4 results about "Traffic flow management" patented technology

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.

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.