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

Bansal et al., 2021 - Google Patents

Htfm: Hybrid traffic-flow forecasting model for intelligent vehicular ad hoc networks

Bansal et al., 2021

Document ID
3050298871270070945
Author
Bansal N
Bali R
Jakhar K
Obaidat M
Kumar N
Tanwark S
Rodrigues J
Publication year
Publication venue
ICC 2021-IEEE International Conference on Communications

External Links

Snippet

Increased vehicular flow on roads along with proposed deployment of autonomous vehicles has necessitated the need for accurate traffic forecasting so as to achieve effective route guidance, traffic management, public safety and congestion avoidance. Although a number …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • G06F17/30424Query processing
    • G06F17/30533Other types of queries
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks

Similar Documents

Publication Publication Date Title
Ali et al. Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
Diao et al. A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction
Tedjopurnomo et al. A survey on modern deep neural network for traffic prediction: Trends, methods and challenges
Zhang et al. Traffic flow prediction model based on deep belief network and genetic algorithm
Elhenawy et al. Dynamic travel time prediction using data clustering and genetic programming
Bansal et al. Htfm: Hybrid traffic-flow forecasting model for intelligent vehicular ad hoc networks
George et al. Traffic prediction using multifaceted techniques: A survey
Ma et al. Short-term traffic flow forecasting by selecting appropriate predictions based on pattern matching
Khedkar et al. Prediction of traffic generated by IoT devices using statistical learning time series algorithms
Modi et al. Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies
Rabie et al. Smart electrical grids based on cloud, IoT, and big data technologies: state of the art
Shao et al. The Traffic Flow Prediction Method Using the Incremental Learning‐Based CNN‐LTSM Model: The Solution of Mobile Application
Ranjan et al. Large-scale road network congestion pattern analysis and prediction using deep convolutional autoencoder
Ran et al. Short-term travel time prediction: a spatiotemporal deep learning approach
Zheng et al. An ensemble model for short-term traffic prediction in smart city transportation system
Xia et al. A parallel NAW-DBLSTM algorithm on Spark for traffic flow forecasting
Tygesen et al. Unboxing the graph: Towards interpretable graph neural networks for transport prediction through neural relational inference
Song et al. Sparse trip demand prediction for shared E-scooter using spatio-temporal graph neural networks
Ahmed et al. Enhancement of traffic forecasting through graph neural network-based information fusion techniques
Li et al. How spatial features affect urban rail transit prediction accuracy: A deep learning based passenger flow prediction method
Mallick et al. Graphpartitioning-based diffusion convolution recurrent neural network for large-scale traffic forecasting
Zhan et al. Parallel framework of a multi-graph convolutional network and gated recurrent unit for spatial–temporal metro passenger flow prediction
Waikhom et al. Dynamic temporal position observant graph neural network for traffic forecasting
Abideen et al. Regional‐based multi‐module spatial–temporal networks predicting city‐wide taxi pickup/dropoff demand from origin to destination
Xue et al. Urban population density estimation based on spatio‐temporal trajectories