A Smart City Traffic Optimization Method and System Based on 5G Network

Abstract

The present invention discloses a method and system for optimizing smart city traffic based on a 5G network, specifically related to the field of intelligent transportation technology. The method for optimizing smart city traffic based on a 5G network collects traffic data by deploying 5G base stations and installing sensors and traffic equipment on roads. The traffic data includes traffic flow data and traffic signal status. The collected traffic data is quickly transmitted to a cloud platform for storage through the 5G network. The collected traffic data is preprocessed in the cloud platform and deeply analyzed through machine learning algorithms to identify traffic patterns. Based on the analyzed results, the traffic flow demand is predicted, congestion situations that occur are anticipated in advance, and optimization is carried out by adjusting the signal light cycle. The system for optimizing smart city traffic based on a 5G network includes a data collection module, a data analysis module, and a traffic flow control module.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation. More specifically, the present invention relates to a method and system for optimizing urban transportation based on a 5G network. Background Art

[0002] With the rapid development of information technology, the construction of smart cities has been accompanied by increasingly complex challenges in traffic management, which is an important part of urban management. With the increase in urban population and economic development, traditional traffic management methods are no longer sufficient to meet the growing traffic demand. Problems such as traffic congestion, frequent traffic accidents, and environmental pollution have become the core issues to be solved. Therefore, the proposal of intelligent transportation has become one of the effective ways to solve this problem.

[0003] As a new generation of communication technology, 5G technology has characteristics such as high speed, low latency, and large connection capacity, providing important technical support for the optimization of smart city traffic systems. The low latency feature of the 5G network enables more efficient real-time data transmission, while the large connection capacity makes it possible for intelligent devices to work together. These advantages enable the smart traffic system based on the 5G network to achieve more accurate data collection and analysis, real-time monitoring and scheduling, thereby improving traffic flow, reducing the accident rate, and effectively reducing energy consumption and carbon emissions. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a method and system for optimizing urban transportation based on a 5G network to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solution. A method for optimizing urban transportation based on a 5G network specifically includes the following steps:

[0006] Step S1: Deploy 5G base stations and install sensors and traffic devices on the road to collect traffic data, where the traffic data includes traffic flow data and traffic signal status, and use the 5G network to quickly transmit the collected traffic data to the cloud platform for storage;

[0007] Step S2: Preprocess the traffic data collected in Step S1 in the cloud platform and perform in-depth analysis through machine learning algorithms to identify traffic patterns;

[0008] Step S3: Based on the results analyzed in Step S2, predict traffic flow requirements, anticipate congestion situations in advance, and optimize by adjusting the signal light cycle.

[0009] In a preferred embodiment, in step S1, by deploying 5G base stations and installing sensors and traffic devices on the road to collect traffic data, the traffic data includes traffic flow data and traffic signal status, and the collected traffic data is quickly transmitted to the cloud platform for storage through the 5G network. The specific steps are as follows:

[0010] Step A1, Build network infrastructure: Deploy 5G base stations at transportation hubs, road intersections, and high-traffic areas, and install traffic flow sensors and traffic signal monitoring devices on the road to obtain traffic flow data and traffic signal status, and connect all installed sensors and traffic devices to the 5G base station wirelessly;

[0011] Step A2, Data transmission: Quickly upload the real-time collected traffic data to the cloud platform through the 5G network, and classify and store it according to the type of data collection to improve the efficiency of retrieval and query.

[0012] In a preferred embodiment, in step S2, the traffic data collected in step S1 is preprocessed in the cloud platform and deeply analyzed through machine learning algorithms to identify traffic patterns. The specific steps are as follows:

[0013] Step B1, Feature extraction: Preprocess the received traffic data, remove duplicate and incomplete data records, fill in missing values to ensure data quality, and represent the traffic flow data and traffic signal status as and , respectively, where represents a red light, represents a green light, represents a yellow light; Calculate the average traffic flow within the past n time points as: , and obtain the peak traffic flow and the lowest traffic flow to extract statistical features, where represents the average traffic flow at time t, n is the time window size for calculating the average value, representing the number of past time periods, is the traffic flow data at time t−i, representing the traffic flow in the past n time points, and i represents the offset index in the time sliding window;

