Urban road management system based on real-time traffic

Abstract

The invention discloses an urban road management system based on real-time traffic, and the system comprises a data collection end which is used for obtaining the data of urban road traffic in real time; the data processing module is used for carrying out real-time processing and analysis on the collected data, and the processing and analysis comprises data cleaning, verification, real-time flow processing and traffic flow prediction and congestion identification based on a machine learning model; the decision support module is used for generating a real-time traffic management strategy according to the analysis result, and the traffic management strategy comprises signal lamp time adjustment, vehicle navigation route optimization and parking demand management; the user service module is used for providing intelligent navigation, real-time traffic information and parking space management services; and the security protection module is used for protecting the security of data transmission and storage. According to the invention, a high-performance distributed real-time data processing platform is adopted to process and analyze urban road traffic data in real time so as to accurately predict the traffic flow, the congestion condition and the environmental influence, and real-time decision generation is supported.

Description

Technical Field

[0001] The invention relates to the field of municipal road operation management, and in particular to an urban road management system based on real-time traffic. Background Art

[0002] Modern urban traffic management faces many challenges, such as traffic congestion, air quality problems and parking management problems. Traditional traffic management methods often rely on static timetables and simple signal control systems, which cannot effectively cope with the challenges of complex urban traffic environment changes and peak hours. In addition, the data collection and processing capabilities of traffic management systems are limited, and it is impossible to accurately monitor and quickly respond to real-time traffic conditions.

[0003] With the development of big data technology, urban road management systems have ushered in new development opportunities. Big data technology enables urban traffic management to make accurate predictions and intelligent decisions based on real-time and historical data, thereby optimizing traffic flow, reducing congestion, improving air quality, and enhancing the travel experience of drivers and citizens.

[0004] Although the urban road management system based on big data has many advantages, it also has some challenges and disadvantages. For example, the data format and quality from different devices and systems may not be unified, which makes data integration and analysis difficult. The data volume is large and the complexity is high. Processing and analyzing data requires strong computing power and rapid decision support. Summary of the invention

[0005] The purpose of the present invention is to overcome the above problems and propose an urban road management system based on real-time traffic to solve the problems that the data format and quality from different devices and systems may not be unified, resulting in difficulties in data integration and analysis, large data volume and high complexity, and the processing and analysis of data requires powerful computing power and rapid decision support.

[0006] In order to achieve the above object, the present invention is achieved in the following ways: A city road management system based on real-time traffic includes a data collection terminal for acquiring city road traffic data in real time; a data processing module for processing and analyzing the collected data in real time, wherein the processing and analysis include data cleaning, verification, real-time stream processing, and traffic flow prediction and congestion identification based on machine learning models; a decision support module for generating real-time traffic management strategies based on analysis results, wherein the traffic management strategies include adjusting signal light time, optimizing vehicle navigation routes, and managing parking needs; a user service module for providing intelligent navigation, real-time traffic information, and parking space management services; and a security protection module for protecting the security of data transmission and storage.

[0007] The urban road management system based on real-time traffic, the data collection terminal includes Traffic cameras, connected to traffic intersections and important roads, transmit video streams to the data processing module in real time via Ethernet or Wi-Fi; Vehicle geomagnetic sensors, installed in parking spaces or on roads, send vehicle detection data to the data processing module via LoRa or Wi-Fi; GPS track data, installed on vehicles or road facilities, transmits real-time position and track data to the data processing module via satellite signals; Environmental sensors, installed on the edge of the road or in urban areas, send air quality and noise level data to the data processing module via LoRa or Wi-Fi.

[0008] The urban road management system based on real-time traffic, the data processing module includes A distributed real-time data processing platform composed of a server cluster is connected by high-speed Ethernet to form a distributed real-time data processing platform to process data streams from various data collection terminals; The database system uses high-performance storage devices to store historical data and real-time analysis results through high-speed network connections to ensure data reliability and fast access.

[0009] The urban road management system based on real-time traffic, the decision support module includes The decision support algorithm module is deployed on the server and connected to the data processing system through the internal network to obtain real-time analysis results and generate decision recommendations; The user interface establishes communication with the user's PC, smartphone or tablet through an Internet connection to provide users with real-time traffic information and personalized services.

[0010] The urban road management system based on real-time traffic, the user service module includes a mobile application and an online service platform, and the user service module provides personalized intelligent navigation, real-time traffic information query and parking space management services to improve the driver's travel efficiency and experience.

