86 results about "Traffic network" patented technology

Multimodal input data associated with a transportation network environment, including at least one of image data and light detection and ranging (LiDAR) data, is received. Based on an artificial intelligence (AI) component and the multimodal input data, at least one attribute set associated with at least one infrastructure asset within the transportation network environment is identified. Based on the at least one attribute set, output data associated with a multi-objective infrastructure management operation is generated. The output data is provided for display via a graphical user interface rendered by a computing device.

The embodiment of the application discloses a kind of traffic network scheduling control method, system based on space-time big data, electronic equipment, belong to the technical field of electronic data processing, method steps include: the following steps are executed in edge side: receiving and analyzing terminal raw data, filtering and trajectory generation using the rules issued by cloud end;Call fusion engine to perform space-time alignment and matching on multi-source trajectory, generate fusion trajectory;Based on fusion trajectory, traffic flow, average speed are calculated synchronously, and input to event risk assessment model to output risk probability value;After comparing risk probability value with threshold, event level is generated and corresponding alarm is triggered.In cloud side, server converges multi-dimensional information uploaded by each node for macro analysis and model iteration optimization, and periodically issues updated strategy and configuration to edge node to dynamically adjust its processing logic, so as to realize intelligent traffic control from cloud end global optimization to edge real-time execution.

This invention discloses a system for identifying and simulating key nodes in traffic networks based on deep reinforcement learning. Addressing the high computational complexity of key node search and the lack of dynamic verification in traffic networks, this invention employs the following system: a road network topology mapping module parses GIS map data and removes redundant information to generate a weighted topology map; a deep reinforcement learning identification module, based on a deep Q-network, maps node features to graph embedding information through graph representation learning, with the optimization objective of minimizing cumulative normalized connectivity, outputting the optimal key node sequence; and a traffic effect simulation verification module uses a traffic simulation platform to establish a realistic road network model, quantitatively analyzing the impact of key node failures on average travel time and waiting time. This invention features low time complexity, the ability to generalize from small synthetic networks to extremely large-scale real networks, and simulations have verified its high application value in actual traffic management and disaster prevention.

This invention discloses a highly integrated edge computing security gateway with end-to-end encryption suitable for industrial environments. It comprises a routing module with firewall functionality, a switching module, a storage module, an edge computing module, a high-performance security encryption / decryption module, a low-performance security encryption / decryption module, and an industrial field interface module. All modules are connected via an internal high-speed data bus. The routing module coordinates the operation of all modules within the security gateway. This invention can be applied to numerous fields with network security connectivity requirements, such as factories, construction sites, and transportation networks, significantly expanding the application scenarios of related equipment.

The application discloses a land-air collaborative composite traffic network design method considering park-and-ride, and plans and designs a land-air collaborative composite traffic network with a vertical take-off and landing field as a center in a future land-air collaborative composite traffic network background, and proposes a corresponding network design model and a solving algorithm, mainly including: (1) a two-level planning model related to site selection, capacity allocation and integration of a landing field of an electric vertical take-off and landing aircraft (eVTOL) and ground parking facilities; (2) for a network equilibrium model in the two-level planning model, a modified improved gradient projection (M-iGP) algorithm is designed in an augmented Lagrange multiplier (ALM) framework; (3) a sensitivity analysis (SAB) algorithm is used to solve the two-level planning model, and the model and the algorithm are verified and analyzed in a Sioux Falls network.

This invention belongs to the field of path planning technology, specifically relating to a method and system for electric vehicle charging path planning based on TCPN. This method maps data such as the travel time and maximum capacity of each road segment in the traffic network, the number of idle charging piles, charging power, queuing time, and total charging service cost in charging stations, as well as grid load factor, charging carbon emissions, and electricity price fluctuation coefficients in the power grid, into locations, transitions, and time constraints in a time-constrained Petri net. A structural controller is added to control the model. The optimal charging path is obtained by solving all feasible charging paths in the model with the comprehensive goal of minimizing time, cost, and carbon emissions. This shortens the charging path planning solution time, establishes a more refined electric vehicle charging path model, and the planned paths better meet users' real-time response needs, achieving synergistic optimization of charging efficiency and grid stability.

