior art, the beneficial effects of this application are: innovations have been made in the module composition of the system. In order to distinguish it from the existing traffic system, this intelligent traffic control system not only makes innovations in hardware, but also introduces new functional modules in module design, and improves the overall control capability of the system through self-created algorithms. Among them, the distributed traffic data collection module collects data from multiple city nodes through the 5G network, including road conditions, traffic light status, vehicle speed, weather, etc. Different from the existing centralized data collection, this system collects data through multiple cloud terminal nodes in a distributed manner, and the computing load of each node is optimized through dynamic load balancing to ensure the stability of data transmission during peak hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of the intelligent traffic control method based on 5G cloud computing terminal technology in this application document. DETAILED DESCRIPTION

[0016] Below, the present application is further described in conjunction with specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0017] In a preferred embodiment, see Figure 1 , this application document provides a smart traffic control method based on 5G cloud computing terminal technology, including: step S10 uses the 5G network to collect data such as traffic flow parameters, weather condition parameters, and emergency parameters in real time; step S20 integrates the data of traffic flow parameters, weather condition parameters, and emergency parameters, and judges the comprehensive traffic status based on the fusion algorithm; step S31 generates and applies control strategies according to the traffic status, controls traffic lights and electronic signs through 5G terminals, dynamically adjusts traffic flow, and guides vehicles to avoid congested areas; step S32 synchronously uses a traffic flow prediction algorithm to predict future traffic flow; step S42 implements dynamic speed limit adjustment when it is predicted that future traffic flow may exceed the carrying capacity of the road section.

[0018] Preferably, when the system predicts that future traffic conditions will reach a critical value for congestion, route optimization suggestions and traffic warning notifications are sent to vehicle drivers to guide vehicles to detour and alleviate traffic pressure.

[0019] This application document also provides a smart traffic control system based on 5G cloud computing terminal technology. The system is applicable to the smart traffic control method based on 5G cloud computing terminal technology as mentioned above. The system includes: a distributed traffic data acquisition module and a multi-source data fusion and analysis module; wherein the distributed traffic data acquisition module collects traffic flow parameters, weather condition parameters and emergency parameters of the road section in real time; the multi-source data fusion and analysis module performs real-time fusion processing on traffic flow, weather and emergency data based on a fusion algorithm to generate an evaluation result of the comprehensive traffic status.

[0020] Therefore, preferably, during the traffic data collection stage, the distributed traffic data collection module collects data from multiple road sections in real time through a variety of collection devices, and uses the high-speed transmission function of the 5G network to ensure low-latency transmission of data to the cloud for analysis and processing.

[0021] The collection equipment includes surveillance cameras, vehicle-mounted sensors, road surface sensing devices and other equipment installed on various road sections, which collect traffic flow data, vehicle type, vehicle speed and other information in real time. Through the low-latency transmission of the 5G network, all collected data is uploaded to the 5G cloud computing terminal in real time.

[0022] Further preferably, the system also includes: an intelligent prediction module and a path guidance module. The intelligent prediction module generates a control plan based on the traffic flow prediction results, controls the traffic light timing and dynamically adjusts the traffic light timing through the 5G terminal, and the path guidance module realizes traffic sign guidance information and completes the speed limit and diversion plan.

[0023] Further preferably, the system also includes: an end-edge-cloud collaborative computing module, which dynamically allocates complex computing tasks to cloud or edge nodes through distributed cloud computing terminals, thereby improving the computing efficiency of the system under high traffic load.

[0024] Therefore, innovations were made in the module composition of the system. In order to distinguish it from the existing traffic system, this intelligent traffic control system not only made innovations in hardware, but also introduced new functional modules in module design, and improved the overall control capability of the system through self-created algorithms. Among them, the distributed traffic data collection module collects data from multiple city nodes through the 5G network, including road conditions, traffic light status, vehicle speed, weather, etc. Different from the existing centralized data collection, this system collects data through multiple cloud terminal nodes in a distributed manner, and the computing load of each node is optimized through dynamic load balancing to ensure the stability of data transmission during peak hours.

