James, 2021 - Google Patents
Citywide estimation of travel time distributions with Bayesian deep graph learningJames, 2021
- Document ID
- 18326991855752346850
- Author
- James J
- Publication year
- Publication venue
- IEEE Transactions on Knowledge and Data Engineering
External Links
Snippet
Estimation of road link travel time serves a critical role in intelligent transportation operation and management. Due to the uncertainty nature contributed by the volatile traffic, travel time estimates are better described by probability distributions than deterministic models. Existing …
- 230000001537 neural 0 abstract description 35
Classifications
-
- 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
- G06F17/30386—Retrieval requests
- G06F17/30424—Query processing
- G06F17/30533—Other types of queries
-
- 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
- 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
- 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
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- 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
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
-
- 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
- G06F17/10—Complex mathematical operations
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yu et al. | Real-time traffic speed estimation with graph convolutional generative autoencoder | |
CN110827544B (en) | Short-term traffic flow control method based on graph convolution recurrent neural network | |
Liu et al. | GraphSAGE-based traffic speed forecasting for segment network with sparse data | |
Pan et al. | AutoSTG: Neural Architecture Search for Predictions of Spatio-Temporal Graph✱ | |
Zhang et al. | Spatial-temporal graph attention networks: A deep learning approach for traffic forecasting | |
Ren et al. | Mtrajrec: Map-constrained trajectory recovery via seq2seq multi-task learning | |
Yan et al. | Spatial-temporal chebyshev graph neural network for traffic flow prediction in iot-based its | |
James | Citywide estimation of travel time distributions with Bayesian deep graph learning | |
Tang et al. | Joint modeling of dense and incomplete trajectories for citywide traffic volume inference | |
Ye et al. | Short-term prediction of available parking space based on machine learning approaches | |
CN114036135A (en) | Method and system for estimating urban mobile source pollution emission by using incomplete information | |
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 | |
Zhang et al. | Off-deployment traffic estimation—a traffic generative adversarial networks approach | |
Pan et al. | Traffic speed prediction based on time classification in combination with spatial graph convolutional network | |
Zhao et al. | STCGAT: A spatio-temporal causal graph attention network for traffic flow prediction in intelligent transportation systems | |
Zhao et al. | Traffic emission estimation under incomplete information with spatiotemporal convolutional GAN | |
Hussain et al. | A Novel Graph Convolutional Gated Recurrent Unit Framework for Network-Based Traffic Prediction | |
Chen et al. | Next location prediction with a graph convolutional network based on a seq2seq framework | |
CN118133084A (en) | Method and device for predicting air quality of area without monitoring station based on hierarchical graph convolutional network | |
Li et al. | An innovative supervised learning structure for trajectory reconstruction of sparse LPR data | |
Waikhom et al. | Dynamic temporal position observant graph neural network for traffic forecasting | |
Chen et al. | Dynamic path flow estimation using automatic vehicle identification and probe vehicle trajectory data: A 3D convolutional neural network model | |
Cuza et al. | Spatio-temporal graph convolutional network for stochastic traffic speed imputation | |
Pourmoradnasseri et al. | Real-time calibration of disaggregated traffic demand |