726 results about "Traffic management" patented technology

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses a road traffic accident cause analysis and responsibility judgment method and system, and relates to the technical field of traffic management data processing, and the method comprises the steps: carrying out the credible collection of multi-source data, and constructing an evidence chain; performing multi-source data preprocessing and cross-modal fusion optimization; performing multi-dimensional cause intelligent analysis; performing responsibility judgment based on a quantification rule; and the responsibility judgment result is subjected to multi-dimensional rechecking and rule iteration adaptation. According to the road traffic accident cause analysis and responsibility judgment method and system, through a full-process closed-loop design of data acquisition, preprocessing, cause analysis, responsibility judgment, re-checking iteration and report evidence storage, technologies such as multi-source perception and an AI algorithm are integrated; the method solves the problems of uncredible evidence chain, one-sided cause traceability, non-uniform judgment standard, low cooperation efficiency and the like in traditional accident processing, realizes intelligence, standardization, compliance and traceability of accident processing, remarkably improves the credibility, processing efficiency and judicial suitability of a judgment result, and provides core technical support for modernization of traffic control.

The invention belongs to the technical field of traffic management and control, and particularly relates to a congestion feed-forward intervention method based on traffic flow phase change critical point recognition, which comprises the following steps: collecting and preprocessing multi-source heterogeneous traffic data; carrying out multi-scale traffic flow feature engineering; identifying a traffic flow phase change critical point based on a space-time dynamic graph neural network and critical moderation effect analysis; generating a multi-objective optimization congestion feedforward intervention strategy; and performing intervention, evaluating the effect and performing adaptive learning. According to the technical scheme, accurate prevention and early intervention can be performed before congestion occurs, and the operation efficiency and reliability of an urban traffic system are remarkably improved.

The invention discloses a dynamic airspace gridding management method and system for low-altitude economy, and belongs to the technical field of unmanned aerial vehicle traffic management, and the method comprises the steps: collecting airspace state data in real time through a multi-source sensing device, and constructing a four-dimensional space-time grid model; generating a four-dimensional space-time grid with a block chain hash code by fusing meteorological data, an airspace control rule and a real-time flight demand; receiving a space-time grid use request submitted by the aircraft through the smart contract, and calculating an optimal grid allocation scheme based on a deep reinforcement learning model; and the edge computing node executes local track prediction, issues a navigation instruction to the aircraft through the distributed account book, monitors a grid occupation state in real time, and triggers a dynamic grid recombination mechanism when sudden conflicts are detected. According to the method, the rigid constraint of static airspace division can be broken through, the cooperative conflict of multiple aircrafts is eliminated, and the marketization configuration of airspace resources is realized.

The invention relates to the technical field of traffic event identification, and discloses an abnormal traffic event identification method and system based on a traffic large model, and the method comprises the steps: obtaining a traffic data flow, extracting an abnormal feature vector, and obtaining an abnormal signal candidate set; grouping the candidate sets and calculating a deviation degree, and if the deviation degree exceeds a threshold value, taking the deviation degree as a risk signal to form an input subset; environment variables are extracted from the subsets, a mapping relation is established, and anomaly recognition embedding representation is obtained; classifying the embedded representation, judging a congestion precursor and generating an early warning signal to obtain an early warning signal sequence; matching the sequence to obtain an abnormal event chain; if the integrity is higher than a threshold value, analyzing the type to obtain an abnormal event type; extracting a correlation feature vector from the type, pushing the correlation feature vector to a traffic management platform to obtain an instruction, and obtaining an emergency response trigger instruction sequence; and executing the instruction sequence to extract a feedback data stream, inputting the traffic large model to judge the accuracy rate, and if the judgment accuracy rate is met, determining an optimized anomaly recognition framework. The method can solve the problem of insufficient early warning capability.

The invention discloses a global dynamic perception space-time traffic flow prediction method based on graph packet representation learning, and belongs to the technical field of traffic flow prediction, and the method comprises the following steps: S1, traffic data input, S2, traffic graph packet construction, S3, graph packet initial feature extraction, S4, time sequence feature extraction, S5, spatial feature extraction, and S6, traffic flow prediction and output. Through a space-time modeling technology of graph packet representation learning and global dynamic perception, space-time characteristic elements of a traffic road network can be comprehensively covered, traditional traffic indexes such as flow and speed are concerned, elements such as road network topological association and cross-regional multi-hop association are also included, a dynamic dependency relationship between a time sequence and a spatial dimension is deeply mined, and a real-time dynamic perception effect is achieved. Therefore, the prediction result can reflect the real evolution law of the traffic flow more accurately, and a more scientific basis is provided for traffic management and decision making.

