Cai et al., 2023 - Google Patents
Fine-Grained Pavement Performance Prediction Based on Causal-Temporal Graph Convolution NetworksCai et al., 2023
- Document ID
- 16743431080243755134
- Author
- Cai W
- Song A
- Du Y
- Liu C
- Wu D
- Li F
- Publication year
- Publication venue
- IEEE Transactions on Intelligent Transportation Systems
External Links
Snippet
Pavement performance prediction is the foundation of maintenance decisions, which is the key problem of infrastructure management. Most prediction methods focus on section-based and annual deterioration on pavement, while it is hardly supporting daily and preventive …
Classifications
-
- 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
- G06Q10/0639—Performance analysis
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] 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
- 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
- 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
- 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
- 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
-
- 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
- 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
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Mackenzie et al. | An evaluation of HTM and LSTM for short-term arterial traffic flow prediction | |
Pan et al. | Urban traffic prediction from spatio-temporal data using deep meta learning | |
Chiabaut et al. | Traffic congestion and travel time prediction based on historical congestion maps and identification of consensual days | |
Khaled et al. | TFGAN: Traffic forecasting using generative adversarial network with multi-graph convolutional network | |
Chen et al. | Surrogate‐based optimization of expensive‐to‐evaluate objective for optimal highway toll charges in transportation network | |
Dell'Acqua et al. | Time-aware multivariate nearest neighbor regression methods for traffic flow prediction | |
Liu et al. | Multivariate time-series forecasting with temporal polynomial graph neural networks | |
CN111767517B (en) | BiGRU multi-step prediction method, system and storage medium applied to flood prediction | |
ChikkaKrishna et al. | Short-term traffic prediction using fb-prophet and neural-prophet | |
Zhang et al. | Study on water quality prediction of urban reservoir by coupled CEEMDAN decomposition and LSTM neural network model | |
Koukaras et al. | Introducing a novel approach in one-step ahead energy load forecasting | |
CN109615860A (en) | A kind of signalized intersections method for estimating state based on nonparametric Bayes frame | |
Lu et al. | Traffic speed forecasting for urban roads: A deep ensemble neural network model | |
Liu et al. | Dynamic traffic demand uncertainty prediction using radio‐frequency identification data and link volume data | |
Ma et al. | A transfer learning framework for proactive ramp metering performance assessment | |
Nie et al. | Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs | |
Miao et al. | Examining the impact of different periodic functions on short‐term freeway travel time prediction approaches | |
Zhong et al. | Estimating link flows in road networks with synthetic trajectory data generation: Inverse reinforcement learning approach | |
Cai et al. | Fine-Grained Pavement Performance Prediction Based on Causal-Temporal Graph Convolution Networks | |
Petelin et al. | Models for forecasting the traffic flow within the city of Ljubljana | |
Raja et al. | Drought prediction and validation for desert region using machine learning methods | |
Mahajan et al. | Predicting network flows from speeds using open data and transfer learning | |
Deng et al. | Use of recurrent neural networks considering maintenance to predict urban road performance in Beijing, China | |
Niea et al. | Towards better traffic volume estimation: Tackling both underdetermined and non-equilibrium problems via a correlation-adaptive graph convolution network | |
Cao et al. | Probabilistic runoff forecasting considering stepwise decomposition framework and external factor integration structure |