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/trustagi-lab/mtgode
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs
https://github.com/umeiko/mindspore-seq2seq
https://github.com/uw-mad-dash/pgt_index
Spatial-Temporal Large Language Model for Traffic Prediction
https://github.com/vGkatsis/Chat_Bot_DL
https://github.com/victorchan314/DCRNN
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
https://github.com/vivekpandian08/French_English_Translation-NMT-
https://github.com/weiylu/NLP
https://github.com/wengwenchao123/DDGCRN
https://github.com/wengwenchao123/PDG2Seq
https://github.com/wengwenchao123/RGDAN
https://github.com/xlwang233/pytorch-dcrnn
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
https://github.com/xuehaouwa/lm-load-forecasting
PromptCast: A New Prompt-based Learning Paradigm for Time Series Forecasting
https://github.com/yangjun1994/CAGCN
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
https://github.com/yangyucheng000/ascend_gru
https://github.com/yash-nishaant/Seq2Seq-Chatbot
https://github.com/yscope75/CS2225.CH2001020
https://github.com/zekun-cai/koodos
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
https://github.com/zhiyongc/Graph_Convolutional_LSTM
https://github.com/zouchangjie/T-GCN--------
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction
https://github.com/zzwells/jdd2018-population-forecast
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.