Li et al., 2023 - Google Patents
Multimodal Transport Demand Forecasting via Federated LearningLi et al., 2023
- Document ID
- 3592679897066090046
- Author
- Li C
- Liu W
- Publication year
- Publication venue
- IEEE Transactions on Intelligent Transportation Systems
External Links
Snippet
Multi-source data enhances demand prediction performance by learning from multiple transport modes simultaneously. However, existing multimodal demand forecasting methods often require direct sharing of raw data, which can be infeasible or at least very difficult, due …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
- G06F15/163—Interprocessor communication
- G06F15/173—Interprocessor communication using an interconnection network, e.g. matrix, shuffle, pyramid, star, snowflake
-
- 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
- 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
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- 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
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- 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
- G06N5/02—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
- G06F17/50—Computer-aided design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Diao et al. | A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction | |
Ali et al. | Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks | |
Ye et al. | Meta graph transformer: A novel framework for spatial–temporal traffic prediction | |
Jiang et al. | DeepCrowd: A deep model for large-scale citywide crowd density and flow prediction | |
Xia et al. | Short-term traffic flow prediction based on graph convolutional networks and federated learning | |
James | Graph construction for traffic prediction: A data-driven approach | |
Chen et al. | Multiple local 3D CNNs for region-based prediction in smart cities | |
Liu et al. | Online metro origin-destination prediction via heterogeneous information aggregation | |
Liu et al. | Spatio-temporal autoencoder for traffic flow prediction | |
Li et al. | Multimodal Transport Demand Forecasting via Federated Learning | |
Wang et al. | Spatial–temporal multi-feature fusion network for long short-term traffic prediction | |
Jiang et al. | S-GCN-GRU-NN: A novel hybrid model by combining a Spatiotemporal Graph Convolutional Network and a Gated Recurrent Units Neural Network for short-term traffic speed forecasting | |
Wang et al. | Passenger mobility prediction via representation learning for dynamic directed and weighted graphs | |
Wang et al. | On prediction of traffic flows in smart cities: a multitask deep learning based approach | |
Li et al. | Dmgf-net: an efficient dynamic multi-graph fusion network for traffic prediction | |
Fan et al. | RGDAN: A random graph diffusion attention network for traffic prediction | |
Wang et al. | Knowledge fusion enhanced graph neural network for traffic flow prediction | |
Liang et al. | Cross-mode knowledge adaptation for bike sharing demand prediction using domain-adversarial graph neural networks | |
Su et al. | Spatial-temporal graph convolutional networks for traffic flow prediction considering multiple traffic parameters | |
Mo et al. | Cross-city multi-granular adaptive transfer learning for traffic flow prediction | |
Wu et al. | Learning spatial-temporal dynamics and interactivity for short-term passenger flow prediction in urban rail transit | |
Bai et al. | Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction | |
Yang et al. | Spatiotemporal DeepWalk gated recurrent neural network: a deep learning framework for traffic learning and forecasting | |
Kaur et al. | Federated Learning based Spatio-Temporal framework for real-time traffic prediction | |
Zhou et al. | Deep flexible structured spatial–temporal model for taxi capacity prediction |