1352 results about "Road networks" patented technology

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a vehicle path planning system based on multi-agent collaboration, and aims to solve the problems of path response lag, insufficient global optimization ability and conflict between individuals and group targets in a dynamic traffic environment. The system adopts a three-level collaborative architecture of a central coordination server, a regional scheduling agent and a vehicle-mounted agent, realizes macroscopic regulation and local resource allocation through a global guidance potential field and an improved auction algorithm, and improves the dynamic adaptive capacity by combining event-driven replanning and a robustness verification mechanism. Different scheduling and multi-time scale coupling operation of heterogeneous vehicles are supported, and the road network passing efficiency and the path reliability are effectively improved.

The invention discloses a congestion management system and method based on vehicle-road cooperation and dynamic group path optimization, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: S1, collecting original traffic data through a multi-modal sensor array to carry out space-time alignment, and constructing a microscopic traffic flow data set; s2, bidirectional information interaction is carried out through a vehicle-road cooperative communication network; s3, calculating a multi-modal fusion confidence coefficient parameter based on the data quality index, and generating traffic state information and road section real-time saturation; s4, fusing historical and static road network data, constructing a dynamic digital twinborn model and a road network state feature map, and generating a road network load balancing index; and S5, making a decision by adopting a hierarchical-distributed architecture, and generating a group path induction strategy through multi-objective optimization. The efficient collaborative decision is realized through the graph convolutional network and the attention mechanism, and the road network traffic efficiency is comprehensively improved through stable and reliable multi-objective optimization on the premise of guaranteeing the user fairness.

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 flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

The invention discloses an electric vehicle charging guide strategy based on hierarchical hypergraph reinforcement learning, and aims to solve the problem of supply and demand mismatching between massive electric vehicle charging demands and limited charging resources. The method comprises the following steps: firstly, constructing a feature extraction model based on a space-time hypergraph convolutional network, predicting charging demand time distribution by using a time convolutional network, and realizing relevance learning of space-time features of a traffic-power heterogeneous coupling network through a hypergraph structuring method and the hypergraph convolutional network; secondly, constructing a double-layer finite Markov decision process; the upper layer aggregates the environment state to output the target charging station and is embedded into the lower layer, and the lower layer dynamically adjusts the path by combining the real-time road network and the upper layer decision. And finally, a multi-agent reinforcement learning solution strategy is adopted, and the robustness of the strategy is enhanced through a probability distribution mechanism and Q variance constraint.

The invention relates to the field of smart cities, in particular to an urban landscape semantic segmentation method and system based on a large model, and the method comprises the steps: obtaining street view image data of a target geographic region; inputting the streetscape image data into a preset image semantic segmentation model, analyzing the streetscape image data through the image semantic segmentation model, and identifying various predefined environmental element categories contained in each image; calculating an urban landscape quantitative index based on the recognition result of the environmental element category; associating the calculated value of the urban landscape quantitative index to a corresponding road section or node in the road network spatial data corresponding to the target geographic area; generating a road network space visualization layer representing the value of the index value in a color grading manner based on the result after association; and according to a preset index threshold value, identifying the spatial road sections or nodes meeting the early warning conditions in the visual layer and generating prompt information. And the scientificity and comparability of an analysis result are ensured.

The invention relates to the field of traffic engineering, and discloses an urban road network toughness management platform and evaluation method based on dynamic cascade failure, and the platform comprises a disturbance scene simulation and random capacity degradation module, a dynamic cascade failure process simulation module, and a multi-dimensional toughness index calculation and uncertainty evaluation module. A toughness three-dimensional vector is formed by integrating system robustness, restorability and performance accumulated loss, and a time-varying dynamic traffic distribution model considering road network structure change is used for depicting a cascade failure dynamic process; a random variable is introduced into a damaged road section capacity index, and a new evaluation method is provided for solving the defect that the existing toughness evaluation method is difficult to comprehensively quantify the robustness and the restorability of a road network. A failure diffusion mechanism in an abnormal scene is truly restored, and an effective method is provided for overcoming the double defects that a traditional model is insufficient in quantification of travel behavior dynamics and a system recovery process.

