Smart city traffic management method based on machine learning technology

Abstract

The invention discloses a smart city traffic management method based on a machine learning technology. The method comprises the following steps: establishing a training set based on a big data technology, setting a prediction time window, training an initial model based on the training set, and constructing a traffic prediction model; acquiring traffic data based on multiple types of sensors, inputting the traffic data into the traffic prediction model, and outputting traffic flow prediction information in a prediction time window; setting a signal lamp timing scheme based on the traffic flow prediction information to obtain signal lamp duration information; acquiring traffic flow data of each time node, and analyzing traffic flow trend information; generating correction information based on the traffic flow trend information, and dynamically adjusting a signal lamp timing scheme according to the correction information; by analyzing the traffic data and performing traffic flow prediction on the later prediction time, the traffic state information is accurately analyzed, and accurate timing is performed on the duration of the signal lamp according to the traffic state information, so that the control accuracy of the signal lamp is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of urban traffic management, and particularly to a smart city traffic management method based on machine learning technology. Background Art

[0002] Smart city traffic management is to comprehensively perceive, deeply analyze and make intelligent decisions on elements such as people, vehicles, and roads in the urban traffic system by means of new generation information technologies such as the Internet of Things, big data, artificial intelligence, and cloud computing, so as to realize the optimal allocation of traffic resources, the efficient and orderly operation of traffic, the convenient and high-quality travel service, and further improve the overall operation efficiency of the city and the living quality of residents.

[0003] Existing urban traffic management methods are difficult to predict traffic data according to traffic prediction models, so the time of traffic lights cannot be dynamically adjusted, which affects the traffic effect. Summary of the Invention

[0004] The object of the present invention is to propose a smart city traffic management method based on machine learning technology, including the following steps: Establish a training set based on big data technology, set a prediction time window, train an initial model based on the training set, and construct a traffic prediction model; Obtain traffic data based on multi-type sensors, input the traffic data into the traffic prediction model, and output traffic flow prediction information within the prediction time window; Set a signal timing plan based on the traffic flow prediction information to obtain signal light duration information; Set several time nodes, obtain traffic flow data at each time node, and analyze traffic flow trend information; Generate correction information based on the traffic flow trend information, and dynamically adjust the signal timing plan according to the correction information.

[0005] Further, establishing a training set based on big data technology, setting a prediction time window, training an initial model based on the training set, and constructing a traffic prediction model specifically includes: Obtain historical traffic data based on big data technology, and establish a training set according to the historical traffic data; Iteratively train the initial model based on the training set to obtain a training result; Judge whether the training result converges; If it converges, generate a traffic prediction model; If it does not converge, adjust the number of iterations or adjust the model parameters until the model converges.

[0006] Further, obtaining traffic data based on multi-type sensors, inputting the traffic data into the prediction model, and outputting traffic flow prediction information within the prediction time window specifically includes: Obtain traffic flow, vehicle speed, and road condition information based on multiple types of sensors; Analyze traffic state information based on traffic flow, vehicle speed, and road condition information, and obtain traffic data based on the traffic state information; Analyze and predict data change information within a prediction time window based on traffic data; Analyze traffic state information based on the data change information; Analyze traffic flow prediction information within a prediction time window based on traffic state information.

[0007] Furthermore, set a signal light timing plan based on the traffic flow prediction information to obtain signal light duration information, specifically including: Obtain traffic flow prediction information, and analyze traffic congestion information at intersections based on the traffic flow prediction information; Compare the traffic congestion information with the set congestion information to obtain a congestion deviation rate; Determine whether the congestion deviation rate is greater than or equal to the set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, generate optimization information, and set the time of the signal light based on the optimization information to obtain a signal light timing plan; If it is less than the set deviation rate threshold, monitor the current signal light timing plan in real time.

[0008] Furthermore, set several time nodes, obtain traffic flow data at each time node, and analyze traffic flow trend information, specifically including: Obtain traffic flow data corresponding to each time node; Compare the traffic flow data of adjacent time nodes to obtain traffic state change information; Analyze traffic flow data based on the traffic state change information, and analyze traffic flow state information at different time nodes according to the traffic flow data; Analyze traffic flow trend information based on traffic flow state information at different time nodes.