[0014] Step B2, Build an LSTM model: Create a data set, where each row contains the traffic flow at the current time point t and the traffic signal status , as well as statistical features, the statistical features include the peak traffic flow , the lowest traffic flow and the average traffic flow , use the extracted statistical features as the input feature vector X, and the target output is the traffic flow at the future time point t+1, represented as , build an LSTM model using a deep learning framework, including an input layer, an LSTM layer, and an output layer. It further includes the following steps:

[0015] Step B201, Input layer: used to receive feature data, and the input is a vector containing traffic flow statistics features and traffic signal states ;

[0016] Step B202, LSTM layer: used to capture long-term dependencies in the time series. The output of the LSTM layer is: ;

[0017] Step B203, Output layer: used to predict future traffic flow. The traffic flow at the future time t+1 of the output layer is , where W is the weight matrix, b is the bias term, and the output is the predicted traffic flow, is the hidden state at the current moment, represents that, based on the hidden state at the previous moment and the current input data , the LSTM layer updates the hidden state;

[0018] Step B3, After the model is trained, use a clustering algorithm to cluster the features to identify different traffic patterns, including peak traffic periods, off-peak periods, and normal periods. It further includes the following steps:

[0019] Step B301, Standardize the feature data obtained after the model is trained to ensure that all features have the same scale and combine them into a vector: , select k initial cluster centers for K-means clustering, denoted as , and execute the following steps:

[0020] Step B302, For each data point , calculate its distance from each cluster center , and select the nearest cluster center: , and assign the data point to the cluster center with the minimum distance;

[0021] Step B303, Calculate the new center of each cluster, and its position is the mean of all data points within the cluster: , where is the number of data points in cluster , represents the set of all data points in cluster ;

[0022] Step B304: Repeat the above process of allocating and updating the cluster centers until the cluster centers no longer change; after clustering is completed, each cluster represents a different traffic pattern. By analyzing the characteristics of each cluster, the categories of traffic patterns are identified. The traffic peak period is when the cluster center has a high average traffic flow and peak traffic flow, and large, and the traffic signal is characterized by long green and yellow light durations; the low period is when the traffic flow at the cluster center is low, and small, and the red light time is long; the normal period is when the traffic flow is moderate and the traffic flow fluctuation is small, and the traffic signal change rule is stable.

[0023] In a preferred embodiment, in step S3, based on the result analyzed in step S2, the traffic flow demand is predicted, the congestion situation that will occur is pre-judged, and optimization is performed by adjusting the signal light cycle. The specific steps are as follows:

[0024] Step C1: Pre-judge the congestion situation: Set the congestion threshold as , based on the LSTM model, predict the traffic flow demand at a future time point as , according to the prediction result and traffic pattern recognition, pre-judge the congested area that will occur. For each intersection j, when the predicted traffic flow is greater than the congestion threshold , and the current is the peak period, record the status of this intersection as congested, denoted as ; when the predicted traffic flow is less than the congestion threshold R, record the status of this intersection as unobstructed, denoted as ;

[0025] Step C2: Optimize traffic signal control: According to the prediction result and congestion situation, optimize the traffic signal control strategy. The signal cycle of each intersection includes the green light cycle , the red light cycle and the yellow light cycle . Set the total signal cycle as: . By optimizing the green light cycle to improve the overall traffic flow and maximize the passing capacity of the intersection. For each intersection, set the relationship between the passing capacity and the traffic flow, and maximize this capacity by adjusting the green light cycle. Set the objective function as , and the constraint condition is that the total cycle and the green light cycle of each intersection do not exceed an upper limit value. The mathematical expression is: , where, is the green light cycle of the jth intersection, is the traffic capacity corresponding to the traffic volume at the j-th intersection, representing the maximum number of vehicles passing through the intersection, and M is the total number of intersections. is the maximum signal cycle of the intersection. is the maximum green light cycle of the intersection.

