Intelligent traffic data fusion analysis system

Abstract

The invention provides an intelligent traffic data fusion analysis system. The intelligent traffic data fusion analysis system comprises a data acquisition module, a data fusion module and a data analysis module, the data acquisition module is configured to obtain traffic data of an intersection to be detected; the data fusion module is configured to fuse the traffic data, generate target traffic data and store the target traffic data; the data analysis module is configured to obtain a vehicle occupancy rate of the intersection to be detected based on the target traffic data; and based on the vehicle occupancy, adjusting the green light time of the to-be-detected intersection, so that the vehicle occupancy is reduced, thereby solving the problem that data of various detectors in a current traffic intersection cannot be fused and matched for use due to incompatibility of interfaces or protocols of signal machines of different brands; and the current traffic management system cannot adjust the traffic light duration of the intersection signal lamp according to the intersection congestion condition, and the urban traffic cannot be optimized fundamentally.

Description

Technical Field

[0001] This application relates to the field of data analysis technology, and in particular to an intelligent transportation data fusion and analysis system. Background Technology

[0002] With the acceleration of urbanization and the rapid growth of car ownership, urban traffic management is facing unprecedented challenges. Traditional traffic management systems often rely on multiple detectors to obtain traffic flow data.

[0003] However, the data from various detectors at traffic intersections cannot be integrated and used together due to incompatibility of interfaces or protocols between different brands of traffic signal controllers. For example, the currently popular millimeter-wave radar and lidar detection cannot interface with older traffic signal controllers, or with foreign traffic signal controllers like SCASS (which only support on / off signals). Furthermore, current traffic management systems cannot adjust the red and green light durations at intersections based on congestion levels, thus failing to fundamentally optimize urban traffic. Summary of the Invention

[0004] This application provides an intelligent traffic data fusion and analysis system to solve the technical problems of the current traffic management system, which cannot integrate and use the data of various detectors at traffic intersections due to the incompatibility of interfaces or protocols of different brands of traffic signal controllers. Furthermore, the current traffic management system cannot adjust the red and green light durations of traffic lights at intersections according to the congestion situation, thus failing to fundamentally optimize urban traffic.

[0005] This application provides an intelligent transportation data fusion and analysis system, including:

[0006] The system includes a data acquisition module, a data fusion module, and a data analysis module; the data fusion module is communicatively connected to the data acquisition module and the data analysis module.

[0007] The data acquisition module is configured as follows:

[0008] Obtain traffic data for the intersection to be tested;

[0009] The data fusion module is configured as follows:

[0010] The traffic data is fused to generate target traffic data and stored.

[0011] The data analysis module is configured as follows:

[0012] Based on the target traffic data, obtain the vehicle occupancy rate of the intersection to be tested;

[0013] Based on the vehicle occupancy rate, the green light time at the intersection under test is adjusted to reduce the vehicle occupancy rate.

[0014] In some embodiments, the traffic data includes: vehicle occupancy rate, vehicle arrival rate, vehicle dissipation rate, average vehicle speed, and headway.

[0015] In some embodiments, the system further includes:

[0016] A preprocessing module, which is communicatively connected to the data acquisition module and the data fusion module, is configured to:

[0017] The traffic data is cleaned to remove abnormal data and redundant information.

[0018] The traffic data format is standardized.

[0019] In some embodiments, the data fusion module is further configured to:

[0020] Obtain the baseline values ​​of each parameter in the traffic data;

[0021] Based on the aforementioned benchmark values, the target traffic data is generated and stored through fusion.

[0022] In some embodiments, the data analysis module is further configured to:

[0023] Based on the vehicle occupancy rate, it is determined whether the vehicle occupancy rate exceeds the vehicle occupancy rate threshold. If so, the green light time of the intersection to be tested is increased within a preset period.

[0024] In some embodiments, after the step of determining whether the vehicle occupancy rate exceeds the vehicle occupancy rate threshold, the method further includes:

[0025] If not, determine whether the vehicle occupancy rate is 0; if so, reduce the green light time of the intersection under test.

[0026] If not, it will operate according to the preset green light time within the preset cycle.

[0027] In some embodiments, the system further includes:

[0028] The sending module is communicatively connected to the data fusion module, and is configured to:

[0029] The target traffic data is received and sent to a designated device; the designated device is an electronic device capable of receiving information.

