A smart city traffic information system based on 5G network

Abstract

The present invention provides a smart city traffic information system based on 5G network, which relates to the field of data processing technology. The method includes: a digital traffic processing module, which is used to collect and analyze the operation data of various traffic facilities and vehicles in real time, so as to identify traffic flow patterns, predict traffic status, obtain analysis results, and automatically adjust traffic signals and optimize traffic flow according to the analysis results; a digital government affairs collaboration module, which is used to automatically obtain and distribute updated information on traffic policies and regulations, automatically handle traffic violations, and collect and analyze public feedback on traffic policies to obtain analysis reports. The present invention integrates multiple digital modules, realizes real-time collection, intelligent analysis and efficient management of traffic information, not only improves traffic operation efficiency and safety, but also enhances public participation and satisfaction, and effectively promotes the intelligence, collaboration and sustainable development of urban transportation.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a 5G network-based smart city traffic information system. Background Art

[0002] The network latency of traditional technologies is relatively high, especially during peak hours or when the network is congested. The existence of latency may affect key applications such as real-time control of traffic signals and the accuracy of vehicle navigation. The number of connected devices in traditional networks is limited, and it is difficult to support the interconnection needs of a large number of traffic devices in smart cities. This limits the scalability and intelligence level of traffic information systems.

[0003] The infrastructure maintenance cost of traffic information technology is high, including routine maintenance and upgrades of base stations and transmission lines. With the aging of equipment and technological advancement, maintenance costs may continue to rise. Traditional traffic information technology has loopholes in network security and may be subject to hacker attacks or data leaks. As the scale and complexity of the network expand, the difficulty of security protection is also increasing. In traditional traffic information systems, users' travel data, location information, etc. may be collected and analyzed, posing the risk of privacy leakage. Especially in the process of data sharing and exchange, how to protect user privacy is an urgent problem to be solved. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a smart city traffic information system based on 5G network to optimize traffic signal control and improve road traffic efficiency.

[0005] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0006] A 5G network-based smart city traffic information system, comprising:

[0007] Digital traffic processing module, used to collect and analyze the operation data of various traffic facilities and vehicles in real time, so as to identify traffic flow patterns, predict traffic status, obtain analysis results, and automatically adjust traffic signals and optimize traffic flow based on the analysis results;

[0008] The digital government collaboration module is used to automatically obtain and distribute updated information on traffic policies and regulations, automatically handle traffic violations, and collect and analyze public feedback on traffic policies to obtain analysis reports;

[0009] Digital education module, which uses cloud data platforms and 5G networks to automatically update and distribute online education resources in the field of transportation and provide educational experiences, including simulating traffic scenarios through VR technology to conduct traffic safety and planning education;

[0010] Digital park traffic linkage module, used to share traffic and logistics information within the park in real time, and automatically conduct intelligent scheduling of traffic and logistics;

[0011] The digital water service collaborative management module is used to monitor the hydrological conditions around transportation facilities in real time, including water level and rainfall. It uses prediction models to automatically warn of water risks that affect transportation, including floods and waterlogging, and obtains warning results. Based on the warning results, it automatically generates traffic control plans.

[0012] Digital medical response module, used for real-time communication and data sharing. When a traffic accident occurs, it automatically locates the injured, dispatches medical resources for rescue, and provides rescue guidance information.

[0013] The digital community traffic service module is used to provide traffic information services, monitor community traffic conditions in real time, generate and publish traffic monitoring reports, including congestion and parking information, and automatically optimize and adjust traffic management plans based on residents' feedback.

[0014] Furthermore, the operation data of various traffic facilities and vehicles are collected and analyzed in real time to identify traffic flow patterns, predict traffic conditions, obtain analysis results, and automatically adjust traffic signals and optimize traffic flow based on the analysis results, including:

[0015] Collect real-time operation data of traffic facilities and vehicles, including vehicle speed, location, flow, and road conditions;

[0016] Extracting features related to traffic flow pattern recognition from the data, including time features, spatial features and flow features, and combining the relevant features into feature vectors;

[0017] Input the feature vector into a preset neural network model for deep learning and pattern recognition to identify traffic flow patterns;

[0018] Based on the identified traffic flow patterns and historical data, the neural network model predicts future traffic conditions, including congestion and traffic speed, and obtains prediction results;

[0019] Based on the prediction results, the traffic signal control plan is automatically adjusted, including adjusting the duration of traffic lights and optimizing signal timing.

[0020] Furthermore, it automatically obtains and distributes updated information on traffic policies and regulations, automatically handles traffic violations, and collects and analyzes public feedback on traffic policies to obtain feedback reports, including:

[0021] Send requests to the digital government platform regularly at preset intervals to check whether there are any new traffic policies and regulations updated;

[0022] The digital government platform receives and analyzes traffic policy and regulation update data, extracts key information, including policy name, release time, and content, and distributes the extracted key information to traffic management departments, traffic participants, and the public in real time through the 5G network;

[0023] Obtain traffic violation data in real time, and process and analyze the data based on traffic policies and regulations to identify the type, time, and location of violations; automatically impose penalties on violations based on preset processing rules;

[0024] Receive real-time feedback from the public on transportation policies, including opinions, suggestions, and complaints, and classify and organize the feedback information to obtain feedback reports.

[0025] Furthermore, the traffic and logistics information in the park is shared in real time, and intelligent scheduling of traffic and logistics is automatically performed, including:

[0026] Collect raw data on traffic flow, vehicle location, road conditions, and cargo status in real time, and convert the raw data into state vectors;

[0027] According to the state vector, the objective function is constructed and a set of scheduling parameters are initialized. The scheduling parameters include the control scheme of traffic signals, the path planning of vehicles, and the loading and unloading sequence of goods.

[0028] The state vector is updated in real time through the park traffic and logistics information sharing platform to obtain an updated state vector;

[0029] According to the updated state vector, the gradient of the objective function with respect to the scheduling parameters is calculated, and the scheduling parameters are iteratively adjusted to obtain the optimized scheduling parameters;

[0030] The optimized scheduling parameters are passed to the park's traffic management system and logistics operating system to automatically perform intelligent scheduling of traffic and logistics, including adjusting traffic signals, guiding vehicle driving, and arranging loading and unloading, transportation, and distribution of goods.

[0031] Furthermore, the calculation formula of the objective function is:

[0032] ;

[0033] in, represents the objective function; represents the weight coefficient; Indicates the average speed of vehicles in the park; Represents the weight coefficient of the congestion index; represents the congestion index; represents the weight coefficient; Indicates the average transportation time of goods; Indicates the loading and unloading time of the cargo; The weight coefficient representing the on-time delivery rate; Indicates the on-time delivery rate; represents the weight coefficient; Indicates the control efficiency of traffic signals; Indicates the path planning efficiency of the vehicle; Indicates the efficiency of the loading and unloading sequence of cargo.

[0034] Furthermore, the optimized scheduling parameter calculation formula is:

[0035] ;

[0036] in, Indicates The parameter vector after the iteration update; Indicates The parameter vector after iterations; Indicates The learning rate of the iteration; Represents the objective function In the parameter vector The gradient vector at ; , represents the regularization parameter; , represents the momentum parameter; , Indicates Second and The parameter vector after iterations.

[0037] Furthermore, the hydrological conditions around transportation facilities are monitored in real time, including water level and rainfall. The prediction model is used to automatically warn of water risks that affect transportation, including floods and waterlogging. The warning results are obtained and traffic control plans are automatically generated based on the warning results, including:

[0038] Input the real-time monitored hydrological data into the prediction model to predict water affairs risks in the future and obtain water affairs risk prediction results, including risk type, probability of occurrence, and impact range;

[0039] Based on the water affairs risk prediction results, the probability and impact of flood and waterlogging water affairs risks are assessed to obtain risk assessment results;

[0040] When the risk assessment results exceed the preset threshold, early warning information is automatically issued, including risk type, warning level, and impact scope;

[0041] According to the early warning information, a set of traffic control schemes are initialized, including closing certain road sections and restricting vehicle traffic, to obtain an initial set of traffic control schemes;

[0042] Each solution in the initial traffic control solution set is taken as a solution vector, and the solution vector is randomly mutated to generate a new candidate solution;

[0043] Cross the new candidate solution with the original solution vector to generate a new solution vector combination;

[0044] Evaluate the new solution vector combination according to the fitness function and determine the solution vector to enter the next round of iteration;

[0045] The mutation, crossover and evaluation processes are repeated until the preset number of iterations is reached, and the final solution is determined from the solution vector set as the traffic control solution.

[0046] Furthermore, the calculation formula of the fitness function is:

[0047] ;

[0048] in, Indicates in the traffic control scheme The fitness value under ; The weight coefficient representing the traffic flow; Indicates The average vehicle speed on the road section; Indicates the total number of road segments; Indicates The weight coefficient of each road segment; , Represents an index variable; represents the weight coefficient of risk mitigation; Indicates in the traffic control scheme The area of ​​the region at risk from water services; An adjustment factor representing the area of ​​the risk zone; Indicates the total area of ​​the region; represents the weight coefficient of implementation cost; Indicates the total number of cost items; Indicates Implementation costs; Indicates The weight coefficient of the item cost; Indicates the weight coefficient of safety; Indicates in the traffic control scheme The predicted number of traffic accidents; Represents the baseline number of incidents.

[0049] Furthermore, real-time communication and data sharing are carried out. When a traffic accident occurs, the injured are automatically located, medical resources are dispatched for rescue, and rescue guidance information is provided, including:

[0050] Real-time monitoring of traffic conditions to detect abnormal traffic conditions, including sudden stops of vehicles, sharp decreases in speed, and to determine whether the abnormal traffic conditions are the scene of an accident;

[0051] If it is determined to be an accident scene, the data from the accident scene is analyzed, including the location of the vehicle and camera surveillance footage, to determine the location of the injured, and digital maps and GPS data are accessed to determine the geographic location of the injured;

[0052] The digital medical platform receives information from the accident scene, dispatches medical resources for rescue based on the location and condition of the injured, and provides real-time rescue guidance information to rescuers through the 5G network, including rescue routes, descriptions of the injured's location, and injury assessments.

[0053] Furthermore, it provides traffic information services and monitors community traffic conditions in real time, generates and publishes traffic monitoring reports, including congestion and parking information, and automatically optimizes and adjusts traffic management plans based on residents' feedback, including:

[0054] Generate traffic monitoring reports including congestion, parking information, and average vehicle speed based on traffic data within the community;

[0055] Publish traffic monitoring reports on community bulletin boards, and automatically collect feedback on traffic conditions and management plans through questionnaires, online comments, and community meetings to form a feedback data set;

[0056] Based on the feedback data set, the existing traffic management plan is optimized and adjusted, including adjusting traffic signal timing, increasing parking spaces, and improving congested sections.