This invention discloses a method for generating differentiated maintenance strategies for power equipment, belonging to the field of power system and automation technology. The method involves: constructing a two-layer graph model of the power grid based on power grid topology data and traffic network data; calculating key indicators and reachability costs for any power equipment node within the model to obtain a dynamically corrected criticality matrix; performing hierarchical response based on the dynamically corrected criticality matrix to obtain a candidate resource point scheduling set; performing path planning based on real-time traffic data and the candidate resource point scheduling set to obtain a target scheduling path set; obtaining material inventory data and path traffic data corresponding to any target scheduling path in the target scheduling path set; and performing weighted scoring on the material inventory data and path traffic data to obtain a maintenance resource allocation scheme that meets preset scoring conditions. This invention discloses a method for generating differentiated maintenance strategies for power equipment, enabling differentiated maintenance of power equipment in a power grid system.

This invention discloses a method for emergency communication recovery of distribution networks using air-ground coordination, considering cyber-physical coupling, belonging to the field of distribution network resilience technology. This invention aims to solve the technical problem of load recovery difficulties caused by combined faults in distribution and communication networks under extreme disasters. The method constructs a communication network data flow model, a UAV and mobile communication vehicle channel model, an emergency resource working model, and a traffic network model, and combines cyber-physical coupling and distribution network operation constraints to establish an optimization model with the goal of minimizing distribution network load loss. This invention, by jointly optimizing the deployment and trajectory of air-ground emergency communication resources, prioritizes the restoration of communication at key nodes that significantly support the power grid, while meeting communication quality constraints, achieving efficient distribution network load recovery. It is mainly used for emergency repair and power restoration of distribution networks after disasters.

The application discloses a kind of based on high-efficiency structure diagram clustering realizes the method for urban traffic network planning, first using the characteristics that core intersection node in high threshold under SCAN algorithm and collaborative traffic function area are necessarily contained in low threshold result, the traffic area division result under different intersection correlation degree threshold and traffic area scale threshold is pre-organized into a kind of urban traffic road network hierarchical index model;Second, in online query stage, by parameter analysis and index positioning processing to the intersection correlation degree threshold input by user, quickly locate to corresponding target traffic hierarchical tree;Subsequently, using the hierarchical relationship and threshold screening processing of target traffic hierarchical tree, replace traditional complex full road network traversal operation, quickly extract traffic subnet node set;Finally, based on the initial clustering member list extracted, accurately determine pivot node and outlier by label mapping and extraction processing in closed neighborhood structure, to realize the efficient clustering of large-scale urban road network data.

This application belongs to the field of power system management, specifically disclosing a method for predicting the spatiotemporal distribution of charging load probabilistically, taking into account the random fluctuations in commuting demand. This application constructs a multi-dimensional travel strategy set to simulate users' comprehensive decisions regarding energy, time, and space. Uncertainty is introduced from the commuting demand side, and Monte Carlo sampling is used to generate demand fluctuation samples, fundamentally characterizing the random fluctuations in load. A utility evaluation model is constructed based on cumulative prospect theory to correct for users' bounded rationality characteristics. A multi-layered nested discrete choice model is adopted, decomposing the decision into three levels: charging, departure, and route, and iterating to traffic network equilibrium using a heuristic algorithm. In the equilibrium state, the strategy selection ratio is combined with the demand sample, input into the dynamic traffic network model, and the spatiotemporal probability distribution of charging load is output. This achieves more accurate prediction of the spatiotemporal distribution of electric vehicle charging load probabilistically during evening peak hours, providing a scientific basis for charging facility planning and grid dispatching.

