Intelligent traffic information processing platform

Abstract

The invention relates to the technical field of traffic monitoring, and particularly discloses an intelligent traffic information processing platform, which comprises a data acquisition module, a data preprocessing module, a traffic mode recognition module, a traffic road flow control module, a traffic road flow prediction module and an information issuing module, and is characterized in that the data acquisition module acquires congested road data in real time; the data preprocessing module performs cleaning, noise reduction and formatting processing on original data acquired by the data preprocessing module; the traffic mode recognition module establishes a congestion solving model by using real-time traffic data to reduce the current road congestion situation; the traffic road flow prediction module analyzes real-time data and historical data, establishes a traffic jam prediction model, and guides a vehicle to select a route in advance. The information issuing module informs a driver of the traffic flow and the prediction result; according to the invention, the road traffic efficiency is improved, the traffic jam is effectively relieved and dealt with in advance, and the road traffic is more intelligent and efficient.

Description

Technical Field

[0001] The present invention relates to the field of traffic monitoring technology, and more specifically, to an intelligent traffic information processing platform. Background Art

[0002] With the acceleration of urbanization, the number of vehicles has increased dramatically, resulting in an imbalance between the supply and demand of road resources, and traffic congestion has become a common problem in people's daily travel. Existing means of solving traffic congestion mainly rely on manual intervention and preset signal control strategies, which lack flexibility and intelligence, and are difficult to respond to complex and dynamic traffic conditions in real time, resulting in the inability to effectively alleviate road congestion during peak hours.

[0003] In addition, the existing means of collecting traffic information have a single data source, which makes it difficult for traffic managers to accurately grasp road conditions, adjust traffic signals in real time or take effective diversion measures, thereby exacerbating congestion. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a smart traffic information processing platform, which collects multi-source data of congested roads in real time through traffic cameras, and cleans, reduces noise and formats the original data, establishes a congestion solution model based on the multi-source data collected in real time, dynamically adjusts traffic lights in real time to alleviate congestion, and establishes a prediction model based on real-time data and historical data to predict road congestion, guide vehicle diversion in advance, and avoid congestion, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A smart traffic information processing platform includes a data acquisition module, a data preprocessing module, a traffic mode recognition module, a traffic road flow control module, a traffic road flow prediction module and an information release module; the data acquisition module collects congested road data in real time; the data preprocessing module establishes a noise reduction model to perform noise reduction processing on the collected data to improve the quality of the data, and the formula of the noise reduction model is: ,in, is the input image, size is , the pixel value range is , is the horizontal position of the pixel, is the vertical position of the pixel; is the image output after noise reduction, and its pixel value is still ; is a two-dimensional Gaussian filter kernel, is the standard deviation of the Gaussian filter kernel, which determines the smoothness of the filter; It is a convolution operation that implements smoothing to remove high-frequency noise while preserving edge details; To input the image Split into blocks, and get The pixel value of each block. The purpose of the block is to perform adaptive processing on the local area and reduce the artifacts introduced by the global histogram equalization. It is the histogram clipping threshold, which is used to limit excessive contrast enhancement and prevent excessive enhancement of the light spot; To apply contrast limited adaptive histogram equalization to the blocks; The traffic road flow control module uses real-time traffic data to establish a congestion solution model to alleviate the congestion situation. The formula of the congestion solution model is: , Where: The objective function for solving the current congestion situation measures the optimal state of solving the congestion; The total number of directions controlled by the signal light; For the The congestion index in each direction, satisfy ,in, For the The vehicle density in each direction, The maximum vehicle density allowed for road design, is the weight of the impact of vehicle speed on the congestion index, For the The average speed of all vehicles in each direction, is the average speed of all vehicles under non-congestion conditions; is the first adjustment coefficient, which controls the weight of queue length deviation; For the The actual length of the vehicle queue in the direction; is the preset vehicle queue length threshold, satisfy ,in, is the maximum permissible queue length on the road, The vehicle queue length threshold used to control the preset sensitivity to change; is the second adjustment coefficient, which controls the weight of the time deviation of the priority lane; For the The duration of the green light in each direction, satisfy ,in, The default green light duration for initial setting; The green light duration of the priority lane; the congestion solution model re-plans the control time of the signal light, flexibly adjusts the duration ratio of the green light in each direction, and also introduces the ability to regulate the green light duration of the priority lane, thereby alleviating road congestion.

