261 results about "Traffic simulation" patented technology

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

Apparatuses, systems, and techniques to retrieve a set of retrieved scenarios using at least one example scenario, to use at least one first neural network to combine at least the set of retrieved scenarios to obtain combined information, and to use at least one second neural network to infer a new scenario based at least in part on the combined information. In at least one embodiment, scenarios are retrieved from a set of real-world driving scenarios and the new scenario is used to generate a simulation of automobile traffic.

The invention provides a dynamic traffic flow distribution method based on multi-agent reinforcement learning, and the method achieves the dynamic optimization and real-time response of a large-scale vehicle path through a cloud-edge collaborative architecture, and comprises the steps: constructing a multi-agent traffic simulation environment, and carrying out the real-time traffic flow distribution according to the real-time traffic flow distribution. Generating an intelligent body vehicle with a random starting point through the Poisson distribution model; constructing a multi-dimensional observation vector comprising road topological coding, current road density, a neighborhood speed mean value, a target distance ratio and a congestion coefficient; adopting a near-end strategy optimization algorithm to carry out parallel strategy optimization on the multi-agent enhanced strategy network under a Ray RLlib framework; designing a multi-target reward mechanism based on path efficiency, congestion punishment and progress reward; a steering decision is generated according to the real-time observation state, and a vehicle driving route is dynamically updated through a Nash equilibrium solution of the game theory; and a cooperative emergency mechanism is triggered when the traffic jam suddenly occurs. According to the method, the problems of path convergence, response delay and secondary congestion of a traditional method are solved.

According to the urban traffic automatic simulation plug-in based on the large language model and the implementation method, after a user inputs a request for a road network and a request for vehicle routing by using a natural language, the request is transmitted into a full-process automatic simulation module, and the full-process automatic simulation module carries out simulation; the input analysis agent extracts a key value from a natural language to generate a character string in a Json format, then the character string is spliced, a prompt input simulation input construction agent containing detailed simulation information is generated, the simulation input construction agent calls a packaged tool function, a vehicle route xml file and a road network xml file are generated respectively, and the vehicle route xml file and the road network xml file are connected with the simulation input construction agent. And the simulation execution intelligent body opens the SUMO-gu i to start simulation, and returns a result through a graphical interface. According to the method, the technical complexity of traditional traffic simulation is effectively simplified, a simulation tool is promoted to be transformed from professional modeling to intelligent decision support, and an innovative technical path is provided for optimization and management of a dynamic traffic system.

The invention discloses an urban cross-domain multi-mode network space modeling method, and belongs to the technical field of traffic simulation. In order to solve the problem that urban traffic cross-domain spatial modeling is complex, the method comprises the steps that road, hub and track BIM models are classified, traffic semantics in different BIM objects in the BIM models are recognized, and traffic elements are extracted; geometric element abstraction is carried out, spatial topology connection relations and attribute information of geometric elements are defined, and single-mode traffic network model modeling is carried out; coordinate conversion is carried out, and the relative position coordinates of the single-mode traffic network model are converted into 1984 world geodetic surveying geographic coordinates; a walking traffic network is used as an intermediate network, a walking traffic mode and transfer rules of roads, rails, buses and hubs are used as strategies, network fusion is achieved through a map matching algorithm, and a multi-mode network space is modeled; and carrying out topological structure verification on the obtained multi-mode network space. According to the invention, full-link calculation from design to simulation is realized.

The invention aims to provide a large-range urban road network intelligent management and control method based on mobile phone signaling data, and belongs to the technical field of traffic management and control. Real-time traffic flow parameters are obtained by preprocessing the mobile phone signaling data, a high-fidelity microscopic traffic simulation environment is constructed, and on the basis, the real-time traffic flow parameters are obtained; the method comprises the following steps: establishing a multi-dimensional evaluation index system containing operation safety and efficiency, constructing a macro-micro collaborative double-layer planning model, adopting a deep reinforcement learning algorithm, taking continuous-discrete mixed decision variables such as intersection signal timing and a variable lane strategy as optimization objects, carrying out strategy learning and iterative optimization through an Actor-Critic architecture, and carrying out optimization on the optimization objects. And outputting the optimal control strategy combination. According to the invention, dynamic and accurate cooperative management and control of the large-range urban road network are realized, and the traffic efficiency is remarkably improved while the operation safety is guaranteed.