[0026] This application also provides a smart city traffic optimization system based on the 5G network, which specifically includes a data collection module, a data analysis module, and a traffic flow control module.

[0027] Data collection module: By deploying 5G base stations and installing sensors and traffic equipment on the road to collect traffic data, the traffic data includes traffic volume data and traffic signal status, and uses the 5G network to quickly transmit the collected traffic data to the cloud platform for storage.

[0028] Data analysis module: Preprocess the traffic data collected by the data collection module in the cloud platform, and perform in-depth analysis through machine learning algorithms to identify traffic patterns.

[0029] Traffic flow control module: Based on the results analyzed by the data analysis module, predict the traffic flow demand, anticipate the congestion situation in advance, and optimize by adjusting the signal light cycle.

[0030] The beneficial effects of the present invention are as follows: By deploying 5G base stations and installing sensors and traffic equipment on the road to collect traffic data, the traffic data includes traffic volume data and traffic signal status, and uses the 5G network to quickly transmit the collected traffic data to the cloud platform for storage. Preprocess the collected traffic data in the cloud platform and perform in-depth analysis through machine learning algorithms to identify traffic patterns. Based on the analyzed results, predict the traffic flow demand, anticipate the congestion situation in advance, and optimize by adjusting the signal light cycle. The present invention utilizes the high-speed and low-latency characteristics of the 5G network to be able to transmit a large amount of traffic data to the cloud platform for storage and processing in a short time, improving the data transmission efficiency and reliability. Based on the prediction results of the traffic flow demand, it is possible to adjust the signal light cycle in advance, optimize the allocation and switching of traffic signals, effectively relieve traffic congestion, and improve traffic fluency. Brief Description of the Drawings

[0031] Figure 1 is the method flow chart of the present invention.

[0032] Figure 2 is the structural block diagram of the present invention. Detailed Embodiments

[0033] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0034] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

[0035] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present application is not necessarily construed as being more preferred or advantageous than other embodiments. In order for any person skilled in the art to implement and use the present invention, the following description is given. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid unnecessary details from obscuring the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present application.

[0036] Embodiment 1

[0037] This embodiment provides a Figure 1 smart city traffic optimization method based on a 5G network as shown in the figure, specifically including the following steps:

[0038] Step S1: Deploy 5G base stations and install sensors and traffic devices on the road to collect traffic data, where the traffic data includes traffic flow data and traffic signal status, and use the 5G network to quickly transmit the collected traffic data to the cloud platform for storage;

[0039] Step S2: Preprocess the traffic data collected in Step S1 in the cloud platform, and perform in-depth analysis through machine learning algorithms to identify traffic patterns;

[0040] Step S3: Based on the results analyzed in Step S2, predict traffic flow requirements, anticipate congestion situations in advance, and optimize by adjusting the signal light cycle.

[0041] Preferably, in step S1, 5G base stations are deployed, and sensors and traffic devices are installed on roads to collect traffic data, where the traffic data includes traffic flow data and traffic signal status, and the collected traffic data is quickly transmitted to the cloud platform for storage through the 5G network. The specific steps are as follows:

[0042] Step A1, Build network infrastructure: Deploy 5G base stations at transportation hubs, road intersections, and high-traffic areas, and install traffic flow sensors and traffic signal monitoring devices on roads to obtain traffic flow data and traffic signal status. Connect all installed sensors and traffic devices to the 5G base station wirelessly to ensure efficient communication between the devices and the network;

[0043] Step A2, Data transmission: Quickly upload the real-time collected traffic data to the cloud platform through the 5G network, and classify and store it according to the type of data collection to improve the efficiency of retrieval and query.