[0011] The urban road management system based on real-time traffic, the safety protection module includes The data encryption transmission unit uses SSL / TLS encryption protocol to protect data transmission security between the data acquisition terminal and the data processing module, and between the user interface and the data processing module; Secure storage devices, the database system uses data encryption and access control measures to ensure that the stored data cannot be accessed by unauthorized visitors.

[0012] In the above-mentioned urban road management system based on real-time traffic, the data processing module adopts a distributed real-time data processing platform, supports efficient data stream processing and machine learning algorithm application, so as to improve the speed and accuracy of traffic data processing; The traffic flow prediction function is expressed as follows: [ f(x) = ax^3 + bx^2 + cx + d ] Where (x) represents the time variable, which indicates the time point in a day, and (a, b, c, d) represent the model parameters, which are obtained by machine learning training based on historical traffic flow data. The above function is used to predict the traffic flow at a specific time point; The congestion identification algorithm is expressed as follows: [ g(t, v) = \begin{cases} 1&\text{if} v<\theta \ 0&\text{otherwise}\end{cases} ] Among them, (t) represents the time point, (v) represents the traffic speed on a specific road section at a specific time point, and (\theta) represents the speed threshold. If the speed is lower than this value, the road section is considered to be congested.

[0013] Furthermore, the decision support module generates real-time traffic management strategies through real-time data analysis and prediction models to optimize urban traffic flow, reduce traffic congestion and improve traffic safety; The signal light adjustment strategy is expressed as follows: [ h(T) = T_0 + k(\Delta Q) ] Where (T) represents the traffic light cycle, (T_0) represents the basic traffic light cycle time, (k) represents the adjustment coefficient, which is adjusted according to the traffic flow prediction, and (\Delta Q) represents the difference between the predicted traffic flow and the actual traffic flow; The vehicle navigation route optimization function is expressed as follows: [ p(n) = \sum_{i=1}^{n} w_i \times l_i ] Where (n) is the number of segments in the route, (w_i) is the weight of the (i)th segment, based on traffic flow and segment length, and (l_i) is the length of the (i)th segment; The parking space management algorithm is expressed as follows: [ s(d, c) = \begin{cases} 1&\text{if} c>d \ 0&\text{otherwise} \end{cases} ] Among them, (d) represents the parking demand, that is, the number of parking spaces required, and (c) represents the number of currently available parking spaces.

[0014] Furthermore, the security protection module uses data encryption and secure transmission technology to protect the security and privacy of user data; The data encryption function is expressed as follows: [ E(k, m) = k \oplus m ] Where (k) represents the encryption key and (m) represents the message or data to be encrypted.

[0015] A method for using a real-time traffic-based urban road management system, comprising: Step 1: Deploy multiple data collection devices in the urban road network, including high-definition traffic cameras, vehicle geomagnetic sensors, GPS trajectory data collection equipment and environmental sensors to collect road traffic flow, vehicle location and speed, air quality and noise level data in real time; Step 2: The collected real-time data is transmitted to a distributed real-time data processing platform composed of a server cluster, the data is pre-processed, cleaned and verified on the data processing platform, and the cleaned data is stored in a database system for subsequent analysis and historical data archiving; Step 3: Use the distributed data processing platform to perform data analysis and machine learning model training to predict traffic congestion, optimize vehicle navigation routes, and predict environmental impacts. Generate real-time traffic management strategies based on data analysis results, including adjusting traffic light cycles, managing parking needs, and optimizing route planning; Step 4: Feedback real-time traffic information and optimization suggestions to drivers and citizens through web applications and mobile application interfaces, support drivers in making optimal travel decisions, and provide real-time parking space management services.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention is different from the traditional system based on timed scheduling. The system can acquire, process and analyze large-scale real-time traffic data in real time, respond quickly to traffic changes, effectively reduce congestion and optimize road use efficiency. It also uses big data analysis and machine learning models to generate efficient traffic management strategies based on complex traffic patterns and urban environments, thereby improving road safety and traffic fluidity. Furthermore, the present invention adopts a high-performance distributed real-time data processing platform to process and analyze urban road traffic data in real time, so as to accurately predict traffic flow, congestion and environmental impact, and support real-time decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1The figure is a connection diagram of a city road management system based on real-time traffic according to the present invention.

