66 results about "Traffic flow control" patented technology

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The invention discloses an intelligent traffic flow dynamic regulation and control method and system based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: executing multi-source data acquisition through a data acquisition interface to obtain a multi-source traffic data set; performing spatial fusion on the multi-source traffic data set to obtain a flow marking map; performing current traffic state analysis based on the flow marking map, identifying local and global congestion, and constructing a traffic situation map; and performing traffic flow state prediction in a preset future time zone based on the traffic situation map, performing regulation and control decision based on a prediction result, and generating a traffic flow regulation and control strategy. The technical problem that traffic flow prediction and regulation are not accurate enough in the prior art is solved, and the technical effects that intelligent traffic flow dynamic regulation is achieved through multi-source data fusion and space-time analysis, and the traffic management precision and the response speed are improved are achieved.

The invention discloses an intelligent traffic light coordination control method and system for traffic flow optimization, and relates to the technical field of intelligent control. The method comprises the following steps: collecting traffic flow data of lanes in all directions in a target area in real time through heterogeneous sensor networks deployed at all intersections; identifying the traffic flow data to obtain tidal traffic flow features; performing dynamic intensity evaluation based on the tidal traffic flow characteristics to obtain a tidal direction intensity index; a multi-agent cooperative network is constructed, and a continuous intersection green light phase offset sequence is generated based on cooperative calculation of the multi-agent cooperative network; and when the tide direction intensity index is greater than a preset threshold value, inputting the continuous intersection green light phase offset sequence into a traffic signal lamp control system so as to start a tide green wave mode. The technical problems that in the prior art, traffic flow control is not flexible enough, and traffic lights cannot be intelligently adjusted according to the traffic flow condition are solved, and the technical effects of improving the road passing efficiency and reducing congestion are achieved.

Disclosed are techniques for traffic flow control. In an aspect, an on-board computer (OBC) of a vehicle of a secondary traffic flow determines that a primary traffic flow is passing through an intersection associated with a traffic light, wherein the determination that the primary traffic flow is passing through the intersection is based on sensor data from one or more sensors of the vehicle, vehicle-to-vehicle (V2V) communication, vehicle-to-everything (V2X) communication, or any combination thereof, and reduces a speed of the vehicle to prevent the vehicle from tripping a traffic light sensor associated with the traffic light until after the primary traffic flow has passed through the intersection.

The invention discloses a smart city traffic optimization method and system based on a 5G network, and particularly relates to the technical field of smart traffic, the smart city traffic optimization method based on the 5G network comprises the steps: deploying a 5G base station, and installing a sensor and traffic equipment on a road to collect traffic data; the traffic data comprises traffic flow data and a traffic signal state, rapidly transmitting the acquired traffic data to a cloud platform for storage by using a 5G network, preprocessing the acquired traffic data in the cloud platform, performing deep analysis through a machine learning algorithm to identify a traffic mode, and based on an analysis result, determining the traffic mode. The invention discloses a smart city traffic optimization system based on a 5G network, which is used for predicting a traffic flow demand, pre-judging an appearing congestion condition in advance and optimizing by adjusting a signal lamp period, and comprises a data acquisition module, a data analysis module and a traffic flow control module.

The invention relates to the technical field of traffic transportation, in particular to a CAV and HV vehicle mixed traffic flow control method and system and a storage medium, and the CAV and HV vehicle mixed traffic flow control method comprises the steps: S2, building a traffic flow control model which introduces a front vehicle speed effect and also introduces a gathering lane changing strategy; s2, obtaining related parameters of the traffic flow control model; s3, inputting the related parameters obtained in the step S2 into the traffic flow control model established in the step S1, and outputting a simulation result describing a traffic control result; compared with the prior art, the method has the advantages that according to road conditions, CAV queuing conditions of front and back vehicles can be acquired, and the gathering condition of homogeneous vehicles is further considered during lane changing decision making, so that more CACC car-following modes appear on a road, the road traffic flow efficiency is further improved, meanwhile, due to the high interconnection of the CAVs, the road safety is further improved, and the road traffic flow efficiency is improved. And the mutual interference between the CAV vehicle and the HAV vehicle is reduced.