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.

The invention discloses a multi-source fusion real-time traffic perception and emergency early warning system and method based on vehicle and road cloud cooperation, and relates to the technical field of traffic management. The vehicle end system comprises an information acquisition and transmission module, a local decision module, a data receiving module, an emergency response module and a data feedback module, and the information acquisition and transmission module acquires vehicle peripheral information through a vehicle-mounted radar and a vehicle-mounted camera. According to the invention, a vehicle end collects local information such as obstacles around a vehicle and dynamic states of adjacent vehicles through a vehicle-mounted radar and a camera, a road end collects regional information such as road section traffic flow density, road damage and traffic facility states through a roadside camera and a sensor, and a cloud end integrates data at the two ends and carries out time-space synchronization through a multi-source fusion technology. The defect of a single vehicle end or road end sensing blind area is overcome, and complete sensing of the traffic state of the whole road network is achieved.

The invention relates to the technical field of urban traffic management and carbon emission reduction accounting, and provides a bus travel carbon emission reduction accounting method fused with Beidou spatio-temporal data, which comprises the following steps: acquiring spatio-temporal trajectory data based on a Beidou satellite navigation system, and constructing a passenger-trajectory-station three-dimensional correlation model for passenger flow analysis; constructing a passenger transfer analysis system based on the passenger flow analysis result; constructing a dynamic'perception-prediction-optimization 'dynamic scheduling optimization system for passenger flow detail analysis on the basis of space-time trajectory data and a reinforcement learning algorithm; and constructing a mileage-frequency distribution model and a station efficiency evaluation model based on the spatio-temporal trajectory data, respectively generating a trajectory matching optimization strategy and a differentiated station optimization scheme, and realizing adaptive optimization of the public transportation system under dynamic demand change. Compared with a traditional static data dependence method, the method achieves the dynamic and fine-grained monitoring of the operation process of the public transportation system, and effectively improves the timeliness and integrity of data.

The invention discloses a low-altitude economic flight data management method based on a block chain technology, and relates to the technical field of low-altitude traffic management, and the method comprises the steps: verifying an encrypted positioning proof, generating a verification state set, decrypting flight coordinates according to the verification state set, and generating a credible coordinate data stream; performing sensitive airspace intrusion detection according to the credible coordinate data stream and the high-security protection node, performing collision risk analysis by the traffic management node to obtain an encrypted aggregated threat vector, and identifying a threat level according to the encrypted aggregated threat vector; and dynamically adjusting the working parameters of the airborne equipment according to the threat level, generating an airborne equipment regulation and control instruction set, scanning the signal-to-noise ratio and the threat intensity in real time, dynamically allocating a communication frequency band, and generating a new positioning strategy parameter. According to the method, the real-time signal-to-noise ratio and the threat intensity scanning result are combined, communication frequency band dynamic allocation and encryption strategy dynamic optimization are driven, the airspace situation awareness capacity is formed, the resource utilization rate is increased, and the anti-interference capacity is enhanced.

The invention belongs to the field of urban traffic management, and provides a hierarchical dynamic routing system and method for a computing power-energy fusion network, and the method comprises the steps: dividing a fusion network graph into a plurality of region sub-graphs, and dividing a path table and a connection point path table based on each region sub-graph; monitoring the running states of the road, the edge computing node and the charging station in real time, and predicting the road congestion intensity, the edge computing node queuing estimation information and the charging constraint; and determining a candidate path set in the partition path table and the connection point path table according to an online request of the vehicle, screening each candidate path in the candidate path set based on the road congestion intensity, the edge computing node queuing estimation information and the charging constraint, and determining an optimal path. According to the method, online retrieval is converted into rapid combination and evaluation of small candidate sets from full-graph traversal; traffic, energy and computing power are incorporated into the same decision framework, and systematicness deviation caused by evaluation of the future at present is avoided.

