207 results about "Transportation forecasting" 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 relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal edge computing resource dynamic allocation method and system.The method comprises the steps that firstly, real-time and historical traffic data of multiple intersections are obtained, a traffic prediction model is constructed, and future flow changes are pre-judged; thirdly, forming an edge computing resource demand matrix according to a prediction result and computing resource demands, and dynamically adjusting resource allocation to generate a resource slicing scheme; then, the flow gradient between intersections is analyzed, an edge node cooperative calculation model is established, and calculation task distribution and migration are optimized; and finally, in combination with system operation and traffic control effects, forming closed-loop feedback, continuously optimizing resource allocation and a cooperative strategy, and through traffic flow prediction driven resource demand modeling, realizing active pre-allocation of computing resources, converting resource allocation from passive response to active pre-adaptation, and improving the system resource utilization rate by 40%-60%.

The invention belongs to the technical field of intelligent traffic systems, and particularly relates to an intersection signal timing dynamic coordination system and method based on real-time traffic prediction.The method comprises the steps of collecting multi-source traffic data, generating a space-time prediction digital twin model, dynamically defining an intersection coordination cluster and executing cluster coordination optimization control. And signal timing and closed loop feedback are carried out. By adopting the technical scheme, the cooperative operation efficiency and the intelligent management level of the urban intersection group can be effectively improved, and the fundamental conversion from local and reactive active cooperative control to global and predictive active cooperative control is realized.

The invention provides a non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.

The invention relates to the technical field of intelligent logistics distribution, in particular to an automobile market logistics distribution scheduling system based on AI optimization, which comprises a data acquisition module, an order management module, a traffic flow analysis module and a scheduling decision module. Wherein the data acquisition module is used for acquiring current position data and traffic flow data of a delivery vehicle and order information of a customer in real time; the order management module is used for carrying out priority ranking on the orders; the traffic flow analysis module is used for predicting traffic conditions in a period of time in the future and identifying a road section and a time period which are about to be congested; and the scheduling decision module is used for optimizing the scheduling and distribution paths of the vehicles to generate an optimal scheduling scheme. According to the invention, by combining real-time traffic prediction, order priority ranking and a path optimization algorithm, the logistics distribution efficiency is effectively improved, distribution delay and resource waste are reduced, and timely distribution of high-priority orders is ensured.

The invention relates to the technical field of smart city traffic management, in particular to an urban traffic jam intelligent prediction and dispersion system and a method thereof, and the system integrates a data acquisition module, a traffic prediction module, an event influence evaluation module and an intelligent dispersion module. The data acquisition module updates multi-source traffic data in real time, and a solid foundation is provided for prediction; and the traffic prediction module accurately predicts the traffic flow by using a deep learning model combining a graph neural network and a space-time attention mechanism based on the received data. The event influence evaluation module analyzes the influence of emergencies on traffic and ensures the pertinence of a dispersion strategy; the intelligent dispersion module optimizes a traffic signal lamp control scheme in real time by using deep reinforcement learning in combination with the prediction result and the event influence; the system comprehensively senses the traffic condition through multi-source heterogeneous data fusion, the traffic flow prediction accuracy is remarkably improved, and intelligent and efficient traffic management is achieved.

The invention relates to a gated multi-graph convolution perception modeling method for traffic flow prediction. The method integrates multi-graph structure construction, gating graph convolution and time feature extraction, and aims to solve the problems of strong time fluctuation and heterogeneous spatial relationship in traffic data. The method comprises the following steps of: firstly, respectively constructing a geographic map and a semantic map according to the maximum mutual information measurement between the spatial distribution information of a sensor and historical traffic data; and then, designing a dual-adaptive gating graph convolution module, and dynamically adjusting an information propagation path of a multi-graph structure by introducing an attention mechanism and a gating factor, thereby improving the modeling performance of the model on spatial isomerism dependence. On the time dimension, a time sequence interactive sensing module is constructed in combination with multi-scale causal convolution and an attention mechanism, time dependence characteristics of a short period and a long period are captured, and fusion and expression of time characteristics are completed. According to the method, the modeling precision and stability of the traffic prediction model in a complex traffic scene can be effectively enhanced, and the method has relatively high practical application value.