The invention discloses a deep reinforcement learning-based cooperative control method for an expressway confluence area, and the method comprises the steps: building a LiikeSim-Python joint simulation environment, and setting a coil detector in the simulation environment to obtain the traffic flow data of the upstream and downstream of the expressway confluence area; an EM algorithm based on Gaussian mixture distribution is used as a traffic state classifier, traffic flow data of the highway confluence area are used as input, and traffic states of the highway confluence area are divided; designing a state space, an action space and a reward function; taking the state space of the highway confluence area as input, taking the actions of the variable speed-limiting intelligent agent and the ramp metering intelligent agent as output, and constructing a network model of multi-agent sharing experience under time sequence characteristics; an independent experience pool is set for each of the variable speed limit agent and the ramp metering agent, and interaction experience of the agents and the traffic simulation environment is collected with the control period as the frequency; training an agent model by using the sampled samples; and realizing cooperative control of the highway confluence area by using the trained intelligent agent model. According to the invention, the traffic travel delay of the highway confluence area can be reduced.

The invention relates to the technical field of artificial intelligence and automatic driving, and discloses an automatic driving digital twin agent system based on reinforcement learning training, which comprises an agent library construction module, a simulation and configuration module, a distributed simulation execution module, a scheduling and learning module, a strategy training module and a simulation migration module. The method comprises the following steps: constructing a modular digital twin agent library, combining to generate an urban traffic simulation scene, executing large-scale parallel simulation on a distributed computing cluster, implementing dynamic task scheduling and course learning according to an agent learning progress, and training a reinforcement learning strategy and a value network based on simulation data. And verifying the strategy model and implementing migration deployment from simulation to reality. According to the method, the training efficiency can be improved, the diversity of simulation environments is enriched, and the sim-to-real migration difficulty is reduced, so that the robustness of the strategy model is improved.

A main line traffic flow management and control optimization method based on a high-performance traffic simulation agent comprises the steps that a target area road network is selected, and a main line road section and intercommunication road section structure is constructed; a multi-scene sample set is generated through microscopic traffic simulation, and traffic operation states under different traffic requirements, speed limiting and hard shoulder open conditions are simulated; training a neural network model based on a deep learning method, and constructing an efficient traffic simulation agent by taking the target road section and upstream and downstream multilayer associated topological information as input features and taking travel time and average speed as labels; and in combination with a multi-objective optimization algorithm, taking total travel time and carbon emission as optimization objectives, and dynamically iterating to generate an optimal management and control scheme of road section speed limitation and hard road shoulder opening. According to the method, traditional low-efficiency microscopic simulation is replaced with the simulation agent, the calculation performance is remarkably improved, and collaborative optimization of the complex road network and scientific formulation of a low-carbon management and control strategy are achieved.

The invention discloses a track digital twinning real-time online deduction method and system, and belongs to the technical field of traffic simulation based on machine learning. The problem that in the prior art, a traditional traffic simulation and rail passenger flow dynamic allocation method is low in solving efficiency, and consequently the large-scale rail traffic network dynamic passenger flow allocation requirement is difficult to meet is solved. The method comprises the following steps: constructing a track road network model; generating road network passenger flow data and line shift data; further, simulation individuals of the train and the passengers are generated, path track matching based on time and space is carried out, and passenger-train dynamic traffic distribution and passenger-train interactive operation deduction are completed; according to the real-time detection data and the simulation result, dynamic line shift and passenger flow adjustment is carried out until a preset convergence condition is reached, and dynamic traffic checking is completed; and simulation is carried out to realize reduction and prediction of the passenger flow / flow direction of the whole line network. The method effectively improves the rail passenger flow dynamic distribution efficiency, and can be applied to large-scale traffic network simulation.

