Papers with Code 아카이브

Traffic Prediction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 33
보존된 평가 기록: 192

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/AmitRoy7781/SST-GNN

공식 코드

SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network

https://github.com/AmitRoy7781/USTGCN

공식 코드

Unified Spatio-Temporal Modeling for Traffic Forecasting using Graph Neural Network

https://github.com/BUAABIGSCity/PDFormer

공식 코드

PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

https://github.com/ChenxiLiu-HNU/ST-LLM

공식 코드

Spatial-Temporal Large Language Model for Traffic Prediction

https://github.com/ChyaZhang/ChatTraffic

공식 코드

ChatTraffic: Text-to-Traffic Generation via Diffusion Model

https://github.com/Dreamzz5/PCA-Embedding

공식 코드

Unveiling the Inflexibility of Adaptive Embedding in Traffic Forecasting

https://github.com/Graph-Machine-Learning-Group/grin

공식 코드

Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

https://github.com/Graph-Machine-Learning-Group/spin

공식 코드

Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations

https://github.com/Hope-Rita/RepeatMixer

공식 코드

Repeat-Aware Neighbor Sampling for Dynamic Graph Learning

https://github.com/JingqingZ/BaiduTraffic

공식 코드

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

https://github.com/LYZ98/Adversarial-Diffusion-Attacks-on-Graph-based-Traffic-Prediction-Models

공식 코드

Adversarial Diffusion Attacks on Graph-based Traffic Prediction Models

https://github.com/LeiBAI/AGCRN

공식 코드

Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting

https://github.com/Lemma1/DPFE

공식 코드

Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data

https://github.com/LiuAoyu1998/STIDGCN

공식 코드

Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

https://github.com/LucaHermes/graph-unet-traffic-prediction

공식 코드

A Graph-based U-Net Model for Predicting Traffic in unseen Cities

https://github.com/MengzhangLI/STFGNN

공식 코드

Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

https://github.com/MonBG/DCGCN

공식 코드

Dynamic Causal Graph Convolutional Network for Traffic Prediction

https://github.com/Mu-Bilal/Temporal-Graph-MLP-Mixer

공식 코드

Temporal Graph MLP Mixer for Spatio-Temporal Forecasting

https://github.com/OsamaAhmad369/VMGCN

공식 코드

Variational Mode-Driven Graph Convolutional Network for Spatiotemporal Traffic Forecasting

https://github.com/Pachark/SVQ-Forecasting

공식 코드

Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization Approach

https://github.com/Practicing-Federated-Learning-for-IoT/FedGRU

공식 코드

Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach

https://github.com/TeRyZh/Network-Level-Travel-Prediction-Hierarchical-Attention-LSTM

공식 코드

Network Level Spatial Temporal Traffic State Forecasting with Hierarchical Attention LSTM (HierAttnLSTM)

https://github.com/VeritasYin/STGCN_IJCAI-18

공식 코드

Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

https://github.com/WenjieDu/PyGrinder

공식 코드

PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

https://github.com/WenjieDu/PyPOTS

공식 코드

BRITS: Bidirectional Recurrent Imputation for Time Series

https://github.com/WenjieDu/TSDB

공식 코드

PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

https://github.com/Yiru-Jiao/spclt

공식 코드

Structure-preserving contrastive learning for spatial time series

https://github.com/ZhangJiayi24/KAN_GCN_Traffic

공식 코드

TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization

https://github.com/adsafetyjointlab/pptnet

공식 코드

PPTNet: A Hybrid Periodic Pattern-Transformer Architecture for Traffic Flow Prediction and Congestion Identification

https://github.com/ai4cts/lightcts

공식 코드

LightCTS: A Lightweight Framework for Correlated Time Series Forecasting

https://github.com/alextimans/traffic4cast-uncertainty

공식 코드

Uncertainty Quantification for Image-based Traffic Prediction across Cities

https://github.com/alvinzhaowei/gamcn

공식 코드

A Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction

https://github.com/barbara20901/cluster-link-prediction

공식 코드

A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems

https://github.com/bigscity/steve_code

공식 코드

Seeing the Unseen: Learning Basis Confounder Representations for Robust Traffic Prediction

https://github.com/bjtu-ccd-lab/corrstn

공식 코드

A Correlation Information-based Spatiotemporal Network for Traffic Flow Forecasting

https://github.com/blisky-li/dfdgcn

공식 코드

Dynamic Frequency Domain Graph Convolutional Network for Traffic Forecasting

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.