The invention belongs to the field of intelligent traffic, and particularly relates to a construction area traffic flow guiding and speed cooperative control system which realizes accurate traffic flow control through cooperative operation of an area sensing module, a distributed guiding module, a communication module and an emergency response module. The area sensing module is based on three-dimensional modeling and digital twinning technologies, fuses real-time traffic flow, vehicle states and environment data, and constructs a three-dimensional simulation space of a target area; the distributed guide module establishes a drainage control model by using a distributed reinforcement learning algorithm, dynamically optimizes a sub-region congestion control strategy in combination with a vehicle-road-cloud three-level communication node, and adjusts the acceleration and speed of a vehicle in real time; and the emergency response module rapidly generates an emergency instruction through an anomaly analysis algorithm and a strategy library and feeds back the emergency instruction to the guide model to solve an emergency abnormal event, and the application integrates multi-source data real-time sensing, distributed decision optimization and a multi-stage communication architecture, and realizes dynamic traffic flow distribution and intelligent traffic control in a construction area.

The invention discloses a road intelligent traffic multifunctional monitoring optimization system and method, and the system comprises a controller, a deceleration strip, and a traffic light assembly. The controller comprises an embedded single-chip microcomputer, a traffic flow detection module, a short-time traffic flow prediction module, a traffic state analysis module and a real-time traffic flow control adjustment module. The road intelligent traffic multifunctional monitoring optimization system not only has the capability of detecting and predicting the traffic flow in real time, but also has the function of monitoring the road traffic operation condition to give an alarm, and improves the travel experience of urban residents through intelligent regulation and control of the traffic flow. The technical problem that unnecessary waiting time or traffic jam occurs under some conditions due to the fact that traffic conditions cannot be intelligently regulated and controlled by an existing road intelligent traffic multifunctional monitoring optimization system and method, and traffic light time circulates in a fixed time interval is effectively solved.

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 invention discloses an urban road dynamic noise traceability evaluation method and system based on digital twinning, and the method comprises the steps: cooperatively collecting noise and road vibration signals through a non-uniform sound pressure sensor array and a vibration sensor, and dynamically compensating sound wave attenuation in combination with environment parameters; voiceprint features are extracted through wavelet packet decomposition, and dynamic parameters such as digital twinborn associated traffic flow and architectural acoustic reflection are constructed; improving a time difference positioning algorithm and fusing vibration spectrum verification to suppress environmental reflection noise; in the digital twin model, sound field distribution is predicted through sound ray tracking, health risks are evaluated based on a frequency band weighting model, and a parameterized sound insulation screen design and traffic flow regulation and control strategy is generated. The system integrates a multi-source sensing module, a digital twin engine module and an anti-interference positioning and decision optimization module, and realizes full-flow intelligentization of noise signal acquisition, feature fusion modeling, anti-interference positioning and treatment scheme generation.

The invention relates to the technical field of intelligent traffic, in particular to a traffic flow multi-dimensional control method based on mutual feedback adjustment under vehicle-road cooperation, which comprises the following steps: collecting real-time traffic data of each intersection; after every preset time period, recommendation information is obtained in a preset mode; the preset mode comprises the following steps: respectively predicting the traffic flow of the entrance lane corresponding to the passing direction controlled by each signal phase in each intersection at the moment t; calculating the predicted vehicle waiting time of each entrance lane at the moment t based on the current queuing length and the signal phase state of each entrance lane; planning an optimal path for the target vehicle based on the predicted waiting time of each entrance lane and the road section driving time in the subsequent passing path; based on the predicted traffic flow of each entrance lane, determining the total duration of the signal period of the intersection, and optimizing the green light time ratio of each signal phase; and pushing the recommendation information to a vehicle navigation system corresponding to the target vehicle.

