169 results about "Traffic capacity" patented technology

The invention relates to the technical field of bridge structure health monitoring, and particularly provides a bridge earthquake damage rapid assessment method and system, and the method comprises the steps: carrying out the cross-modal coupling calibration of a time-history sudden change feature and a phase of a space energy spectrum, and generating a high-confidence three-dimensional dynamic response field; inputting the spatial gradient distribution characteristics into a nonlinear material state inversion program, performing path integrity verification in combination with an energy dissipation path, and outputting a potential damage topology boundary of the bridge; dividing an output bridge potential damage topological boundary into discretized damage units, and mapping strain constraints of the discretized damage units into triple decision-making indexes including a traffic capacity attenuation coefficient, an emergency disposal aging threshold value and an aftershock risk level through a bridge structure function attenuation rule. The system comprises a mixed field dynamic response reconstruction module, a damage sensitive domain reverse locking module and a function level damage quantitative mapping module. According to the method, deterministic mapping of the bridge structure damage state and the engineering function index is realized.

The invention discloses a traffic network toughness diagnosis method under flood disasters based on a space-time diagram neural network, and relates to the crossing field of traffic engineering and artificial intelligence. The method comprises the following steps: collecting traffic topology, flood monitoring and traffic flow data, and carrying out space-time alignment; a flood coupling dynamic space-time diagram is constructed, a water depth-traffic capacity response mechanism is introduced, and real-time mapping from a disaster physical state to a network topology is realized by utilizing an attenuation function meeting physical monotonicity constraint and dynamically updating an edge weight of a diagram structure according to real-time water depth; inputting the dynamic graph into a pre-trained space-time graph neural network model, extracting space-time evolution characteristics and outputting a toughness diagnosis result; model training adopts a toughness label generated based on an anti-fact baseline to carry out supervised learning, and introduces a physical constraint loss function. According to the method, the problems of decoupling of disaster features and graph structures and unavailability of toughness labels in the prior art are solved, and the physical consistency and accuracy of diagnosis are improved.

The invention relates to the technical field of intelligent traffic control, in particular to a single-intersection signal lamp dynamic timing method and system based on holographic perception, and the method comprises the steps: collecting the information of a single intersection and surrounding traffic participants in real time through a multi-source holographic perception device, and carrying out the preprocessing, thereby obtaining a standardized traffic data set; based on the data set, combining vehicle, pedestrian and non-motor vehicle demand weights, constructing a traffic demand evaluation model, and calculating a demand priority coefficient of each entrance lane; constructing a signal lamp dynamic timing optimization objective function by taking minimization of intersection total vehicle waiting time, minimization of pedestrian crossing waiting time and maximization of intersection traffic volume per unit time as objectives according to the priority coefficient in combination with intersection traffic capacity constraint conditions, solving to obtain an optimal signal lamp timing parameter, and issuing the optimal signal lamp timing parameter to a control terminal; and traffic state feedback data of the intersection are collected in real time, and timing optimization is carried out again if the change exceeds a preset threshold value. The system can wait for a long time, improves the intersection passing efficiency, and relieves traffic congestion.

The invention specifically relates to a tunnel group road section traffic capacity assessment method based on intelligent network connection, which relates to the technical field of traffic engineering, and comprises the steps of constructing an AI-driven dynamic assessment model based on fusion data and a digital twinborn model in combination with tunnel engineering constraints, and calculating the traffic capacity, congestion risk and bottleneck position of each road section of a tunnel group in real time. In the invention, a multi-source data acquisition system is combined with an edge and cloud two-stage fusion architecture to construct an LSTM-XGBoost double-model architecture; the LSTM model can calculate and predict the traffic capacity of four time nodes in the future 10 minutes in real time, the XGBoost model accurately locates the bottleneck position and quantifies the contribution degree of four types of causes, meanwhile, the precision is ensured through multi-dimensional verification, the tunnel group whole domain can be covered, instantaneous traffic changes can be captured, and a panoramic decision basis of real-time data, prediction trend and bottleneck causes is provided for traffic management.