The invention discloses a BIM-based highway construction period traffic guide and change optimization system, and relates to the technical field of road traffic management. According to the BIM-based highway construction period traffic guide and change optimization system, traffic management in the construction period is dynamically adjusted in real time through space-time modeling, data fusion and optimization decision. The system generates a road network space-time model, matches vehicle trajectory data with lane space data, counts the number and speed of vehicles, and optimizes traffic flow. According to the optimization result, the system outputs the tailgating length, the number of lanes and the speed limit value and generates a corresponding control instruction and a speed limit issuing instruction, traffic management of the highway construction period is accurately optimized through space-time modeling and real-time data fusion based on BIM, the system can obtain vehicle track data in real time, the lanes and the speed limit strategy can be dynamically adjusted, and the speed limit efficiency is improved. Traffic delay in the construction period is minimized, and traffic safety and smoothness of a construction section are effectively guaranteed by generating an accurate lane control and speed limiting instruction.

The invention provides a bridge health monitoring and intelligent control method and system based on machine learning, and the method comprises the steps: carrying out the distributed sensing data collection of a bridge structure, generating a structure monitoring data set, carrying out the spatial-temporal feature extraction, and obtaining a vibration response feature sequence and a traffic load feature sequence of the bridge structure; performing correlation analysis on the two sequences, generating a structural damage sensitive feature matrix, performing damage evolution trend prediction based on the structural damage sensitive feature matrix, generating damage development probability distribution of the bridge key component, and generating a dynamic traffic control instruction according to the damage development probability distribution. The dynamic traffic control instruction comprises a vehicle passing parameter dynamically adjusted based on a damage degree quantitative index, and sending the dynamic traffic control instruction to a traffic management system. According to the invention, the accuracy of bridge health monitoring and the dynamic adaptability of control measures can be improved.

The invention belongs to the technical field of traffic management evaluation, and particularly discloses and provides an urban traffic toughness quantitative evaluation method based on multi-mode traffic collaboration, and the method comprises the steps: carrying out the time-space standardization processing of multi-source data of motor vehicles, public traffic, slow traffic and external disturbance events, and generating a standardized time-space data set; based on the data set, respectively calculating a comprehensive transportation efficiency index, an alternative path redundancy index and a connection connectivity index, and constructing a toughness evaluation index set; based on the historical sequence of each index, generating a dynamic combination weight through a subjective and objective combination weighting method; and based on the index set and the dynamic weight at the current moment, calculating a comprehensive toughness index and generating a time sequence thereof to output an evaluation result. According to the method, the defects that in the prior art, an isolated analysis single mode depends on a static model are effectively overcome, and accurate multi-dimensional quantitative evaluation of the real toughness level of the complex traffic system under disturbance is achieved.

The invention provides an expressway lane dynamic optimization management method, system and device and a medium, and belongs to the technical field of intelligent traffic management. The method comprises the following steps: firstly, collecting traffic flow, environment and vehicle credit data through high-speed key node multi-source sensing equipment and a data center, and fusing to generate a time-space fusion data set; inputting the data into a space-time diagram convolution network model, dynamically mapping a road topology into a variable weight directed graph, and generating future lane-level traffic flow, vehicle speed and occupancy rate prediction data through convolution operation; inputting the prediction data into a hierarchical reinforcement learning model, generating a single-vehicle differential management and control instruction in combination with vehicle credit data, and determining an optimal strategy through digital twinborn simulation; then, a modularized lane controller is used for executing a strategy and collecting feedback data; and finally, inputting feedback data into a causal inference engine to analyze the effect, optimizing the reinforcement learning model, and dynamically updating credit data according to vehicle behavior data.

The invention discloses a flight flow intelligent prediction and air traffic collaborative optimization system based on big data, and relates to the technical field of air traffic management. Comprising the steps that a data acquisition module acquires multi-source data from a civil aviation database and aligns the multi-source data to generate a fusion data matrix; the traffic prediction module extracts traffic feature vectors, generates a feature similarity matrix by calculating distribution differences, and generates a basic traffic prediction result based on migration prediction model parameters; the disturbance correction module detects a sudden disturbance event based on real-time weather and airspace state data and corrects a basic prediction result; the capacity adjusting module generates a capacity adjusting scheme when the capacity deviation value exceeds a threshold value according to the correction result and the airport operation capability data; and the collaborative optimization module finally generates a multi-airport collaborative scheduling scheme through a game equilibrium algorithm based on the correction result, the capacity scheme and the regional coordination data. According to the invention, the accuracy of flight flow prediction and the efficiency of multi-airport collaborative management are effectively improved.