The application provides a construction and solution method and system of an electric traffic flow queuing-battery model, relates to the technical field of intelligent traffic, and comprises the following steps: acquiring traffic network basic parameters, electric vehicle performance parameters and commuting flow data; performing traffic network framework modeling according to the traffic network basic parameters to obtain a traffic network structure; performing electric quantity evolution modeling according to the traffic network structure and the electric vehicle performance parameters to obtain an electric vehicle network model based on a road section; performing traffic flow parameter modeling according to the electric vehicle network model to obtain road section supply-demand parameters; performing network flow distribution modeling according to the road section supply-demand parameters and the commuting flow data, calculating flow transfer between upstream and downstream road sections through a node flow function, and constructing a queuing-battery model; and performing numerical solution according to the queuing-battery model to obtain complete state results of the electric traffic system. The application takes into account the calculation efficiency and accuracy requirements of large-scale electric traffic system modeling.

Method for avoiding collisions between vehicles, the method comprising: Receiving (310) data representing a first journey path (MPP1) of a first vehicle (110-1), wherein the first journey path (MPP1) is a most likely journey path of the first vehicle (110-1) calculated by a horizon provider of the first vehicle (110-1) and represents in a traffic network from a current position of the first vehicle (110-1) a future journey path along which a driver of the first vehicle (110-1) is most likely to travel; Receiving (320) data representing at least one second route (MPP2 - MPPn) of at least one other vehicle (110-2 - 110-n), wherein the second route (MPP2) is a most likely route of one of the other vehicles (110-2 - 110-n) calculated by a horizon provider of the respective other vehicle (110-2 - 110-n) and represents in the traffic network from a current position of the respective other vehicle (110-2 - 110-n) a future route along which a driver of the respective other vehicle (110-2 - 110-n) is most likely to travel; wherein the data representing the routes (MPP1 - MPPn) identify one or more nodes (510, 520) of the transport network and / or one or more links (530) between nodes (510, 520) of the transport network and / or one or more form points of the respective route (MPP1 - MPPn); Identifying a collision risk (220) within the transport network; Calculating (330) a first arrival time, when the vehicle (110-1) belonging to the first travel path (MPP1) arrives at the collision-prone location (220), depending on the first travel path (MPP1); Calculate (330) a second arrival time, when the vehicle (110-2 - 110-n) belonging to the second travel path (MPP2) arrives at the collision-prone location (220), depending on the second travel path (MPP2); Calculate (330) a time difference between the first and second arrival times; Determine (340) whether the first travel path (MPP1) collides with the second travel path (MPP2); and to detect a path collision, to initiate (350) a signal generation to warn the driver of at least one of the vehicles (110) of a possible collision with another vehicle (110); wherein the detection (340) of a path collision and / or the initiation (350) of a signal generation is carried out depending on the calculated time difference, wherein a threshold value for the time difference is chosen depending on attributes of the links (530) or attributes of the nodes (510, 520).

The application discloses a self-adaptive multi-modal data fusion bridge health monitoring method and system, generates a response sample database through establishment of a bridge nonlinear dynamics equation, pre-processes bridge sensor data and trains a deep learning model to extract damage features, and self-adaptively optimizes model parameters; based on model output, real-time and historical data are combined to perform bridge health evaluation, and a monitoring strategy is dynamically adjusted or early warning is triggered; an edge computing device is deployed to perform pre-processing and damage identification, and long-term trend analysis is realized in cooperation with a cloud platform; macro-structure damage and micro-crack data are fused through cross-scale feature extraction; an artificial intelligence algorithm is used to generate a bridge health evaluation result, a bridge maintenance and repair scheme and a decision-level early warning signal. The application combines nonlinear dynamics modeling, self-adaptive deep learning, edge cloud collaborative computing and cross-scale analysis, improves complex damage identification precision and monitoring intelligent level, and is suitable for large-scale traffic network bridge monitoring management.

