Toll station ramp control method and device based on artificial intelligence and medium

Abstract

The invention discloses a toll station ramp control method and device based on artificial intelligence, and a medium, and the method comprises the steps: collecting a video stream and sensor data of a to-be-controlled road segment, and carrying out the pre-processing of the video stream to determine a key frame image; analyzing the key frame image in combination with sensor data, determining traffic data and traffic influence factor values corresponding to the to-be-controlled road section, and inputting the traffic data and the traffic influence factor values into a trained traffic flow prediction model to output a pre-estimated traffic condition corresponding to a preset time period; generating a recommended planning path corresponding to the driving vehicle based on the vehicle driving demand and the estimated traffic condition which are acquired in real time, and optimizing the estimated traffic condition based on the confirmation condition of the driving vehicle on the corresponding recommended planning path; and determining a congestion level corresponding to the optimized pre-estimated traffic condition, and determining a ramp management and control strategy corresponding to the congestion level based on a preset ramp management and control library so as to perform ramp management and control on the toll station according to the ramp management and control strategy.

Description

Technical Field

[0001] The present application relates to the field of traffic control technology, and in particular to a toll station ramp control method, device and medium based on artificial intelligence. Background Art

[0002] With the continuous growth of the global economy and the acceleration of urbanization, highways, as important infrastructure connecting major cities and economic regions, have continued to increase in size and complexity, becoming an indispensable traffic artery in modern society. The rapid development of highways has not only greatly promoted the flow of people and materials, but also put forward higher requirements for traffic management and services. Toll stations, as key nodes and transportation hubs in the highway network, undertake multiple functions such as vehicle toll collection, traffic flow control and safety supervision. Their operating efficiency and management level directly affect the smoothness of the entire road network and user experience.

[0003] Traditional toll station ramp management methods mostly rely on manual toll collection, semi-automatic toll collection systems and basic video surveillance. Under this model, the real-time monitoring of traffic flow, rapid identification of vehicle types and emergency response capabilities for special traffic conditions (such as accidents and bad weather) are relatively limited. Manual operations are easily affected by factors such as fatigue and emotions, resulting in low efficiency; and simple monitoring systems often lack intelligent analysis functions, making it difficult to accurately assess traffic conditions in real time and make corresponding adjustments. The combined effect of these factors can easily lead to traffic congestion and extend vehicle travel time, which not only wastes time and resources, but may also increase the risk of traffic accidents due to vehicle backlogs, affecting road safety and public travel experience.

[0004] In recent years, with the rapid development of advanced technologies such as artificial intelligence, big data, and cloud computing, intelligent transportation systems (ITS) have become a new way to solve modern traffic management problems. The application of artificial intelligence technology in the field of traffic management, such as intelligent traffic signal control, vehicle automatic driving, and road condition prediction, has shown remarkable results and improved the operating efficiency and safety of the traffic system. However, for the specific scenario of highway toll station ramps, although there have been some preliminary explorations, such as automatic license plate recognition and the promotion of ETC systems, there is still a lack of a comprehensive and efficient intelligent management and control solution as a whole. Summary of the invention

[0005] The embodiments of the present application provide a toll station ramp control method, device and medium based on artificial intelligence to solve the above-mentioned technical problems.

[0006] On the one hand, an embodiment of the present application provides a toll station ramp control method based on artificial intelligence, including:

[0007] Based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is pre-processed to determine the key frame image;

[0008] Analyze the key frame image in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, and input the traffic data and the traffic influencing factor values ​​into a trained traffic flow prediction model to output an estimated traffic condition corresponding to a preset time period;

[0009] Based on the real-time acquired vehicle driving demand and the estimated traffic conditions, a recommended planned path for the corresponding driving vehicle is generated, and based on the confirmation status of the driving vehicle on the corresponding recommended planned path, the estimated traffic conditions are optimized;

[0010] Determine the congestion level corresponding to the optimized estimated traffic condition, and determine the ramp control strategy corresponding to the congestion level based on a preset ramp control library, so as to perform ramp control on the toll station according to the ramp control strategy.

[0011] In one implementation of the present application, based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is preprocessed to determine the key frame image, specifically including:

[0012] Through the preset cameras and sensors of the road section to be controlled, the video stream and sensor data corresponding to the road section to be controlled are collected in real time, and the video stream and sensor data uploaded by the cameras and the sensors are received;

[0013] The video stream is analyzed to identify the latter image of two adjacent images with inconsistent information between the previous and the next images in the video stream, so as to determine the key frame image corresponding to the video stream.

[0014] In one implementation of the present application, the key frame image is analyzed in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, specifically including:

[0015] Obtaining the historical ramp control records corresponding to the road section to be controlled, and determining the traffic influencing factors corresponding to the road section to be controlled based on the historical ramp control records; wherein the traffic influencing factors include time periods, holidays, and weather, and the time periods include peak periods, trough periods, and regular periods;

[0016] Segmenting the key frame image to determine the moving vehicle in the key frame image, and identifying the vehicle model, vehicle color and license plate number corresponding to the moving vehicle;

[0017] Determine the speed of the traveling vehicle according to the sensor data corresponding to the traveling vehicle on the adjacent road section of the road section to be controlled, so as to obtain the traffic data corresponding to the road section to be controlled;

[0018] The data collection time corresponding to the traffic data is obtained, so as to determine the traffic influencing factor value corresponding to the road section to be controlled according to the data collection time and in combination with the sensor data.