[0018] Figure 2 The present invention is a data collection terminal connection diagram of a real-time traffic-based urban road management system.

[0019] Figure 3 The present invention is a data processing module connection diagram of a real-time traffic-based urban road management system.

[0020] Figure 4 The figure is a connection diagram of a decision support module of a real-time traffic-based urban road management system according to the present invention.

[0021] Figure 5 This is a schematic diagram of user service module connections for a real-time traffic-based urban road management system according to the present invention.

[0022] Figure 6 The present invention is a schematic diagram of the connection of a safety protection module of an urban road management system based on real-time traffic. DETAILED DESCRIPTION

[0023] The technical solutions of the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection requested by the present application.

[0024] A real-time traffic-based urban road management system comprises a data acquisition terminal for acquiring real-time urban road traffic data; preferably, the data acquisition terminal comprises a traffic camera connected to traffic intersections and important road sections, and transmitting video streams to a data processing module in real time via Ethernet or Wi-Fi; a vehicle geomagnetic sensor installed in a parking space or on the road, and sending vehicle detection data to the data processing module via LoRa or Wi-Fi; GPS track data installed on a vehicle or road facility, and transmitting real-time position and track data to the data processing module via satellite signals; an environmental sensor installed at the edge of a road or in an urban area, and sending air quality and noise level data to the data processing module via LoRa or Wi-Fi.

[0025] in: The data processing module is used to process and analyze the collected data in real time, and the processing and analysis include data cleaning, verification, real-time stream processing, and traffic flow prediction and congestion identification based on machine learning models; preferably, the data processing module includes a distributed real-time data processing platform composed of a server cluster, which is connected by high-speed Ethernet to form a distributed real-time data processing platform to process data streams from various data collection terminals; the database system uses high-performance storage devices to store historical data and real-time analysis results through high-speed network connections to ensure data reliability and rapid accessibility.

[0026] A decision support module is used to generate real-time traffic management strategies based on the analysis results, and the traffic management strategies include adjusting traffic light time, optimizing vehicle navigation routes, and managing parking needs; preferably, the decision support module includes a decision support algorithm module, which is deployed on the server and connected to the data processing system through an internal network to obtain real-time analysis results and generate decision recommendations; a user interface is connected to the Internet to establish communication with the user's PC, smartphone or tablet computer to provide the user with real-time traffic information and personalized services.

[0027] A user service module is used to provide intelligent navigation, real-time traffic information and parking space management services; preferably, the user service module includes a mobile application and an online service platform, and the user service module provides personalized intelligent navigation, real-time traffic information query and parking space management services to improve the driver's travel efficiency and experience.

[0028] A security protection module is used to protect the security of data transmission and storage; preferably, the security protection module includes a data encryption transmission unit, which uses the SSL / TLS encryption protocol to protect the security of data transmission between the data acquisition terminal and the data processing module, and between the user interface and the data processing module; the secure storage device and the database system adopt data encryption and access control measures to ensure that the stored data cannot be obtained by unauthorized visitors.

[0029] Local magnetic sensors, GPS trajectory data collection devices and environmental sensors are installed at urban traffic intersections and important roads to collect real-time data on road traffic conditions, vehicle driving trajectories, air quality and noise levels. The collected data are sent to the distributed real-time data processing platform together with images and videos captured by high-definition traffic cameras. On this platform, the data is pre-processed, cleaned and verified to ensure its accuracy and completeness. Then, the data is transmitted to the data analysis and model training module, which uses machine learning algorithms to conduct real-time analysis and prediction of traffic flow, congestion patterns and environmental impacts. Based on these analysis results, the decision support module generates real-time traffic management strategies, such as adjusting traffic light cycles, optimizing vehicle navigation routes and managing parking needs. Finally, through the user interface, real-time traffic information and optimization suggestions are fed back to drivers and citizens to give the best travel decisions, thereby realizing the effective operation and service functions of the entire system.

