Traffic data acquisition and real-time road condition analysis system and method based on big data, equipment and medium

Abstract

The invention provides a traffic data acquisition and real-time road condition analysis system, method and device based on big data and a medium, and belongs to the technical field of traffic big data processing. The data integration and processing module carries out data processing, preliminary analysis, traffic feature extraction and data integration on traffic data from different sources to obtain a preliminary road condition; the real-time traffic monitoring and predicting module is used for publishing road conditions through real-time channels, predicting traffic flow and giving out travel suggestions; the data analysis module and the behavior recognition module are used for analyzing traffic flows under different conditions, recognizing and early warning illegal traffic behaviors, recognizing traffic abnormal events and processing the traffic abnormal events; and the road condition evaluation and information publishing module is used for evaluating and grading the road conditions and then publishing the road conditions. According to the invention, the efficiency and response speed of traffic management can be improved, and more convenient and efficient travel experience is provided for users.

Description

Background Art

[0002] With the development of transportation, traffic collection systems are becoming more and more important. Traffic collection systems collect and analyze traffic data in real time, provide data support for traffic management departments to obtain road condition information so as to make dispatch and decision in time, and improve the efficiency and response speed of traffic management. Traffic collection systems also send road condition information to drivers or passengers in real time so that they can make reasonable travel choices, avoid congested roads, and improve travel efficiency.

[0003] The current traffic data collection system still has the following technical problems: First, the data accuracy is not high. The accuracy of the sensors that collect traffic data may be affected by factors such as weather, environmental noise, and equipment aging, resulting in errors in the collected data, affecting the real-time and accuracy of traffic conditions. Second, the data processing capacity is insufficient. As the amount of data increases, the existing data processing and analysis technologies may not be able to process large-scale, high-frequency traffic data in real time and efficiently, which may lead to information delays or omissions. Third, there are many different sources of traffic data, such as GPS, cameras, and radars. The formats of various types of traffic data are different, and it is quite difficult to integrate these traffic data into a unified traffic information system. Fourth, the ability to identify and respond to traffic events such as congestion and accidents is low, which affects the speed of emergency response and the efficiency of traffic diversion.

[0004] This is a shortcoming of the prior art. Therefore, in view of the above-mentioned defects in the prior art, it is very necessary to provide a traffic data collection and real-time road condition analysis system, method, equipment and medium based on big data. Summary of the invention

[0005] Traffic collection systems can improve the efficiency of traffic management and travel efficiency, but existing traffic collection systems are limited by sensor accuracy, low accuracy of collected data, insufficient data processing capabilities, difficulty in integrating multiple types of traffic data, and low ability to identify traffic events. The present invention provides a traffic data collection and real-time road condition analysis system, method, equipment and medium based on big data to solve the above technical problems.

[0006] In a first aspect, the present invention provides a traffic data collection and real-time road condition analysis system based on big data, comprising:

[0007] Data collection module, which collects traffic data through various sensor devices;

[0008] The data integration and processing module cleans traffic data from different sources and converts them into a unified format, then performs preliminary analysis on the cleaned and converted traffic data, extracts traffic features, and integrates traffic data with related data to obtain preliminary road conditions;

[0009] The real-time traffic monitoring and prediction module will integrate and process traffic data and preliminary routes in real time, publish road conditions through real-time channels, predict traffic flow through self-correcting prediction models, and give travel suggestions based on users' travel information;

[0010] The data analysis module and behavior recognition module analyze traffic flow under different conditions, identify and warn illegal traffic behaviors, identify abnormal traffic events and handle them according to emergency plans;

[0011] The road condition assessment and information release module assesses and grades the road conditions and then releases the road conditions.

[0012] Furthermore, the sensing devices of the data acquisition module include GPS, camera, and vehicle speed sensor;

[0013] Traffic data includes vehicle flow, speed, direction of travel, and public transportation information.