[0057] The above solution of the present invention includes at least the following beneficial effects:

[0058] Collect and analyze traffic data in real time, accurately identify traffic flow patterns, effectively predict traffic conditions, and provide a scientific basis for traffic management. Automatically adjust traffic signals and optimize traffic flows to reduce traffic congestion and improve road efficiency. Through real-time data analysis, timely detect traffic anomalies and improve traffic safety. Seamlessly connect with the digital government platform to ensure timely updating and widespread dissemination of traffic policies and regulations.

[0059] Automatically process traffic violations, improve efficiency, and reduce human intervention. Collect and analyze public feedback to provide data support for policy formulation and adjustment, and enhance public participation. Use cloud data platforms and 5G networks to achieve real-time updates and widespread distribution of online education resources in the field of transportation.

[0060] Use VR technology to simulate real traffic scenarios, provide immersive traffic safety and planning education experience, and improve education effectiveness. Expand the audience range of traffic education and enhance the public's traffic safety awareness and planning ability. Share traffic and logistics information within the park in real time to achieve optimal allocation and intelligent scheduling of resources. Improve the collaborative efficiency of traffic and logistics within the park, reduce operating costs, and enhance the overall competitiveness of the park. Monitor the hydrological conditions around transportation facilities in real time and issue timely warnings of water risks such as floods and waterlogging. Automatically generate traffic control plans based on warning results to ensure traffic safety and reduce disaster losses. Enhance the city's ability to cope with water risks and ensure the stable operation of the transportation system.

[0061] Real-time communication and data sharing ensure that the injured can be quickly located when a traffic accident occurs.

[0062] Efficiently dispatch medical resources for rescue, provide timely rescue guidance information, and reduce injuries. Improve rescue response speed and service quality. Provide convenient traffic information services to help residents understand traffic conditions in a timely manner and plan travel routes reasonably. Monitor community traffic conditions in real time, generate and publish traffic monitoring reports, and provide residents with accurate traffic information. Automatically optimize and adjust traffic management plans based on resident feedback to meet residents' needs and improve community traffic management levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a schematic diagram of a 5G network-based smart city traffic information system provided by an embodiment of the present invention.

[0064] Figure 2 It is a flowchart of a 5G network smart city traffic information system based on which real-time sharing of traffic and logistics information within a park and automatic intelligent scheduling of traffic and logistics are provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0066] like Figure 1 As shown, an embodiment of the present invention proposes a 5G network-based smart city traffic information system, including:

[0067] The digital traffic processing module 11 is used to collect and analyze the operation data of various traffic facilities and vehicles in real time to identify the traffic flow pattern, predict the traffic status, obtain the analysis results, and automatically adjust the traffic signals and optimize the traffic flow according to the analysis results;

[0068] Digital government collaboration module 12, used to automatically obtain and distribute updated information on traffic policies and regulations, automatically handle traffic violations, and collect and analyze public feedback on traffic policies to obtain analysis reports;

[0069] Digital education module 13, which uses cloud data platforms and 5G networks to automatically update and distribute online education resources in the field of transportation and provide educational experiences, including simulating traffic scenarios through VR technology to conduct traffic safety and planning education;

[0070] The digital park traffic linkage module 14 is used to share the traffic and logistics information in the park in real time and automatically perform intelligent scheduling of traffic and logistics;

[0071] The digital water service collaborative management module 15 is used to monitor the hydrological conditions around the transportation facilities in real time, including water level and rainfall, and use the prediction model to automatically warn of water service risks that affect transportation, including floods and waterlogging, obtain warning results, and automatically generate traffic control plans based on the warning results;

[0072] The digital medical response module 16 is used for real-time communication and data sharing. When a traffic accident occurs, it automatically locates the injured, dispatches medical resources for rescue, and provides rescue guidance information;

[0073] The digital community traffic service module 17 is used to provide traffic information services, monitor community traffic conditions in real time, generate and publish traffic monitoring reports, including congestion conditions and parking information, and automatically optimize and adjust traffic management plans based on residents' feedback.

[0074] In the embodiments of the present invention, by collecting and analyzing the operation data of traffic facilities and vehicles in real time, the traffic flow pattern can be accurately identified, providing a scientific basis for traffic management. The traffic status can be predicted, potential traffic congestion points can be discovered in advance, and traffic signals can be automatically adjusted and traffic flow can be optimized according to the analysis results, effectively alleviating traffic congestion and improving road traffic efficiency. Real-time traffic data and analysis reports are provided to traffic managers to support them in making more scientific and reasonable decisions, ensuring the timely updating and wide dissemination of traffic policies and regulations, and improving the efficiency and accuracy of policy implementation.

[0075] Automatically process traffic violations, reduce the burden of manual processing, and improve law enforcement efficiency. Collect and analyze public feedback on traffic policies, enhance the transparency of policy making and public participation, and improve policy satisfaction. Use cloud data platforms and 5G networks to achieve real-time updates and wide distribution of online education resources in the field of transportation, and ensure the timeliness and accuracy of educational content. Use VR technology to simulate traffic scenes, provide immersive traffic safety and planning education experience, and improve educational effectiveness and learning interest. Break geographical restrictions, allow more people to access high-quality traffic education resources, and enhance the public's traffic safety awareness and planning capabilities. Share traffic and logistics information within the park in real time, achieve optimal allocation and intelligent scheduling of resources, and improve the collaborative efficiency of traffic and logistics within the park.

[0076] Through intelligent scheduling, we can reduce resource waste and duplication of work, lower the park's operating costs, and improve the park's overall competitiveness. Effectively alleviate the problem of traffic congestion in the park, improve the smoothness of the park's traffic and the travel experience of employees. Real-time monitoring of the hydrological conditions around transportation facilities, using prediction models to automatically warn of water risks such as floods and waterlogging, and provide a time window for traffic management to respond in advance. Automatically generate traffic control plans based on the warning results to ensure that response measures can be taken quickly and effectively in emergency situations to ensure traffic safety. Through timely warning and effective response, reduce the impact and losses of water disasters on the transportation system.

[0077] Real-time communication and data sharing ensure that the injured can be quickly located and medical resources can be dispatched for rescue when a traffic accident occurs. Provide accurate rescue guidance information to help rescuers quickly and effectively carry out rescue work, save lives and reduce injuries. Improve the quality of medical services through an efficient medical response mechanism and enhance the public's trust and satisfaction with the medical system. Provide convenient traffic information services to help residents understand traffic conditions in a timely manner, plan travel routes reasonably, and improve travel efficiency. Monitor community traffic conditions in real time, generate and publish accurate traffic monitoring reports, and provide residents with reliable traffic information references. Automatically optimize and adjust traffic management plans based on resident feedback to meet residents' needs and improve community traffic management levels.

[0078] In a preferred embodiment of the present invention, real-time collection and analysis of operation data of various traffic facilities and vehicles to identify traffic flow patterns, predict traffic conditions, obtain analysis results, and automatically adjust traffic signals and optimize traffic flow based on the analysis results may include:

[0079] Collect real-time operation data of traffic facilities and vehicles, including vehicle speed, location, flow, and road conditions;

[0080] Extracting features related to traffic flow pattern recognition from the data, including time features, spatial features and flow features, and combining the relevant features into feature vectors;

[0081] Input the feature vector into a preset neural network model for deep learning and pattern recognition to identify traffic flow patterns;

[0082] Based on the identified traffic flow patterns and historical data, the neural network model predicts future traffic conditions, including congestion and traffic speed, and obtains prediction results;

[0083] Based on the prediction results, the traffic signal control plan is automatically adjusted, including adjusting the duration of traffic lights and optimizing signal timing.

[0084] In an embodiment of the present invention, the vehicle's speed, location, driving direction and other information are collected in real time through vehicle-mounted GPS devices, vehicle-mounted sensors, smart phone applications, etc. Roadside sensors, cameras, traffic detectors and other equipment are used to collect data such as road conditions (such as road damage, construction information), traffic flow (such as vehicle counts, lane occupancy) in real time. Weather data (such as rainfall, visibility), public transportation data (such as bus arrival time, subway operation status), etc. are integrated to provide more comprehensive information for traffic flow pattern recognition and prediction. The collected data is cleaned to remove outliers, duplicate values, etc. The data is normalized or standardized to ensure that data from different sources and types are comparable. The processed data is sorted according to timestamps.

[0085] Extract time information from the data, such as hours, minutes, days of the week, holidays, etc., to reflect the periodic changes in traffic flow. Extract geographic location information from the data, such as longitude and latitude, road types (such as main roads, branch roads), intersection types, etc., to reflect the spatial distribution of traffic flow. Calculate statistical indicators related to traffic flow, such as average speed, traffic volume, lane occupancy, etc., to directly reflect the status of traffic flow. Combine features into feature vectors according to certain rules as input data for the neural network model.

[0086] Select a neural network architecture, such as a convolutional neural network (CNN), to model traffic flow data based on its temporal characteristics. Design the neural network’s parameters, such as the number of layers, number of neurons, and activation functions, to ensure that the model can effectively learn traffic flow patterns. Use historical traffic data as a training set and input feature vectors into the neural network model for training. Adjust model parameters using backpropagation algorithms and optimizers (such as gradient descent, Adam, etc.) to minimize the model’s prediction error on the training set. Use a validation set to validate the model and evaluate the model’s generalization ability to avoid overfitting. Input the feature vectors collected in real time into the trained neural network model for deep learning and pattern recognition. The model outputs the identified traffic flow patterns, such as congestion patterns during peak hours and smooth patterns during off-peak hours.

[0087] Based on the identified traffic flow patterns and historical data, the neural network model predicts future traffic conditions, including congestion, traffic speed, etc. The prediction results can be presented in the form of time series, such as traffic status predictions for the next few hours. Based on the prediction results, a traffic signal control plan is formulated, such as adjusting the duration of traffic lights, optimizing signal timing, etc. Different optimization goals can be set, such as minimizing average waiting time, maximizing traffic efficiency, etc., and appropriate optimization strategies can be selected according to actual needs. The optimized traffic signal control plan is sent to the traffic signal control system to achieve real-time adjustment and optimization. The effect of traffic signal optimization is monitored in real time, and actual traffic data is collected as feedback.

