102 results about "Traffic flow modeling" patented technology

The invention belongs to the field of intelligent traffic systems, and relates to a multi-mode space-time traffic flow modeling method supporting large-scale road network real-time prediction, and the method comprises the steps: firstly designing a space-time prediction framework facing a dynamic traffic network, and then carrying out the training to obtain a final prediction model; the space-time prediction framework comprises a data embedding layer, a space-time coding module and a deep modeling and output module based on an expert hybrid mechanism; the data embedding layer comprises two parallel channels of time embedding and spectral domain space embedding and a time-space data fusion module; the space-time coding module comprises a block-level sparse time attention module, a space attention-message passing module and a weighted fusion layer which are parallel; the deep modeling and output module based on the expert hybrid mechanism comprises an MoE dynamic expert modeling module, a full-connection mapping module, a jump connection layer and an output layer; according to the design, the response speed, the prediction precision and the cross-regional adaptive capacity of the model in a high-heterogeneity scene are improved.

The invention discloses an expressway digital twinborn dynamic construction method, which relates to the technical field of traffic, acquires and preprocesses multi-dimensional data of an expressway system, and constructs a traffic flow model of an expressway based on a deep learning technology and in combination with the multi-dimensional data of the expressway system. The method comprises the steps of mapping multi-dimensional data, collected in real time, of an expressway system to a traffic flow model, dynamically updating a traffic state in the traffic flow model, performing short-term traffic flow prediction by using the traffic flow model, simulating influences when different traffic events occur, and analyzing existing risks. According to the method, the valuable traffic information is extracted from the mass data by capturing and analyzing the traffic flow data in real time and utilizing the machine learning and data mining technology, so that the digital twinborn body can accurately predict the future traffic flow and provide a decision-making basis, the occurrence of traffic congestion and accidents is reduced, and the traffic flow prediction efficiency is improved. And the traffic capacity and the safety of the expressway are improved.

The invention provides a high-precision perception-driven intelligent road network collaborative optimization method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting road sensing data through multi-sensor fusion, and obtaining multi-source perception data; performing dynamic traffic flow modeling according to the multi-source sensing data to obtain a traffic state prediction result; performing optimization decision on the traffic state prediction result to obtain an intelligent optimization decision; performing road network level cooperative control on the intelligent optimization decision to obtain a road network level cooperative control result; and performing real-time dynamic adjustment by using the road network level cooperative control result to obtain a road network scheduling result. According to the method and the device, the technical targets of global collaborative scheduling, improvement of traffic flow prediction precision, optimization of a real-time dynamic scheduling scheme and improvement of the intelligent level of urban traffic management can be realized, and the technical effects of reducing traffic congestion, improving road traffic efficiency, optimizing traffic resource allocation and improving emergency response capability are achieved.

The invention discloses a sensing intelligent driving multi-mode traffic signal optimization method and system, and relates to the technical field of traffic signal optimization, and the method comprises the steps: collecting traffic flow data; constructing a multi-mode traffic flow model; connecting a plurality of traffic lights in the target area to obtain a street lamp control signal; based on the traffic flow data and the street lamp control signal, using the multi-mode traffic flow model to predict a traffic flow change trend; and configuring a timing optimization scheme of a plurality of traffic lights in the target area according to the traffic flow change trend prediction result. According to the method and the device, the technical problem of low traffic intersection passing efficiency caused by the fact that a traffic signal lamp timing scheme cannot be dynamically adjusted accurately according to real-time traffic conditions and multi-mode traffic behavior interaction relations in the prior art is solved, and the purposes of optimizing the traffic signal lamp timing of the target area according to the prediction result and improving the traffic signal lamp timing efficiency are achieved. And the traffic efficiency of the traffic intersection is effectively improved.

The invention discloses a non-stationary time sequence flow prediction method and device based on delay space-time dependence, and a medium, and relates to the technical field of intelligent traffic. The method comprises the following steps: constructing a prediction problem of non-stationary traffic flow, and decomposing a traffic flow time sequence into a time-varying component and a time-invariant component by using Fourier transform; based on the time-varying component and the time-invariant component, constructing a Moran operator and a spatial-temporal feature fusion framework, and extracting spatial and temporal features of traffic flow; and based on the space and time characteristics of the traffic flow, predicting the traffic flow and dynamically adjusting parameters by using function-to-function regression and a Bayesian Kriging optimization model. The method is superior to a traditional baseline model in a complex traffic flow prediction scene, and new technical support is provided for non-stationary traffic flow modeling and prediction optimization.