[0019] In one implementation of the present application, before inputting the traffic data and the traffic influencing factor values ​​into a trained traffic flow prediction model to output an estimated traffic condition corresponding to a preset time period, the method further includes:

[0020] Based on the deep learning algorithm, a traffic flow prediction model corresponding to the road section to be controlled is constructed;

[0021] Obtain the historical traffic data corresponding to the road section to be controlled and the historical traffic influencing factor value corresponding to the historical traffic data, and mark the traffic conditions of the next time period corresponding to the historical traffic data and the historical traffic influencing factor value;

[0022] The historical traffic data and the historical traffic influencing factor values ​​are input into the traffic flow prediction model, and the corresponding estimated traffic conditions are output until the estimated traffic conditions match the marked traffic conditions for the next time period, thereby completing the training of the traffic flow prediction model.

[0023] In one implementation of the present application, the traffic data and the traffic influencing factor values ​​are input into a trained traffic flow prediction model to output an estimated traffic condition corresponding to a preset time period, specifically including:

[0024] Determine whether the data format corresponding to the traffic data and the traffic influencing factor value is consistent with the input format corresponding to the traffic flow prediction model, and if not, convert the traffic data and the traffic influencing factor value into the input format;

[0025] Input the traffic data and the traffic influencing factor values ​​in the input format into the trained traffic flow prediction model, and output the estimated traffic conditions corresponding to the road section to be controlled within a preset time period;

[0026] The estimated traffic condition is verified, and if the verification fails, the estimated traffic condition is corrected based on a preset expert database to obtain a target estimated traffic condition.

[0027] In one implementation of the present application, based on the real-time acquired vehicle driving demand and the estimated traffic conditions, a recommended planning path for the corresponding driving vehicle is generated, specifically including:

[0028] Determine whether there is a potential risk in the road section to be controlled according to the estimated traffic conditions, and if so, obtain driving demand information corresponding to vehicles traveling in the road section to be controlled; wherein the driving demand information includes the destination, expected arrival time, and preferred route;

[0029] Based on the driving demand information and in combination with the estimated traffic conditions, generating at least one corresponding recommended planning route for the driving vehicle; wherein the recommended planning route includes an estimated duration, an estimated cost, and congestion conditions;

[0030] The recommended planning path is sent to the corresponding traveling vehicle through wireless communication for the vehicle occupants to select and confirm.

[0031] In one implementation of the present application, based on the confirmation status of the driving vehicle on the corresponding recommended planned path, optimizing the estimated traffic condition specifically includes:

[0032] Receive confirmation of the corresponding recommended planned path from all vehicles in the road section to be controlled, and dynamically update the traffic influencing factor value corresponding to the road section to be controlled based on the feedback data;

[0033] According to the feedback data and in combination with the updated traffic influencing factor values, the model parameters of the traffic flow prediction model are iteratively optimized to generate a target estimated traffic condition through the optimized traffic flow prediction model.

[0034] In one implementation of the present application, the congestion level corresponding to the estimated traffic condition after optimization is determined, and based on a preset ramp control library, a ramp control strategy corresponding to the congestion level is determined, specifically including:

[0035] Based on the preset congestion level classification standard and according to the optimized estimated traffic conditions, determine the congestion level corresponding to the road section to be controlled;

[0036] According to the congestion level, a corresponding ramp control strategy is determined in a preset ramp control library, and according to the traffic data collected in real time from the road section to be controlled, the actual situation corresponding to the road to be controlled is determined;

[0037] According to the actual situation, the ramp control strategy is adjusted, and a target ramp control strategy corresponding to the road section to be controlled is generated.

[0038] On the other hand, the embodiment of the present application also provides a toll station ramp control device based on artificial intelligence, the device comprising:

[0039] at least one processor;

[0040] and, a memory communicatively coupled to the at least one processor;

[0041] Among them, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned artificial intelligence-based toll station ramp control method.

[0042] On the other hand, an embodiment of the present application further provides a non-volatile computer storage medium storing computer executable instructions, which, when executed, implements a toll station ramp control method based on artificial intelligence as described above.

[0043] The embodiments of the present application provide a method, device and medium for toll station ramp control based on artificial intelligence, which at least have the following beneficial effects:

[0044] Through efficient roadside equipment collection and preprocessing technology, it is possible to obtain real-time and accurate traffic video and sensor data of the road section to be controlled, providing a solid foundation for subsequent traffic analysis and prediction; by integrating video and sensor data, it is possible to have a more comprehensive understanding of the traffic conditions of the road section to be controlled, including key indicators such as vehicle flow, speed, density, and influencing factors such as weather and road conditions; by inputting these data into the trained traffic flow prediction model, it is possible to more accurately predict the traffic conditions in the future and provide a basis for traffic management and decision-making; by obtaining vehicle driving needs in real time and combining them with the estimated traffic conditions, it is possible to provide personalized recommended planning routes for driving vehicles, which helps to reduce vehicle driving time and improve road traffic efficiency. Reduce the possibility of traffic congestion and accidents; by collecting the confirmation status of the recommended planned routes by the traveling vehicles, timely feedback and adjustment of the estimated traffic conditions can be made, which helps to improve the accuracy and practicality of traffic forecasts and better meet the actual traffic needs; by determining the congestion level of the optimized estimated traffic conditions, it can more accurately evaluate the current and future traffic conditions, and combined with the preset ramp control library, it can quickly formulate and implement corresponding ramp control strategies, effectively alleviate traffic congestion and improve road capacity; by implementing the ramp control strategy, it can reasonably regulate the vehicle flow entering the toll station and avoid excessive congestion at the toll station entrance, which helps to improve the traffic efficiency of the toll station, reduce vehicle waiting time, and improve the overall traffic smoothness and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0046] Figure 1A flowchart of a toll station ramp control method based on artificial intelligence provided in an embodiment of the present application;

[0047] Figure 2 A schematic diagram of the internal structure of an artificial intelligence-based toll station ramp control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0049] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0050] Figure 1 A flowchart of a toll station ramp control method based on artificial intelligence is provided in an embodiment of the present application.