[0088] By collecting data such as vehicle speed, location, traffic flow, and road conditions in real time, the operating status of the traffic system can be fully and accurately understood. Based on the analysis of real-time data, traffic managers can make more scientific and reasonable decisions to improve the efficiency and accuracy of traffic management. By extracting time features (such as hours, minutes, days of the week, holidays, etc.), spatial features (such as longitude and latitude, road type, intersection type, etc.) and flow features (such as average speed, traffic flow, lane occupancy, etc.), traffic flow patterns can be more accurately identified, providing strong support for predicting traffic conditions. With the continuous accumulation and analysis of data, the feature extraction method can be continuously optimized to improve the accuracy and stability of traffic flow pattern recognition.

[0089] Using neural network models for deep learning and pattern recognition can automatically learn the inherent laws and characteristics of traffic flow data and realize intelligent recognition of traffic flow patterns. Neural network models have strong adaptability and can automatically adjust and optimize according to different traffic environments and scenarios to improve the generalization ability of traffic flow pattern recognition. By predicting future traffic conditions, including congestion, traffic speed, etc., traffic managers can formulate response measures in advance to reduce traffic congestion and delays and improve road traffic efficiency.

[0090] According to the prediction results, traffic resources, such as police and rescue forces, can be reasonably deployed to ensure that traffic problems can be quickly responded to and handled at critical moments. Automatically adjust the control plan of traffic signals according to the prediction results, including adjusting the duration of traffic lights and optimizing signal timing, to achieve intelligent control of traffic signals and improve the adaptability and flexibility of traffic signals. By optimizing the traffic signal control plan, traffic congestion problems can be effectively alleviated, vehicle waiting time and queue length can be reduced, and road capacity and efficiency can be improved. The optimized traffic signal control plan can reduce the probability of traffic accidents and improve traffic safety.

[0091] When specifically applied, this includes:

[0092] Collect vehicle data from vehicle GPS, vehicle sensors, smartphone applications, etc. in real time, including speed, location, driving direction, etc. Collect road condition (such as road damage, construction information) and traffic flow (such as vehicle count, lane occupancy) data from roadside sensors, cameras, traffic detectors, etc. Integrate weather data (such as rainfall, visibility) and public transportation data (such as bus arrival time, subway operation status) to provide more comprehensive traffic flow information.

[0093] Clean the collected data, remove outliers and duplicate values, etc., to ensure data quality. Normalize or standardize the data to make data from different sources and types comparable. Sort the processed data by timestamp. Extract time information from the data, such as hours, minutes, day of the week, whether it is a holiday, etc., to reflect the periodic changes in traffic flow. Extract geographic location information from the data, such as longitude and latitude, road type, intersection type, etc., to reflect the spatial distribution of traffic flow. Calculate statistical indicators related to traffic flow, such as average speed, traffic volume, lane occupancy, etc., to directly reflect the status of traffic flow.

[0094] Combine the extracted features into feature vectors according to certain rules as input data for the neural network model. The construction of feature vectors should take into account the temporal and spatial characteristics of traffic flow data. Select a suitable neural network architecture, such as convolutional neural network (CNN), to model traffic flow data based on its temporal characteristics. Design the parameters of the neural network, such as the number of layers, number of neurons, and activation function, to ensure that the model can effectively learn traffic flow patterns. Use historical traffic data as a training set and input the feature vectors into the neural network model for training. Adjust the model parameters through backpropagation algorithms and optimizers (such as gradient descent, Adam, etc.) to minimize the prediction error of the model on the training set. Use a validation set to validate the model and evaluate the generalization ability of the model to avoid overfitting. The validation set should contain data that is different from the training set but has similar characteristics. Test the model to evaluate its performance and accuracy in actual applications. Testing can include comparing the differences between the model prediction results and the actual traffic flow data, and the performance of the model in different traffic scenarios.

[0095] The feature vectors collected in real time are input into the trained neural network model for deep learning and pattern recognition. The model outputs the identified traffic flow patterns, such as congestion patterns during peak hours and smooth traffic patterns during off-peak hours. These patterns can reflect the current traffic flow status and possible future trends. Based on the identified traffic flow patterns and historical data, the neural network model predicts future traffic conditions, including congestion conditions, vehicle speed, etc. The prediction results can be presented in the form of time series, such as traffic status predictions in the next few hours. According to the prediction results, a control plan for traffic signals is formulated, such as adjusting the duration of traffic lights and optimizing signal timing. The optimization goals can be to minimize the average waiting time, maximize traffic efficiency, etc., and select the appropriate optimization strategy according to actual needs. The optimized traffic signal control plan is sent to the traffic signal control system for real-time adjustment and optimization. This can be achieved by communicating with the interface or API of the traffic signal control system. The effect of traffic signal optimization is monitored in real time, and actual traffic data is collected as feedback. This can be achieved through roadside sensors, cameras and other equipment. The model is iterated and optimized based on the feedback data to improve the prediction accuracy and signal optimization effect. This can include adjusting model parameters, adding features, improving optimization strategies, etc.

[0096] Assume that at a certain intersection, the vehicle speed and location data are collected through the vehicle GPS device, the traffic flow data is collected through the roadside sensor, and the rainfall data is obtained through the weather forecast. After data preprocessing and feature extraction, the feature vector is obtained. This feature vector is input into the trained CNN model to identify the current traffic flow mode as "peak hour congestion mode". According to historical data and the identified traffic flow mode, it is predicted that the traffic state in the next hour will be "severe congestion" and the average vehicle speed will drop to 20km / h. Therefore, the traffic signal control plan is optimized to extend the red light duration by 10 seconds and shorten the green light duration by 10 seconds to reduce vehicle waiting time and improve traffic efficiency. After the optimization is implemented, the traffic conditions are monitored in real time, and it is found that the congestion has been alleviated and the average vehicle speed has increased to 25km / h, indicating that the optimization plan is effective.

[0097] In a preferred embodiment of the present invention, the updated information of traffic policies and regulations is automatically obtained and distributed, traffic violations are automatically processed, and feedback information on traffic policies from the public is collected and analyzed to obtain feedback reports, which may include:

[0098] Send requests to the digital government platform regularly at preset intervals to check whether there are any new traffic policies and regulations updated;

[0099] The digital government platform receives and analyzes traffic policy and regulation update data, extracts key information, including policy name, release time, and content, and distributes the extracted key information to traffic management departments, traffic participants, and the public in real time through the 5G network;

[0100] Obtain traffic violation data in real time, and process and analyze the data based on traffic policies and regulations to identify the type, time, and location of violations; automatically impose penalties on violations based on preset processing rules;

[0101] Receive real-time feedback from the public on transportation policies, including opinions, suggestions, and complaints, and classify and organize the feedback information to obtain feedback reports.

[0102] In an embodiment of the present invention, a reasonable time interval (such as every hour, every day, or every week) is pre-set for regularly sending query requests to the digital government platform. When the set time interval is reached, the background service of the traffic management department automatically sends an HTTP or HTTPS request to the digital government platform to inquire whether there are new traffic policies and regulations updated. After receiving the query request, the digital government platform checks the traffic policy and regulation data in its database or content management system, generates response data containing the latest update information, and returns it to the traffic management department or related system.

[0103] After receiving the response data returned by the digital government platform, the traffic management department uses the preset parsing algorithm to parse the data and extract key information such as policy name, release time, content, etc. The extracted key information is distributed to the traffic management department, traffic participants and the public in real time through 5G network or other high-speed communication network. This can be achieved through SMS, email, APP push, social media release and other methods. The distributed traffic policy and regulation update information is stored in the local database or cloud storage for subsequent query and backup. Traffic violation data is obtained in real time through traffic monitoring cameras, traffic sensors, vehicle-mounted equipment, etc., including vehicle information, violation type, time, location, etc. The obtained violation data is matched with the latest traffic policies and regulations to determine the nature and handling method of the violation. According to the preset processing rules, the violation is automatically punished, such as sending a fine notice, a deduction notice, etc. At the same time, the processing results are recorded in the violation processing system.

[0104] Receive feedback from the public on traffic policies in real time through official websites, social media, hotlines and other channels, including opinions, suggestions, complaints, etc. Classify and organize the feedback information received and archive it according to different topics or categories. Natural language processing (NLP) technology can be used to assist in classification and organization. Generate a detailed feedback report based on the classified and organized feedback information. The report should include the public's main opinions, suggestions, complaints and the focus of problems on traffic policies, so that the traffic management department can understand the public's needs in a timely manner and optimize traffic policies. Submit the feedback report to the traffic management department or relevant decision-making body as an important reference for formulating or adjusting traffic policies. At the same time, improve and optimize traffic management work based on the problems and suggestions in the feedback report. Maintain and update the system regularly to ensure the stability and security of the system. According to the system operation, optimize the data processing, distribution, storage and other links to improve the system performance and response speed. Provide users with necessary training and support to ensure that users can use the system proficiently and understand the latest traffic policies and regulations.

[0105] By sending requests to the digital government platform regularly, traffic management departments can obtain the latest traffic policy and regulation updates in real time. This information is quickly distributed to traffic management departments, traffic participants and the public through the 5G network, ensuring the timeliness and accuracy of the information. Using multiple distribution channels (such as APP push, social media, text messages, etc.), updated information on traffic policies and regulations can quickly cover a large number of traffic participants, improving policy awareness and enforcement. The system can obtain traffic violation data in real time and automatically process and analyze it according to the latest traffic policies and regulations. This reduces the time for manual processing and improves processing efficiency. By matching violation data with traffic policies and regulations, the system can accurately identify the type, time, location and other information of violations, and automatically impose penalties according to preset processing rules. This ensures the fairness and accuracy of penalties. The public can provide feedback on traffic policies to traffic management departments in real time through multiple channels. This convenient feedback mechanism enhances public participation and satisfaction with traffic management. By collecting and analyzing public feedback information, traffic management departments can timely understand public needs and opinions, so as to accurately improve and optimize traffic policies. By collecting and analyzing a large amount of traffic data and public feedback, traffic management departments can make more scientific and reasonable decisions. These data provide strong support for the formulation and adjustment of traffic policies. With the continuous operation of the system and the continuous accumulation of data, traffic management departments can continuously optimize and adjust traffic policies, rules for handling violations, etc. This helps to form a virtuous circle and continuously improve the level and efficiency of traffic management.