The invention discloses an intelligent traffic system and digital twinborn technology integration method under a dual-carbon strategy. A data acquisition module, a data preprocessing module, a carbon emission model, an air quality model, a traffic flow model, a carbon fusion model, a digital twinborn visualization module and a dynamic simulation and prediction module are included. The system also comprises data synchronization and integration, and real-time monitoring and feedback. According to the invention, the monitoring and prediction precision of carbon emission, air quality and traffic flow is improved; real-time dynamic monitoring and optimization of the traffic system are realized, and the intelligent level of traffic management is enhanced; through a multi-objective optimization algorithm, traffic flow, air quality and carbon emission are comprehensively considered, and an optimized traffic management strategy and an optimized carbon emission reduction measure are provided; and by adopting a digital twinning technology, visualization and real-time feedback of data are realized, and transparency and operability of traffic system management are improved.

The invention discloses a road construction dustproof evaluation method, which comprises the following steps: constructing a macroscopic traffic flow model based on a cellular automaton, taking a visibility distribution field as an input condition, simulating behavior responses of drivers under different visibility, including vehicle speed selection and vehicle distance maintenance, and obtaining a traffic flow state evolution process in a construction area; the visibility, the vehicle speed and the vehicle distance serve as state input, a deep reinforcement learning algorithm is adopted to train a microscopic driving behavior model, a driving behavior strategy is optimized through a reward and punishment mechanism, and accurate description and prediction of driving behaviors under different visibility conditions are achieved; and calling a preset traffic control strategy library according to the influence degree of the road construction dust on the traffic safety, and selecting a proper traffic control scheme for dust prevention, including adjustment of a lane speed limit value and release of warning information. According to the invention, the traffic safety risk caused by construction dust is effectively reduced, and the road traffic safety is guaranteed.

The invention provides an urban traffic dynamic deduction and real-time planning method and system based on multi-source position data fusion. Time-space references of signaling, MDT and MR data are unified through a dynamic rasterization mechanism, and the problem of data isomerism is solved by adopting anchor point selection of signal weighting and a triple criterion time expansion algorithm; generating a minute-level updated dynamic OD matrix based on the hierarchical road network constraint model; a METANET macroscopic traffic flow model and EKF extended Kalman filtering are fused to realize traffic state deduction, and parameter online calibration is supported; signal timing optimization, path induction and emergency control schemes are generated in combination with real-time deduction results, and a sensing-deduction-decision-evaluation closed loop is formed. The method can improve road network traffic efficiency, and is especially suitable for response of sudden traffic events.

The embodiment of the invention discloses a multi-source big data traffic control management method and system based on a distributed architecture, and belongs to the technical field of intelligent traffic. The problem that in the prior art, traffic situation prediction precision is insufficient, and consequently traffic regulation and control are not accurate is solved. Comprising the following steps: constructing a dynamic traffic flow model based on extracted feature data and a dynamic space-time diagram corresponding to a traffic network; deducing the traffic situation through a central node in a traffic road network to obtain a plurality of traffic prediction scenes, and matching corresponding traffic regulation and control strategies; after the abnormal traffic event is detected, an affected area is delimited according to the corresponding propagation influence trend of the abnormal traffic event on the space axis; dynamically combining a plurality of operation units corresponding to the traffic regulation and control strategy through the central node, and generating a customized regulation and control scheme; and each edge node performs joint optimization on the customized regulation and control scheme by using a distributed optimization algorithm based on local traffic flow data so as to realize traffic control management.

The invention provides a signal lamp synchronization method and device, processing equipment, a program product and a medium, and relates to the technical field of Internet of Vehicles, the method comprises the following steps: obtaining signal lamp configuration information of a simulated traffic environment and mapping relation information between the simulated traffic environment and a real traffic environment, testing a signal phase and time sequence SPAT message of a real intersection received by the vehicle in a real traffic environment; according to the signal lamp configuration information, the mapping relation information and the SPAT message, determining a signal lamp group state of a simulation intersection corresponding to the real intersection; updating the simulated traffic environment according to the signal lamp group state corresponding to the simulated intersection so as to realize signal lamp synchronization between the simulated traffic environment and the real traffic environment; the simulated traffic environment is a traffic flow model which is established in simulation software and is used for simulating a real traffic environment. According to the scheme, the problem that the simulation world signal lamp and the real world signal lamp are difficult to accurately synchronize in the prior art is solved.