[0030] In some embodiments, the system further includes:

[0031] A data display module, which is communicatively connected to the data fusion module, is configured as follows:

[0032] Based on the target traffic data, obtain the speed, distance, and angle of vehicles at the intersection to be tested;

[0033] Based on the speed, distance, and angle of the vehicles at the intersection to be tested, a two-dimensional map of the intersection is generated and displayed.

[0034] In some embodiments, the data display module is further configured to:

[0035] Based on the target traffic data, obtain the vehicle type, outline size, position, direction angle, and object information of the vehicles at the intersection to be tested;

[0036] Based on the vehicle type, outline dimensions, position, direction angle, and object information of the vehicles at the intersection to be tested, a 3D map of the intersection to be tested is generated and displayed.

[0037] In some embodiments, the data acquisition module supports multiple data acquisition interfaces, serial ports, networks, and switch signals, and supports integration with multiple data formats and protocols.

[0038] This application provides an intelligent traffic data fusion and analysis system, including: a data acquisition module, a data fusion module, and a data analysis module; the data fusion module is communicatively connected to the data acquisition module and the data analysis module; the data acquisition module is configured to: acquire traffic data of the intersection to be tested; the data fusion module is configured to: fuse the traffic data to generate target traffic data and store it; the data analysis module is configured to: obtain the vehicle occupancy rate of the intersection to be tested based on the target traffic data; and adjust the green light time of the intersection to be tested based on the vehicle occupancy rate to reduce the vehicle occupancy rate. This addresses the current problem that data from various detectors at traffic intersections cannot be fused and used together due to incompatibility of interfaces or protocols between different brands of traffic signal controllers, and that current traffic management systems cannot adjust the red and green light durations of traffic lights at intersections according to traffic congestion, thus failing to fundamentally optimize urban traffic problems. Attached Figure Description

[0039] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the intelligent transportation data fusion and analysis system in this application;

[0041] Figure 2 This is a flowchart of the intelligent transportation data fusion and analysis method in this application;

[0042] Figure 3 This is a schematic diagram of a two-dimensional view of the intersection to be tested in this application under one embodiment.

[0043] Figure 4 This is a schematic diagram of another embodiment of the two-dimensional diagram of the intersection to be tested in this application.

[0044] Figure 5 This is a schematic diagram of the three-dimensional view of the intersection to be tested in this application.

[0045] Explanation of reference numerals in the attached figures:

[0046] 1-Data acquisition module; 2-Data fusion module; 3-Data analysis module; 4-Preprocessing module; 5-Sending module; 6-Data display module. Detailed Implementation

[0047] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of this application.

[0048] Because data from various detectors at traffic intersections cannot be integrated and used together due to incompatibility of interfaces or protocols between different brands of traffic signal controllers in some technologies, and because current traffic management systems cannot adjust the red and green light durations of traffic lights according to intersection congestion, they cannot fundamentally optimize urban traffic. To solve this technical problem, this application provides an intelligent traffic data fusion and analysis system. The structure of each part of the intelligent traffic data fusion and analysis system is described below:

[0049] For example, intelligent traffic intersection equipment plays a crucial role in current urban traffic management. However, with continuous technological development and increasing traffic demands, these devices also face numerous challenges. One significant problem is that some traffic signal controllers cannot effectively interface and fuse data. Traffic signal controllers are important nodes in the traffic system, responsible for controlling traffic flow, regulating traffic signals, and interacting with other traffic equipment. Currently, traffic signal controllers only have a single interface or communication protocol, making them difficult to be compatible with other types of monitoring equipment. In modern intelligent transportation systems, various monitoring devices are needed to collect real-time data at intersections, such as cameras, sensors, and radar. These devices can provide rich information about traffic flow, vehicle speed, vehicle type, etc. However, if the interface of the traffic signal controller is too limited or does not support the communication protocols of these devices, data fusion and analysis cannot be achieved. Data fusion is a crucial aspect of intelligent transportation systems. By fusing data from different sources, more comprehensive and accurate traffic information can be obtained, leading to more scientific and rational traffic management decisions. However, if traffic signal controllers cannot fuse data, information silos will emerge, making traffic management one-sided and inefficient. Furthermore, real-time performance is also a crucial characteristic of intelligent transportation systems. Traffic flow changes dynamically and rapidly, necessitating the real-time acquisition and analysis of traffic information. If traffic signals cannot interface and integrate data with other devices in real time, this requirement cannot be met, thus impacting the effectiveness of traffic management.