[0044] Preferably, in step S2, the traffic data collected in step S1 is preprocessed in the cloud platform and deeply analyzed through machine learning algorithms to identify traffic patterns. The specific steps are as follows:

[0045] Step B1, Feature extraction: Preprocess the received traffic data, remove duplicate and incomplete data records, fill in missing values to ensure data quality, and represent the traffic flow data and traffic signal status as and , respectively, where represents a red light, represents a green light, represents a yellow light; Calculate the average traffic flow within the past n time points as: , and obtain the peak traffic flow, the lowest traffic flow to extract statistical features, where represents the average traffic flow at time t, n is the time window size for calculating the average value, representing the number of past time periods, is the traffic flow data at time t−i, representing the traffic flow in the past n time points, and i represents the offset index in the time sliding window;

[0046] Step B2, Build an LSTM model: Create a data set, where each row contains the traffic flow at the current time point t and the traffic signal status , as well as statistical features, where the statistical features include the peak traffic flow , the lowest traffic flow and the average traffic flow . Use the extracted statistical features as the input feature vector X, and the target output is the traffic flow at the future time point t+1, represented as , build an LSTM model using a deep learning framework, including an input layer, an LSTM layer, and an output layer, further including the following steps:

[0047] Step B201, input layer: used to receive feature data, and the input is a vector containing traffic flow statistical features and traffic signal states ;

[0048] Step B202, LSTM layer: used to capture long-term dependencies in the time series, and the output of the LSTM layer is: ;

[0049] Step B203, output layer: used to predict future traffic flow, and the traffic flow at the future t+1 moment of the output layer is , where W is the weight matrix, b is the bias term, and the output is the predicted traffic flow, is the hidden state at the current moment, represents that based on the hidden state at the previous moment and the current input data , the LSTM layer updates the hidden state;

[0050] Step B3, after the model is trained, use a clustering algorithm to cluster the features to identify different traffic patterns, including peak traffic periods, off-peak periods, and normal periods, further including the following steps:

[0051] Step B301, standardize the feature data obtained after the model is trained to ensure that all features have the same scale and combine them into a vector: , select k initial cluster centers for K-means clustering, denoted as , and perform the following steps:

[0052] Step B302, for each data point , calculate its distance from each cluster center , and select the nearest cluster center: , and assign the data point to the cluster center with the minimum distance;

[0053] Step B303, calculate the new center of each cluster, and its position is the mean of all data points within the cluster: , where is the number of data points in cluster , represents the set of all data points in cluster ;

[0054] Step B304: Repeat the above process of allocating and updating the cluster centers until the cluster centers no longer change; after clustering is completed, each cluster represents a different traffic pattern. By analyzing the characteristics of each cluster, identify the categories of traffic patterns. The traffic peak period is when the cluster center has a high average traffic flow and peak traffic flow, and large, and the traffic signal is characterized by longer green and yellow light durations; the low period is when the traffic flow at the cluster center is low, and small, and the red light time is long; the normal period is when the traffic flow is moderate, and the traffic flow fluctuation is small, and the traffic signal change rule is stable.

[0055] Preferably, in step S3, based on the result analyzed in step S2, predict the traffic flow demand, anticipate the congestion situation in advance, and optimize it by adjusting the signal cycle. The specific steps are as follows:

[0056] Step C1: Anticipate the congestion situation: Set the congestion threshold as , and based on the LSTM model, predict the traffic flow demand at future time points as . According to the prediction result and traffic pattern recognition, anticipate the congested areas in advance. For each intersection j, when the predicted traffic flow is greater than the congestion threshold , and it is currently the peak period, record the status of this intersection as congested, denoted as ; when the predicted traffic flow is less than the congestion threshold R, record the status of this intersection as unobstructed, denoted as ;

[0057] Step C2: Optimize traffic signal control: According to the prediction result and congestion situation, optimize the traffic signal control strategy. The signal cycle of each intersection includes the green light cycle , the red light cycle and the yellow light cycle . Set the total signal cycle as: . By optimizing the green light cycle to improve the overall traffic flow and maximize the traffic capacity of the intersection. For each intersection, set the relationship between the traffic capacity and the traffic flow, and maximize this capacity by adjusting the green light cycle. Set the objective function as , and the constraint conditions are that the total cycle and the green light cycle of each intersection do not exceed an upper limit value. The mathematical expression is: , where, is the green light cycle of the jth intersection, is the traffic capacity corresponding to the traffic volume at the j-th intersection, representing the maximum number of vehicles passing through the intersection, and M is the total number of intersections. is the maximum signal cycle of the intersection. is the maximum green light cycle of the intersection.

