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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.
STAHGNet: Modeling Hybrid-grained Heterogenous Dependency Efficiently for Traffic Prediction
Evaluation of Bio-Inspired Models under Different Learning Settings For Energy Efficiency in Network Traffic Prediction
Global Spatio-Temporal Fusion-based Traffic Prediction Algorithm with Anomaly Aware
FairTP: A Prolonged Fairness Framework for Traffic Prediction
Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective
Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation
An Experimental Evaluation of Imputation Models for Spatial-Temporal Traffic Data
Federated Learning in Mobile Networks: A Comprehensive Case Study on Traffic Forecasting
Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction
Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting
Federated Contrastive Learning of Graph-Level Representations
A Traffic Prediction-Based Individualized Driver Warning System to Reduce Red Light Violations
DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
Strada-LLM: Graph LLM for traffic prediction
SSMT: Few-Shot Traffic Forecasting with Single Source Meta-Transfer
FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputation
Incorporating Long-term Data in Training Short-term Traffic Prediction Model
A Time Series is Worth Five Experts: Heterogeneous Mixture of Experts for Traffic Flow Prediction
Graph Pruning Based Spatial and Temporal Graph Convolutional Network with Transfer Learning for Traffic Prediction
Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting
On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver
Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge
Causally-Aware Spatio-Temporal Multi-Graph Convolution Network for Accurate and Reliable Traffic Prediction
Self-Refined Generative Foundation Models for Wireless Traffic Prediction
OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignment
DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series Forecasting
IBB Traffic Graph Data: Benchmarking and Road Traffic Prediction Model
Spatio-Temporal Partial Sensing Forecast for Long-term Traffic
MSCT: Addressing Time-Varying Confounding with Marginal Structural Causal Transformer for Counterfactual Post-Crash Traffic Prediction
Individualized Federated Learning for Traffic Prediction with Error Driven Aggregation
Traffic Prediction considering Multiple Levels of Spatial-temporal Information: A Multi-scale Graph Wavelet-based Approach
Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction
Rethinking Spatio-Temporal Transformer for Traffic Prediction:Multi-level Multi-view Augmented Learning Framework
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