42 results about "Traffic flow modeling" patented technology

The invention provides a bridge parallel steel wire pull sling corrosion fatigue residual life prediction method, and relates to the technical field of bridge safety assessment, and the method comprises the steps: obtaining first information and second information; establishing a random traffic flow model based on the first information; based on the second information and preset bridge pavement irregularity, cable force time history analysis considering vehicle-bridge dynamic response is carried out, and a response time history of a bridge system is obtained through analysis; processing the response time history based on a rain flow counting method to obtain all stress amplitudes of the bridge and corresponding cycle times; and calculating the corrosion fatigue duration of the bridge parallel steel wire pull sling by adopting a time increment method based on all the stress amplitudes and the corresponding cycle times, and outputting the fatigue life of the bridge parallel steel wire pull sling. The method is used for evaluating the residual life of the pull sling in the service period along with the change of the external environment, the evaluation model can be dynamically updated in combination with an actual inspection result, and effective combination of a theoretical model and actually measured data is realized.

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 a self-adaptive control method and platform for an intelligent traffic signal lamp, and relates to the technical field of intelligent control, and the method comprises the steps: collecting traffic flow data in real time through a multi-source sensing module, including the number of vehicles, the vehicle speed, the vehicle type and the pedestrian flow; according to the data, traffic condition prediction is carried out by using a traffic flow model, and traffic pressure values of roads in all directions are calculated and generated; and based on the traffic pressure value, determining the phase duration of the signal lamp in each direction, performing signal lamp control analysis, generating a corresponding signal control instruction, and executing adaptive control of the traffic signal lamp. The technical problems of low traffic efficiency and serious traffic jam caused by the fact that the existing traffic signal lamp control method cannot dynamically adjust the time length of the signal lamp according to the real-time traffic condition are solved, and the purposes of dynamically optimizing the phase time length of the signal lamp and giving consideration to the requirements of pedestrians are achieved. The traffic efficiency is improved; and the congestion is relieved.

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.

A device includes a memory and one or more processors coupled to the memory. The processor(s) are configured to obtain historical flight data that represents a plurality of flights associated with a plurality of airports and to generate a directed graph, based on the historical flight data, that includes a plurality of nodes and a plurality of edges connecting pairs of nodes. The plurality of nodes correspond to the plurality of airports and the plurality of edges correspond to the plurality of flights. The processor(s) are configured to perform a ranking operation on the directed graph to identify one or more target nodes of the plurality of nodes. The one or more target nodes correspond to one or more airports of the plurality of airports. The processor(s) are configured to output a graphical user interface that indicates the one or more airports as recommended airports for modeling predicted traffic flow.

The embodiment of the invention provides a vehicle lane changing influence prediction method and related equipment, and belongs to the technical field of traffic control. The method comprises the following steps: acquiring traffic environment data around a lane changing vehicle, wherein the traffic environment data comprises traffic flow data and vehicle-level data; according to the vehicle-level data, the influence of the lane changing vehicle on other vehicles when the lane changing vehicle is changed to the target lane is predicted through an improved car-following model, and a vehicle influence estimation index is obtained; according to the traffic flow data, the influence on the traffic flow when the lane changing vehicle is converted to the target lane is predicted through a traffic flow model, and a traffic flow influence estimation index is obtained; and performing lane changing benefit calculation according to the vehicle influence estimation index and the traffic flow influence estimation index to obtain a benefit index of changing the lane changing vehicle to the target lane. According to the embodiment of the invention, the accuracy of vehicle lane changing influence prediction can be improved, and an accurate reference is provided for the lane changing behavior of the lane changing vehicle.

The invention is suitable for the technical field of power distribution networks, and provides a flexible interconnected power distribution network and charging facility collaborative expansion planning method, which comprises the following steps: constructing a semi-dynamic traffic flow model according to obtained traffic network basic parameters and electric vehicle inventory data; based on the semi-dynamic traffic flow model, an electric vehicle charging and discharging space-time adjustment model is established in combination with electric vehicle charging and discharging adjustment constraints under the influence of the traffic flow; according to the obtained basic parameters of the power distribution network, a semi-dynamic traffic flow model and the electric vehicle charging and discharging space-time adjustment model, constructing a flexible interconnection power distribution network and charging facility collaborative expansion planning model; and converting and solving the collaborative expansion planning model to obtain a final planning scheme of the flexible interconnection power distribution network and the charging facility. The problems that power grid and traffic network coupling operation cannot be finely modeled and the multi-resource flexible adjustment capability cannot be fully excavated in power distribution network extension planning are solved, and the scientificity of a planning scheme and the power grid operation reliability are improved.

