19 results about "Traffic flow management" patented technology

The invention discloses a traffic video abnormity management and control method and system based on a three-dimensional live-action map, and relates to the technical field of big data analysis, and the method comprises the steps: building a target road three-dimensional live-action model; based on the traffic monitoring video stream of the target road and the three-dimensional live-action modeling of the target road, generating spatial mapping of the traffic monitoring video stream and the three-dimensional live-action modeling of the target road, and marking abnormal event information of a dynamic object in the target road; performing real-time three-dimensional rendering according to the abnormal event information of the dynamic object in the target road, and analyzing an abnormal risk index of the dynamic object in the target road; and performing three-dimensional live-action modeling visual labeling on the target road based on the abnormal risk index of the dynamic object in the target road, determining an abnormal event type of the dynamic object, and generating a traffic abnormality management and control scheme of the target road. The method has the advantages that the traffic management efficiency is improved, the accident occurrence probability is reduced, and traffic flow management and emergency response are optimized.

The invention provides a railway traffic depot dispatching system which is used for managing railway transportation activities in the jurisdiction range of a traffic depot. The railway traffic depot scheduling system comprises a road network diagram module, a marshalling plan module, a data integration module, a traffic flow management module and a depot technical operation chart. The road network diagram module is used for maintaining the position of each station in the jurisdiction and the association between the stations; the marshalling planning module is used for determining a traffic flow path and a train marshalling plan in the train depot according to the position, association and train marshalling requirements; the data integration module is used for collecting train operation plans and train operation actual performance nationwide and current train data of each station; and the traffic flow management module is used for predicting traffic flow data of a traffic depot and traffic flow data of each station in a future preset time period according to the train operation plan, the train operation actual performance, the current train data, the traffic flow path, the train marshalling plan, the position and association, and displaying the traffic flow data and the traffic flow data through a depot technology operation chart. The problems in the prior art can be solved.

The invention discloses a dynamic management method, system and device for mixed traffic flow, a medium and a product. The method comprises the following steps: acquiring participants of a mixed traffic flow scene; the participants comprise controllable participants and uncontrollable participants; clustering the controllable participants according to a preset group scale range to obtain at least one microscopic group; when a state change notification from the participant is received, searching an affected first microscopic group from the microscopic groups, and recombining the first microscopic group according to the state change notification; and reordering the microcosmic groups at each time step, regarding each reordered microcosmic group as a formation control object, and performing formation control on the participants. By adopting the embodiment of the invention, the efficient division of the traffic flow can be realized, the compactness of the microscopic group is ensured to be maintained in real time, the computing complexity of the cloud during traffic flow management is effectively reduced, and the management efficiency and the resource utilization rate are improved.

The invention relates to a method and system for controlling priority vehicle traffic flow using secure coded signals. By establishing a mechanism for issuing digital identifiers (EC) and a set of anonymous certificates (Certx) that decay over time Ti = T0 . e - ƛi, with ƛ>0 independent along with the establishment of a shared session key between the vehicle and the intelligent system (V2X), allows vehicles to participate, requiring priority traffic flow management while ensuring security, preventing tracking, and eliminating fraudulent vehicles. The process and system for controlling priority vehicle traffic flow using secure coded signals as described in the invention also reduces computational costs and saves system resources, which is beneficial for transportation systems.

The embodiment of the application provides a traffic congestion discrimination method and device based on map coordinates and vehicle big data, fully utilizes high-precision coordinate information provided by modern map services and massive vehicle driving data, and realizes rapid and accurate discrimination of a traffic congestion state through comprehensive analysis of multidimensional data such as vehicle driving directions and speeds, thereby providing timely and reliable decision support for a traffic management department, optimizing traffic flow management, improving road passing efficiency, and reducing traffic delay and environmental pollution. The method innovatively combines map geometric analysis and vehicle big data processing, overcomes many limitations of traditional methods, and has remarkable application value and popularization prospect.

The invention discloses a multi-source traffic data driven signal lamp accurate control method and system, and the method comprises the following steps: S1, collecting multi-source traffic data, and carrying out the data preprocessing, and forming a multi-source traffic original data set; s2, according to a flow conservation relationship, performing unification processing on the original data, and outputting calibrated traffic state data; s3, utilizing a shock wave neural operator module to predict motorcade wavefront arrival time and queuing emptying time of each entrance lane; s4, constructing a sequential logic constraint set and an uncertainty scene set; s5, through distributed robust optimization solution, generating a signal control scheme satisfying constraints, and generating a plan time axis; and S6, a control scheme is issued to a signal controller, adaptive phase switching is implemented, and after execution, recovery data are updated online. According to the accurate signal lamp control method driven by the multi-source traffic data, signal lamp scheduling is optimized in real time, the traffic flow management efficiency is improved, and traffic congestion is reduced.

