Papers with Code 아카이브
Traffic Prediction
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Traffic Prediction is a task that involves forecasting traffic conditions, such as the volume of vehicles and travel time, in a specific area or along a particular road. This task is important for optimizing transportation systems and reducing traffic congestion.
<span style="color:grey; opacity: 0.6">( Image credit: BaiduTraffic )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/littleflow3r/Sequence_to_sequence_learning_with_NN
https://github.com/liulingbo918/ACFM
Dynamic Spatial-Temporal Representation Learning for Traffic Flow Prediction
https://github.com/lmissher/stgnn
Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective
https://github.com/lmissher/stwave
Efficient Large-Scale Traffic Forecasting with Transformers: A Spatial Data Management Perspective
https://github.com/lvyufeng/mindspore-nlp-tutorial
https://github.com/lwm412/STFGNN-Pytorch
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
https://github.com/macournoyer/neuralconvo
https://github.com/martinwhl/T-GCN-PyTorch
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
https://github.com/matken11235/keras-seq2seq
https://github.com/microsoft/StemGNN
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
https://github.com/mindspore-ai/models/blob/master/research/cv/tgcn/
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
https://github.com/mindspore-ai/models/tree/master/research/nlp/seq2seq
https://github.com/minsoo9506/NLP-study
https://github.com/mits58/Pointer-Networks
https://github.com/mlennox/summarisers
https://github.com/moon23k/LSTM_Anchors
https://github.com/moon23k/RNN_Seq2Seq
https://github.com/munir-bd/Korean-POS-Tagger-LSTM
https://github.com/nouhadziri/THRED
https://github.com/octoberzzzzz/ml-based-tms-cav-survey
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
https://github.com/panwangwin/SpatialTemporalNetTrainer-pytorch
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
https://github.com/pursuer0123/T-GCN
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
https://github.com/pwc-1/Paper-9/tree/main/6/Cybertron
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
https://github.com/q-rz/kdd23-ditto
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations
https://github.com/radandreicristian/adn
https://github.com/razvanc92/enhancenet
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
https://github.com/rileynwong/pytorch-seq2seq-joke2punchline
https://github.com/sayhitosandy/Chatbot
https://github.com/semink/LargeScale-DLM
Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs
https://github.com/simonvino/DCRNN_brain_connectivity
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
https://github.com/simonvino/graphwavenet_brain_connectivity
https://github.com/starry91/NMT-Lab
https://github.com/tijsmaas/TrafficPrediction
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
https://github.com/torchspatiotemporal/tsl
Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks
https://github.com/tpepin96/NYCDatasetProcessing
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.