[0050] Depend on Figure 1As can be seen, to address the aforementioned problems, this application provides an intelligent traffic data fusion and analysis system, comprising: a data acquisition module 1, a data fusion module 2, and a data analysis module 3; the data fusion module 2 is communicatively connected to the data acquisition module 1 and the data analysis module 3; the data acquisition module 1 is configured to: acquire traffic data of the intersection to be tested; the traffic data includes: vehicle occupancy rate, vehicle arrival rate, vehicle dissipation rate, average vehicle speed, and headway; the data fusion module 2 is configured to: fuse the traffic data to generate target traffic data and store it; since an intersection may have multiple detectors, and different detectors may acquire the same data, it is necessary to fuse the traffic data, select the more accurate data to retain, and delete the identical data to generate target traffic data for subsequent analysis; the data analysis module 3 is configured to: acquire the vehicle occupancy rate of the intersection to be tested based on the target traffic data; and adjust the green light time of the intersection to be tested based on the vehicle occupancy rate to reduce the vehicle occupancy rate. The vehicle occupancy rate represents the density and number of vehicles at the intersection under test. A high vehicle occupancy rate indicates that there are many vehicles at the intersection under test, which is a congested state. The green light time of the intersection under test can be adjusted based on the vehicle occupancy rate. For example, if the vehicle occupancy rate is high, the green light time of the intersection under test will be increased, so that vehicles can pass through the intersection quickly and avoid vehicles from staying at the intersection for a long time, which would cause congestion.

[0051] This application provides an intelligent traffic data fusion and analysis system. This system not only overcomes key challenges encountered in current traffic management, such as the inability to fuse data from multiple detectors and incompatibility issues with interfaces or protocols of different brands of traffic signal controllers, but also addresses the inability of popular millimeter-wave radar and lidar monitoring to interface with older traffic signal controller models, as well as compatibility issues with specific foreign brands such as SCATS traffic signal controllers (which only support on / off signals). The intelligent traffic data fusion and analysis system provides a novel solution for urban traffic management, significantly improving the level of intelligence. In current urban traffic networks, various monitoring devices, such as millimeter-wave radar and lidar, can capture real-time dynamic information of vehicles, pedestrians, and non-motorized vehicles. However, due to technological differences and compatibility issues, this valuable data is often not effectively utilized. The intelligent traffic data fusion and analysis system proposed in this application, through the adoption of advanced data fusion technology, achieves efficient integration and in-depth analysis of traffic flow data from multiple platforms. This system can comprehensively integrate data from various monitoring devices to monitor vehicle dynamics, pedestrian status, and non-motorized vehicle status within the channelized sections of intersections in real time, providing comprehensive data support for traffic management. By monitoring, predicting trends, and intelligently scheduling these data in real time, the system can accurately identify traffic congestion points, formulate traffic management plans in advance, and effectively alleviate traffic pressure. Furthermore, the system boasts high flexibility and scalability. It supports multiple communication protocols and interface standards, easily connecting to traffic monitoring equipment and signal controllers of different brands and models, achieving true "plug and play." Simultaneously, the system supports cloud deployment and remote management, allowing users to view and operate data anytime, anywhere. In summary, the intelligent traffic data fusion and analysis system provided in this application not only solves the technical challenges currently existing in traffic management but also brings revolutionary changes to urban traffic management by improving the intelligence level of data integration and analysis, thereby enhancing urban traffic efficiency, alleviating congestion, and optimizing the travel experience.

[0052] Depend on Figure 1 It is understood that the system further includes a preprocessing module 4, which is communicatively connected to the data acquisition module 1 and the data fusion module 2. The preprocessing module 4 is configured to: clean the traffic data, removing abnormal data and redundant information; and standardize the traffic data format. It is understood that the data acquisition module 1 can collect data from different types of detectors. Because the detector models are different, the acquired data protocol formats are different. Therefore, in order to standardize the data protocol format, the preprocessing module 4 changes the data protocol format to the internally defined format of the intelligent traffic data fusion analysis system according to the data content, so that the intelligent traffic data fusion analysis system can read the data acquired by the data acquisition module 1.