The invention discloses a traffic flow prediction method based on multi-view fusion and diffusion diagram convolution. The traffic flow prediction method is suitable for dynamic modeling and space-time dependence extraction in a complex traffic scene. The method comprises the following steps: firstly, extracting short-term fluctuation and long-term periodic characteristics through double-path time slice convolution, and modeling multi-scale time dependence; in the aspect of spatial modeling, three types of graph structures including a static physical graph, a historical semantic graph and a current feature graph are fused, a sparse dynamic graph is generated through a Top-K mechanism, multi-order diffusion graph convolution is executed in combination with a static graph, and local and global spatial features are extracted. Furthermore, a space-time fusion strategy of bidirectional cross gating is provided, the information circulation direction between short-term and long-term features is dynamically adjusted, and coordinated fusion of multi-scale features is realized. And finally, the traffic state of multiple time steps in the future is output through a gating structure and a non-regression prediction module. According to the method, the modeling capability of the model for the complex traffic dependency relationship is effectively enhanced, and the prediction precision and robustness are remarkably improved.

The invention relates to the technical field of traffic prediction, in particular to a multi-layer space-time structure correlation perception traffic flow prediction model, which comprises a space-time structure decomposition layer for decomposing original traffic flow data into a road hierarchical structure feature matrix, a dynamic time period feature matrix and a hidden space topology feature matrix; the dynamic adjacency matrix generation layer is used for constructing a self-adaptive adjacency weight matrix based on the hidden space topological feature matrix; the cross-level correlation perception layer performs bidirectional feature modulation on the road hierarchical structure feature matrix and the dynamic time period feature matrix to generate a space-time coupling feature tensor; and the prediction layer inputs the space-time coupling feature tensor into a space-time diagram convolution prediction network and outputs a traffic flow prediction value in a future time period. According to the method, the expression ability of the model on potential heterogeneous association between the nodes is improved, and the generalization ability across regions and time periods is effectively improved.

The invention provides a traffic control strategy self-adaption method and system based on simulation feedback, and belongs to the technical field of traffic prediction and control, and the method comprises the steps: firstly obtaining traffic control scene data containing traffic flow data and road condition information, then constructing a simulation evaluation environment, configuring scene parameters based on the traffic control scene data, and carrying out the simulation evaluation environment; the method comprises the following steps: simulating traffic operation states under different traffic control strategies, calling a pre-trained reinforcement learning model to perform simulation evaluation on each strategy in a traffic control strategy set, generating a strategy effect feedback set comprising a traffic operation efficiency index and a traffic order stability index, and according to the strategy effect feedback set, calculating the traffic order stability of the traffic control strategy. And performing parameter adjustment on the strategy in consideration of the index association relationship to obtain an adjusted strategy, and finally outputting the adjusted strategy to the traffic control system to realize strategy updating, thereby effectively improving traffic operation efficiency, ensuring traffic order stability, and realizing adaptive optimization of the traffic control strategy.

The invention provides a traffic flow prediction method based on a dual hypergraph neural network. The method comprises the following steps: preprocessing traffic data collected by a sensor in a traffic road network in a certain time period; forming a data set by the preprocessed traffic data, and dividing the data set into a training set, a test set and a verification set; respectively constructing a traffic flow graph and a dual hypergraph according to the topological graph of the traffic network; constructing a traffic flow prediction model based on the space-time dual hypergraph convolution layer, wherein the traffic flow prediction model comprises an input layer, the space-time dual hypergraph convolution layer and an output layer which are connected in sequence; inputting the preprocessed traffic data in the training set into a traffic flow prediction model for training; testing the trained traffic flow prediction model by using the preprocessed traffic data in the test set to obtain a final traffic flow prediction model; and inputting the traffic data preprocessed in the verification set into the final traffic flow prediction model to obtain the predicted traffic flow. According to the method, multi-source data are fused and external knowledge is utilized, so that the spatial-temporal correlation in the data can be better understood and utilized, and the accuracy of a traffic prediction model is improved.