The invention provides an expressway lane level variable speed limit control method and device in severe weather, and is applied to the technical field of data processing. The method comprises the following steps: acquiring vehicle track and weather type data of each lane of a road section, and preprocessing to extract car-following and lane-changing data; a 2D-IDM and UDCL combined traffic flow model is constructed, parameters are calibrated based on a genetic algorithm, and driving behavior parameters are adjusted in combination with weather; an efficiency and safety double-target optimization model is established, and after multiple constraints are applied, an ant colony algorithm is used for solving a target speed limiting sequence; on the basis of a model predictive control framework, the traffic state is obtained in real time, evolution of 20-30 minutes in the future is predicted, target speed limit of each lane is output, closed-loop iteration is carried out, and a lane-level variable speed limit control model is formed; and finally, establishing different severe weather scenes, comparing with a non-control and integral control scheme, and combining macroscopic indexes and speed-time thermodynamic diagram analysis to generate a target speed limit control result.

The application discloses a kind of based on gradual large-scale short-time traffic flow prediction method, comprising: S1, the original data set collected by sensor is obtained, and the original data set is preprocessed;S2, construct based on gradual large-scale short-time traffic flow model, the processed multiple periodic time series data and driving distance adjacency matrix are used as the input of model, and the large-scale short-time traffic flow model is trained;S3, the current traffic data of input is predicted by the obtained model future traffic data.The method is based on the idea of gradual, time characteristics and spatial characteristics are fused by step-by-step fusion to more fully, more fully perform space-time fusion operation, effectively solve the problem of insufficient space-time fusion in existing method in large-scale traffic flow prediction, and greatly reduce the time overhead of calculation and space-dependent modeling;At the same time, the prediction effect is more accurate, can provide more accurate reference for urban traffic management.

The application discloses a macro dynamic traffic flow generation and evolution method and system for mixed traffic flow, and the method comprises the following steps: acquiring traffic flow basic data containing average speed, vehicle density, vehicle type ratio and automatic driving penetration rate; introducing a heterogeneous disturbance factor dynamically adjusted according to a heavy truck ratio in a continuity equation of a classical LWR model to obtain an improved traffic flow model to simulate fluctuation effects caused by mixed flow; calculating macro traffic flow by using the improved model; generating a virtual vehicle injection sequence based on a non-homogeneous Poisson process; detecting downstream density and speed changes in real time, dynamically correcting upstream injection intensity when approaching saturation, and suppressing reverse shock waves; deducing a start-end point matrix according to intersection flow by using a maximum entropy model; and finally outputting a virtual vehicle sequence with position, speed, vehicle type and target path, and injecting the virtual vehicle sequence into an automatic driving simulation platform. The application can accurately reflect congestion wave evolution characteristics under high heavy truck ratio mixed flow and improve test fidelity.

The invention belongs to the technical field of traffic group travel prediction methods, and particularly relates to a multi-modal data fusion group travel intelligent analysis method based on a large model, and the method comprises the steps: collecting user historical travel track truth value data; performing time serialization processing on all switched base stations of the user from the starting time to the ending time of the travel to obtain base station zipper data of the user; in combination with a user travel track path reconstruction technology, filling up zipper data of the base station, and constructing full-chain base station data; carrying out vectorization and normalization on the discrete characteristics of the data of the whole-chain base station based on a Sichuan outflow line big model through an embedding technology to obtain multi-dimensional vector representation data of user travel; inputting the multi-dimensional vector representation data as a time sequence algorithm, and training to obtain a user travel prediction model; and reasoning and predicting a user travel mode based on the user travel prediction model. The technical problem that an existing user travel mode prediction method is not high in data accuracy is solved.