[0053] For example, the data acquired by the data acquisition module 1 often contains various outliers, duplicate information, incorrect inputs, unnecessary fields, or inconsistent formatting. These factors can all negatively impact the results of data analysis. To ensure data accuracy and consistency, the data cleaning process needs to be meticulous and rigorous. First, outliers need to be identified and removed. Outliers may be caused by equipment malfunctions, human error, or oversights during data entry; they may manifest as extreme values, missing values, or outliers that are clearly inconsistent with other data. If these outliers are not processed, they may mislead the analysis process. Therefore, it is necessary to identify and remove these outliers by setting reasonable thresholds, using statistical methods, or combining domain knowledge. Second, redundant information needs to be removed. During data acquisition, some information irrelevant to the analysis objective or duplicated may be included. This information not only increases the complexity of data processing but may also interfere with the analysis results. Therefore, it is necessary to carefully examine each field and record in the dataset and delete information irrelevant to the analysis objective or duplicated. Furthermore, data cleaning also needs to focus on data consistency and format standardization. In practical applications, due to diverse data sources, different data entry personnel, or system differences, data may exhibit inconsistencies in format, units, or naming conventions. These issues affect data readability and analyzability. Therefore, data formatting is necessary to standardize data formats, units, and naming conventions, ensuring data consistency and comparability. By removing outliers and redundant information, ensuring data consistency and format standardization, and paying attention to data integrity and security, more accurate, reliable, and valuable traffic data can be obtained, providing strong support for subsequent data analysis.

[0054] In this embodiment, the data fusion module 2 is further configured to: acquire baseline values ​​for each parameter in the traffic data; fuse the data based on the baseline values ​​to generate target traffic data and store it. The data to be fused includes average vehicle speed, headway, queue length, traffic flow statistics corresponding to lane numbers, lane occupancy, etc. Monitoring average vehicle speed and headway is crucial; since multiple identical data points may exist, it is necessary to fuse identical data in the traffic data. For example, data such as queue length obtained from millimeter-wave radar and lidar detectors are more accurate, so the data from these two devices is used as the baseline value. If multiple baseline values ​​exist, the target baseline value is obtained by averaging. Data such as traffic flow statistics corresponding to lane numbers and lane occupancy are more accurate from video detectors and magnetic induction coils, so the data from these two devices is used as the baseline value, with data from lidar and millimeter-wave radar, etc., as supplementary data. When the road environment is complex, such as when there are too many non-motorized vehicles or metal guardrails in the middle of the road, the traffic flow data for these lanes needs to be fused with data from multiple detection methods to eliminate the impact of the above situations on monitoring equipment such as millimeter-wave radar and ensure the accuracy of the data.

[0055] In this embodiment, the data analysis module 3 is further configured to: determine whether the vehicle occupancy rate exceeds a vehicle occupancy rate threshold based on the vehicle occupancy rate; if so, increase the green light time of the intersection under test within a preset period. The presence or absence of a vehicle occupancy rate threshold determines whether the intersection is congested. If the vehicle occupancy rate is greater than the threshold, the intersection is considered congested. By increasing the green light time within a preset period, vehicles at the intersection have more time to pass. The preset period is the total time from when the traffic light turns red, yellow, and green once until it turns off. Through multiple iterations of data and real-time analysis of the road surface condition, the system finds that the vehicle dissipation pattern at the intersection matches the vehicle arrival pattern, thus achieving an optimal green light signal time setting.

[0056] In this embodiment, after the step of determining whether the vehicle occupancy rate exceeds the vehicle occupancy rate threshold, the method further includes: if not, determining whether the vehicle occupancy rate is 0; if yes, reducing the green light time of the intersection to be tested; if no, operating according to the preset green light time within a preset cycle. A vehicle occupancy rate of 0 means there are no vehicles at the intersection to be tested, so the green light time of the intersection to be tested is reduced in real time, thereby increasing traffic efficiency; if the vehicle occupancy rate is not 0, it means there are vehicles at the intersection to be tested but it has not reached a congested state, so the traffic lights can operate according to the preset green light time within a preset cycle.

[0057] Depend on Figure 1It is understood that the system further includes a sending module 5, which is communicatively connected to the data fusion module 2. The sending module 5 is configured to receive the target traffic data and send it to a designated device; the designated device is an electronic device capable of receiving information. Through the sending module 5, the target traffic data can be sent to the designated device in real time, allowing users to understand the road conditions in real time and take action based on these conditions, such as dispatching traffic police to congested sections to alleviate traffic, and adjusting the green light time in conjunction with the analysis module 3 to disperse vehicles at the intersection under test.