[0051] The analysis method involved in the embodiments of the present application can be implemented by a terminal device or a server, and the present application does not impose any special restrictions on this. For the convenience of understanding and description, the following embodiments are described in detail by taking a server as an example.

[0052] It should be noted that the server may be a single device or a system consisting of multiple devices, that is, a distributed server, and this application does not make any specific limitation on this.

[0053] like Figure 1 As shown, an artificial intelligence-based toll station ramp control method provided in an embodiment of the present application includes:

[0054] 101. Based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is pre-processed to determine the key frame image.

[0055] Specifically, in one embodiment of the present application, based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is preprocessed to determine the key frame image, which specifically includes:

[0056] Through the preset cameras and sensors of the road section to be controlled, the video stream and sensor data corresponding to the road section to be controlled are collected in real time, and the video stream and sensor data uploaded by the cameras and sensors are received;

[0057] The video stream is analyzed to identify the latter image of two adjacent images whose preceding and following information are inconsistent in the video stream, so as to determine the key frame image corresponding to the video stream.

[0058] In one embodiment, hardware facilities such as cameras, radars, infrared sensors, and pressure sensors are deployed at the entrance and exit of the toll station ramp to achieve real-time collection of information such as vehicle type, speed, and traffic volume. A license plate recognition system is deployed in the toll station area to monitor vehicle information (license plate number, vehicle model, driving direction, etc.) entering and leaving the toll station. The collected data is transmitted to the backend server or cloud platform using the 5G high-speed communication network to ensure the real-time and integrity of the data.

[0059] The server cleans the video stream and sensor data, including: removing abnormal data, supplementing missing values, normalizing vehicle speed and traffic volume data, etc., to ensure the accuracy and consistency of the data, and using the random forest algorithm to predict missing values. Other non-missing features are used as input to predict missing values. At the same time, historical traffic data is also classified and marked according to peak hours, off-peak hours, holidays and other time periods for subsequent AI model training and prediction.

[0060] In one embodiment, in order to more effectively manage the highway sections to be controlled in a certain city, the traffic management department decided to adopt an advanced intelligent monitoring system. The system mainly includes multiple high-definition cameras and various sensors preset along the sections to be controlled, such as vehicle detectors, speed sensors, and meteorological sensors. These cameras and sensors are carefully arranged in key locations to collect video streams and various sensor data corresponding to the sections to be controlled in real time. The high-definition cameras are responsible for capturing real-time traffic images of the sections, while the sensors are responsible for collecting key information such as vehicle flow, speed, vehicle model distribution, weather, and road conditions. The collected video streams and sensor data are uploaded to the central monitoring center in real time via a wireless network to ensure the timeliness and accuracy of the data.

[0061] At the central monitoring center, a set of specialized video analysis software performs in-depth analysis of the received video stream. Using advanced image recognition algorithms, the software can automatically detect two adjacent images in the video stream that have inconsistent information. For example, when a vehicle suddenly changes lanes or stops, this change will produce images in the video stream that have inconsistent information. The software can accurately identify this change and mark the latter image as a key frame image. In this way, the system can automatically filter out the most important image frames in the video stream, providing key basis for subsequent traffic analysis and prediction.

[0062] The determined key frame images are used to further analyze traffic conditions. Combined with sensor data, the system can more accurately understand the real-time traffic conditions of the road section to be controlled, including vehicle behavior, traffic flow changes, and potential safety hazards. This information is used to generate real-time traffic reports and warnings, helping traffic management departments to take necessary measures in a timely manner, such as adjusting traffic signals, dispatching rescue vehicles, or implementing ramp control, to ensure road safety and smooth traffic.

[0063] 102. Analyze the key frame images in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, and input the traffic data and traffic influencing factor values ​​into the trained traffic flow prediction model to output the estimated traffic conditions corresponding to the preset time period.

[0064] Specifically, in one embodiment of the present application, the key frame image is analyzed in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, specifically including:

[0065] Obtain the historical ramp control records corresponding to the road section to be controlled, and determine the traffic influencing factors corresponding to the road section to be controlled based on the historical ramp control records; the traffic influencing factors include time periods, holidays, and weather, and the time periods include peak periods, off-peak periods, and regular periods;

[0066] Segment the key frame image to determine the moving vehicle in the key frame image, and identify the vehicle model, vehicle color and license plate number corresponding to the moving vehicle;

[0067] Determine the speed of the vehicle according to the sensor data of the vehicle traveling on the adjacent road section in the road section to be controlled, so as to obtain the traffic data corresponding to the road section to be controlled;

[0068] The data collection time corresponding to the traffic data is obtained, so as to determine the traffic influencing factor value corresponding to the road section to be controlled according to the data collection time and in combination with the sensor data.