The invention provides a traffic signal intelligent regulation and control method and system, and relates to the technical field of traffic management, and the method comprises the steps: obtaining radar sensing data, video sensing data and traffic signal lamp sensing data in a monitoring range of a target single intersection, which are correspondingly collected by a plurality of sensors, according to a preset interval time; fusing the radar sensing data, the video sensing data and the traffic light sensing data to obtain multi-source fusion data of the target single intersection; inputting the multi-source fusion data into a pre-trained timing scheme generation model to obtain a timing scheme of the traffic lights at the target single intersection; performing simulation verification on the timing scheme, and issuing the timing scheme to a signal control machine of a target single intersection after the verification is passed; and determining the vehicle patency at the target single intersection based on the issued timing scheme, and optimizing the timing scheme based on the vehicle patency. According to the invention, the timing scheme of the traffic signal is dynamically adjusted, and the flexibility and the control precision of traffic flow control are improved.

A mainline traffic flow control optimization method based on a high-performance traffic simulation agent includes: selecting a target area road network and constructing a mainline section and interchange section structure; generating a multi-scenario sample set through micro-traffic simulation to simulate traffic operation status under different traffic demands, speed limits, and hard shoulder opening conditions; training a neural network model based on a deep learning method, using the target road section and its upstream and downstream multi-layer associated topology information as input features, and using travel time and average speed as labels to construct an efficient traffic simulation agent; combining a multi-objective optimization algorithm, using total travel time and carbon emissions as optimization targets, and dynamically iterating to generate the optimal control plan for road section speed limits and hard shoulder opening. The method of the present invention replaces traditional inefficient micro-simulation with a simulation agent, significantly improving computing performance, and achieving the scientific formulation of collaborative optimization of complex road networks and low-carbon control strategies.

The invention discloses a mixed traffic flow regulation and control method based on communication delay adaptation, and aims to overcome the defect that a traffic flow control method aiming at communication delay adaptation is lacked in the prior art. The method comprises the following steps: introducing communication delay to a cooperative adaptive cruise control (CACC) system, and constructing a mixed traffic flow model in combination with a vehicle type and a communication mode; constructing a critical equation, dividing a control compensation dominant area and a communication delay dominant area according to the mixed traffic flow model, and determining critical communication delay; and obtaining a safe time headway function according to the mixed traffic flow model, defining a ratio of a control compensation item to a communication delay item based on the function, performing safe time headway judgment according to the ratio, and realizing communication delay adaptive mixed traffic flow regulation and control. The blank of current research is filled, and a more refined optimization scheme is provided for actual traffic management.

To provide a traffic flow control device and a traffic flow control program that suppress an increase in energy consumption of a traffic flow.SOLUTION: A traffic flow control device comprises a communication unit 45, and a control unit 50, wherein: the control unit 50 acquires: information about a first controlling vehicle 11 communicating with the communication unit 45; information about a subsequent vehicle 15 traveling between the first controlling vehicle 11 and a second controlling vehicle 12 traveling behind the first controlling vehicle 11 and communicating with the communication unit 45; and information about a traffic light 20 located in front of the first controlling vehicle 11, and the control unit causes the first controlling vehicle 11 to behave in a manner that is based on consumption energy of the first controlling vehicle 11 and the subsequent vehicle 15 with respect to a behavior of the first controlling vehicle 11, which is obtained from the information about the first controlling vehicle 11, the information about the subsequent vehicle 15, and the information about the traffic light 20.SELECTED DRAWING: Figure 1

The application discloses a large railway hub overhead off-ramp dynamic traffic flow control method, realizes dynamic self-adaptive adjustment of vehicle stay time threshold according to real-time traffic state (queue length, lane occupancy rate and driving-in flow) of the ramp, automatically compresses the allowed stay time to accelerate vehicle turnover in peak congestion period, and appropriately relaxes in low peak idle period to improve humanized experience; a multi-dimensional dynamic correction factor system is constructed, time period characteristics (peak / flat / low), environmental factors (adverse weather such as rain, snow and fog, holidays / large events), historical overtime statistical rules and the like are brought into a dynamic control calculation model, and the adaptability and robustness of the control strategy are improved; a self-learning mechanism based on historical overtime rate feedback is introduced, so that the algorithm can automatically adjust the laxity according to actual operation data, stabilize the overtime rate in a preset target range, and realize continuous optimization iteration of the control strategy.

