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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.
Predicting the Skies: A Novel Model for Flight-Level Passenger Traffic Forecasting
Global-Aware Enhanced Spatial-Temporal Graph Recurrent Networks: A New Framework For Traffic Flow Prediction
TelTrans: Applying Multi-Type Telecom Data to Transportation Evaluation and Prediction via Multifaceted Graph Modeling
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time Changes
Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecasting
A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction
SPD-DDPM: Denoising Diffusion Probabilistic Models in the Symmetric Positive Definite Space
A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective
Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal Deep Learning
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning
Spatial-Temporal-Decoupled Masked Pre-training for Spatiotemporal Forecasting
An Adaptive Framework for Generalizing Network Traffic Prediction towards Uncertain Environments
ChatTraffic: Text-to-Traffic Generation via Diffusion Model
Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction
Estimating time of arrival of vehicle fleets with GCN based traffic prediction
Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach
Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction
STDA-Meta: A Meta-Learning Framework for Few-Shot Traffic Prediction
Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks
Enhancing Traffic Prediction with Learnable Filter Module
B^2SFL: A Bi-level Blockchained Architecture for Secure Federated Learning-based Traffic Prediction
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning
Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach
Spatio-Temporal Graph Mixformer for Traffic Forecasting
Interaction-aware Traffic Prediction and Scenario-based Model Predictive Control for Autonomous Vehicles on Highways
Revealing the Power of Masked Autoencoders in Traffic Forecasting
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
MLPST: MLP is All You Need for Spatio-Temporal Prediction
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Uncertainty-aware Traffic Prediction under Missing Data
STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting
Minimalist Traffic Prediction: Linear Layer Is All You Need
Enhancing Spatiotemporal Traffic Prediction through Urban Human Activity Analysis
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