[0025] The multi-source data fusion and analysis module transmits multi-source data (such as traffic flow, lane occupancy, real-time events, weather conditions, etc.) to the cloud server through the 5G network, and cleans, integrates and analyzes the data. It not only simply processes a single data source, but also includes complex data such as weather changes and major events in the analysis scope. Combined with 5G's ultra-high-speed data transmission, the system can fuse these data within milliseconds and generate instant control suggestions. Furthermore, in the intelligent prediction and control module, through intelligent algorithms based on distributed computing, future traffic conditions are predicted, and traffic lights, electronic billboards and other facilities are adjusted in real time to guide traffic and reduce traffic jams. Unlike traditional control methods, the system combines the low latency characteristics of 5G, can process tens of billions of data points in real time, and based on innovative algorithms, predict traffic congestion points and perform instant control. The innovative algorithms include fusion algorithms and traffic flow prediction algorithms.

[0026] In the end-edge-cloud collaborative computing module, based on the distributed architecture of cloud computing, the system dynamically allocates computing tasks to edge nodes and cloud terminals to achieve faster response. Combined with 5G's edge computing technology, the system can dynamically determine the best execution location for computing tasks and reduce the computing pressure on the central server. This is especially effective when the road network is large or data-intensive.

[0027] Furthermore, the fusion algorithm is implemented based on the following formula: D=α·T1+β·W+γ·E; wherein D is the traffic status assessment result, T1 is the real-time traffic flow parameter, W is the weather condition parameter, such as applied to rainfall and temperature, E is the emergency parameter, such as applied to accidents and closed roads, and α, β, and γ are weight parameters dynamically adjusted according to the traffic conditions.

[0028] Preferably, the weight parameters α, β, and γ in the fusion algorithm are dynamically adjusted according to the current traffic environment, the α weight is higher to reflect the importance of real-time traffic flow, the β weight changes according to weather conditions, and the γ weight is set according to the severity of the emergency.

[0029] Among them, in a specific application scenario, the traffic state within a unit time t is evaluated, and the unit time t can be set differently according to different actual application scenarios, preferably between 30 minutes and 60 minutes. Therefore, in a unit time t, the unit time t is preferably 60 minutes. The following data is collected in the traffic management system in a certain city. The current traffic flow parameter T1 is 500 vehicles / hour, the weather state parameter W is 0.8, the weather state parameter value range is 0 to 1, 0.8 indicates moderate rainfall, and the emergency parameter E is 1, and the value 1 indicates an accident. Therefore, according to the above situation, the system sets the weight parameters to α=0.6, β=0.2, γ=0.2, and according to the fusion algorithm formula, the current traffic state evaluation result D is 0.6×500+0.2×0.8+0.2×1=300+0.16+0.2=300.36, so the current traffic flow is high, and there are bad weather and accidents, and traffic congestion is expected.

[0030] Among them, it should be noted that traffic flow, weather and traffic accidents are the main factors affecting traffic conditions. Therefore, the value of α is set to 0.6 because traffic flow is the most important factor affecting traffic conditions, with a higher proportion. The value of β is 0.2, which represents the impact of weather. Although the impact of weather on traffic is greater, its weight is slightly lower than that of traffic flow. The value of γ is 0.2, which represents the impact of emergencies (such as traffic accidents). Although serious emergencies may cause congestion, their frequency of occurrence is low, so the weight is set to 20%. Dynamic adjustment means that the system can flexibly adjust these parameters according to different traffic conditions. For example, in extreme weather (such as heavy rain), the weight of β may increase, while on sunny days, the weight of α may increase, reflecting the system's adaptability to actual environmental changes. Therefore, in a preferred embodiment, W takes a value of 0, indicating that the weather conditions are ideal and have no negative impact on traffic, while W takes a value of 0.5, indicating that there is slight rainfall, which has a certain impact on traffic flow. When the weather is bad, which greatly affects traffic flow and safety, W takes a value of 1. The above-mentioned traffic state evaluation result D is explained, and the calculation result of 300.36 represents the current comprehensive traffic state, which takes into account the comprehensive impact of traffic flow, weather and emergencies. This result indicates that the traffic conditions of the road section have reached a high load level and may face traffic congestion. In general, if the calculated value of the traffic state evaluation result D exceeds a certain critical value, preferably 300 in this embodiment, it means that the traffic volume carried by the road section is large, and combined with the current weather and accident impact, the system predicts that congestion may occur in a short time. The system will make further judgments in combination with the traffic flow prediction algorithm.

[0031] Furthermore, the traffic flow prediction algorithm is implemented using the following formula: T2=λ·T1+(1-λ)·T3; wherein T1 is a real-time traffic flow parameter, which is also the real-time traffic flow within the above-mentioned unit time t, t is preferably 60 minutes, T2 is the predicted traffic flow value, T3 is a historical traffic flow parameter, which represents the traffic flow at the same time in history, preferably the traffic flow 60 minutes ago, and λ is a balancing weight parameter.