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 an expressway traffic state estimation method and system fusing a traffic flow model and a graph neural network, and the method comprises the steps: discretizing an expressway into cells with a fixed length, and representing the traffic state of each cell through three parameters: flow, density and speed; determining an active influence region and a region which will be influenced of the cells in a time period by using the traffic wave velocity between any two cells, and establishing a cell traffic state space-time evolution model; converting the discretized cellular traffic state and the adjacency information into graph structure data, and designing a graph neural network to update all cellular traffic states; and based on the updated cellular traffic state, optimizing the cellular traffic state space-time evolution model by using a gradient descent method to obtain a highway traffic state estimation result. The expressway traffic flow time-space evolution modeling method can effectively improve interpretability, accuracy and generalization ability of expressway traffic flow time-space evolution modeling, and accurately grasp an expressway traffic flow time-space evolution mechanism.

The invention discloses a method for evaluating the time-varying reliability of a prestressed concrete continuous box girder bridge based on actually measured traffic flow, which comprises the following steps of: establishing a finite element model of the prestressed concrete continuous box girder bridge according to a bridge structure design drawing, and calculating and defining the section attribute of each section in the longitudinal bridge direction; determining the most unfavorable section position of the bridge structure; the time-varying bearing capacity of the most unfavorable section is calculated through the constructed degradation model; preprocessing the data acquired by the dynamic weighing system; establishing the preprocessed dynamic weighing data into a numerical model to realize the construction of a traffic flow model; performing bridge dynamic analysis to obtain a load effect; calculating a time-varying reliability index in the service period of the bridge; and comparing the calculated time-varying reliable index with a target reliable index in the specification. According to the method, the time-varying reliability of the in-service prestressed concrete continuous box girder bridge in the service period can be accurately evaluated, and subsequent management and maintenance work is guided.

The invention relates to a traffic flow prediction model, and aims to solve the problems that in the prior art, the model input length is limited, time-space dependency relationship modeling is difficult, and traffic flow periodic feature modeling is insufficient. The invention provides a traffic flow prediction model based on space-time masking and feature fusion. According to the model, firstly, traffic flow data is randomly masked by using a space-time mask mechanism so as to rebuild missing information; then, short-term traffic flow modeling is carried out in combination with a graph convolutional network; and finally, designing a self-adaptive periodic modeling mechanism, comprehensively considering periodic characteristics of festival and holiday data and workday data, and generating effective spatio-temporal characteristic representation through a model hidden layer fusion mask mechanism. According to the invention, the problems of overlong traffic flow prediction sequence and complex space-time dependence can be solved, and more accurate traffic flow prediction can be carried out in the future.

The invention discloses an electric vehicle charging scheduling method and system considering a power-carbon market, and the method comprises the steps: firstly constructing a power-carbon market cooperation model which comprises a power transmission network low-carbon operation model, a power distribution network cooperation operation model, a power transmission network carbon emission flow tracking model, and a power distribution network carbon emission flow tracking model; then, on the basis of a user equilibrium theory, a traffic flow model of the electric vehicle is constructed with the purpose of minimizing the total driving time, the charging time and the charging cost of the electric vehicle, and then a charging station planning and charging integrated management model of the electric vehicle is constructed on the basis of the electricity-carbon market cooperation model and the traffic flow model; and finally, solving the power-carbon market cooperation model, the traffic flow model and the charging station planning and charging integrated management model to obtain an electric vehicle charging scheduling strategy. By adopting the method, the charging strategy of the electric vehicle can be optimized, the carbon emission is reduced, the load pressure of a power grid is relieved, and the renewable energy consumption capability is improved.

