[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Xu et al., 2023 - Google Patents

Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction

Xu et al., 2023

View PDF
Document ID
13190329391206157074
Author
Xu M
Qiu T
Fang J
He H
Chen H
Publication year
Publication venue
Expert Systems with Applications

External Links

Snippet

Forecasting the forthcoming intersection movement-based traffic volume enables adaptive traffic control systems to dynamically respond to the fluctuation of traffic demands. In this paper, a deep-learning based Signal-control Refined Dynamic Traffic Graph (ScR-DTG) …
Continue reading at papers.ssrn.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/04—Architectures, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386—Retrieval requests
    • G06F17/30424—Query processing
    • G06F17/30533—Other types of queries

Similar Documents

Publication Publication Date Title
Wang et al. Traffic flow prediction via spatial temporal graph neural network
Lu et al. LSTM variants meet graph neural networks for road speed prediction
Khaled et al. TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network
Yin et al. Multi-stage attention spatial-temporal graph networks for traffic prediction
Zheng et al. Traffic flow forecast through time series analysis based on deep learning
Huang et al. Diffusion convolutional recurrent neural network with rank influence learning for traffic forecasting
Xu et al. AGNP: Network-wide short-term probabilistic traffic speed prediction and imputation
Xu et al. Signal-control refined dynamic traffic graph model for movement-based arterial network traffic volume prediction
Rajeh et al. Modeling multi-regional temporal correlation with gated recurrent unit and multiple linear regression for urban traffic flow prediction
Zhou et al. Spatial–temporal deep tensor neural networks for large-scale urban network speed prediction
Wang et al. Spatial–temporal multi-feature fusion network for long short-term traffic prediction
Sun et al. Modeling global spatial–temporal graph attention network for traffic prediction
Wang et al. A trend graph attention network for traffic prediction
Xu et al. A flexible deep learning-aware framework for travel time prediction considering traffic event
Fan et al. RGDAN: A random graph diffusion attention network for traffic prediction
Geng et al. Dynamic-learning spatial-temporal Transformer network for vehicular trajectory prediction at urban intersections
Luo et al. Dynamic Graph Convolutional Network with Attention Fusion for Traffic Flow Prediction
Su et al. Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters
Azadani et al. STAG: A novel interaction-aware path prediction method based on Spatio-Temporal Attention Graphs for connected automated vehicles
Feng et al. A macro–micro spatio-temporal neural network for traffic prediction
Wang et al. TransGAT: A dynamic graph attention residual networks for traffic flow forecasting
Ouyang et al. Domain adversarial graph neural network with cross-city graph structure learning for traffic prediction
Yin et al. Spatiotemporal dynamic graph convolutional network for traffic speed forecasting
Wang et al. TYRE: A dynamic graph model for traffic prediction
Wang et al. LSTM-based graph attention network for vehicle trajectory prediction