[0106] When specifically applied, this includes:

[0107] Assume that the traffic management department selects 1:00 a.m. every day as the query time interval to check whether there are new traffic policies and regulations updated. At the set time (i.e. 1:00 a.m. every day), the background service of the traffic management department automatically sends an HTTP / HTTPS request to the API of the digital government platform to query the latest traffic policies and regulations. After receiving the request, the digital government platform checks the traffic policy and regulation data in its database and generates a JSON format response data containing the latest update information. For example, the response data may contain the newly released traffic regulations "New Urban Traffic Management Regulations 2023". After receiving the response data, the traffic management department uses a preset parsing algorithm (such as a JSON parser) to parse the data and extract key information such as the policy name (such as "New Urban Traffic Management Regulations 2023"), release time, content, etc. The extracted key information is distributed in real time to the internal system of the traffic management department, traffic participants, and the public through 5G networks or other high-speed communication networks. For example, through APP push, a summary of the new regulations, including major changes and impacts, is sent to the public. The distributed traffic policy and regulation update information is stored in the local database or cloud storage of the traffic management department for subsequent query and backup. For example, detailed information on the new regulation “New Regulations on Urban Traffic Management 2023” is stored in the database and marked as the latest regulation.

[0108] Traffic violation data is obtained in real time through traffic monitoring cameras, traffic sensors and other devices. For example, a vehicle's violation of running a red light at a certain intersection is recorded by the camera, and a record containing vehicle information, violation type (running a red light), time, location and other data is generated. The obtained violation data is matched with the latest traffic policies and regulations. For example, the violation of running a red light is matched with the penalty provisions for running a red light in the "New Urban Traffic Management Regulations 2023". According to the matching results and the preset processing rules, the violation is automatically punished. For example, according to the "New Urban Traffic Management Regulations 2023", vehicles that run a red light will be fined 200 yuan and deducted 3 points from the driver's license. The system automatically sends a fine notice and a deduction notice to the owner, and records the processing results in the violation processing system. Receive public feedback on traffic policies in real time through official websites, social media and other channels. For example, the public may make comments or suggestions on certain clauses in the "New Urban Traffic Management Regulations 2023". Classify and organize the received feedback information, and use NLP technology to assist in the classification work. For example, public feedback can be classified into categories such as "support", "opposition", and "suggestions for improvement" and archived. A detailed feedback report can be generated based on the classified and organized feedback information. The report may point out the main areas of public opinion on the new regulations, such as the excessive amount of fines and the lack of clarity of certain regulations.

[0109] Submit feedback reports to the traffic management department or relevant decision-making bodies as an important reference for formulating or adjusting traffic policies. For example, based on public feedback, the traffic management department may revise or explain certain clauses in the "New Urban Traffic Management Regulations 2023". Regularly maintain and update the system to ensure the stability and security of the system. For example, regularly back up and clean up the database, and update system software and hardware equipment. Provide users with necessary training and support to ensure that users can use the system proficiently and understand the latest traffic policies and regulations. For example, organize training courses, provide online help documents and customer service support, etc.

[0110] In a preferred embodiment of the present invention, cloud data platform and 5G network are used to automatically update and distribute online education resources in the field of transportation and provide educational experience, including simulating traffic scenes through VR technology to conduct traffic safety and planning education, which may include:

[0111] The cloud data platform is connected to the 5G network to provide high-speed, low-latency data transmission services. The high bandwidth and low latency characteristics of the 5G network are used to optimize the transmission speed and stability of educational resources to ensure that users can smoothly access and download resources. Design a user-friendly front-end interface, including functions such as course list, resource browsing, and video playback. Develop a back-end system to manage course resources, user information, and learning progress. Implement an automatic update mechanism so that when educational resources on the cloud data platform are updated, they can be automatically synchronized to the online education platform. Use professional VR development tools (such as Unity and UnrealEngine) to develop VR content that simulates traffic scenes, including traffic rules simulation, traffic accident reproduction, and traffic planning simulation. Ensure that the developed VR content is compatible with mainstream VR devices on the market, such as VR helmets and VR glasses. Design intuitive VR interaction methods so that users can naturally interact with virtual traffic scenes and improve the educational experience. Test the various functions of the online education platform to ensure normal operation.

[0112] In the 5G network environment, the platform's performance was tested, including resource loading speed, VR scene rendering speed, etc. Some users were invited to try it out and feedback was collected for platform optimization and improvement.

[0113] Promote through social media, transportation industry websites and other channels to attract users to register and use. Continuously update educational resources and VR content according to user needs and technological development to keep the platform vibrant and attractive.

[0114] Assume that online education platform data, 5G network and VR technology provide online education resources and immersive education experience in the field of transportation. The platform stores a wealth of education resources in the field of transportation, including traffic rules explanation videos, traffic accident case analysis documents, traffic planning tutorials, etc. These resources are regularly updated and maintained by a professional team of traffic education experts. Users can enjoy high-speed and smooth resource downloads and online playback experience by accessing the "Traffic Cloud Education" platform through the 5G network. Whether at home, school or on transportation, users can learn traffic knowledge anytime and anywhere. The platform provides multiple VR traffic scene simulations, such as the "Urban Traffic Rules Simulation" scene, where users wear VR devices to enter a virtual urban traffic environment and learn traffic rules intuitively through interactive methods such as simulated driving and pedestrian crossing the road; the "Traffic Accident Reproduction" scene helps users understand the causes and preventive measures of accidents by simulating real traffic accident processes; the "Traffic Planning Simulation" scene allows users to play the role of traffic planners, optimizing traffic flow and improving road traffic efficiency by adjusting road layouts and setting traffic signals.

[0115] In a preferred embodiment of the present invention, real-time sharing of traffic and logistics information within the park and automatic intelligent scheduling of traffic and logistics may include:

[0116] Collect raw data on traffic flow, vehicle location, road conditions, and cargo status in real time, and convert the raw data into state vectors;

[0117] According to the state vector, the objective function is constructed and a set of scheduling parameters are initialized. The scheduling parameters include the control scheme of traffic signals, the path planning of vehicles, and the loading and unloading sequence of goods.

[0118] The state vector is updated in real time through the park traffic and logistics information sharing platform to obtain an updated state vector;

[0119] According to the updated state vector, the gradient of the objective function with respect to the scheduling parameter is calculated, and the scheduling parameter is iteratively adjusted to obtain the optimized scheduling parameter; the calculation formula of the optimized scheduling parameter is:

[0120] ;

[0121] in, Indicates The parameter vector after the iteration update; Indicates The parameter vector after iterations; Indicates The learning rate of the iteration; Represents the objective function In the parameter vector The gradient vector at ; , represents the regularization parameter; , represents the momentum parameter; , Indicates Second and The parameter vector after iterations;

[0122] The optimized scheduling parameters are passed to the park's traffic management system and logistics operating system to automatically perform intelligent scheduling of traffic and logistics, including adjusting traffic signals, guiding vehicle driving, and arranging loading and unloading, transportation, and distribution of goods.

[0123] In the embodiment of the present invention, sensors (such as geomagnetic sensors, infrared sensors or cameras) installed at key locations on the park roads are used to count the number and type of vehicles passing through in real time. The location information of vehicles in the park is obtained in real time through GPS positioning technology or wireless positioning systems in the park (such as Wi-Fi positioning, Bluetooth positioning). Information on road congestion, accidents, construction and other conditions is collected through camera monitoring, sensor detection or manual reporting. RFID tags, sensors or logistics management systems are used to obtain real-time status information such as the location, temperature, humidity, damage and other conditions of goods.

[0124] Clean the collected raw data to remove invalid, erroneous or redundant data. Convert the cleaned data into a state vector. A state vector is a vector containing multiple dimensions, each of which represents a specific state of traffic and logistics in the park, such as vehicle density, average speed, road congestion index, cargo waiting time, etc. Construct an objective function based on the actual needs of the park's traffic and logistics. The purpose of the objective function is to measure the pros and cons of scheduling parameters (including traffic signal control schemes, vehicle path planning, cargo loading and unloading sequence, etc.).

[0125] Initialize a set of scheduling parameters as the starting point for optimization. These parameters include the initial timing plan for traffic lights, the initial path planning for vehicles, the initial loading and unloading order of goods, etc. Update the state vector in real time through the park traffic and logistics information sharing platform. This requires the platform to have efficient data transmission and processing capabilities to quickly pass new state information to the optimization algorithm.

[0126] For In iterations, we first calculate the objective function In the current parameter vector The gradient vector at . The gradient vector indicates the direction of descent of the objective function in the parameter space. Calculate the momentum term The momentum term takes into account the gradient information of the previous two iterations, which helps to accelerate convergence and reduce oscillation. According to the formula Calculate the new parameter vector .in, It is The learning rate of the iteration controls the size of the iteration step. is the objective function In the parameter vector The gradient vector at . and is a regularization parameter used to prevent overfitting and maintain parameter sparsity. is the parameter vector The square of the norm of is used to measure the complexity of the parameters. is the momentum parameter, which controls the influence of the momentum term. It is the momentum term of the difference between the gradients of the first two iterations, which helps accelerate convergence. Check whether the stopping conditions are met (such as the gradient is close to zero, the number of iterations reaches the upper limit, the objective function value converges, etc.). If the conditions are met, stop the iteration and output the optimized scheduling parameters; otherwise, continue to the next iteration.

[0127] The optimized scheduling parameters (i.e., the parameter vector obtained from the last iteration) are passed to the traffic management system and logistics operation system of the park.

[0128] According to the optimized traffic signal control scheme, the timing of traffic lights is automatically adjusted to optimize traffic flow and reduce congestion. At the same time, according to the real-time location and path planning of vehicles, vehicles are guided to improve road traffic efficiency. According to the optimized cargo loading and unloading sequence and transportation and distribution plan, the loading, unloading, transportation and distribution of goods are automatically arranged. Through intelligent scheduling, the waiting time and transportation costs of goods are reduced and the logistics efficiency is improved. Real-time monitoring of the operation status of traffic and logistics in the park, and feedback data (such as traffic flow, vehicle speed, cargo status, etc.) is collected. According to the feedback data, the objective function, scheduling parameters and optimization algorithm are continuously adjusted and optimized. Through continuous optimization, the accuracy and efficiency of the intelligent scheduling system are improved to meet the ever-changing needs of traffic and logistics in the park.

