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 Flow Prediction with Neural Networks(SAEs、LSTM、GRU).
Traffic Graph Convolutional Recurrent Neural Network
Some TrafficFlowForecasting Solutions(交通流量预测解决方案)
Traffic prediction is the task of predicting future traffic measurements (e.g. volume, speed, etc.) in a road network (graph), using historical data (timeseries).
Summary of open source code for deep learning models in the field of traffic prediction
[AAAI2023] A PyTorch implementation of PDFormer: Propagation Delay-aware Dynamic Long-range Transformer for Traffic Flow Prediction.
ST-SSL (STSSL): Spatio-Temporal Self-Supervised Learning for Traffic Flow Forecasting/Prediction
Codes for AAAI 2019 DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
[Pattern Recognition] Decomposition Dynamic Graph Conolutional Recurrent Network for Traffic Forecasting
A PyTorch implementation of T-GCN
[ICML'2024] "FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction"
Attention Feature Fusion base on spatial-temporal Graph Convolutional Network(AFFGCN)
Long Short-Term Memory(LSTM) is a particular type of Recurrent Neural Network(RNN) that can retain important information over time using memory cells. This project includes understanding and implementing LSTM for traffic flow prediction along with the introduction of traffic flow prediction, Literature review, methodology, etc.
A time series task- predicting traffic flow using LSTM model
This work considers combine multi-tricks with highway network to achieve traffic flow prediction accurately.
DeepSTD: Mining Spatio-temporal Disturbances of Multiple Context Factors for Citywide Traffic Flow Prediction
Predict traffic flow by affinity propagation clustering and LSTM
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