[0030] In this embodiment, the data processing module adopts a distributed real-time data processing platform to support efficient data stream processing and machine learning algorithm application to improve the speed and accuracy of traffic data processing; The traffic flow prediction function is expressed as follows: [ f(x) = ax^3 + bx^2 + cx + d ] Where (x) represents the time variable, which indicates the time point in a day, and (a, b, c, d) represent the model parameters, which are obtained by machine learning training based on historical traffic flow data. The above function is used to predict the traffic flow at a specific time point; Based on the embodiment of the above formula, it is assumed that there is a set of historical traffic flow data that records the traffic volume of a certain road every day and every hour. Here, a cubic polynomial (f(x) = ax^3 + bx^2 + cx + d) is used to simulate and predict traffic flow; Specifically, it includes collecting traffic flow data at the same time point every day within a certain period (such as a year), selecting a cubic polynomial model to capture the peaks and troughs in traffic flow based on the volatility characteristics of the data, and using statistical methods such as the least squares method to fit the model parameters (a, b, c, d), including constructing a loss function based on the sum of the squares of the differences between the predicted values ​​and the actual values, and then finding the optimal parameters through the gradient descent method.

[0031] The congestion identification algorithm is expressed as follows: [ g(t, v) = \begin{cases} 1&\text{if} v<\theta \ 0&\text{otherwise}\end{cases} ] Where (t) represents the time point, (v) represents the traffic speed at a specific time point on a specific road section, and (\theta) represents the speed threshold, below which the road section is considered to be congested; Based on the embodiment of the above formula, it is assumed that when the vehicle speed is lower than 30 km / h, the road section is congested. Specifically, the real-time vehicle speed ( v ) and time ( t ) are received. If ( v<30 ) km / h (threshold ( \theta )), then ( g(t, v) = 1 ) indicates congestion, otherwise ( g(t, v) = 0 ) indicates smooth traffic.

[0032] Furthermore, the decision support module generates real-time traffic management strategies through real-time data analysis and prediction models to optimize urban traffic flow, reduce traffic congestion and improve traffic safety; The signal light adjustment strategy is expressed as follows: [ h(T) = T_0 + k(\Delta Q) ] Where (T) represents the traffic light cycle, (T_0) represents the basic traffic light cycle time, (k) represents the adjustment coefficient, which is adjusted according to the traffic flow prediction, and (\Delta Q) represents the difference between the predicted traffic flow and the actual traffic flow; In the embodiment based on the above formula, it is assumed that the basic signal light cycle (T_0) is set to 60 seconds, and the increase in traffic flow requires an increase in the signal light cycle to reduce congestion; Specifically, the difference between the real-time traffic flow ( Q ) and the historical average flow ( Q_{avg} ) is calculated ( \DeltaQ = Q - Q_{avg} ), and the traffic light cycle ( T = T_0 + k \times \Delta Q ) is adjusted according to ( \Delta Q ). Where ( k ) is an empirically determined coefficient, for example, for every 100 additional vehicles, the traffic light increases by 5 seconds.

[0033] The vehicle navigation route optimization function is expressed as follows: [ p(n) = \sum_{i=1}^{n} w_i \times l_i ] Where (n) is the number of segments in the route, (w_i) is the weight of the (i)th segment, based on traffic flow and segment length, and (l_i) is the length of the (i)th segment; Based on the embodiment of the above formula, it is assumed that, considering the road congestion and the length of the road section, a weight can be assigned to each road section; Specifically, according to the congestion and length of the road section, a weight ( w_i ) is assigned, all possible routes are evaluated, and the route with the smallest sum of weight and length is selected ( p(n) = \sum_{i=1}^{n} w_i \times l_i ).

[0034] The parking space management algorithm is expressed as follows: [ s(d, c) = \begin{cases} 1&\text{if} c>d \ 0&\text{otherwise} \end{cases} ] Among them, (d) represents the parking demand, that is, the number of parking spaces required, and (c) represents the number of currently available parking spaces; Based on the embodiment of the above formula, it is assumed that the parking demand (d) is 100 parking spaces and the currently available (c) parking spaces are 80; Specifically, compare (d) and (c). If (c>d), output 1 (there are enough parking spaces), otherwise output 0 (there are not enough parking spaces).

[0035] Furthermore, the security protection module uses data encryption and secure transmission technology to protect the security and privacy of user data; The data encryption function is expressed as follows: [ E(k, m) = k \oplus m ] Wherein, (k) represents the encryption key, (m) represents the message or data to be encrypted; In the embodiment based on the above formula, it is assumed that a simple XOR encryption technique is used and the key ( k ) is a fixed value; Specifically, a key (k) is selected, and the message (m) is XORed with the key (k) (E(k, m)= k \oplus m) to generate encrypted data.