[0014] Furthermore, the data integration and processing module includes:

[0015] The data preprocessing unit performs preliminary processing on the collected traffic data, identifies erroneous data by checking the data value range and format verification, deletes or corrects the erroneous data, and performs null value checks on the timestamp and vehicle ID fields to remove null value data; supplements the missing time series traffic data by the time difference method, and uses the classification prediction model to fill in the missing traffic data of each category; uses the Z score method to detect outliers, checks the detected outliers, deletes them if confirmed to be outliers, and recovers them if they are identified as errors;

[0016] The data conversion and formatting unit unifies the timestamp, unit and data type of the pre-processed traffic data and converts it into a unified data format;

[0017] The data preliminary analysis and integration unit conducts time series analysis on the traffic data after preprocessing and format unification, extracts the changing trend of traffic flow, and uses clustering algorithms to identify traffic hot spots and traffic bottlenecks; extracts peak hours, average speeds, and congestion levels as traffic characteristics, and visualizes the extracted traffic characteristics using heat maps; integrates traffic data with weather data and historical traffic data to obtain preliminary road conditions.

[0018] Furthermore, the real-time traffic monitoring and prediction module includes:

[0019] A real-time data acquisition unit, which acquires cleaned and converted traffic data, preliminary analysis results, and preliminary road conditions in real time;

[0020] Traffic condition publishing unit, which publishes traffic conditions through real-time channels;

[0021] The traffic flow prediction unit predicts the overall traffic flow, the traffic flow of a pre-calibrated road section, and the traffic flow of a pre-calibrated time period through a self-correcting prediction model;

[0022] The route optimization suggestion unit obtains the user's departure place, destination, and departure time, and gives the optimal driving route suggestion based on real-time road conditions and predicted traffic flow.

[0023] Furthermore, the data analysis module and the behavior recognition module include:

[0024] Traffic flow analysis unit, which analyzes the changes in traffic flow at different times and under different weather conditions, and the impact of road construction on traffic flow;

[0025] The behavior pattern recognition unit builds a user behavior database, identifies the user's behavior pattern through a neural network model, issues early warnings for traffic violations based on the identified behavior pattern and the user behavior database, and analyzes the user's driving habits to provide personalized safe driving suggestions;

[0026] The anomaly detection unit combines sensor data to quickly detect traffic accidents, road obstructions, and abnormal parking-related traffic anomalies, and classifies and prioritizes traffic anomalies, and handles them through emergency plans in conjunction with emergency management departments.

[0027] Furthermore, the road condition assessment and information release module includes:

[0028] The road condition assessment unit assesses the current traffic conditions in combination with the predicted traffic flow, and then classifies the road conditions according to the assessment results;

[0029] The information release unit pushes real-time traffic information to users and relevant departments through API interfaces, mobile applications, websites or in-vehicle navigation systems.

[0030] In a second aspect, the present invention provides a method for traffic data collection and real-time road condition analysis based on big data, comprising the following steps:

[0031] S1. Collect traffic data through various sensor devices;

[0032] S2. Clean the traffic data from different sources and convert them into a unified format, then conduct a preliminary analysis on the cleaned and converted traffic data, extract traffic characteristics, and integrate the traffic data with related data to obtain preliminary road conditions;

[0033] S3. Integrate and process traffic data and preliminary routes in real time, publish traffic conditions through real-time channels, predict traffic flow through self-correcting prediction models, and give travel suggestions based on user travel information;

[0034] S4. Analyze traffic flow under different conditions, identify and warn of illegal traffic behaviors, identify abnormal traffic events and handle them according to emergency plans;

[0035] S5. Evaluate and grade the road conditions, and then publish the road conditions.