[0006] As a further solution of the present invention, the data acquisition module collects congested road data in real time, including the following specific contents: the data acquisition module monitors the traffic conditions of the road in real time through traffic cameras arranged along the road, the traffic cameras can collect image data in the traffic flow in real time, and analyze the image data to obtain key data such as vehicle density, road occupancy rate and traffic flow, and the captured images are parsed by image processing algorithms, and the vehicles are accurately identified and counted, so as to obtain the number of vehicles passing a certain position in a unit time, that is, the traffic flow. In addition, the traffic camera can also identify the occupancy of the road, such as whether there is a lane closure caused by construction, accidents, etc., or whether there are other obstacles that affect the normal use of the road. In order to ensure the accuracy of the data, the traffic camera has high-resolution imaging technology and night vision capabilities, and can collect high-quality images under different lighting conditions, including traffic conditions during the day and at night, road conditions under complex weather, etc. The collected data is transmitted to the data preprocessing module through a communication network, and the communication network adopts a high-speed and low-latency transmission method, such as a 5G network or a dedicated optical fiber line, to ensure the real-time and stability of the data.

[0007] As a further solution of the present invention, the data preprocessing module cleans, reduces noise and formats the collected raw data, including the following specific contents: First, the data preprocessing module cleans the raw data, the purpose of which is to remove the erroneous values, redundant information and missing data in the data. For example, in the data collection process, some incomplete records may be generated due to equipment failure or communication interruption. If these incomplete data are not processed, they will cause deviations in subsequent analysis. Therefore, in the data cleaning process, outliers based on statistical methods are used to detect and eliminate abnormal values, as well as rule-based logical judgments to mark obviously unreasonable vehicle speeds, excessively high or low vehicle density and other values ​​as abnormal.

[0008] Secondly, the images collected by the traffic camera under complex lighting conditions will be interfered by light spots and shadows. The data preprocessing module performs noise reduction on the collected raw data by establishing a noise reduction model to improve the quality of the data.

[0009] The data collected from different sensors have different timestamps, different data units and different data types, so the data preprocessing module synchronizes and calibrates the timestamps, converts the units uniformly, and integrates all data into a unified structured database to facilitate unified management and analysis of the system.

[0010] As a further solution of the present invention, the traffic pattern recognition module recognizes traffic patterns in different time periods and road conditions, including the following specific contents: the traffic pattern recognition module obtains key traffic feature data such as vehicle density, road occupancy rate and average speed in real time through traffic cameras arranged along the road, and analyzes the key traffic feature data, taking vehicle density and average speed as core features, and comprehensively evaluates the degree of road smoothness. For example, under normal traffic conditions, the vehicle density and average speed remain within a preset range, and this state is marked as "smooth"; as the number of vehicles increases and the average speed decreases, when the density exceeds a preset threshold, the traffic pattern recognition module will identify it as "congested". In addition, the traffic pattern recognition module can also detect the occurrence of special events such as traffic accidents. For example, when the speed of a certain location suddenly drops and the vehicle density is abnormally concentrated, it is inferred that there may be an accident based on the characteristic anomaly.

[0011] As a further solution of the present invention, the traffic road flow prediction module establishes a traffic congestion prediction model by analyzing real-time data and historical data, and guides vehicles to select routes in advance, including the following specific contents: the traffic road flow prediction module establishes a traffic congestion prediction model based on real-time data and historical data, predicts congestion conditions, releases traffic information in advance, and guides vehicles to select different routes. The formula of the traffic congestion prediction model is: , Where: To predict the time Congestion index; is the total number of roads; For the Road Vehicle density at the time; For the The maximum design vehicle density of the road; For the Road The average speed of all vehicles at the moment; For the Road The average speed of all vehicles at the moment; for Moment The slipperiness of the road is affected by the temperature; the congestion index To classify, if , indicating that the traffic is unobstructed and there is no need to guide the vehicle; if , indicating that there is slight congestion, it is recommended that vehicles give priority to alternative routes; if , indicating that there is severe congestion. The red light on the main road is extended through traffic lights, and the green light on the auxiliary road is increased to guide vehicle diversion in advance.