[0036] A method for using a real-time traffic-based urban road management system comprises the following steps: Step 1: deploy multiple data collection devices in the urban road network, including high-definition traffic cameras, vehicle geomagnetic sensors, GPS trajectory data collection equipment and environmental sensors to collect real-time road traffic flow, vehicle location and speed, air quality and noise level data; Step 2: The collected real-time data is transmitted to a distributed real-time data processing platform composed of a server cluster, and the data is pre-processed, cleaned and verified on the data processing platform, and the cleaned data is stored in a database system for subsequent analysis and historical data archiving; Step 3: Use the distributed data processing platform to perform data analysis and machine learning model training to predict traffic congestion, optimize vehicle navigation routes, and predict environmental impacts. Generate real-time traffic management strategies based on data analysis results, including adjusting traffic light cycles, managing parking demand, and optimizing route planning; Step 4: Feedback real-time traffic information and optimization suggestions to drivers and citizens through web applications and mobile application interfaces, support drivers in making optimal travel decisions, and provide real-time parking space management services.

[0037] Working principle: First, use the cubic polynomial (f(x) = ax^3 + bx^2 + cx + d) to predict the traffic flow in a specific time period. Based on this prediction result, it is detected in real time whether congestion will occur. If the predicted traffic flow exceeds a certain threshold, it can be considered that congestion may occur in that time period. Once congestion is detected, the operation cycle of the traffic light is adjusted to alleviate traffic pressure; by using the function (g(t, v)) to determine whether a certain intersection is congested ((v<30) km / h), and then adjust the traffic light cycle (T = T_0 + k \times \Delta Q), where (\Delta Q) is the difference between the predicted flow and the average flow, so that the traffic lights can be flexibly adjusted according to the real-time traffic conditions to improve traffic efficiency; the adjustment of the traffic lights can provide data support for route optimization. According to the adjustment of the traffic lights on each section, the weight of the road (w_i) is updated in real time, and then combined with the section length (l_i ), calculate the optimal path, and the navigation system uses this data to provide drivers with the best route to avoid congested sections; when the vehicle approaches the destination, the system uses the parking space management algorithm to predict parking demand (d) and currently available parking spaces (c). If (c>d), the system will directly guide the vehicle to the nearest parking lot. Otherwise, the system can recommend alternative parking lots or provide public transportation options; throughout the system, it is necessary to ensure the security of all transmitted and stored data, and use encryption functions (E(k, m) = k \oplus m) to encrypt sensitive data such as vehicle location and user information to ensure privacy and security. In this way, a coherent system is built from predicting traffic flow to route navigation to parking management. Each part supports another part, improving the efficiency and user experience of the entire urban transportation system. Through real-time data and algorithm adjustments, it is possible to dynamically respond to various traffic conditions, thereby effectively managing urban traffic flow.

[0038] The above are only embodiments provided for the present application and are not intended to limit the present application. Although the present application is described in detail with reference to the embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or make equivalent substitutions for some of the technical features therein. However, any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. An urban road management system based on real-time traffic, characterized by: It includes a data collection terminal for obtaining real-time data on urban road traffic; A data processing module is used to process and analyze the collected data in real time, including data cleaning, verification, real-time stream processing, and traffic flow prediction and congestion identification based on machine learning models; a decision support module is used to generate real-time traffic management strategies based on the analysis results, including adjusting signal light timing, optimizing vehicle navigation routes, and managing parking needs; User service module, used to provide intelligent navigation, real-time traffic information and parking space management services; Security protection module, used to protect the security of data transmission and storage.

2. The urban road management system based on real-time traffic according to claim 1 is characterized in that: The data collection terminal includes Traffic cameras, connected to traffic intersections and important roads, transmit video streams to the data processing module in real time via Ethernet or Wi-Fi; Vehicle geomagnetic sensors, installed in parking spaces or on roads, send vehicle detection data to the data processing module via LoRa or Wi-Fi; GPS track data, installed on vehicles or road facilities, transmits real-time position and track data to the data processing module via satellite signals; Environmental sensors, installed on the edge of the road or in urban areas, send air quality and noise level data to the data processing module via LoRa or Wi-Fi.

3. The urban road management system based on real-time traffic according to claim 1 is characterized in that: The data processing module includes A distributed real-time data processing platform composed of a server cluster is connected by high-speed Ethernet to form a distributed real-time data processing platform to process data streams from various data collection terminals; The database system uses high-performance storage devices to store historical data and real-time analysis results through high-speed network connections to ensure data reliability and fast access.

