Stgcn_ijcai 18

[IJCAI'18] Spatio-Temporal Graph Convolutional Networks
Alternatives To Stgcn_ijcai 18
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
3 days ago205mitDart
FL Chart is a highly customizable Flutter chart library that supports Line Chart, Bar Chart, Pie Chart, Scatter Chart, and Radar Chart.
Ogb1,666136 days ago16February 23, 202216mitPython
Benchmark datasets, data loaders, and evaluators for graph machine learning
Cogdl1,4371a month ago16June 01, 202234mitPython
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
7 months ago1gpl-3.0Python
A parallel implementation of "graph2vec: Learning Distributed Representations of Graphs" (MLGWorkshop 2017).
Stgcn_ijcai 18723
3 months ago9bsd-2-clausePython
[IJCAI'18] Spatio-Temporal Graph Convolutional Networks
Knowledge Graph Learning662
4 months ago336mit
A curated list of awesome knowledge graph tutorials, projects and communities.
Sr Gnn607
2 years ago6Python
[AAAI 2019] Source code and datasets for "Session-based Recommendation with Graph Neural Networks"
7 months ago22mitPython
Convolutional 2D Knowledge Graph Embeddings resources
3 years ago24mitPython
KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
a month ago12otherJupyter Notebook
Cleora AI is a general-purpose model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data.
Alternatives To Stgcn_ijcai 18
Select To Compare

Alternative Project Comparisons

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

Paper Github PyG_Temporal"


We propose a novel deep learning framework, STGCN, to tackle time series prediction problem in traffic domain. Instead of applying regular convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures. To the best of our knowledge, it is the first time that to apply purely convolutional structures to extract spatio-temporal features simultaneously from graph-structured time series in a traffic study.

Problem Formulation

Traffic forecast is a typical time-series prediction problem, i.e. predicting the most likely traffic measurements (e.g. speed or traffic flow) in the next $H$ time steps given the previous $M$ observations from traffic network $G$ as,

Fig.1 Graph-structured traffic data.
Each $v_t$ indicates a frame of current traffic status at time step $t$, which is recorded in a graph-structured data matrix.

Network Structure

Fig. 2 Architecture of spatio-temporal graph convolutional networks.
The framework STGCN consists of two spatio-temporal convolutional blocks (ST-Conv blocks) and a fully-connected output layer in the end. Each ST-Conv block contains two temporal gated convolution layers and one spatial graph convolution layer in the middle. The residual connection and bottleneck strategy are applied inside each block. The input $v_{t-M+1}, ..., v_t$ is uniformly processed by ST-Conv blocks to explore spatial and temporal dependencies coherently. Comprehensive features are integrated by an output layer to generate the final prediction $\hat{v}$.


Model PeMSD7(M) (15/30/45 min) PeMSD7(L) (15/30/45 min)
HA 4.01 10.61 7.20 4.60 12.50 8.05
LSVR 2.50/3.63/4.54 5.81/8.88/11.50 4.55/6.67/8.28 2.69/3.85/4.79 6.27/9.48/12.42 4.88/7.10/8.72
ARIMA 5.55/5.86/6.27 12.92/13.94/15.20 9.00/9.13/9.38 5.50/5.87/6.30 12.30/13.54/14.85 8.63/8.96/9.39
FNN 2.74/4.02/5.04 6.38/9.72/12.38 4.75/6.98/8.58 2.74/3.92/4.78 7.11/10.89/13.56 4.87/7.02/8.46
FC-LSTM 3.57/3.94/4.16 8.60/9.55/10.10 6.20/7.03/7.51 4.38/4.51/4.66 11.10/11.41/11.69 7.68/7.94/8.20
GCGRU 2.37/3.31/4.01 5.54/8.06/9.99 4.21/5.96/7.13 2.48/3.43/4.12∗ 5.76/8.45/10.51∗ 4.40/6.25/7.49∗
STGCN(Cheb) 2.25/3.03/3.57 5.26/7.33/8.69 4.04/5.70/6.77 2.37/3.27/3.97 5.56/7.98/9.73 4.32/6.21/7.45
STGCN(1st) 2.26/3.09/3.79 5.24/7.39/9.12 4.07/5.77/7.03 2.40/3.31/4.01 5.63/8.21/10.12 4.38/6.43/7.81

Table 1: Performance comparison of different approaches on the dataset PeMSD7.

Fig. 3: Speed prediction in the morning peak and evening rush hours of the dataset PeMSD7.