[0009] Furthermore, generate correction information based on traffic flow trend information, and dynamically adjust the signal light timing plan according to the correction information, specifically including: Obtain traffic flow trend information, compare the traffic flow trend information with the set trend information to obtain a trend deviation rate; Determine whether the trend deviation rate is greater than or equal to the set trend deviation rate threshold; If it is greater than or equal to, generate correction information, and adjust the signal light timing plan based on the correction information; If it is less than, determine that the signal light timing plan meets the requirements, and obtain the signal light timing information in real time.

[0010] Further, based on obtaining traffic data by multiple types of sensors, it further includes: Obtain traffic data, perform encryption processing on the traffic data based on the safety condition information to obtain encrypted data; Analyze the encryption level based on the encrypted data, and generate an encryption key according to the encryption level; Perform encryption processing on the encrypted data based on the encryption level to obtain ciphertext data; Match the ciphertext data with the encryption key to obtain a decryption key; Transmit the decryption key to the terminal.

[0011] Further, it further includes: Obtain traffic data, and analyze traffic hazard information based on the traffic data; Analyze traffic accident information based on the traffic hazard information; Analyze the traffic condition based on the traffic accident information to obtain traffic monitoring information; Give a warning about the road condition based on the traffic monitoring information to obtain warning information; Transmit the warning information to the terminal in real time.

[0012] The present invention also provides a smart city traffic management system based on machine learning technology, including a processor, a memory, and at least one program, where the program is stored in the memory and is configured to be executed by the processor, and the program includes instructions for executing the smart city traffic management method based on machine learning technology as described in any one of the above.

[0013] The present invention also provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program enables a computer to execute to implement the smart city traffic management method based on machine learning technology as described in any one of the above.

[0014] Due to the application of the above technical solutions, the present invention has the following advantages compared with the prior art: By analyzing traffic data, the present invention predicts the traffic flow for the subsequent prediction time, thereby accurately analyzing the traffic state information, and accurately allocating the duration of the signal lights according to the traffic state information, thereby improving the control accuracy of the signal lights. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Shows a schematic flowchart of the smart city traffic management method based on machine learning technology provided by an embodiment of the present invention; Figure 2 Shows a flowchart of the traffic prediction model construction and training method of the smart city traffic management method based on machine learning technology provided by this embodiment; Figure 3It shows the flowchart of traffic flow prediction information analysis for the smart city traffic management method provided in this embodiment based on machine learning technology. Detailed implementation manners

[0016] To enable those skilled in the art to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0017] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to implement the embodiments of this application described here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will describe this application in detail with reference to the drawings and in conjunction with the embodiments.

[0019] As Figures 1-3 shown, the embodiments of the present invention provide a smart city traffic management method based on machine learning technology, including the following steps: S101, establish a training set based on big data technology, set a prediction time window, train an initial model based on the training set, and construct a traffic prediction model; S102, obtain traffic data based on multiple types of sensors, input the traffic data into the traffic prediction model, and output traffic flow prediction information within the prediction time window; S103, set a signal light timing plan based on the traffic flow prediction information to obtain signal light duration information; S104, set several time nodes, obtain traffic flow data for each time node, and analyze traffic flow trend information; S105, generate correction information based on the traffic flow trend information, and dynamically adjust the signal light timing plan according to the correction information.

[0020] Specifically, by installing various sensors on road facilities (such as traffic lights, bridges, tunnels, etc.) and vehicles, real-time collection of traffic data is achieved, such as traffic flow, vehicle speed, road conditions, etc., providing basic data support for traffic management.

[0021] Big data technology: storing, processing, and analyzing a large amount of traffic data to mine the laws and potential values behind the data. For example, by analyzing historical traffic data, predicting the change trend of traffic flow, providing a basis for traffic planning and management decisions.

[0022] Artificial intelligence technology: using algorithms such as machine learning and deep learning to achieve intelligent recognition and prediction of traffic behaviors. For example, an intelligent traffic signal system can automatically adjust the signal duration according to real-time traffic flow to optimize traffic flow.

[0023] Cloud computing technology: providing powerful computing capabilities and storage resources to support the rapid processing and sharing of traffic data. Traffic management departments can achieve data interaction and collaborative work between different departments through the cloud computing platform.