[0036] Furthermore, in step S1, vehicle flow, speed, driving direction and public transportation information are collected through GPS, camera and vehicle speed sensor as sensing devices;

[0037] The specific steps of step S2 are as follows:

[0038] S21. Perform preliminary processing on the collected traffic data, identify erroneous data by checking the data value range and format verification, delete or correct the erroneous data, check the null value of the timestamp and vehicle ID fields, and remove the null value data; supplement the missing time series type traffic data by the time difference method, and use the classification prediction model to fill the missing traffic data of each category; use the Z score method to detect outliers, check the detected outliers, delete them if confirmed to be outliers, and restore them if they are identified as errors;

[0039] S22. Unify the timestamp, unit and data type of the pre-processed traffic data and convert it into a unified data format;

[0040] S23. Perform time series analysis on the traffic data after preprocessing and format unification, extract the changing trend of traffic flow, use clustering algorithm to identify traffic hotspots and traffic bottlenecks; extract peak hours, average speed and congestion level as traffic features, and visualize the extracted traffic features using heat maps; integrate traffic data with weather data and historical traffic data to obtain preliminary road conditions; the specific steps of step S3 are as follows:

[0041] S31. Real-time acquisition of cleaned and converted traffic data, preliminary analysis results and preliminary road conditions;

[0042] S32. Release traffic conditions through real-time channels;

[0043] S33. The overall traffic flow, the traffic flow of the pre-calibrated road section, and the traffic flow of the pre-calibrated time period are predicted by the self-correcting prediction model;

[0044] S34. Obtain the user's departure place, destination, and departure time, and give the optimal driving route suggestion based on real-time traffic conditions and predicted traffic flow;

[0045] The specific steps of step S4 are as follows:

[0046] S41. Analyze the changes in traffic flow at different times and under different weather conditions, and analyze the impact of road construction on traffic flow;

[0047] S42. Establish a user behavior database, identify the user's behavior pattern through a neural network model, issue warnings for traffic violations based on the identified behavior pattern and the user behavior database, and analyze the user's driving habits to provide personalized safe driving suggestions;

[0048] S43. Rapidly detect traffic accidents, road obstructions, and abnormal parking-related traffic abnormalities by combining sensor data, and classify and prioritize traffic abnormalities, and handle them through emergency plans in conjunction with emergency management departments;

[0049] The specific steps of step S5 are as follows:

[0050] S51. Evaluate the current traffic conditions in combination with the predicted traffic flow, and then classify the traffic conditions according to the evaluation results;

[0051] S52. Push real-time traffic information to users and relevant departments through API interfaces, mobile applications, websites or in-vehicle navigation systems.

[0052] In a third aspect, the present invention provides an electronic device, including a processor and a memory;

[0053] The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the device executes the method described in the second aspect above.

[0054] In a fourth aspect, the present invention provides a storage medium,

[0055] The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in the second aspect.

[0056] The beneficial effects of the present invention are:

[0057] The traffic data collection and real-time road condition analysis system, method, equipment and medium based on big data provided by the present invention integrate and process the traffic data collected by the sensor equipment, effectively reduce the data errors caused by factors such as weather, environmental noise and equipment aging, and significantly improve the accuracy and reliability of the traffic data. The invention can also process large-scale and high-frequency traffic data in real time and efficiently, effectively avoid the problem of information delay or omission, and improve the efficiency of data processing and analysis; by integrating traffic data from different sources, data unification is achieved, and the problem of difficulty in integrating multi-source traffic data is solved; the identification of abnormal traffic events significantly improves the emergency response speed and traffic diversion efficiency; the optimal driving route recommendation improves the user's travel experience and efficiency; the real-time road condition information push provides timely and accurate road condition information for traffic management departments and the public.

[0058] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect.

[0059] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0061] Figure 1 It is a schematic diagram of the traffic data collection and real-time road condition analysis system based on big data of the present invention.

[0062] Figure 2 It is a flow chart of the traffic data collection and real-time road condition analysis method based on big data of the present invention. DETAILED DESCRIPTION

[0063] The traffic data collection and real-time road condition analysis method based on big data can be applied to one or more devices, which are devices that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and whose hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0064] An electronic device can be any electronic product that can interact with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), an interactive network television (IPTV), etc. The network in which the electronic device is located includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0065] 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 should fall within the scope of protection of the present invention.