The invention belongs to the technical field of heterogeneous traffic flow modeling and intelligent traffic management, and relates to a heterogeneous traffic flow macroscopic fundamental diagram experimental modeling method considering takeover behaviors, which comprises the following steps of: firstly, performing stage division on driving behaviors of L3-level man-machine co-driving vehicles, and selecting a car-following model; integrating TOV permeability and takeover rate, four typical car-following modes in heterogeneous traffic flow and a function degradation mechanism of cooperative adaptive cruise control to deduce theoretical scale models of vehicles of various models in the traffic flow. A joint simulation platform is built to restore a heterogeneous traffic flow scene, and a two-stage multi-cycle experiment process is designed to collect comprehensive traffic flow data under different density levels. Whether the experimental vehicle model proportion is matched with the theoretical vehicle model proportion or not is checked, and if yes, the improved Underwood model is used for fitting experimental data to obtain an experimental model of the macroscopic fundamental diagram. The method has the advantage that an accurate model basis is provided for heterogeneous traffic flow characteristic analysis and CAV cooperative control strategy formulation.

The application provides a signal system passing capacity evaluation method based on a sparse Gaussian process regression, and belongs to the field of intelligent traffic signal control. Specifically, the green signal ratio variable is included in the input feature and is simultaneously modeled to capture the mutual influence relationship between the signal configuration and the traffic state variable, so that a data-driven traffic flow model is developed by using the GP regression to reflect the complex nonlinear mapping between the high-dimensional variables, reduce the complexity of the calculation, obtain the uncertainty of the quantitative model, and accurately and dynamically evaluate the passing capacity of the intersection. The modified Gaussian kernel is used for modeling to capture the mutual influence relationship between the signal configuration and the traffic state variable, the data sparsification calculation is used to develop a data-driven traffic flow model by using the GP regression, the relationship that the signal timing (g / C) influences the traffic flow fluctuation with the change of the traffic flow is embodied, and the passing capacity of the intersection is dynamically evaluated.

The application belongs to the technical field of mixed traffic control, and discloses a mixed traffic multi-scale congestion suppression method mixed with slow-moving vehicles in a near-signal area, which comprises the following steps: S1. setting a three-lane mixed traffic scene mixed with network-connected automatic vehicles, human-driven vehicles and slow-moving vehicles in a near-signal area; S2. respectively analyzing the laws and influences of the red-green light interruption effect, the lane-changing behavior in the near-signal area and the slow-moving vehicle moving bottleneck effect; S3. establishing a mixed traffic flow model mixed with slow-moving vehicles in the near-signal area; S4. designing a network-connected automatic vehicle congestion feedback control method considering the flow difference; and S5. designing a speed guiding method of the network-connected automatic vehicle, so as to make speed suggestions and speed guidance for the network-connected automatic vehicle at the road section and vehicle scales. The application can effectively suppress the traffic congestion and parking problems caused by slow-moving vehicles and red-green lights, reduce fuel consumption, reduce the average travel and waiting time, improve the traffic efficiency, and provide a new idea for the traffic cooperative control problem in the near-signal area.

The invention discloses an urban road traffic state intelligent estimation method based on microphysical representation, and the method comprises the steps: constructing a road section boundary condition based on the arrival and departure accumulated flow information of an intersection and a gate, and forming multi-source traffic perception input through combining with road section observation data; constructing a physical-data hybrid driving model, constructing a deep learning model in a data driving branch, learning a mapping relation between road section boundary cumulative flow and a traffic state in a road section, and converting an indistinguishable Newell traffic flow model into a distinguishable computational graph structure through function approximation and structural conversion in a distinguishable physical branch; and constructing a loss function of the hybrid drive model, so that data drive output and a physical model are kept coordinated, and traffic flow parameters are jointly estimated. Through deep embedding of the microphysical calculation graph, the dependence of a pure physical model on an ideal assumed condition is made up, and meanwhile, the problem that a pure data driving method is insufficient in generalization ability in a sensing blind area is solved.

