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astgcn

Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN) AAAI 2019

awesome-public-datasets

A topic-centric list of HQ open datasets. PR ☛☛☛

awesome-pytorch-chinese

【干货】史上最全的PyTorch学习资源汇总

berkeley-stat-157

Homepage for STAT 157 at UC Berkeley

blog_pic

博客图片

courseml-pr-final

dcrnn

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

dcrnn_pytorch

Diffusion Convolutional Recurrent Neural Network Implementation in PyTorch

dgl

Python package built to ease deep learning on graph, on top of existing DL frameworks.

graph-wavenet

graph wavenet

lstnet

open-traffic-datasets

open traffic datasets

python-100-days

Python - 100天从新手到大师

smartcitylargescreen

智慧城市大屏

st_gat

Variations of Spatiotemporal Graph Convolution Network

stdn

Code for our Spatiotemporal Dynamic Network

stgat

Implementation of Spatial-Temporal Graph Attention Networks for Traffic Flow Forecasting(for IJCAI 2020)

stgcn

implementation of STGCN for traffic prediction in IJCAI2018

stgcn_ijcai-18

Spatio-Temporal Graph Convolutional Networks

t-gcn

Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

tcn-with-attention

Character based Temporal Convolutional Networks + Attention Layer

tm_prediction

tpa-lstm

Temporal Pattern Attention for Multivariate Time Series Forecasting

traffic-dataset-of-beijing-road

Fig. 1 illustrates the structure of the traffic system, which contains six road segments of the Xueyuan Road in Beijing, China. The segments R4, R5, R6 are in the main road of a highway, and R1, R2, R3 are in the side road. The data set that contains the 30 days’ traffic speeds of the segments in April, 2016.

traffic-flow-forecast

Using GAT with temporal attention to forecast urban traffic flow.