[0012] As a further solution of the present invention, the information release module informs the driver of the traffic flow and the prediction results, including the following specific contents: the information release module timely conveys the real-time traffic data and the prediction results to the driver, thereby guiding him to choose the best route, avoid congestion and optimize the utilization of road resources. The information release module realizes efficient information dissemination through a variety of communication methods, including channels such as mobile phone applications, vehicle navigation systems and roadside display screens, so as to cover different driving scenarios and user needs. In terms of mobile phone applications, the module presents the current road traffic status, estimated travel time and alternative route suggestions to the driver in a visual way through real-time push notifications or dynamic navigation services, so as to facilitate the driver to quickly adjust the route before or during driving; for the vehicle navigation system, the information release module directly displays detailed traffic information on the driver's navigation screen through docking with the vehicle's embedded system, and combines voice prompts to help the driver quickly understand the road conditions without affecting safe driving; the roadside display screen mainly serves drivers who cannot access mobile phones or vehicle systems, and displays the traffic conditions, estimated delay time and diversion suggestions of specific sections of the road in real time through electronic screens, providing a basis for temporary decision-making. In addition, the information release module has an intelligent information screening function, which only pushes the most relevant information according to the driver's location, destination and current road conditions to avoid redundancy or unnecessary interference.

[0013] As a further solution of the present invention, the data preprocessing module is connected to the data acquisition module, the traffic pattern recognition module is connected to the data preprocessing module, the traffic road flow control module is connected to the traffic pattern recognition module, the traffic road flow prediction module is connected to the traffic road flow control module, and the information release module is connected to the traffic road flow prediction module.

[0014] The technical effects and advantages of a smart traffic information processing platform of the present invention are as follows: the present invention uses a traffic camera to collect congested road data in real time through a data acquisition module; the data preprocessing module cleans, reduces noise and formats the original data to improve the quality of the data; the traffic mode recognition module identifies different traffic modes based on key traffic feature data, and the traffic road flow control module establishes a congestion solution model based on real-time data, dynamically adjusts the signal light time, etc. to alleviate congestion; the traffic road flow prediction module uses real-time and historical data to establish a prediction model to predict congestion and classify it, and guide vehicle diversion in advance; the information release module accurately pushes traffic information to drivers through multiple channels. The platform effectively improves the accuracy and completeness of traffic data, enhances the accuracy of traffic mode recognition, improves road travel efficiency, achieves effective relief and early response to traffic congestion, optimizes the driver's travel experience, and makes traffic management more intelligent and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a structural schematic diagram of an intelligent traffic information processing platform of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example 1 See also Figure 1 As shown in the structural schematic diagram, an embodiment of the present invention provides a smart traffic information processing platform, which includes a data acquisition module, a data preprocessing module, a traffic mode recognition module, a traffic road flow control module, a traffic road flow prediction module and an information release module; the data acquisition module collects congested road data in real time; the data preprocessing module cleans, reduces noise and formats the collected original data; the traffic mode recognition module identifies traffic modes in different time periods and road conditions; the traffic road flow control module uses real-time traffic data to establish a congestion solution model based on the current congestion situation to alleviate the current road congestion situation; the traffic road flow prediction module establishes a traffic congestion prediction model by analyzing real-time data and historical data, and guides vehicles to choose routes in advance; the information release module informs the driver of the traffic flow and prediction results.

[0018] Furthermore, the data acquisition module collects congested road data in real time, including the following specific contents: the data acquisition module monitors the traffic conditions of the road in real time through traffic cameras arranged along the road, the traffic cameras can collect image data in the traffic flow in real time, and analyze the image data to obtain key data such as vehicle density, road occupancy rate and traffic flow, and analyze the captured images through image processing algorithms, so that the vehicles are accurately identified and counted, thereby obtaining the number of vehicles passing through a certain position per unit time, that is, the traffic flow. In addition, the traffic camera can also identify the occupancy of the road, such as whether there are lane closures caused by construction, accidents, etc., or whether there are other obstacles that affect the normal use of the road. In order to ensure the accuracy of the data, the traffic camera has high-resolution imaging technology and night vision capabilities, and can collect high-quality images under different lighting conditions, including traffic conditions during the day and at night, road conditions under complex weather, etc. The collected data is transmitted to the data preprocessing module through a communication network, and the communication network adopts a high-speed and low-latency transmission method, such as a 5G network or a dedicated optical fiber line, to ensure the real-time and stability of the data.