A method for providing coordinated traffic flow management among autonomous vehicles is disclosed. The method includes: providing a decentralized architecture upon which autonomous vehicles from different manufacturers with proprietary APIs and communication systems interact with each other; establishing real-time communication over a cellular network using a network slice that enables the autonomous vehicles to exchange information; leveraging edge computing and low-latency services associated with the network slice to ensure low-latency data transmission between the autonomous vehicles; enabling each autonomous vehicle equipped with a network slice protocol associated with the network slice to continuously share its location, speed, and intended maneuvers; and facilitating dynamic traffic adjustments and collision avoidance based on information shared by the autonomous vehicles using the network slice.

The invention discloses a traffic flow management and control method for port unmanned driving and manned driving mixed passing, which relates to the technical field of road traffic and comprises the steps of constructing an orthogonal road network and establishing a plane intersection, managing and controlling storage yard traffic based on storage yard road layout and standardizing driving behaviors of automatic driving container trucks at nodes. And the traffic control of the mixed vehicles is completed. According to the invention, safe, orderly and efficient operation of unmanned container trucks and manned container trucks in a complex storage yard environment is effectively realized, the path conflict risk of two types of vehicles at key nodes such as intersections and box area entrances and exits is obviously reduced, and the road traffic efficiency and the operation safety are improved; the turning space utilization of a large container truck is optimized, traffic blocking caused by insufficient turning radius is reduced, and efficient operation and sustainable development of an intelligent port are powerfully supported.

The application discloses a traffic video abnormality management and control method and system based on a three-dimensional real scene map, relates to the technical field of big data analysis, and comprises the following steps: establishing three-dimensional real scene modeling of a target road; based on traffic monitoring video streams of the target road and the three-dimensional real scene modeling of the target road, generating spatial mapping of the traffic monitoring video streams and the three-dimensional real scene modeling of the target road, and marking abnormal event information of dynamic objects in the target road; performing real-time three-dimensional rendering according to the abnormal event information of the dynamic objects in the target road, analyzing abnormal risk indexes of the dynamic objects in the target road; based on the abnormal risk indexes of the dynamic objects in the target road, performing visual labeling of the three-dimensional real scene modeling of the target road, determining an abnormal event type of the dynamic objects, and generating a traffic abnormality management and control scheme of the target road. The application has the advantages of improving traffic management efficiency, reducing the probability of accidents, and optimizing traffic flow management and emergency response.

1. Name of the product in this design: Graphical User Interface for Traffic Intersection Management in Electronic Equipment. 2. Intended use of this design: for use in electronic devices. 3. The key design features of this product are its graphical user interface content. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: The graphical user interface is used for traffic intersection management. 6. Human-computer interaction method of graphical user interface: The main view is the main page for traffic situation visualization, real-time monitoring of equipment status, and traffic flow management at the intersection. Refer to the main view interface change status reference diagram. By clicking the "View" button in the "Asset List" in the main view, you can enter the interface change status diagram 1, which displays the distribution and status of traffic flow (speed, traffic volume, etc. are indicated by different colors), traffic lights, signal controllers, and other equipment at the intersection. Refer to the interface change status reference diagram 1. By clicking the "Intersection Information" drop-down information in the interface change status diagram 1, you can enter the interface change status diagram 2, which displays a page where you can select a time period to view traffic data such as average vehicle speed and traffic volume. Refer to the interface change status reference diagram 2.

This invention discloses a scalable multi-strategy collaborative optimization method for highway traffic flow control, comprising: S1. Defining corresponding reinforcement learning action spaces based on different types of traffic flow control strategies; S2. Defining corresponding reinforcement learning state spaces based on traffic flow observation states; S3. Defining reward functions for reinforcement learning based on different target coefficients; S4. Constructing a simulation training environment; S5. Constructing reinforcement learning agent strategies based on action spaces, state spaces, reward functions, and the simulation training environment; S6. Optimizing the constructed reinforcement learning agent strategies in the simulation training environment to obtain the optimal reinforcement learning agent strategy; S7. Obtaining the actual environmental state and calculating the optimal traffic flow control strategy for the actual environmental state based on the optimal reinforcement learning agent strategy.

The application relates to the technical field of intelligent traffic, and discloses an intelligent traffic control method and system, a roadside edge computing device and a storage medium, wherein the method comprises the following steps: collecting traffic state information acquired by video monitoring equipment, radar equipment and manual input equipment arranged at a traffic intersection and a surrounding area of the traffic intersection, wherein the traffic state information comprises vehicle queue length, average speed and waiting time; performing normalization processing on the traffic state information, calculating entropy values of various data sources, calculating and generating weights of the various data sources based on the entropy values; performing weighted fusion on the traffic state information based on the weights, obtaining a fused traffic state estimation vector, and constructing a Bayesian belief distribution based on the vector to represent a belief value of the traffic state. Through combination of the Bayesian belief distribution and a game model, real-time intelligent optimization scheduling of traffic signals is realized, traffic flow management is effectively improved, and signal waste is reduced.