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 relates to the technical field of vehicle scheduling, provides a mobile charging vehicle scheduling method and device, electronic equipment and a storage medium, and aims at a plurality of candidate vehicles which send charging requests or are in a low-electric-quantity emergency state to obtain real-time vehicle state data and real-time road network data of each candidate vehicle in real time; according to the method, real-time vehicle state data and real-time road network data are acquired, a corresponding spatial feature risk threshold is acquired from a spatial feature risk database, meanwhile, a time path threshold is determined according to the real-time vehicle state data and the real-time road network data, and then the spatial feature risk threshold and the time path threshold are fused to generate a comprehensive scheduling weight; and finally, according to the comprehensive scheduling weight and the service attribute of the mobile charging vehicle, determining a target vehicle needing to be served and a target parking lot for parking and charging, and scheduling the corresponding mobile charging vehicle to go to the target parking lot to complete the charging service of the target vehicle, so that the user does not need to wait for a long time or empty driving to find a fixed charging pile in the whole process. And the intelligent level and the user experience of the emergency charging service are improved.

The invention relates to the technical field of smart traffic, and discloses a smart traffic monitoring system and method based on multi-source data fusion, and the system comprises a multi-source data collection module, a spatial-temporal feature fusion engine, a hierarchical decision core, a decision execution module, and an online learning module. The method corresponds to the system. According to the method, heterogeneous traffic data are unified under the same time-space reference through a multi-modal fusion technology, and comprehensive and accurate road network state feature representation is constructed; the hierarchical decision-making core forms a closed-loop decision-making link from event identification to control strategy generation through close cooperation of an event sensing layer and a regional optimization layer, and realizes rapid response and accurate control of traffic abnormity; on-line learning continuously optimizes decision logic based on complete historical operation data so as to adapt to a continuously changing traffic environment; finally, the accuracy of traffic state perception and the timeliness of decision response are improved, and reliable technical guarantee is provided for efficient management and control of intelligent traffic.

The invention discloses a power distribution network space-time coupling electric vehicle fast charging bearing capacity evaluation method and system, and belongs to the technical field of electric vehicle fast charging control, and the method comprises the steps: generating a space-time charging load of an electric vehicle through Monte Carlo simulation based on a road network-power grid coupling model; monte Carlo simulation comprises vehicle group initialization, dynamic path planning and driving, energy consumption and state updating, charging decision and load event recording and all-weather cycle simulation, and a charging load event set is obtained; and based on the charging load event set, constructing an optimization model taking maximization of the number of the electric vehicles acceptable by the power distribution network as a target function, solving the optimization model, and solving the optimization model through a second-order cone relaxation technology and a genetic algorithm to obtain a power distribution network time-space coupling electric vehicle fast charging bearing capacity evaluation result. According to the method, calculation of a large-scale complex network can be completed within reasonable time, and technical support is provided for planning capacity expansion of the power distribution network, optimized layout of the charging piles and scheduling strategies of the electric vehicles.

The invention belongs to the technical field of near-roadside pollution diffusion simulation, and particularly relates to a road traffic pollution diffusion rapid simulation method based on a physical structured proxy model. Comprising the following steps: S1, meteorological characteristic engineering and sampling; s2, road network standardization decomposition: carrying out topology discretization decomposition on the complex urban road network into standardized unit line sources which can be independently calculated; s3, reconstructing spatial geometric features; s4, physical mechanism dual partitioning: constructing an orthogonal physical mechanism partitioning system according to the atmospheric stability level and the relative position of the receptor point; s5, proxy model training: respectively training an XGBoost regression model for each physical partition; and S6, area field rapid reconstruction: receiving real-time boundary conditions for parallel inference, and rapidly reconstructing an area pollutant concentration field through linear superposition. On the premise of ensuring the simulation precision highly consistent with that of a full-physical model, the calculation efficiency is remarkably improved, and the method is suitable for real-time air quality simulation of a large-scale urban road network.

The invention relates to the technical field of road traffic management and maintenance, in particular to a method for configuring ice and snow removing and traffic keeping mechanical equipment in a regional road network, which comprises the following steps of: formulating a standard road section length standard for configuring ice and snow removing mechanical equipment; dividing the roads into different grades based on the requirements of unobstructed and unobstructed road maintenance of different road sections in the regional road network; corresponding ice and snow removing operation time limit requirements are provided according to different grades of roads and different snow conditions; according to the regional characteristics, selecting proper conventional ice and snow removing mechanical equipment types; determining the configuration number of ice and snow removing equipment based on the equipment operation parameters and the actual condition of the road; and in combination with different snow conditions and different grades of roads, an applicable ice and snow removal operation combination mode is calculated, and the number of operation combination tables is determined. According to the method, the configuration number of the equipment is determined by combining the operation parameters of the equipment with the actual condition of the road, so that the allocation of the equipment is more scientific and reasonable, the problem of resource waste is reduced, and the unobstructed and smooth capability of the road in winter is improved.