The invention discloses a C-V2X-based traffic monitoring management method and a C-V2X-based traffic monitoring management system, relates to the technical field of path planning, and solves the problems of relatively single influence factor and insufficient individuation degree of path planning in the prior art. Various data including vehicle driving data and traffic flow data are collected, a traffic flow model is constructed based on the traffic flow data to predict the traffic flow, then a clustering algorithm is adopted to construct a vehicle driving behavior model, vehicles are classified based on the vehicle driving data, and finally a target function is established to predict the traffic flow. And calculating an optimal driving route for each vehicle based on the traffic flow model and the vehicle driving behavior model. According to the method, data of more dimensions, such as driving behaviors, driving habits and vehicle types of vehicles, can be introduced into path planning, so that more personalized and accurate path planning is realized, the requirements of different drivers are met, and the overall efficiency and safety of a traffic system are improved.

The present application relates to the field of traffic control, and particularly relates to a vehicle behavior recognition method and system based on an intelligent expressway system, the method comprising: collecting state information of all vehicles located on a set road section at a fixed frequency, the state information comprising a heading angle and a planar coordinate and a horizontal velocity vector of the vehicle in a planar rectangular coordinate system with the starting point of the set road section as the origin; calculating a real-time lane-changing probability of a target vehicle, and determining that the target vehicle has a high lane-changing risk when the real-time lane-changing probability is greater than or equal to a preset threshold value. The present application does not rely on fixed road markings such as physical lane lines at all, and only performs traffic flow modeling and behavior analysis based on the motion trajectory of the vehicle itself, has excellent all-weather and all-road section adaptability, and can timely identify lane-changing risks by calculating the lane-changing probability of the vehicle in real time, providing a longer reaction time for the traffic management system and the rear vehicle, and effectively improving road safety.

The application provides a networked vehicle cooperative control method based on road network traffic information, and relates to the technical field of networked vehicle cooperative control, and comprises the following steps: establishing a vehicle dynamics model and a road network traffic flow model; at the upper intersection control layer, designing a consistency controller based on a Lagrange traffic flow model, adjusting the expected speed of an approaching vehicle platoon according to the traffic density information of adjacent intersections, so that the traffic density entering each intersection is consistent, and determining the upper limit of the communication time delay based on a system time-delay differential equation to ensure the consistency of the road network; at the lower tracking control layer, based on the vehicle dynamics model, using a leader-follower communication topology, designing a distributed tracking controller for a follower vehicle, so that the follower vehicle tracks the expected speed of a leader vehicle at an expected inter-vehicle distance, and determining the upper limit of the communication time delay based on a subsystem characteristic equation to ensure the stability of the vehicle platoon; and verifying the internal stability of the vehicle platoon and the stability of the platoon.

The modeling method and following method of car-following model considering the influence of multiple vehicles in front and acceleration information belong to the technical fields of vehicle-road cooperation and traffic flow modeling. In order to solve the problem that the existing car-following model does not consider comprehensive factors, thereby the overall starting time is long and the operation efficiency of the team is affected. The application considers the influence of the position and speed of the front vehicle and the second front vehicle on the host vehicle, and establishes a car-following model considering the influence of the acceleration of the vehicle in front, and then describes the car-following behavior based on the established car-following model. The application is suitable for modeling and following process of car-following model.

The invention relates to a system and method for directed graph-based airport prioritization for traffic flow modeling. An apparatus includes a memory and one or more processors coupled to the memory. The processor is configured to obtain historical flight data representing a plurality of flights associated with a plurality of airports and generate a directed graph comprising a plurality of nodes and a plurality of edges connecting pairs of nodes based on the historical flight data. The plurality of nodes correspond to a plurality of airports and the plurality of edges correspond to a plurality of flights. The processor is configured to perform a sorting operation on the directed graph to identify one or more target nodes of the plurality of nodes. The one or more target nodes correspond to one or more airports of the plurality of airports. The processor is configured to output a graphical user interface indicating one or more airports as recommended airports for modeling predicted traffic flow.

A server accesses vehicle data from each connected vehicle (CV) of a subset of a plurality of CVs on a roadway portion, the vehicle data comprising at least one of: a position, a velocity, or a headway. The server generates, based on the accessed vehicle data, a long-term shared world model. The server generates, using the long-term shared world model, a data structure representing predicted future velocities on the roadway portion by position and time by applying a traffic flow model to the long-term shared world model. The server transmits, to a connected autonomous vehicle (CAV), a control signal for controlling operation of the CAV based on the generated data structure.