[0024] According to an embodiment of the present invention, a training set is established based on big data technology, a prediction time window is set, and an initial model is trained based on the training set to construct a traffic prediction model, specifically including: S201, obtaining historical traffic data based on big data technology and establishing a training set according to the historical traffic data; S202, performing iterative training on the initial model based on the training set to obtain a training result; S203, determining whether the training result converges; S204, if it converges, generating a traffic prediction model; S205, if it does not converge, adjusting the number of iterations or adjusting the model parameters until the model converges.

[0025] It should be noted that the model is trained with a large amount of data to ensure that the output result of the traffic prediction model is closer to the actual result and improve the model prediction accuracy.

[0026] According to an embodiment of the present invention, traffic data is obtained based on multiple types of sensors, and the traffic data is input into the prediction model to output traffic flow prediction information within the prediction time window, specifically including: S301, obtaining traffic flow, vehicle speed, and road condition information based on multiple types of sensors; S302, analyzing traffic state information based on traffic flow, vehicle speed, and road condition information, and obtaining traffic data based on the traffic state information; S303, analyzing data change information within the prediction time window based on the traffic data; S304. Analyze traffic state information based on data change information; S305. Analyze traffic flow prediction information within a predicted time window based on traffic state information.

[0027] It should be noted that by analyzing data change information, the traffic state is predicted, and the accuracy of traffic flow prediction within the predicted time window is improved.

[0028] According to an embodiment of the present invention, a signal light timing scheme is set based on traffic flow prediction information to obtain signal light duration information, which specifically includes: Obtain traffic flow prediction information and analyze traffic congestion information at intersections based on the traffic flow prediction information; Compare the traffic congestion information with the set congestion information to obtain a congestion deviation rate; Determine whether the congestion deviation rate is greater than or equal to the set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, generate optimization information, and set the time of the signal light based on the optimization information to obtain a signal light timing scheme; If it is less than the set deviation rate threshold, monitor the current signal light timing scheme in real time.

[0029] Specifically include: applying algorithms such as machine learning and deep learning to realize intelligent recognition and prediction of traffic behaviors. For example, an intelligent traffic signal light system can automatically adjust the signal light duration according to the real-time traffic flow, optimize the traffic flow, dynamically adjust the signal light timing scheme based on the real-time traffic flow data, and improve the passing efficiency of intersections. For example, during peak hours, the green light duration of the main road is automatically increased according to the traffic volume to relieve traffic congestion.

[0030] According to an embodiment of the present invention, several time nodes are set, traffic flow data at each time node is obtained, and traffic flow trend information is analyzed, which specifically includes: Obtain traffic flow data corresponding to each time node; Compare the traffic flow data of adjacent time nodes to obtain traffic state change information; Analyze traffic flow data based on the traffic state change information, and analyze traffic flow state information at different time nodes according to the traffic flow data; Analyze traffic flow trend information based on traffic flow state information at different time nodes.

[0031] According to an embodiment of the present invention, correction information is generated based on traffic flow trend information, and the signal light timing scheme is dynamically adjusted according to the correction information, which specifically includes: Obtain traffic flow trend information, compare the traffic flow trend information with the set trend information to obtain a trend deviation rate; Determine whether the trend deviation rate is greater than or equal to the set trend deviation rate threshold; If it is greater than or equal to, generate correction information and adjust the signal timing plan based on the correction information; If it is less than, determine that the signal timing plan meets the requirements and obtain the signal timing information in real time.

[0032] Specifically, use devices such as high-definition cameras and radars to monitor the urban roads comprehensively and master the traffic conditions in real time. At the same time, combine video analysis technology to automatically identify traffic violations, such as running red lights, speeding, illegal lane changes, etc.

[0033] Traffic signal optimization: Based on real-time traffic flow data, dynamically adjust the signal timing plan of traffic lights to improve the traffic efficiency at intersections. For example, increase the green light duration of the main roads during peak hours to relieve traffic congestion.