The invention relates to the field of automatic driving traffic flow control, in particular to an automatic driving travel conflict control method and system based on a crossing occupancy rate, and the control method comprises the steps: S1, taking a vehicle intersection at a fork crossing as a conflict control point; s2, delimiting a first control area and a second control area which take the conflict control point as the center; s3, if the front vehicle runs in the first control area at the preset speed and the rear vehicle enters the second control area, the speed of the rear vehicle is adjusted to the first speed, and the first speed is made to be smaller than the preset speed; if the front vehicle runs in the first control area at the preset speed and the rear vehicle running in the same direction as the front vehicle enters the first control area, the speed of the rear vehicle is adjusted to a second speed, and the second speed is smaller than the preset speed and larger than the first speed; the method can control the vehicle speed in advance according to the positions of the front vehicle and the rear vehicle, improves the traffic efficiency of the crossroad crossing, and reduces the collision risk.

The present invention discloses a traffic flow control method, system, terminal and medium based on in-transit information, which relates to the field of intelligent transportation. The key points of its technical solution are as follows: The present invention collects the travel time information of traveling vehicles at adjacent network nodes, and performs linear classification processing on the travel time information of multiple neighboring traveling vehicles. Without adding hardware devices, it can batch-screen out the traveling vehicles that did not stay in the service area at the previous network node as target vehicles, and generate a guiding instruction to timely guide the target to drive into the service area of the current network node for rest, enabling comprehensive and timely control and management of the traffic flow on the highway, thereby reducing the probability of traffic accidents.

A traffic flow control device has a communication unit and a control unit. The control unit receives information about a first controlled vehicle that is in communication with the communication unit, information about a following vehicle that is between the first controlled vehicle and a second controlled vehicle behind the first controlled vehicle and is in communication with the communication unit, and information about a traffic light that is positioned in front of the first controlled vehicle.The control unit causes the first controlled vehicle to behave based on the energy consumption of the first controlled vehicle and the following vehicle in relation to the behavior of the first controlled vehicle, whereby the energy consumption is obtained from the information relating to the first controlled vehicle, the information relating to the following vehicle and the information relating to the traffic light.

The present invention relates to the technical field of traffic flow control, and discloses an automatic control system for electronic information traffic flow based on a wireless sensor network. The multi-source perception module collects multi-dimensional traffic data of the road network through the wireless sensor network, and generates traffic situation characteristics through a specific data fusion method. The spatiotemporal feature fusion module uses the spatiotemporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs the fused features into a pre-trained distributed decision model to generate a control instruction set. The dynamic game optimization module constructs a multi-objective game optimization model and uses a dynamic game strategy decomposition algorithm to optimize traffic signal parameters. The hierarchical execution module executes control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer, and an intersection execution layer. The system can comprehensively collect and integrate traffic data, realize scientific decision-making and precise control, effectively improve road traffic efficiency, balance road network load, and alleviate traffic congestion.

The invention discloses an urban digital governance system and method based on digital twinning, and relates to the technical field of urban digital management, the system discloses a road type statistics module, the road type statistics module is used for obtaining road type data in a preset area, and the road type data comprises a main elevated road area, an expressway area, a main road and a secondary road; the region division module is used for dividing the road types in the preset region into different sub-range regions according to the road type data after obtaining the road type data in the preset region, counting the number of street lamps in each region after the sub-range regions are divided, and numbering the street lamps one by one; according to factors such as function layout, road types and traffic flow distribution of a city, a street lamp network of the whole city is divided carefully, intelligent control is performed on street lamp illumination by combining traffic flow control illumination conditions, and sustainable development of the city is assisted.