[0066] See also Figure 1 The following is a specific implementation of a traffic data collection and real-time traffic condition analysis system based on big data, including:

[0067] Data collection module, which collects traffic data through various sensor devices;

[0068] The data integration and processing module cleans traffic data from different sources and converts them into a unified format, then performs preliminary analysis on the cleaned and converted traffic data, extracts traffic features, and integrates traffic data with related data to obtain preliminary road conditions;

[0069] The real-time traffic monitoring and prediction module will integrate and process traffic data and preliminary routes in real time, publish road conditions through real-time channels, predict traffic flow through self-correcting prediction models, and give travel suggestions based on users' travel information;

[0070] The data analysis module and behavior recognition module analyze traffic flow under different conditions, identify and warn illegal traffic behaviors, identify abnormal traffic events and handle them according to emergency plans;

[0071] The road condition assessment and information release module assesses and grades the road conditions and then releases the road conditions.

[0072] In this embodiment, the sensor equipment of the data acquisition module includes GPS, camera, and vehicle speed sensor;

[0073] Traffic data includes vehicle flow, speed, direction of travel, and public transportation information;

[0074] Data integration and processing modules include:

[0075] The data preprocessing unit performs preliminary processing on the collected traffic data, identifies erroneous data by checking the data value range and format verification, deletes or corrects the erroneous data, and performs null value checks on the timestamp and vehicle ID fields to remove null value data; supplements the missing time series traffic data by the time difference method, and uses the classification prediction model to fill in the missing traffic data of each category; uses the Z score method to detect outliers, checks the detected outliers, deletes them if confirmed to be outliers, and recovers them if they are identified as errors;

[0076] The data conversion and formatting unit unifies the timestamp, unit and data type of the pre-processed traffic data and converts it into a unified data format;

[0077] The preliminary data analysis and integration unit conducts time series analysis on the traffic data after preprocessing and format unification, extracts the changing trend of traffic flow, and uses clustering algorithms to identify traffic hot spots and traffic bottlenecks; extracts peak hours, average speeds, and congestion levels as traffic features, and visualizes the extracted traffic features using heat maps; integrates traffic data with weather data and historical traffic data to obtain preliminary road conditions;

[0078] Real-time traffic monitoring and prediction modules include:

[0079] A real-time data acquisition unit, which acquires cleaned and converted traffic data, preliminary analysis results, and preliminary road conditions in real time;

[0080] Traffic condition publishing unit, which publishes traffic conditions through real-time channels;

[0081] The traffic flow prediction unit predicts the overall traffic flow, the traffic flow of a pre-calibrated road section, and the traffic flow of a pre-calibrated time period through a self-correcting prediction model;

[0082] The route optimization suggestion unit obtains the user's departure place, destination, and departure time, and gives the optimal driving route suggestion based on real-time road conditions and predicted traffic flow;

[0083] The data analysis module and behavior recognition module include:

[0084] Traffic flow analysis unit, which analyzes the changes in traffic flow at different times and under different weather conditions, and the impact of road construction on traffic flow;

[0085] The behavior pattern recognition unit builds a user behavior database, identifies the user's behavior pattern through a neural network model, issues early warnings for traffic violations based on the identified behavior pattern and the user behavior database, and analyzes the user's driving habits to provide personalized safe driving suggestions;

[0086] The anomaly detection unit combines sensor data to quickly detect traffic accidents, road obstructions, and abnormal parking. It also classifies and prioritizes traffic anomalies and handles them through emergency plans linked to emergency management departments.

[0087] The road condition assessment and information release module includes:

[0088] The road condition assessment unit assesses the current traffic conditions in combination with the predicted traffic flow, and then classifies the road conditions according to the assessment results;

[0089] The information release unit pushes real-time traffic information to users and relevant departments through API interfaces, mobile applications, websites or in-vehicle navigation systems.

[0090] The following is an embodiment of the method for traffic data collection and real-time road condition analysis based on big data provided by the embodiments of the present disclosure. This method and the system for traffic data collection and real-time road condition analysis based on big data in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the method for traffic data collection and real-time road condition analysis based on big data, please refer to the embodiment of the system for traffic data collection and real-time road condition analysis based on big data.