Fig. 4: Time consumptions of training on the dataset PeMSD7 (M, left) and (L, right)


Our code is based on Python3 (>= 3.6). There are a few dependencies to run the code. The major libraries are listed as follows:

  • TensorFlow (>= 1.9.0)
  • NumPy (>= 1.15)
  • SciPy (>= 1.1.0)
  • Pandas (>= 0.23)

The implementation of Spatio-Temporal Graph Convolutional Layer with PyTorch is available in PyG Temporal. You might refer to STConv that supports ChebConv Graph Convolutions.


Data Source

PeMSD7 was collected from Caltrans Performance Measurement System (PeMS) in real-time by over 39, 000 sensor stations, deployed across the major metropolitan areas of California state highway system. The dataset is also aggregated into 5-minute interval from 30-second data samples. We randomly select a medium and a large scale among the District 7 of California containing 228 and 1, 026 stations, labeled as PeMSD7(M) and PeMSD7(L), respectively, as data sources. The time range of PeMSD7 dataset is in the weekdays of May and June of 2012. We select the first month of historical speed records as training set, and the rest serves as validation and test set respectively.

Dataset PeMSD7(M/L) is now available under dataset folder (station list included). Please refer issue #6 for how to download metadata from PeMS.

Data Format

You can make your customized dataset by the following format:

  • PeMSD7_V_{$num_route}.csv : Historical Speed Records with shape of [len_seq * num_road] (len_seq = day_slot * num_dates).
  • PeMSD7_W_{$num_route}.csv : Weighted Adjacency Matrix with shape of [num_road * num_road].

Note: please replace the $num_route with the number of routes in your dataset. '*.csv' should not contain any index or header in the file.

Data Preprocessing

The standard time interval is set to 5 minutes. Thus, every node of the road graph contains 288 data points per day (day_slot = 288). The linear interpolation method is used to fill missing values after data cleaning. In addition, data input are normalized by Z-Score method.
In PeMSD7, the adjacency matrix of the road graph is computed based on the distances among stations in the traffic network. The weighted adjacency matrix W can be formed as,

All of our experiments use 60 minutes as the historical time window, a.k.a. 12 observed data points (M = 12) are used to forecast traffic conditions in the next 15, 30, and 45 minutes (H = 3, 6, 9).

Model Details


python --n_route {$num_route} --graph {$weight_matrix_file}

Default settings:

  • Training configs: argparse is used for passing parameters.
    • n_route=228, graph='default', ks=3, kt=3, n_his=12, n_pred=9
    • batch_size=50, epoch=50, lr=0.001, opt='RMSProp', inf_mode='merge', save=10
  • Data source will be searched in dataset_dir = './dataset', including speed records and the weight matrix.
  • Trained models will be saved in save_path = './output/models' every epochs.
  • Training logs will be saved in sum_path = './output/tensorboard'.

Note: it normally takes around 6s on a NVIDIA TITAN Xp for one epoch with the batch size of 50 and n_route of 228.

Folder structure

├── data_loader
│   ├──
│   └──
├── dataset
│   ├── PeMSD7_V_228.csv
│   ├── PeMSD7_W_228.csv
│   ├── PeMSD7_V_1026.csv
│   └── PeMSD7_W_1026.csv
├── models
│   ├──
│   ├──
│   ├──
│   ├──
│   └──
├── output
│   ├── models
│   └── tensorboard
└── utils


Feb. 22, 2022:

  • Sensor Station List of PeMSD7-M released.

Feb. 11, 2022:

  • Dataset PeMSD7-L (1,026 nodes) released.
  • Fix the issue in size calculation of temporal channel. Thanks to @KingWang93 and @cheershuaizhao.

Apr. 18, 2019:

  • Dataset PeMSD7-M (228 nodes) released.

Jan. 14, 2019:

  • Code refactoring based on the Tensorflow-Project-Template, following the PEP 8 code style;
  • Function model_save(), model_test() and tensorboard support are added;
  • The process of model training and inference is optimized;
  • Corresponding code comments are updated.


Please refer to our paper. Bing Yu*, Haoteng Yin*, Zhanxing Zhu. Spatio-temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018

    title={Spatio-temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting},
    author={Yu, Bing and Yin, Haoteng and Zhu, Zhanxing},
    booktitle={Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI)},
Popular Graph Projects
Popular Dataset Projects
Popular Computer Science Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.