The application relates to a traffic flow prediction method and device based on space-time sequence deep learning, which comprises the following steps: acquiring road shape and traffic flow historical data of the road; preprocessing the traffic flow historical data according to relevant time correlation sequences, and further arranging the traffic flow historical data in the form of batches into the form of tensors to construct a multivariate space-time sequence data set of the traffic flow historical data; dividing the multivariate space-time sequence data set into a training data set, a verification data set and a test data set; training a traffic flow prediction model by using the training data set; collecting traffic flow data at a current time, inputting the collected traffic flow data at the current time into the trained traffic flow prediction model, and predicting time sequence values at future times. The application can predict effective change conditions of traffic flow in actual traffic flow prediction applications, and can provide reference and safety risk assessment for traffic flow management personnel on current traffic flow conditions.

This invention discloses a spatiotemporal traffic flow prediction method based on dual-flow decoupling. The method first decomposes the temporal data of road network traffic flow into trend and periodic components using the exponential moving average method. Then, features are extracted and fused using a deep linear network and a local feature hybrid network with temporal block embedding to obtain the global temporal prediction component. After the traffic flow temporal data is encoded in the temporal domain by gated dilated convolution, multi-view spatial aggregation is performed using forward, backward, and normalized graph convolutions with adaptive adjacency matrices to obtain the local spatiotemporal prediction component. Finally, dynamic weights are generated by a gated network, and the two types of components are weighted and fused to obtain the final prediction result. This invention accurately separates long-term and short-term traffic flow features, adaptively balances global patterns and local details, improves the accuracy and robustness of long-term temporal prediction, and reduces computational complexity, making it suitable for intelligent traffic flow management scenarios.

The application belongs to the technical field of unmanned driving, and particularly relates to an intelligent traffic flow management system and method for unmanned vehicles. Firstly, environmental data and traffic flow data around the vehicle are collected and preprocessed. Then, obstacle detection and identification are performed to detect and identify characteristic indexes of the traffic flow data. The output result is used to predict the probability of the current vehicle passing through the obstacle and predict the future traffic flow dynamics. Then, an adaptive algorithm is called to calculate the dynamic passing threshold of the target vehicle according to the probability of the vehicle passing through the obstacle, the obstacle detection result and the traffic index identification result. Whether the dynamic passing threshold of the target vehicle exceeds the preset passing threshold is judged. If yes, a warning command is generated and transmitted to the background. If no, the dynamic behavior of the target vehicle is generated according to the predicted future traffic flow dynamics. The application can solve the problem that the evaluation of the influence of obstacles on the passing function of the unmanned driving technology in the prior art is not accurate.

A method for performing traffic flow management with aid of auxiliary information and associated apparatus are provided. The method may include: carrying first auxiliary information in at least one first data unit of first data units in a first stream, wherein the first stream and a second stream are assigned to a same traffic identifier (TID); and sending the at least one first data unit carrying the first auxiliary information and at least one second data unit in the second stream to the second device, wherein the first auxiliary information comprises an indication of the first data units being part of the first stream.

The application relates to the technical field of traffic optimization, in particular to a city traffic optimization system, which comprises a sensor comprehensive module, a camera monitoring module and a comparison and optimization module, wherein: the sensor comprehensive module collects vehicle traffic sensing data by using various sensors, and the vehicle traffic sensing data is subjected to vehicle quantity prediction by using a decision tree algorithm model; the camera monitoring module collects vehicle traffic picture data by using a camera, and the vehicle traffic picture data is subjected to vehicle quantity prediction by using a YOLO neural network model; the comparison and optimization module collects a vehicle quantity threshold range stipulated by traffic flow management, accepts the vehicle quantity predicted by the sensor comprehensive module and the camera monitoring module, carries out summation and averaging, compares the summation and averaging result with the threshold range, determines a final result according to the comparison result, and optimizes the methods in the sensor comprehensive module and the camera monitoring module.

To provide a moving object control device that can prevent a decrease in a movement efficiency of a moving object.SOLUTION: A moving object control device according to the present invention comprises: a moving object information acquisition unit 11 that acquires moving object information; an environment information acquisition unit 12 that acquires environment state information which is movement constraint imposed by a travel environment; and an area control unit 13 that uses the moving object information and the environment state information to set an occupation area 5 exclusively for a moving object. The environment state information includes restriction state information indicating whether the movement constraint is imposed and information on a restriction area 6 that restricts movement, an area in which the moving object will exist until some future time is set as a provisional occupation area 5', and an area that overlaps with the restriction area 6 out of the provisional occupation area 5' is set as an intersection area 8. When the restriction state information restricts the advancement to the restriction area 6, the area control unit 13 sets an area that does not include the intersection area 8 out of the provisional occupation area 5' as the occupation area 5; and when the restriction state information allows the advancement to the restriction area 6, the provisional occupation area 5', which includes the intersection area 8, is set as the occupation area 5.SELECTED DRAWING: Figure 2