[0069] In one embodiment, in order to more finely manage the sections of expressways to be controlled in a certain city, the traffic management department introduced an intelligent traffic analysis system. The system aims to provide a scientific basis for ramp control by analyzing historical data and real-time data. The system first obtains the historical ramp control records corresponding to the sections to be controlled. These records include detailed information such as the implementation time, reasons, and effects of ramp control in the past period of time. Based on these historical data, the system uses data mining technology to determine the main factors affecting the traffic of the sections to be controlled, including time periods (such as peak periods, trough periods, and regular periods), holidays, and weather conditions. These factors are used as an important reference for subsequent traffic analysis and forecasting.

[0070] The system receives keyframe images from preset cameras on the road section to be controlled. These images are automatically selected by the system based on video stream analysis and contain important traffic information. By segmenting the keyframe images, the system can accurately identify the moving vehicles in the images. Through further image recognition technology, the system can also determine the model, color and license plate number of each vehicle, providing detailed information for subsequent traffic data analysis.

[0071] The system not only analyzes the key frame images, but also combines the sensor data corresponding to the vehicles traveling on the adjacent sections of the road section to be controlled. These data include key information such as the vehicle's passing time and speed. By comparing the sensor data of adjacent sections, the system can accurately calculate the speed of the vehicle before entering the section to be controlled, thereby obtaining the traffic data corresponding to the section to be controlled. The system obtains the data collection time corresponding to the traffic data. This moment is the time point when the system records the data, which is used for subsequent analysis of changes in traffic conditions. Combining the data collection time and sensor data, the system can determine the traffic influencing factor values ​​of the section to be controlled at a specific time point. These values ​​reflect the extent to which the traffic conditions of the section are affected by factors such as time period, holidays and weather, and provide a scientific basis for ramp control decisions.

[0072] In one embodiment of the present application, before inputting the traffic data and the traffic influencing factor values ​​into the trained traffic flow prediction model to output the estimated traffic conditions corresponding to the preset time period, the method further includes:

[0073] Based on the deep learning algorithm, a traffic flow prediction model corresponding to the road section to be controlled is constructed;

[0074] Obtain the historical traffic data corresponding to the road section to be controlled and the historical traffic influencing factor values ​​corresponding to the historical traffic data, and mark the traffic conditions of the next period corresponding to the historical traffic data and the historical traffic influencing factor values;

[0075] The historical traffic data and historical traffic influencing factor values ​​are input into the traffic flow prediction model, and the corresponding estimated traffic conditions are output until the estimated traffic conditions match the marked traffic conditions for the next time period, completing the training of the traffic flow prediction model.

[0076] In one embodiment, in order to improve the traffic management level of a section of a city's main road to be controlled, the traffic management department decided to use deep learning technology to build a traffic flow prediction model. The model aims to learn the changing patterns of traffic flow through historical data, so as to accurately predict the traffic conditions in the future. The traffic management department first builds a traffic flow prediction model corresponding to the section to be controlled based on deep learning algorithms, such as long short-term memory networks (LSTM) or convolutional neural networks (CNN). These algorithms have excellent performance in processing time series data and image data, and can capture complex patterns and trends in traffic flow.

[0077] Next, the system obtains the historical traffic data corresponding to the road section to be controlled, including key indicators such as vehicle flow, speed, density, etc. At the same time, it also obtains the historical traffic influencing factor values ​​corresponding to these historical traffic data, such as time periods, holidays, weather conditions, etc. In order to train the model, these historical data need to be labeled. Specifically, the historical traffic data and influencing factor values ​​at each time point are matched with the traffic conditions in the next time period to form a training data set. In this way, the model can learn the mapping relationship from historical data to future traffic conditions.

[0078] The annotated historical traffic data and influencing factor values ​​are input into the traffic flow prediction model. The model gradually approaches the real traffic conditions by continuously learning and adjusting parameters. During the training process, the system uses appropriate loss functions and optimization algorithms, such as mean square error (MSE) and stochastic gradient descent (SGD), to evaluate the model's prediction performance and optimize model parameters. The training process continues until the model's estimated traffic conditions match the annotated traffic conditions for the next period, that is, a high prediction accuracy is achieved. At this point, it can be considered that the model has successfully learned the changing patterns of traffic flow.

[0079] The trained traffic flow prediction model can be used to predict the future traffic conditions of the road section to be controlled in real time. By inputting the current traffic data and influencing factor values, the model can output the estimated traffic conditions for the next period, that is, the traffic conditions such as peak traffic time and congestion duration in a certain period of time in the future, to provide scientific decision-making basis for the traffic management department. For example, before the peak period arrives, the model can predict the congestion of the road section, thereby helping the traffic management department to take ramp control measures in advance, such as adjusting the timing of traffic lights, restricting vehicle access, etc., to alleviate traffic pressure. Based on the real-time collected data and historical data, the AI ​​model is continuously optimized so that it can more accurately predict future traffic flow and dynamically adjust the ramp control strategy.

[0080] In one embodiment of the present application, traffic data and traffic influencing factor values ​​are input into a trained traffic flow prediction model to output an estimated traffic condition corresponding to a preset time period, specifically including:

[0081] Determine whether the data format corresponding to the traffic data and the traffic influencing factor value is consistent with the input format corresponding to the traffic flow prediction model, and if not, convert the traffic data and the traffic influencing factor value into the input format;

[0082] Input the traffic data and traffic influencing factor values ​​in the input format into the trained traffic flow prediction model, and output the estimated traffic conditions corresponding to the road section to be controlled within the preset time period;

[0083] The estimated traffic conditions are verified. If the verification fails, the estimated traffic conditions are corrected based on the preset expert database to obtain the target estimated traffic conditions.