[0034] According to the embodiments of the present invention, based on obtaining traffic data by multiple types of sensors, it further includes: Obtain traffic data, perform encryption processing on the traffic data based on the security condition information to obtain encrypted data; Analyze the encryption level based on the encrypted data and generate an encryption key according to the encryption level; Perform encryption processing on the encrypted data based on the encryption level to obtain ciphertext data; Match the ciphertext data with the encryption key to obtain a decryption key; Transmit the decryption key to the terminal.

[0035] It should be noted that in order to ensure the security of traffic data, during the process of saving traffic data, through encryption processing, encrypted transmission of data is realized.

[0036] According to the embodiments of the present invention, it further includes: Obtain traffic data and analyze traffic hazard information based on the traffic data; Analyze traffic accident information based on the traffic hazard information; Analyze the traffic conditions based on the traffic accident information to obtain traffic monitoring information; Warn the road conditions based on the traffic monitoring information to obtain warning information; Transmit the warning information to the terminal in real time.

[0037] The present invention also provides a smart city traffic management system based on machine learning technology, including a processor, a memory, and at least one program. The program is stored in the memory and is configured to be executed by the processor. The program includes instructions for executing the smart city traffic management method based on machine learning technology as described in any one of the above.

[0038] The present invention also provides a computer-readable storage medium storing a computer program, which causes a computer to execute to implement the smart city traffic management method based on machine learning technology described in any one of the above.

[0039] In summary, the present invention analyzes traffic data, predicts traffic flow for subsequent prediction times, thereby accurately analyzing traffic state information, and accurately allocating the duration of traffic lights according to the traffic state information, so as to improve the control accuracy of traffic lights.

[0040] This embodiment also provides a smart city traffic management system based on machine learning technology, including a processor, a memory, and at least one program. The program is stored in the memory and is configured to be executed by the processor. The program includes instructions for executing any one of the above smart city traffic management methods based on machine learning technology.

[0041] Those skilled in the art can understand that, for the sake of convenience, an example is given in which the number of memories and processors is both set to one. In an actual terminal or server, there may be multiple processors and memories. The memory may also be referred to as a storage medium or a storage device, etc., and the embodiments of the present application do not limit this.

[0042] It should be understood that in the embodiments of the present application, the processor may be a central processing unit (Central Processing Unit, abbreviated as CPU), and the processor may also be other general-purpose processors, digital signal processors (Digital Signal Processing, abbreviated as DSP), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), field-programmable gate arrays (Field-Programmable Gate Array, abbreviated as FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may also adopt a general-purpose microprocessor, a graphics processing unit (GPU), or one or more integrated circuits to execute relevant programs to implement the functions required to be executed in the embodiments of the present application.

[0043] The processor can also be an integrated circuit chip with the ability to process signals. In the implementation process, each step of this application can be completed by the integrated logic circuit in the hardware of the processor or instructions in the form of software. The above-mentioned processor can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of this application. The steps of the method disclosed in combination with the embodiments of this application can be directly embodied as being executed and completed by the hardware decoding processor, or executed and completed by a combination of the hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the functions required to be executed by the units included in the method, device and storage medium of the embodiments of this application.

[0044] It should also be understood that the memory mentioned in the embodiments of this application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache.

[0045] By way of example but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0046] The memory may also be a Compact Disc Read-Only Memory (CD-ROM) or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may exist independently and be connected to the processor via a bus. The memory may also be integrated with the processor. The memory may store a program, and when the program stored in the memory is executed by the processor, the processor is used to execute each step of the determination method in the above embodiments of the present application.

[0047] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, the memory (storage module) is integrated in the processor. It should be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0048] It should be understood that the term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0049] In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly implemented by the execution of the hardware processor, or implemented by the combination of the hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0050] Those of ordinary skill in the art can realize that the various illustrative logical blocks (ILBs) and steps described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0051] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer-programmed program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a processor, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a computer network, or other programmable devices.

[0052] This embodiment also provides a computer-readable storage medium storing a computer program, and the computer program enables a computer to execute to implement the above-mentioned smart city traffic management method based on machine learning technology.

[0053] It should be noted that computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from a website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber) or wirelessly (such as infrared, wireless, microwave, etc.), or from a website, computer, server, or data center to a mobile phone processor by wire. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive), etc.

