104 results about "Traffic forecast" patented technology

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 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.

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 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 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 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 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.

The invention discloses a travel scene traffic flow time sequence prediction method, device and equipment, a storage medium and a program product, and the method comprises the steps: carrying out the preprocessing of original traffic flow time sequence data, and generating a preparation time sequence, extracting an input sequence feature and a basic sequence feature from the preprocessed original traffic flow time sequence data, extracting a traffic flow time feature, performing spatial feature analysis based on a graph structure constructed based on a preparation time sequence and the preparation time sequence, and splicing and fusing the traffic flow time feature and the spatial feature to obtain a fused traffic flow time sequence; a traffic flow time sequence prediction result of the travel scene is generated based on the spatial-temporal feature fusion result and the basic sequence features, local features and global features in the time sequence are accurately captured, the spatial-temporal features of tourist flow in the travel scene are accurately captured, the accuracy of traffic flow prediction of the travel scene is greatly improved, and the traffic flow prediction efficiency is improved. And the traffic management efficiency of the travel scene is improved.

The present application relates to the technical field of smart tourism and passenger flow management, and particularly relates to a smart tourism destination tourist carrying capacity dynamic evaluation method and system, the method comprising: collecting entrance ticket scanning data, device signal density data and video passenger flow statistical data to form multi-source passenger flow data through ticket scanning gate, wireless network probe and video passenger flow camera respectively; reconstructing a node real-time tourist density distribution map through a spatial interpolation algorithm; obtaining a node engineering carrying threshold parameter and performing upstream and downstream coupling conduction modulation on the node engineering carrying threshold parameter based on an upstream node congestion index to obtain a node dynamic carrying threshold; performing a three-hour passenger flow prediction based on historical passenger flow time series data and external passenger flow influence factors, comparing the node predicted passenger flow with the node dynamic carrying threshold to trigger a shunt early warning; and generating a low-density tour route suggestion based on the node real-time tourist density distribution map and the shunt early warning and pushing the low-density tour route suggestion to a tourist terminal.

The invention provides an operation optimization scheduling method of an expressway tunnel micro-storage system. The system is composed of a photovoltaic power generation unit, a wind power generation unit, a diesel power generation unit, a storage battery, a super-capacitor energy storage unit and a power grid interaction interface. The method comprises the following steps: acquiring system inherent parameters, sun position, weather and traffic flow prediction data; calculating wind power and ideal photovoltaic output; aiming at shadow influence of a mountain in a tunnel environment, dynamically calculating a tunnel shading loss coefficient based on a geometric projection principle, and correcting photovoltaic output; calculating tunnel dynamic electrical load according to traffic flow prediction; establishing a multi-objective optimization model and solving by taking system power balance and equipment constraint as conditions and taking operation cost, expected power shortage and carbon emission minimization as objectives to obtain a day-ahead optimization scheduling scheme of each unit and energy storage; according to the invention, the photovoltaic output prediction precision in the tunnel environment and the reliability, economy and environmental protection of the scheduling scheme are improved.

The invention discloses a motor vehicle charging pile management optimization method and system based on artificial intelligence, and the method comprises the following steps: collecting operation data, and generating an original flow data set; mapping the original traffic data set to a double-layer graph structure, and constructing a space-time traffic feature matrix; inputting the space-time traffic characteristic matrix into a traffic prediction module to obtain a prediction result; inputting a prediction result into a scheduling strategy optimization module, and initializing a state space and a constraint condition; calculating an initial control action based on the state space and the constraint condition; substituting the initial control action into an obstacle constraint optimization process, and carrying out iterative solution to obtain an optimal scheduling action; inputting the optimal scheduling action into a system execution module to obtain a feedback data set; and carrying out decomposition operation on flow and power flow change data, updating gradient flow, rotation flow and harmonic flow components, and realizing online closed-loop optimization of the obstacle Lyapunov constraint strategy model. According to the invention, artificial intelligence motor vehicle charging pile management optimization is realized.

The invention discloses a railway hub large passenger flow intelligent prediction method based on a space-time attention mechanism, and belongs to the technical field of intelligent traffic and traffic transportation prediction. The method comprises the following steps: collecting and preprocessing multi-source data; based on an LSTM network and a random forest algorithm, constructing a fusion model, and using the trained fusion model to obtain a third passenger flow prediction result; performing cross-model residual error correction on the third passenger flow volume prediction result to obtain a final fusion prediction result; and according to the fusion prediction result and the early warning threshold, carrying out early warning grade division, and carrying out corresponding dynamic response and scheduling output. According to the invention, based on multi-source decision fusion of a deep learning prediction result and a nonlinear feature model result, intelligent early warning and dynamic management and control output of railway hub large passenger flow are realized; a multi-model decision confidence fusion mechanism is established through a D-S evidence theory, and credibility enhancement and early warning grading response of a prediction result are realized in combination with a passenger flow threshold grading strategy.