[0084] In one embodiment, in order to more accurately predict the future traffic conditions in a busy section of a city to be controlled, the traffic management department decided to adopt an advanced traffic flow prediction model, and combined it with data format conversion and expert verification mechanisms to ensure the accuracy of the prediction. First, the system collects traffic data and traffic influencing factor values ​​from various sensors and cameras. These data may exist in different formats, such as CSV, JSON, or database records. The system checks whether the format of these data is consistent with the input format required by the traffic flow prediction model. If inconsistencies are found, the system will automatically perform data format conversion to ensure that the traffic data and influencing factor values ​​can be correctly read and processed by the model. This step is crucial to avoid data errors and improve the accuracy of model predictions.

[0085] Once the data format conversion is completed, the system will input the traffic data and traffic influencing factor values ​​that conform to the input format into the trained traffic flow prediction model. The model uses this data, combined with its internally learned traffic flow rules, to output the estimated traffic conditions corresponding to the road section to be controlled within the preset time period. These estimated data may include key indicators such as vehicle flow, average speed, and congestion index.

[0086] To ensure the accuracy of the prediction, the system verifies the output estimated traffic conditions. The verification process may include comparison with historical data, trend analysis, and rationality checks. If the verification fails, that is, the estimated traffic conditions deviate significantly from the actual situation or are illogical, the system will trigger a correction mechanism. This mechanism is based on a preset expert database, which contains the knowledge and experience of experts in the field of transportation. The system corrects the estimated traffic conditions according to the rules and suggestions in the expert database to obtain a more accurate and reliable target estimated traffic condition. The correction process may involve adjusting the prediction parameters, introducing new influencing factors, or adopting a more complex prediction model. Finally, the verified and corrected target estimated traffic conditions are used to guide the decision-making of the traffic management department. For example, based on the prediction results, the management department can adjust traffic signals, deploy police forces, or implement ramp control measures in advance to alleviate traffic congestion and improve road traffic efficiency.

[0087] 103. Based on the real-time vehicle driving demand and estimated traffic conditions, a recommended planned path for the corresponding moving vehicle is generated, and based on the moving vehicle's confirmation of the corresponding recommended planned path, the estimated traffic conditions are optimized.

[0088] Specifically, in one embodiment of the present application, based on the real-time acquired vehicle driving demand and estimated traffic conditions, a recommended planning path for the corresponding driving vehicle is generated, specifically including:

[0089] According to the estimated traffic conditions, determine whether there is a potential risk in the road section to be controlled. If so, obtain the driving demand information corresponding to the vehicles traveling in the road section to be controlled; wherein the driving demand information includes the destination, expected arrival time, and preferred route;

[0090] Based on the driving demand information and in combination with the estimated traffic conditions, at least one corresponding recommended planning route is generated for the driving vehicle; wherein the recommended planning route includes the estimated duration, estimated cost and congestion conditions;

[0091] The recommended planning path is sent to the corresponding driving vehicle through wireless communication for the vehicle personnel to select and confirm.

[0092] In one embodiment, in order to optimize the traffic management of the road sections to be controlled in the core area of ​​a city and ensure that the vehicles can pass efficiently and safely, the traffic management department has developed an intelligent path planning system. The system combines the estimated traffic conditions and driving demand information to provide personalized driving route suggestions for vehicles. First, based on the estimated traffic conditions, the potential risks of the road sections to be controlled are evaluated. Factors considered in the evaluation include vehicle flow, average speed, congestion index, and historical accident data. If the system determines that there is a potential risk, such as severe congestion or frequent accidents, it will trigger subsequent steps to obtain driving demand information of the moving vehicle.

[0093] The system communicates with vehicles traveling on the road section to be controlled through wireless communication methods, such as vehicle networking or mobile communication networks. Driving demand information is obtained from the vehicle, including the destination, expected arrival time, and preferred route. This information is crucial for generating a planned path that meets the driver's expectations. Based on the obtained driving demand information, the system combines the estimated traffic conditions to generate at least one recommended planned path for each traveling vehicle. The planned path contains detailed driving instructions, including the estimated driving time, estimated costs (such as fuel or electricity costs), and congestion along the way. This information helps the driver make informed decisions.

[0094] The system sends the generated recommended planning paths to the corresponding moving vehicles through wireless communication. Vehicle personnel can view these paths on the vehicle display or mobile device and select and confirm them according to their needs and preferences. The confirmed paths will be used as driving guidance for the vehicle. During the driving process of the vehicle, the system continuously monitors changes in traffic conditions and updates and optimizes the planned paths in real time according to actual conditions. If new congestion or accidents occur, the system will immediately notify the vehicle personnel and provide alternative path suggestions to ensure driving safety and efficiency.

[0095] In one embodiment of the present application, based on the confirmation status of the corresponding recommended planned path by the moving vehicle, the estimated traffic condition is optimized, specifically including:

[0096] Receive the confirmation of the corresponding recommended planned routes by all vehicles in the road section to be controlled, and dynamically update the traffic influencing factor value corresponding to the road section to be controlled based on the feedback data;

[0097] According to the feedback data and in combination with the updated traffic influencing factor values, the model parameters of the traffic flow prediction model are iteratively optimized to generate the target estimated traffic conditions through the optimized traffic flow prediction model.