The invention discloses an urban traffic three-dimensional model vegetation element automatic generation method and system based on streetscape recognition, a terminal and a storage medium. The method comprises the steps of constructing a vegetation element knowledge graph, defining a vegetation type and typical attributes, directionally collecting a three-dimensional model and a multi-view image, unifying metadata and storing the metadata. Pixel-level semantic segmentation is carried out on the streetscape image, vegetation categories are identified based on transfer learning and an attention mechanism, and ROI and feature vectors are output; screening the candidate set based on ontology mapping, selecting a best matched vegetation model for the local feature vector of the recognized ROI and a pre-stored feature vector in a material library, and recording mapping; based on reference object correction, knowledge graph fusion and automatic discrimination or optimization strategies, reliable scale correction, number or position deduction and batch three-dimensional model import placement meeting ecological constraints are carried out on the vegetation ROI. According to the invention, efficient, intelligent and standardized technical support is provided for traffic simulation and urban visualization.

The invention discloses an automatic driving scene generation method and electronic equipment, and the method comprises the steps: obtaining a driving scene material used for generating an automatic driving scene, and obtaining driving scene description information used for describing the automatic driving scene according to the obtained driving scene material through employing a multi-mode large model; based on the obtained driving scene description information, generating a road network structure in an automatic driving scene by using a multi-modal large model and a traffic simulation device; based on the obtained driving scene description information, obtaining a driving scene script file used for generating an automatic driving scene by using a multi-modal large model; according to the obtained driving scene script file and the automatic driving simulator, controlling traffic facilities and road obstacles in the automatic driving scene to be displayed in the generated road network structure, and controlling traffic participants in the automatic driving scene to move in the generated road network structure so as to obtain the automatic driving scene. Therefore, the generation rate of the automatic driving scene is improved.

The invention provides an ultra-large city comprehensive traffic data and intelligent research and strategy system, which establishes a multi-dimensional traffic research and strategy task through a traffic research and strategy task module, and processes the traffic research and strategy task through a traffic characteristic diagnosis module to obtain a traffic characteristic index vector rank and a candidate traffic root cause index set. The traffic traceability analysis module performs further processing to obtain main traffic root cause indexes and measure indexes, and the traffic strategy generation module outputs traffic characteristic indexes according to the main traffic root cause indexes and the measure indexes to optimize a pre-generation strategy; the traffic simulation deduction module optimizes a simulation task item distribution model generated by a pre-generation strategy according to traffic characteristic indexes to obtain a pre-selection result, and the traffic strategy evaluation module and the traffic strategy release module finally perform evaluation and release, thereby solving a problem that a single factor outputs a single execution scheme with relatively high precision. Therefore, when single execution schemes are overlapped and fused into a set of optimal executable solutions for the comprehensive traffic of the super-large city, scheme execution conflicts are easy to occur.

The invention discloses a mixed traffic scene-oriented different-intelligence network-connected vehicle collaborative decision-making method and system, and relates to the technical field of multi-vehicle collaborative decision-making, and the method comprises the specific steps: building a mixed traffic simulation environment for multi-agent reinforcement learning training; constructing a different intelligence network vehicle physical model, and performing function module configuration based on the grade of each vehicle; the different-intelligence networked vehicle physical model comprises a sensing module, a decision planning module, a control module and a communication module. A heterogeneous multi-agent deep reinforcement learning method is adopted to carry out cooperative training on the different-intelligence network vehicle simulation queue based on a mixed traffic simulation environment, and an optimal cooperative decision strategy is obtained; and deploying the optimal collaborative decision-making strategy to the different intelligence network connection vehicle queue for collaborative decision-making information generation. Through the parameter sharing and independent optimization mechanism of the high-level and low-level intelligent vehicle strategy and state value networks, hierarchical cooperation between different-intelligence vehicles is realized; and the overall traffic efficiency in the complex mixed traffic scene is improved.

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 sewage treatment plant intelligent layout method based on a genetic optimization algorithm, and the method comprises the steps: enabling a system to obtain parameters through the automatic importing and sorting of multi-source data, and completing the machine learning preprocessing; mapping the functional characteristics of the monomers and the process dependency relationship into a map structure, and realizing dynamic grouping according to a reasoning result; a multi-target adaptive algorithm and interactive visualization are adopted, and automatic arrangement of buildings and pipelines is achieved in a local range; during overall layout, an evolutionary genetic algorithm and a self-adaptive reward and punishment mechanism are introduced, and parameters of each group are globally optimized; determining road details based on external traffic simulation and future capacity expansion requirements; performing comprehensive optimization in a whole plant pipeline arrangement stage; updating the design scheme; and outputting a plurality of achievement forms. According to the method, the modification requirement can be quickly met, and the algorithm can be restarted and a new scheme can be efficiently generated only by updating parameters no matter the size of a single body, the land condition or the technological process is changed.