A real-time air traffic flow prediction method based on aviation situational awareness includes: constructing a traffic flow dataset that integrates spatiotemporal features; achieving efficient conversion from trajectory data to air traffic flow data based on time slice partitioning; extracting multi-source spatiotemporal features; constructing a prediction model using LightGBM, the core of which lies in: using the Tweedie loss function to model the zero-inflation and long-tail distribution characteristics of traffic flow data, and ensuring model robustness through five-fold cross-validation; establishing a multi-dimensional evaluation system; and in the application phase, dividing the airspace into a dynamic grid and combining radar-returned trajectory data to predict flight traffic flow within the airspace for the next few minutes in real time. This invention supports dynamic feature extraction and traffic flow prediction based on real-time radar data. Practical testing has verified that it effectively supports air traffic flow control decisions, enabling rapid response when airspace traffic changes, meeting the timeliness requirements of dynamic air traffic control, and significantly improving the scientific nature and response speed of air traffic control decisions.

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.

The utility model belongs to the technical field of urban traffic flow control, and particularly relates to a traffic flow control reversible lane isolation system, which comprises a control host, a fixed pile, a group of movable piles and two groups of fixed chassis, the two sets of fixed chassis are fixedly connected to marking lines on the two sides of a reversible lane respectively, the movable pile is in signal connection with the control host, the control host can control the movable pile to move on a road, and the movable pile can be fixedly connected with the fixed chassis when moving to one side of the fixed chassis. Isolation ropes are connected between every two adjacent movable piles and between the fixed piles and the movable piles. The movable pile and the fixed chassis are fixedly connected together, the stability of the movable pile after positioning is completed can be greatly improved, the phenomenon that the movable pile moves due to external force in the using process is reduced, and the stability of the reversible lane isolation system after positioning is completed is improved.

This invention discloses a smart city traffic optimization method and system based on 5G networks, belonging to the field of smart city traffic management technology. It addresses the issues of information consistency and conflict resolution in urban traffic flow control and shared parking space guidance. The method includes uploading traffic flow and signal phase status, combining parking space occupancy status and vehicle owner location information for path guidance and parking space recommendation, and identifying and managing parking space status conflicts across multiple heterogeneous platforms. It constructs parking space status behavior sequences, heterogeneous contention indices, state disturbance propagation graphs, and conflict score clusters, performing state masking and path correction for high-conflict parking spaces. Simultaneously, it collects vehicle owner and vehicle behavior data to complete parking space usage records and settlements, identify and process illegal parking, and constructs a shared parking space reservation permission management mechanism. This method is applicable to optimization and guidance under dynamic urban traffic control and multi-platform access to parking space resources.

A traffic flow control device includes a communication unit and a control unit. The control unit obtains information related to a first controlled vehicle communicating with the communication unit. Information related to a following vehicle traveling between the first controlled vehicle and a second controlled vehicle traveling behind the first controlled vehicle and communicating with the communication unit; and information related to a traffic light located in front of the first controlled vehicle. The control unit causes the first controlled vehicle to act on the basis of energy consumption of the first controlled vehicle and the following vehicle related to the behavior of the first controlled vehicle, the energy consumption being acquired from information related to the first controlled vehicle, information related to the following vehicle, and information related to the traffic light.

Techniques for traffic flow control are disclosed. In one aspect, an on-board computer (OBC) of a vehicle in a secondary traffic flow determines that a primary traffic flow is passing through an intersection associated with a traffic light, wherein the determination that the primary traffic flow is passing through the intersection is based on sensor data from one or more sensors of the vehicle, vehicle-to-vehicle (V2V) communication, vehicle-to-everything (V2X) communication, or any combination thereof, and reducing the speed of the vehicle to prevent the vehicle from triggering a traffic light sensor associated with the traffic light before the primary traffic flow has passed the intersection.

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 generally relates to establishment of a traffic flow system model and a reinforcement learning traffic flow control method based on a Lyapunov value function. Comprises: establishing a mathematical model of a traffic flow system; constructing an environment simulator, and converting a boundary control problem of the system into a Markov process; and designing an SAC reinforcement learning algorithm based on a Lyapunov value function, training an Actor-Critic network through interaction with an environment simulator, and realizing flow control of the traffic flow system through an optimal controller output by the Actor network.