[0098] In one embodiment, in order to further improve the traffic management efficiency of an important section of a city to be controlled and ensure the accuracy and practicality of the traffic flow prediction model, the traffic management department implemented a dynamic feedback and optimization system. The system collects the confirmation of the recommended planned path by the vehicle, dynamically updates the value of the traffic influencing factor, and iteratively optimizes the parameters of the traffic flow prediction model. First, the confirmation of the corresponding recommended planned path by all vehicles in the section to be controlled is received. These feedback data include whether the vehicle is driving along the recommended path, the actual traffic conditions during driving, and the driver's satisfaction. By collecting these feedback data, the system can understand the actual effect of the recommended path in real time, as well as the driver's suggestions and opinions on the path planning.

[0099] Based on the collected feedback data, the system dynamically updates the traffic influencing factor values ​​corresponding to the road section to be controlled. These influencing factor values ​​may include the actual traffic capacity of the road section, the driving habits of the driver, the timing of traffic signals, etc. By updating the traffic influencing factor values, the system can more accurately reflect the real-time traffic conditions of the road section and provide a more reliable data basis for subsequent traffic flow predictions. The system iteratively optimizes the model parameters of the traffic flow prediction model based on the updated traffic influencing factor values ​​and feedback data. The optimization process may include adjusting the input features of the model, modifying the prediction algorithm of the model, or introducing new influencing factors. Through iterative optimization, the system can continuously improve the accuracy and generalization ability of the traffic flow prediction model, so that it can better adapt to changes in actual traffic conditions.

[0100] The traffic flow prediction model after iterative optimization is used to generate target estimated traffic conditions. These estimated data include key indicators such as vehicle flow, average speed, and congestion index in the future time period. The target estimated traffic conditions provide a scientific basis for decision-making for traffic management departments, helping them to formulate more effective ramp control measures and traffic diversion plans. The system continuously monitors the traffic conditions of the road sections to be controlled, and regularly collects feedback data for model updates and optimization. Through continuous improvement and perfection, the system can gradually improve the efficiency of traffic management and driver satisfaction.

[0101] 104. Determine the congestion level corresponding to the estimated traffic condition after optimization, and determine the ramp control strategy corresponding to the congestion level based on the preset ramp control library, so as to perform ramp control on the toll station according to the ramp control strategy.

[0102] Specifically, in one embodiment of the present application, the congestion level corresponding to the estimated traffic condition after optimization is determined, and based on a preset ramp control library, a ramp control strategy corresponding to the congestion level is determined, specifically including:

[0103] Based on the preset congestion level classification standards and the optimized estimated traffic conditions, determine the congestion level corresponding to the road section to be controlled;

[0104] According to the congestion level, the corresponding ramp control strategy is determined in the preset ramp control library, and the actual situation of the road to be controlled is determined based on the traffic data collected in real time from the road section to be controlled;

[0105] The ramp control strategy is adjusted according to the actual situation, and a target ramp control strategy corresponding to the road section to be controlled is generated.

[0106] In one embodiment, based on the output of the prediction model, the ramps and toll booths are dynamically controlled in real time. If the system detects that the traffic volume is about to reach the congestion threshold, the traffic light duration of the ramp is automatically adjusted to appropriately extend the green light time to improve traffic efficiency.

[0107] It should be noted that the key indicators of the model output in the embodiment of the present application include predicted traffic volume, speed distribution, queue length, peak traffic flow period and lane utilization. The predicted traffic volume is used to indicate the estimated vehicle throughput in the next few minutes or hours; the speed distribution is used to indicate the change in vehicle speed at different time periods on the ramp, which can reflect the smoothness of traffic flow; the queue length is used to indicate the number and length of vehicles queued on the ramp predicted to measure the degree of congestion; the peak traffic flow period is used to indicate the time and duration of the future traffic peak; the lane utilization rate is used to indicate the utilization rate of each lane at the ramp and toll station entrance, which measures whether the vehicles are evenly distributed.

[0108] The signal duration is adjusted for average waiting time, maximum queue length, throughput rate, alternating control and emergency response.

[0109] Average waiting time: Reducing the average waiting time of vehicles on the ramp is one of the main goals. If the model predicts an increase in traffic volume, the green light duration of the signal can be appropriately extended to allow more vehicles to pass through in one cycle.

[0110] Maximum queue length: Control the queue length by adjusting the green light duration to prevent the queue from exceeding the ramp or affecting the traffic on the main road.

[0111] Pass rate: The ratio of the number of vehicles passing through the ramp divided by the time, which measures the traffic volume passing through the ramp per unit time. Adjusting the signal light duration can increase this ratio.

[0112] Alternating control: If the traffic volume at some ramp entrances is large, the system can give priority to lanes with high traffic volume or temporarily close lanes with low traffic volume based on the prediction results to optimize the overall traffic efficiency.

[0113] Emergency response: In the event of an accident or emergency, the duration of the traffic light can be adjusted dynamically based on the severity of the accident or the impact on traffic flow.

[0114] The server controls the opening time of the ramp entrance gate according to the traffic volume and speed to prevent too many vehicles from entering the ramp and causing congestion. If necessary, the backup channel is opened to relieve the traffic pressure on the main ramp.

[0115] Through image recognition technology and sensor data, traffic accidents, vehicle failures or other emergencies can be automatically identified. When an emergency is detected, the system will quickly calculate the impact of the accident on traffic and take action according to the preset emergency plan. Some ramp entrances or exits can be automatically closed to prevent more vehicles from entering the accident area, and traffic lights can be adjusted to give priority to vehicles to detour. At the same time, detour suggestions and traffic conditions information will be released to drivers in real time through high-speed information boards and high-speed radios to help them choose a more suitable route.