[0129] By collecting raw data such as traffic flow and vehicle location in real time and converting them into state vectors, the system can accurately grasp the traffic conditions in the park. By constructing the objective function based on the state vector and iteratively adjusting the control scheme of the traffic signal, the traffic flow can be optimized, congestion and waiting time can be reduced, and the traffic efficiency of the road can be improved. Real-time collection of cargo status information, such as location, temperature, humidity, etc., enables the system to understand the current status of the cargo in a timely manner. By optimizing the loading and unloading sequence and transportation and distribution plan of the cargo, the waiting time and transportation cost of the cargo can be reduced, and the response speed and accuracy of logistics can be improved. The park traffic and logistics information sharing platform provides managers with comprehensive real-time data support, enabling them to make more scientific and reasonable decisions. The optimized scheduling parameters provide clear guidance for the traffic management system and logistics operating system, realizing automated and intelligent management. The intelligent scheduling of traffic and logistics coordinates with each other to form an efficient and orderly park operation system. By continuously optimizing the scheduling parameters, the system can adapt to the continuous changes in the park's traffic and logistics and maintain efficient operation. Reducing traffic congestion and waiting time reduces the fuel consumption and emissions of vehicles, which is beneficial to environmental protection. Optimizing the logistics process reduces unnecessary transportation and loading and unloading operations, and reduces logistics costs and energy consumption. The efficient transportation and logistics system has improved the overall operational efficiency and service quality of the park. Intelligent management methods have made the park more competitive in attracting enterprises and improving customer satisfaction.

[0130] When specifically applied, this includes:

[0131] Geomagnetic sensors, infrared sensors and cameras are deployed at the main road intersections, entrances and exits, and key sections of the park to count the number, type and speed of passing vehicles in real time. GPS base stations are set up in the park or existing wireless facilities such as Wi-Fi and Bluetooth are used to build a wireless positioning system to obtain the precise location of vehicles and personnel in the park in real time. RFID readers and sensors are set up in cargo storage areas, loading and unloading areas, and during transportation to monitor the location, temperature, humidity and other conditions of cargo in real time.

[0132] Assume that there are three main intersections in the park, and geomagnetic sensors and cameras are deployed at each intersection for vehicle counting and type recognition. Multiple Wi-Fi access points are deployed in the park, forming a wireless positioning system covering the entire park, which can track the location of all vehicles in the park in real time. RFID readers are installed in the cargo warehouse and loading and unloading area to monitor the storage and loading and unloading status of cargo in real time.

[0133] The collected raw data is preprocessed to remove invalid, erroneous or redundant data, such as abnormal vehicle speed, repeated counts, etc. Based on the cleaned data, a state vector containing multiple dimensions is generated, such as vehicle density, average vehicle speed, road congestion index, cargo waiting time, etc. After data cleaning, the number, type and average speed of vehicles at each intersection in the park are obtained. Based on these data, a state vector is generated, which contains information such as the overall vehicle density, average vehicle speed, congestion index of each intersection and waiting time of cargo warehouses.

[0134] According to the actual needs of the park's transportation and logistics, an objective function is constructed to minimize traffic congestion and cargo waiting time within the park. Initialize the timing plan for traffic lights and set the green light time for each intersection. Plan the initial route for vehicles within the park to avoid congested sections as much as possible. Formulate the initial loading and unloading sequence of goods to ensure that the goods can be loaded, unloaded and transported efficiently and in an orderly manner.

[0135] Update the state vector in real time to reflect the number of vehicles, positions and status changes of goods in the park. Perform multiple iterations of optimization to continuously adjust the timing plan of traffic lights, the path planning of vehicles and the loading and unloading order of goods. After multiple iterations, the optimized scheduling parameters are obtained, which reduces traffic congestion and waiting time for goods in the park. Apply the optimized traffic light timing plan to the traffic management system of the park to realize intelligent control of traffic signals. Provide real-time path planning services for vehicles through the park's navigation system or mobile phone APP to guide vehicles to avoid congested sections. According to the optimized cargo loading and unloading order and transportation and distribution plan, automatically dispatch loading and unloading equipment and transportation vehicles to ensure that the goods can be delivered to the destination efficiently and accurately.

[0136] Real-time monitoring of traffic flow, vehicle speed, cargo status and other data within the park. According to the changes in these data, regular adjustments and optimizations are made to the objective function, scheduling parameters and optimization algorithms. Through continuous optimization, the intelligent scheduling system can better adapt to the actual needs of the park's transportation and logistics, and improve the overall operation efficiency of the park.

[0137] In a preferred embodiment of the present invention, the calculation formula of the objective function is:

[0138] ;

[0139] in, represents the objective function; represents the weight coefficient; Indicates the average speed of vehicles in the park; Represents the weight coefficient of the congestion index; represents the congestion index; represents the weight coefficient; Indicates the average transportation time of goods; Indicates the loading and unloading time of the cargo; The weight coefficient representing the on-time delivery rate; Indicates the on-time delivery rate; represents the weight coefficient; Indicates the control efficiency of traffic signals; Indicates the path planning efficiency of the vehicle; Indicates the efficiency of the loading and unloading sequence of cargo.

[0140] In the embodiment of the present invention, the objective function is minimized by adjusting the traffic signal control, vehicle path planning and cargo loading and unloading sequence within the park. to improve the overall efficiency of the park's transportation and logistics. Determine the optimization variables, including the control scheme of traffic signals (influencing ), vehicle path planning (affecting ) and the order of cargo loading and unloading (affecting ). Collect the speed data of vehicles in the park in real time and calculate the average driving speed Calculate the road congestion index through monitoring equipment or algorithms . Record the transit time of the goods and loading and unloading time . Statistical distribution on-time rate. Evaluate the control efficiency of traffic signals. , vehicle path planning efficiency and cargo loading and unloading sequence efficiency .

[0141] Initialize the scheduling parameters, including the initial setting of the traffic signal control scheme, the initial route of vehicle path planning, and the initial arrangement of the cargo loading and unloading sequence. Set the weight coefficient according to the actual situation and expert experience , , And the weight coefficient of the congestion index , weight coefficient of delivery on-time rate , calculate the objective function value under the current scheduling parameters . Adjust the scheduling parameters and generate a new scheduling plan. Calculate the objective function value under the new scheduling plan '.Compare and ',if ' < , indicating that the new scheduling scheme is better, accept the new scheme, and update the current optimal scheduling parameters and minimum objective function value. ' ≥ , indicating that the new scheduling scheme has not been improved, and the current optimal scheduling parameters and minimum objective function value remain unchanged.

[0142] Repeat the generation of new solutions, calculation of new target values, and comparison and selection until the stopping condition is met (such as reaching the maximum number of iterations). Adjust the timing of traffic lights according to the optimized traffic signal control plan. Guide vehicle driving according to the optimized vehicle path planning. Arrange loading and unloading operations according to the optimized cargo loading and unloading sequence.

[0143] Monitor the operation status of the park's traffic and logistics in real time and collect actual operation data. Regularly evaluate the optimization effect, such as average vehicle speed, road congestion index, cargo transportation time, loading and unloading time, delivery on-time rate, traffic signal control efficiency, vehicle path planning efficiency, cargo loading and unloading sequence efficiency, etc. According to the evaluation results, feedback adjustment is made to the weight coefficient and scheduling parameters to adapt to the dynamic changes of the park's traffic and logistics.

[0144] By optimizing the control efficiency of traffic signals ( ), which can reduce the waiting time of vehicles at the intersection, increase the speed of vehicles, and thus increase the average speed of vehicles in the park ( ). In the objective function, The increase of will lead to a decrease in the value of -1 / v, thus reducing The value of this item has an impact on the overall objective function The improvement of vehicle driving efficiency can also reduce fuel consumption and emissions, which is beneficial to environmental protection. By optimizing the vehicle's path planning efficiency ( ), which can plan a more reasonable and smooth driving route for vehicles and avoid congestion caused by vehicles concentrating on certain sections of the road. Congestion Index ( ) will directly lead to the reduction of Reducing the value of this item helps to reduce the overall objective function Relieving traffic congestion can also improve the utilization of road resources, allowing more vehicles to complete transportation tasks in a shorter time. Optimizing the efficiency of cargo loading and unloading sequence ( ) and the vehicle’s path planning efficiency ( ) can shorten the average transportation time of goods ( ) and loading and unloading time ( ). In the objective function, and The reduction will directly lead to The value of this item decreases, which has an impact on the overall objective function. The reduction of freight transportation time can improve the response speed of logistics services and customer satisfaction.

[0145] By optimizing traffic signal control, vehicle routing and cargo loading and unloading sequence, the on-time rate of delivery can be improved ( ). In the objective function, The increase will lead to The value of this item increases (note the negative sign here), but because there is a negative sign in front, it actually increases the overall objective function. Improved delivery punctuality can enhance customer trust and loyalty to logistics services. Improved vehicle driving efficiency, eased traffic congestion, and shortened cargo transportation time can reduce logistics operating costs, including vehicle maintenance costs, fuel costs, and labor costs. Cost reduction can improve the competitiveness of logistics companies and create more economic benefits for the company.

[0146] In a preferred embodiment of the present invention, the hydrological conditions around the traffic facilities are monitored in real time, including water level and rainfall, and a prediction model is used to automatically warn of water risks that affect traffic, including floods and waterlogging, and obtain warning results. Based on the warning results, a traffic control plan is automatically generated, which may include:

[0147] Input the real-time monitored hydrological data into the prediction model to predict water affairs risks in the future and obtain water affairs risk prediction results, including risk type, probability of occurrence, and impact range;

[0148] Based on the water affairs risk prediction results, the probability and impact of flood and waterlogging water affairs risks are assessed to obtain risk assessment results;

[0149] When the risk assessment results exceed the preset threshold, early warning information is automatically issued, including risk type, warning level, and impact scope;

[0150] According to the early warning information, a set of traffic control schemes are initialized, including closing certain road sections and restricting vehicle traffic, to obtain an initial set of traffic control schemes;

[0151] Each solution in the initial traffic control solution set is taken as a solution vector, and the solution vector is randomly mutated to generate a new candidate solution;

[0152] Cross the new candidate solution with the original solution vector to generate a new solution vector combination;

[0153] Evaluate the new solution vector combination according to the fitness function and determine the solution vector to enter the next round of iteration;

[0154] The mutation, crossover and evaluation processes are repeated until the preset number of iterations is reached, and the final solution is determined from the solution vector set as the traffic control solution.

[0155] In an embodiment of the present invention, hydrological monitoring equipment such as water level monitors and rain gauges are installed at key locations around traffic facilities to ensure that hydrological data such as water level and rainfall can be collected in real time and accurately. A data acquisition system is established to transmit the hydrological data collected by the monitoring equipment to the central data center in real time for storage and processing. A water affairs risk prediction model is constructed, which should be able to predict water affairs risks in the future based on historical hydrological data, weather forecast information, topography and other factors. The hydrological data monitored in real time is input into the prediction model, and the model is run to predict water affairs risks to obtain water affairs risk prediction results, including risk types (such as floods, waterlogging), occurrence probability, impact range, etc. According to the water affairs risk prediction results, the occurrence probability and impact degree of water affairs risks such as floods and waterlogging are evaluated to obtain risk assessment results. A risk assessment threshold is set. When the risk assessment result exceeds the preset threshold, the early warning mechanism is automatically triggered to generate early warning information. The early warning information should include risk type, early warning level (such as low, medium, high), impact range, etc., and be promptly released to relevant departments and personnel through SMS, email, APP push, etc.