[0058] Depend on Figure 1 It is understood that the system further includes a data display module 6, which is communicatively connected to the data fusion module 2. The data display module 6 is configured to: acquire the speed, distance, and angle of vehicles at the intersection to be tested based on the target traffic data; and generate and display a two-dimensional map of the intersection based on the speed, distance, and angle of the vehicles at the intersection to be tested. By using millimeter-wave radar with multi-target radar technology, the speed, distance, and angle of multiple vehicles at the intersection to be tested are monitored to obtain a two-dimensional map of the intersection to be tested, such as... Figure 3 As shown, the fan-shaped area represents the detection range of the millimeter-wave radar, and the squares represent the speeds of vehicles. If an intelligent traffic data fusion and analysis system integrates multiple millimeter-wave radars, it can fuse the two-dimensional images from multiple different locations according to direction into a complete two-dimensional image of the intersection under test. Figure 4 As shown in the figure. The density and speed of vehicles at each intersection can be obtained through the two-dimensional map of the intersection under test.

[0059] In this embodiment, the data display module 6 is further configured to: acquire the vehicle type, outline size, position, direction angle, and object information of vehicles at the intersection to be tested based on the target traffic data; and generate and display a 3D map of the intersection based on the vehicle type, outline size, position, direction angle, and object information of the vehicles at the intersection to be tested. The point cloud data of vehicles at the intersection to be tested is collected by LiDAR to identify the vehicle type, outline size, position, direction angle, and object information. After the intelligent traffic data fusion analysis system accesses the point cloud data, it refers to the point cloud image of the LiDAR to display the intersection status completely in 3D, such as... Figure 5 The 3D map of the intersection under test allows for a more intuitive observation of the traffic conditions, avoiding the impact on traffic data caused by complex road conditions such as excessive non-motorized vehicles or metal guardrails in the middle of the road.

[0060] In this embodiment, the data acquisition module 1 supports multiple data acquisition interfaces, serial ports, networks, and digital inputs, as well as multiple data formats and protocols. By supporting multiple data acquisition interfaces, serial ports, networks, digital inputs, and multiple data formats and protocols, the flexibility and adaptability of the system are greatly enhanced, enabling the module to be widely applied in various complex scenarios and needs. Specifically, the functions of the data acquisition module 1 are mainly reflected in the following aspects: Wide adaptability: By supporting multiple data acquisition interfaces (such as serial ports, networks, etc.) and multiple data formats (such as text, binary, XML, JSON, etc.), the data acquisition module 1 can easily interface with various devices and systems, achieving efficient data exchange and transmission for both traditional hardware devices and modern network services. Diverse data source access: In addition to supporting different interfaces and formats, the data acquisition module 1 also supports different data types such as digital inputs, meaning that the data acquisition module 1 can process signals from various detectors, realizing the access and integration of multiple data sources. Efficient protocol integration: The data acquisition module 1 provides excellent support for different communication protocols, including common protocols such as Modbus, TCP / IP, and HTTP, as well as proprietary protocols used by specific industries or devices. The data acquisition module 1 can quickly and accurately integrate with these protocols to ensure the real-time performance and accuracy of traffic data.

[0061] This application provides an intelligent transportation data fusion and analysis system, such as Figure 2 The diagram shows a flowchart of the intelligent traffic data fusion and analysis system in operation. Traffic data is collected by the data acquisition module 1 by detection devices such as millimeter-wave radar or lidar, which tracks the dynamics of all vehicles entering the intersection under test. The traffic data for the intersection is then obtained by the data fusion module 2, which performs protocol fusion to supplement the traffic data of the intersection under test, resulting in target traffic data. The target traffic data is used for real-time traffic analysis of the intersection under test. If the intersection is congested, the green light duration is increased; if the intersection is in a normal state, the green light duration is increased or decreased in real-time based on the presence of vehicles. Simultaneously, because millimeter-wave radar and lidar detection technologies can provide accurate vehicle information and three-dimensional coordinates, the fused data can generate three-dimensional point cloud data to display the road conditions. Figure 5 As shown, this allows users to more intuitively observe the current road traffic conditions and make real-time evaluations of the current optimization results.