[0116] In addition, during the operation of this application, the implementation effect will be continuously recorded, and traffic flow data and emergency response results after implementation will be collected. Through the continuous accumulation of data and the self-learning of the AI ​​model, the system's traffic control decision-making ability will be optimized, and the accuracy of prediction and the efficiency of emergency response will be improved. The system's hardware equipment will be regularly maintained and upgraded to ensure the long-term stable operation of the system.

[0117] In one embodiment, in order to more effectively manage the section of a city's highway to be controlled and alleviate traffic congestion, the traffic management department has developed an intelligent ramp control system. The system dynamically adjusts and generates target ramp control strategies based on preset congestion level classification standards, optimized estimated traffic conditions, and real-time collected traffic data. First, the optimized estimated traffic conditions are evaluated according to the preset congestion level classification standards. These standards may include indicators such as vehicle flow, average speed, congestion length and density. Through evaluation, the system determines the congestion level corresponding to the section to be controlled, such as light congestion, moderate congestion, or severe congestion. This helps to provide a basis for the subsequent selection of ramp control strategies.

[0118] According to the determined congestion level, the system searches for the corresponding ramp control strategy in the preset ramp control library. These strategies may include adjusting the ramp signal timing, restricting vehicle access, opening emergency lanes, etc. The system selects the ramp control strategy that best suits the current congestion level to alleviate traffic pressure and improve road traffic efficiency to the greatest extent. In order to ensure the effectiveness of the ramp control strategy, the system also needs to consider the actual situation of the road section to be controlled. Therefore, the system collects traffic data of the road section to be controlled in real time, including vehicle flow, speed, vehicle model distribution, etc. By analyzing these data, the system can more accurately understand the real-time traffic conditions of the road section and provide a basis for adjusting the ramp control strategy.

[0119] According to the actual situation, the system adjusts the selected ramp control strategy. Adjustments may include modifying the signal light timing plan, adjusting the conditions for restricting vehicle access, or optimizing the emergency lane opening strategy. The adjusted ramp control strategy is generated as the target ramp control strategy and sent to relevant traffic control equipment or personnel via wireless communication. These strategies will be used to guide actual ramp control operations. The target ramp control strategy is implemented and the traffic conditions of the road section to be controlled are continuously monitored. Through real-time feedback and adjustments, the system can ensure the effectiveness and adaptability of the ramp control strategy. If the traffic conditions change or new congestion occurs, the system will immediately trigger a new estimation and strategy adjustment process to keep the road safe and unobstructed.

[0120] The above is an embodiment of the method proposed in this application. Based on the same inventive concept, the embodiment of this application also provides a toll station ramp control device based on artificial intelligence, and its structure is as follows: Figure 2 shown.

[0121] Figure 2 The internal structure diagram of a toll station ramp control device based on artificial intelligence provided in the embodiment of the present application. Figure 2 As shown, the device includes:

[0122] at least one processor;

[0123] and, a memory communicatively coupled to the at least one processor;

[0124] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor to enable the at least one processor to:

[0125] Based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is pre-processed to determine the key frame image;

[0126] Analyze the key frame images in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, and input the traffic data and traffic influencing factor values ​​into the trained traffic flow prediction model to output the estimated traffic conditions corresponding to the preset time period;

[0127] Generate a recommended planned route for the corresponding vehicle based on the real-time vehicle driving demand and estimated traffic conditions, and optimize the estimated traffic conditions based on the confirmation of the corresponding recommended planned route by the vehicle;

[0128] Determine the congestion level corresponding to the optimized estimated traffic conditions, and determine the ramp control strategy corresponding to the congestion level based on the preset ramp control library, so as to perform ramp control on the toll station according to the ramp control strategy.

[0129] The present application also provides a non-volatile computer storage medium storing computer executable instructions. When the computer executable instructions are executed, they can:

[0130] Based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is pre-processed to determine the key frame image;

[0131] Analyze the key frame images in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, and input the traffic data and traffic influencing factor values ​​into the trained traffic flow prediction model to output the estimated traffic conditions corresponding to the preset time period;

[0132] Generate a recommended planned route for the corresponding vehicle based on the real-time vehicle driving demand and estimated traffic conditions, and optimize the estimated traffic conditions based on the confirmation of the corresponding recommended planned route by the vehicle;

[0133] Determine the congestion level corresponding to the optimized estimated traffic conditions, and determine the ramp control strategy corresponding to the congestion level based on the preset ramp control library, so as to perform ramp control on the toll station according to the ramp control strategy.

[0134] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0135] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0136] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0137] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0138] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0142] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0143] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0144] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0145] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A toll station ramp control method based on artificial intelligence, characterized in that: The method comprises: Based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is pre-processed to determine the key frame image; Analyze the key frame image in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, and input the traffic data and the traffic influencing factor values ​​into a trained traffic flow prediction model to output an estimated traffic condition corresponding to a preset time period; Based on the real-time acquired vehicle driving demand and the estimated traffic conditions, a recommended planned path for the corresponding driving vehicle is generated, and based on the confirmation status of the driving vehicle on the corresponding recommended planned path, the estimated traffic conditions are optimized; Determine the congestion level corresponding to the optimized estimated traffic condition, and determine the ramp control strategy corresponding to the congestion level based on a preset ramp control library, so as to perform ramp control on the toll station according to the ramp control strategy.