The invention relates to a traffic simulation agent system construction method based on a large model, and the method comprises the steps: constructing a simulation tool library, and achieving the precise evaluation and continuous optimization of a simulation result through the fusion of multi-source heterogeneous traffic data, the construction of standardized input, and the establishment of a quantitative evaluation system. A large language model is utilized to understand a natural language instruction of a user, tasks are intelligently disassembled, an execution process is planned, dependence management and parallel scheduling are carried out in combination with a directed acyclic graph, and professional tools are driven to automatically execute. And performing evaluation, problem diagnosis and adaptive re-planning on an execution result through a large language model reflection mechanism to form an understanding-planning-execution-reflection closed loop. According to the method, the problems of how to assist a user to interact with a traffic system by utilizing an agent technology driven by a large language model, reducing the technical threshold of traffic simulation software use and saving time cost and labor cost are solved, the traffic simulation use threshold is reduced, the automation and intelligence level is improved, and efficient and accurate traffic system interaction and optimization are realized.

The invention discloses a traffic simulation parameter calibration method based on GMM clustering and Bayesian optimization, and the method comprises the following steps: inputting and preprocessing data, including data collection, driving style clustering analysis and simulation environment construction; calculating simulation repetition times of the simulation parameter sample points; according to a simulation result of the simulation parameter sample point, estimating expectation and variance distribution of an objective function; and determining a next simulation parameter sampling point based on a Bayesian optimization method. The traffic simulation parameter calibration method considering the heterovariance noise is established, the accuracy of parameter calibration is improved by identifying different driving styles, the mean square percentage error is reduced from 20.2% to 3.1%, and an effective tool is provided for the accuracy of highway traffic simulation.

The invention discloses an intelligent decision-making system construction method for traffic signal control. The method comprises a model training and deployment stage and an application and evolution stage, and specifically comprises the following steps of: S1, generating a pairing training sample set of traffic state structured data and conflict-free signal control instructions based on a pre-stored road traffic conflict rule in the model training and deployment stage; utilizing the paired training sample set to supervise and finely adjust a large language model to obtain a basic model; s2, accessing the basic model into a traffic simulation environment for reinforcement learning training; and in each training step, the basic model outputs a signal control instruction according to the current traffic state, performs safety verification on the instruction according to the road traffic conflict rule, generates a safety reward signal and the like. The traffic signal intelligent decision-making system which is safe, credible, sustainable in evolution and suitable for edge independent deployment is constructed.

The invention discloses an encrypted domain name resolution protocol simulation and characterization system, and relates to the technical field of network security and network traffic analysis. The invention aims to simulate an encrypted domain name resolution process in a real network environment, collect the flow of the process and extract features to construct a data set, and ensure the quality of the generated data set through data enhancement and a data set evaluation scheme. The system comprises a traffic simulation module, a traffic representation module, a data enhancement module and a data set evaluation module. The flow simulation and characterization module simulates and encrypts domain name resolution flow and extracts a structured feature vector containing 34 side channel features; and the data enhancement and data set evaluation module is used for enhancing a feature set based on a conditional table generative adversarial network CTGAN so as to construct a feature data set which is closer to traffic in a real network environment, and ensuring that the constructed data set has engineering availability and theoretical rationality through evaluation. The system can be used for constructing a current scarce encrypted domain name resolution protocol side channel feature data set, and provides data support for related security detection and research.

