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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.
Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
DetectorNet: Transformer-enhanced Spatial Temporal Graph Neural Network for Traffic Prediction
Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
5G Traffic Prediction with Time Series Analysis
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
Short-term traffic prediction using physics-aware neural networks
Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting
Spatially Focused Attack against Spatiotemporal Graph Neural Networks
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis
Completion and Augmentation based Spatiotemporal Deep Learning Approach for Short-Term Metro Origin-Destination Matrix Prediction under Limited Observable Data
A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
A Novel Method for Analyzing Weather Effect on Smart City Traffic
Incorporating Reachability Knowledge into a Multi-Spatial Graph Convolution Based Seq2Seq Model for Traffic Forecasting
Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting
QFCNN: Quantum Fourier Convolutional Neural Network
SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction
Traffic-Aware Service Relocation in Cloud-Oriented Elastic Optical Networks
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Low-Rank Autoregressive Tensor Completion for Spatiotemporal Traffic Data Imputation
Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs
Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network
Applications of Artificial Intelligence, Machine Learning and related techniques for Computer Networking Systems
Multi-fold Correlation Attention Network for Predicting Traffic Speeds with Heterogeneous Frequency
Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models
Bayesian Graph Convolutional Network for Traffic Prediction
SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction
Scalable Learning With a Structural Recurrent Neural Network for Short-Term Traffic Prediction
Event-Driven Source Traffic Prediction in Machine-Type Communications Using LSTM Networks
Modeling Spatial Nonstationarity via Deformable Convolutions for Deep Traffic Flow Prediction
A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.