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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.
When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks
Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning
A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models
Newell's theory based feature transformations for spatio-temporal traffic prediction
STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction
FDTI: Fine-grained Deep Traffic Inference with Roadnet-enriched Graph
LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting
Dynamic Causal Graph Convolutional Network for Traffic Prediction
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series
Revisiting Random Forests in a Comparative Evaluation of Graph Convolutional Neural Network Variants for Traffic Prediction
Building Transportation Foundation Model via Generative Graph Transformer
Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Survey of Federated Learning Models for Spatial-Temporal Mobility Applications
Analysis of different temporal graph neural network configurations on dynamic graphs
Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies
Leveraging Neo4j and deep learning for traffic congestion simulation & optimization
TraffNet: Learning Causality of Traffic Generation for What-if Prediction
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
Graph Neural Rough Differential Equations for Traffic Forecasting
Adaptive Modeling of Uncertainties for Traffic Forecasting
Traffic Prediction with Transfer Learning: A Mutual Information-based Approach
Enhancing Peak Network Traffic Prediction via Time-Series Decomposition
LSTM-ACB-Based Random Access for Mixed Traffic IoT Networks
Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction
TrafFormer: A Transformer Model for Predicting Long-term Traffic
Semantic-Fused Multi-Granularity Cross-City Traffic Prediction
LightCTS: A Lightweight Framework for Correlated Time Series Forecasting
A Dynamic Temporal Self-attention Graph Convolutional Network for Traffic Prediction
Traffic Prediction in Cellular Networks using Graph Neural Networks
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