[0156] According to the warning information, combined with the actual situation of traffic facilities and traffic control needs, a set of traffic control plans are initialized. These plans can include measures such as closing certain sections of roads, restricting vehicle traffic, adjusting signal light timing, etc. The initialized traffic control plan set is represented as a solution vector, and each solution vector represents a specific traffic control plan. The initial solution vector is randomly mutated to generate new candidate solutions. The mutation operation can be achieved by changing certain elements in the solution vector (such as closed sections of roads, types of vehicles restricted from passing, etc.). The new candidate solution is cross-operated with the original solution vector to generate a new solution vector combination. The cross-operation can generate a new combination by exchanging some elements in the two solution vectors. Define a fitness function to evaluate the quality of the solution vector. The fitness function can comprehensively consider factors such as the impact of the traffic control plan on traffic flow, implementation difficulty, and cost. Evaluate the new solution vector combination according to the fitness function, and select the solution vector with higher fitness to enter the next round of iteration. Repeat the mutation, cross-over and evaluation process until the preset number of iterations is reached or other stop conditions are met. After the iteration, the solution vector with the highest fitness is selected from the solution vector set as the final solution, that is, the optimal traffic control plan. The finalized traffic control plan is presented to decision makers in a visual form, and specific steps and suggestions for implementation are provided.

[0157] Organize relevant departments and personnel to implement the finalized traffic control plan. During the implementation process, continuously monitor the hydrological and traffic conditions, and dynamically adjust and optimize the traffic control plan according to the actual situation. Evaluate and summarize the implementation effect of the traffic control plan to provide experience and reference for future water risk response and traffic control.

[0158] By real-time monitoring of hydrological data such as water level and rainfall, combined with prediction models, the probability and scope of water risks such as floods and waterlogging can be predicted in advance. This allows traffic management departments to have enough time for early warning and preparation, thereby reducing the impact of water risks on the transportation system and protecting the lives and property of the public. The process of automatically generating traffic control plans is based on risk assessment results and optimization technologies such as genetic algorithms, which can quickly and intelligently generate a set of feasible traffic control plans. This avoids the subjectivity and delays that may be caused by manual decision-making and improves the efficiency and accuracy of responding to water risks. In the process of implementing traffic control plans, by continuously monitoring hydrological and traffic conditions, the plans can be dynamically adjusted and optimized according to actual conditions. This flexibility ensures that traffic control measures can adapt to changes in water risks at any time and maintain the stable operation of the transportation system. Through optimization technologies such as genetic algorithms, the optimal solution or approximate optimal solution can be found among multiple feasible traffic control plans. This means that while achieving the same or better results, the impact on the transportation system and cost investment can be minimized, and the optimal allocation of resources can be achieved. Timely and accurate warning information and effective traffic control measures can enhance the public's trust and sense of security in traffic management departments. At the same time, by reducing the impact of water risks on the transportation system, the inconvenience and dissatisfaction caused by traffic congestion and delays can be reduced, and public satisfaction can be improved. The implementation of this process relies on advanced hydrological monitoring technology, prediction models, intelligent optimization algorithms and other scientific and technological means to promote the intelligent and modern process of traffic management. This not only improves the efficiency and accuracy of traffic management, but also provides more possibilities and space for future traffic management innovation.

[0159] When specifically applied, this includes:

[0160] Water level monitors and rain gauges are installed at key locations around transportation facilities (such as bridges, tunnels, and highways). For example, water level monitors and rain gauges are installed along the main rivers and at the entrances of highways in a city to monitor water level changes and rainfall in real time. The monitoring data is transmitted to the central data center of the city water administration bureau in real time via a wireless network, and the data center stores and preprocesses the data. A flood prediction model is constructed using historical hydrological data, weather forecast information, and topographic data. When real-time monitoring shows a rapid rise in water levels and an increase in rainfall, the model predicts that a flood may occur within the next 24 hours. Risks are assessed based on the prediction results, and thresholds are set to trigger warnings. For example, the model predicts that the probability of flooding is 80%, and the impact area includes the main traffic arteries in the north of the city. The risk assessment results exceed the preset threshold, and the system automatically triggers a high-level flood warning. Based on the warning information and the actual situation of the transportation facilities, the traffic control plan is initialized. For example, an initial traffic control plan was formulated, including closing the main traffic arteries in the north of the city, restricting vehicle traffic, and adjusting the timing of surrounding traffic lights.

[0161] The initial scheme was optimized by genetic algorithm, taking into account factors such as traffic flow impact, implementation difficulty and cost. After multiple iterations, a final traffic control scheme was obtained that can effectively alleviate the impact of floods and keep traffic relatively smooth. The final traffic control scheme was presented to the urban traffic management department in the form of a map, indicating the closed sections, restricted areas and signal light adjustment plan, and providing detailed implementation steps and timetable. The urban traffic management department quickly organized the implementation of traffic control measures based on the final plan. At the same time, the water level and traffic flow were continuously monitored, and the control scheme was fine-tuned according to the actual situation to ensure the safety and smoothness of traffic. The implementation effect was evaluated and lessons learned were summarized. For example, after the flood, the implementation effect of the urban traffic control scheme was evaluated. The results showed that the scheme effectively reduced the impact of floods on traffic and ensured the safety of citizens' travel. At the same time, the experience and shortcomings in the implementation of the scheme were summarized, providing a reference for future water risk response and traffic control.

[0162] In a preferred embodiment of the present invention, the calculation formula of the fitness function is:

[0163] ;

[0164] in, Indicates in the traffic control scheme The fitness value under ; The weight coefficient representing the traffic flow; Indicates The average vehicle speed on the road section; Indicates the total number of road segments; Indicates The weight coefficient of each road segment; , Represents an index variable; represents the weight coefficient of risk mitigation; Indicates in the traffic control scheme The area of ​​the region at risk from water services; An adjustment factor representing the area of ​​the risk zone; Indicates the total area of ​​the region; represents the weight coefficient of implementation cost; Indicates the total number of cost items; Indicates Implementation costs; Indicates The weight coefficient of the item cost; Indicates the weight coefficient of safety; Indicates in the traffic control scheme The predicted number of traffic accidents; Represents the baseline number of incidents.

[0165] In the embodiment of the present invention, the weight coefficient is determined (weight of traffic flow), (weight of risk mitigation), (weight of implementation cost), (Weight of security). Determine other parameters, (Total number of sections), (Total number of cost items), (Adjustment factor for the area of ​​risk area), (Total area), (Baseline number of incidents).

[0166] For each road segment (from 1 to ):

[0167] Get the average speed of vehicles on this road section . Get the weight coefficient of the road section (Set according to the importance of the road section or traffic flow). Calculate the weighted speed sum (cumulative for all road segments).

[0168] Calculate the sum of road segment weights (The weights of all road sections are accumulated). Calculate the traffic flow index = According to the weight coefficient , calculate the contribution of traffic fluency to the fitness value According to the traffic control plan , determine the area at risk of water damage . Calculate the risk mitigation index = ( is the adjustment factor, used to adjust the impact of the risk area).

[0169] According to the weight coefficient , calculate the contribution of risk mitigation to the fitness value .

[0170] For each cost (from 1 to ):

[0171] Get the value of the cost . Get the weight coefficient of this cost . Calculate the sum of weighted costs (All cost items are accumulated). According to the weight coefficient , calculate the contribution of the implementation cost to the fitness value (note that this is a negative contribution, because the higher the cost, the lower the fitness) .

[0172] According to the traffic control plan , predict the number of traffic accidents that may occur . Calculate the safety index = ( is the number of baseline accidents, used to standardize the predicted number of accidents). According to the weight coefficient , calculate the contribution of safety to the fitness value Adding up the contributions of the four parts, we get the traffic control solution The fitness value under .

[0173] In the formula The term represents the weight of traffic smoothness, which encourages maintaining a higher average vehicle speed in the traffic control scheme, especially on sections with larger weights. This helps reduce traffic congestion and improve overall traffic efficiency, thus bringing a smoother travel experience to the public. The project focuses on risk mitigation, which aims to reduce the impact of potential water disasters by reducing the area at risk. This helps protect transportation facilities and public safety, and reduces traffic disruptions and property losses caused by water risks. The first item takes into account the cost required to implement the traffic control plan, including the input of manpower, material resources, etc. By minimizing the sum of weighted costs, the implementation cost can be reduced and the resource utilization efficiency can be improved while ensuring the effect of traffic control. The project emphasizes the importance of safety and evaluates the safety of traffic control schemes by predicting and reducing the number of traffic accidents. This helps to reduce the incidence of traffic accidents, protect the lives and property of the public, and improve the overall safety level of the transportation system.

[0174] In a preferred embodiment of the present invention, real-time communication and data sharing are performed. When a traffic accident occurs, the position of the injured is automatically located, medical resources are dispatched for rescue, and rescue guidance information is provided, which may include:

[0175] Real-time monitoring of traffic conditions to detect abnormal traffic conditions, including sudden stops of vehicles, sharp decreases in speed, and to determine whether the abnormal traffic conditions are the scene of an accident;

[0176] If it is determined to be an accident scene, the data from the accident scene is analyzed, including the location of the vehicle and camera surveillance footage, to determine the location of the injured, and digital maps and GPS data are accessed to determine the geographic location of the injured;

[0177] The digital medical platform receives information from the accident scene, dispatches medical resources for rescue based on the location and condition of the injured, and provides real-time rescue guidance information to rescuers through the 5G network, including rescue routes, descriptions of the injured's location, and injury assessments.

[0178] In an embodiment of the present invention, traffic monitoring equipment such as cameras, radars, and induction coils are deployed at key sections and intersections to monitor traffic flow data in real time, including vehicle speed, location, and driving direction. A traffic data analysis system is established to receive and process real-time data from monitoring equipment, analyze traffic flow patterns, and detect abnormal traffic conditions, such as sudden vehicle stops and sharp speed drops. A judgment logic is set. When abnormal traffic conditions are detected, historical data and on-site monitoring images are combined to determine whether it is a traffic accident scene. Once it is determined to be an accident scene, the data at the accident scene is immediately analyzed in depth, including vehicle location data, camera monitoring images, etc., to confirm the specific circumstances of the accident and the possible location of the injured. Digital maps and GPS data are accessed, and the geographic location of the injured is accurately determined by combining the vehicle location information and the geographic coordinates of the camera monitoring images. The exact location of the injured is marked on the digital map. Digital medical platform access: The accident scene information is transmitted to the digital medical platform in real time, including the location of the injured, preliminary injury assessment, etc. According to the location and injury of the injured, the digital medical platform automatically or manually dispatches the nearest medical resources, including ambulances, emergency personnel, etc., to the accident scene for rescue. Medical resources should be rationally allocated according to the number of injured and the severity of their injuries to ensure that every injured person receives timely and effective treatment.