This invention discloses a lane-level conflict prediction method for expressway weaving areas based on real-time traffic flow data, comprising the following steps: S1, selecting a suitable expressway weaving area as the survey location and conducting on-site investigation; S2, collecting traffic flow data and traffic conflict data for each lane, and defining thresholds for different conflict types; S3, building a traffic simulation model using the traffic parameters obtained from the survey, defining different conflict types according to the conflict thresholds obtained in step S2, and obtaining traffic conflict data for different types; S4, extracting feature variables from the data obtained in step S3, and constructing datasets for prediction models of different conflict types; S5, constructing a conflict analysis model; S6, constructing a conflict prediction model. This invention can achieve real-time lane-level traffic conflict prediction in expressway weaving areas, thereby providing more accurate lane-level conflict risk information for vehicles in each lane, helping drivers choose safer lanes and routes.

The invention relates to the technical field of intelligent traffic, in particular to an optimization method and device for a traffic control scheme generation model, equipment and a medium. The method comprises the steps of obtaining a traffic event training data set; inputting each traffic event into a traffic control scheme generation model, and generating at least one candidate control scheme of the traffic event; sequentially inputting the at least one candidate control scheme into a traffic simulation model for deduction to obtain parameters of at least one simulation index of each candidate control scheme; calculating a reward value of each candidate control scheme based on each candidate control scheme, the parameter of the at least one simulation index and a reward function; wherein the reward function comprises a format reward function, a rule reward function and a quality reward function; and based on the reward value of each candidate control scheme and the real annotation data of the traffic event, optimizing the traffic control scheme generation model by adopting a reinforcement learning algorithm based on strategy gradient to obtain an optimized traffic control scheme generation model.

The invention discloses a traffic signal optimization method based on multi-agent deep reinforcement learning, and belongs to the technical field of traffic signal control, and the method comprises the following steps: carrying out the tracking and track simulation of a vehicle according to the vehicle operation data, and constructing an urban traffic simulation model and a reinforcement intelligent learning body corresponding to the traffic signal lamp of each intersection; constructing a context enhanced state space, performing normalization processing on feature parameters in the context state space, and performing combination to obtain a real-time traffic environment state vector; a congestion index self-adaptive reward is obtained through calculation; according to a heuristic reward shaping method, defining a flow matching degree index and an indication signal period position reward, and combining a congestion index adaptive reward to obtain a traffic signal optimization reward; and according to the traffic signal optimization reward, a multi-agent double-depth Q network is adopted to train and strengthen an intelligent learning body to control traffic signal phase switching. According to the invention, the problem of insufficient traffic signal control flexibility and efficiency in a complex scene is solved.

The invention discloses a traffic conflict risk evaluation method, system and device based on simulation modeling and a storage medium, and relates to the technical field of traffic safety evaluation method research, and the method comprises the steps: collecting road data and vehicle driving data, and constructing a traffic data set; carrying out traffic simulation modeling based on the traffic data set; carrying out traffic conflict index calculation based on traffic simulation modeling; evaluating the traffic conflict risk according to the traffic conflict index; according to the method, the traffic conflict is dynamically analyzed, the occurrence of the traffic conflict can be more accurately judged, the traffic conflict risk problem can be effectively solved, and a new thought is provided for judging the regional traffic conflict risk condition.

The invention relates to an automatic parallel traffic simulation analysis method and device. The method comprises the following steps: in response to a received region delimiting instruction, generating an initial digital road network from a map data source, and executing topology calibration and connectivity repair operation on the initial digital road network to generate a digital road network file; generating structured traffic demand data based on the input of the user; generating a basic traffic scene file based on the digital road network file and the traffic demand data, and forming a traffic simulation operation scene based on the configuration of the basic traffic scene file by a user; generating a plurality of signal timing schemes based on the traffic simulation operation scene; and executing traffic simulation based on the plurality of signal timing schemes, analyzing a simulation result to obtain a multi-level key performance index, performing visual comparison on the multi-level key performance index, and generating a natural language evaluation report by using a large language model. Therefore, simulation efficiency, usability and scene construction flexibility can be remarkably improved.