The invention discloses a traffic prediction method, belongs to the technical field of traffic flow and trajectory optimization, and particularly relates to a traffic prediction method based on a hybrid LSTM-GCN architecture. A data set is preprocessed, and a solid data foundation is provided for a model through data cleaning, integration and feature engineering; meanwhile, a traffic prediction model based on LSTM and GCN effectively captures the dynamic change and long-term dependence of traffic flow by virtue of the excellent time sequence analysis capability, the GCN model improves the accuracy of traffic flow change prediction by analyzing the topological structure of a road network, and meanwhile, the Dijkstra algorithm is adjusted and optimized by dynamic weight, so that the traffic flow change prediction accuracy is improved. According to the method and the system, path planning can adapt to real-time traffic conditions, the practicability of the model is further improved, the method and the system have important significance in improving the intelligent level of an urban traffic system, and powerful support can be provided for urban traffic management through traffic prediction and path planning.

A real-time traffic signal optimization system that includes: (a) a traffic data acquisition module that collects real-time traffic data from sensors, cameras, GPS and vehicle-to-infrastructure communication; b) a machine learning-based traffic prediction engine that analyses historical and real-time data to predict congestion patterns; c) an adaptive signal control unit that dynamically adjusts traffic light switching times based on real-time traffic flow and predictive insights; (d) an emergency and priority vehicle management system that overrides normal signal operation to give priority to emergency vehicles and public transport vehicles; (e) a central traffic monitoring and control dashboard for real-time monitoring, manual interventions and system configuration; and (f) a vehicle-to-infrastructure communication interface enabling data exchange between traffic signals and connected vehicles to improve driver awareness and traffic coordination.

The invention discloses a federated traffic prediction method for cross-domain space-time diagram data, and belongs to the field of federated calculation. Each platform establishes a local time-space sub-graph by using historical traffic flow trajectory data, inputs the local time-space sub-graph into a local time-space graph model to extract local spatial dependence, and realizes safe aggregation of cross-party spatial dependence through assistance of a central server; and after each platform completes local model training, keeping personalized global model part federated averaging through the central server, and when a local loss function is converged, completing joint model training and predicting traffic state quantity of each platform. According to the invention, under the condition that privacy sensitive information is not leaked, aggregation of cross-square space dependence information is realized, and the accuracy of a traffic prediction result is improved.

The invention discloses a traffic flow prediction method based on time-space trend perception information fusion, and belongs to the technical field of traffic prediction planning. The prediction method comprises the following steps: sequentially inputting preprocessed historical traffic sequences of a plurality of sensors into a high-dimensional spatio-temporal data embedding module, a time trend sensing module, a space trend sensing module, a spatio-temporal trend sensing information fusion module and a prediction module; high-dimensional space-time representation is extracted step by step, time features and space features with trend perception are captured, space-time information with trend perception is fused, and finally a flow sequence in future time is obtained. According to the method, the flow sequence is converted into the high-dimensional space-time representation, the space-time correlation modeling capability is improved, the trend of the flow is considered, the traffic flow space-time characteristics of trend perception can be captured, the space-time dependence of the traffic flow sequence is fully fused, and the prediction precision is improved.

The invention discloses a traffic prediction method and device based on multi-modal feature fusion, and a medium, and relates to the technical field of traffic prediction. The method comprises the following steps: performing unmanned aerial vehicle video acquisition and preprocessing on an interleaving area to obtain a target area video; traffic flow parameters are extracted from the target area video based on a multi-target detection and tracking algorithm, wherein the traffic flow parameters comprise a space average speed, a space occupancy rate and a vehicle interleaving conflict index; a deep learning model is adopted to process the image sequence of the target area video, potential semantic features are extracted, and the deep learning model comprises a variational auto-encoder; carrying out weighting processing on the potential semantic features by adopting a double attention mechanism; and splicing the weighted potential semantic features and the traffic flow parameters into a multi-modal feature vector, and carrying out traffic state prediction. According to the method, the accuracy and robustness of traffic state prediction of the interlaced area are improved, and more accurate data support and decision basis are provided for dynamic traffic management of complex road sections.

