161 results about "Traffic forecast" patented technology

The invention discloses a subway ventilation demand dynamic decision-making method and system fused with people flow prediction, and belongs to the technical field of intelligent traffic and environment control, and the method comprises the steps: collecting real-time people flow data, historical passenger flow records, station layout information and external environment parameters of each region in a subway station; constructing a deep learning prediction model through multi-source data fusion, and generating short-term and medium-term people flow distribution prediction results; based on the prediction result, combining the ventilation equipment operation parameters, the air quality index and the energy consumption cost to construct a multi-objective optimization model; dynamic decision making is carried out on a ventilation system by utilizing the model, and an optimal ventilation strategy in different regions and different time periods is output; through a real-time feedback mechanism, prediction and control parameters are continuously corrected according to actual people flow changes, closed-loop regulation and control are formed, the response precision and the energy efficiency level of a subway ventilation system can be remarkably improved, and passenger comfort and environment safety are guaranteed.

The invention relates to the technical field of charging pile power dispatching, in particular to a service area new energy charging management method and system based on multi-device data analysis. The method comprises the following steps: acquiring real-time operation monitoring parameters of all charging piles in a service area; multi-dimensional feature perception and charging behavior time popularity distribution analysis are carried out, and a dynamic charging behavior hotspot map is constructed; carrying out multi-time-point power sampling according to the dynamic charging behavior hotspot map, carrying out power cooperation path topology evolution, and constructing a power cooperation path network; performing charging pile power trend prediction and power peak congestion evolution according to the power cooperation path network, and generating a power peak congestion state feature of the charging pile; and obtaining new energy traffic flow data of the service area, carrying out unit time traffic flow average calculation, and carrying out traffic flow prediction to obtain a traffic flow prediction thermodynamic diagram. Charging power scheduling is carried out through demand prediction, and the operation efficiency and stability of charging equipment are improved.

The invention provides a region-level aviation flow prediction method based on Mamb-GCN, and belongs to the technical field of air traffic flow prediction, and the method comprises the steps: constructing a Mamb-GNC collaborative network model; historical flight path data of a target airspace is collected, the target airspace is divided into space grids, the number of aircrafts in each space grid is counted, and the time feature and the space feature of each aircraft are coded to construct a space-time tensor; constructing a dynamic adjacency matrix and a dynamic weight map based on the 8-neighborhood topology of the space grid; inputting the space-time tensor and the dynamic weight graph into a Mamba-GCN collaborative network model for training, and optimizing model parameters; and preprocessing the aviation trajectory data of the target airspace acquired in real time, and inputting the preprocessed aviation trajectory data into the trained Mamba-GCN collaborative network model to obtain an aviation flow prediction result. According to the method, the long-time dependence of the aviation flow in the time dimension and the grid correlation in the space dimension can be captured, and the prediction efficiency is high.

The invention provides a traffic prediction method and device for a highway, electronic equipment and a storage medium. The method comprises the following steps: acquiring highway multi-source data; inputting the highway multi-source data into a knowledge graph construction module of a traffic prediction model to obtain a traffic prediction knowledge graph of the highway multi-source data; inputting the traffic flow data, the meteorological data and the holiday and festival data into a time feature extraction module to obtain time features of the highway multi-source data; inputting the road portrait data and the flow prediction knowledge graph into a spatial feature extraction module to obtain spatial features of the highway multi-source data; inputting the time features, the spatial features, the event data and the charging policy data into a feature fusion module to obtain fusion features of the highway multi-source data; and inputting the fused features into a prediction module to obtain a traffic prediction result of the expressway. Thus, through the constructed flow prediction model, the multi-source data of the expressway can be fused for flow prediction, and the accuracy of expressway flow prediction is improved.

The invention provides a smart city construction system based on big data, and relates to the technical field of smart cities, and the system comprises a traffic data collection module, a transmission and preprocessing module, a flow prediction and analysis module, an intelligent signal optimization module, a parking lot scheduling and induction module, a decision support module and a user service module. The traffic flow prediction and analysis module predicts a traffic state in a set time length in the future through a space-time diagram convolutional network in combination with a seasonal autoregression model; and the intelligent traffic signal optimization module is used for dynamically adjusting signal lamp timing based on a deep reinforcement learning algorithm and realizing intersection linkage through a regional cooperative control algorithm. According to the invention, the space-time diagram convolutional network, the deep reinforcement learning intelligent algorithm, the block chain and the distributed computing technology are fused to construct a multi-source data-driven traffic whole-flow intelligent management system, so that the whole-chain intelligence from real-time sensing and dynamic optimization to cross-platform service is realized; and the collaboration, the prediction accuracy and the service ecological openness of the urban traffic system are obviously improved.

