aptx1231/Traffic-Prediction-Open-Code-Summary
Summary of open source code for deep learning models in the field of traffic prediction
Curated collection of 30+ spatio-temporal deep learning architectures for traffic prediction across six task categories (flow, speed, travel time, accidents, etc.), with implementations in TensorFlow, PyTorch, and MXNet. Models progress from CNN-LSTM baselines to graph neural networks (GCN, GNN-ODE) and attention mechanisms, capturing both spatial road network topology and temporal dependencies. Includes peer-reviewed papers from top venues (AAAI, KDD, NIPS) with direct links to official and community implementations across frameworks.
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