2. The method for toll station ramp control based on artificial intelligence according to claim 1 is characterized in that: Based on the preset roadside equipment of the road section to be controlled, the video stream and sensor data of the road section to be controlled are collected, and the video stream is preprocessed to determine the key frame image, specifically including: Through the preset cameras and sensors of the road section to be controlled, the video stream and sensor data corresponding to the road section to be controlled are collected in real time, and the video stream and sensor data uploaded by the cameras and the sensors are received; The video stream is analyzed to identify the latter image of two adjacent images with inconsistent information between the previous and the next images in the video stream, so as to determine the key frame image corresponding to the video stream.

3. The method for toll station ramp control based on artificial intelligence according to claim 1 is characterized in that: Analyzing the key frame image in combination with the sensor data to determine the traffic data and traffic influencing factor values ​​corresponding to the road section to be controlled, specifically including: Obtaining the historical ramp control records corresponding to the road section to be controlled, and determining the traffic influencing factors corresponding to the road section to be controlled based on the historical ramp control records; wherein the traffic influencing factors include time periods, holidays, and weather, and the time periods include peak periods, trough periods, and regular periods; Segmenting the key frame image to determine the moving vehicle in the key frame image, and identifying the vehicle model, vehicle color and license plate number corresponding to the moving vehicle; Determine the speed of the traveling vehicle according to the sensor data corresponding to the traveling vehicle on the adjacent road section of the road section to be controlled, so as to obtain the traffic data corresponding to the road section to be controlled; The data collection time corresponding to the traffic data is obtained, so as to determine the traffic influencing factor value corresponding to the road section to be controlled according to the data collection time and in combination with the sensor data.

4. The method for controlling ramps at toll booths based on artificial intelligence according to claim 1, characterized in that: Before inputting the traffic data and the traffic influencing factor values ​​into a trained traffic flow prediction model to output an estimated traffic condition corresponding to a preset time period, the method further includes: Based on the deep learning algorithm, a traffic flow prediction model corresponding to the road section to be controlled is constructed; Obtain the historical traffic data corresponding to the road section to be controlled and the historical traffic influencing factor value corresponding to the historical traffic data, and mark the traffic conditions of the next time period corresponding to the historical traffic data and the historical traffic influencing factor value; The historical traffic data and the historical traffic influencing factor values ​​are input into the traffic flow prediction model, and the corresponding estimated traffic conditions are output until the estimated traffic conditions match the marked traffic conditions for the next time period, thereby completing the training of the traffic flow prediction model.

5. The method for toll station ramp control based on artificial intelligence according to claim 1 is characterized in that: The traffic data and the traffic influencing factor values ​​are input into the trained traffic flow prediction model to output the estimated traffic conditions corresponding to the preset time period, specifically including: Determine whether the data format corresponding to the traffic data and the traffic influencing factor value is consistent with the input format corresponding to the traffic flow prediction model, and if not, convert the traffic data and the traffic influencing factor value into the input format; Input the traffic data and the traffic influencing factor values ​​in the input format into the trained traffic flow prediction model, and output the estimated traffic conditions corresponding to the road section to be controlled within a preset time period; The estimated traffic condition is verified, and if the verification fails, the estimated traffic condition is corrected based on a preset expert database to obtain a target estimated traffic condition.

6. The method for toll station ramp control based on artificial intelligence according to claim 1 is characterized in that: Based on the real-time acquired vehicle driving demand and the estimated traffic conditions, a recommended planning path for the corresponding driving vehicle is generated, specifically including: Determine whether there is a potential risk in the road section to be controlled according to the estimated traffic conditions, and if so, obtain driving demand information corresponding to vehicles traveling in the road section to be controlled; wherein the driving demand information includes the destination, expected arrival time, and preferred route; Based on the driving demand information and in combination with the estimated traffic conditions, generating at least one corresponding recommended planning route for the driving vehicle; wherein the recommended planning route includes an estimated duration, an estimated cost, and congestion conditions; The recommended planning path is sent to the corresponding traveling vehicle through wireless communication for the vehicle occupants to select and confirm.

7. The method for toll station ramp control based on artificial intelligence according to claim 1 is characterized in that: Based on the confirmation status of the driving vehicle on the corresponding recommended planned path, optimizing the estimated traffic condition specifically includes: Receive confirmation of the corresponding recommended planned path from all vehicles in the road section to be controlled, and dynamically update the traffic influencing factor value corresponding to the road section to be controlled based on the feedback data; According to the feedback data and in combination with the updated traffic influencing factor values, the model parameters of the traffic flow prediction model are iteratively optimized to generate a target estimated traffic condition through the optimized traffic flow prediction model.

8. The method for controlling ramps at toll stations based on artificial intelligence according to claim 1, characterized in that: Determine the congestion level corresponding to the optimized estimated traffic conditions, and determine the ramp control strategy corresponding to the congestion level based on the preset ramp control library, specifically including: Based on the preset congestion level classification standard and according to the optimized estimated traffic conditions, determine the congestion level corresponding to the road section to be controlled; According to the congestion level, a corresponding ramp control strategy is determined in a preset ramp control library, and according to the traffic data collected in real time from the road section to be controlled, the actual situation corresponding to the road to be controlled is determined; According to the actual situation, the ramp control strategy is adjusted, and a target ramp control strategy corresponding to the road section to be controlled is generated.

9. A toll station ramp control device based on artificial intelligence, characterized in that: The device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; In which, the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute an artificial intelligence-based toll station ramp control method as described in any one of claims 1-8.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: When the computer executable instructions are executed, an artificial intelligence-based toll station ramp control method as described in any one of claims 1 to 8 is implemented.

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