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Traffic Prediction
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Traffic Prediction is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.
<span style="color:grey; opacity: 0.6">( Image credit: BaiduTraffic )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction
Traffic flow prediction using Deep Sedenion Networks
Traffic4cast 2020 -- Graph Ensemble Net and the Importance of Feature And Loss Function Design for Traffic Prediction
AST-GCN: Attribute-Augmented Spatiotemporal Graph Convolutional Network for Traffic Forecasting
Multi-agent Trajectory Prediction with Fuzzy Query Attention
Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting
From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Data
Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework
Demystifying Deep Learning in Predictive Spatio-Temporal Analytics: An Information-Theoretic Framework
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services
Short-term Traffic Prediction with Deep Neural Networks: A Survey
IGANI: Iterative Generative Adversarial Networks for Imputation with Application to Traffic Data
Scalable Low-Rank Tensor Learning for Spatiotemporal Traffic Data Imputation
Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics
Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic Cameras
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
Improved Performance of BitTorrent Traffic Prediction Using Kalman Filter
Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Learning to Recommend Signal Plans under Incidents with Real-Time Traffic Prediction
An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images
Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic Prediction
A Nonconvex Low-Rank Tensor Completion Model for Spatiotemporal Traffic Data Imputation
Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach
BuildSenSys: Reusing Building Sensing Data for Traffic Prediction with Cross-domain Learning
QTIP: Quick simulation-based adaptation of Traffic model per Incident Parameters
Machine Learning for Predictive Deployment of UAVs with Multiple Access
DALC: Distributed Automatic LSTM Customization for Fine-Grained Traffic Speed Prediction
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Traffic Data Imputation using Deep Convolutional Neural Networks
Spatial-Temporal Transformer Networks for Traffic Flow Forecasting
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