The invention discloses a traffic road network monitoring method and device based on artificial intelligence, and relates to the technical field of road network monitoring. The method comprises the following steps: acquiring a traffic network of a management and control area, and positioning traffic monitoring equipment distributed in the traffic network; constructing a traffic supervision module through big data driven training by taking traffic flow trend and traffic behavior analysis as guidance; establishing interactive connection between the traffic monitoring equipment and the flow prediction layer and between the traffic monitoring equipment and the behavior decision-making layer; the traffic monitoring equipment samples road network traffic data, transmits the data to the traffic supervision module through a data interface, makes a decision and outputs a traffic guidance strategy; and managing the traffic network of the management and control area according to the traffic guidance strategy. The technical problems of low traffic flow prediction precision and untimely traffic behavior decision response in the prior art are solved, and the technical effects of improving the traffic flow prediction accuracy, optimizing the traffic behavior decision efficiency and improving the traffic network intelligent management level are achieved.

A method, device and system for active management and control of road traffic safety are disclosed. The method includes acquiring traffic data of a target road in real time, wherein the target road includes management and control sections; for each management and control section, judging whether there is a traffic accident according to the acquired traffic data; if so, formulating an emergency management and control strategy; if not, extracting traffic flow data from the traffic data, and generating predicted traffic flow data according to the traffic flow data by a traffic flow prediction model; generating a risk level according to the predicted traffic flow data by a risk prediction model; determining the current active management and control strategy of the management and control section according to the risk level and the predicted traffic data; and issuing the corresponding management and control strategy of each of the management and control sections.

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

The invention discloses a traffic flow prediction method and device, a storage medium and electronic equipment. The method relates to the technical field of traffic flow analysis, and comprises the following steps: constructing a multi-source traffic data pool, and preprocessing historical traffic data in the multi-source traffic data pool to obtain a target multi-source traffic data pool; performing data enhancement processing on the historical traffic data in the target multi-source traffic data pool by adopting a pre-trained GANs model to obtain an enhanced sample set for training a target traffic flow prediction model; performing model training on an initial traffic flow prediction model by adopting a Dropout method based on the enhanced sample set to obtain a target traffic flow prediction model meeting a preset condition; and performing traffic flow prediction on multi-source traffic data acquired in real time by using the target traffic flow prediction model to obtain a traffic flow prediction result. According to the method, the accuracy of urban traffic flow prediction can be improved.

The invention discloses a building group energy-saving management and control method and system based on big data analysis, and relates to the field of big data analysis, and the method comprises the steps: carrying out the standardization processing of the real-time visitor flow data of a commercial district, and the subentry energy consumption data, time information and environmental parameters of the commercial district and a residential district, and constructing a current scene feature vector; screening matched similar historical scene samples, and generating a commercial district people flow prediction curve in a future target time period through weighted fitting calculation; screening matched historical regulation and control records, and performing weighted calculation to obtain a mean value as a historical reference regulation and control parameter; generating a candidate parameter combination according to a preset step length, calling the corresponding total energy consumption and the indoor temperature of the residential area, and selecting an energy-saving regulation and control instruction; and adjusting operation parameters of the commercial district energy system according to the energy-saving regulation and control instruction. According to the building group energy-saving management and control method and system based on big data analysis, the problem that the use comfort of a residential area is poor due to the fact that the energy consumption fluctuation of a commercial area is too large is solved.

The invention discloses an intelligent traffic signal control method and system based on Agent-ARIMA, and a program product. The intelligent traffic signal control method comprises the steps of calculating the shortest green light time of each intersection direction, distributing the green light time of each direction, distributing the green light time of left-turn and straight lanes, adjusting the traffic cycle, calculating the time weight offset of multiple intersections, and predicting the traffic flow in the future by using an ARIMA model. And inputting the prediction time of the ARIMA algorithm into the LLM-Agent agent for time prediction, and the like. The method is suitable for different city scales, time distribution of the traffic lights is dynamically adjusted through accurate prediction and analysis of the traffic flow, the traffic jam condition of a large city can be optimized, and the traffic efficiency of a small city can also be improved.