The invention discloses a test method and device based on vehicle-in-the-loop, electronic equipment and a storage medium. The method is realized by a test system based on vehicle-in-the-loop, and the system comprises a scene simulation platform and a vehicle platform. The method comprises the following steps: constructing a traffic simulation scene by adopting a scene simulation platform, and obtaining virtual perception information; sending the virtual perception information to a vehicle platform for processing to obtain virtual scene information, and displaying the virtual scene information on a front windshield of the tested vehicle; and the tested vehicle is controlled according to the virtual scene information on the front windshield of the tested vehicle, and the pose information of the tested vehicle is collected and fed back to the scene simulation platform. By adopting the technical scheme of the embodiment of the invention, the tested vehicle in-the-loop test system is constructed to test the head-up display, and the vehicle head-up display function is comprehensively and accurately tested and verified in combination with the real running state of the tested vehicle and the simulated traffic simulation scene.

The invention provides a traffic flow intelligent sensing scheduling method and system under a special meteorological condition, and relates to the technical field of traffic management, and the method comprises the steps: carrying out the conjoint analysis of a historical traffic data set and a historical special meteorological data set, and constructing a meteorological-traffic correlation model; connecting a meteorological system to obtain real-time special meteorological data; inputting the real-time special meteorological data into the meteorological-traffic association model to obtain predicted traffic data of the next time period, and generating an initial traffic flow scheduling scheme; constructing a traffic accident thermodynamic diagram through traffic simulation; and optimizing the initial traffic flow scheduling scheme according to the traffic accident thermodynamic diagram to obtain a traffic flow scheduling optimization scheme. Through the traffic flow scheduling method and device, the technical problem of poor traffic flow scheduling efficiency caused by incomplete risk identification under the special meteorological condition in the prior art can be solved, the traffic risk under the special meteorological condition is comprehensively identified through joint analysis of the traffic flow data and the special meteorological data, and the traffic flow scheduling efficiency is improved.

The invention provides a traffic simulation analysis method for an expressway construction area based on VISSIM. The method comprises the following steps: S1, collecting point cloud data, vehicle data and traffic video data of the landform of the construction area in real time; s2, preprocessing the collected multi-source data; s3, constructing a dynamic simulation model of the construction area; s4, simulating the traffic condition of the construction area; s5, dynamically planning an optimal driving path of the vehicle according to the simulated traffic condition and construction information; and analyzing the vehicle overlapping probability of the intersection in real time, and performing early warning according to an analysis result. Through the technical means of multi-source data fusion, high-precision dynamic modeling, real-time analogue simulation, intelligent path planning and the like, accurate simulation and optimal management of the traffic influence during the blasting demolition construction of the expressway overline overbridge group are realized, and remarkable technical advantages and application values are achieved; and a scientific and efficient solution is provided for urban traffic management.

The invention relates to a semantic-driven traffic simulation scene generation method based on a large language model. The method comprises the following steps: receiving traffic simulation scene natural language description input by a user; constructing a tool knowledge base, performing retrieval in the tool knowledge base based on user input, and combining a retrieval result with the user input to obtain extended input; calling the large language model to carry out multi-step reasoning on the extended input, determining a tool needing to be called in a tool knowledge base, and generating a structured simulation configuration element by utilizing the determined tool; and converting the generated simulation configuration elements into a configuration file which can be directly loaded to a traffic simulation platform, and executing simulation verification in the traffic simulation platform. Compared with the prior art, natural language input can be automatically converted into a structured and executable traffic simulation configuration file under the condition that manual intervention is not needed, and high efficiency and intelligence of scene construction are achieved.

The invention discloses an OSM-based urban traffic three-dimensional simulation scene modeling method and system, and the method comprises the steps: obtaining map data of a target simulation region, carrying out the preprocessing of the map data, and obtaining the preprocessing data; road shoulders, curbs and sidewalks are subjected to parameterization batch filling according to the preprocessed data, and a two-dimensional simulation model is correspondingly generated; defining a target data standard, generating a target high-precision map according to the target data standard and the two-dimensional simulation model, and converting the target high-precision map into a three-dimensional road network model through target software; based on three-dimensional visualization software, a three-dimensional simulation model is generated according to the three-dimensional road network model, and three-dimensional visualization simulation is generated based on a digital twinborn tool and a blueprint tool. Aiming at traffic simulation modeling requirements, the OSM map is used as a data source, the data conversion relation between the simulation model and the three-dimensional model is deeply explored, the automatic modeling efficiency of traffic simulation is improved, and the three-dimensional visualization capability of the simulation model is enhanced.