The invention discloses a multi-target road transportation path planning method and system based on multiple parameters A * and NSGA-II, and belongs to the technical field of road transportation path optimization. The method comprises the following steps: constructing a large-scale road network directed graph structure; establishing a satellite orbit model and a risk distribution model, calculating a dynamic risk value of a roadside, and forming dynamic road network data; a three-stage initialization strategy is adopted to generate an initial population, transportation time and reconnaissance risks are taken as double targets, evolutionary optimization is carried out through an NSGA-II algorithm, and a Pareto optimal path set is output. The system comprises a corresponding road network module, a risk module and a planning module. According to the method, the technical problems of multi-target conflict, low initial population quality and satellite risk avoidance of a traditional method are solved, and the timeliness, the concealment and the algorithm efficiency of path planning are remarkably improved.

A traffic flow prediction method based on a dynamic space-time fusion attention mechanism is characterized in that a space-time decoupling embedding strategy, a dynamic weighting time attention module and a self-adaptive dynamic graph modeling module are innovatively adopted, so that space and time characteristics can be effectively decoupled and independently modeled; and meanwhile, the long-term dependency relationship of the time dimension and the spatial relevance of the road network are captured. According to the method, the accuracy and generalization ability of the model in a complex traffic flow prediction task are remarkably improved, a more reliable and more efficient solution is provided for flow prediction in an intelligent traffic system, and the method has important application value and prospect.

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 discloses a container terminal truck intelligent scheduling method and system based on multiple driving modes. The method comprises the steps that a whole-process system architecture integrating a central scheduling platform, a vehicle-mounted intelligent terminal and a sensing and communication network is constructed; the method comprises the following steps: acquiring basic data of vehicles, tasks, road networks and environments, constructing a multi-objective optimization function, establishing a dangerous goods risk integral model, respectively setting mathematical constraints for task assignment, path planning, hybrid driving coordination, dynamic path tracking and mode switching, and forming a multi-dimensional mathematical model; an improved non-dominated sorting genetic algorithm is adopted to solve the model, and a personalized scheduling scheme including vehicle-task assignment, path planning, time requirements and risk control rules is generated; through cooperation of the central scheduling platform and the vehicle-mounted intelligent terminal, MPC dynamic path tracking is adopted for the unmanned vehicle, visual and voice dual-mode guidance is provided for the artificial vehicle, and hybrid driving cooperative management and control are implemented, so that the vehicle operation efficiency and the dangerous goods transportation safety are guaranteed.

The invention relates to the technical field of intelligent traffic, in particular to a traffic abnormal event dynamic detection method and system based on trajectory analysis, and the method comprises the steps: receiving original space-time trajectory data; preprocessing the original spatio-temporal trajectory data, and mapping the original spatio-temporal trajectory data to a road section sequence of a road network through a map matching algorithm; constructing a behavior model based on the track sequence after map matching, learning the track sequence of the normal behavior mode on the road section, and establishing an observation emission model and a state transition matrix; in the online stage, the log-likelihood value of an observation sequence and / or the similarity between the observation sequence and a normal behavior pattern cluster are / is calculated for a real-time track in a set sliding window, and when the log-likelihood value and / or the similarity exceed a set threshold value, the track is marked as abnormal; performing time-space aggregation on abnormal trajectories of the same road section or intersection in unit time according to single vehicle abnormality judgment, and triggering group abnormal event alarm when aggregation data exceed a preset value.

The invention discloses a road subsidence detection method and system based on a deep learning model. The method comprises the following steps: acquiring continuous image sequence data of a to-be-detected road; inputting the continuous image sequence data into a deep learning model; extracting multi-scale visual features of each frame of image in the continuous image sequence data through an encoder network in the deep learning model, and fusing the multi-scale visual features of each frame of image to generate a fused feature map corresponding to each frame; through a time sequence analysis network in the deep learning model, time sequence modeling is carried out on the continuous multi-frame fusion feature map, dynamic deformation features of the road surface are captured, and a road subsidence area is identified based on the dynamic deformation features; and through a decoder network in the deep learning model, carrying out spatial positioning and contour reconstruction on the road subsidence area, and outputting a detection result containing the position and contour information of the road subsidence area. According to the method, the road subsidence detection precision and robustness are improved, and large-range road network automatic continuous monitoring is realized.