[0019] Furthermore, the data preprocessing module cleans, reduces noise and formats the collected raw data, including the following specific contents: First, the data preprocessing module cleans the raw data, the purpose of which is to remove the erroneous values, redundant information and missing data in the data. For example, in the data collection process, some incomplete records may be generated due to equipment failure or communication interruption. If these incomplete data are not processed, they will cause deviations in subsequent analysis. Therefore, in the data cleaning process, outliers based on statistical methods are used to detect and eliminate abnormal values, as well as rule-based logical judgments to mark obviously unreasonable vehicle speeds, excessively high or low vehicle density and other values ​​as abnormal.

[0020] Secondly, the images collected by the traffic camera under complex lighting conditions will be interfered by light spots and shadows. The data preprocessing module performs noise reduction processing on the collected raw data by establishing a noise reduction model to improve the quality of the data. The formula of the noise reduction model is: , Where: is the input image, size is , the pixel value range is , is the horizontal position of the pixel, is the vertical position of the pixel; is the image output after noise reduction, and its pixel value is still ; is a two-dimensional Gaussian filter kernel, is the standard deviation of the Gaussian filter kernel, which determines the smoothness of the filter; It is a convolution operation that implements smoothing to remove high-frequency noise while preserving edge details; To input the image Split into blocks, and get The pixel value of each block. The purpose of the block is to perform adaptive processing on the local area and reduce the artifacts introduced by the global histogram equalization. It is the histogram clipping threshold, which is used to limit excessive contrast enhancement and prevent excessive enhancement of the light spot; To apply contrast limited adaptive histogram equalization to the blocks.

[0021] The meanings and values ​​of the parameters in the noise reduction model are as follows:

[0022] The data collected from different sensors have different timestamps, different data units, and different data types. Therefore, the data preprocessing module synchronizes and calibrates the timestamps, converts the units uniformly, and integrates all data into a unified structured database to facilitate unified management and analysis of the system. In addition, during the formatting process, the data is also interpolated to complete the missing data points. Especially in the monitoring of traffic flow, some data may be lost due to equipment problems at certain times. At this time, time series interpolation or interpolation algorithms based on historical trends can be used to fill these gaps to ensure the continuity and integrity of the data.

[0023] Furthermore, the traffic pattern recognition module recognizes traffic patterns in different time periods and road conditions, including the following specific contents: the traffic pattern recognition module obtains key traffic feature data such as vehicle density, road occupancy rate and average speed in real time through traffic cameras arranged along the road, and analyzes the key traffic feature data, taking vehicle density and average speed as core features, and comprehensively evaluates the degree of road smoothness. For example, under normal traffic conditions, the vehicle density and average speed remain within a preset range, and this state is marked as "smooth"; as the number of vehicles increases and the average speed decreases, when the density exceeds a preset threshold, the traffic pattern recognition module will identify it as "congested". In addition, the traffic pattern recognition module can also detect the occurrence of special events such as traffic accidents. For example, when the speed of a certain location suddenly drops and the vehicle density is abnormally concentrated, it is inferred that there may be an accident based on the characteristic anomaly.

[0024] Furthermore, the traffic road flow control module uses real-time traffic data to establish a congestion solution model based on the current congestion situation to alleviate the congestion situation, including the following specific contents: the traffic road flow control module dynamically adjusts the allocation of road resources and signal light time based on real-time traffic data to effectively alleviate the congestion situation and improve road traffic efficiency. Based on the vehicle density, road occupancy rate and vehicle flow data obtained by the data acquisition module, a congestion solution model is established to alleviate the congestion situation. The formula of the congestion solution model is: , Where: The objective function for solving the current congestion situation measures the optimal state of solving the congestion; The total number of directions controlled by the signal light; For the The congestion index in each direction, satisfy ,in, For the The vehicle density in each direction, The maximum vehicle density allowed for road design, is the weight of the impact of vehicle speed on the congestion index, For the The average speed of all vehicles in each direction, is the average speed of all vehicles under non-congestion conditions; is the first adjustment coefficient, which controls the weight of queue length deviation; For the The actual length of the vehicle queue in the direction; is the preset vehicle queue length threshold, satisfy ,in, is the maximum permissible queue length on the road, The vehicle queue length threshold used to control the preset sensitivity to change; is the second adjustment coefficient, which controls the weight of the time deviation of the priority lane; For the The duration of the green light in each direction, satisfy ,in, The default green light duration for initial setting; The green light duration of the priority lane; the congestion solution model re-plans the control time of the signal light, flexibly adjusts the duration ratio of the green light in each direction, and also introduces the ability to regulate the green light duration of the priority lane, thereby alleviating road congestion.