The invention discloses a multi-source sensing airport scene moving target cooperative monitoring and conflict early warning system, and relates to the technical field of airport scene traffic management, a millimeter wave radar, a laser radar and the like form a heterogeneous network, and the whole area of an airport is synchronously covered through a TSN network; cascading filtering denoising is adopted, GPS time service and Kalman filtering are combined to realize space-time calibration, and a data complementation mechanism is established; based on a multi-modal model classification target, the tracking continuity is kept through an improved algorithm; fusing multiple factors and predicting a path by using an ST-GNN model to adapt to different scenes; constructing a four-dimensional judgment space to evaluate risks in a grading manner, and performing grading response and multi-channel information pushing according to the risks; and the system performance is continuously optimized through federated learning and digital twinborn verification. According to the invention, the method achieves the precise monitoring of the whole area of the airport scene, improves the target recognition and tracking stability, has the self-optimization capability, balances the operation safety and efficiency, and supports the high-quality development of aviation logistics and general aviation services.

A system and method for strategic airspace deconfliction is disclosed, centered on a Cooperative Multi-Agent Platform (CMAP) embedded within an Automated Data Service Provider (ADSP) operating within a federated Unmanned Aircraft Systems (UAS) Traffic Management (UTM) network. The system's inventive feature resides in a specific technical architecture that synergistically integrates a real-time safety constraint into a strategic negotiation engine. A Regulatory Compliance Monitor (RCM) generates a real-time Conflict Risk Score that is incorporated directly into the Local Observation Vector of a Multi-Agent Reinforcement Learning (MARL) policy network. The MARL engine is thereby configured to select a strategic negotiation primitive (e.g., BID, YIELD, TRADE) that is dynamically determined based on this real-time Conflict Risk Score. This transforms abstract resource allocation into a safety-aware, risk-adaptive protocol, allowing an ADSP agent to maximize fleet efficiency while guaranteeing collective safe separation

The invention provides a smart city traffic abnormity monitoring method and system based on an Internet of Things large model, and relates to the field of Internet of Things and smart city traffic management. The system comprises an abnormity judgment module and a diversion module. The abnormity judgment module is configured to perform abnormity judgment on the target area according to the multi-source data and determine a plurality of hidden danger hot areas; the diversion module is configured to determine a plurality of standby routes according to the judgment result and the regional road network topological map; determining a plurality of main routes according to the plurality of standby routes and the position information of the plurality of variable information boards, generating a diversion instruction, and sending the diversion instruction to the emergency supervision object platform; and based on the diversion instruction, controlling a plurality of variable information boards to display a sketch of a corresponding main route, and controlling traffic lights on a plurality of standby routes to perform green light signal display according to a passing period. According to the method, the main pushing route of the variable information boards can be reasonably determined and controlled, potential traffic accident hidden dangers are prevented, and traffic safety is guaranteed.

The invention provides a traffic carbon emission and ecological environment influence feedback type combined prediction system based on digital twinning. The system aims to solve the problems of insufficient dynamic response, single prediction dimension and lack of an effective feedback mechanism in traditional traffic management through a virtual-real combined bidirectional interaction mechanism. The system adopts the Internet of Things technology to collect multi-dimensional data such as traffic flow, carbon emission and environment quality in real time, constructs a digital twinborn body ('virtual ') of a traffic system, and realizes dynamic mapping and real-time updating of an actual traffic system ('real'). In the digital twin body, the system performs joint prediction and virtual simulation analysis on ecological environment indexes such as carbon emission, air quality and vegetation coverage through a multi-objective optimization algorithm and an intelligent prediction model, and provides an optimization treatment strategy according to an analysis result. The optimized strategy is transmitted to an actual traffic system through a closed-loop feedback mechanism, the treatment effect is implemented and verified, and a virtual-real closed-loop iteration process is formed. The system not only can accurately predict traffic carbon emission in real time, but also can comprehensively evaluate the influence of traffic activities on air quality, vegetation coverage and other ecological environment indexes, and provides scientific decision support for intelligent traffic management and low-carbon environmental governance.