[0032] Furthermore, the balance weight parameter is set to balance the influence of the current traffic flow parameter and the historical traffic flow parameter, and the value of the balance weight parameter is adjusted in real time to adapt to the changes in the traffic environment.

[0033] Among them, when it is detected that the real-time traffic flow parameter T1 within a certain unit time t is 500 vehicles / hour and the historical traffic flow parameter T3 is 450 vehicles / hour, the balance weight parameter is preferably taken as 0.7, indicating that the system is more inclined to use the current real-time traffic for prediction, and the importance of historical data is relatively low.

[0034] Therefore, the predicted traffic flow value for the next 60 minutes is calculated as T2=0.7×500+(1-0.7)×450=485 vehicles / hour, and the traffic flow will decrease in the future.

[0035] Among them, a λ value of 0.7 indicates that the current traffic flow is more important to future traffic flow prediction than historical data. For example, a λ value of 0.3 corresponds to historical data in the previous unit time. The setting of λ reflects the system's balance between real-time and historical rules. For example, when real-time traffic dominates during peak hours, the system is more inclined to make predictions based on current traffic flow; while during off-peak or relatively stable periods, the weight of historical traffic data may be higher. The system uses λ to decide whether to rely more on current real-time data or historical data. A larger λ value indicates that real-time data is the main reference, and a lower λ value indicates that the system refers to historical trends.

[0036] Then, the system can predict the future traffic conditions by predicting the traffic flow value and give control suggestions. For example, if the predicted traffic flow value far exceeds the current traffic flow value, the system will take measures in advance, such as diverting vehicles.

[0037] Therefore, in this embodiment, the value of λ is 0.7, which means that the system focuses more on the impact of current traffic data, while the impact of historical data is relatively small. If the value of λ is 0.5 in another implementation, it means that the system regards current traffic and historical traffic as equally important. The predicted results of future traffic flow can be used for signal light control schemes and guiding driver route planning to prevent traffic congestion.

[0038] Furthermore, in order to realize a more intelligent traffic control system, the present application further introduces a traffic congestion risk assessment method. The traffic congestion risk assessment method is based on the calculation results of the above-mentioned traffic state fusion formula and the traffic flow prediction formula, and is then used to assess the congestion risk of a specific road section, and further determine the signal light duration adjustment and traffic flow guidance strategy.

[0039] The traffic congestion risk assessment formula is: Among them, R represents the predicted traffic congestion risk assessment value of the unit time period, and the reference value of R is preferably set to 350. If the calculated result is greater than the reference value, it means that the traffic assessment of the unit time period is not congested. If the calculated result is less than the reference value, it means that the traffic in the unit time period is assessed as congested. κ represents the sensitivity factor of the traffic control system, which is used to adjust the amplification or reduction effect of the assessment result. D represents the comprehensive traffic state at the current moment, which is calculated by the front fusion algorithm. T2 is the traffic flow predicted in the future, which is obtained by the front traffic flow prediction formula. C represents the design carrying capacity of the road section, that is, the maximum tolerable traffic flow of the road section per unit time. E represents the severity parameter of the current emergency, such as the impact of traffic accidents, with a value range of 0 to 1. A higher value indicates that the emergency is more serious. ε represents the adjustment factor that amplifies the impact of the emergency, and amplifies the impact of the event on the traffic risk. In a specific embodiment, D takes a value of 300.36, T2 takes a value of 485 vehicles / hour, and the road section design carrying capacity C takes a value of 600 vehicles / hour, which represents the maximum traffic capacity of the road section. The current emergency parameter E takes a value of 0.3, indicating that a certain section of the road is under construction. The adjustment factor ε takes a value of 1.1, the accident has a small impact, and the sensitivity factor κ takes a value of 1.2. Substituting into the calculation, we get 1.2×300.36×485 / 600×(1+0.3×1.1), and the calculated R is about 219. Therefore, the calculation result indicates that the traffic congestion risk of this section of road after 60 minutes is high, and high congestion may occur. Therefore, since the evaluation results show that this section of road will be congested in a short time, according to the evaluation results, the system will take immediate measures, such as adjusting the timing of the signal lights, guiding vehicles to avoid this section of road through dynamic traffic signs, diverting traffic flow, and reducing the risk of congestion. Therefore, based on the current risk assessment results, the system decides to further optimize the timing of the signal lights and reduce the green light time of the section of road to reduce the number of new vehicles entering. Traffic guidance can send route adjustment suggestions in real time through the 5G network, suggesting drivers to detour other sections of the road and reduce the amount of traffic entering the section. The warning can be issued through the system using the 5G network to send warnings to municipal management departments, prompting early intervention to prevent the aggravation of potential congestion.

