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

2017-09-14 · Bing Yu, Haoteng Yin, Zhanxing Zhu

Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect spatial and temporal dependencies. In this paper, we propose a novel deep learning framework, Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the 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, which enable much faster training speed with fewer parameters. Experiments show that our model STGCN effectively captures comprehensive spatio-temporal correlations through modeling multi-scale traffic networks and consistently outperforms state-of-the-art baselines on various real-world traffic datasets.

📄 PDF Abstract BibTeX arXiv:1709.04875

Code (7)

VeritasYin/STGCN_IJCAI-18 공식 구현 tf

2jungeuni/stgcn-pytorch pytorch

Aguin/STGCN-PyTorch pytorch

hazdzz/STGCN pytorch

ldphenshuai/STGCN mindspore

octoberzzzzz/ml-based-tms-cav-survey

zachysun/taxi_traffic_benchmark pytorch

Tasks

Time SeriesTime Series Analysis Time Series Forecasting Time Series Prediction Traffic Prediction

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