[0091] like Figure 2 As shown, the method comprises the following steps:

[0092] S1. Collect traffic data through various sensor devices;

[0093] S2. Clean the traffic data from different sources and convert them into a unified format, then conduct a preliminary analysis on the cleaned and converted traffic data, extract traffic characteristics, and integrate the traffic data with related data to obtain preliminary road conditions;

[0094] S3. Integrate and process traffic data and preliminary routes in real time, publish traffic conditions through real-time channels, predict traffic flow through self-correcting prediction models, and give travel suggestions based on user travel information;

[0095] S4. Analyze traffic flow under different conditions, identify and warn of illegal traffic behaviors, identify abnormal traffic events and handle them according to emergency plans;

[0096] S5. Evaluate and grade the road conditions, and then publish the road conditions.

[0097] In this embodiment, in step S1, vehicle flow, speed, driving direction and public transportation information are collected by using GPS, camera and vehicle speed sensor as sensing devices;

[0098] The specific steps of step S2 are as follows:

[0099] S21. Perform preliminary processing on the collected traffic data, identify erroneous data by checking the data value range and format verification, delete or correct the erroneous data, check the null value of the timestamp and vehicle ID fields, and remove the null value data; supplement the missing time series type traffic data by the time difference method, and use the classification prediction model to fill the missing traffic data of each category; use the Z score method to detect outliers, check the detected outliers, delete them if confirmed to be outliers, and restore them if they are identified as errors;

[0100] S22. Unify the timestamp, unit and data type of the pre-processed traffic data and convert it into a unified data format;

[0101] S23. Perform time series analysis on traffic data after preprocessing and format unification, extract the changing trend of traffic flow, and use clustering algorithm to identify traffic hotspots and traffic bottlenecks; extract peak hours, average speed and congestion level as traffic features, and visualize the extracted traffic features using heat maps; integrate traffic data with weather data and historical traffic data to obtain preliminary road conditions;

[0102] The specific steps of step S3 are as follows:

[0103] S31. Real-time acquisition of cleaned and converted traffic data, preliminary analysis results and preliminary road conditions;

[0104] S32. Release traffic conditions through real-time channels;

[0105] S33. The overall traffic flow, the traffic flow of the pre-calibrated road section, and the traffic flow of the pre-calibrated time period are predicted by the self-correcting prediction model;

[0106] S34. Obtain the user's departure place, destination, and departure time, and give the optimal driving route suggestion based on real-time traffic conditions and predicted traffic flow;

[0107] The specific steps of step S4 are as follows:

[0108] S41. Analyze the changes in traffic flow at different times and under different weather conditions, and analyze the impact of road construction on traffic flow;

[0109] S42. Establish a user behavior database, identify the user's behavior pattern through a neural network model, issue warnings for traffic violations based on the identified behavior pattern and the user behavior database, and analyze the user's driving habits to provide personalized safe driving suggestions;

[0110] S43. Rapidly detect traffic accidents, road obstructions, and abnormal parking-related traffic abnormalities by combining sensor data, and classify and prioritize traffic abnormalities, and handle them through emergency plans in conjunction with emergency management departments;

[0111] The specific steps of step S5 are as follows:

[0112] S51. Evaluate the current traffic conditions in combination with the predicted traffic flow, and then classify the traffic conditions according to the evaluation results;

[0113] S52. Push real-time traffic information to users and relevant departments through API interfaces, mobile applications, websites or in-vehicle navigation systems.

[0114] It should be understood that the order of execution of the steps in the above embodiment does not mean that the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0115] The traffic data collection and real-time road condition analysis method based on big data provided by the present invention is a module and algorithm step of each example described in combination with the embodiments disclosed herein, and can be implemented by electronic hardware, computer software or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0116] Those skilled in the art will appreciate that the various aspects of the traffic data collection and real-time road condition analysis method based on big data provided by the present invention can be implemented as a system, method or program product. Therefore, various aspects of the present disclosure can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which can be collectively referred to as "circuit", "module" or "system" here.