The invention relates to the technical field of path planning, in particular to an unmanned vehicle dynamic path optimization method and system based on artificial intelligence. The method comprises the following steps: collecting regional state data based on real-time navigation information, carrying out abnormal traffic flow fluctuation analysis, carrying out abnormal road section visual rendering, and constructing an abnormal fluctuation rendering road network; performing abnormal disturbance propagation chain reaction mining on the abnormal fluctuation rendering road network, performing dynamic risk effect modeling, and constructing a risk linkage effect prediction engine; performing user behavior intention deep analysis and riding intention situation prediction to obtain a user intention prediction situation map; and performing adjacent vehicle path pre-judgment based on the user intention prediction situation map, and performing path conflict probability calculation to obtain a multi-vehicle path conflict probability. According to the invention, the robustness of the unmanned driving system to deal with sudden situations and complex dynamic environments is improved, and the riding safety and the path accessibility are improved.

Provided are a reinforcement learning (RL)-based traffic signal control (TSC) method and apparatus, a device, a medium, and a product. The TSC method includes: obtaining traffic state data of a target intersection at a current time point and a road network graph, where the traffic state data includes a quantity of lanes at the target intersection and a traffic flow of each of the lanes; inputting the traffic state data and the road network graph into a preset traffic signal prediction model, and obtaining a target phase action output by the traffic signal prediction model, where the traffic signal prediction model includes a spatiotemporal encoder and a return-based action decoder, and the traffic signal prediction model is obtained through training based on return-based contrastive learning; and controlling, based on the target phase action, a traffic light at the target intersection to execute the target phase action.

The invention discloses a method for calculating road network traffic operation carbon emission by using vehicle trajectory data. The method comprises the following steps: firstly, segmenting vehicle GPS trajectory data according to timestamps and serial numbers to obtain a trajectory subset of each vehicle; determining a vehicle stop interval based on the minimum instantaneous speed threshold value, and dividing the trajectory data into driving sections to obtain a driving trajectory; constructing an urban road network model, calculating a spatial distance between a track and a road section, screening candidate road sections according to a shortest distance threshold value, obtaining a matched road section in combination with a minimum difference value of an azimuth angle, mapping track points to the road section, and obtaining re-expanded driving section track data; according to the timestamps and the road section serial numbers, segmenting according to the road section serial numbers to obtain each road section track subset; idle speed and normal driving intervals of a road section are identified through spatial-temporal clustering, a MOVES model database is introduced to perform classified calculation on carbon emissions of the corresponding intervals, the carbon emissions are summarized to obtain road section emissions, and road network traffic carbon emissions are summarized to obtain road network traffic carbon emissions. According to the method, macrotopography and microscopic real-time working condition data are fused, and a road section carbon emission measuring and calculating method is perfected.

The invention discloses an indoor positioning method based on a Bluetooth RSSI (Received Signal Strength Indicator), which comprises the following steps: after a positioning tag receives RSSI data broadcasted by Bluetooth beacons, screening the beacon with the strongest signal and calculating the distance; current data is directly adopted in first positioning, and translation processing is performed in combination with historical positions in non-first positioning so as to smooth a result. And mapping the position points to path points through a road network matching algorithm, preliminarily positioning the path points based on shortest path planning, and outputting a final positioning result through a filtering rule. According to the invention, the problem of positioning fluttering caused by fluttering of the Bluetooth RSSI signal, the problem of back-and-forth jumping of actual positioning caused by instability of the Bluetooth RSSI signal and the problem of signal abnormity caused by signal interference caused by metal, a wall body or a human body are effectively solved.