The invention discloses an expressway reconstruction and extension traffic diversion method, system and device and a medium, and the method comprises the steps: obtaining multi-source dynamic traffic data, and constructing a road network origin-destination matrix through data fusion and demand deduction; a traffic flow distribution result is obtained by constructing a system optimal traffic distribution model; identifying the first congestion time period, and calculating the traffic flow exceeding the residual traffic capacity in the congestion time period to obtain the traffic flow to be shunted; solving an optimal shunting path under the target of minimizing the total travel time of the system through a hybrid intelligent algorithm to obtain an optimal shunting path scheme; and verifying the optimal shunting path scheme by using microscopic traffic simulation software, updating traffic data based on a simulation result, and carrying out iterative optimization to obtain a differentiated traffic organization scheme. According to the invention, the traffic flow passing efficiency during the highway reconstruction and extension period is effectively improved.

The invention discloses a road culvert inner wall damage image detection and traffic capacity evaluation method and system, and relates to the technical field of road engineering detection and computer vision, and the method comprises the steps: collecting multi-source data through an annular camera, a laser section scanner and a mileage encoder carried by a detection robot, and carrying out the panoramic expansion and time-space registration; using a multi-scale attention panoramic segmentation network to identify damages such as lining cracks, joint slab staggering, damage and chipping, steel bar exposure and water leakage; calculating the clearance size and the deposition thickness of the culvert by adopting an ellipse fitting method; according to the method, the culvert detection speed reaches 50 m / h, and the damage detection accuracy reaches 89% or above.

The invention relates to a community low altitude-oriented aircraft path planning and traffic capacity evaluation system. The system comprises an airspace facility modeling module, a facility relation judgment module, a hierarchical airspace segmentation module, a noise-speed-safe distance constraint calculation module, a dynamic obstacle marking module, a multi-target path planning module and a bottleneck identification and traffic capacity evaluation module. The method comprises the following steps: modeling airspace facilities, and judging a facility relationship based on spatial geometric attributes and function use requirements of the facilities; performing hierarchical grid division on the low altitude of the community, calculating a safety distance along the movement direction and a safety distance perpendicular to the flight direction, and performing obstacle marking on grids around the building based on the safety distances and the flight direction; and performing path search based on an improved A * algorithm, identifying a bottleneck region, extracting parameters, and calculating the maximum allowable speed of the bottleneck region and the total traffic capacity of the bottleneck region. The system can relieve environmental troubles such as safety compliance dispute and noise pollution caused by operation of the low-altitude aircraft.

The invention discloses a U-turn control method and device of an automatic driving vehicle and the automatic driving vehicle. The method comprises the steps that a first prediction track of a traffic participation object is acquired, and meanwhile, a second prediction track of a target automatic driving vehicle is acquired; when it is determined that a coincident trajectory exists between the first prediction trajectory and the second prediction trajectory based on a comparison result of the first prediction trajectory and the second prediction trajectory, obtaining first arrival time when the traffic participant arrives at each prediction coincident point on the coincident trajectory; evaluating the first arrival time to obtain the influence degree of the traffic participant on the turning-around operation; adjusting the original turning-around strategy according to the influence degree to obtain a target turning-around strategy; and controlling the target autonomous vehicle to execute a turning-around operation according to the target turning-around strategy. According to the invention, the technical problem that the traffic capacity of the autonomous vehicle is limited due to the obvious insufficiency of the autonomous vehicle turn-round path planning in the aspects of dynamic traffic flow influence processing and adaptive adjustment in the related technology is solved.

The invention relates to the technical field of rail bridge engineering, and particularly discloses a rail-bridge system residual traffic capacity assessment method, medium and equipment, and the assessment method comprises the steps: constructing a rail-bridge system seismic response model and a train-rail-bridge system coupling vibration model of a research object; constructing an earthquake-induced irregularity sample library; establishing a simplified calculation model of the shock-actuated irregularity equivalent coefficient; performing curved surface fitting to obtain a shock-induced composite unsmooth fitting curved surface; putting forward earthquake-induced irregularity thresholds in different driving performance index states, and establishing a linear mapping relation between the earthquake-induced irregularity thresholds and the driving speed; and establishing a mapping relationship among the seismic oscillation intensity, the structure natural vibration period and the post-earthquake driving speed limit value in different driving performance index states, and outputting a residual traffic capacity evaluation result according to the post-earthquake driving speed limit value. According to the method, the problem that in an existing method, the quantitative mapping relation between the current rail surface irregularity after the earthquake and the train operation performance is not clear is effectively solved.