The invention discloses a federal space-time attention adaptive graph learning method and system, and the method comprises the steps: constructing a traffic prediction problem, constructing a space-time attention enhancement dynamic graph convolutional network model composed of a feature enhancement layer, a dynamic graph convolutional recursive network, a multi-head time attention module, a graph attention module and a space-time attention fusion module according to the traffic prediction problem; placing a traffic prediction problem in a federated learning scene, and setting privacy constraints of the model; on the basis of privacy constraints, an activation decomposition strategy is implemented on the model, and the activation decomposition strategy is that a transfer function is applied to a dynamic graph convolutional recursive network in the model; and performing traffic prediction on the traffic data acquired by the sensor by using the model which implements the activation decomposition strategy in a federated learning scene. According to the method, the problems of privacy leakage risk, high communication cost, limited model flexibility and the like in a traditional federal space-time attention adaptive graph learning framework are solved.

The invention relates to the technical field of path planning, and discloses a logistics transportation optimal path planning method, which comprises the following steps: acquiring real-time traffic flow data, weather forecast information and external factors of accident reports from multiple channels through Internet of Things equipment, a mobile application and an API (Application Program Interface) to form an original data set; preprocessing the original data set and storing the original data set in a distributed database to obtain preprocessed traffic data; and based on the original data set and the preprocessed traffic data, constructing a traffic prediction model based on a machine learning algorithm, and taking the preprocessed traffic data and the original data set as a training set. According to the optimal path planning method for logistics transportation, a neural network architecture comprising a plurality of layers of LSTM units is designed, a sliding window method is adopted to extract a plurality of subsequences from the neural network architecture to serve as training samples, the difference between a predicted value and a true value is measured in combination with a mean square error and a loss function of a penalty term, and more accurate traffic prediction is provided.

The invention relates to the field of intelligent traffic, and provides a cross-domain-oriented urban traffic road network toughness pre-judgment and evaluation method, which comprises the following three steps: step 1, based on input source domain and target domain urban traffic data, constructing a space-time cross-domain traffic prediction model, realizing traffic state transfer learning and generalization prediction among different cities, and establishing a space-time cross-domain traffic prediction model; generating traffic prediction in a cross-domain scene and a corresponding traffic map structure; 2, designing a hysteretic toughness index in combination with a prediction result and a traffic map structure, and fusing structure-function toughness and a hysteretic toughness factor to form a toughness index capable of reflecting a road network performance evolution mechanism; and step 3, based on the hysteretic toughness index, establishing a space-time fine-grained toughness quantization algorithm for quantizing node-level toughness of the road network. According to the method, the three major problems of lack of pre-judgment and cross-domain generalization ability, single toughness index representation and rough toughness quantification of an existing evaluation method are effectively solved, and the perspectiveness, the adaptability and the accuracy of road network toughness evaluation are remarkably improved.

The invention discloses an adaptive and interpretable traffic situation prediction method and system based on a large language model, and the method comprises the following steps: obtaining historical traffic data related to a prediction task, inputting a trained time sequence prediction basic model, and generating candidate traffic state tracks of a plurality of prediction periods based on the prediction task; acquiring context information related to a prediction time period, and evaluating the candidate traffic state trajectory based on a configured large language reasoning model to obtain an optimal trajectory conforming to the context information of the prediction time period; and based on the optimal track and the context information, generating a text report by utilizing the configured large language interpretation model. Probability prediction is carried out through the time sequence prediction basic model, context reasoning and text generation are carried out through the large language model, the accuracy, adaptability and interpretability of traffic prediction under abnormal events are remarkably improved, and powerful support is provided for intelligent traffic.

The invention discloses a vehicle cruise control method and system based on traffic prediction, and relates to the technical field of vehicle intelligent control. The method comprises the following steps: preprocessing vehicle state data and driving environment data by utilizing edge calculation; performing information fusion and mapping on the preprocessed data by using a cloud control platform to obtain a road traffic condition simulation result, and generating an optimal vehicle driving strategy according to the road traffic condition simulation result by using reinforcement learning; a hierarchical control strategy is constructed by considering traffic constraints and economic vehicle speed factors, and the vehicle is dynamically regulated and controlled; and the real-time driving state of the vehicle is fed back to the cloud control platform, and parameter optimization is carried out on the optimal driving strategy of the vehicle. According to the invention, traffic information is efficiently mined and fused by using the powerful information processing and fusion capability of the cloud control platform, predictive cruise control of the vehicle is realized in combination with a vehicle control optimization algorithm, and the energy-saving effect and the driving efficiency of the vehicle in a complex traffic environment are improved.