[0040] The above describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above embodiments, and the above embodiments and the specification only describe the principles of the present application. The present application may have various changes and improvements without departing from the spirit and scope of the present application, and these changes and improvements fall within the scope of the present application for which protection is sought. The scope of protection claimed by the present application is defined by the attached claims and their equivalents.

Claims

1. A smart traffic control method based on 5G cloud computing terminal technology, characterized in that: include: Use 5G networks to collect data on traffic flow parameters, weather conditions parameters, emergency parameters, etc. in real time; fusing the data of the traffic flow parameter, the weather condition parameter and the emergency parameter, and judging the comprehensive traffic status based on the fusion algorithm; Generate and apply control strategies based on the traffic status, control traffic lights and electronic signs through 5G terminals, dynamically adjust traffic flow, and guide vehicles to avoid congested areas; Simultaneously, a traffic flow prediction algorithm is used to predict future traffic flow; When it is predicted that future traffic flow may exceed the carrying capacity of the road section, dynamic speed limit adjustment is implemented.

2. The intelligent traffic control method based on 5G cloud computing terminal technology according to claim 1 is characterized in that: The fusion algorithm uses real-time traffic flow parameters, weather condition parameters, emergency parameters, and weight parameters that are dynamically adjusted according to traffic conditions.

3. The intelligent traffic control method based on 5G cloud computing terminal technology according to claim 1 is characterized in that: The traffic flow prediction algorithm uses T1 as the real-time traffic flow parameter, T2 as the predicted traffic flow value, T3 as the historical traffic flow parameter, and λ as the balance weight parameter for calculation.

4. The intelligent traffic control method based on 5G cloud computing terminal technology according to claim 3 is characterized in that: The balancing weight parameter is set to balance the influence of the current traffic flow parameter and the historical traffic flow parameter, and the value of the balancing weight parameter is adjusted in real time to adapt to changes in the traffic environment.

5. The intelligent traffic control method based on 5G cloud computing terminal technology according to claim 2 is characterized in that: The weight parameters α, β, and γ in the fusion algorithm are dynamically adjusted according to the current traffic environment. The higher weight of α is used to reflect the importance of real-time traffic flow, the weight of β changes according to weather conditions, and the weight of γ is set according to the severity of the emergency.

6. The intelligent traffic control method based on 5G cloud computing terminal technology according to claim 3 is characterized in that: When the system predicts that future traffic conditions will reach a critical congestion value, it will send route optimization suggestions and traffic warning notifications to vehicle drivers to guide vehicles to detour and alleviate traffic pressure.

7. A smart traffic control system based on 5G cloud computing terminal technology, characterized in that: The system is applicable to the intelligent traffic control method based on 5G cloud computing terminal technology as described in any one of claims 1 to 6, and the system includes: Distributed traffic data collection module, multi-source data fusion and analysis module; Wherein, the distributed traffic data collection module collects the traffic flow parameters, weather condition parameters and emergency parameters of the road section in real time; The multi-source data fusion and analysis module performs real-time fusion processing on traffic flow, weather, and emergency data based on a fusion algorithm to generate an evaluation result of the comprehensive traffic status.

8. The intelligent traffic control system based on 5G cloud computing terminal technology according to claim 7 is characterized in that: The system further comprises: Intelligent prediction module and path guidance module. The intelligent prediction module generates a control plan based on the traffic flow prediction results, controls the traffic light timing and dynamically adjusts the traffic light timing through the 5G terminal, and the path guidance module implements traffic sign guidance information and completes the speed limit and diversion plan.

9. The intelligent traffic control system based on 5G cloud computing terminal technology according to claim 8 is characterized in that: The system further comprises: The end-edge-cloud collaborative computing module dynamically allocates complex computing tasks to the cloud or edge nodes through distributed cloud computing terminals, thereby improving the computing efficiency of the system under high traffic load.

10. The intelligent traffic control system based on 5G cloud computing terminal technology according to claim 7 is characterized in that: The distributed traffic data collection module collects data from multiple road sections in real time through a variety of collection devices, and uses the high-speed transmission function of the 5G network to ensure low-latency transmission of data to the cloud for analysis and processing.

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