The application provides an electric vehicle fast charging navigation method and device, electronic equipment and storage medium, constructs a coupling network model of a traffic network-power grid in a region where a target electric vehicle user is located, a coupling edge is composed of a road node containing a charging station and a power grid node of the charging station, and the weight of the road node is defined as an average travel time, the weight of the power grid node is defined as a voltage variation, and both of them jointly determine a charging cost of the charging station; a charging navigation model is constructed with a target function of a comprehensive cost of the target electric vehicle user and constraint conditions of minimum residual allowable capacity of a battery and queue tolerance time; and through processing of the charging navigation model, a charging station with the lowest comprehensive cost is taken as a target charging station, and an optimal path to the target charging station is output. The application can realize best charging station recommendation and optimal path planning of the electric vehicle user by using multi-source information such as the traffic network-power grid, and promote benign collaborative operation of the traffic network-power grid coupling network.

This invention provides a traffic flow prediction method based on masked autoencoder clustering and adaptive hybrid expert, belonging to the field of traffic flow prediction technology. The method first extracts deep spatiotemporal features of traffic data using a masked autoencoder and then explicitly decomposes nodes into two subsets—high-flow and low-flow—using K-Means clustering. Next, a dual-channel adaptive hybrid expert network is constructed. The high-flow expert uses a fusion pattern library and a self-similarity multi-graph dynamic graph convolutional network to capture complex spatial dependencies, while the low-flow expert uses a simplified spatial module. Both experts share a temporal feature encoder based on Transformer and temporal embedding. Finally, the prediction results of the two experts are fused and output. This invention achieves end-to-end collaborative optimization of explicit data decomposition and prediction tasks, effectively capturing the long-term and dynamic spatial correlations of traffic networks and significantly improving traffic flow prediction accuracy.

The application discloses a large-scale multi-mode traffic parallel simulation method and system based on CPU and GPU, relates to the technical field of traffic control, and comprises the following steps: acquiring traffic road networks to be simulated and multi-mode traffic demand data, dividing the traffic road networks to be simulated and the multi-mode traffic demand data based on a preset dynamic spectrum division algorithm to obtain a multi-mode traffic network division result; performing dynamic congestion detection and information feedback processing on the multi-mode traffic network division result based on a preset cross-process congestion feedback algorithm and an MPI communication optimization strategy to obtain congestion state information; inputting the congestion state information into a pre-established microscopic traffic intelligent agent model to perform microscopic behavior parallel calculation, and outputting microscopic behavior data based on the limitation of a regionalized Bucket communication mechanism; and performing simulation processing based on the microscopic behavior data to obtain a final traffic parallel simulation result, so that the simulation precision and efficiency are significantly improved.

This invention provides an intelligent event flow control method and system applied to traffic scenarios, belonging to the field of traffic management technology. First, it receives comprehensive perception of traffic events and dynamic correlation information of the traffic network to generate traffic event flow baseline information. Based on the traffic event flow baseline information, it divides the flow into stages and reconstructs the traffic event flow links. According to the traffic event flow stage division and link reconstruction information, it constructs a distributed collaborative response rule set for control resources to generate resource collaborative response configuration information. Following the resource collaborative response configuration information, it drives a distributed control node group to execute collaborative control actions, generating collaborative execution information. Based on feedback traffic event evolution correction data and traffic network operation optimization data, iteratively updates the traffic event flow link reconstruction information and resource collaborative response configuration information to generate traffic event flow control completion information. This invention provides comprehensive perception and precise control, achieving efficient handling of traffic events and improving road network operation efficiency and safety.

This invention relates to a method and system for assessing wheelchair accessibility in slow-traffic networks based on ramp location. The method includes: collecting and preprocessing multi-source basic data; automatically generating the locations of curb ramps based on network data through spatial calculations; using deep learning fine-grained analysis and multimodal large-model scene analysis to identify and assess the absence of ramps in street view images; automatically constructing a slow-traffic network that conforms to the actual conditions for wheelchair access based on the identification results; and calculating the curb ramp absence rate, wheelchair accessibility range, and fairness of space use to achieve multi-dimensional quantitative assessment. This invention solves the problems of low efficiency, limited coverage, and coarse qualitative assessment associated with traditional manual surveys and mapping. It achieves large-scale automated, refined surveys of curb ramps and systematic construction of a wheelchair-accessible slow-traffic network. Through multi-dimensional quantitative indicators, it accurately reveals spatial differences in wheelchair accessibility, providing practical technical support for identifying key areas for barrier-free renovation and making scientific decisions.