The invention provides an urban road network traffic congestion state prediction method and system based on deep learning, and relates to the technical field of intelligent traffic control, and the method comprises the steps: obtaining congestion state image data of an urban road network, and carrying out the preprocessing to extract structured road congestion information; constructing a road network topological graph based on the road congestion information, and performing structure sensing state coding on the road network topological graph to generate a structure sensing state vector fusing local congestion features and global topological position information; and inputting the structure perception state vector into an improved deep learning prediction model, performing feature deepening and enhancement through a graph neural network which dynamically adjusts a graph structure and aggregates node information, and finally outputting road network congestion state prediction results of a plurality of time scales in the future. According to the method, accurate prediction of the urban road network congestion state can be realized, and reliable technical support is provided for intelligent traffic management, resource scheduling optimization, travel path planning and the like.

The invention discloses a traffic jam detection and early warning method based on an unmanned aerial vehicle video stream, and belongs to the technical field of intelligent traffic. The system is combined with a floating vehicle triggering model, collects road videos in real time through an unmanned aerial vehicle, and realizes high-precision vehicle detection and tracking in combination with improved YOLOv8 and DeepSort algorithms; unmanned aerial vehicle data and floating vehicle GPS data are fused, three elements of traffic flow are quantified, and a dual-mechanism congestion detection model is constructed; respectively utilizing a GRU cooperative traffic wave theory to predict the congestion dissipation duration and utilizing an improved GRU + GCN model to predict the traffic situation; the signal lamps are dynamically regulated and controlled through multi-agent reinforcement learning, and active congestion relieving is achieved. The method has the advantages of wide-area coverage, high-precision perception and intelligent response, and the urban traffic control efficiency is remarkably improved.

The invention discloses a traffic flow prediction method for a double-layer multi-scale dynamic graph convolutional network, and relates to the technical field of traffic, and the method comprises the following steps: constructing a double-layer structure of a traffic network, which comprises a node layer composed of traffic sensor nodes and a region layer formed by clustering nodes in the node layer, node layer traffic flow data and area layer traffic flow data are extracted; respectively mapping the node layer traffic flow data and the region layer traffic flow data to a potential space to obtain a node layer initial hidden state and a region layer initial hidden state; inputting the initial hidden state of the node layer and the initial hidden state of the region layer into a sequence containing at least one space-time module for processing so as to capture a dynamic space-time dependency relationship; the method has the effect of providing more reliable decision support for traffic management and travel planning.

The invention discloses an urban traffic flow prediction method fusing a graph attention network and a Transform, and belongs to the field of traffic flow prediction. The method comprises the following steps: (1) constructing an urban refined characteristic traffic flow high-quality data set based on actual license plate recognition (LPR) probe data; (2) building an efficient space-time traffic flow prediction model of a fusion graph attention network GAT and a Transform model; and (3) traffic flow prediction model training based on time-space fusion. According to the method, the traffic flow prediction model fusing the graph attention mechanism and the Transform is established, the space-time dependency relationship of the traffic flow in the urban complex road network is accurately described, the traffic flow prediction level under the urban complex road network is improved, and efficient data support is provided for intelligent traffic management, signal control optimization and urban congestion management.

The invention provides a low-altitude traffic service information fusion and interaction system and method, and relates to the technical field of low-altitude traffic management, and the system comprises a multi-source information collection module which is used for obtaining multi-dimensional data from different information sources; the dynamic weight information fusion module is used for calculating the dynamic weight of each information source based on the data credibility coefficient and the updating frequency factor of each information source, performing weighted fusion and generating low-altitude traffic comprehensive data; the traffic service information generation module is used for matching the low-altitude traffic comprehensive data with a preset service rule base to generate traffic service data; and the multi-terminal information interaction module is used for transmitting the traffic service data to different terminals by adopting different communication protocols. According to the system and the method provided by the invention, by integrating multi-dimensional information, the weight of the information source is dynamically adjusted, and the fusion accuracy is improved; the system adapts to various communication protocols, seamless interaction with different terminals is realized, and the service quality of low-altitude traffic service is remarkably improved.