[0179] Utilize the high speed and low latency characteristics of 5G network to provide real-time communication and data transmission support for rescuers. Plan the best rescue route for rescuers based on the geographic location of the injured and the current traffic conditions to ensure that rescuers can quickly reach the scene of the accident. Provide rescuers with accurate descriptions of the injured's location in the form of text, pictures or videos, so that rescuers can quickly locate the injured. Provide rescuers with guidance and suggestions on injury treatment based on the preliminary injury assessment results to improve rescue efficiency and treatment success rate. After the rescuers arrive at the scene of the accident, they will update the on-site situation in real time through the 5G network, including the condition of the injured, the progress of the rescue, etc., to facilitate the subsequent deployment of medical resources and command decisions. After the rescue is completed, collect feedback on the rescue effect, including rescue time, treatment success rate and other data, for subsequent optimization of the rescue process and improvement of rescue efficiency. Analyze and summarize the data of each rescue operation, identify existing problems and deficiencies, and propose improvement measures. Based on the summary results, optimize and upgrade monitoring equipment, data analysis systems, digital medical platforms, etc. to improve overall rescue capabilities and efficiency.

[0180] The system can monitor traffic conditions in real time and quickly detect abnormal traffic conditions, such as sudden stops and sharp drops in speed, to provide key information for rapid response to traffic accidents. Once the accident scene is determined, the system immediately analyzes the accident data, combines digital maps and GPS data to accurately determine the geographic location of the injured, and provide accurate target positioning for rescue operations. The digital medical platform can quickly receive accident scene information, including the location and injury assessment of the injured, providing a scientific basis for the dispatch of medical resources.

[0181] The platform can automatically or manually dispatch the nearest medical resources, such as ambulances and emergency personnel, according to the number of injured people and the severity of their injuries, to ensure that every injured person can receive timely and effective treatment. Through the high speed and low latency characteristics of the 5G network, the system can provide rescuers with real-time rescue guidance information, including the optimal rescue route, a detailed description of the injured person's location, and suggestions for injury treatment. These guidance information can help rescuers quickly locate the injured and take correct rescue measures, thereby improving rescue efficiency and treatment success rate. The system can record and analyze the data of each rescue operation, identify existing problems and deficiencies, and provide data support for optimizing the rescue process. Through continuous summary and improvement, the system can enhance the overall rescue capability and ensure that rescue can be carried out more quickly and effectively in future traffic accidents. Real-time communication and data sharing mechanisms can strengthen the collaboration and cooperation between traffic management departments, medical emergency departments, and rescue personnel, form a joint force, and jointly deal with traffic accidents. Information sharing can also avoid the phenomenon of information islands, ensuring that all parties can obtain the latest rescue progress and the situation of the injured in a timely manner, providing strong support for decision-making.

[0182] An efficient traffic accident emergency response system can handle traffic accidents quickly, reduce the pain and waiting time of the injured, and thus enhance the public's sense of safety and satisfaction.

[0183] When specifically applied, this includes:

[0184] Suppose that on a highway in a city, a truck suddenly had a tire blowout and lost control, crashing into the roadside guardrail. Subsequently, several cars were unable to avoid it and also had a chain collision. This series of accidents caused serious abnormalities in traffic flow, a sharp drop in vehicle speed, some vehicles stagnated, and people were injured.

[0185] Monitoring equipment such as cameras, radars, and induction coils deployed on highways monitor abnormal data such as vehicle speed reduction and stagnation in real time, and capture the moment when the accident occurs. After receiving this data, the traffic data analysis system immediately conducts real-time analysis, detects abnormal changes in traffic flow, and triggers an alarm. The system combines historical data with on-site monitoring images to quickly determine that the abnormal situation is a traffic accident scene.

[0186] Analyze the accident scene data, including vehicle location data, camera monitoring screen, etc., to confirm the specific circumstances of the accident and the possible location of the injured. By accessing digital maps and GPS data, the system accurately determines the geographic location of the injured and marks the exact location of the injured on the digital map. The digital medical platform receives information from the accident scene, including the location of the injured and the preliminary injury assessment (such as the injury condition obtained through the analysis of the monitoring screen). According to the location and injury of the injured, the platform automatically dispatches the nearest ambulance, emergency personnel and other medical resources to the accident site for rescue. According to the number of injured and the severity of the injury, the platform reasonably allocates medical resources to ensure that each injured can receive timely and effective treatment. Using the high-speed and low-latency characteristics of the 5G network, the system provides real-time communication and data transmission support for rescue personnel, ensuring the rapid communication of rescue instructions. Based on the geographic location of the injured and the current traffic conditions, the system plans the best rescue route for rescue personnel, and provides accurate location descriptions of the injured in the form of text, pictures, etc. On the way to the accident site, the rescue personnel received the injury treatment guidance and suggestions provided by the system through the 5G network, which made full preparations for subsequent rescue work.

[0187] After the rescuers arrived at the scene of the accident, they updated the on-site situation in real time through the 5G network, including the specific injuries of the injured, the progress of the rescue, etc., to facilitate the subsequent deployment of medical resources and command decisions. During the rescue process, the system continued to provide necessary support and guidance to the rescuers to ensure the smooth progress of the rescue work. After the rescue, the system collected feedback on the rescue effect, including data such as rescue time and treatment success rate, and conducted detailed analysis and summary. Through the summary, the system found some problems in the rescue process, such as some rescuers did not have a deep understanding of the guidance on injury treatment. According to the summary results, the system optimized and upgraded the monitoring equipment, data analysis system, digital medical platform, etc., such as strengthening the training of injury treatment guidance and improving the accuracy of the data analysis system, so as to improve the overall rescue capability and efficiency.

[0188] In a preferred embodiment of the present invention, traffic information services are provided, and community traffic conditions are monitored in real time, and traffic monitoring reports are generated and published, including congestion conditions and parking information. At the same time, traffic management plans are automatically optimized and adjusted based on residents' feedback, which may include:

[0189] Generate traffic monitoring reports including congestion, parking information, and average vehicle speed based on traffic data within the community;

[0190] Publish traffic monitoring reports on community bulletin boards, and automatically collect feedback on traffic conditions and management plans through questionnaires, online comments, and community meetings to form a feedback data set;

[0191] Based on the feedback data set, the existing traffic management plan is optimized and adjusted, including adjusting traffic signal timing, increasing parking spaces, and improving congested sections.

[0192] In an embodiment of the present invention, traffic data in the community is collected, including vehicle flow, speed, parking occupancy, traffic signal status, etc. Radars, induction coils, cameras and other sensors are deployed at key sections, intersections and parking lots in the community to monitor vehicle passing conditions, parking status, etc. in real time. Data from different sensors are integrated to form a unified traffic data stream, including timestamp, location, vehicle speed, vehicle flow, parking occupancy rate and other information. Based on the collected traffic data, a traffic monitoring report containing congestion conditions, parking information, average vehicle speed, etc. is generated. The collected traffic data is processed to calculate key indicators such as congestion index, average vehicle speed, and parking space utilization rate. Based on the analysis results, a traffic monitoring report is automatically generated, and the report content should contain clear charts, data analysis and suggestions for easy understanding by residents and traffic managers. The traffic monitoring report is released to community residents to improve the transparency of traffic information. The report is printed out and posted on the bulletin board of the community for residents to read. The electronic version of the report is published on the community website, APP or social media for residents to view at any time. A questionnaire is designed to ask residents about their satisfaction and improvement suggestions on traffic congestion, parking convenience, traffic signals, etc. Set up a comment area on the community website or app to allow residents to express their views on traffic conditions. Hold community meetings regularly and invite residents to participate to directly listen to their opinions and suggestions. Screen and organize the collected feedback to remove invalid and duplicate information. Categorize the feedback according to categories such as traffic congestion, parking problems, and traffic signals. Perform statistical analysis on the classified feedback to identify issues and areas of common concern to residents.

[0193] According to traffic flow and congestion, optimize the timing of traffic signals, reduce waiting time, and improve traffic efficiency. In areas with high parking demand, find suitable space to increase parking spaces, or build multi-storey parking lots to alleviate parking difficulties. Improve road capacity by widening roads, setting up bus lanes, optimizing intersection design, etc. By real-time monitoring and reporting of traffic conditions in the community, including congestion and average vehicle speed, traffic managers can keep abreast of traffic dynamics and adjust traffic signal timing. For example, increase green light time and reduce red light waiting time during peak hours, thereby effectively alleviating traffic congestion and improving road traffic efficiency. Parking information in traffic monitoring reports, such as parking space utilization and the number of vacant parking spaces, provides valuable decision-making basis for traffic managers. Based on this information, managers can add parking spaces in areas with high parking demand, or guide vehicles to distant but vacant parking lots to balance parking resources and reduce traffic congestion caused by finding parking spaces. Collect residents' feedback on traffic conditions and management plans through various channels such as community bulletin boards, questionnaires, online comments, and community meetings, so that traffic management is closer to residents' needs. Residents can feel that their opinions are valued, and their satisfaction with traffic management will naturally increase. Good traffic conditions are an important foundation for the harmonious development of the community. By optimizing traffic management plans and reducing traffic congestion and parking problems, we can reduce friction and conflicts between residents caused by traffic problems and promote a harmonious atmosphere in the community. Traffic management plan optimization based on big data and residents' feedback makes traffic management more scientific and accurate. Through data analysis, we can identify hot spots and time periods of traffic problems, take targeted measures, and improve the efficiency and effectiveness of traffic management.

[0194] When specifically applied, this includes:

[0195] For example, radar sensors are deployed to monitor vehicle speed and traffic flow at key sections of smart communities, such as main entrances, exits, around schools, and commercial centers. Induction coils monitor parking occupancy, and cameras capture traffic signal status and vehicle driving trajectories. Through the Internet of Things technology, these sensor data are transmitted to the community traffic management center in real time, and integrated using a big data processing platform to form a unified traffic data stream containing information such as timestamps, locations, vehicle speeds, vehicle flow, and parking occupancy rates. Using data analysis software, the integrated traffic data is deeply mined to calculate key indicators such as congestion index (such as determining congestion by time periods and areas where the vehicle speed is below a certain threshold), average vehicle speed, and parking space utilization rate. Traffic monitoring reports are automatically generated that include clear charts (such as congestion heat maps, vehicle speed distribution maps, and parking occupancy rate change maps), data analysis (such as congestion causes, parking demand trends), and improvement suggestions (such as optimizing signal timing and adding parking spaces). The printed traffic monitoring reports are posted on the community bulletin board, especially in areas with dense traffic, such as community centers and supermarket entrances.