Generating transportation network solutions using graph neural networks, includes: generating, based on input data describing a product transportation network, a graph neural network representing the product transportation network, wherein the graph neural network comprises multiple edges, multiple source nodes, multiple destination nodes, and multiple intermediary nodes; performing a graph neural network prediction based on the graph neural network, wherein performing the graph neural network prediction comprises predicting inclusion in the transportation network solution for each edge and intermediary node; and generating a transportation network solution by performing a model optimization using an initial state based on the graph neural network prediction.

This invention discloses a global collaborative prediction method for traffic signals based on quantum entanglement states. The method includes: establishing a quantumized model of the dynamic evolution path of the target traffic network within a future time window; constructing a spatiotemporal graph model; mapping the signal phase states of intersections to spatiotemporally integrated quantum states using a hierarchical quantum encoding scheme; constructing a Hamiltonian containing spatial coupling, temporal evolution, and cost terms based on a preset traffic optimization objective; solving for its ground state using a hybrid quantum-classical computation method to determine the optimal dynamic evolution path; encoding the current actual state of the traffic network as an initial state; decoding the collaborative signal control strategies for future time points using the optimal evolution operator and sequential measurements; and executing these strategies through a rolling time-domain control framework. This invention utilizes quantum parallelism for global optimization, enabling the acquisition of the optimal collaborative strategy across the entire spatiotemporal range in a single step, thereby improving the overall operational efficiency, predictability, and robustness of the traffic network.

This invention belongs to the field of resident travel choice prediction technology, and particularly relates to a method and system for predicting resident travel choices in the dynamic evolution of rail transit networks. It includes preprocessing multi-source resident travel data, dividing it into training and test sets; constructing a gradient boosting decision tree model, training the model using the training set, and optimizing the model parameters using a GBDT hyperparameter automatic tuning method based on multi-objective Bayesian optimization; obtaining prediction results on the test set using the trained model, calculating the SHAP value and partial dependency graph of each influencing factor, and obtaining the nonlinear mechanism and dynamic evolution law of each influencing factor. This invention uses multi-source resident travel data from different periods of the dynamic evolution of rail transit networks for analysis and research, and innovatively introduces a multi-objective Bayesian optimization framework for automatic hyperparameter tuning, dynamically capturing the nonlinear laws of resident travel choices during the evolution of rail transit networks.

This invention relates to the field of information processing technology, providing a method, system, and device for discovering traffic operation patterns and generating control strategies based on an AI-driven autonomous traffic scientist system. The method includes: retrieving and analyzing relevant literature in the traffic field based on a traffic research topic to extract structured literature information; constructing initial traffic pattern hypotheses based on the structured literature information; conducting computational experiments in a traffic simulation environment and / or a real traffic data environment to verify the traffic pattern hypotheses; adaptively iteratively updating the traffic pattern hypotheses based on the verification results until a preset convergence condition is met, and selecting target traffic operation patterns from these; and generating traffic control strategies based on the target traffic operation patterns. This invention can achieve the autonomous discovery of traffic operation patterns such as traffic congestion propagation, traffic travel patterns, traffic network resilience, and the evolution of mixed traffic flows, and is applicable to scenarios such as traffic science research and intelligent traffic management.

