Lin et al., 2022 - Google Patents
Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficientLin et al., 2022
- Document ID
- 14981454965979463389
- Author
- Lin G
- Lin A
- Gu D
- Publication year
- Publication venue
- Information Sciences
External Links
Snippet
The prediction of short-term traffic flow is critical for improving service levels for drivers and passengers as well as enhancing the efficiency of traffic management in the urban transportation system. For transportation departments, the issue remains of how to efficiently …
- 238000004642 transportation engineering 0 abstract description 16
Classifications
-
- 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
- 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
- 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
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
-
- 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
- G06F17/5009—Computer-aided design using simulation
-
- 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
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- 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
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lin et al. | Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient | |
Dou et al. | A hybrid CEEMD-GMM scheme for enhancing the detection of traffic flow on highways | |
Ma et al. | Forecasting transportation network speed using deep capsule networks with nested LSTM models | |
Yin et al. | Multi-stage attention spatial-temporal graph networks for traffic prediction | |
Wang et al. | Traffic flow prediction via spatial temporal graph neural network | |
Zhang et al. | TrafficGAN: Network-scale deep traffic prediction with generative adversarial nets | |
Chen et al. | Multiple local 3D CNNs for region-based prediction in smart cities | |
Zhang et al. | Network‐wide traffic speed forecasting: 3D convolutional neural network with ensemble empirical mode decomposition | |
Yao et al. | Short‐term traffic speed prediction for an urban corridor | |
Bai et al. | PrePCT: Traffic congestion prediction in smart cities with relative position congestion tensor | |
Bao et al. | An improved deep belief network for traffic prediction considering weather factors | |
Venkatesh et al. | RETRACTED ARTICLE: Rainfall prediction using generative adversarial networks with convolution neural network | |
Zhou et al. | Spatial–temporal deep tensor neural networks for large-scale urban network speed prediction | |
Zhao et al. | Unsupervised anomaly detection based method of risk evaluation for road traffic accident | |
Wang et al. | A novel hybrid method for achieving accurate and timeliness vehicular traffic flow prediction in road networks | |
Wang et al. | STTF: An efficient transformer model for traffic congestion prediction | |
Su et al. | Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters | |
Xing et al. | RL-GCN: Traffic flow prediction based on graph convolution and reinforcement learning for smart cities | |
Wang et al. | TransGAT: A dynamic graph attention residual networks for traffic flow forecasting | |
Hu et al. | Multi-source information fusion based DLaaS for traffic flow prediction | |
Waikhom et al. | Dynamic temporal position observant graph neural network for traffic forecasting | |
Zhang et al. | Attention-driven recurrent imputation for traffic speed | |
Mrad et al. | An overview of model-driven and data-driven forecasting methods for smart transportation | |
Yang et al. | TARGCN: temporal attention recurrent graph convolutional neural network for traffic prediction | |
Hua et al. | Freeway traffic speed prediction under the intelligent driving environment: A deep learning approach |