The invention discloses an intelligent electric vehicle queue optimization method based on congestion prediction and DRL. The method comprises the following steps: (1) modeling a queue position optimization problem of an electric vehicle queue into a mathematical model which takes energy balance as a target and is constrained by a traffic environment; and (2) solving the mathematical model in the step (1) by adopting a DRL method based on a TRPO algorithm to obtain an optimal queue adjustment strategy of the electric vehicle queue. In the step (2), congestion state information in an external traffic environment is obtained through an LSTM traffic prediction and FCM method, and the congestion state information, the remaining electric quantity of each vehicle in the motorcade and the accumulated driving distance are jointly used as state input of a TRPO algorithm; according to the TRPO algorithm, a strategy network and a value network are adopted to respectively obtain a strategy for adjusting the queue position, and the return expectation in the current state is evaluated. The TRPO algorithm dynamically adjusts the updating step length of the strategy through the KL divergence updated by the constraint strategy, and guarantees the stability and convergence in the strategy updating process.

The invention relates to an intelligent traffic signal control method and system based on traffic flow prediction, and belongs to the technical field of intelligent traffic. Traffic vehicle video streams and traffic flow data are collected through a high-definition camera and a traffic flow sensor group, different vehicles and speeds, directions and movement tracks thereof are identified through the traffic vehicle video streams, special vehicles are judged, traffic violation behaviors and road abnormal conditions are monitored according to the traffic vehicle video streams and the traffic flow data, and the traffic violation behaviors and the road abnormal conditions are monitored. A traffic prediction model is established according to traffic vehicle video streams and traffic flow historical record data, and traffic flow, vehicle positions, vehicle categories, lane line information, vehicle movement tracks, traffic violation, road abnormal conditions, and traffic flow values and vehicle speeds in future time periods of all lanes at all intersections are obtained from a server in real time. And different signal lamp control schemes are generated according to different vehicles. According to the invention, a signal control scheme is designed in combination with vehicle types, traffic violation, road abnormity and other conditions, and accurate traffic congestion dispersion is realized.

The invention provides a short-term traffic flow prediction method and device, equipment and a storage medium, and belongs to the technical field of road traffic prediction. The scheme comprises the following steps: collecting historical road index data, wherein the index data comprises time characteristics, weather characteristics, road section characteristics and holiday and festival characteristics; carrying out one-hot coding processing on the weather features, respectively carrying out normalization processing on the time features and the road segment features, and carrying out binarization processing on holiday and festival features; a decision-making tree and a corresponding prediction function are constructed through a gradient boosting decision-making model according to the processed multiple groups of index data, multiple decision-making trees are generated through multi-round iteration, and the decision-making tree enabling the loss function of the current iteration to be minimum is obtained through each round of iteration on the basis of the previous round of iteration; calculating the weight of each decision tree in the prediction function based on the loss function of each round; accumulating the decision-making trees constructed in each step through the weights to obtain a final decision-making tree; and inputting the latest collected index data into the final decision tree, and outputting a prediction result of the traffic flow of the current road in the next time period. According to the scheme, the problem that the traffic flow prediction precision is not accurate enough due to the fact that the number of researched data dimensions is relatively small in the existing short-time traffic flow prediction technology is solved.

The invention discloses an inland waterway congestion dredging method and device, and relates to the field of traffic control, and the method comprises the steps: obtaining multi-source traffic data and waterway meteorological and hydrological data of each ship in a waterway; performing space-time registration and fusion processing on the multi-source traffic data, generating comprehensive traffic situation information of the whole section of the channel, and extracting traffic flow characteristic parameters and channel congestion indexes; inputting the traffic flow characteristic parameters, the channel congestion index and the channel meteorological and hydrological data into a trained traffic flow prediction model to obtain traffic prediction information of key nodes in the channel in a future preset time window; based on the comprehensive traffic situation information and the traffic prediction information, generating preventive control parameters for channel key node signal lamps; and according to the traffic prediction information and the preventive control parameters, traffic guidance information is generated and sent to the corresponding ship terminals, and the preventive control parameters are sent to the corresponding signal lamp control machines. The overall passing efficiency of the channel can be improved.