The invention discloses an aero-engine air inlet passage secondary flow prediction method based on a neural network. The method comprises the steps that an air inlet passage physical model containing suction holes is established, and grids are divided; setting boundary conditions required for solving; performing numerical simulation to obtain flow field data under different working conditions, calculating mass flow under a suction hole blocking condition and a corresponding surface dimensionless sound velocity flow coefficient, and constructing a training data set; training and constructing a traffic prediction model based on a neural network; re-establishing a simplified air inlet channel physical model without suction holes and dividing grids; and calling a flow prediction model to predict a dimensionless sound velocity flow coefficient of the simplified air inlet physical model, and reversely calculating actual suction mass flow, and applying the actual suction mass flow as a boundary condition to a suction hole area corresponding to the simplified air inlet physical model to realize equivalent numerical simulation of the suction effect. According to the method, calculation efficiency and prediction precision are both considered, and integration can be carried out in a CFD solver to support air inlet parameterization design and rapid performance evaluation.

The invention discloses a TCN-LSTM-Attention prediction model construction method and a highway toll station dynamic lane configuration method, and the method comprises the steps: obtaining traffic data in a time period M, and the traffic flow of each lane, the traffic flow of an ETC lane and the traffic flow of an MTC lane in a time period M + 1, and carrying out the preprocessing of the traffic data, the traffic flow of each lane, the traffic flow of the ETC lane and the traffic flow of the MTC lane; through a TCN-LSTM-Attention prediction model composed of a TCN layer, an LSTM layer, an Attention layer and a full-connection layer, spatial-temporal feature extraction is performed on historical traffic flow data, accurate prediction of traffic flow is realized, and traffic passing efficiency is improved. Meanwhile, based on the predicted traffic flow condition, an expressway toll station dynamic lane configuration strategy with toll station traffic capacity maximization and lane switching cost minimization as objective functions is constructed, dynamic configuration of lanes is achieved according to a real-time traffic flow prediction result, the problems of resource waste and congestion caused by fixed lane configuration are avoided, and the traffic flow prediction efficiency is improved. The technical problem that a lane configuration method in the prior art cannot perform lane configuration according to the traffic flow and the vehicle type proportion in time is solved.

This invention relates to the field of data processing technology, specifically to a bus arrival time prediction system and method based on fusion perception. The system includes a route search subsystem, a location acquisition subsystem, a perception acquisition subsystem, a prediction subsystem, and an arrival display subsystem. The route search subsystem searches for the target bus's route; the location acquisition subsystem obtains the target bus's current location and the location of each bus stop based on the route by collecting the bus's license plate; the perception acquisition subsystem obtains the traffic flow from the current location to each bus stop; the prediction subsystem predicts the remaining time for the target bus to reach each bus stop based on the current location and traffic flow; and the arrival display subsystem installed at each bus stop displays the remaining time for the target bus to arrive at that bus stop, thus solving the problem of passenger anxiety caused by not knowing the exact arrival time of the bus.

The invention discloses a self-adaptive parking space recommendation method for flow fluctuation in festivals and holidays, and relates to the technical field related to intelligent traffic, and the method comprises the steps: obtaining a target travel request of a user side, at least including a destination and travel time; through festival and holiday flow prediction and parking space demand pre-judgment, in combination with a target travel request, a festival and holiday travel scene is identified, and a scene rule base including recommendation priority rules and path planning preferences is loaded; generating a parking space recommendation scheme based on a multi-dimensional decision model by fusing the real-time parking space state, the road condition information and the user portrait; and planning a full-link travel path, and displaying the full-link travel path on a terminal interface. The technical problems that in the prior art, due to the fact that the contradiction between supply and demand of parking spaces in holidays and festivals is sharp and the adaptability of parking space recommendation is poor, users are difficult to find parking spaces, and surrounding traffic jam is aggravated are solved, intelligent and efficient matching recommendation of parking space resources is achieved through scene pre-judgment, and the user experience is improved. And the user travel experience and the regional traffic operation efficiency are improved.

This invention relates to the field of air traffic forecasting technology, and in particular to an air traffic forecasting and uncertainty quantification method and system based on MSGF-Net. The method includes: preprocessing air trajectory data and extracting traffic data; constructing and normalizing input features based on the preprocessed air trajectory data and extracted traffic data; constructing an MSGF-Net-based traffic forecasting model; optimizing and training the constructed traffic forecasting model based on a loss function; predicting the input features based on the trained traffic forecasting model; and outputting the prediction result. This invention, through a parallel multi-scale encoder and gating fusion mechanism of TCN and GRU, solves for the first time the aforementioned deficiency of standard models in capturing multi-scale temporal patterns, greatly improving the model's generalization ability.