The invention discloses a traffic state reconstruction method with physical consistency and uncertainty quantification, which comprises the following steps of: establishing a traffic fundamental diagram model, performing parameter calibration on the traffic fundamental diagram model, and deducing by combining a traffic flow model and the traffic fundamental diagram model to obtain a partial differential conservation equation of flow, density and speed; establishing a traffic state reconstruction model, and solving to obtain a density reconstruction value and an uncertainty quantitative index; the traffic state reconstruction model is combined with a long-short term memory network and a Bayesian physical information neural network, and a loss function of the Bayesian physical information neural network comprises a physical loss value, a data loss value and a regular term of network parameter posteriori distribution; and substituting the density reconstruction value into a traffic flow model to calculate and obtain a flow reconstruction value and a speed reconstruction value. The traffic reconstruction method is high in precision and has excellent interpretability, the reliability of the traffic state reconstruction model is further improved, and key decision support is provided for credible and reliable traffic operation and control.

The invention provides a cable-stayed bridge structure and traffic safety evaluation method under a cable-stayed bridge fracture condition, and relates to the technical field of bridge safety evaluation, and the method comprises the steps: dividing a bridge floor geometric space, constructing a two-dimensional cellular space, and generating a most unfavorable random traffic flow model in combination with limit traffic flow and vehicle weight statistical data; constructing an axle coupling dynamic model according to the vehicle distribution position in the traffic flow model and the physical characteristics of the bridge; performing bridge response simulation before and after single cable fracture based on the axle coupling dynamic model, judging a simulation result through a bridge structure and a driving safety condition, and when the simulation result meets the bridge structure and the driving safety condition, considering that the bridge is still safe under the cable fracture working condition, and obtaining a single cable fracture working condition result; and performing bridge response simulation under multi-cable continuous fracture by gradually increasing the number of the broken cables until the structural or driving safety conditions are not met, and outputting the maximum number of the broken cables. The problem that it is difficult to accurately evaluate the influence of cable-stayed bridge cable breakage on bridge safety operation is solved.

The application discloses a traffic state estimator design method and device for a multi-agent system, comprising: dividing a highway network into a plurality of cells according to preset positions, taking traffic flow density as a traffic state variable, establishing a traffic flow model of the highway network, and determining an output matrix of a traffic sensor; according to observability or detectability of the highway network system composed of a highway network system matrix and an output matrix of each type of traffic sensor, at least one state observer and at least one Kalman filter are constructed; all vehicle density estimation values obtained based on the state observer and the Kalman filter are respectively subjected to weighted summation fusion calculation to obtain first and second total vehicle density estimation values; and the first and second total vehicle density estimation values are subjected to weighted summation fusion calculation to obtain a final vehicle density estimation value. The application improves the state estimation precision of mixed traffic flow and improves the utilization efficiency of different types of sensors.

The invention relates to the field of traffic control, in particular to a vehicle behavior recognition method and system based on an intelligent high-speed system, and the method comprises the steps: collecting the state information of all vehicles located on a set road section at a fixed frequency, the state information comprises a course angle and a plane coordinate and a horizontal velocity vector of the vehicle in a plane rectangular coordinate system taking a starting point of a set road section as an original point; the real-time lane changing probability of the target vehicle is calculated, and when the real-time lane changing probability is larger than or equal to a preset threshold value, it is judged that the target vehicle has the high lane changing risk. The method does not depend on fixed road marks such as physical lane lines, traffic flow modeling and behavior analysis are carried out only based on the motion trail of the vehicle, the excellent all-weather and all-road-section adaptive capacity is achieved, the lane changing risk can be recognized in time by calculating the lane changing probability of the vehicle in real time, and the vehicle lane changing safety is improved. Longer response time is provided for a traffic management system and rear vehicles, and the road safety is effectively improved.