[0054] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart city traffic management method based on machine learning technology, characterized in that: The following steps are involved: Establish a training set based on big data technology, set the prediction time window, train the initial model based on the training set, and build a traffic prediction model; Obtain traffic data based on multiple types of sensors, input the traffic data into the traffic prediction model, and output the traffic flow prediction information within the prediction time window; Set the signal light timing plan based on traffic flow prediction information and obtain signal light duration information; Set several time nodes, obtain traffic flow data at each time node, and analyze traffic flow trend information; Generate correction information based on traffic flow trend information, and dynamically adjust the traffic light timing plan according to the correction information.

2. The smart city traffic management method based on machine learning technology as claimed in claim 1, characterized in that: Based on big data technology, a training set is established, a prediction time window is set, the initial model is trained based on the training set, and a traffic prediction model is constructed, including: Obtain historical traffic data based on big data technology and establish a training set based on the historical traffic data; Iteratively train the initial model based on the training set to obtain the training results; Determining whether the training result converges; If converged, a traffic prediction model is generated; If it does not converge, adjust the number of iterations or adjust the model parameters until the model converges.

3. The smart city traffic management method based on machine learning technology as claimed in claim 2, characterized in that: Based on the acquisition of traffic data from multiple types of sensors, the traffic data is input into the prediction model, and the traffic flow prediction information within the prediction time window is output, including: Obtain traffic flow, speed and road condition information based on multiple types of sensors; Analyze traffic status information based on traffic volume, vehicle speed and road condition information, and obtain traffic data based on the traffic status information; Based on traffic data analysis, predict data change information within the time window; Analyze traffic status information based on data change information; Traffic flow prediction information within the prediction time window is analyzed based on traffic status information.

4. The smart city traffic management method based on machine learning technology as claimed in claim 3, characterized in that: The signal light timing scheme is set based on the traffic flow prediction information to obtain the signal light duration information, including: Obtain traffic flow prediction information, and analyze traffic congestion information at intersections based on the traffic flow prediction information; Compare the traffic congestion information with the set congestion information to obtain the congestion deviation rate; Determining whether the congestion deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to the set deviation rate threshold, then the optimization information is generated, and the time of the traffic light is set based on the optimization information to obtain the traffic light timing plan; If it is less than the set deviation rate threshold, the current traffic light timing plan is monitored in real time.

5. The smart city traffic management method based on machine learning technology as claimed in claim 4, characterized in that: Set several time nodes, obtain traffic flow data at each time node, and analyze traffic flow trend information, including: Obtain the traffic flow data corresponding to each time node; Compare the traffic flow data of adjacent time nodes to obtain traffic status change information; Analyze traffic flow data based on traffic status change information, and analyze traffic flow status information at different time nodes based on traffic flow data; Analyze traffic flow trend information based on traffic flow status information at different time nodes.

6. The smart city traffic management method based on machine learning technology as claimed in claim 5, characterized in that: Generate correction information based on traffic flow trend information, and dynamically adjust the signal light timing plan according to the correction information, including: Obtaining traffic flow trend information, comparing the traffic flow trend information with set trend information, and obtaining a trend deviation rate; Determine whether the trend deviation rate is greater than or equal to a set trend deviation rate threshold; If it is greater than or equal to, then generate correction information, and adjust the signal light timing plan based on the correction information; If it is less than, it is determined that the traffic light timing scheme meets the requirements, and the traffic light timing information is obtained in real time.

7. The smart city traffic management method based on machine learning technology as claimed in claim 6, characterized in that: Acquiring traffic data based on multiple types of sensors, including: Acquire traffic data, and encrypt the traffic data based on security condition information to obtain encrypted data; Analyze the encryption level based on the encrypted data and generate an encryption key according to the encryption level; Encrypting the encrypted data based on the encryption level to obtain ciphertext data; Match the ciphertext data with the encryption key to obtain the decryption key; Transfer the decryption key to the terminal.

8. The smart city traffic management method based on machine learning technology as claimed in claim 7, characterized in that: Also includes: Obtain traffic data and analyze traffic hazard information based on the traffic data; Analyze traffic accident information based on traffic hazard information; Analyze traffic conditions based on traffic accident information to obtain traffic monitoring information; Warning of road conditions based on traffic monitoring information to obtain warning information; Transmit warning information to the terminal in real time.

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