4. The urban road management system based on real-time traffic according to claim 1 is characterized in that: The decision support module includes The decision support algorithm module is deployed on the server and connected to the data processing system through the internal network to obtain real-time analysis results and generate decision recommendations; The user interface establishes communication with the user's PC, smartphone or tablet through an Internet connection to provide users with real-time traffic information and personalized services.

5. The urban road management system based on real-time traffic according to claim 1 is characterized in that: The user service module includes a mobile application and an online service platform. The user service module provides personalized intelligent navigation, real-time traffic information query and parking space management services to improve the driver's travel efficiency and experience.

6. The urban road management system based on real-time traffic according to claim 1 is characterized in that: The security protection module includes The data encryption transmission unit uses SSL / TLS encryption protocol to protect data transmission security between the data acquisition terminal and the data processing module, and between the user interface and the data processing module; Secure storage devices, the database system uses data encryption and access control measures to ensure that the stored data cannot be accessed by unauthorized visitors.

7. The urban road management system based on real-time traffic according to claim 1 or 3 is characterized in that: The data processing module adopts a distributed real-time data processing platform to support efficient data stream processing and machine learning algorithm application to improve the speed and accuracy of traffic data processing; The traffic flow prediction function is expressed as follows: [ f(x) = ax^3 + bx^2 + cx + d ] Where (x) represents the time variable, which indicates the time point in a day, and (a, b, c, d) represent the model parameters, which are obtained by machine learning training based on historical traffic flow data. The above function is used to predict the traffic flow at a specific time point; The congestion identification algorithm is expressed as follows: [ g(t, v) = \begin{cases} 1 & \text{if} v < \theta \ 0 & \text{otherwise} \end{cases} ] Among them, (t) represents the time point, (v) represents the traffic speed on a specific road section at a specific time point, and (\theta) represents the speed threshold. If the speed is lower than this value, the road section is considered to be congested.

8. Furthermore, the decision support module generates real-time traffic management strategies through real-time data analysis and prediction models to optimize urban traffic flow, reduce traffic congestion and improve traffic safety; The signal light adjustment strategy is expressed as follows: [ h(T) = T_0 + k(\Delta Q) ] in, ( T ) represents the traffic light cycle, ( T_0 ) represents the basic traffic light cycle time, ( k ) represents the adjustment coefficient, which is adjusted according to the traffic flow prediction, and ( \Delta Q ) represents the difference between the predicted traffic flow and the actual traffic flow; The vehicle navigation route optimization function is expressed as follows: [ p(n) = \sum_{i=1}^{n} w_i \times l_i ] Where (n) is the number of segments in the route, (w_i) is the weight of the (i)th segment, based on traffic flow and segment length, and (l_i) is the length of the (i)th segment; The parking space management algorithm is expressed as follows: [ s(d, c) = \begin{cases} 1 & \text{if} c > d \ 0 & \text{otherwise} \end{cases} ] Among them, (d) represents the parking demand, that is, the number of parking spaces required, and (c) represents the number of currently available parking spaces.

9. Furthermore, the security protection module uses data encryption and secure transmission technology to protect the security and privacy of user data; The data encryption function is expressed as follows: [ E(k, m) = k \oplus m ] in, (k) represents the encryption key, and (m) represents the message or data to be encrypted.

10. A method for using a real-time traffic-based urban road management system, characterized by: Includes the following step; Step 1: Deploy multiple data collection devices in the urban road network, including high-definition traffic cameras, vehicle geomagnetic sensors, GPS trajectory data collection equipment and environmental sensors to collect road traffic flow, vehicle location and speed, air quality and noise level data in real time; Step 2: The collected real-time data is transmitted to a distributed real-time data processing platform composed of a server cluster, the data is pre-processed, cleaned and verified on the data processing platform, and the cleaned data is stored in a database system for subsequent analysis and historical data archiving; Step 3: Use the distributed data processing platform to perform data analysis and machine learning model training to predict traffic congestion, optimize vehicle navigation routes, and predict environmental impacts. Generate real-time traffic management strategies based on data analysis results, including adjusting traffic light cycles, managing parking needs, and optimizing route planning; Step 4: Feedback real-time traffic information and optimization suggestions to drivers and citizens through web applications and mobile application interfaces, support drivers in making optimal travel decisions, and provide real-time parking space management services.

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