[0025] Furthermore, the traffic road flow prediction module establishes a traffic congestion prediction model by analyzing real-time data and historical data, and guides vehicles to select routes in advance, including the following specific contents: the traffic road flow prediction module establishes a traffic congestion prediction model based on real-time data and historical data, predicts congestion conditions, releases traffic information in advance, and guides vehicles to select different routes. The formula of the traffic congestion prediction model is: , Where: To predict the time Congestion index; is the total number of roads; For the Road Vehicle density at the time; For the The maximum design vehicle density of the road; For the Road The average speed of all vehicles at the moment; For the Road The average speed of all vehicles at the moment; for Moment The slipperiness of the road is affected by the temperature; the congestion index To classify, if , indicating that the traffic is unobstructed and there is no need to guide the vehicle; if , indicating that there is slight congestion, it is recommended that vehicles give priority to alternative routes; if , indicating that there is severe congestion. The red light on the main road is extended through traffic lights, and the green light on the auxiliary road is increased to guide vehicle diversion in advance.

[0026] Furthermore, the information release module informs the driver of the traffic flow and prediction results, including the following specific contents: the information release module timely conveys real-time traffic data and prediction results to the driver, thereby guiding him to choose the best route, avoid congestion and optimize the utilization of road resources. The information release module realizes efficient information dissemination through multiple communication methods, including channels such as mobile phone applications, vehicle navigation systems and roadside display screens, so as to cover different driving scenarios and user needs. In terms of mobile phone applications, the module presents the current road traffic status, estimated travel time and alternative route suggestions to the driver in a visual way through real-time push notifications or dynamic navigation services, so as to facilitate the driver to quickly adjust the route before or during driving; for the vehicle navigation system, the information release module directly displays detailed traffic information on the driver's navigation screen through docking with the vehicle's embedded system, and combines voice prompts to help the driver quickly understand the road conditions without affecting safe driving; the roadside display screen mainly serves drivers who cannot access mobile phones or vehicle systems, and displays the traffic conditions, estimated delay time and diversion suggestions of specific sections of the road in real time through electronic screens, providing a basis for temporary decision-making. In addition, the information release module has an intelligent information screening function, which only pushes the most relevant information according to the driver's location, destination and current road conditions to avoid redundancy or unnecessary interference.

[0027] Furthermore, the data preprocessing module is connected to the data acquisition module, the traffic pattern recognition module is connected to the data preprocessing module, the traffic road flow control module is connected to the traffic pattern recognition module, the traffic road flow prediction module is connected to the traffic road flow control module, and the information release module is connected to the traffic road flow prediction module.

[0028] The present invention uses a traffic camera to collect congested road data in real time through a data acquisition module, including vehicle density, road occupancy rate and vehicle flow; a data preprocessing module cleans, reduces noise and formats the original data to improve data quality; a traffic mode recognition module recognizes different traffic modes based on key traffic feature data, and a traffic road flow control module establishes a congestion solution model based on real-time data, dynamically adjusts the signal light time and other measures to alleviate congestion; a traffic road flow prediction module uses real-time and historical data to establish a prediction model to predict congestion and classify it, and guide vehicle diversion in advance; an information release module accurately pushes traffic information to drivers through multiple channels. The platform effectively improves the accuracy and completeness of traffic data, enhances the accuracy of traffic mode recognition, improves road traffic efficiency, achieves effective relief and early response to traffic congestion, optimizes the driver's travel experience, and makes road traffic more intelligent and efficient.