The invention discloses a highway network traffic flow detection and congestion situation dynamic modeling system, and relates to the technical field of highway traffic management, the highway network traffic flow detection and congestion situation dynamic modeling system comprises a sensing layer, a transmission layer, a first processing layer, a second processing layer and an application layer, the sensing layer deploys multi-modal detection equipment to realize traffic flow data acquisition of highway key nodes; the transmission layer is provided with an edge server cluster for realizing real-time transmission of data acquired by the sensing layer; the first processing layer is used for realizing an RGBT multi-mode traffic flow detection subsystem, the second processing layer is used for realizing GNN congestion situation dynamic modeling, and the application layer provides visual display and decision support for a traffic management department. The RGBT detection technology and GNN modeling need to be deeply fused, a perception-modeling-analysis-early warning closed-loop system is constructed, the requirements of a traffic management department for accurate management and control and efficient scheduling are met, and the practical requirements of current highway management pain points are met.

The invention relates to the technical field of highway intelligent traffic management, in particular to a charging abnormity monitoring and recognition system for a highway. Comprising a data acquisition module used for acquiring vehicle track flow data and road network topology basic data; the first processing module is used for calculating topology inconsistent energy and generating a purification subgraph according to the topology inconsistent energy; the second processing module is used for calculating the antagonism characteristic drift rate and resolving the posterior probability that the target vehicle is a real fee evasion person; the game decision-making module is used for determining the total expected utility in combination with the preset interception income and the social congestion conversion cost, and solving the optimal interception execution probability which maximizes the total expected utility; and the strategy execution module is used for responding to the optimal interception execution probability. According to the method, the interference of environmental noise and equipment faults on a recognition system is remarkably reduced, the purity and credibility of input data are improved, and a solid foundation is laid for subsequent accurate recognition.

The application discloses a traffic management platform construction method and device and a traffic management method and system, acquires traffic basic data and carries out digital analysis to construct a traffic management semantic library, constructs a traffic management element periodic table from the semantic library, and the traffic management element periodic table is composed of various preset traffic management elements and traffic basic data corresponding to each type of traffic management element; the traffic management element is randomly combined to generate a corresponding traffic management scene library; the traffic management scenes in the traffic management scene library are randomly combined to generate a traffic management business library; and the traffic management platform for digital traffic management is constructed according to the traffic management semantic library, the traffic management element periodic table, the traffic management scene library and the traffic management business library, so that the programmable and calculable traffic management full chain can be realized, and templates and demonstrations can be provided for the construction of traffic management command centers in various places.

The invention provides a method and a system for constructing a multi-agent collaborative decision graph for traffic control. The method comprises the following steps: generating a dynamic multi-dimensional traffic decision map through multi-source traffic, traffic resources and multi-source data of traffic events of a road network system, establishing a model, predicting and evaluating a simulation result and an optimization decision result of the road network system, iterating the map, and generating a strategy according to user vehicle priority association nodes in combination with the map. Generating feedback data for a decision instruction of a traffic environment, inputting the feedback data into a map for optimization and updating, simulating, deducing and selecting an optimal scheme according to a vehicle travel urgency degree, and generating an instruction execution scheme; therefore, active deployment, collaborative optimization and adaptive evolution of traffic control are realized, and the operation efficiency and safe operation capability of a road network system are improved.

The invention belongs to an intelligent traffic system and artificial intelligence technology, and discloses a tunnel emergency speed limit decision support system and method. The method comprises the steps that a natural language instruction of a traffic management terminal is received and analyzed, and an unstructured current emergency scene is converted into a standardized parameter; based on the current emergency scene after parameter standardization, similarity retrieval is carried out in the historical simulation case library, a historical case with the highest matching degree is recalled, and an optimal control scheme and a recommendation index thereof are extracted from the historical case and recommended to a traffic management terminal; and performing result integration and output on the optimal control scheme recommended to the traffic management terminal and the recommendation index thereof. According to the method, a complete historical simulation case library and an efficient AI Agent retrieval matching engine are constructed, so that full-process automation from scene recognition to scheme recommendation is realized.