The invention provides a traffic flow prediction method based on a dynamic multi-graph hybrid expert graph neural network. The method can perform feature extraction and flow prediction for different road types and regional features. The method comprises the following steps: obtaining a road network map; obtaining respective traffic flow sequences of N road sections corresponding to the N nodes; for the road network map, constructing a distance-based map according to the distance correlation between every two road sections in the N road sections; obtaining respective attributes of the N road sections and semantic correlation between every two road sections in the N road sections, and constructing a graph based on a knowledge graph; and according to the distance-based graph, the knowledge graph-based graph and the respective traffic flow sequences of the N road sections, predicting respective estimated traffic flows of the N road sections at the next moment.

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 portal data-based highway traffic incident detection method and system, and relates to the technical field of traffic intelligent management, and the incident detection method comprises the steps: obtaining the historical traffic data of an ETC portal, constructing a road network topology and traffic flow reference model, and carrying out the dynamic flow prediction through combining the real-time portal data flow, the system can identify traffic events and carry out accurate positioning. According to the method, a flow deviation accumulation and space-time aggregation mechanism is adopted, and video monitoring linkage and multi-channel information release are combined, so that real-time confirmation, check and release decision of events can be realized; according to the method, the timeliness and accuracy of event response are remarkably improved, the information issuing process is optimized, the robustness of the system is enhanced, and the false alarm rate is reduced. Sufficient technical support is provided for safe and efficient operation of the expressway, and the emergency management and traffic control level is improved.

The present invention discloses a multi-layer attention-based graph fusion traffic flow prediction method, medium and device that can capture the complex traffic flow patterns of different roads and achieve high-precision traffic flow prediction. [Solution] Historical traffic flow data for all roads in a target road network is sampled at different sampling intervals to obtain multiple historical flow sequences representing different data periods for each road. A road spatial relationship graph and a road functional similarity relationship graph corresponding to each data period are then constructed for all roads in the target road network. The road spatial relationship graph is then subjected to adjacency matrix attention fusion with the road functional similarity relationship graph corresponding to each data period. Finally, the fused adjacency matrix and vertex feature matrix for each data period are input into a graph attention network, and weighted fusion is performed on the output results to obtain future flow prediction results for the target road network.

The invention provides a vehicle flow prediction method and system based on a bimodal optimization embedded learning model, and the system comprises a data preprocessing module, a bimodal coding module, an attention alignment module and a sequence prediction module, is suitable for the field of intelligent transportation, and aims at achieving the prediction of the vehicle flow through multi-source data fusion and deep model architecture innovation. The problem that a traditional method is insufficient in prediction precision in a complex traffic scene is solved, and efficient decision support is provided for urban traffic management.

The invention discloses a charging pile dynamic distribution method in a multi-forklift collaborative operation scene, and relates to the technical field of intelligent logistics dispatch, and the method comprises the steps: S1, based on a current traffic flow prediction result, generating the predicted arrival time of a forklift at each charging pile, and S2, according to the predicted arrival time, determining the charging pile according to the predicted arrival time. The method comprises the steps of S1, calculating the actual waiting duration and the corresponding priority weight of each forklift, S3, dynamically regulating and controlling the charging queuing sequence of the forklifts based on the priority weights, and S4, sending the regulated queuing sequence to a target forklift to guide the target forklift to complete charging pile distribution. According to the charging pile dynamic allocation method in the multi-forklift collaborative operation scene, the charging queuing sequence is regulated and controlled according to the current traffic flow prediction result, so that the problems of congestion and overlong waiting time are solved.

The invention discloses a traffic flow prediction and regulation and control method based on Transform, an A3C algorithm and multi-path AGV (Automatic Guided Vehicle) planning. According to the method, the mixed attention is introduced into the Transform model, so that high-precision traffic flow prediction is realized. A traffic signal control method based on an A3C algorithm is constructed, and a strategy gradient method is introduced to optimize a signal scheduling strategy so as to reduce vehicle waiting time and relieve traffic congestion. Through AGV (Automatic Guided Vehicle) multi-path planning, cooperative scheduling of signal control and path optimization is realized, and the traffic efficiency is improved. The method can effectively deal with a complex traffic environment, optimizes a signal control strategy, and improves the adaptability and stability of an intelligent traffic system. The method is suitable for the scenes of urban road management, intelligent traffic scheduling, automatic driving systems and the like.

