Tekouabou et al., 2022 - Google Patents
Improving parking availability prediction in smart cities with IoT and ensemble-based modelTekouabou et al., 2022
View HTML- Document ID
- 7772330298478308872
- Author
- Tekouabou S
- Cherif W
- Silkan H
- et al.
- Publication year
- Publication venue
- Journal of King Saud University-Computer and Information Sciences
External Links
Snippet
Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more …
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
-
- 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
-
- 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
- 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
- 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
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- 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
- 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
- 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
- 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
-
- 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
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Tekouabou et al. | Improving parking availability prediction in smart cities with IoT and ensemble-based model | |
Xu et al. | Real-time prediction of taxi demand using recurrent neural networks | |
Kim et al. | Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects | |
Zheng et al. | Hybrid deep learning models for traffic prediction in large-scale road networks | |
Lu et al. | Lane-level traffic speed forecasting: A novel mixed deep learning model | |
Shao et al. | The Traffic Flow Prediction Method Using the Incremental Learning‐Based CNN‐LTSM Model: The Solution of Mobile Application | |
Sun et al. | Prediction model for short‐term traffic flow based on a K‐means‐gated recurrent unit combination | |
Bai et al. | Deep spatial–temporal sequence modeling for multi-step passenger demand prediction | |
CN113112793A (en) | Traffic flow prediction method based on dynamic space-time correlation | |
Xu et al. | Short‐term traffic flow prediction based on whale optimization algorithm optimized BiLSTM_Attention | |
CN112559585A (en) | Traffic space-time sequence single-step prediction method, system and storage medium | |
Zhou et al. | An attention-based deep learning model for citywide traffic flow forecasting | |
Wang et al. | STTF: An efficient transformer model for traffic congestion prediction | |
Prabowo et al. | Because every sensor is unique, so is every pair: Handling dynamicity in traffic forecasting | |
Ke et al. | AutoSTG+: An automatic framework to discover the optimal network for spatio-temporal graph prediction | |
Guo et al. | Multi‐step traffic speed prediction model with auxiliary features on urban road networks and its understanding | |
Hua et al. | Freeway traffic speed prediction under the intelligent driving environment: a deep learning approach | |
Kong et al. | JointGraph: joint pre-training framework for traffic forecasting with spatial-temporal gating diffusion graph attention network | |
Li et al. | Spatial-temporal attention mechanism and graph convolutional networks for destination prediction | |
Fu et al. | Estimation of short-term online taxi travel time based on neural network | |
Wang et al. | A knowledge-driven memory system for traffic flow prediction | |
Yan et al. | Jointly Modeling Intersections and Road Segments for Travel Time Estimation via Dual Graph Convolutional Networks | |
Feng et al. | AGCN‐T: A Traffic Flow Prediction Model for Spatial‐Temporal Network Dynamics | |
Gao et al. | Traffic Prediction with Self-Supervised Learning: A Heterogeneity-Aware Model for Urban Traffic Flow Prediction Based on Self-Supervised Learning | |
Lin et al. | Predictions of taxi demand based on neural network algorithms |