The invention relates to the technical field of computers, and discloses an outdoor three-dimensional walking navigation road network map generation method fusing public source trajectory data. The method comprises the following steps: carrying out Kalman filtering and spline interpolation preprocessing on a public source track; performing track segment semantic segmentation based on a motion state discrimination criterion; extracting multi-dimensional behavior characteristics of geometry, kinematics, direction stability, time sequence distribution and the like of the track segment; generating a candidate path cluster through an improved space-time clustering algorithm; constructing a traffic intention comprehensive scoring model, and screening a high-intention path cluster; a main curve algorithm is adopted to extract a center line and construct a two-dimensional topology road network; and finally, fusing three-dimensional elevation information of an original track, endowing a road network with a slope, accumulating climbing attributes, and generating a three-dimensional walking road network map supporting refined navigation. The accuracy and practicability of the road network are improved.

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 discloses an unmanned aerial vehicle relay tracking system and method for cloud side-end cooperation in an urban scene, and belongs to the technical field of intelligent traffic and unmanned aerial vehicle cooperative control. In order to solve the problems of insufficient endurance and limited sight distance of existing single-machine tracking in an urban large-range scene, a physically distributed and logically unified cloud-side-end three-level architecture is constructed, wherein a cloud end is responsible for natural language instruction analysis and global road network monitoring; the edge end serves as a regional scheduling hub and is responsible for operating a high-precision detection model, extracting a target Re-ID feature fingerprint and calculating a cross-machine relay time window; and the terminal unmanned aerial vehicle only operates a lightweight tracking model and a visual servo control law. According to the method, the urban area is divided into a plurality of honeycomb grids, seamless locking and relay of a target among unmanned aerial vehicles in different grid responsibility areas are realized by using a cross-view feature relay protocol and a collaborative finite-state machine, and the engineering problem of long-time-sequence and large-range vehicle tracking is effectively solved.

The invention belongs to the technical field of intelligent traffic, and particularly discloses an urban road network traffic state and congestion propagation probability prediction method and system. The method comprises the following steps: firstly, introducing information such as spatial distance and historical flow correlation on the basis of road topology to form a weighted gate road network; secondly, a conditional denoising diffusion model based on a bayonet space-time diagram is provided, the model takes historical multi-step diagram signals and a road network structure as conditions, and modeling is carried out on conditional distribution of future multi-step diagram signals through forward noise adding and reverse denoising processes; according to the method, multiple traffic evolution sample tracks in the future are obtained through multiple times of sampling, node operation indexes are mapped into discrete congestion levels, the occurrence frequency of each node under different congestion levels is counted, and the node congestion level probability is obtained; and further counting a time sequence co-occurrence relationship of congestion states between adjacent nodes, estimating a propagation condition probability of congestion in a road network, and constructing a key node influence degree index and a high-risk propagation path.

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 relates to the technical field of intelligent traffic, and discloses a method and system for predicting traffic flow and a storage medium. The method comprises the steps of obtaining multi-source dynamic data of a mountainous area road network, constructing a road network connection relation model, extracting flow transfer characteristics and transfer dynamic fluctuation characteristics between nodes, performing time sequence trend analysis on a historical data sequence of a flow transfer path, and predicting a transfer influence range caused by road network state change; and when the transfer influence range exceeds a preset range threshold value, screening an affected road subset, generating a flow transfer spatial distribution map, updating weight parameters of the road network connection relation model, performing flow offset simulation, determining adjusted road network connection state characteristics, performing geospatial mapping on the road network connection state characteristics, and obtaining a flow transfer spatial distribution map of the road network connection relation model. And extracting a traffic flow quantitative index, and outputting a traffic flow prediction result when a preset congestion risk condition is satisfied. According to the invention, the accuracy and pertinence of mountain road network traffic flow prediction can be improved.

The invention discloses a highway intelligent risk monitoring method and system based on multi-modal information, and the method comprises the steps: collecting multi-modal traffic data, constructing a road network holographic portrait through a road network holographic portrait risk prediction model, positioning a potential risk point through a dual time-space mask anomaly detection model, and carrying out the detection of the potential risk point. Lane-level risk assessment is completed by means of a lane-level risk dynamic assessment algorithm, and risk information is output after multi-dimensional fusion analysis is carried out through a traffic risk intelligent research and judgment platform. All the models and algorithms are operated cooperatively, data integration, feature mining, anomaly recognition, risk assessment and fusion research and judgment are achieved step by step, a whole-process monitoring system from data collection to result output is constructed, refined and dynamic risk monitoring from the global road network to the local lane is achieved, the risk recognition accuracy and monitoring comprehensiveness are effectively improved, and the risk monitoring efficiency is improved. And technical support is provided for safe operation of the expressway.