LibCity: An Open Library for Urban Spatial-temporal Data Mining
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Updated
Sep 27, 2024 - Python
LibCity: An Open Library for Urban Spatial-temporal Data Mining
Traffic Graph Convolutional Recurrent Neural Network
GMAN: A Graph Multi-Attention Network for Traffic Prediction (GMAN, https://fanxlxmu.github.io/publication/aaai2020/) was accepted by AAAI-2020.
Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)
This repo includes introduction, code and dataset of our paper Deep Sequence Learning with Auxiliary Information for Traffic Prediction (KDD 2018).
This project is a collection of recent research in areas such as new infrastructure and urban computing, including white papers, academic papers, AI lab and dataset etc.
Summary of open source code for deep learning models in the field of traffic prediction
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival
[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.
Useful resources for traffic prediction, including popular papers, datasets, tutorials, toolkits, and other helpful repositories.
Paper list in traffic prediction field
A collection of research on spatio-temporal data mining
Predict traffic flow with LSTM. For experimental purposes only, unsupported!
Traffic data processing tools in LibCity
Paper & Code & Dataset Collection of Spatial-Temporal Data Mining.
[Pattern Recognition] Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting
We have used Support Vector Regression and Random Forest Regression to predict traffic or congestion.
Organize some grid-based traffic flow datasets, mainly New York City bicycle and taxi data
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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