The invention discloses an intersection signal control method based on minimum blocking probability and highest traffic capacity, and relates to the technical field of intelligent traffic systems. A bilevel planning optimization model is constructed, an upper layer model takes minimization of intersection blocking probability as a core target, a blocking probability function is established through dynamic quantification of traffic flow random volatility, and a sequential quadratic programming algorithm is fused to optimize a signal period and effective green light time parameters of each phase; the lower layer model is based on a Webster delay model, and feeds back the traffic state in real time by taking the minimization of the total delay of the vehicle as a target. The upper layer model and the lower layer model realize collaborative optimization through a closed loop mechanism of parameter adjustment-delay feedback-iterative optimization, and meanwhile, a dynamic weight coefficient is introduced to flexibly coordinate multi-target conflicts of the blocking probability, delay and traffic capacity. According to the method, the dynamic response capability of signal timing to the real-time traffic demand is remarkably improved, the peak delay of the intersection is effectively reduced by 20%, the average peak delay is effectively reduced by 15%, and the road network toughness is enhanced.

The invention discloses a multi-agent traffic signal control method based on traffic flow dynamic prediction, and the method comprises the steps: constructing an independent reinforcement learning agent for each intersection, precisely depicting a causal mechanism of signal decision to congestion propagation through an intersection-level queue evolution model, and carrying out the prediction of a traffic signal on the basis of a simple traffic state index. According to the method, the comprehensive traffic state fusing the traffic capacity of vehicles at the intersection and the future queue length is established, the method is used for dynamic prediction of each intersection based on the traffic flow state, an intelligent agent can select the action enabling the queue length to be reduced fastest, and therefore a better control effect is achieved. And a neural network model is constructed based on transformer, and optimal traffic phase control based on a reinforcement learning algorithm is realized by using time sequence characteristics and spatial characteristics of multi-intersection traffic flow. The method can effectively utilize the current signal phase and the vehicle state and distribution to predict the queue change after phase switching, and can be used for traffic agent design of a large-scale road network.

The invention relates to a layered collision detection and path search method for a sweeper structure. According to the method, based on the vehicle body structure characteristic that the sweeper is wide in the upper portion and narrow in the lower portion, environment point cloud data are subjected to hierarchical processing according to height, and at least low-layer obstacles and high-layer obstacles are distinguished; and determining the width difference of the vehicle in the high-layer space and the low-layer space in combination with the vehicle equivalent widths corresponding to different heights, and implementing differential expansion processing on the obstacles with different heights in the grid map. On the basis of completing layered expansion, a unified layered Voronoi diagram is constructed, the layered Voronoi diagram is introduced into a path search process, and the driving path of the sweeper is constrained or guided, so that the effective passing space below the blind compensation radar is fully utilized on the premise of ensuring the obstacle avoidance safety, and the blind compensation efficiency is improved. And the traffic capacity and the path planning efficiency of the sweeper in a complex and narrow scene are improved.

The invention provides a safety-oriented subway station passenger flow dynamic control method and system, and relates to the technical field of rail transit, and the method comprises the steps: calculating the time utility of each directed path and the selection probability of stairs and ladders according to real-time detection data, simulating the pedestrian walking process based on a microscopic pedestrian simulation model, and constructing a subway station simulation model; performing gridding processing on the preset risk detection area, constructing a multi-level comprehensive risk degree evaluation model based on the subway station simulation model in combination with the congestion level, the space risk, the train operation condition and the traffic capacity, and determining the comprehensive risk degree; constructing a prediction model; and with the minimization of the difference between the predicted risk degree and a set safety reference value as a target, optimizing the pull-in service delay time through closed-loop control to obtain the pull-in service delay time. The invention provides a metro passenger flow control method oriented to microscopic crowd safety. The metro passenger flow control method overcomes the defect that in the prior art, only the macroscopic service level or congestion is concerned, and local dynamic risks are ignored.

