[go: up one dir, main page]

Lim et al., 2024 - Google Patents

Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling

Lim et al., 2024

View PDF
Document ID
12014675435139352043
Author
Lim J
Lee J
An C
Park E
Publication year
Publication venue
IET Intelligent Transport Systems

External Links

Snippet

A two‐step framework that integrates real‐time data collection with time series forecasting models for predicting traffic volume is proposed. In the first step, the framework utilizes live highway surveillance video data and YOLO‐v7 object detector to construct accurate traffic …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • 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
    • 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
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • 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
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01Social networking
    • 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
    • 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • 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
Shen et al. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community
Tian et al. Spatial‐temporal attention wavenet: A deep learning framework for traffic prediction considering spatial‐temporal dependencies
Zhang et al. Automated pixel‐level pavement crack detection on 3D asphalt surfaces with a recurrent neural network
Zhuang et al. Innovative method for traffic data imputation based on convolutional neural network
Ye et al. Spatial-temporal traffic data imputation via graph attention convolutional network
Kim et al. Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Khan et al. Development and evaluation of recurrent neural network-based models for hourly traffic volume and annual average daily traffic prediction
Xu et al. MTLM: a multi-task learning model for travel time estimation
Yu et al. Improved convolutional neural network‐based quantile regression for regional photovoltaic generation probabilistic forecast
Fernandes et al. Long short-term memory networks for traffic flow forecasting: exploring input variables, time frames and multi-step approaches
Lim et al. Enhancing real‐time traffic volume prediction: A two‐step approach of object detection and time series modelling
Cui et al. Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond
Riboni et al. Bayesian optimization and deep learning for steering wheel angle prediction
Ye et al. Demand forecasting of online car‐hailing by exhaustively capturing the temporal dependency with TCN and Attention approaches
Awan et al. A novel deep stacking-based ensemble approach for short-term traffic speed prediction
Tang et al. Lane‐level short‐term travel speed prediction for urban expressways: An attentive spatio‐temporal deep learning approach
Zhao et al. Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus
Kumar et al. Enabling internet of things in road traffic forecasting with deep learning models
Wang et al. Remote sensing image analysis and prediction based on improved Pix2Pix model for water environment protection of smart cities
Ku et al. Toward directed spatiotemporal graph: A new idea for heterogeneous traffic prediction
Sarkar et al. Real‐Time Air Quality Index Detection through Regression‐Based Convolutional Neural Network Model on Captured Images
Ran et al. Travel time prediction by providing constraints on a convolutional neural network
Wang et al. The prediction of urban road traffic congestion by using a deep stacked long short-term memory network
Wang et al. An adaptive spatio-temporal graph recurrent network for short-term electric vehicle charging demand prediction
Li et al. A lightweight bus passenger detection model based on YOLOv5