14 results about "Traffic flow control" patented technology

The invention relates to the technical field of traffic flow control, and provides a multi-mode traffic flow control method and system, and the method comprises the steps: obtaining a traffic capacity parameter of a core traffic node based on the preset core traffic node; obtaining an instantaneous personnel entry rate of the core traffic node; calculating a personnel net accumulation rate according to the instantaneous personnel entry rate and the traffic capacity parameters; performing grading and shunting judgment based on the personnel net accumulation rate to obtain a grading judgment result; when the grading judgment result shows that the personnel net accumulation rate reaches a first preset threshold value, generating an information guide shunting instruction to guide the people stream to actively select an alternative traffic mode; and when the grading judgment result shows that the personnel net accumulation rate reaches a second preset threshold value, generating a physical isolation shunting instruction so as to drive a physical isolation device to change a people flow path. The method has the advantages that the cross-mode congestion transmission chain is accurately identified and cut off, and local congestion is prevented from spreading towards systematic paralysis.

The application discloses a kind of urban high-density road network mixed traffic flow cell transmission simulation prediction methods, comprising: with urban high-density road network area as modeling object, with road boundary end as traffic flow input position and output position, the road section between input position and output position includes urban expressway basic road section, access ramp and interchange, and urban non-expressway basic road section and intersection, the road section between input position and output position is divided into cell, and mixed mixed traffic flow cell transmission model is established to the cell, and the state of urban road network is simulated and predicted.The application comprehensively considers the traffic flow of urban expressway and urban non-expressway, effectively connects the cell of urban expressway with the cell of urban non-expressway, and designs and improves the cell division and modeling method of interweaving area and intersection, which is beneficial to the comprehensive study of traffic flow change in urban road network area and the traffic flow state simulation prediction for traffic flow control.

This invention discloses a method and system for resilient traffic flow control on highways under complex construction scenarios, relating to the field of traffic control. The method includes: zoning and calibrating the construction area and traffic sensing points to determine the road network construction topology; sampling and predicting real-time traffic flow data based on the traffic sensing network, and determining traffic congestion waves through wave eigenvector transformation; constructing resilient control units based on the road network construction topology and deploying them at the road network edge; intervening in the traffic congestion waves using wave interference offsetting and detour diversion, and then iteratively allocating the solutions to each zone using a trust region framework to determine the resilient control scheme; and performing dynamic control and management of traffic flow resilience. This application solves the technical problem of existing dynamic traffic flow control methods, which struggle to quantify and identify congestion evolution patterns in real time and implement precise and efficient interventions, achieving the technical effect of real-time quantification and identification of congestion evolution patterns and precise and efficient interventions for traffic flow.

The invention provides a service flow control method and device, equipment, a medium and a product. The method is applied to a policy control unit, a policy control request sent by a policy execution unit is received, and the policy control request comprises a user identifier and network state information. And generating a dynamic strategy rule satisfying the network state information and the service configuration information based on the strategy control request and the service configuration information and the user behavior information in the unified data model. And sending the dynamic policy rule to a policy execution unit through a communication interface, so that the policy execution unit controls the service flow according to the dynamic policy rule. According to the method, the policy decision and the charging rule are based on the same data source, service interruption or charging errors caused by cross-system synchronization delay can be avoided, meanwhile, generation of personalized policies is supported by combining user behaviors, network states and charging states, and the resource utilization rate and the user experience are improved.

The application discloses a kind of intelligent traffic light coordination control method and system of traffic flow optimization, it is related to intelligent control technical field.The method includes: real-time acquisition of the traffic flow data of each direction lane in target area by the heterogeneous sensor network deployed at each intersection;Traffic flow data is identified to obtain tidal flow characteristics;Dynamic intensity evaluation is carried out based on tidal flow characteristics, and tidal direction intensity index is obtained;A multi-agent collaborative network is constructed, and a continuous intersection green phase offset sequence is generated based on multi-agent collaborative network collaborative calculation;When tidal direction intensity index is greater than the preset threshold, the continuous intersection green phase offset sequence is input into the traffic signal control system to start the tidal green wave mode.The technical problems that traffic flow control is not flexible enough in the prior art, and traffic signal lights cannot be intelligently adjusted according to traffic conditions are solved, achieving the technical effects of improving road traffic efficiency and reducing congestion.

The invention discloses a smart highway active traffic flow control method and system, and relates to the field of traffic flow control. A roadside environment sensing unit, a vehicle-mounted state data acquisition terminal, an air auxiliary sensing system and an embedded road environment state monitoring device are deployed; and a driving behavior sensing function module is deployed in a smart phone terminal authorized by a driver to collect data to form multi-source real-time traffic state data. Then, by constructing a multi-agent reinforcement learning system based on a three-dimensional digital twin model environment, dynamic acquisition, fusion prediction and control strategy optimization of multi-source real-time traffic state data are realized, the operation efficiency and safety of the expressway are improved, and by combining a multi-target optimization model and a driver behavior compliance index, the driving safety of the expressway is improved. A differential control strategy is formed to generate an excitation and constraint linkage mechanism, and fine regulation and control of traffic congestion, high-risk driving behaviors and energy consumption emission are enhanced.