The invention relates to the technical field of traffic prediction, and discloses a road traffic flow prediction method and system based on big data, and the system comprises a data fusion processing module, a prediction algorithm matching module and a dynamic prediction execution module. The method solves the time-space sampling frequency difference between meteorological environment data and dynamic traffic flow data, achieves the refined quantitative characterization of impact factors of sudden traffic events, enhances the prediction stability under severe meteorological conditions, fuses road network adjacency relation characteristics through a time-space grid matrix, captures the cascading congestion propagation effect caused by upstream accidents, and improves the prediction accuracy. The prediction reliability of complex road network structures such as interchange junctions is improved, the congestion false alarm rate is reduced, deviation accumulation in the continuous prediction process is continuously corrected through linkage of dynamic prediction model training and a real-time feedback optimization module, the model robustness in a special traffic scene is improved, and the stability of long-term operation of a prediction system is kept.

The embodiment of the invention discloses a multi-source big data traffic control management method and system based on a distributed architecture, and belongs to the technical field of intelligent traffic. The problem that in the prior art, traffic situation prediction precision is insufficient, and consequently traffic regulation and control are not accurate is solved. Comprising the following steps: constructing a dynamic traffic flow model based on extracted feature data and a dynamic space-time diagram corresponding to a traffic network; deducing the traffic situation through a central node in a traffic road network to obtain a plurality of traffic prediction scenes, and matching corresponding traffic regulation and control strategies; after the abnormal traffic event is detected, an affected area is delimited according to the corresponding propagation influence trend of the abnormal traffic event on the space axis; dynamically combining a plurality of operation units corresponding to the traffic regulation and control strategy through the central node, and generating a customized regulation and control scheme; and each edge node performs joint optimization on the customized regulation and control scheme by using a distributed optimization algorithm based on local traffic flow data so as to realize traffic control management.

The invention discloses an urban circle traffic demand prediction method and system, and belongs to the technical field of traffic data processing, and the method comprises the steps: taking a four-stage theory as a basis, efficiently obtaining a travel demand OD matrix of urban circle global traffic middle area granularity in the future, and mastering the overall traffic demand pattern of an urban circle from a macroscopic level; on the basis of a trip chain theory, a city traffic cell granularity trip demand OD matrix with higher time-space precision in the future year is accurately obtained, and the method can adapt to more application scenes and reflect traffic prediction and evaluation analysis of traffic demand details; through a traffic distribution algorithm of motor vehicles and public traffic, traffic flow indexes borne by urban circle traffic supply facilities are obtained, and continuity and consistency of spatial and temporal distribution of the current situation and indexes such as traffic supply facilities, travel demands, road flow and bus passenger volume in the future are achieved. And therefore, the traffic demand prediction and evaluation index result of the urban circle in the future year can scientifically meet the needs of users.

The invention provides a long-term traffic prediction method and device based on discrete wavelet decomposition, and the method comprises the steps: obtaining an original traffic time sequence, and decomposing the original traffic time sequence through discrete wavelet decomposition, obtaining a stable trend component and a fluctuating event component; calling a preset space-time encoder, and extracting time change features and space features of the trend component and the event component through the space-time encoder; codes of historical traffic signals are converted based on the predicted time dimension of future signals, and spatial-temporal variations in a potential space are captured by a preset spatial-temporal decoder to generate potential temporal features and potential spatial features. According to the invention, the robustness of the prediction model to different structure changes can be enhanced, and the problem of distribution offset of unsteady traffic data is effectively relieved.

The invention discloses an abnormal traffic situation generation method based on a large language model and a diffusion model, and relates to the field of computer machine learning, and the method comprises the steps: obtaining a traffic event text description and a historical traffic state, aligning the time stamps of the text description and the historical traffic state, and dividing a training set, a verification set and a test set; acquiring a traffic network structure, and making the traffic network structure into an adjacent matrix for spatial relationship modeling of a subsequent model; constructing an abnormal traffic situation generation model, training by adopting a training set, adjusting parameters by adopting a verification set, and evaluating a generation effect by using a test set; and finally, optimizing the model by adopting a weighted mixed objective function combining noise prediction loss and potential reconstruction loss. According to the method, the abnormal traffic situation with semantic consistency and spatial rationality can be effectively generated, and support is provided for traffic prediction, emergency management and intelligent traffic scheduling.