[0029] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0030] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart traffic information processing platform, characterized in that: It includes a data acquisition module, a data preprocessing module, a traffic mode recognition module, a traffic road flow control module, a traffic road flow prediction module and an information release module; the data acquisition module collects congested road data in real time; the data preprocessing module establishes a noise reduction model to perform noise reduction on the collected data to improve the quality of the data, and the formula of the noise reduction model is: ,in, is the input image; is the image output after denoising; is a two-dimensional Gaussian filter kernel, is the standard deviation of the Gaussian filter kernel, which determines the smoothness of the filter; It is a convolution operation that implements smoothing to remove high-frequency noise; To input the image Split into blocks, and get The pixel value of each block; It is the histogram clipping threshold, which is used to limit excessive contrast enhancement and prevent excessive enhancement of the light spot; To apply contrast limited adaptive histogram equalization to the blocks; The traffic road flow control module uses real-time traffic data to establish a congestion solution model to alleviate the congestion situation. The formula of the congestion solution model is: ， Where: The objective function for solving the current congestion situation measures the optimal state of solving the congestion; The total number of directions controlled by the signal light; For the Congestion index in each direction; is the first adjustment coefficient, which controls the weight of queue length deviation; For the The actual length of the vehicle queue in the direction; is a preset vehicle queue length threshold; is the second adjustment coefficient, which controls the weight of the time deviation of the priority lane; For the Green light duration in each direction; The default green light duration for initial setting; The green light duration for the priority lane.

2. According to claim 1, a smart traffic information processing platform is characterized in that The traffic road flow prediction module establishes a traffic congestion prediction model based on real-time data and historical data, predicts congestion conditions, releases traffic information in advance, and guides vehicles to choose different routes. The formula of the traffic congestion prediction model is: ， Where: To predict the time Congestion index; is the total number of roads; For the Road Vehicle density at the time; For the The maximum design vehicle density of the road; For the Road The average speed of all vehicles at the moment; For the Road The average speed of all vehicles at the moment; for Moment The slipperiness of the road is affected by the temperature; the congestion index To classify, if , indicating that the traffic is unobstructed and there is no need to guide the vehicle; if , indicating that there is slight congestion, it is recommended that vehicles give priority to alternative routes; if , indicating that there is severe congestion. The red light on the main road is extended through traffic lights, and the green light on the auxiliary road is increased to guide vehicle diversion in advance.

3. According to claim 1, the intelligent traffic information processing platform is characterized in that The data preprocessing module is connected to the data acquisition module, the traffic pattern recognition module is connected to the data preprocessing module, the traffic road flow control module is connected to the traffic pattern recognition module, the traffic road flow prediction module is connected to the traffic road flow control module, and the information release module is connected to the traffic road flow prediction module.

4. The intelligent traffic information processing platform according to claim 1, characterized in that: The data acquisition module monitors the road traffic conditions in real time through traffic cameras arranged along the road, collects traffic flow image data and analyzes to obtain vehicle density, road occupancy rate, vehicle volume and road occupancy. The traffic cameras have high-resolution imaging technology and night vision capabilities. The collected data is transmitted to the data preprocessing module through a high-speed and low-latency communication network.

5. The intelligent traffic information processing platform according to claim 1, characterized in that: The data preprocessing module performs data cleaning on the collected data, with the aim of removing erroneous values, redundant information and missing data problems in the data. During the data collection process, incomplete records are generated due to equipment failure or communication interruption. If the incomplete data is not processed, it will cause deviations in subsequent analysis. Therefore, in the data cleaning process, outliers based on statistical methods are used to detect and eliminate abnormal values, as well as rule-based logical judgments.

6. The intelligent traffic information processing platform according to claim 1, characterized in that: The data preprocessing module formats the collected data. The data collected from different sensors have different timestamps, different data units and different data types. Therefore, the data preprocessing module synchronizes and calibrates the timestamps, converts the units uniformly, and integrates all data into a unified structured database to facilitate unified management and analysis of the system.

7. The intelligent traffic information processing platform according to claim 1, characterized in that: The traffic mode recognition module obtains key traffic feature data such as vehicle density, road occupancy rate and average vehicle speed in real time through traffic cameras, comprehensively evaluates the road smoothness with vehicle density and average vehicle speed as core features, identifies different traffic modes and detects the occurrence of special events.

8. The intelligent traffic information processing platform according to claim 1, characterized in that: The information release module informs the driver of real-time traffic data and prediction results through mobile phone applications, on-board navigation systems and roadside display screens. The mobile phone application provides real-time push notifications or dynamic navigation services. The on-board navigation system connects with the vehicle's embedded system to display and voice prompt traffic information. The roadside display screen provides traffic information to the driver and has an intelligent information screening function.

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