[0196] Publish electronic reports on the community's official website, APP and social media platforms simultaneously to ensure that residents can view them at any time through their mobile phones or computers. Send questionnaires through the community APP or email to ask residents about their satisfaction with traffic congestion, parking convenience, traffic signals and other aspects and specific improvement suggestions. Encourage residents to express their views on traffic conditions in the community APP and website comment area, especially for the analysis and suggestions in the report. Hold community traffic management meetings regularly, invite resident representatives, traffic experts, etc. to participate, and directly listen to their opinions and suggestions. Screen the collected feedback, remove invalid (such as malicious comments, repeated submissions) and duplicate information, and ensure the effectiveness of the feedback. Classify the feedback according to categories such as traffic congestion, parking problems, and traffic signals, and use statistical analysis methods to identify issues and areas of common concern to residents. Optimize the timing of the traffic lights at the main entrance of the community according to traffic flow and congestion. For example, during the morning rush hour, increase the green light time, reduce vehicle waiting time, and improve traffic efficiency.

[0197] In the vicinity of commercial centers, use vacant land to build multi-storey parking lots, or set up temporary parking spaces around schools to ease the parking problem. Widen and renovate seriously congested sections of roads, such as changing two-way two-lane roads to two-way four-lane roads; set up bus lanes at major intersections to improve bus traffic efficiency; optimize intersection design, such as setting up left-turn waiting areas and right-turn lanes, etc., to improve road traffic capacity.

[0198] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A 5G network-based smart city traffic information system, characterized in that: include: Digital traffic processing module, used to collect and analyze the operation data of various traffic facilities and vehicles in real time, so as to identify traffic flow patterns, predict traffic status, obtain analysis results, and automatically adjust traffic signals and optimize traffic flow based on the analysis results; The digital government collaboration module is used to automatically obtain and distribute updated information on traffic policies and regulations, automatically handle traffic violations, and collect and analyze public feedback on traffic policies to obtain analysis reports; Digital education module, which uses cloud data platforms and 5G networks to automatically update and distribute online education resources in the field of transportation and provide educational experiences, including simulating traffic scenarios through VR technology to conduct traffic safety and planning education; Digital park traffic linkage module, used to share traffic and logistics information within the park in real time, and automatically conduct intelligent scheduling of traffic and logistics; The digital water service collaborative management module is used to monitor the hydrological conditions around transportation facilities in real time, including water level and rainfall. It uses prediction models to automatically warn of water risks that affect transportation, including floods and waterlogging, and obtains warning results. Based on the warning results, it automatically generates traffic control plans. Digital medical response module, used for real-time communication and data sharing. When a traffic accident occurs, it automatically locates the injured, dispatches medical resources for rescue, and provides rescue guidance information. The digital community traffic service module is used to provide traffic information services, monitor community traffic conditions in real time, generate and publish traffic monitoring reports, including congestion and parking information, and automatically optimize and adjust traffic management plans based on residents' feedback; Share the traffic and logistics information in the park in real time, and automatically conduct intelligent scheduling of traffic and logistics, including: Collect raw data on traffic flow, vehicle location, road conditions, and cargo status in real time, and convert the raw data into state vectors; According to the state vector, the objective function is constructed and a set of scheduling parameters are initialized. The scheduling parameters include the control scheme of traffic signals, the path planning of vehicles, and the loading and unloading sequence of goods. The state vector is updated in real time through the park traffic and logistics information sharing platform to obtain an updated state vector; According to the updated state vector, the gradient of the objective function with respect to the scheduling parameters is calculated, and the scheduling parameters are iteratively adjusted to obtain the optimized scheduling parameters; The optimized dispatching parameters are transmitted to the park's traffic management system and logistics operating system to automatically carry out intelligent dispatching of traffic and logistics, including adjusting traffic signals, guiding vehicle driving, and arranging the loading, unloading, transportation and distribution of goods; The calculation formula of the objective function is: ； in, represents the objective function; represents the weight coefficient; Indicates the average speed of vehicles in the park; Represents the weight coefficient of the congestion index; represents the congestion index; represents the weight coefficient; Indicates the average transportation time of goods; Indicates the loading and unloading time of the cargo; The weight coefficient representing the on-time delivery rate; Indicates the on-time delivery rate; represents the weight coefficient; Indicates the control efficiency of traffic signals; Indicates the path planning efficiency of the vehicle; Indicates the efficiency of the loading and unloading sequence of cargo.

2. According to claim 1, the 5G network-based smart city traffic information system is characterized in that: Real-time collection and analysis of operating data of various traffic facilities and vehicles to identify traffic flow patterns, predict traffic conditions, obtain analysis results, and automatically adjust traffic signals and optimize traffic flow based on the analysis results, including: Collect real-time operation data of traffic facilities and vehicles, including vehicle speed, location, flow, and road conditions; Extracting features related to traffic flow pattern recognition from the data, including time features, spatial features and flow features, and combining the relevant features into feature vectors; Input the feature vector into a preset neural network model for deep learning and pattern recognition to identify traffic flow patterns; Based on the identified traffic flow patterns and historical data, the neural network model predicts future traffic conditions, including congestion and traffic speed, and obtains prediction results; Based on the prediction results, the traffic signal control plan is automatically adjusted, including adjusting the duration of traffic lights and optimizing signal timing.

3. The 5G network-based smart city traffic information system according to claim 2 is characterized in that: Automatically obtain and distribute updated information on traffic policies and regulations, automatically handle traffic violations, and collect and analyze public feedback on traffic policies to obtain feedback reports, including: Send requests to the digital government platform regularly at preset intervals to check whether there are any new traffic policies and regulations updated; The digital government platform receives and analyzes traffic policy and regulation update data, extracts key information, including policy name, release time, and content, and distributes the extracted key information to traffic management departments, traffic participants, and the public in real time through the 5G network; Obtain traffic violation data in real time, and process and analyze the data based on traffic policies and regulations to identify the type, time, and location of violations; automatically impose penalties on violations based on preset processing rules; Receive real-time feedback from the public on transportation policies, including opinions, suggestions, and complaints, and classify and organize the feedback information to obtain feedback reports.

4. The 5G network-based smart city traffic information system according to claim 3 is characterized in that: The optimized scheduling parameter calculation formula is: ； in, Indicates The parameter vector after the iteration update; Indicates The parameter vector after iterations; Indicates The learning rate of the iteration; Represents the objective function In the parameter vector The gradient vector at ; , represents the regularization parameter; , represents the momentum parameter, which takes into account the gradient information of the previous two iterations; , Indicates Second and The parameter vector after iterations.

5. The 5G network-based smart city traffic information system according to claim 4 is characterized in that: Real-time monitoring of hydrological conditions around transportation facilities, including water level and rainfall, and automatic warning of water risks affecting transportation, including floods and waterlogging, using prediction models, and obtaining warning results. Based on the warning results, traffic control plans are automatically generated, including: Input the real-time monitored hydrological data into the prediction model to predict water affairs risks in the future and obtain water affairs risk prediction results, including risk type, probability of occurrence, and impact range; Based on the water affairs risk prediction results, the probability and impact of flood and waterlogging water affairs risks are assessed to obtain risk assessment results; When the risk assessment results exceed the preset threshold, early warning information is automatically issued, including risk type, warning level, and impact scope; According to the early warning information, a set of traffic control schemes are initialized, including closing certain road sections and restricting vehicle traffic, to obtain an initial set of traffic control schemes; Each solution in the initial traffic control solution set is taken as a solution vector, and the solution vector is randomly mutated to generate a new candidate solution; Cross the new candidate solution with the original solution vector to generate a new solution vector combination; Evaluate the new solution vector combination according to the fitness function and determine the solution vector to enter the next round of iteration; The mutation, crossover and evaluation processes are repeated until the preset number of iterations is reached, and the final solution is determined from the solution vector set as the traffic control solution.

6. The 5G network-based smart city traffic information system according to claim 5 is characterized in that: The calculation formula of the fitness function is: ； in, Indicates in the traffic control scheme The fitness value under ; The weight coefficient representing the traffic flow; Indicates The average vehicle speed on the road section; Indicates the total number of road segments; Indicates The weight coefficient of each road segment; , Represents an index variable; represents the weight coefficient of risk mitigation; Indicates in the traffic control scheme The area of ​​the region at risk from water services; An adjustment factor representing the area of ​​the risk zone; Indicates the total area of ​​the region; represents the weight coefficient of implementation cost; Indicates the total number of cost items; Indicates Implementation costs; Indicates The weight coefficient of the item cost; The weight coefficient representing safety; Indicates in the traffic control scheme The predicted number of traffic accidents; Represents the baseline number of incidents.

7. The 5G network-based smart city traffic information system according to claim 6 is characterized in that: Real-time communication and data sharing. When a traffic accident occurs, the system automatically locates the injured, dispatches medical resources for rescue, and provides rescue guidance information, including: Real-time monitoring of traffic conditions to detect abnormal traffic conditions, including sudden stops of vehicles, sharp decreases in speed, and to determine whether the abnormal traffic conditions are the scene of an accident; If it is determined to be an accident scene, the data from the accident scene is analyzed, including the location of the vehicle and camera surveillance footage, to determine the location of the injured, and digital maps and GPS data are accessed to determine the geographic location of the injured; The digital medical platform receives accident scene information, dispatches medical resources for rescue based on the location and condition of the injured, and provides real-time rescue guidance information to rescuers through the 5G network, including rescue routes, descriptions of the injured’s location, and injury assessments.

8. The 5G network-based smart city traffic information system according to claim 7 is characterized in that: Provide traffic information services and monitor community traffic conditions in real time, generate and publish traffic monitoring reports, including congestion and parking information, and automatically optimize and adjust traffic management plans based on resident feedback, including: Generate traffic monitoring reports including congestion, parking information, and average vehicle speed based on traffic data within the community; Publish traffic monitoring reports on community bulletin boards, and automatically collect feedback on traffic conditions and management plans through questionnaires, online comments, and community meetings to form a feedback data set; Based on the feedback data set, the existing traffic management plan is optimized and adjusted, including adjusting traffic signal timing, increasing parking spaces, and improving congested sections.

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