[0058] Embodiment 2

[0059] This embodiment provides an intelligent city traffic optimization system based on a 5G network, specifically including a data collection module, a data analysis module, and a traffic flow control module.

[0060] Data collection module: By deploying 5G base stations and installing sensors and traffic equipment on the road to collect traffic data, the traffic data includes traffic volume data and traffic signal status, and the collected traffic data is quickly transmitted to the cloud platform for storage using the 5G network.

[0061] Data analysis module: Preprocess the traffic data collected by the data collection module in the cloud platform, and perform in-depth analysis through machine learning algorithms to identify traffic patterns.

[0062] Traffic flow control module: Based on the results analyzed by the data analysis module, predict traffic flow requirements, anticipate congestion situations in advance, and optimize by adjusting the signal light cycle.

[0063] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0064] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0065] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, and the combination of processes and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate for realizing the processes Figure 1one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0066] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the functions in the process Figure 1 one or more processes and / or blocks Figure 1 the functions specified in one or more blocks

[0067] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions in the process Figure 1 one or more processes and / or blocks Figure 1 the steps of the functions specified in one or more blocks

[0068] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn the basic inventive concept. Therefore, the appended claims are intended to be construed to cover the preferred embodiments as well as all changes and modifications falling within the scope of the present invention

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations

Claims

1. A smart city traffic optimization method based on 5G network, characterized in that, Specifically, it includes the following steps: Step S1: Deploy 5G base stations and install sensors and traffic devices on the road to collect traffic data, where the traffic data includes traffic flow data and traffic signal status, and use the 5G network to quickly transmit the collected traffic data to the cloud platform for storage; Step S2: Preprocess the traffic data collected in Step S1 in the cloud platform and conduct in-depth analysis through machine learning algorithms to identify traffic patterns. The specific steps are as follows: Step B1, Feature Extraction: Preprocess the received traffic data, remove duplicate and incomplete data records, fill in missing values to ensure data quality, and represent the traffic flow data and traffic signal status as and , where represents a red light, represents a green light, represents a yellow light; Calculate the average traffic flow within the past n time points as: , and obtain the peak traffic flow and the lowest traffic flow to extract statistical features, where represents the average traffic flow at time t, n is the time window size for calculating the average value, representing the number of past time periods, is the traffic flow data at time t−i, representing the traffic flow in the past n time points, and i represents the offset index in the time sliding window; Step B2: Build an LSTM model: Create a dataset where each row contains the traffic flow at the current time point t and the traffic signal status , as well as statistical features, where the statistical features include the peak traffic flow , the minimum traffic flow and the average traffic flow . Use the extracted statistical features as the input feature vector X, and the target output is the traffic flow at the future time point t+1, denoted as . Build an LSTM model using a deep learning framework, including an input layer, an LSTM layer, and an output layer; Step B3: After model training, use a clustering algorithm to cluster the features to identify different traffic patterns, including peak traffic periods, off-peak traffic periods, and normal periods; In Step B2 for constructing the LSTM model, use a deep learning framework to construct the LSTM model, including an input layer, an LSTM layer, and an output layer. It further includes the following steps: Step B201, Input Layer: Used to receive feature data, and the input is a vector containing traffic flow statistics features and traffic signal status ; Step B202, LSTM layer: used to capture long-term dependencies in the time series, and the output of the LSTM layer is: ; Step B203, Output Layer: Used to predict future traffic flow. The traffic flow at the future time t+1 of the output layer is , where W is the weight matrix, b is the bias term, and the output is the predicted traffic flow, is the hidden state at the current time, indicates that based on the hidden state at the previous time and the current input data , the LSTM layer updates the hidden state; In Step B3, after model training, use a clustering algorithm to cluster the features to identify different traffic patterns, including peak traffic periods, off-peak traffic periods, and normal periods. It further includes the following steps: Step B301: Standardize the feature data obtained after model training to ensure that all features have the same scale, and combine them into a vector: , select k initial cluster centers for K-means clustering, denoted as , and perform the following steps: Step B302: For each data point , calculate its distance from each cluster center , and select the nearest cluster center: , assign the data point to the cluster center with the minimum distance; Step B303: Calculate the new center of each cluster , whose position is the mean value of all data points within the cluster: , where is the number of data points in cluster , represents the set of all data points in cluster ; Step B304: Repeat the above process of allocating and updating the cluster centers until the cluster centers no longer change; after clustering is completed, each cluster represents a different traffic pattern, and by analyzing the features of each cluster, identify the category of the traffic pattern; Step S3: Based on the results analyzed in Step S2, predict the traffic flow demand, anticipate congestion in advance, and optimize it by adjusting the signal light cycle; In Step S3, based on the results analyzed in Step S2, predict the traffic flow demand, anticipate congestion in advance, and optimize it by adjusting the signal light cycle. The specific steps are as follows: Step C1, Predict congestion situation: Set the congestion threshold to , predict the traffic flow demand at future time points based on the LSTM model, which is . According to the prediction results and traffic pattern recognition, anticipate the congestion areas in advance. For each intersection j, when the predicted traffic flow is greater than the congestion threshold , and it is currently the peak period, record the status of this intersection as congested, denoted as ; when the predicted traffic flow is less than the congestion threshold R, record the status of this intersection as unobstructed, denoted as ; Step C2, Traffic Signal Control Optimization: Optimize the traffic signal control strategy according to the prediction results and congestion conditions. The signal cycle at each intersection includes a green light cycle , a red light cycle and a yellow light cycle . Set the total signal cycle as: . Improve the overall traffic flow by optimizing the green light cycle to maximize the traffic capacity of the intersection. For each intersection, set the relationship between the traffic capacity and the traffic flow, and maximize this capacity by adjusting the green light cycle. Set the objective function as . The constraint conditions are the total cycle and the green light cycle at each intersection not exceeding an upper limit value. The mathematical expression is: . Where is the green light cycle of the j-th intersection, is the traffic capacity corresponding to the traffic flow at the j-th intersection, representing the maximum number of vehicles passing through this intersection. M is the total number of intersections, is the maximum signal cycle of the intersection, is the maximum green light cycle of the intersection.

