LibCity: An Open Library for Urban Spatial-temporal Data Mining
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Updated
Sep 11, 2024 - Python
LibCity: An Open Library for Urban Spatial-temporal Data Mining
Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)
A collection of research on spatio-temporal data mining
Pattern-Matching Dynamic Memory Network for Dual-Mode Traffic Prediction
Fast Temporal Wavelet Graph Neural Networks (NeurIPS 2023)
Repository for advanced traffic forecasting models integrating GCN, LSTM/Bi-LSTM, and attention mechanisms for improved accuracy, including weather data processing.
🌎 🚙📚 Predicting travel times and traffic density on a highway in Slovenia
[Neural Networks] RGDAN: A random graph diffusion attention network for traffic prediction
Here is a time series analysis using R and Arima models to predict air traffic for Hong Kong Airport.
Paper list in traffic prediction field
Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)
Paper & Code & Dataset Collection of Spatial-Temporal Data Mining.
Welcome to quote our published papers, and the codes have been uploaded.
[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"
[CIKM'2023] "CL4ST: Spatio-Temporal Meta Contrastive Learning"
[Pattern Recognition] Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting
M-LibCity: An Open Source Library for Urban Spatio-temporal Prediction Models Based on MindSpore
Pedestrians destination prediction
Using transfer learning for indirect estimation of network-wide traffic flows from link speeds
Traffic prediction with graph neural network using PyTorch Geometric. The implementation uses the MetaLayer class to build the GNN which allows for separate edge, node and global models.
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