Techniques for load balancing secure network traffic are disclosed. A system, process, and / or computer program product for load balancing secure network traffic includes monitoring network traffic for one branch of a plurality of branches for an enterprise network, and splitting the network traffic of the one branch into a plurality of network segments based on a determination that the network traffic exceeds traffic capacity of at least one security processing node (SPN) of a plurality of SPNs using a network load balancer (NLB) in communication with a plurality of Network Processing Nodes (NPNs), the plurality of monitored branches being distributed to the plurality of SPNs via a plurality of tunnels.

The invention discloses a highway passenger and freight mixed traffic flow modeling method based on METANET model optimization. The method comprises the following steps: constructing a dynamic critical density correction function based on a freight car proportion; establishing a differentiated lane changing behavior decision-making mechanism, respectively setting lane changing probabilities based on a Logit utility function and a suppression coefficient for cars and trucks, and enhancing the dynamic simulation capability of lane transverse flow distribution; and constructing a ramp interaction model coupled with the truck proportion, and analyzing a traffic capacity attenuation mechanism of the confluence area. According to the method, the macroscopic modeling advantage of the METANET model is kept, and the method has good structural compatibility and generalizability. According to the method, the prediction precision of the flow, the speed and the density in a multi-vehicle mixed traffic scene is superior to that of a traditional model, and the method is suitable for modeling, simulation and control of an intelligent high-speed system.

The invention discloses an intersection traffic capacity assessment method and system based on multi-source data fusion. The method comprises the steps of obtaining intersection static model data and delay index data; counting the number of effective lanes in each flow direction based on the intersection static model data, determining the maximum number of lanes in each phase, and calculating the basic green time ratio of the corresponding phase; calculating a flow direction comprehensive delay index based on the real-time delay index data and the flow direction lane number, and taking the ratio of the flow direction comprehensive delay index to the intersection comprehensive delay index as a flow direction delay green time ratio; time period division is carried out based on historical delay index data, and fusion weights of a basic green time ratio and a flow direction delay green time ratio in each time period are distributed; calculating a final green time ratio of each flow direction; and evaluating the traffic capacity of each flow direction of the intersection according to the final green signal ratio and the theoretical traffic capacity. According to the method, the basic lane configuration data and the real-time delay data are combined, intersection traffic capacity evaluation is carried out under the condition of not depending on intersection equipment arrangement and flow data, and the accuracy and universality of traffic capacity evaluation are remarkably improved.

The invention discloses a river-sea combined transport ship collaborative scheduling method and system based on multi-objective optimization, and the method comprises the steps: collecting and fusing dynamic data, static data, scheduling application data and channel environment data of river ships and sea ships, and obtaining fused multi-source heterogeneous data; constructing a channel digital model, and calculating the dynamic traffic capacity of each channel according to the fused multi-source heterogeneous data; constructing a multi-objective optimization model by taking the minimization of the total scheduling time and the total waiting time of all ships as an objective; solving the multi-objective optimization model based on an improved DE-NSGA-II algorithm to obtain a Pareto optimal solution set; and selecting a final scheduling scheme from the Pareto optimal solution set and executing the final scheduling scheme. The ship operation cost is remarkably reduced, carbon emission is reduced, and green port construction is assisted. And the waiting time and the total voyage of the ship are reduced, so that the fuel consumption and the operation cost of the ship are directly reduced.

The application relates to a traffic capacity estimation method based on traffic basic graph parameter calibration, belongs to the technical field of intelligent traffic systems and traffic flow modeling, and particularly relates to a traffic capacity estimation method based on traffic basic graph parameter calibration. The application aims to solve the problem that the existing method cannot obtain accurate traffic basic graph parameters, resulting in low traffic capacity estimation precision. The process is as follows: step one, obtaining historical traffic flow single vehicle data samples of a road section; processing the obtained single vehicle data samples to obtain a final aggregated data set; step two, obtaining adaptive weights based on kernel density estimation based on the obtained final aggregated data set; step three, obtaining an optimal parameter set of a traffic basic graph model by adopting a particle swarm optimization algorithm based on the adaptive weights; and step four, obtaining traffic capacity based on the optimal parameter set of the traffic basic graph model.

