6 results about "Traffic flow management" patented technology

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.

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.

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 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.