This central unit (CU) may comprise at least one processor. The at least one processor is configured to: transmit, to a radio link control (RLC) entity, a first message for requesting radio quality assistance information, according to a determination that an amount of downlink traffic stored in a buffer of a packet data convergence protocol (PDCP) entity of the CU exceeds a threshold value; determine, on the basis of receiving a second message including the radio quality assistance information from the RLC entity, whether congestion occurs in a downlink on the basis of the radio quality assistance information; and, according to a determination that the congestion occurs in the downlink, set an explicit congestion notification echo (ECE) flag in a transmission control protocol (TCP) header of uplink traffic, and then transmit the uplink traffic to a server.

This invention discloses a hybrid traffic flow simulation method based on an improved multi-type traffic flow cellular transmission model. The method achieves hybrid traffic flow simulation by making the following four improvements to the multi-type traffic flow cellular transmission model: 1) Changing the vehicle equivalent conversion from being based on fixed vehicle length to dynamically calculating the equivalent function of a small car based on vehicle speed differences; 2) Changing the determination method of the maximum capacity of the link and the downstream shock wave coefficient from exogenous calibration to dynamic lookup of a segmented index table based on historical monitoring data; 3) Removing the relative occupancy rate from the downstream cell reception calculation to avoid redundant calculations; 4) Introducing a state adaptive correction term in the calculation of the actual transmission flow between upstream and downstream cells to achieve accurate flow allocation. The advantages are that it can accurately describe the dynamic evolution characteristics of real hybrid traffic flow, provide an accurate assessment basis for the road capacity of hybrid traffic flow, and improve the scientificity and effectiveness of traffic flow control for hybrid traffic flow on highways.

The application discloses a kind of traffic AI scheduling decision-making methods based on multi-source geographic big data, it is related to intelligent transportation technology field, including, collection multi-source traffic data and pre-processing, generate after processing traffic dataset;Using trained long short-term memory network, the multi-scale time series analysis is carried out to after processing traffic dataset, generates traffic flow prediction result;Using particle swarm optimization algorithm, traffic flow prediction result is adjusted, generates candidate traffic scheduling scheme;Candidate traffic scheduling scheme is dynamically optimized, generates after optimization traffic flow control scheme;Optimization after traffic flow control scheme is compared and analyzed with newly collected multi-source traffic data, generates adjusted traffic flow control scheme, and based on real-time traffic flow feedback, adjusted traffic flow control scheme is dynamically adjusted, forms continuous feedback cycle.The application improves the traffic capacity of road and resource use efficiency.

This invention relates to the field of tunnel traffic lighting engineering and intelligent adaptive control technology, specifically to a wireless network-based intelligent timing control system for tunnel lighting and its control method. The system includes: a data acquisition hardware group that collects real-time velocity vectors of moving objects and ambient brightness values ​​of the tunnel environment; a central control processor configured to perform the following operations: constructing a virtual fluid medium model; calculating the virtual shear viscosity parameters of the virtual fluid medium based on human visual adaptation characteristics and ambient brightness values ​​of the tunnel environment; substituting the real-time velocity vector as a disturbance source into the fluid dynamics control equations to generate a spatiotemporally continuous virtual light pressure control data field; and parsing the virtual light pressure control data field into discrete pulse width modulation commands according to spatial coordinates, sending them to the drive power supply via a wireless ad hoc network to control the brightness of the corresponding lighting units. This invention effectively reduces the risk of rear-end collisions and achieves deep integration of the lighting system and traffic flow control.

Packet traffic flow control includes receiving a packet, by a first virtual machine network security appliance (VMNSA), and determining whether the packet is to be processed by a first path or a second path; in response to determining that the packet is to be processed by the first path, processing the packet by a first traffic flow controller (TFC) of the VMNSA, forwarding the packet by the first TFC to a first transmitter, and sending the packet by the first transmitter; and in response to determining that the packet is to be processed by the second path, processing the packet by the first TFC, forwarding the packet by the first TFC to a second TFC of the VMNSA, processing the packet by the second TFC, forwarding the packet by the second TFC to a second transmitter, and sending the packet by the second transmitter.

A method for modeling and controlling traffic flow in a traffic flow system is provided. The method includes establishing a mathematical model of the traffic flow system, constructing an environment simulator that transforms the boundary control problem of the system into a Markov process, and designing a control method utilizing reinforcement learning algorithms to stabilize the traffic flow system. The control method interacts with the environment simulator to train an Actor-Critic network. The optimal traffic flow controller is implemented through the output of the Actor network, thereby enabling effective control of the traffic flow.