[0117] In the non-transitory computer-readable storage medium storing the traffic data collection and real-time road condition analysis method based on big data, it can be but not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples of readable storage media (non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0118] Although the present invention has been described in detail by referring to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person skilled in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. 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 by the present invention, and they shall be within the scope of protection of the present invention.

Claims

1. A traffic data collection and real-time road condition analysis system based on big data, characterized in that: include: Data collection module, which collects traffic data through various sensor devices; The data integration and processing module cleans traffic data from different sources and converts them into a unified format, then performs preliminary analysis on the cleaned and converted traffic data, extracts traffic features, and integrates traffic data with related data to obtain preliminary road conditions; The real-time traffic monitoring and prediction module will integrate and process traffic data and preliminary routes in real time, publish road conditions through real-time channels, predict traffic flow through self-correcting prediction models, and give travel suggestions based on users' travel information; The data analysis module and behavior recognition module analyze traffic flow under different conditions, identify and warn illegal traffic behaviors, identify abnormal traffic events and handle them according to emergency plans; The road condition assessment and information release module assesses and grades the road conditions and then releases the road conditions.

2. The traffic data collection and real-time road condition analysis system based on big data as claimed in claim 1, characterized in that: The sensing devices of the data acquisition module include GPS, camera, and vehicle speed sensor; Traffic data includes vehicle flow, speed, direction of travel, and public transportation information.

3. The traffic data collection and real-time road condition analysis system based on big data as claimed in claim 1, characterized in that: Data integration and processing modules include: The data preprocessing unit performs preliminary processing on the collected traffic data, identifies erroneous data by checking the data value range and format verification, deletes or corrects the erroneous data, and performs null value checks on the timestamp and vehicle ID fields to remove null value data; supplements the missing time series traffic data by the time difference method, and uses the classification prediction model to fill in the missing traffic data of each category; uses the Z score method to detect outliers, checks the detected outliers, deletes them if confirmed to be outliers, and recovers them if they are identified as errors; The data conversion and formatting unit unifies the timestamp, unit and data type of the pre-processed traffic data and converts it into a unified data format; The data preliminary analysis and integration unit conducts time series analysis on the traffic data after preprocessing and format unification, extracts the changing trend of traffic flow, and uses clustering algorithms to identify traffic hot spots and traffic bottlenecks; extracts peak hours, average speeds, and congestion levels as traffic characteristics, and visualizes the extracted traffic characteristics using heat maps; integrates traffic data with weather data and historical traffic data to obtain preliminary road conditions.

4. The traffic data collection and real-time road condition analysis system based on big data as claimed in claim 1, characterized in that: Real-time traffic monitoring and prediction modules include: A real-time data acquisition unit, which acquires cleaned and converted traffic data, preliminary analysis results, and preliminary road conditions in real time; Traffic condition publishing unit, which publishes traffic conditions through real-time channels; The traffic flow prediction unit predicts the overall traffic flow, the traffic flow of a pre-calibrated road section, and the traffic flow of a pre-calibrated time period through a self-correcting prediction model; The route optimization suggestion unit obtains the user's departure place, destination, and departure time, and gives the optimal driving route suggestion based on real-time road conditions and predicted traffic flow.

5. The traffic data collection and real-time road condition analysis system based on big data as claimed in claim 4, characterized in that: The data analysis module and behavior recognition module include: Traffic flow analysis unit, which analyzes the changes in traffic flow at different times and under different weather conditions, and the impact of road construction on traffic flow; The behavior pattern recognition unit builds a user behavior database, identifies the user's behavior pattern through a neural network model, issues early warnings for traffic violations based on the identified behavior pattern and the user behavior database, and analyzes the user's driving habits to provide personalized safe driving suggestions; The anomaly detection unit combines sensor data to quickly detect traffic accidents, road obstructions, and abnormal parking-related traffic anomalies, and classifies and prioritizes traffic anomalies, and handles them through emergency plans in conjunction with emergency management departments.