The invention discloses a multi-level linkage ramp control method and related equipment based on multi-source data fusion, and the method comprises the steps: carrying out the clustering analysis of target multi-source traffic trajectory data, and obtaining travel trajectory feature data; on the basis of the travel track feature data, performing weight calibration on the on-bridge ramp of the expressway to obtain on-bridge ramp control priority data; performing short-time prediction on the current traffic flow data to obtain a short-time flow prediction result; performing congestion prediction according to the short-time flow prediction result and the dynamic bearing capacity of the expressway to obtain a congestion section prediction result; and according to the short-time flow prediction result, the congestion section prediction result and the on-bridge ramp management and control priority data, carrying out ramp control on the predicted on-bridge ramps of the expressway congestion section by adopting a layered management and control strategy. The method can achieve the precise dynamic sorting of the turn-off priorities of the ramps, balances the load of the road network, avoids the secondary congestion, improves the overall traffic efficiency of the road network, and can be widely applied to the technical field of traffic control.

The invention provides a multi-modal traffic flow prediction method and system based on deep learning. The method comprises the steps that an electronic device obtains multi-modal vehicle driving collection data of a specific intersection; predicting by using the traffic flow prediction model to obtain traffic flow prediction data of the specific intersection; and inputting the traffic flow prediction data into the reinforcement learning model of the double-Q network structure to obtain a traffic signal lamp regulation and control strategy of the specific intersection. According to the embodiment of the invention, the traffic flow prediction model is utilized to obtain the traffic flow prediction data, the reinforcement learning model of the double-Q network structure is utilized to construct the Markov decision-making model with the intersection traffic efficiency maximization as the target, the Markov decision-making model is solved, and the traffic signal lamp regulation and control strategy of the specific intersection is obtained. According to the mode, the traffic signal lamp control strategy is determined, model parameters do not need to be calculated again, calculation time consumption is reduced, and sudden events can be handled.

The invention belongs to the technical field of traffic flow green wave control, and particularly relates to a green wave control method based on traffic flow prediction driving. The method comprises the following steps: acquiring multi-dimensional real-time data in a distributed manner; performing abnormal value detection and data interpolation based on space-time neighborhood analysis to generate high-quality time sequence traffic flow data; modeling a road network into a dynamic graph fusing static connectivity and real-time traffic flow coupling degree, and constructing a double-time-scale time sequence diagram sequence; inputting an improved space-time diagram neural network, extracting time features by optimizing Bi-GRU, capturing spatial association by a multi-head attention mechanism, and outputting an accurate road section-level traffic flow prediction result through residual fusion; and finally, constructing a multi-objective optimization model based on prediction, solving by using an improved multi-objective particle swarm algorithm, obtaining a signal timing scheme, and issuing and executing the signal timing scheme. The method can dynamically adapt to traffic changes, improves the traffic efficiency, and avoids green wave failure and congestion.

The present application relates to a system for ingress traffic management. The system includes a collection system within a network configured to collect traffic arrival information for peering links of the network. The system includes a training system configured to train a model based on the traffic arrival information to predict a probability of a traffic flow arriving on a peering link. The system includes a congestion mitigation system configured to predict based on the model, for traffic flows arriving on one or more peering links, other peering links to which the traffic flows would be shifted due to a condition affecting the one or more peering links. The congestion mitigation system may determine, in response to the condition, a set of prefixes to withdraw based on the other peering links to which traffic would be shifted.

The invention discloses a highway edge calculation flow regulation and control method based on OpenHarmony, and belongs to the technical field of intelligent traffic control systems. The method comprises the following steps: traffic flow detection adopts a lightweight improved YOLOv8 model, a backbone network is replaced by an OfficientViT, a neck network is optimized by phantom convolution, a feature fusion module is enhanced by SCConv, and a dynamic non-monotonic focusing loss function is introduced; according to traffic flow prediction, a spatio-temporal feature fusion Transform model is constructed, a multi-head convolution low-rank decomposition attention mechanism is adopted to capture time dependence, spatial topological features are extracted in combination with attention graph convolution, spatio-temporal information is fused through a gating unit, and a prediction error is controlled within 1.5-3.0 times of historical standard deviation; a triple lightweight LSTM-AutoEncoder is adopted for abnormal early warning, so that the false alarm rate is reduced; the OpenHarmony distributed task scheduling is cooperated with the above modules, the variable information sign and the dynamic speed limit sign are driven to execute regulation and control, the response delay is reduced, the problem of real-time accurate regulation and control under the condition that the computing power of the edge device is limited is solved, and the traffic efficiency is improved.

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 discloses a flight plan prediction method based on Prophet and LSTM model fusion holiday and seasonal factors, and the method comprises the steps: collecting historical air traffic flow data, and carrying out the preprocessing of the data, and obtaining a historical data time sequence; constructing a Prophet model, predicting the air traffic flow in a future time period according to the historical data time sequence, and constructing prediction results into a prediction result sequence; and constructing a Seq2Seq model to carry out prediction again to obtain a final air traffic flow prediction result. The prediction result sequence is input into a Seq2Seq model, and joint training is carried out; and carrying out joint prediction on actual data by using the model subjected to joint training, and carrying out visual display on a result. According to the method, seasonal and holiday factors, long-term trend information, historical data and the influence of other characteristics are fused in prediction, and accurate and comprehensive prediction of flight plans in spring and autumn in the future is achieved.

