PEMS-BAY

PEMS-BAY Traffic Dataset Dataset Description This dataset contains traffic flow data for time series forecasting tasks, commonly used with Graph Neural Networks and specifically the Diffusion Convolutional Recurrent Neural Network (DCRNN) model. Dataset Structure Data Format Format: Parquet files for efficient loading

Papers using PEMS-BAY (14)

Graph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting2026WeaGAN++: An Efficient Weather-Aware Graph Attention Network for Traffic Prediction2026Spatio-Temporal Meta-Graph Learning for Traffic Forecasting2022 · 11 citesMegaCRN: Meta-Graph Convolutional Recurrent Network for Spatio-Temporal Modeling2022 · 2 citesGT-CausIn: a novel causal-based insight for traffic prediction2022 · 1 citesSemi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Prediction2024 · 1 citesFederated Learning with Graph-Based Aggregation for Traffic Forecasting2025 · 1 citesFast Temporal Wavelet Graph Neural Networks2023Improving Traffic Flow Predictions with SGCN-LSTM: A Hybrid Model for Spatial and Temporal Dependencies2024Adaptive Graph Pruning with Sudden-Events Evaluation for Traffic Prediction using Online Semi-Decentralized ST-GNNs2025Residual Correction in Real-Time Traffic Forecasting2022Persistent Homology-induced Graph Ensembles for Time Series Regressions2025Temporal Attention Evolutional Graph Convolutional Network for Multivariate Time Series Forecasting2025STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting2025