The invention discloses a highway toll station toll lane rotation method and related equipment. The method comprises the following steps: firstly, acquiring a survey data set containing toll services and departure time of different vehicle types on ETC and mixed lanes; predicting two types of lane traffic flows of the current toll station according to vehicle types and time periods based on historical traffic data; carrying out clustering analysis on the predicted traffic flow, and dividing a whole-day time period into a plurality of stages; inputting the survey data set, the traffic flow prediction result and the time period division result into a traffic flow arrival rate model and a lane service rate model to obtain traffic flow arrival rates and service rates of the two types of lanes; and inputting the data into a multi-toll lane rotation value plan model, solving to obtain an optimal solution of the ETC and mixed lane opening number in different time periods, and performing lane rotation value according to the optimal solution of the ETC lane and mixed lane opening number in the corresponding time period. The invention aims to greatly save the toll station operation cost and the vehicle passing time cost.

The invention provides a traffic prediction method, device and equipment for an urban road network under an abnormal event, and the method comprises the steps: constructing a fusion feature matrix based on the data of a current road network and the information of the current abnormal event; performing space-time calibration on the fusion feature matrix by using a space-time attention mechanism to obtain a space-time calibration feature matrix; determining time characteristic representation based on the abnormal event individual causal effect and historical traffic space-time state data, and determining an adjacency matrix for dynamic causal based on the space-time calibration characteristic matrix and the time characteristic representation; determining spatial feature representation based on the dynamic causal adjacency matrix and the space-time calibration feature matrix; and determining a flow prediction result of the target road network at a future moment by using the dynamic causal adjacency matrix, the time feature representation and the spatial feature representation. By adopting the traffic prediction method, device and equipment under the abnormal event of the urban road network, the prediction precision of the speed of the urban road network is improved.

The invention relates to a metering station instantaneous flow prediction method which comprises the following steps: S1, acquiring historical instantaneous flow data which comprises gas instantaneous flow per hour in multiple days; s2, preprocessing the historical instantaneous flow data, and dividing the historical instantaneous flow data into a training set and a verification set; s3, constructing a CNN + LSTM-based hybrid neural network model, wherein the model comprises a CNN module and an LSTM module; and S4, taking instantaneous flow data of two consecutive days as input, and training the CNN + LSTM model to predict an instantaneous flow sequence of the next day. The method has more remarkable superiority and practical value in the aspect of urban gas volume prediction.

The invention relates to the technical field of intelligent planning, in particular to an aerocar station site selection method, device and equipment based on multi-stage optimization, and the method comprises the steps: selecting map data of an analysis target area and user travel data in different periods, selecting a plurality of aggregation blocks as candidate stations according to the highest demand quantity, and generating a travel demand thermodynamic diagram, substituting candidate site data into a site capability scoring formula, a site connectivity degree scoring formula, a service traffic prediction model formula and a decision model formula, constructing a deployment optimization model taking profit maximization as a core, performing probability modeling on user travel selection behaviors, and establishing a deployment optimization model taking profit maximization as a core. The method truly reflects the substitution potential of eVTOL in a multi-mode traffic system, improves the economic feasibility, scheduling efficiency and service capability of a site selection scheme, and is suitable for the intelligent planning of dynamic demands and large-scale candidate regions. Therefore, the problems of poor eVTOL station scheduling collaboration, low operation flexibility, poor urban scale multi-source data processing performance, low calculation efficiency and the like are solved.

The invention belongs to the technical field of building planning management, and particularly relates to a building area route planning system applied to building planning design, which comprises a geographic information integration module, a function demand analysis module, a people flow prediction module, a route intelligent generation module, a multi-scheme evaluation optimization module and a result visualization display module. According to the invention, various building area route planning schemes are intelligently generated through the route intelligent generation module according to geographic information, function requirements and people flow prediction information, the various schemes are evaluated to select an optimal scheme, and the optimal route planning scheme is visually displayed. According to the method, key factors in multiple aspects of building area planning can be comprehensively considered, and based on tight cooperation and data interaction of all modules, the whole process of route planning from basic data collection to final scheme display is scientific, intelligent and visual, and the quality and level of building area route planning are remarkably improved; and the working difficulty of planning and designing personnel can be reduced.

This invention relates to the field of intelligent transportation technology, and more particularly to a multi-strategy improved traffic flow prediction method, device, and medium. Based on acquired traffic spatiotemporal data, this method obtains the tuning parameters of a preset traffic flow prediction model through an optimization algorithm layer; updates the preset traffic flow prediction model based on the tuning parameters to obtain a first traffic flow prediction model; trains the first traffic flow prediction model using training set data to obtain a second traffic flow prediction model; updates the second traffic flow prediction model to obtain an improved traffic flow prediction model; the improved traffic flow prediction model is used to output traffic flow prediction values. This invention uses a whale optimization algorithm to update the tuning parameters, and the improved preset traffic flow prediction model enhances the model's ability to extract multi-scale spatiotemporal features of traffic spatiotemporal data and its ability to focus on key time step information.