The invention discloses a traffic flow prediction method and system based on a multi-scale dynamic space diagram, and belongs to the technical field of traffic flow prediction. The method comprises the following steps: firstly, acquiring traffic time data, inputting the traffic time data into a pre-constructed traffic flow prediction model, and outputting a corresponding traffic flow prediction result; the traffic flow prediction model comprises an input layer, a time coding module, a multi-scale dynamic space diagram module, a GCN module, a self-adaptive convolution module and an output layer which are connected in sequence. By introducing a multi-scale dynamic graph structure and a self-adaptive convolution mechanism, the spatial dimension dynamic evolution rule of the traffic flow under different scales can be effectively modeled, so that high-precision future traffic state prediction is provided, the limitation of a traditional method is broken through, the problem of complex multi-scale and dynamic space dependence can be solved, and the real-time performance of the traffic state prediction method is improved. And the method has good expandability and practical value, and is suitable for being applied to actual traffic management and intelligent traffic systems.

The invention discloses an expressway flow prediction method based on multi-modal fusion and sequence decomposition, and relates to the technical field of traffic flow prediction, and the method comprises the steps: obtaining expressway traffic flow historical monitoring data, meteorological condition data and sampling time mark data, and constructing a multi-modal feature input sample; calling a two-channel neural network prediction model to extract trend term and remainder term features according to the sample, and calculating a traffic flow prediction result; combining prediction results of the trend term and the remainder term features, adjusting model parameters by adopting a feedback mechanism based on multi-modal coding parameter optimization, and generating a final traffic flow prediction model; and by using the trained prediction model and combining highway traffic flow monitoring data, realizing accurate prediction of regional traffic flow and inter-regional traffic flow at a specified moment and period in the future. Through multi-modal data fusion, feature extraction and model parameter optimization, the highway traffic flow can be accurately predicted, and powerful support is provided for traffic planning and management.

The invention relates to the technical field of flow control, and particularly discloses a flow control system of a flow battery, which comprises a flow data acquisition module, a first flow analysis module, a second flow analysis module, a flow prediction analysis module, a flow control judgment module, a flow control regulation module and a flow control optimization module, the method comprises the following steps: acquiring flow data, analyzing to obtain a first flow influence coefficient and a second flow influence coefficient, further obtaining a flow prediction index, analyzing to obtain predicted flow according to the flow prediction index, comparing the predicted flow with actual flow, analyzing to obtain a flow control deviation coefficient, and judging whether flow control regulation is performed or not. The flow deviation abnormal data is controlled and adjusted through the controller; the intelligent level of flow control of the flow battery is improved, the operation efficiency, the reliability and the economical efficiency of the system are also improved, and the method has important significance in promoting further development and application of the flow battery technology.

The technical scheme of the invention discloses a traffic flow prediction method based on a dynamic space-time diagram convolutional network. The invention provides a traffic flow prediction method fused with a dynamic space-time diagram convolutional network, which captures road network change in real time through a dynamic adjacency matrix, designs a multi-scale time attention mechanism to fuse local convolutional features and global time sequence dependence, develops an intelligent mixed precision training system to realize computing resource optimization, and improves the traffic flow prediction efficiency. And the training efficiency is improved while the prediction error is reduced. Meanwhile, traffic flow data time features and multi-section spatial features are comprehensively considered, and prediction is carried out based on a single step length and multiple step lengths. The method realizes high-precision prediction of the short-time traffic flow, and is suitable for precise flow prediction of a main line and an arterial highway of a highway network.

The invention discloses a high-speed rail station passenger flow volume prediction method and device, and the method comprises the steps: firstly obtaining historical multi-modal data which specifically comprise historical ticket business data, historical weather data, historical holiday and festival data, historical urban activity data, historical network media data and historical abnormal traffic data; extracting features from the historical multi-modal data, inputting the features into a to-be-trained prediction model, and training the features to obtain a prediction model; and then multi-modal data of the prediction day is input into the prediction model to obtain the passenger flow volume of the prediction day, so that the problem of inaccurate prediction caused by single data training is avoided, and the accuracy of predicting the passenger flow volume of the high-speed rail station is improved.