Techniques for load balancing secure network traffic are disclosed. A system, process, and / or computer program product for load balancing secure network traffic includes monitoring network traffic for one branch of a plurality of branches for an enterprise network, and splitting the network traffic of the one branch into a plurality of network segments based on a determination that the network traffic exceeds traffic capacity of at least one security processing node (SPN) of a plurality of SPNs using a network load balancer (NLB) in communication with a plurality of Network Processing Nodes (NPNs), the plurality of monitored branches being distributed to the plurality of SPNs via a plurality of tunnels.

The invention discloses an intersection turning lane line correction method and device, electronic equipment and a storage medium, and the method comprises the steps: generating the global path planning information of a vehicle according to the position information of the vehicle and the information of an intersection to be turned around; and according to an initial lane line in the global path planning information, obtaining an optimized lane line after path control point resampling, curve fitting and path remodeling in sequence, so that the self-vehicle realizes path planning at the intersection to be turned round according to the optimized lane line. According to the invention, the traffic capacity of the automatic driving vehicle at a complex intersection is improved, and an important guarantee is provided for the safety and stability of the automatic driving technology.

The invention relates to the technical field of vehicle control, in particular to a vehicle passing processing method, electronic equipment and a vehicle. The method comprises the following steps: acquiring environment sensing data and vehicle driving data; performing region division processing on the environment sensing data by using a region extraction model to obtain a region division result; performing traffic strategy prediction processing on the region division result by using a traffic prediction model to obtain a traffic strategy prediction result; and based on the region division result, the passage strategy prediction result and the vehicle driving data, performing fusion analysis processing by using a fusion analysis model, and determining and outputting a passage index of the passage strategy prediction result. According to the method, the traffic difficulty corresponding to the corresponding traffic strategy prediction result can be determined according to the traffic index of the traffic strategy prediction result, a user can be assisted to carry out accurate vehicle driving control, the traffic index can be responded and output in real time, the timely control effect of the vehicle is ensured, and the traffic capacity of the vehicle in various complex environments can be improved.

The present application relates to the technical field of intelligent traffic control, and more particularly to a single intersection signal lamp dynamic timing method and system based on holographic perception, comprising: real-time collection of single intersection and surrounding traffic participant information through multi-source holographic perception equipment, preprocessing to obtain standardized traffic data set; based on the data set, combining vehicle, pedestrian and non-motor vehicle demand weight, constructing a traffic demand evaluation model, calculating the demand priority coefficient of each entrance; according to the priority coefficient, combining the intersection traffic capacity constraint condition, taking the minimization of the total vehicle waiting time at the intersection, the minimization of the pedestrian crossing waiting time and the maximization of the intersection unit time traffic volume as the target, constructing a signal lamp dynamic timing optimization objective function, solving to obtain the optimal signal lamp timing parameters and issuing to the control terminal; real-time collection of intersection traffic state feedback data, and if the change exceeds the preset threshold, re-timing optimization. It can reduce waiting time, improve intersection traffic efficiency and relieve traffic congestion.

The invention provides a busy waypoint traffic capacity simulation evaluation method based on a release interval. The method comprises the following steps: step 1, preprocessing flight track data of a flight; step 2, extracting waypoint operation characteristic parameters; step 3, waypoint traffic capacity evaluation simulation; according to the method, traffic flow distribution conditions of different height layers at waypoints, passing-point flight release time interval distribution, aircraft model hybrid ratio and other characteristic data are counted based on historical data, a waypoint topological structure is combined, a busy flight height layer of a busy waypoint serves as an experimental scene, and the simulation evaluation method is constructed.

