🌎 🚙📚 Predicting travel times and traffic density on a highway in Slovenia
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Updated
Jun 7, 2024 - CSS
🌎 🚙📚 Predicting travel times and traffic density on a highway in Slovenia
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.)
[Pattern Recognition] Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting
Paper & Code & Dataset Collection of Spatial-Temporal Data Mining.
M-LibCity: An Open Source Library for Urban Spatio-temporal Prediction Models Based on MindSpore
Pedestrians destination prediction
Here is a time series analysis using R and Arima models to predict air traffic for Hong Kong Airport.
[Neural Networks] RGDAN: A random graph diffusion attention network for traffic 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.
Welcome to quote our published papers, and the codes have been uploaded.
A collection of research on spatio-temporal data mining
[CIKM'2023] "CL4ST: Spatio-Temporal Meta Contrastive Learning"
[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"
Dijkstra adjacency distance matrices were calculated for 40 cities from traffic sensor locations provide by UTD19 https://utd19.ethz.ch/.
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
Fast Temporal Wavelet Graph Neural Networks
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