6. The traffic data collection and real-time road condition analysis system based on big data as claimed in claim 1, characterized in that: The road condition assessment and information release module includes: The road condition assessment unit assesses the current traffic conditions in combination with the predicted traffic flow, and then classifies the road conditions according to the assessment results; The information release unit pushes real-time traffic information to users and relevant departments through API interfaces, mobile applications, websites or in-vehicle navigation systems.

7. A method for traffic data collection and real-time road condition analysis based on big data, characterized in that: The steps include: S1. Collect traffic data through various sensor devices; S2. Clean the traffic data from different sources and convert them into a unified format, then conduct a preliminary analysis on the cleaned and converted traffic data, extract traffic characteristics, and integrate the traffic data with related data to obtain preliminary road conditions; S3. Integrate and process the traffic data and preliminary routes in real time, publish the traffic conditions through real-time channels, predict the traffic flow through the self-correcting prediction model, and give travel suggestions based on the user's travel information; S4. Analyze traffic flow under different conditions, identify and warn of illegal traffic behaviors, identify abnormal traffic events and handle them according to emergency plans; S5. Evaluate and grade the road conditions, and then publish the road conditions.

8. The method for collecting traffic data and analyzing real-time road conditions based on big data as claimed in claim 7, characterized in that: In step S1, vehicle flow, speed, driving direction and public transportation information are collected through GPS, camera and vehicle speed sensor as sensing devices; The specific steps of step S2 are as follows: S21. Perform preliminary processing on the collected traffic data, identify erroneous data by checking the data value range and format verification, delete or correct the erroneous data, perform null value checks on the timestamp and vehicle ID fields, and remove null value data; The missing time series traffic data is supplemented by the time difference method, and the missing traffic data of each category is filled by the classification prediction model; the Z-score method is used for outlier detection, and the detected outliers are checked. If confirmed as outliers, they are deleted, and if they are identified incorrectly, they are restored; S22. Unify the timestamp, unit and data type of the pre-processed traffic data and convert it into a unified data format; S23. Perform time series analysis on traffic data after preprocessing and format unification, extract the changing trend of traffic flow, and use clustering algorithm to identify traffic hotspots and traffic bottlenecks; extract peak hours, average speed and congestion level as traffic features, and visualize the extracted traffic features using heat maps; integrate traffic data with weather data and historical traffic data to obtain preliminary road conditions; The specific steps of step S3 are as follows: S31. Real-time acquisition of cleaned and converted traffic data, preliminary analysis results and preliminary road conditions; S32. Release traffic conditions through real-time channels; S33. The overall traffic flow, the traffic flow of the pre-calibrated road section, and the traffic flow of the pre-calibrated time period are predicted by the self-correcting prediction model; S34. Obtain the user's departure place, destination, and departure time, and give the optimal driving route suggestion based on real-time traffic conditions and predicted traffic flow; The specific steps of step S4 are as follows: S41. Analyze the changes in traffic flow at different times and under different weather conditions, and analyze the impact of road construction on traffic flow; S42. Establish a user behavior database, identify the user's behavior pattern through a neural network model, issue warnings for traffic violations based on the identified behavior pattern and the user behavior database, and analyze the user's driving habits to provide personalized safe driving suggestions; S43. Rapidly detect traffic accidents, road obstructions, and abnormal parking-related traffic abnormalities by combining sensor data, and classify and prioritize traffic abnormalities, and handle them through emergency plans in conjunction with emergency management departments; The specific steps of step S5 are as follows: S51. Evaluate the current traffic conditions in combination with the predicted traffic flow, and then classify the traffic conditions according to the evaluation results; S52. Push real-time traffic information to users and relevant departments through API interfaces, mobile applications, websites or in-vehicle navigation systems.

9. An electronic device, characterized in that: including a processor and a memory; The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the device executes the method described in any one of claims 7 to 8.

10. A storage medium, characterized in that: The storage medium stores instructions, which, when executed on a computer, enable the computer to execute the method described in any one of claims 7 to 8.

Images