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 relates to the technical field of traffic scheduling, and discloses a multi-source data fusion smart city entrance and exit flow monitoring and intelligent regulation and control system, which comprises the following steps: collecting traffic flow data, environmental data, weather data and data of social activity data sources, carrying out fusion processing on the collected data, and carrying out data fusion processing on the fused data; establishing a non-linear traffic flow model based on the flow data and the environment data, then defining a flow optimization target, a safety optimization target, an environment optimization target and an energy efficiency optimization target, solving the optimization targets by adopting a multi-target optimization algorithm, and carrying out optimization on traffic signals according to an optimal regulation and control strategy. And dynamically adjusting the real-time traffic flow data, the meteorological data and the social activity data based on Bayesian reasoning. According to the invention, by introducing a multi-source heterogeneous data fusion mechanism, unified modeling of traffic flow, weather changes and social activities is realized, and a high-precision and full-time-space perception effect of traffic states at entrances and exits of a complex city is obtained.

The invention discloses a traffic capacity assessment method, application and system for a traffic flow channel with passenger exchange. The method comprises the following steps: abstracting a vehicle queuing system into a single-lane queuing system, simulating a vehicle queuing process by using an M / M / 1 queuing model controlled by a threshold value, and deducing to obtain a traffic flow model. And fitting a traffic flow model according to measured data, determining fitting parameters, and obtaining a macroscopic fundamental diagram and an optimal traffic flow density. And comparing the traffic flow density with the optimal traffic flow density, and when the traffic flow density is smaller than the optimal traffic flow density, releasing the vehicle to enter the vehicle queuing area. And when the traffic flow density is greater than the optimal traffic flow density, limiting the vehicles from entering the vehicle queuing area. The traffic flow model is constructed according to the single-lane queuing model controlled by the threshold value, the traffic capacity of the landside channel of the large hub can be dynamically adjusted, and the departure efficiency of passengers is effectively guaranteed.

The invention is suitable for the technical field of intelligent traffic, and provides a traffic signal control method and control equipment, and the method comprises the steps: determining the first driving-out flow of each lane at the current moment through employing a traffic flow model based on the current traffic data and historical traffic data of each lane in a traffic region, the second driving-out flow of each lane at the current moment is determined through attention mechanism processing; determining the predicted driving-out flow of each lane at the current moment based on each first driving-out flow and each second driving-out flow; determining a predicted traffic state of each lane at a future moment based on a law of conservation of vehicles and each predicted driving-out flow; adjusting the passing duration of each lane based on a preset adjustment target and each predicted traffic state until a target passing duration conforming to the adjustment target is obtained; and controlling the passing duration of the traffic signal lamp of each lane to be each target passing duration. The accuracy of traffic signal control can be improved, so that the traffic efficiency of the traffic area is improved.

The invention discloses a traffic speed prediction method based on a dynamic fundamental diagram and spatial-temporal characteristics, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: selecting a space region and a time range, obtaining structural data through a traffic monitoring system, and dividing a training set and a test set; a neural network model based on graph information is constructed, and the neural network model comprises a customized NF traffic flow model calibration module used for obtaining traffic flow parameters of different months and spatial positions, a spatial feature extraction module and a time feature extraction module used for extracting spatial and temporal features of traffic flow speed and flow data; and constructing a composite loss function, training the neural network model based on the graph information, and completing prediction of the traffic speed. Therefore, by adopting the traffic speed prediction method based on the dynamic basic diagram and the spatial-temporal characteristics, the result is ensured to conform to the traffic engineering theory, so that the traffic speed prediction problem under the spatial-temporal heterogeneous condition is solved, and meanwhile, the prediction precision is improved.

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 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.

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.

This invention discloses a perception-driven intelligent multi-modal traffic signal optimization method and system, belonging to the field of traffic signal optimization technology. The method includes: collecting traffic flow data; constructing a multi-modal traffic flow model; connecting multiple traffic lights within a target area to obtain street light control signals; predicting traffic flow trends based on the traffic flow data and the street light control signals, using the multi-modal traffic flow model; and configuring a timing optimization scheme for multiple traffic lights within the target area based on the traffic flow trend prediction results. This invention solves the technical problem in the prior art where traffic light timing schemes cannot be accurately adjusted dynamically based on real-time traffic conditions and multi-modal traffic behavior interactions, leading to low traffic intersection efficiency. It achieves the technical effect of optimizing traffic light timing in a target area based on prediction results, effectively improving the traffic intersection efficiency.

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.