The application provides a bridge parallel steel wire pull cable corrosion fatigue residual life prediction method, and relates to the technical field of bridge safety evaluation, comprising: obtaining first information and second information; establishing a random traffic flow model based on the first information; performing cable force time history analysis considering vehicle-bridge dynamic response based on the second information and preset bridge road roughness, and obtaining the response time history of the bridge system; processing the response time history based on the rain flow counting method to obtain all stress amplitudes of the bridge and corresponding cycle times; based on all the stress amplitudes and corresponding cycle times, the time increment method is used to calculate the corrosion fatigue duration of the bridge parallel steel wire pull cable, and the fatigue life of the bridge parallel steel wire pull cable is output. The application is used for evaluating the residual life of the pull cable changing with the external environment during the service period, the evaluation model can be dynamically updated combined with the actual inspection results, and the effective combination of the theoretical model and the measured data is realized.

The application relates to the field of campus wisdom systems, and discloses a campus wisdom pick-up and drop-off management method, which comprises the following steps: acquiring real-time observation data of a target vehicle in a parking lot; updating a belief state of an internal traffic state of the parking lot based on the real-time observation data and a preset system dynamic model, wherein the system dynamic model is coupled with a traffic flow model for simulating micro behaviors of vehicles; and based on the updated belief state, a partially observable Markov decision process optimization algorithm is used to decide a target berth and a preliminary guide path for the target vehicle. The application combines the partially observable Markov decision process with the cellular automaton micro traffic flow simulation, and combines a particle filter to update the belief state, so that highly intelligent prediction and decision of the parking lot dynamics are realized, and the problems of poor adaptability, easy local congestion or uneven resource allocation of the parking lot in a complex dynamic environment are effectively overcome.

The embodiment of the application provides a vehicle lane-changing influence prediction method and related equipment, and belongs to the technical field of traffic control. The method comprises the following steps: acquiring traffic environment data around a lane-changing vehicle, wherein the traffic environment data comprises traffic flow data and vehicle-level data; predicting, according to the vehicle-level data, the influence of the lane-changing vehicle on other vehicles when the lane-changing vehicle changes to a target lane by using an improved car following model to obtain a vehicle influence estimation index; predicting, according to the traffic flow data, the influence of the lane-changing vehicle on traffic flow when the lane-changing vehicle changes to the target lane by using a traffic flow model to obtain a traffic flow influence estimation index; and performing lane-changing benefit calculation according to the vehicle influence estimation index and the traffic flow influence estimation index to obtain a benefit index of the lane-changing vehicle changing to the target lane. The embodiment of the application can improve the accuracy of vehicle lane-changing influence prediction and provide an accurate reference for the lane-changing behavior of the lane-changing vehicle.

The application discloses a highway traffic state estimation method and system fusing a traffic flow model and a graph neural network, and the method comprises the following steps: discretizing a highway into cells with a fixed length, and representing the traffic state of each cell by three parameters of flow, density and speed; determining the active influence area of the cell in a time period and the area to be affected by using the traffic wave speed between any two cells, and establishing a cell traffic state space-time evolution model; converting the discretized cell traffic state and adjacent information into graph structure data, and designing a graph neural network to update the traffic state of all cells; and based on the updated cell traffic state, using the gradient descent method to optimize the cell traffic state space-time evolution model, and obtaining the highway traffic state estimation result. The application can effectively improve the explainability, accuracy and generalization ability of the highway traffic flow space-time evolution modeling, and accurately grasp the highway traffic flow space-time evolution mechanism.

The invention discloses an intelligent network connection mixed traffic flow modeling method fusing perception risks. The method comprises the steps that mixed traffic data are collected and preprocessed, a two-dimensional risk field is constructed with the mass center of a vehicle as the core, the real-time perception risk value of each vehicle is calculated, and risk sensitivity parameters of different types of vehicles are set; after a risk-behavior coupled lane changing decision model and a risk-decision coupled longitudinal behavior model are constructed based on a real-time perception risk value, the model and the differentiated risk perception parameters are integrated to a unified simulation platform, permeability combinations of different types of vehicles are set, dynamic simulation of mixed traffic flow is executed, and a simulation operation result is output. According to the method, operation mechanism analysis and management and control strategy evaluation under multi-type vehicle permeability combination can be realized, and the behavior authenticity of a hybrid simulation model is improved.