This invention discloses a method for assessing the capacity demand of regional modular reconfigurable mobile energy storage considering multiple support requirements. The method includes: 1. Constructing the configuration scale and resource constraints of regional modular reconfigurable mobile energy storage; 2. Constructing a traffic network model for regional modular reconfigurable mobile energy storage; 3. Constructing a charging and discharging model for regional modular reconfigurable mobile energy storage; 4. Constructing the power flow constraints and objective function for regional modular reconfigurable mobile energy storage; 5. After transforming the power flow constraints into linearized constraints through linearization and second-order cone relaxation, solving the objective function to obtain the optimal assessment method for the capacity demand of regional modular reconfigurable mobile energy storage. This invention achieves efficient reuse of mobile energy storage resources in the spatiotemporal dimensions through modular reconfiguration, significantly reducing the total installed capacity demand while effectively improving the equipment utilization efficiency, power supply resilience, and optimal system operation performance of the distribution network.

This invention discloses a multi-mobile resource distribution network load restoration method based on a simplified transportation network. First, multi-source data is collected, including input distribution network line fault conditions, transportation network topology, and mobile resource configuration parameters. Then, the original transportation network is simplified according to the functional characteristics of line repair teams, mobile energy storage, and V2G, resulting in simplified transportation networks for each resource. Finally, based on the simplified transportation networks for each resource, a multi-mobile resource collaborative scheduling model is constructed with the objective of maximizing the weighted load restoration of the distribution network. Constraints are introduced for the collaborative optimization scheduling of line repair teams, mobile energy storage, and V2G to achieve distribution network load restoration. Load loss is reduced through the collaborative complementarity among multiple mobile resources.

This invention discloses a method for decomposing complex networks based on graph contrastive learning and multi-hop aggregation, comprising the following steps: collecting the number of neighbor nodes, connecting edges, and average clustering coefficients of a complex traffic network to construct a network decomposition model; inputting the original graph into a role graph generation module to obtain a role graph; inputting the original graph and the role graph into a multi-view representation learning module to obtain multi-angle graph representations of the role graph and the original graph; obtaining an importance score for each node; calculating and optimizing the joint loss function to train the network decomposition model; decomposing the traffic network to obtain the importance values ​​of traffic nodes in the traffic network, and setting stronger security measures for traffic nodes with high importance and weaker security measures for nodes with low importance. This invention uses intra-graph contrastive learning and cross-contrast learning to improve graph representation performance and proposes a multi-hop aggregation mechanism to predict node importance by combining multi-hop neighbor information, achieving high-performance network decomposition.

The application relates to the technical field of intelligent traffic systems, and discloses a sparse perception traffic scene blind area information interpolation estimation method. Traffic data collected by sensors at traffic network nodes is acquired, and a plurality of characteristic information is adaptively fused; dynamic time characteristic extraction is carried out according to the multi-modal fusion characteristics; the extraction result of the dynamic time characteristics is added to the multi-modal fusion characteristics and then input into a space attention module and a bidirectional time attention module to obtain a new adjacency matrix and a dynamic transfer matrix; the new adjacency matrix and the dynamic transfer matrix are input into an S-fusion layer module to obtain a road network adjacency matrix with forward and backward long and short term space-time dependence; the road network adjacency matrix and the multi-modal fusion characteristics are input into a graph attention gate recurrent module and a decoding output module to obtain interpolation estimation of nodes with missing values. The application can accurately capture the complex dynamic space-time correlation characteristics between scene nodes, and guarantee the accuracy and robustness of scene space-time data completion.

The application relates to an event-driven traffic network modeling method, an event prediction method and a system, and proposes a spatiotemporal interaction Hawkes process, solves the problem of event-driven dynamic network modeling, can effectively learn the influence mode of historical interaction events on subsequent events, and describes the network dynamic change process. The spatiotemporal interaction Hawkes process proposed in the application describes a series of event occurrence processes, and utilizes relevant historical events and relevant adjacent nodes including geographical adjacent nodes and semantic adjacent nodes to establish an interaction event occurrence rate model between any two nodes on the network. Model parameters are estimated by maximizing the likelihood function of the Hawkes process. The estimation result can fully reveal the complex spatiotemporal influence mode between interaction events, and realizes prediction of the number of network events at a future moment.