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

Abstract

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.

Deep Multi-attributed Graph Translation with Node-Edge Co-Evolution

“…Node attributes prediction methods: 1) Interaction Network (IN) [4] is a node state updating network considering the interaction of neighboring nodes; 2) DCRNN [5] is a node attribute prediction network for tranffic flow prediction; 3) Spatio-Temporal Graph Convolutional Networks (STGCN) [6] is a node attribute prediction model for traffic speed forecast.…”

“…predicting future states of a system in the physical domain based on the fixed relations (e.g. gravitational forces) among nodes [4] and the traffic speed forecasting on the road networks [5], [6]. Though they can work on generic graph-structured data, they assume that the graphs from the input domain and target domain share the same graph topology but cannot model or predict the change of the graph topology.…”

“…The existing Graph structured data translation either deal with the node attributes prediction or translate the graph topology. Node attributes prediction aims at predicting the node attributes given the fixed graph topology [4]- [6], [28]. Li et al [5] propose a Diffusion Convolution Recurrent Neural Network (DCRNN) for traffic forecasting which incorporates both spatial and temporal dependency in the traffic flow.…”

“…Li et al [5] propose a Diffusion Convolution Recurrent Neural Network (DCRNN) for traffic forecasting which incorporates both spatial and temporal dependency in the traffic flow. Yu et al [6] formulated the node attributes prediction problem of graphs based on the complete convolution structures. Graph topology translation considers the change of graph topology from one domain distributions to another.…”

Cluster-Based LSTM Network for Short-Term Passenger Flow Forecasting in Urban Rail Transit

“…Recent studies constructed multi-graph networks to capture several kinds of adjacent information, such as proximity, connectivity, and functionality, to improve precision [18,19]. Yu et al introduced STGCN model [20], enabling much faster training speed with fewer parameters and performing better than many models. The GCN-based models, however, generally use one to four GCN layers.…”