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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.
A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting
Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns
Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting
A spatial-temporal short-term traffic flow prediction model based on dynamical-learning graph convolution mechanism
Wavelet-Based Hybrid Machine Learning Model for Out-of-distribution Internet Traffic Prediction
Transfer Learning Based Efficient Traffic Prediction with Limited Training Data
ST-ExpertNet: A Deep Expert Framework for Traffic Prediction
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction
An Empirical Study on Internet Traffic Prediction Using Statistical Rolling Model
Deep Sequence Modeling for Anomalous ISP Traffic Prediction
Learning spatiotemporal features from incomplete data for traffic flow prediction using hybrid deep neural networks
Implementation and Experimental Validation of Data-Driven Predictive Control for Dissipating Stop-and-Go Waves in Mixed Traffic
TrafPS: A Visual Analysis System Interpreting Traffic Prediction in Shapley
Few-Sample Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases
A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
A GAN-Based Short-Term Link Traffic Prediction Approach for Urban Road Networks Under a Parallel Learning Framework
A Graph-based U-Net Model for Predicting Traffic in unseen Cities
Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities
Structured Time Series Prediction without Structural Prior
SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers
Network Level Spatial Temporal Traffic State Forecasting with Hierarchical Attention LSTM (HierAttnLSTM)
GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]
A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems
A novel stacking framework based on hybrid of gradient boosting-adaptive boosting-multilayer perceptron for crash injury severity prediction and analysis
Graph Neural Controlled Differential Equations for Traffic Forecasting
STJLA: A Multi-Context Aware Spatio-Temporal Joint Linear Attention Network for Traffic Forecasting
Traffic4cast -- Large-scale Traffic Prediction using 3DResNet and Sparse-UNet
LibCity: An Open Library for Traffic Prediction
Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks
Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks
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