2. The smart city traffic optimization method based on the 5G network according to claim 1, wherein: In Step S1, deploy 5G base stations and install sensors and traffic devices on the road to collect traffic data, where the traffic data includes traffic flow data and traffic signal status, and use the 5G network to quickly transmit the collected traffic data to the cloud platform for storage. The specific steps are as follows: Step A1: Build network infrastructure: Deploy 5G base stations at transportation hubs, road intersections, and high-traffic areas, and install traffic flow sensors and traffic signal monitoring devices on the road to obtain traffic flow data and traffic signal status. Connect all installed sensors and traffic devices to the 5G base stations wirelessly to ensure efficient communication between the devices and the network; Step A2: Data transmission: Quickly upload the real-time collected traffic data to the cloud platform through the 5G network and classify and store it according to the type of data collection to improve the efficiency of retrieval and query.

3. A smart city traffic optimization system based on a 5G network is applied to a smart city traffic optimization method based on a 5G network as described in any one of claims 1-2, characterized in that: It includes a data collection module, a data analysis module, and a traffic flow control module; Data collection module: Deploy 5G base stations and install sensors and traffic devices on the road to collect traffic data, where the traffic data includes traffic flow data and traffic signal status, and use the 5G network to quickly transmit the collected traffic data to the cloud platform for storage; Data analysis module: Preprocess the traffic data collected by the data collection module in the cloud platform and conduct in-depth analysis through machine learning algorithms to identify traffic patterns; Traffic flow control module: Based on the results analyzed by the data analysis module, it predicts the traffic flow demand, anticipates congestion situations in advance, and optimizes by adjusting the signal light cycle.

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