201 results about "Traffic forecast" patented technology

The invention discloses a full-period traffic flow prediction method based on spatial-temporal feature deep fusion, and aims to deeply integrate complex spatial-temporal dependence of traffic flow, form composite traffic situation perception and realize traffic multi-period comprehensive flow prediction. The method comprises the following steps: firstly, through spatio-temporal information comprehensive representation, extracting time sequence characteristics of multiple time modals and constructing a concrete real spatial relationship; in order to further construct a digital urban traffic flow prediction method fusing multi-scale and multi-granularity comprehensive elements, a comprehensive traffic flow prediction model deeply fusing short-term space-time dependence is provided for short-term traffic flow prediction, so that the sensitivity to instantaneous flow change is improved; the short-term dynamic change of the traffic flow in the multifunctional airspace node is fully modeled; in addition, a deep airspace deconstruction and time sequence feature fusion model is designed for medium and long term traffic prediction and deep extraction of complex traffic modes. Simulation results show that the method provided by the invention is superior to the existing traffic flow prediction 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 discloses an urban subway passenger flow prediction method and system based on a large language model. The method comprises the following steps: generating a patch embedding vector according to subway passenger flow time sequence data; fusing the patch embedding vectors by using a multi-head attention mechanism to obtain an attention mechanism patch embedding vector; constructing a prefix prompt according to the subway passenger flow time sequence data; and fusing the prefix prompt and the attention mechanism patch embedding vector to obtain an output projection. According to the method, complex characteristics of subway passenger flow volume data can be fully mined and utilized, so that the prediction accuracy and reliability are improved, effective decision support can be provided for urban subway operation management, and optimization of traffic scheduling and passenger service is facilitated.

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 BIM-based municipal road construction method and system, and the method comprises the steps: collecting traffic flow data, accident information and weather conditions, and generating original traffic data and traffic state data; collecting construction data; cleaning and processing the collected original traffic data and traffic state data to obtain traffic data and traffic information characteristics; generating traffic flow prediction data and an initial model framework based on the traffic data and the traffic information features; generating a construction scheme and a stage schedule by using an optimization algorithm in combination with the predicted traffic flow data and construction data; and filling the construction scheme and the stage schedule into the initial model framework to generate an initial BIM model, and generating the BIM model according to the construction data through dynamic traffic simulation. Through collection, processing and analysis of comprehensive traffic data and construction data, a construction scheme is optimized, and dynamic traffic simulation is carried out, so that the construction efficiency is remarkably improved, traffic congestion is reduced, and the construction safety is 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 relates to a traffic flow prediction model training method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring sample traffic flow data and traffic state influence data of a sample traffic road section at different moments in a sample time period, and obtaining traffic flow sequence information according to the sample traffic flow data and the traffic state influence data; inputting the traffic flow sequence information into a to-be-trained traffic flow prediction model to obtain corresponding time characteristics and spatial characteristics of the traffic flow sequence information in the sample time period, and obtaining spatial-temporal characteristics according to the time characteristics and the spatial characteristics; obtaining predicted traffic flow data of the sample traffic road section at the prediction moment according to the spatial-temporal characteristics; and according to the difference between the predicted traffic flow data and the actual traffic flow data, training a traffic flow prediction model to obtain a trained traffic flow prediction model. By adopting the method, the prediction precision of the traffic flow can 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 satellite network flow sensing method based on a space-time diagram neural network, and the method comprises the following steps: calculating the telemetering time delay Li, j, the telemetering overhead Ci, j and the path reliability Ri, j from a node i to a node j along a path # imgabs0 # according to the satellite network topology and the node link flow; dividing the satellite network into K sub-networks GS (VS, ES) = {C1, C2,..., CK} based on a spectral clustering method; a Hamiltonian loop-based telemetering path with the minimum weighted sum of telemetering delay and telemetering overhead is searched in the sub-network k, and a flow telemetering threshold Ttr is set; acquiring a space-time dependency relationship between the network topology and the service traffic by using the space-time diagram neural network, and predicting traffic data at the next moment; according to the collected flow data and the predicted flow data, a satellite network flow state view is obtained through analysis, and satellite network flow perception is achieved. According to the method, the telemetering overhead in the satellite network is reduced, the traffic prediction accuracy is improved, and the problems of high perception overhead and low accuracy caused by space-time coupling and random burst of the satellite network service traffic are solved.

The invention provides a base station power distribution method and device, equipment and a storage medium, and the method comprises the steps: obtaining target data corresponding to each base station in a target region; determining traffic change information based on the target data; predicting a base station power consumption demand of the base station based on the traffic change information, and predicting a base station power demand of the base station based on the base station power consumption demand and the real-time traffic of the base station; determining a power distribution strategy based on the power demand of each base station; and carrying out dynamic power distribution on each base station based on the power distribution strategy. The embodiment of the invention can dynamically adjust the power distribution of the base station and reduce the power consumption of the base station.

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 relates to a high-speed traffic flow prediction method combining a self-attention mechanism and a state space model, belongs to the field of intelligent traffic systems, and aims at reducing the calculation complexity of capturing a long-range dependency relationship by introducing the state space model and modeling the dynamic change of a time dimension through the self-attention mechanism. In order to further improve the performance of the model, the invention also designs a learnable space-time embedding module which can adaptively learn unique traffic characteristics of different highway nodes and time periods, including multi-dimensional information such as traffic flow, time, date, node position and the like, so that the prediction precision is improved.

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 road intelligent traffic multifunctional monitoring optimization system and method, and the system comprises a controller, a deceleration strip, and a traffic light assembly. The controller comprises an embedded single-chip microcomputer, a traffic flow detection module, a short-time traffic flow prediction module, a traffic state analysis module and a real-time traffic flow control adjustment module. The road intelligent traffic multifunctional monitoring optimization system not only has the capability of detecting and predicting the traffic flow in real time, but also has the function of monitoring the road traffic operation condition to give an alarm, and improves the travel experience of urban residents through intelligent regulation and control of the traffic flow. The technical problem that unnecessary waiting time or traffic jam occurs under some conditions due to the fact that traffic conditions cannot be intelligently regulated and controlled by an existing road intelligent traffic multifunctional monitoring optimization system and method, and traffic light time circulates in a fixed time interval is effectively solved.

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 invention relates to the technical field of traffic flow prediction, and provides an urban traffic flow prediction method based on a graph convolutional network and related equipment. The method comprises the following steps: dividing a target city area into unit areas according to a plurality of division scales; according to all the traffic flow data and all the unit areas under the division scale, traffic flow time characteristics of the target city area are calculated; performing feature calculation on the traffic flow time features by using a graph convolutional network to obtain final traffic flow features of the target city region under each division scale; based on all the final traffic flow characteristics, obtaining predicted traffic flow data of the target urban area under each division scale, and determining a target division scale according to all the predicted traffic flow data; and performing traffic flow prediction on the to-be-predicted urban area according to the target division scale to obtain final predicted traffic flow data of the to-be-predicted urban area. According to the method, the accuracy of traffic flow prediction can be improved.