[0062] This application provides an intelligent traffic data fusion and analysis system that can uniformly process, fuse, and directly analyze traffic data. The fusion and comparison of traffic data improves data accuracy and real-time performance. The data acquisition module 1 can interface with mainstream detectors currently on the market and can interface with multiple detectors simultaneously. The data display module 6, in conjunction with millimeter-wave radar or lidar, can directly present the real-time status of the intersection.

[0063] The above detailed embodiments further illustrate the purpose, technical solution, and beneficial effects of the embodiments of this application. It should be understood that the above are merely specific embodiments of the embodiments of this application and are not intended to limit the protection scope of the embodiments of this application. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solutions of the embodiments of this application should be included within the protection scope of the embodiments of this application.

Claims

1. An intelligent transportation data fusion and analysis system, characterized in that, include: The system includes a data acquisition module (1), a data fusion module (2), and a data analysis module (3); the data fusion module (2) is communicatively connected to the data acquisition module (1) and the data analysis module (3). The data acquisition module (1) is configured as follows: Obtain traffic data for the intersection to be tested; The data fusion module (2) is configured as follows: The traffic data is fused to generate target traffic data and stored. The data analysis module (3) is configured as follows: Based on the target traffic data, obtain the vehicle occupancy rate of the intersection to be tested; Based on the vehicle occupancy rate, the green light time at the intersection under test is adjusted to reduce the vehicle occupancy rate.

2. The intelligent transportation data fusion and analysis system according to claim 1, characterized in that, The traffic data includes: vehicle occupancy rate, vehicle arrival rate, vehicle dispersal rate, average vehicle speed, and headway.

3. The intelligent transportation data fusion and analysis system according to claim 1, characterized in that, The system also includes: The preprocessing module (4) is communicatively connected to the data acquisition module (1) and the data fusion module (2), and the preprocessing module (4) is configured as follows: The traffic data is cleaned to remove abnormal data and redundant information. The traffic data format is standardized.

4. The intelligent transportation data fusion and analysis system according to claim 1, characterized in that, The data fusion module (2) is also configured to: Obtain the baseline values ​​of each parameter in the traffic data; Based on the aforementioned benchmark values, the target traffic data is generated and stored through fusion.

5. The intelligent transportation data fusion and analysis system according to claim 1, characterized in that, The data analysis module (3) is also configured as follows: Based on the vehicle occupancy rate, it is determined whether the vehicle occupancy rate exceeds the vehicle occupancy rate threshold. If so, the green light time of the intersection to be tested is increased within a preset period.

6. The intelligent transportation data fusion and analysis system according to claim 5, characterized in that, After the step of determining whether the vehicle occupancy rate exceeds the vehicle occupancy rate threshold, the method further includes: If not, determine whether the vehicle occupancy rate is 0; if so, reduce the green light time of the intersection under test. If not, it will operate according to the preset green light time within the preset cycle.

7. The intelligent transportation data fusion and analysis system according to claim 1, characterized in that, The system also includes: The sending module (5) is communicatively connected to the data fusion module (2), and the sending module (5) is configured as follows: The target traffic data is received and sent to a designated device; the designated device is an electronic device capable of receiving information.

8. The intelligent transportation data fusion and analysis system according to claim 1, characterized in that, The system also includes: A data display module (6) is communicatively connected to the data fusion module (2), and the data display module (6) is configured as follows: Based on the target traffic data, obtain the speed, distance, and angle of vehicles at the intersection to be tested; Based on the speed, distance, and angle of the vehicles at the intersection to be tested, a two-dimensional map of the intersection is generated and displayed.

9. The intelligent transportation data fusion and analysis system according to claim 8, characterized in that, The data display module (6) is also configured as follows: Based on the target traffic data, obtain the vehicle type, outline size, position, direction angle, and object information of the vehicles at the intersection to be tested; Based on the vehicle type, outline dimensions, position, direction angle, and object information of the vehicles at the intersection to be tested, a 3D map of the intersection to be tested is generated and displayed.

10. The intelligent transportation data fusion and analysis system according to claim 1, characterized in that, The data acquisition module (1) supports multiple data acquisition interfaces, serial ports, networks, and switch signals, and supports multiple data formats and protocol interfaces.

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