LibCity: An Open Library for Urban Spatial-temporal Data Mining
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Updated
Jun 25, 2024 - Python
LibCity: An Open Library for Urban Spatial-temporal Data Mining
GMAN: A Graph Multi-Attention Network for Traffic Prediction (GMAN, https://fanxlxmu.github.io/publication/aaai2020/) was accepted by AAAI-2020.
This repo includes introduction, code and dataset of our paper Deep Sequence Learning with Auxiliary Information for Traffic Prediction (KDD 2018).
[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.
Traffic data processing tools in LibCity
HetETA: Heterogeneous Information Network Embedding for Estimating Time of Arrival
[Pattern Recognition] Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting
Predict traffic flow with LSTM. For experimental purposes only, unsupported!
Source codes of CIKM2022 Full Paper "Domain Adversarial Spatial-Temporal Network: A Transferable Framework for Short-term Traffic Forecasting across Cities"
[CIKM'2023] "STExplainer: Explainable Spatio-Temporal Graph Neural Networks"
[CIKM'2023] "CL4ST: Spatio-Temporal Meta Contrastive Learning"
[Neural Networks] RGDAN: A random graph diffusion attention network for traffic prediction
We have used Support Vector Regression and Random Forest Regression to predict traffic or congestion.
Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction
Spatial-Temporal Graph Convolutional Neural Network with LSTM layers
Reproduce - Traffic_prediction - StemGNN(NeurIPS20)
ALL is a pre-training method based on Federated Meta-Learning and Reinforcement Learning, which is used to address the small-sample issues of parking occupancy prediction.
Dynamic Modification Neural Network Model for Short-term Traffic Prediction
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.
ST-MAN: Spatio-Temporal Multimodal Attention Network for Traffic Prediction (KSEM 2023)
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