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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.
Spatio-temporal neural structural causal models for bike flow prediction
PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction
Causal conditional hidden Markov model for multimodal traffic prediction
Smart Journey in Istanbul: A Mobile Application in Smart Cities for Traffic Estimation by Harnessing Time Series
GT-CausIn: a novel causal-based insight for traffic prediction
MegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling
STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction
Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction
Laplacian Convolutional Representation for Traffic Time Series Imputation
Federated Learning for 5G Base Station Traffic Forecasting
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting
RNTrajRec: Road Network Enhanced Trajectory Recovery with Spatial-Temporal Transformer
Multi-task Learning for Sparse Traffic Forecasting
GC-GRU-N for Traffic Prediction using Loop Detector Data
Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach
Large scale traffic forecasting with gradient boosting, Traffic4cast 2022 challenge
STGC-GNNs: A GNN-based traffic prediction framework with a spatial-temporal Granger causality graph
Intelligent Traffic Steering in Beyond 5G Open RAN based on LSTM Traffic Prediction
Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic Prediction
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
HAGCN : Network Decentralization Attention Based Heterogeneity-Aware Spatiotemporal Graph Convolution Network for Traffic Signal Forecasting
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
Towards Spatio-Temporal Cross-Platform Graph Embedding Fusion for Urban Traffic Flow Prediction
Traffic state data imputation: An efficient generating method based on the graph aggregator
Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecasting
Short Duration Traffic Flow Prediction Using Kalman Filtering
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
Spatial-Temporal Feature Extraction and Evaluation Network for Citywide Traffic Condition Prediction
NodeTrans: A Graph Transfer Learning Approach for Traffic Prediction
Traffic-Twitter Transformer: A Nature Language Processing-joined Framework For Network-wide Traffic Forecasting
Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting
Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
Energy-Efficient Wake-Up Signalling for Machine-Type Devices Based on Traffic-Aware Long-Short Term Memory Prediction
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