The invention discloses a main line intersection CAV special lane setting method and a self-adaptive control system in a mixed driving environment. The method comprises the following steps: collecting information in real time; according to the current CAV permeability, the traffic capacity in the trunk line direction in three different states is calculated, the maximum net income of the traffic capacity is calculated, and then lane function switching judgment conditions are set in a mode of comparing the maximum net income of the traffic capacity; the lane function switching judgment condition is used for judging whether to open or close a CAV special lane, and determining that the CAV special lane is set as a CAV full-steering special lane or a CAV straight special lane; and iteratively optimizing the generated signal control scheme by adopting a PS-PPO-based multi-agent deep reinforcement learning algorithm, and generating an optimal signal control scheme in real time. According to the invention, the hybrid lane and CAV lane functions are dynamically adjusted according to the change of the CAV permeability, the cooperative optimization of the lane functions and the signal control scheme is realized, and the traffic efficiency of the trunk line intersection is improved to the greatest extent.

The invention discloses a road construction area intelligent sealing control and scheduling method fused with edge calculation, and relates to the technical field of road construction traffic management, and the method comprises the steps: S1, data collection and fusion: deploying edge calculation nodes at the periphery of a road construction area, and enabling the edge calculation nodes to access and fuse data collected by a multi-source sensor in real time, the data comprises traffic flow data acquired by a traffic camera, traffic flow speed data measured by a millimeter wave radar, global positioning system (GPS) positioning data of a construction vehicle, and visibility and precipitation data acquired by a weather sensor. By fusing edge calculation and multi-source sensor data, the traffic flow change can be responded in real time, the sealing control range is shrunk in the peak to reduce congestion, and road resources are reasonably adjusted and prevented from being idle in the flat peak; the device can adapt to weather condition changes, a safe buffer area is expanded in low-visibility environments such as rainstorm and heavy fog, and the traffic capacity is recovered in time when the weather turns good.

The invention relates to a BIM-based traffic engineering electronic sand table simulation and analysis system. The method comprises the following steps: fusing a satellite image and laser radar data through an initial state modeling module, constructing a three-dimensional road model through point cloud processing and image fusion, establishing a lane-level reference in combination with environmental data, and determining the initial state of a traffic flow through trajectory smoothing, behavior recognition and particle filtering. And the simulation risk identification module predicts a braking distance by using a neural network based on the initial state of the traffic flow, simulates the multi-scene traffic flow after adjusting the traffic capacity, and identifies the high-risk area distribution. And the risk quantification early warning module calculates a risk value through Monte Carlo simulation according to high-risk area distribution, extracts a key factor optimization model and triggers graded early warning. By adopting the system, the modeling precision and the risk prediction accuracy can be improved, quantitative support is provided for traffic management, and the management scientificity and the intelligent level are improved.

The application discloses a kind of based on flight flow control restriction information autonomous release sequencing method and system, method includes: flow control restriction information is structured as including time, waypoint and digitalization constraint of traffic capacity and storage;According to the constraint screening affected flight, and it is divided into A class flight with fixed calculation takeoff time and B class flight without fixed time;With A class flight time as reference, under multiple constraints such as meeting calculation wheel block time and flow control capacity, dynamically calculate distribution suggestion takeoff time slot for B class flight;Finally, generate flight autonomous release sequencing sequence and output are integrated.This application method and system realize the change from artificial experience decision to intelligent automatic calculation in release sequencing, while guaranteeing instruction compliance, significantly improve sequencing efficiency, rationality and system response dynamic change robustness.

This invention belongs to the field of intelligent unmanned systems and path planning technology. It discloses a dynamic evaluation method for the path mobility of unmanned vehicles applicable to complex environments. This method constructs a mapping relationship between traffic influencing factors and traffic coefficients; and builds a closed-loop learning system of "perception-decision-verification-update" using a convolutional neural network (ResNet) and a graph attention network (GAT). Compared to traditional mobility evaluation methods with their single evaluation conditions and the high computational complexity and lack of adaptability of traditional overlay analysis methods, this invention effectively improves the accuracy and adaptability of mobility evaluation by introducing multi-source environmental factor modeling and machine learning mechanisms, providing scientific and efficient decision support for unmanned vehicle path planning.