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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.
STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
Beyond Attention: Learning Spatio-Temporal Dynamics with Emergent Interpretable Topologies
MetaSTNet: Multimodal Meta-learning for Cellular Traffic Conformal Prediction
Cellular Traffic Prediction via Byzantine-robust Asynchronous Federated Learning
PPTNet: A Hybrid Periodic Pattern-Transformer Architecture for Traffic Flow Prediction and Congestion Identification
HQSI: Hybrid Quantum Swarm Intelligence -- A Case Study of Online Certificate Status Protocol Request Flow Prediction
Enhancing short-term traffic prediction by integrating trends and fluctuations with attention mechanism
DG-STMTL: A Novel Graph Convolutional Network for Multi-Task Spatio-Temporal Traffic Forecasting
STEI-PCN: an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring
DP-LET: An Efficient Spatio-Temporal Network Traffic Prediction Framework
FlowDistill: Scalable Traffic Flow Prediction via Distillation from LLMs
MNT-TNN: Spatiotemporal Traffic Data Imputation via Compact Multimode Nonlinear Transform-based Tensor Nuclear Norm
Graph ODEs and Beyond: A Comprehensive Survey on Integrating Differential Equations with Graph Neural Networks
Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap
MSCMHMST: A traffic flow prediction model based on Transformer
Effective and Efficient Cross-City Traffic Knowledge Transfer: A Privacy-Preserving Perspective
TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization
Fed-KAN: Federated Learning with Kolmogorov-Arnold Networks for Traffic Prediction
SUSTeR: Sparse Unstructured Spatio Temporal Reconstruction on Traffic Prediction
Highly Dynamic and Flexible Spatio-Temporal Spectrum Management with AI-Driven O-RAN: A Multi-Granularity Marketplace Framework
NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches
Structure-preserving contrastive learning for spatial time series
Using Causality for Enhanced Prediction of Web Traffic Time Series
Data Mining in Transportation Networks with Graph Neural Networks: A Review and Outlook
Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Prediction
T-Graphormer: Using Transformers for Spatiotemporal Forecasting
MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing Networks
Dynamic Trend Fusion Module for Traffic Flow Prediction
Virtual Nodes Improve Long-term Traffic Prediction
Temporal Graph MLP Mixer for Spatio-Temporal Forecasting
Efficient Traffic Prediction Through Spatio-Temporal Distillation
BiDepth Multimodal Neural Network: Bidirectional Depth Deep Learning Architecture for Spatial-Temporal Prediction
MHGNet: Multi-Heterogeneous Graph Neural Network for Traffic Prediction
SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling Network for Traffic Prediction
FasterSTS: A Faster Spatio-Temporal Synchronous Graph Convolutional Networks for Traffic flow Forecasting
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.